Multi-omics evaluation

Abstract

Methods for assessing disease, such as multi-omics methods, are described herein. A multi-omics method can integrate proteomic data, transcriptomic data, genomic data, lipidomic data, or metabonomic data. The method screens for a disease or a disease state. Also described herein are methods of screening a disease or a disease state from a biological sample. Multi-omics databases and methods of using them are also described herein.

Description

[0001] Cross-references

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 349,937, filed on June 7, 2022; U.S. Provisional Application No. 63 / 399,998, filed on August 22, 2022; and U.S. Provisional Application No. 63 / 486,247, filed on February 21, 2023, which are incorporated herein by reference.

[0003] Sequence Listing Incorporated by Reference

[0004] This application is submitted with a sequence listing in electronic format. The sequence listing is provided as a file named "PrognomIQ712.601sequencelisting.xml," created on June 3, 2023, and 160,536 bytes in size. The information in the electronic sequence listing is incorporated herein by reference in its entirety. Background Art

[0005] There is a need for methods to accurately detect disease states, such as cancer, at early stages. Accurate and early disease detection can improve treatment and prognosis for subjects suffering from the disease. Summary of the Invention

[0006] In some aspects, polyomics methods are disclosed herein. These methods can be used for biomarker discovery, or for assessing a disease or disease state. Some aspects include a method comprising obtaining a polyomics database comprising polyomics data generated by biological fluid samples of groups with different disease states and patient characteristics; and querying the polyomics database to identify biomarkers or biomarker groups that can distinguish between individuals of the group with a first disease state or patient characteristic and other individuals of the group with a second disease state or patient characteristic. In some aspects, the polyomics data include proteomics, metabolomics, lipidomics, transcriptomics, fragmentomics, methylomics or genomics, or a combination thereof. Polyomics data include proteomics, metabolomics, lipidomics, transcriptomics, fragmentomics, methylomics and genomics. In some aspects, the query includes identifying the biomarker or biomarker group as being useful for identifying a third disease state or patient characteristic, and determining that the biomarker or biomarker group is also useful for identifying the first disease state or patient characteristic or the second first disease state or patient characteristic. In some aspects, the query includes identifying other biomarkers or biomarker groups, which can be used to distinguish between the described colony individuals with the first disease state or patient characteristics and the described colony other individuals with the second disease state or patient characteristics, and determine that between individuals in the colony, the biomarker or biomarker group is related to the described other biomarkers or biomarker group. In some aspects, the query includes comparing or associating the measured values ​​of the multi-omics data. In some aspects, the query includes associating the values ​​of the multi-omics data with the first disease state or patient characteristics or the second disease state or patient characteristics. In some aspects, the query includes using machine learning. In some aspects, the multi-omics data are generated from a biofluid sample of more than 500, more than 1000, more than 1500, more than 2000, more than 2500 or more than 3000 colony members. In some aspects, the multi-omics data is generated from a biofluid sample of no more than 500, no more than 1000, no more than 1500, no more than 2000, no more than 2500 or no more than 3000 colony members. In some aspects, the multi-omics data is generated using a non-targeted omics measurement method. In some aspects, at least some multi-omics data is generated after using nanoparticle enrichment. In some aspects, the biomarker or biomarker panel includes a secretory biomarker. In some aspects, the biomarker or biomarker panel includes a protein, a lipid, a nucleic acid, a metabolite or a combination thereof. In some aspects, the biomarker panel corresponds to a metabolic pathway. In some aspects, the first disease state or patient characteristic includes a cancer state.In some aspects, the first disease state or patient characteristic or the second disease state or patient characteristic comprises a co-morbidity state. In some aspects, the second disease state or patient characteristic comprises a healthy state. In some aspects, the first patient characteristic or the second patient characteristic comprises age, sex, race, weight, height, dietary consumption, exercise habits, activity level or smoking status. Some aspects include using the biomarker or biomarker group to classify the object as having the first disease state or patient characteristic or having the second disease state or patient characteristic. Some aspects include identifying, recommending or administering disease treatment based on the use of the biomarker or biomarker group. In some aspects, the biological fluid sample comprises blood, serum or plasma sample. In some aspects, the colony comprises human subjects.

[0007] In some aspects, the present invention discloses the following method, comprising: obtaining multi-omic data from one or more biological fluid samples of an object identified as having a lung nodule; and applying a classifier to the multi-omic data to evaluate whether the lung nodule is cancerous or non-cancerous. In some aspects, the multi-omic data include metabolomics data, lipidomics data, proteomics data, or transcriptomics data. In some aspects, the proteomics data include targeted proteomics data. In some aspects, the proteomics data include non-targeted proteomics data. In some aspects, the transcriptomics data include mRNA data. In some aspects, the transcriptomics data include microRNA data. In some aspects, when distinguishing between biological fluid samples to indicate whether a lung nodule is cancerous, the area under the curve determined by the classifier in a receiver operating characteristic curve is at least about 0.6. Any one or more biomarkers disclosed herein can be used for evaluation, or as a feature in a classifier feature. Some examples of biomarkers that can be included in multi-omics data can include STVLTIPEIIIK (SEQ ID NO: 12), TLAFPLTIR (SEQ ID NO: 13), LIQGAPTIR (SEQ ID NO: 14), SSGLVSNAPGVQIR (SEQ ID NO: 15), DGSFSVVITGLR (SEQ ID NO: 16), LGPISADSTTAPLEK (SEQ ID NO: 17), SEAACLAAGPGIR (SEQ ID NO: 18), TDTGFLQTLGHNLFGIYQK (SEQ ID NO: 19), LKPEDITQIQPQQLVLR (SEQ ID NO: 20), GLPAPIEK (SEQ ID NO: 21), LLGPGPAADFSVSVER (SEQ ID NO: 22), YEYLEGGDR (SEQ ID NO: 23), HLEDVFSK (SEQ ID NO: 24), ILGPLSYSK (SEQ ID NO: 25), NCQTVLAPCSPNPCENAAVCK (SEQ ID NO: 26). NO:26), TVTATFGYPFR (SEQ ID NO:27), STDTSVNPPTVQNAHILSR (SEQ ID NO:28), FSLVSGWGQLLDR (SEQ ID NO:29), ELLALIQLER (SEQ ID NO:30), DAHSVLLSHIFHGR (SEQ ID NO:31), EHAVEGDCDFQLLK (SEQ ID NO:32), SQASSCSLQSSDSVPVGLCK (SEQ ID NO:33), GEFAIDGYSVR (SEQ IDNO: 34), ALVEGVDQLFTDYQIK (SEQ ID NO: 35), LLPYIVGVAQR (SEQ ID NO: 36), HTLNQIDEVK (SEQ ID NO: 37), IDILVNNGGMSQR (SEQ ID NO: 38), LMMDGHEVTVVDNFFTGR (SEQ ID NO: 39), MYGEILSPNYPQAYPSEVEK (SEQ ID NO: 40), NNEEWTVDSCTECHCQNSVTICK (SEQ ID NO: 41), IDTQDIEASHYR (SEQ ID NO: 42), TFIFSDLDYMGMSSGFYK (SEQ ID NO: 43), PDAELSASSVYNLLPEK (SEQ ID NO: 44), ASIHEAWTDGK (SEQ ID NO: 45), LYPWGVVEVENPEHNDFLK (SEQ ID NO: 46), YHWEHTGLTLR (SEQ ID NO: 47) or IGGAIEEVYVSLGVSVGK (SEQ IDNO:48), ENSG00000224067.2, ENSG00000196735.13, ENSG00000287647.1, ENSG00000230797.3, ENSG00000 287219.1, ENSG00000271543.1, ENSG00000223711.1, ENSG00000177602.5, ENSG00000144671.11, ENSG0000 0129673.10, ENSG00000265817.4, ENSG00000108924.14, ENSG00000232125.5, ENSG00000252800.1, ENSG00 000287537.1, ENSG00000196405.13, ENSG00000250893.1, ENSG00000153446.16, ENSG00000284630.1, ENSG 00000284687.1, PC(20:3_20:4)+AcO, DAG(18:2_20:2)+NH4, PC(18:2_20:5)+AcO, LPE(18:1)-H, LPE(16:0) -H, TAG(58:6_FA18:0)+NH4, DAG(20:1_20:5)+NH4, PC(14:0_20:2)+AcO, PC(18:2_20:3)+AcO, PE(18:1_22: 4)-H, PE(18:0_20:1)-H, CER(d18:1 / 26:1)+H, PC(14:0_18:2)+AcO, PE(18:0_22:4)-H, PI(15:0_22:5)-H, P E(P-18:1_18:0)+H, TAG(54:5_FA18:3)+NH4, TAG(58:5_FA18:1)+NH4, DAG(20:5_22:4)+NH4, LPE(20:3)-H. Biomarkers can include PC(20:3_20:4)+AcO, sedoheptulose 1,7-bisphosphate, glucuronate, biopterin, reduced glutathione, N-acetylarginine, cotinine, indole-3-lactic acid, 13C4-ketoglutarate, propionyl-CoA, AICAR, 3-methyl-3-hydroxyglutarate, imidazoleacetic acid, shikimic acid, 1-methyladenosine, dopamine, carnosine, homocitrulline, indolepyruvate, 2-phosphoglycerate or glutaric acid.

[0008] In some aspects, disclosed herein are methods comprising: obtaining multi-omic data from one or more biological fluid samples from a subject suspected of having pancreatic cancer; and applying a classifier to the multi-omic data to assess the likelihood that the subject has pancreatic cancer. In some aspects, the classifier exhibits an area under the curve (AUC) of at least 0.85, at least 0.86, at least 0.87, at least 0.88, at least 0.89, at least 0.90, at least 0.91, at least 0.92, at least 0.93, at least 0.94, at least 0.95, at least 0.96, at least 0.97, or at least 0.98 when distinguishing between a biological fluid sample indicating pancreatic cancer and a sample indicating pancreatic cancer.98. In some aspects, the pancreatic cancer comprises stage 1 or stage 2 pancreatic cancer. In some aspects, the pancreatic cancer comprises stage 3 or stage 4 pancreatic cancer. In some aspects, the multi-omics data comprises copy number variation, fragmentomics, mRNA, protein, metabolite, or lipid data. In some aspects, the multi-omics data comprises copy number variation data, fragmentomics data, transcriptomics data, proteomics data, metabolomics data, and lipidomics data. Any one or more biomarkers disclosed herein can be used for evaluation or as a feature in a classifier feature. Some examples of biomarkers that may be included in multi-omics data may include P00488, P15144, P01833, P58335, P05109, P02750, O95445, P02654, P06702, O14786, P08637, P02766, Q9NQ79, P05362, Q13740, P24821, P06396, P054 52. P18065, Q8WWA0, Q06033, P19320, P02656, Q01628, P01011, Q9H4F8, P01009, P26022, Q 9BYE9, Q16777, P09237, P10643, P07355, Q08830, P62805, P49748, TELVEPTEYLVVHLK (SEQ ID NO:1), TFVIIPELVLPNR (SEQ ID NO:2), LQELHLSSNGLESLSPEFLRPVPQLR (SEQ ID NO:3), ITLLSALVETR (SEQ ID NO:4), VVATTQMQAADAR (SEQ ID NO:5), TFVIIPELVLPNR (SEQ ID NO:6), LQHLENELTHDIITK (SEQ ID NO:7), FLENEDRR (SEQ ID NO:8), LWYENPGVFSPAQLTQIK (SEQ ID NO:9), QWMENPNNNPIHPNLR (SEQ ID NO:10), LEIYQEDQIHFMCPLAR (SEQ ID NO:11), ENSG00000170088.14, ENSG00000274641.2, ENSG00000248180.1, ENSG00000271270.7, ENSG00000132846.6, ENSG000002802 47.1, ENSG00000284035.1, ENSG00000277681.1, ENSG00000264559.1, ENSG00000264764.1, ENSG00000216101.3, ENSG00000266297.1、ENSG00000266320.1、ENSG00000273836.1、ENSG00000199135.1、ENSG00000207604.3、ENSG00000221656.1、ENSG00000207639.1、ENSG00000207607.3、ENSG00000199121.4、ENSG00000265253.1、ENSG00000283728.1、ENSG00000207563.1、ENSG00000283978.1、ENSG00000208015.1、ENSG00000207993.3、ENSG00000208012.1、ENSG00000284195.1、ENSG00000264796.1、ENSG00000278549.1、ENSG00000283764.1、ENSG00000221540.1、ENSG00000263381.1、ENSG00000265435.1、ENSG00000198976.1、ENSG00000208037.1、ENSG00000207757.1、ENSG00000263409.1、ENSG00000221493.1、ENSG00000207807.1、ENSG00000207870.1、CER(d18:1 / 16:0)+H、CER(d18:1 / 18:0)+H、PA(18:0_20:5)-H、DAG(18:1_20:0)+NH4、PC(18:2_20:5)+AcO、PC(20:3_20:4)+AcO、PE(O-18:0_22:5)-H、PE(14:0_22:5)-H、PC(16:0_20:2)+AcO、PI(18:3+20:4)-H、PA(20:2+20:3)-H、17:0-18:1PE-d5-H_USPLASH.IS, PC (16:0_16:0) + AcO, PC (17:0_20:1) + AcO, CER (d18:0 / 24:0) + H, PE (P = 16:0 + 22:5) + H, PE (18:2 + 20:1) - H, PE (P-16:0 + 20:5) + H, TAG (48:0 + FA16:0) + NH4, PC (16:0 + 18:1) + AcO, PE (18:0 + 20:2) = H, PE (18:1 + 20:1) - H, AICAR, CMP, dimethylglycine, epinephrine, sorbitol, 5-thymidine (5-thymidine) acid, dTMP), taurocholic acid, glycocholate, fructose-6-phosphate, farnesyl pyrophosphate, ATP, cystamine, taurocholate, glycine, choline, hydroxyphenyllactic acid, inosine, glutarylcarnitine, 1-methylimidazole acetate, AMP, gluconate, reduced glutathione, glutamate, creatine, L-dihydroorotic acid, thymidine, imidazoleacetic acid, or UMP.

[0009] In some aspects, disclosed herein are methods for detecting pancreatic cancer. The methods may include obtaining a biomarker from a biological fluid sample of a subject; and applying a classifier to the biomarker to evaluate pancreatic cancer, wherein the classifier distinguishes between biological fluid samples of subjects with and without pancreatic cancer as characterized by a receiver operating characteristic (ROC) curve having a mean or median area under the curve (AUC) of at least 0.7, and wherein the biomarker comprises any one of the following chromosomal regions: ThXX chr10:113000001-113100000, chr7:45200001-45300000, chr9:104900001-105000000, chr18:58600001-58700 000, chr17:17400001-17500000, chr2:150700001-150800000, chr7:149300001-149400000, chr4:88700001-888 00000, chr20:28900001-29000000 or chr8:55300001-55400000; any of the following mRNA transcripts: TMEM192, H2BC17, GAPDHP60, ENSG00000271270.7, ZBED3 or GRCh38; any of the following microRNAs: MIR5187, MIR6739, MIR3162, MIR4772, MIR877, MIR744, MIR3909, MIR6842, MIR101-1, MIR206, MIR1225, MIR193B, MIR200A, MIR26B, MIR4446, MIR7108, MIR23B, MIR365B, MIR362, M IR134, MIRLET7F2, MIR6852, MIR5009, MIR6736, MIR6850, MIR1180, MIR5584, MIR3121, MIR429, MIR320A, MIR93, MI R4747, MIR320C1 or MIR221; any of the following proteins: F13A_human, AMPN_human, PIGR_human, ANTR2_human, S10A8_human, A2GL_human, APOM_human, APOC1_human, S10A9_human, NRP1_human, FCG3A_human, TTHY_human, CRAC1_human, ICAM1_human, CD166_human, TENA_human, GELS_human, TETN_human, IBP2_human, ITLN1_human, ITIH3_human,VCAM1_human or APOC3_human; any of the following peptides: TELVEPTEYLVVHLK (SEQ ID NO: 1), TFVIIPELVLPNR (SEQ ID NO: 2), LQELHLSSNGLESLSPEFLRPVPQLR (SEQ ID NO: 3), ITLLSALVETR (SEQ ID NO: 4), VVATTQMQAADAR (SEQ ID NO: 5), TFVIIPELVLPNR (SEQ ID NO: 6), LQHLENELTHDIITK (SEQ ID NO: 7), FLENEDRR (SEQ ID NO: 8), LWYENPGVFSPAQLTQIK (SEQ ID NO: 9), QWMENPNNNPIHPNLR (SEQ ID NO: 10), or LEIYQEDQIHFMCPLAR (SEQ ID NO: 11). NO:11); any of the following proteins: IFM3_human, AMPN_human, A2GL_human, AACT_human, SMOC1_human, A1AT_human, PTX3_human, CDHR2_human, H2A2C_human, ANTR2_human, MMP7_human, CO7_human, ANXA2_human, FGL1_human, H4_human, or ACADV_human; any of the following lipids: CER (d18:1 / 16:0) + H, CER ( d18:1 / 18:0)+H, PA(18:0_20:5)-H, DAG(18:1_20:0)+NH4, PC(18:2_20:5)+AcO, PC(20:3_20:4)+A cO, PE(O-18:0_22:5)-H, PE(14:0_22:5)-H, PC(16:0_20:2)+AcO, PI(18:3+20:4)-H, PA(20:2+20:3 )-H, 17:0-18:1PE-d5-H_USPLASH.IS, PC(16:0_16:0)+AcO, PC(17:0_20:1)+AcO, CER(d18:0 / 24:0 )+H, PE(P=16:0+22:5)+H, PE(18:2+20:1)-H, PE(P-16:0+20:5)+H, TAG(48:0+FA16:0)+NH4, PC(16: 0+18:1)+AcO or PE(18:0+20:2)=H, PE(18:1+20:1)-H; or any of the following metabolites: AICAR, CMP, dimethylglycine, epinephrine, sorbitol, 5-thymidylic acid (dTMP), taurocholic acid, glycocholate, fructose-6-phosphate, farnesyl pyrophosphate, ATP, cystamine, taurocholate, glycine, choline, hydroxyphenyllactic acid, inosine, glutarylcarnitine, 1-methylimidazole acetate,AMP, gluconate, reduced glutathione, glutamate, creatine, L-dihydroorotic acid, thymidine, imidazoleacetic acid or UMP. In some aspects, the biomarker includes any one of the following chromosomal regions: chr10:113000001-113100000, chr7:45200001-45300000, chr9:104900001-105000000, chr18:58600001-58700000, chr17:174 In some aspects, the biomarker comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 of the chromosomal regions. In some aspects, the biomarker comprises any one of the following mRNA transcripts: TMEM192, H2BC17, GAPDHP60, ENSG00000271270.7, ZBED3, or GRCh38. In some aspects, the biomarker comprises 1, 2, 3, 4, 5, or 6 mRNA transcripts. In some aspects, the biomarkers include any one of the following microRNAs: MIR5187, MIR6739, MIR3162, MIR4772, MIR877, MIR744, MIR3909, MIR6842, MIR101-1, MIR206, MIR1225, MIR193B, MIR200A, MIR26B, MIR4446, MIR7108, MIR23B, MIR365B, MIR362, MIR134, MIRLET7F2, MIR6852, MIR5009, MIR6736, MIR6850, MIR1180, MIR5584, MIR3121, MIR429, MIR320A, MIR93, MIR4747, MIR320C1, or MIR221. In some aspects, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, or 33 microRNAs. In some aspects, the biomarkers include any one of the following proteins: F13A_human, AMPN_human, PIGR_human, ANTR2_human, S10A8_human, A2GL_human, APOM_human, APOC1_human, S10A9_human, NRP1_human, FCG3A_human, TTHY_human,In some aspects, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 proteins. In some aspects, the biomarkers include any one of the following peptides: TELVEPTEYLVVHLK (SEQ ID NO: 1), TFVIIPELVLPNR (SEQ ID NO: 2), LQELHLSSNGLESLSPEFLRPVPQLR (SEQ ID NO: 3), ITLLSALVETR (SEQ ID NO: 4), VVATTQMQAADAR (SEQ ID NO: 5), TFVIIPELVLPNR (SEQ ID NO: 6), LQHLENELTHDIITK (SEQ ID NO: 7), FLENEDRR (SEQ ID NO: 8), LWYENPGVFSPAQLTQIK (SEQ ID NO: 9), QWMENPNNNPIHPNLR (SEQ ID NO: 10), or LEIYQEDQIHFMCPLAR (SEQ ID NO: 11). In some aspects, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 peptides. In some aspects, the biomarkers include any one of the following proteins: IFM3_Human, AMPN_Human, A2GL_Human, AACT_Human, SMOC1_Human, A1AT_Human, PTX3_Human, CDHR2_Human, H2A2C_Human, ANTR2_Human, MMP7_Human, CO7_Human, ANXA2_Human, FGL1_Human, H4_Human, or ACADV_Human. In some aspects, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 proteins. In some aspects, the biomarkers include any one of the following lipids: CER (d18:1 / 16:0)+H, CER (d18:1 / 18:0)+H, PA (18:0-20:5)-H, DAG (18:1-20:0)+NH4, PC (18:2-20:5)+AcO, PC (20:3-20:4)+AcO, PE (0-18:0-22:5)-H, PE (14:0-22:5)-H, PC (16:0-20:2)+AcO, PI (18:3+20:4)-H, PA (20:2+20:3)-H, 17:0-18:1 PE-d5-H_USPLASH.IS,PC (16:0-16:0) + AcO, PC (17:0-20:1) + AcO, CER (d18:0 / 24:0) + H, PE (P = 16:0 + 22:5) + H, PE (18:2 + 20:1) - H, PE (P-16:0 + 20:5) + H, TAG (48:0 + FA16:0) + NH4, PC (16:0 + 18:1) + AcO, PE (18:0 + 20:2) = H, or PE (18:1 + 20:1) - H. In some aspects, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, or 22 lipids. In some aspects, the biomarkers include any one of the following metabolites: AICAR, CMP, dimethylglycine, epinephrine, sorbitol, 5-thymidylic acid (dTMP), taurocholic acid, glycocholate, fructose-6-phosphate, farnesyl pyrophosphate, ATP, cystamine, taurocholate, glycine, choline, hydroxyphenyllactic acid, inosine, glutarylcarnitine, 1-methylimidazole acetate, AMP, gluconate, reduced glutathione, glutamic acid, creatine, L-dihydroorotic acid, thymidine, imidazoleacetic acid or UMP. In some aspects, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27 or 28 metabolites. In some aspects, the biomarker includes any one of the following biomarkers: APOM_human, G6PE_human, F13A_human, A1AT_human, AACT_human, A2MG_human, CO5_human, IGHG2_human, APOC1_human, APOC3_human, APOB_human, ICAM1_human, ITB1_human, GELS_human, S10A9_human, CO8B_human, TSP1_human, MMP7_human or CO7_human. In some aspects, the classifier includes the performance characterized by a receiver operating characteristic (ROC) curve, and the receiver operating characteristic (ROC) curve has an average or median area under the curve (AUC) of at least 0.8. In some aspects, the object is suspected of having pancreatic cancer. In some aspects, the evaluation includes identifying the biomarker as indicating pancreatic cancer. In some aspects, the method also includes administering pancreatic cancer treatment to the object when the object suffers from pancreatic cancer. In some aspects, the method further comprises monitoring the subject when the subject does not have pancreatic cancer.

[0010] In some aspects, disclosed herein are methods for detecting lung cancer. The methods may include identifying a biomarker from a biological fluid sample of a subject; and applying a classifier to the biomarker to evaluate lung cancer, wherein the classifier distinguishes between biological fluid samples of subjects with and without lung cancer by a performance characterized by a receiver operating characteristic (ROC) curve having a mean or median area under the curve (AUC) of at least 0.7, and wherein the biomarker comprises any one of the following RNAs: BAT2, HLA-DQA1, antisense to AK5, YY2, ENSG00000287219.1, RPL6 pseudogene, ENSG00000223711.1, HASPIN, SL C22A14, AANAT, FSBP, HLF, DYTN, ENSG00000252800.1, a novel human transcript from chromosome 12 at positions 49,536,677 to 49,538,894 from the reverse strand of genome build GRCh38, EVL, a novel human transcript from chromosome 4 at positions 40,426,119 to 40,427,585 from the forward strand of genome build GRCh38, C16orf89, a novel human transcript from chromosome 22 at positions 21,657,811 to 21,661,021 from the forward strand of genome build GRCh38, or RBFOX1; any of the following lipids :PC(20:3_20:4)+AcO, DAG(18:2_20:2)+NH4, PC(18:2_20:5)+AcO, LPE(18:1)-H, LPE(16:0)-H, TAG(58:6_FA18:0)+NH4, DAG(20:1_20:5)+NH4, PC(14:0_20:2)+AcO, PC(18:2_20:3)+AcO, PE(18:1_22:4)-H, PE(18:0_20:1)-H, CER(d18:1 / 26:1)+H, PC(14:0_18:2)+AcO, PE(18:0_22:4)-H, P I(15:0_22:5)-H, PE(P-18:1_18:0)+H, TAG(54:5_FA18:3)+NH4, TAG(58:5_FA18:1)+NH4, DAG(20:5_22:4)+NH4 or LPE(20:3)-H; any of the following metabolites: sedoheptulose 1,7-bisphosphate, glucuronide, biopterin, reduced glutathione, N-acetylarginine, cotinine, indole-3-lactic acid, 13C4-ketoglutarate, propionyl-CoA, AICAR, 3-methyl-3-hydroxyglutarate, imidazoleacetic acid, shikimic acid, 1-methyladenosine, dopamine, carnosine, homocitrulline, indolepyruvate,2-phosphoglycerate or glutaric acid; any of the following peptides: STVLTIPEIIIK (SEQ ID NO: 12), TLAFPLTIR (SEQ ID NO: 13), LIQGAPTIR (SEQ ID NO: 14), SSGLVSNAPGVQIR (SEQ ID NO: 15), DGSFSVVITGLR (SEQ ID NO: 16), LGPISADSTTAPLEK (SEQ ID NO: 17), SEAACLAAGPGIR (SEQ ID NO: 18), TDTGFLQTLGHNLFGIYQK (SEQ ID NO: 19), LKPEDITQIQPQQLVLR (SEQ ID NO: 20), GLPAPIEK (SEQ ID NO: 21), LLGPGPAADFSVSVER (SEQ ID NO: 22), YEYLEGGDR (SEQ ID NO: 23), HLEDVFSK (SEQ ID NO: 24), ILGPLSYSK (SEQ ID NO: 25), NCQTVLAPCSPNPCENAAVCK (SEQ ID NO: 26). NO:26), TVTATFGYPFR (SEQ ID NO:27), STDTSCVNPPTVQNAHILSR (SEQ ID NO:28), FSLVSGWGQLLDR (SEQ ID NO:29), ELLALIQLER (SEQ ID NO:30), or DAHSVLLSHIFHGR (SEQ ID NO:31), or fragments thereof; or any of the following peptides: EHAVEGDCDFQLLK (SEQ ID NO:32), SQASSCSLQSSDSVPVGLCK (SEQ ID NO:33), GEFAIDGYSVR (SEQ ID NO:34), ALVEGVDQLFTDYQIK (SEQ ID NO:35), LLPYIVGVAQR (SEQ ID NO:36), HTLNQIDEVK (SEQ ID NO:37), IDILVNNGGMSQR (SEQ ID NO:38), LMMDGHEVTVVDNFFTGR (SEQ ID NO:39), NO:39), MYGEILSPNYPQAYPSEVEK (SEQ ID NO:40), NNEEWTVDSCTECHCQNSVTICK (SEQ ID NO:41), IDTQDIEASHYR (SEQ ID NO:42), TFIFSLDDYMGMSSGFYK (SEQ ID NO:43), PDAELSASSVYNLLPEK (SEQ ID NO:44),ASIHEAWTDGK (SEQ ID NO: 45), LYPWGVVEVENPEHNDFLK (SEQ ID NO: 46), YHWEHTGLTLR (SEQ ID NO: 47) or IGGAIEEVYVSLGVSVGK (SEQ ID NO: 48), or fragments thereof. In some aspects, the biomarkers include any one of the following RNAs: BAT2, HLA-DQA1, antisense to AK5, YY2, ENSG00000287219.1, RPL6 pseudogene, ENSG00000223711.1, HASPIN, SLC22A14, AANAT, FSBP, HLF, DYTN, ENSG00000252800.1, the first reverse strand from genomic build GRCh38. In some aspects, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 RNAs. In some aspects, the biomarkers include any of the following lipids: PC (20:3_20:4) + AcO, DAG (18:2_20:2) + NH4, PC (18:2_20:5) + AcO, LPE (18:1) -H, LPE (16:0) -H, TAG (58:6_FA18:0) + NH4, DAG (20:1_20:5) + NH4, PC (14:0_20:2) + AcO, PC (18:2_20:3) + AcO, PE (18:1) -H, LPE (16:0) -H, :1-22:4)-H, PE (18:0-20:1)-H, CER (d18:1 / 26:1)+H, PC (14:0-18:2)+AcO, PE (18:0-22:4)-H, PI (15:0-22:5)-H, PE (P-18:1-18:0)+H, TAG (54:5-FA18:3)+NH4, TAG (58:5-FA18:1)+NH4, DAG (20:5-22:4)+NH4 or LPE (20:3)-H. In some aspects, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19 or 20 lipids. In some aspects, the biomarker includes any one of the following metabolites: sedoheptulose 1,7-bisphosphate, glucuronate, biopterin, reduced glutathione, N-acetylarginine, cotinine, indole-3-lactic acid, 13C4-ketoglutarate, propionyl-CoA, AICAR, 3-methyl-3-hydroxyglutarate, imidazoleacetic acid, shikimic acid, 1-methyladenosine, dopamine, carnosine, homocitrulline, indolepyruvate, 2-phosphoglycerate or glutaric acid. In some aspects, the biomarker includes wherein the biomarker includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 metabolites. In some aspects, the biomarkers include any of the following peptides: STVLTIPEIIIK (SEQ ID NO: 12), TLAFPLTIR (SEQ ID NO: 13), LIQGAPTIR (SEQ ID NO: 14), SSGLVSNAPGVQIR (SEQ ID NO: 15), DGSFSVVITGLR (SEQ ID NO: 16), LGPISADSTTAPLEK (SEQ ID NO: 17), SEAACLAAGPGIR (SEQ ID NO: 18), TDTGFLQTLGHNLFGIYQK (SEQ ID NO: 19), LKPEDITQIQPQQLVLR (SEQ ID NO: 20), GLPAPIEK (SEQ ID NO: 21), LLGPGPAADFSVSVER (SEQ ID NO: 22), YEYLEGGDR (SEQ ID NO: 23), HLEDVFSK (SEQ ID NO: 24), ILGPLSYSK (SEQ ID NO: 25), NCQTVLAPCSPNPCENAAVCK (SEQ ID NO: 26). NO: 26), TVTATFGYPFR (SEQ ID NO: 27), STDTSCVNPPTVQNAHILSR (SEQ ID NO: 28), FSLVSGWGQLLDR (SEQ ID NO: 29), ELLALIQLER (SEQ ID NO: 30), or DAHSVLLSHIFHGR (SEQ ID NO: 31), or fragments thereof. In some aspects, the biomarker comprises wherein the biomarker comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 peptides. In some aspects, the biomarker comprises any one of the following peptides: EHAVEGDCDFQLLK (SEQ ID NO: 32),SQASSCSLQSSDSVPVGLCK(SEQ ID NO:33), GEFAIDGYSVR(SEQ ID NO:34), ALVEGVDQLFTDYQIK(SEQ ID NO:35), LLPYIVGVAQR(SEQ ID NO:36), HTLNQIDEVK(SEQ ID NO:37), IDILVNNGGMSQR(SEQ ID NO:38), LMMDGHEVTVVDNFFTGR (SEQ ID NO:39), MYGEILSPNYPQAYPSEVEK (SEQ ID NO:40), NNEEWTVDSCTECHCQNSVTICK (SEQ ID NO:41), IDTQDIEASHYR (SEQ ID NO:42), TFIFSLDDYMGMSSGFYK (SEQ ID NO:43), PDAELSASSVYNLLPEK (SEQ ID NO:44), ASIHEAWTDGK (SEQ ID NO:45), LYPWGVVEVENPEHNDFLK (SEQ ID NO:46), YHWEHTGLTLR (SEQ ID NO:47) or IGGAIEEVYVSLGVSVGK (SEQ ID NO:48), or fragments thereof. In some aspects, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17 peptides. In some aspects, the classifier includes a performance characterized by a receiver operating characteristic (ROC) curve having an average or median area under the curve (AUC) of at least 0.8. In some aspects, the subject is suspected of having lung cancer. In some aspects, the evaluation includes identifying the biomarker as indicating lung cancer. In some aspects, the method further includes administering lung cancer treatment to the subject when the subject has lung cancer or obtaining a lung nodule biopsy from the subject. In some aspects, the method further includes monitoring the subject when the subject does not have lung cancer. In some aspects, the lung cancer includes non-small cell lung cancer (NSCLC). In some aspects, the biological fluid sample is obtained from a subject identified as having a lung nodule. In some aspects, the method further comprises identifying the subject as having a lung nodule by performing medical imaging. In some aspects, the classifier distinguishes between cancerous and non-cancerous lung nodules.

[0011] In some aspects, disclosed herein are methods for detecting lung cancer, comprising: (a) obtaining a biomarker from a biological fluid sample of a subject; and (b) applying a classifier to the biomarker to evaluate lung cancer, wherein the classifier distinguishes between biological fluid samples of subjects with and without lung cancer by performance characterized by a receiver operating characteristic (ROC) curve having a mean or median area under the curve (AUC) of at least 0.7 and wherein the biomarker comprises any one of the following mRNA transcripts: ENSG00000155744.10, ENSG0000008 1052.14, ENSG00000173726.11, ENSG00000143995.20, ENSG00000108528.14, ENSG00000177427.13, ENSG00000163961.4, ENSG00 000049130.16, ENSG00000008405.12, ENSG00000135090.14, ENSG00000151778.11, ENSG00000172116.23, ENSG00000144218.21, ENSG00000131196.18, ENSG00000129351.18, ENSG00000105518.14, ENSG00000182162.11, ENSG00000126368.6, ENSG0000017635 8.16, ENSG00000112599.9, ENSG00000142864.15, ENSG00000163159.15, ENSG00000165661.17, ENSG00000165661.18, ENSG00000 007923.17, ENSG00000054116.12, ENSG00000113811.12, ENSG00000100644.17, ENSG00000133997.12, ENSG00000120925.16, ENSG00000110048.12, ENSG00000197863.9, ENSG00000174307.7, or ENSG00000109381.21; any of the following peptides: LC (UniMod: 4) PSGMYTEYIHSR (SEQ ID NO:49), LCPSGMYTEYIHSR (SEQ ID NO:139), NADLQVLKPEPELVYEDLR (SEQ ID NO:50), ASTPGAAAQIQEVK (SEQ ID NO:51),PYC(UniMod:4)NHPC(UniMod:4)YAAMFGPK(SEQ ID NO:52)、PYCNHPCYAAMFGPK(SEQ ID NO:140)、QLLQENEVQFLDK(SEQ ID NO:53)、AISAFHGSLSSSQPAEIITQSK(SEQ ID NO:54)、FEGIAC(UniMod:4)EISK(SEQ ID NO:55)、FEGIACEISK(SEQ ID NO:141)、FIINDWVK(SEQ ID NO:56)、YVGGQEHFAHLLILRDTK(SEQ ID NO:57)、SVGFHLPSR(SEQ ID NO:58)、GSPMEISLPIALSK(SEQ ID NO:59)、M(UniMod:35)VVSMTLGLHPWIANIDDTQYLAAK(SEQ ID NO:60)、MVVSMTLGLHPWIANIDDTQYLAAK(SEQ ID NO:142)、TVTAM(UniMod:35)DVVYALK(SEQ ID NO:61)、TVTAMDVVYALK(SEQ ID NO:143)、C(UniMod:4)SC(UniMod:4)DPGYELAPDKR(SEQ ID NO:62)、CSCDPGYELAPDKR(SEQ ID NO:144)、GNPTVEVDLHTAK(SEQ ID NO:63)、HLQLAIRNDEELNK(SEQ ID NO:64)、FQDGDLTLYQSNTILR(SEQ ID NO:65)、IRPNDFIPNVI(SEQ ID NO:66)、TKLEEHLEGIVNIFHQYSVRK(SEQ ID NO:67)、GDPEC(UniMod:4)HLFYNEQQEAR(SEQ ID NO:68)、GDPECHLFYNEQQEAR(SEQ ID NO:145)、ALNSIIDVYHK(SEQ ID NO:69)、DDPDAPLQPVTPLQLFEGR(SEQ ID NO:70)、KSEEENLFEIITADEVHYFLQAATPK(SEQ ID NO:71)、FPNGVQLSPAEDFVLVAETTMAR(SEQ ID NO:72)、LYFMHFNLESSYLC(UniMod:4)EYDYVK(SEQ IDNO:73)、LYFMHFNLESSYLCEYDYVK(SEQ ID NO:146)、LFDYC(UniMod:4)DIPLC(UniMod:4)ASSSFDC(UniMod:4)GK(SEQ ID NO:74)、LFDYCDIPLCASSSFDCGK(SEQ ID NO:147)、AEQC(UniMod:4)C(UniMod:4)EETASSISLHGK(SEQ ID NO:75)、AEQCCEETASSISLHGK(SEQ ID NO:148)、VALEGLRPTIPPGISPHVC(UniMod:4)K(SEQ ID NO:76)、VALEGLRPTIPPGISPHVCK(SEQ IDNO:149)、VWEQIDQMK(SEQ ID NO:77)、FTDEEVDELYREAPIDK(SEQ ID NO:78)、DTHFPIC(UniMod:4)IFC(UniMod:4)C(UniMod:4)GC(UniMod:4)C(UniMod:4)HR(SEQ ID NO:79)、DTHFPICIFCCGCCHR(SEQ ID NO:150)、RQDNEILIFWSK(SEQ ID NO:80)、QDNEILIFWSK(SEQ IDNO:81)、EVGTVLSQVYSK(SEQ ID NO:82)、MVTALGTHWHPEHFC(UniMod:4)C(UniMod:4)VSC(UniMod:4)GEPFGDEGFHER(SEQ ID NO:83)、MVTALGTHWHPEHFCCVSCGEPFGDEGFHER(SEQ IDNO:151)、EVTFHC(UniMod:4)HEGYILHGAPK(SEQ ID NO:84)、EVTFHCHEGYILHGAPK(SEQ IDNO:152)、GAGGQSMSEAPTGDHAPAPTR(SEQ ID NO:85)、DGSFSVVITGLR(SEQ ID NO:86)、GISLNPEQWSQLK(SEQ ID NO:87)、LVHVEEPHTETVR(SEQ ID NO:88)、RVEPYGENFNK(SEQ IDNO:89)、LDDC(UniMod:4)GLTEAR(SEQ ID NO:90)、LDDCGLTEAR(SEQ ID NO:153)、LVQAAQMLQSDPYSVPAR(SEQ ID NO:91)、DFLGFYVVDSHR(SEQ ID NO:92)、YGTC(UniMod:4)IYQGR(SEQ ID NO:93)、YGTCIYQGR(SEQ ID NO:154)、WLQEGGQEC(UniMod:4)EC(UniMod:4)K(SEQ ID NO:94)、WLQEGGQECECK(SEQ ID NO:155)、ASGPPVSELITK(SEQ ID NO:95)、ELSDFISYLQR(SEQ ID NO:96)、EGHVLQGPSVLK(SEQ ID NO:97)、MNLASEPQEVLHIGSAHNR(SEQID NO:98)、FLILPDMLK(SEQ ID NO:99)、GISQEQMNEFR(SEQ ID NO:100)、DPNHFRPAGLPEK(SEQ ID NO:101)、VPSHLQAETLVGK(SEQ ID NO:102)、NLHFLTTQEDYTLK(SEQ ID NO:103)、SEAYNTFSER(SEQ ID NO:104)、AVLDVFEEGTEASAATAVK(SEQ ID NO:105)、VIQYLAYVASSHK(SEQ ID NO:106)、ASYAQQPAESR(SEQ ID NO:107)、YLEESNFVHR(SEQ ID NO:108)、GSFTYFAPSNEAWDNLDSDIR(SEQ ID NO:109)、ALTDMPQM(UniMod:35)R(SEQ ID NO:110)、LAVNM(UniMod:35)VPFPR(SEQ ID NO:111)、TSC(UniMod:4)LLFMGR(SEQ ID NO:112)、QQQHLFGSNVTDC(UniMod:4)SGNFC(UniMod:4)LFR(SEQ ID NO:113)、ALTDMPQMR(SEQ ID NO:156)、LAVNMVPFPR(SEQ ID NO:157)、TSCLLFMGR(SEQ ID NO:158)、QQQHLFGSNVTDCSGNFCLFR(SEQ ID NO:159)、DYVSQFEGSALGK(SEQ ID NO:114)、DSITTWEILAVSMSDK(SEQ ID NO:115)、FC(UniMod:4)NIMGSSNGVDQEHFSNVVK(SEQ ID NO:116)、FCNIMGSSNGVDQEHFSNVVK(SEQ IDNO:160)、SEHPGLSIGDTAK(SEQ ID NO:117), QFVEQHTPQLLTLVPR(SEQ ID NO:118), NQDLAPNSAEQASILSLVTK(SEQ ID NO:119), TDGALLVNAMFFK(SEQ ID NO:120), DDFEGQLESDRFLLMSGGK(SEQ ID NO:121), SIQC(UniMod:4)LTVHK(SEQ ID NO:122), SIQCLTVHK(SEQ ID NO:161), EDITQSAQHALR(SEQ ID NO:123), VVAC(UniMod:4)TSAFLLWDPTK(SEQ ID NO:124), VVACTSAFLLWDPTK(SEQ ID NO:162), NYPMHVFAYR (SEQ ID NO:125), MEEVEAMLLPETLK (SEQ ID NO:126), ADVQAHGEGQEFSITC(UniMod:4)LVDEEEM(UniMod:35)K(SEQ ID NO:127), ADVQAHGEGQEFSITCLVDEEEMK(SEQ ID NO:163), DFALLSLQVPLK(SEQ ID NO:128), LLIYAVLPTGDVIGDSAK(SEQ ID NO:129),VDIVAINDPFIDLNYMVYMFQYDSTHGK(SEQ ID NO:130),AEQINQAAGEASAVLAK(SEQ ID NO:131),TPAYYPNAGLIK(SEQ ID NO:132),QGENGQMM(UniMod:35)SC(UniMod:4)TC(UniMod:4)LGNGK(SEQ ID NO:133), QGENGQMMSCTCLGNGK (SEQ ID NO:164), YWEMQPATFR (SEQ ID NO: 134), HGEYWLGNK (SEQ ID NO: 135), FVPAEMGTHTVSVK (SEQ ID NO: 136), NALGPGLSPELGPLPALR (SEQ ID NO: 137), or TKLEEHLEGIVNIFHQYSVR (SEQ ID NO: 138); any of the following lipids: 1-palmitoyl-GPE (16:0), phosphatidylcholine (18:0 / 20:2, 20:0 / 18:2), linoleamide (18:2n6), linolenamide (18:3), 2-aminooctanoate,1-Linoleoyl-2-arachidonoyl-GPC (18:2 / 20:4n6), 1-Palmitoylglycerol (16:0), 1-Oleoyl-GPC (18:1), 1-Linoleoyl-GPC (18:3), Pregnanolone / Isopregnanolone Sulfate, Sphingomyelin (d18:2 / 24:1, d18:1 / 24:2), Myristamide (14:1), 1-Linoleoylglycerol (18:2), 11β-Hydroxyandrosterone Glucuronide, 2S,3R-Dihydroxybutyrate, Glycosyl-N-Behenoylsphingosine (d18:1 / 22:0), 1-Palmitoyl-2-Linoleoyl-GPC (16:0 / 18:2), 1-Stearoyl-2-Arachidonoyl-GPS (18:0 / 20:4) , 1-tetracosyl-GPC (24:0), 3β-hydroxy-5-cholestenol ester, 5α-androstane-3α,17β-diol monosulfate (2), hexadecenedioate (C16:1-DC), myristamide (14:0), 1-stearoyl-GPE (18:0), 1-myristoyl-2-arachidonoyl-GPC (14:0 / 20:4), 1-arachidonoyl-GPC (20:0), 4-hydroxy-2-oxoglutaric acid, nicotinic acid ester (24:6n3), sphingomyelin (d17:1 / 16:0, d18:1 / 15:0, d16:1 / 17:0), 3-hydroxyoctanoate, 1-arachidonoylglycerol (20:4), 1-stearoyl-2-oleoyl- GPS (18:0 / 18:1), 1-eicosenoyl-GPE (20:1), sphingosine, glycoursodeoxycholic acid sulfate (1), 1-stearoyl-2-linoleoyl-GPC (18:0 / 18:2), erucate (22:1n9), phosphoethanolamine, androstenone glucuronide, behenyl dihydrosphingomyelin (d18:0 / 22:0), androstenediol (3α,17α) monosulfate (2), isoursodeoxycholate, N-stearoylsphingosine (d18:1 / 18:0), margaramide (17:0), 1-eicosenoyl-GPC (20:1), tetrahydrocortisone glucuronide (5), linoleoylcarnitine (C18:2), hydroxypalmitoyl or any of the following metabolites: N-acetylcarnosine, indole lactate, lanthionine, 3-(4-hydroxyphenyl) lactate, hydantoin-5-propionate, urea, homoarginine, β-citrylglutamate, S-1-pyrroline-5-carboxylate, aspartic acid, isovalerylcarnitine (C5), creatine, N-acetylglucosamine / N-acetylgalactosamine, galactonic acid, N-acetylneuraminic acid, 3-phosphoglyceric acid, bilirubin (E, Z or Z, E), retinol (vitamin A), heme, nicotinamide, carotene diol (1), bilirubin (Z, Z), 1-methylnicotinamide, α-ketoglutarate,Xanthine, phenylacetylcarnitine, HWESASXX, 5-acetamido-6-formylamino-3-methyluracil, 2-keto-3-deoxygluconate, iminodiacetate (IDA), 4-acetaminophen sulfate, caffeic acid sulfate, 2-hydroxyacetaminophen sulfate, 3-formyl indole, X-18779, X-24473, X-23593, X-24307, X-24027, X-14939, X-12456, X-25790, X-17146, X-15220, X-12740, X-17765, X-25420, X-23639, X-12462, X-15728, or X-25422. In some embodiments, the biomarkers include any of the following mRNA transcripts: ENSG00000155744.10, ENSG00000081052.14, ENSG00000173726.11, ENSG00000143995.20, ENSG00000108528.14, ENSG00000177427.13, ENSG00000163961.4, ENSG00000143995.20, G00000049130.16, ENSG00000008405.12, ENSG00000135090.14, ENSG00000151778.11, ENSG0000017 2116.23, ENSG00000144218.21, ENSG00000131196.18, ENSG00000129351.18, ENSG00000105518.14, E NSG00000182162.11, ENSG00000126368.6, ENSG00000176358.16, ENSG00000112599.9, ENSG0000014 2864.15, ENSG00000163159.15, ENSG00000165661.17, ENSG00000165661.18, ENSG00000007923.17, In some embodiments, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81In some embodiments, the biomarker comprises any one of the following peptides: LC(UniMod:4)PSGMYTEYIHSR (SEQ ID NO:49), LCPSGMYTEYIHSR (SEQ ID NO:139), NADLQVLKPEPELVYEDLR (SEQ ID NO:50), ASTPGAAAQIQEVK (SEQ ID NO:51), PYC(UniMod:4)NHPC(UniMod:4)YAAMFGPK (SEQ ID NO:52), PYCNHPCYAAMFGPK (SEQ ID NO:140), QLLQENEVQFLDK (SEQ ID NO:53), AISAFHGSLSSSQPAEIITQSK (SEQ ID NO:54), FEGIAC(UniMod:4)EISK (SEQ ID NO:55), FEGIACEISK (SEQ ID NO:141), FIINDWVK (SEQ ID NO:56), ID NO:56), YVGGQEHFAHLLILRDTK (SEQ ID NO:57), SVGFHLPSR (SEQ ID NO:58), GSPMEISLPIALSK (SEQ ID NO:59), M (UniMod:35) NO:142), TVTAM(UniMod:35)DVVYALK(SEQ ID NO:61), TVTAMDVVYALK(SEQ ID NO:143), C(UniMod:4)SC(UniMod:4)DPGYELAPDKR(SEQ ID NO:62), CSCDPGYELAPDKR(SEQ ID NO:144), GNPTVEVDLHTAK(SEQ ID NO:63), HLQLAIRNDEELNK(SEQ ID NO:64), FQDGDLTLYQSNTILR(SEQ ID NO:65), IRPNDFIPNVI(SEQID NO:66), TKLEEHLEGIVNIFHQYSVRK(SEQ ID NO:67), GDPEC(UniMod:4)HLFYNEQQEAR(SEQID NO:68), GDPECHLFYNEQQEAR(SEQ ID NO:145), ALNSIIDVYHK (SEQ ID NO:69),DDPDAPLQPVTPLQLFEGR(SEQ ID NO:70)、KSEEENLFEIITADEVHYFLQAATPK(SEQ ID NO:71)、FPNGVQLSPAEDFVLVAETTMAR(SEQ ID NO:72)、LYFMHFNLESSYLC(UniMod:4)EYDYVK(SEQ IDNO:73)、LYFMHFNLESSYLCEYDYVK(SEQ ID NO:146)、LFDYC(UniMod:4)DIPLC(UniMod:4)ASSSFDC(UniMod:4)GK(SEQ ID NO:74)、LFDYCDIPLCASSSFDCGK(SEQ ID NO:147)、AEQC(UniMod:4)C(UniMod:4)EETASSISLHGK(SEQ ID NO:75)、AEQCCEETASSISLHGK(SEQ ID NO:148)、VALEGLRPTIPPGISPHVC(UniMod:4)K(SEQ ID NO:76)、VALEGLRPTIPPGISPHVCK(SEQ IDNO:149)、VWEQIDQMK(SEQ ID NO:77)、FTDEEVDELYREAPIDK(SEQ ID NO:78)、DTHFPIC(UniMod:4)IFC(UniMod:4)C(UniMod:4)GC(UniMod:4)C(UniMod:4)HR(SEQ ID NO:79)、DTHFPICIFCCGCCHR(SEQ ID NO:150)、RQDNEILIFWSK(SEQ ID NO:80)、QDNEILIFWSK(SEQ IDNO:81)、EVGTVLSQVYSK(SEQ ID NO:82)、MVTALGTHWHPEHFC(UniMod:4)C(UniMod:4)VSC(UniMod:4)GEPFGDEGFHER(SEQ ID NO:83)、MVTALGTHWHPEHFCCVSCGEPFGDEGFHER(SEQ IDNO:151)、EVTFHC(UniMod:4)HEGYILHGAPK(SEQ ID NO:84)、EVTFHCHEGYILHGAPK(SEQ IDNO:152)、GAGGQSMSEAPTGDHAPAPTR(SEQ ID NO:85)、DGSFSVVITGLR(SEQ ID NO:86)、GISLNPEQWSQLK(SEQ ID NO:87)、LVHVEEPHTETVR(SEQ ID NO:88)、RVEPYGENFNK(SEQ IDNO:89)、LDDC(UniMod:4)GLTEAR(SEQ ID NO:90)、LDDCGLTEAR(SEQ ID NO:153)、LVQAAQMLQSDPYSVPAR(SEQ ID NO:91)、DFLGFYVVDSHR(SEQ ID NO:92)、YGTC(UniMod:4)IYQGR(SEQ ID NO:93)、YGTCIYQGR(SEQ ID NO:154)、WLQEGGQEC(UniMod:4)EC(UniMod:4)K(SEQ ID NO:94)、WLQEGGQECECK(SEQ ID NO:155)、ASGPPVSELITK(SEQ ID NO:95)、ELSDFISYLQR(SEQ ID NO:96)、EGHVLQGPSVLK(SEQ ID NO:97)、MNLASEPQEVLHIGSAHNR(SEQID NO:98)、FLILPDMLK(SEQID NO:99)、GISQEQMNEFR(SEQ ID NO:100)、DPNHFRPAGLPEK(SEQID NO:101)、VPSHLQAETLVGK(SEQ ID NO:102)、NLHFLTTQEDYTLK(SEQ ID NO:103)、SEAYNTFSER(SEQ ID NO:104)、AVLDVFEEGTEASAATAVK(SEQ ID NO:105)、VIQYLAYVASSHK(SEQ ID NO:106)、ASYAQQPAESR(SEQ ID NO:107)、YLEESNFVHR(SEQ ID NO:108)、GSFTYFAPSNEAWDNLDSDIR(SEQ ID NO:109)、ALTDMPQM(UniMod:35)R(SEQ ID NO:110)、LAVNM(UniMod:35)VPFPR(SEQ ID NO:111)、TSC(UniMod:4)LLFMGR(SEQ ID NO:112)、QQQHLFGSNVTDC(UniMod:4)SGNFC(UniMod:4)LFR(SEQ ID NO:113)、ALTDMPQMR(SEQ ID NO:156)、LAVNMVPFPR(SEQ ID NO:157)、TSCLLFMGR(SEQ ID NO:158)、QQQHLFGSNVTDCSGNFCLFR(SEQ ID NO:159)、DYVSQFEGSALGK(SEQ ID NO:114)、DSITTWEILAVSMSDK(SEQ ID NO:115)、FC(UniMod:4)NIMGSSNGVDQEHFSNVVK(SEQ ID NO:116)、FCNIMGSSNGVDQEHFSNVVK(SEQ IDNO:160)、SEHPGLSIGDTAK(SEQ ID NO:117)、QFVEQHTPQLLTLVPR(SEQ ID NO:118)、NQDLAPNSAEQASILSLVTK(SEQ ID NO:119)、TDGALLVNAMFFK(SEQ ID NO:120)、DDFEGQLESDRFLLMSGGK(SEQ ID NO:121)、SIQC(UniMod:4)LTVHK(SEQ ID NO:122)、SIQCLTVHK(SEQ ID NO:161)、EDITQSAQHALR(SEQ ID NO:123)、VVAC(UniMod:4)TSAFLLWDPTK(SEQ ID NO:124)、VVACTSAFLLWDPTK(SEQ ID NO:162)、NYPMHVFAYR(SEQ IDNO:125)、MEEVEAMLLPETLK(SEQ ID NO:126)、ADVQAHGEGQEFSITC(UniMod:4)LVDEEEM(UniMod:35)K(SEQ ID NO:127)、ADVQAHGEGQEFSITCLVDEEEMK(SEQ ID NO:163)、DFALLSLQVPLK(SEQ ID NO:128)、LLIYAVLPTGDVIGDSAK(SEQ ID NO:129)、VDIVAINDPFIDLNYMVYMFQYDSTHGK(SEQ ID NO:130)、AEQINQAAGEASAVLAK(SEQ ID NO:131)、TPAYYPNAGLIK(SEQ ID NO:132)、QGENGQMM(UniMod:35)SC(UniMod:4)TC(UniMod:4)LGNGK(SEQ ID NO:133)、QGENGQMMSCTCLGNGK(SEQ ID NO:164)、YWEMQPATFR(SEQ ID NO:134)、HGEYWLGNK(SEQ ID NO:135)、In some embodiments, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, or 28 peptides. In some embodiments, the biomarkers include any of the following lipids: 1-palmitoyl-GPE (16:0), phosphatidylcholine (18:0 / 20:2, 20:0 / 18:2), linoleamide (18:2n6), linolenamide (18:3), 2-aminocaprylate, 1-linoleoyl-2-arachidonoyl-GPC (18:2 / 20:4n6), 1-palmitoylglycerol (16:0), 1-oleoyl-GPC (18:1), 1-linolenoyl-GPC (18:3), pregnanolone / isopregnanolone sulfate, sphingomyelin ( d18:2 / 24:1, d18:1 / 24:2), myristamide (14:1), 1-linoleoylglycerol (18:2), 11β-hydroxyandrosterone glucuronide, 2S,3R-dihydroxybutyrate, glycosyl-N-behenoyl sphingosine (d18:1 / 22:0), 1-palmitoyl-2-linoleoyl-GPC (16:0 / 18:2), 1-stearoyl-2-arachidonoyl-GPS (18:0 / 20:4), 1-tetracosyl-GPC (24:0), 3β-hydroxy-5-cholestenol ester, 5α-androstane-3α ,17β-diol monosulfate (2), hexadecenedioate (C16:1-DC), myristamide (14:0), 1-stearoyl-GPE (18:0), 1-myristoyl-2-arachidonoyl-GPC (14:0 / 20:4), 1-arachidonoyl-GPC (20:0), 4-hydroxy-2-oxoglutarate, nicotinic acid ester (24:6n3), sphingomyelin (d17:1 / 16:0, d18:1 / 15:0, d16:1 / 17:0), 3-hydroxyoctanoate, 1-arachidonoylglycerol (20:4), 1 -stearoyl-2-oleoyl-GPS (18:0 / 18:1), 1-eicosenoyl-GPE (20:1), sphingosine, glycoursodeoxycholic acid sulfate (1), 1-stearoyl-2-linoleoyl-GPC (18:0 / 18:2), erucate (22:1n9), phosphoethanolamine, androstenone glucuronic acid ester, behenyl dihydrosphingomyelin (d18:0 / 22:0), androstenediol (3α,17α) monosulfate (2), isoursodeoxycholate, N-stearoylsphingosine (d18:1 / 18:0), heptadecanamide (17:0),1-eicosenoyl-GPC (20:1), tetrahydrocortisone glucuronide (5), linoleoylcarnitine (C18:2), hydroxypalmitoyl sphingomyelin (d18:1 / 16:0(OH)), or 1-eicosapentaenoyl-GPC (20:5). In some embodiments, the biomarker includes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, or 28 lipids. In some embodiments, the biomarker comprises any one of the following metabolites: N-acetylcarnosine, indole lactate, lanthionine, 3-(4-hydroxyphenyl) lactate, hydantoin-5-propionate, urea, homoarginine, β-citrylglutamate, S-1-pyrroline-5-carboxylate, aspartic acid, isovalerylcarnitine (C5), creatine, N-acetylglucosamine / N-acetylgalactosamine, galactonic acid, N-acetylneuraminic acid, 3-phosphoglyceric acid, bilirubin (E, Z or Z, E), retinol (vitamin A), heme, nicotinamide, carotene diol (1), bilirubin (Z, Z), 1-methylnicotinamide, α-ketoglutarate, xanthine, phenylacetyl Carnitine, HWESASXX, 5-acetamido-6-formylamino-3-methyluracil, 2-keto-3-deoxygluconate, iminodiacetate (IDA), 4-acetaminophen sulfate, caffeic acid sulfate, 2-hydroxyacetaminophen sulfate, 3-formyl indole, X-18779, X-24473, X-23593, X-24307, X-24027, X-14939, X-12456, X-25790, X-17146, X-15220, X-12740, X-17765, X-25420, X-23639, X-12462, X-15728, or X-25422. In some embodiments, the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, or 28 metabolites. In some embodiments, the classifier includes performance characterized by a receiver operating characteristic (ROC) curve having a mean or median area under the curve (AUC) of at least 0.8. In some embodiments, the subject is suspected of having lung cancer. In some embodiments, the evaluation includes identifying the biomarker as indicative of lung cancer. In some embodiments, the method further includes administering a lung cancer treatment to the subject when the subject has lung cancer. In some embodiments, the method further includes monitoring the subject when the subject does not have lung cancer. In some embodiments, the lung cancer includes non-small cell lung cancer. In some embodiments, the lung cancer includes stage 1,Stage 2 or stage 3 lung cancer. In some embodiments, the lung cancer comprises stage 4 lung cancer.

[0012] In some aspects, disclosed herein are multi-omics methods. The methods may include obtaining multi-omics data generated from one or more biological fluid samples collected from subjects suspected of having a disease state, the multi-omics data including proteomic measurements and nucleic acid sequencing measurements; applying a classifier to the multi-omics data to evaluate the disease state; and any of (i)-(iv): (i) wherein the proteomic measurements are generated after a sample in the one or more biological fluid samples has undergone an enrichment protocol that enriches for one protein or peptide without enriching for another protein or peptide, (ii) wherein the proteomic measurements are generated based on the amount of protein or peptide in the sample added to the one or more biological fluid samples, or (iii) wherein the classifier comprises a performance characteristic comprising a mean or median area under the receiver operating characteristic (ROC) curve (AUC) of at least 0.9, as determined in a data set derived from a randomized controlled trial of at least 20 subjects having the disease state and more than 20 control subjects not having the disease state, or (iv) wherein the evaluation comprises selecting a cancer therapy based on the multi-omics data, the proteomic measurements being generated using mass spectrometry. In some aspects, proteomic measurements are generated after the sample in the one or more biological fluid samples has experienced an enrichment scheme that enriches some proteins but does not enrich other proteins. In some aspects, proteomic measurements are generated by proteins adsorbed to nanoparticles. In some aspects, proteomic measurements are generated based on the amount of the protein in the sample added to the one or more biological fluid samples. In some aspects, the protein added to the sample is labeled. In some aspects, nucleic acid sequencing measurements include mRNA sequencing measurements. In some aspects, nucleic acid sequencing measurements include mRNA sequencing measurements and miRNA sequencing measurements. In some aspects, multi-omics data include measurements of more than 45 peptides or protein groups. In some aspects, the evaluation has a performance higher than at least 4% when the classifier is applied to only one type of omics data, wherein the performance is included in the sensitivity under a given specificity, as determined in the data set derived from more than 25 objects with disease states and more than 25 control subjects without disease states. In some aspects, the classifier is characterized by a mean area under the curve (AUC) of a receiver operating characteristic (ROC) curve of at least 0.9, as determined in a data set derived from a randomized controlled trial of at least 20 subjects with the disease state and more than 20 control subjects without the disease state.In some aspects, applying a classifier to multi-omic data to evaluate a disease state comprises applying a first classifier to proteomic measurements to generate a first marker corresponding to the presence, absence, or likelihood of a disease state, applying a second classifier to nucleic acid sequencing measurements to generate a second marker corresponding to the presence, absence, or likelihood of a disease state, and evaluating the disease state based on (a), (b), or (c): (a) an unweighted average of the first and second markers, (b) a weighted average of the first and second markers, or (c) a majority vote score based on the first and second markers. Some aspects include evaluating the disease state based on a weighted average of the first and second markers, wherein the weighted average is generated by assigning weights to the results of the first and second classifiers based on area under the ROC curve, area under the precision-recall curve, accuracy, precision, recall, sensitivity, F1-score, specificity, or a combination thereof. In some aspects, classifier is applied to multi-omics data to evaluate disease state and comprises: obtain the subset of feature from proteomic measurement value; obtain at least one subset of feature from nucleic acid sequencing measurement value; the subset of feature in the first group of data and the feature in the second group of data are collected to obtain the feature through collection; and the disease state is evaluated based on the feature through collection. In some aspects, the subset of feature obtained from the first group of data or the second group of data includes obtaining the top feature (top feature) based on univariate data. In some aspects, classifier is trained using deep learning, hierarchical cluster analysis, principal component analysis, partial least squares discriminant analysis, random forest classification analysis, support vector machine analysis, k nearest neighbor analysis, naive Bayes (naiveBayes) analysis, K-means cluster analysis or hidden Markov (hidden Markov) analysis. In some aspects, multi-omics data further includes metabolomics data. In some aspects, disease state includes cancer. In some aspects, cancer is selected from the group consisting of: lung cancer, pancreatic cancer, breast cancer, colon cancer, liver cancer and ovarian cancer. In some aspects, the evaluation comprises selecting a cancer therapy based on the multi-omics data. In some aspects, the evaluation comprises administering chemotherapy, medication, radiation, or surgical cancer therapy to the subject based on the evaluation. In some aspects, the one or more biological fluid samples comprise blood, serum, or plasma samples. In some aspects, the subject is a human.In some aspects, disclosed herein are multi-omic methods comprising: obtaining multi-omic data generated from one or more blood, serum, or plasma samples collected from a human subject suspected of having cancer, the multi-omic data comprising proteomic measurements and RNA sequencing measurements; applying a classifier to the multi-omic data to evaluate the cancer; selecting or administering a cancer therapy to the subject based on the evaluation; and any of (i)-(iii): (i) wherein the proteomic measurement is generated after a sample in the one or more blood, serum, or plasma samples has been enriched for proteins or peptides with an affinity reagent, (ii) wherein the proteomic measurement is generated based on the amount of labeled proteins or peptides in a sample added to the one or more blood, serum, or plasma samples, or (iii) wherein the classifier comprises a performance characteristic comprising a mean area under the curve (AUC) of a receiver operating characteristic (ROC) curve of at least 0.9, as determined in a held-out dataset derived from a randomized controlled trial of at least 25 subjects with a disease state and more than 25 control subjects without the disease state. In some embodiments, the proteomic measurements are generated after the sample in the one or more blood, serum, or plasma samples has been enriched with an affinity reagent. In some embodiments, the proteomic measurements are generated based on the amount of labeled protein in the sample added to the one or more blood, serum, or plasma samples. In some embodiments, the classifier is characterized by a receiver operating characteristic (ROC) curve with a mean area under the curve (AUC) of at least 0.9, as determined in a data set derived from a randomized controlled trial of at least 25 subjects with the disease state and more than 25 control subjects without the disease state.

[0013] In some aspects, disclosed herein are multi-omic disease detection methods comprising: obtaining multi-omic data generated from one or more biological fluid samples collected from a subject, the multi-omic data comprising first omic data, the first omic data comprising proteomic data, metabolomic data, transcriptomic data, or genomic data; and second omic data, the second omic data comprising proteomic data, metabolomic data, transcriptomic data, or genomic data that is different from the first omic data; and assigning a first label comprising the presence, absence, or likelihood of a disease state to the first omic data using a first classifier, assigning a second label comprising the presence, absence, or likelihood of a disease state to the second omic data using a second classifier, and identifying the multi-omic data as indicating or not indicating a disease state based on the first and second labels. In some aspects, the first omic data comprises proteomic data, and the second omic data comprises metabolomic data, transcriptomic data, or genomic data. In some aspects, the proteomic data is generated by contacting a biological fluid sample of the biological fluid sample with particles such that the particles adsorb biomolecules comprising proteins. In some aspects, the particles include carboxylate particles, polyacrylic acid particles, dextran particles, polystyrene particles, dimethylamine particles, amino particles, silica particles or N-(3-trimethoxysilylpropyl) diethylenetriamine particles. In some aspects, the particles include different nanoparticle groups in physicochemical terms. In some aspects, proteomics data are generated using mass spectrometry, chromatography, liquid chromatography, high performance liquid chromatography, solid phase chromatography, lateral flow assay, immunoassay, enzyme-linked immunosorbent assay, Western blotting, dot blotting or immunostaining or a combination thereof. In some aspects, genomics data or transcriptomics data are generated by sequencing, microarray analysis, hybridization, polymerase chain reaction, electrophoresis or a combination thereof. In some aspects, the second group of studies data include transcriptomics data. In some aspects, transcriptomics data include mRNA or microRNA expression data. In some aspects, the second group of studies data include genomics data. In some aspects, genomics data include DNA sequence data or epigenetic data. In some aspects, multi-omics data are identified as indicating or not indicating disease state and include identifying multi-omics data as indicating or not indicating disease state based on the first mark or the second mark. In some aspects, multi-omics data are identified as indicating or not indicating disease state and include generating or obtaining majority vote score based on the first and second marks. In some aspects, multi-omics data are identified as indicating or not indicating disease state and include generating or obtaining the weighted average of the first and second marks. Some aspects include assigning weights to a classifier and a second classifier based on receiver operating characteristic (ROC) area under the curve, precision-recall area under the curve, accuracy, precision, recall, sensitivity, F1 score, specificity or their combination, so as to obtain weighted average.In some aspects, the first group of data is generated from a first biological fluid sample of a biological fluid sample, and the second group of data is generated from a second biological fluid sample of the biological fluid sample. In some aspects, the first biological fluid sample is collected in a first container, the first container comprising a first collection component, the first collection component comprising heparin, ethylenediaminetetraacetic acid (EDTA), citrate, or an anti-lysate, wherein the second biological fluid sample is collected in a second container, the second container comprising a second collection component different from the first collection component, and the second collection component comprising heparin, EDTA, citrate, or an anti-lysate. In some aspects, the multi-omics data further includes a third group of data, the third group of data comprising a third group of data types. The third group of data may include a group of data types or subtypes different from the first and second group of data. Some aspects include using a third classifier to assign a third marker corresponding to the presence, absence, or possibility of a disease state to the third group of data. In some aspects, identifying the multi-omics data as indicating or not indicating a disease state includes identifying the multi-omics data as indicating or not indicating a disease state based on a combination of the first, second, or third markers. Some aspects include using the third sorter to assign the third mark including the presence, absence or possibility of disease state to the third group of data that is different from the first and second group of data, and wherein based on the first and second marks, multiple group of data are identified as indicating or not indicating disease state, including based on the first, second and third marks, multiple group of data are identified as indicating or not indicating disease state. In some aspects, the first group of data types includes proteomics data, the second group of data types includes mRNA transcriptomics data, and the third group of data types includes microRNA transcriptomics data (that is, microRNA data). Some aspects include transmitting or exporting the information related to identification. Some aspects include recommending the treatment of disease state.

[0014] In some aspects, disclosed herein are methods comprising: obtaining combined data generated from one or more biological fluid samples from a subject, the combined data comprising two, three, or four of the following: proteomic data, metabolomic data, transcriptomic data, or genomic data; and using a classifier to identify the combined data as indicating or not indicating one or more disease states. In some aspects, the one or more biological fluid samples comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more biological fluid samples. In some aspects, the combined data are generated simultaneously. In some aspects, simultaneous data generation comprises simultaneously determining two, three, or four of the proteomic data, metabolomic data, transcriptomic data, or genomic data. In some aspects, simultaneous data generation comprises determining two, three, or four of the proteomic data, metabolomic data, transcriptomic data, or genomic data at separate locations on a determination substrate. In some aspects, the separate locations comprise separate wells, and the determination substrate comprises an assay plate. In some aspects, the one or more biological fluid samples include two or more of a whole blood sample, a plasma sample, a serum sample or a urine sample. In some aspects, proteomic data are generated from the biological fluid sample in the one or more biological fluid samples. In some aspects, metabolomics data are generated from the biological fluid sample or from the other biological fluid sample in the one or more biological fluid samples, wherein the proteomic data and the metabolomics data are combined to obtain the combined data. In some aspects, the classifier identifies the combined data as indicating or not indicating one or more disease states with a sensitivity or specificity higher than independent proteomic data, metabolomics data, transcriptomics data or genomics data. In some aspects, the classifier includes the feature selected from proteomic data, metabolomics data, genomics data or transcriptomics data. In some aspects, the classifier includes the feature selected from the combination of proteomic data, metabolomics data, genomics data or transcriptomics data. In some aspects, the classifier includes numerous classifiers. In some aspects, the numerous classifiers include 2,3 or 4 or more classifiers. In some aspects, the numerous classifiers separately comprise features selected from proteomic data, metabolomics data, genomic data, transcriptomics data or a combination thereof. In some aspects, using the classifier to identify the combined data as indicating or not indicating one or more disease states comprises using the numerous classifiers to identify the combined data as indicating or not indicating one or more disease states. In some aspects, using the classifier to identify the combined data as indicating or not indicating one or more disease states comprises selecting the output of any one of the numerous classifiers. In some aspects, using the classifier to identify the combined data as indicating or not indicating one or more disease states comprises a majority vote across numerous classifiers.In some aspects, the data identified as being combined using the classifier as indicating or not indicating one or more disease states include a majority vote across a subset of numerous classifiers. In some aspects, the data identified as being combined using the classifier as indicating or not indicating one or more disease states include a weighted average of the numerous classifiers. In some aspects, the data identified as being combined using the classifier as indicating or not indicating one or more disease states include a weighted average of the numerous classifiers. In some aspects, the weight of the weighted average is assigned based on the area under the receiver operating characteristic (ROC) curve. In some aspects, the weight of the weighted average is assigned based on the area under the precision-recall curve. In some aspects, the weight of the weighted average is assigned based on accuracy. In some aspects, the weight of the weighted average is assigned based on precision. In some aspects, the weight of the weighted average is assigned based on recall. In some aspects, the weight of the weighted average is assigned based on sensitivity. In some aspects, the weight of the weighted average is assigned based on F1 score. In some aspects, the weight of the weighted average is assigned based on specificity.

[0015] In some aspects, disclosed herein are methods comprising: obtaining proteomic data generated from a biological fluid sample from a subject; obtaining metabolomic data, transcriptomic data, or genomic data generated from the biological fluid sample or from another biological fluid sample from the subject, wherein the proteomic data and the metabolomic data, transcriptomic data, or genomic data are combined to obtain combined data; and using a classifier to identify the combined data as indicative of or not indicative of one or more disease states. In some aspects, the proteomic data is generated by contacting the biological fluid sample from the subject with particles such that the particles adsorb biomolecules comprising proteins. Some aspects include contacting the biological fluid sample from the subject with particles such that the particles adsorb biomolecules. Some aspects include analyzing the biomolecules adsorbed to the particles to generate the proteomic data. Some aspects include analyzing the biological fluid sample or another biological fluid sample to generate the metabolomic data. Some aspects include using a classifier to identify the combined data as indicative of or not indicative of the one or more disease states. In some aspects, the proteomic data is generated by measuring a readout indicating the presence, absence, or amount of a biomolecule. In some aspects, proteomic data are generated using mass spectrometry, chromatography, liquid chromatography, high performance liquid chromatography, solid phase chromatography, lateral flow assay, immunoassay, enzyme-linked immunosorbent assay, Western blotting, dot blotting or immunostaining or a combination thereof. In some aspects, proteomic data are generated using mass spectrometry. In some aspects, the proteins include secreted proteins. In some aspects, the particles include nanoparticles. In some aspects, the particles include lipid particles, metal particles, silica particles or polymer particles. In some aspects, the particles include carboxylate particles, polyacrylic acid particles, dextran particles, polystyrene particles, dimethylamine particles, amino particles, silica particles or N-(3-trimethoxysilylpropyl)diethylenetriamine particles. In some aspects, the particles include physicochemically different groups of nanoparticles. In some aspects, metabolomics data are generated by biological fluid samples different from proteomic data. In some respects, metabolomics data are to use mass spectrometry, electrophoresis, colorimetry, fluorimetry, chromatography, liquid chromatography, high performance liquid chromatography, solid phase chromatography, lateral flow assay, immunoassay or their combination to generate. In some respects, metabolomics data are to use mass spectrometry to generate. In some respects, metabolomics data are to generate from the biological fluid sample identical with proteomics data. In some respects, metabolomics data are to generate by analyzing the analyte adsorbed on the particle. In some respects, metabolomics data comprise lipid metabolite data, carbohydrate metabolite data, vitamin metabolite data or cofactor metabolite data or their combination. In some respects, biological fluid sample comprises blood sample, plasma sample or serum sample.In some aspects, the additional biofluid sample is collected from the subject in a container separate from the biofluid sample. In some aspects, the combined data is generated from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more samples. In some aspects, the 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more samples are collected separately in 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more containers. In some aspects, the 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more containers also contain multiple components in addition to the sample. In some aspects, the biofluid sample and the additional biofluid sample are collected in separate containers, and the separate containers contain different components. In some aspects, the first container in the separate containers contains a first component, and the first component is different from the second component in the second container in the separate container. In some aspects, the biological fluid sample comprises serum; has been collected in a container containing ethylenediaminetetraacetic acid (EDTA), citrate or heparin; or comprises a preservative to prevent cell lysis. In some aspects, the biological fluid sample has been collected in a container containing ethylenediaminetetraacetic acid (EDTA). In some aspects, the other biological fluid sample comprises a blood sample, a plasma sample or a serum sample. In some aspects, the other biological fluid sample has been processed to obtain cell-free DNA or obtain RNA. Some aspects include obtaining genomics or transcriptomics data generated from the biological fluid sample, from the other biological fluid sample or from a third biological fluid sample from an object. In some aspects, the combined data further includes genomics or transcriptomics data. Some aspects include analyzing the biological fluid sample, the other biological fluid sample or the third biological fluid sample to generate genomics or transcriptomics data. In some aspects, the third biological fluid sample comprises a blood sample, a plasma sample or a serum sample. In some aspects, the third biological fluid sample has been processed to obtain cell-free DNA or obtain RNA. In some aspects, genomics or transcriptomics data are generated by the reading of the presence, absence or amount of indicator nucleic acid. In some aspects, genomics or transcriptomics data are generated by order-checking, microarray analysis, hybridization, polymerase chain reaction, electrophoresis or their combination. In some aspects, genomics or transcriptomics data are generated from the biofluid sample different from metabolomics data. In some aspects, genomics or transcriptomics data are generated from the biofluid sample identical with metabolomics data. In some aspects, genomics or transcriptomics data are generated from the biofluid sample different from p data. In some aspects, genomics or transcriptomics data are generated from the biofluid sample identical with proteomics data.In some aspects, genomics or transcriptomics data are generated by analyzing nucleic acids adsorbed to particles. In some aspects, genomics or transcriptomics data include genomics data. In some aspects, genomics data include DNA sequence data. In some aspects, genomics data include DNA polymorphism data. In some aspects, genomics data include epigenetic data. In some aspects, genomics data include DNA methylation data. In some aspects, epigenetic data include histone modification data. In some aspects, histone modification data include acetylation data, methylation data, ubiquitination data, phosphorylation data, sumoylation data, ribosylation data or citrullination data. In some aspects, genomics or transcriptomics data include transcriptomics data. In some aspects, transcriptomics data include RNA sequence data. In some aspects, transcriptomics data include RNA expression data. In some aspects, transcriptomics data include mRNA, tRNA, rRNA, microRNA, snRNA, snoRNA or lncRNA expression data. In some aspects, transcriptomics data include mRNA expression data. In some aspects, transcriptomics data include microRNA expression data. In some aspects, classifier includes the feature for identifying the data through combination as indicating the one or more disease states. In some aspects, the feature includes control protein measured value, control metabolite measured value, control nucleic acid measured value, mass spectrum, m / z ratio, chromatographic result, immunoassay result, light or fluorescence intensity or sequence information. In some aspects, classifier is to use deep learning, hierarchical cluster analysis, principal component analysis, partial least squares discriminant analysis, random forest classification analysis, support vector machine analysis, k nearest neighbor analysis, naive Bayes (naive Bayes) analysis, K-means cluster analysis or hidden Markov (hidden Markov) analysis to train. In some aspects, the one or more disease states include one or more cancers. In some aspects, the one or more cancers include lung cancer, breast cancer, prostate cancer, colorectal cancer, colon cancer, melanoma, bladder cancer, lymphoma, leukemia, kidney cancer, uterine cancer, pancreatic cancer or a combination thereof. In some aspects, classifier discriminates the one or more disease states. In some aspects, the classifier distinguishes lung cancer, colon cancer and pancreatic cancer. In some aspects, the classifier distinguishes lung cancer, colon cancer and pancreatic cancer. In some aspects, lung cancer includes non-small cell lung cancer (NSCLC). Some aspects include generating a report based on the use of the classifier for identifying the combined data as indicating or not indicating the one or more disease states. In some aspects, the report includes the possibility or indication that a biological fluid or object contains the one or more disease states. In some aspects, the report includes outputting or transmitting the report. In some aspects, the report is used by a medical professional for diagnosing at least one of the one or more disease states, giving medical advice or providing treatment.Some aspects include identifying the combined data as indicative of the one or more disease states. In some aspects, the one or more disease states include cancer, and further including recommending a cancer treatment for the subject when the combined data is identified as indicative of cancer. In some aspects, the one or more disease states include cancer, and further including administering a cancer treatment to the subject when the combined data is identified as indicative of cancer. In some aspects, the cancer treatment includes chemotherapy, radiotherapy, ablative therapy, embolization, or surgery. Some aspects include using a classifier to identify the combined data as indicative of a first disease state among the one or more disease states, but not a second disease state among the one or more disease states. Some aspects include administering or recommending treatment for the first disease state but not the second disease state. Some aspects include identifying the combined data as not indicative of the one or more disease states. Some aspects include observing the subject without providing treatment to the subject when the combined data is identified as not indicative of the one or more disease states. In some aspects, observing the subject without providing treatment includes analyzing biomolecules in a biological fluid sample obtained later from the subject. In some aspects, the subject is a mammal. In some aspects, the subject is a human. In some aspects, the classifier includes the feature selected from proteomic data, metabolomics data, genomic data or transcriptomics data. In some aspects, the classifier includes the feature selected from the combination of proteomic data, metabolomics data, genomic data or transcriptomics data. In some aspects, the classifier includes numerous classifiers. In some aspects, the numerous classifiers include 2,3 or 4 or more classifiers. In some aspects, the numerous classifiers separately comprise the feature selected from proteomic data, metabolomics data, genomic data, transcriptomics data or their combination. In some aspects, using the classifier the data identified as combined to indicate or not indicate one or more disease states include using the numerous classifiers the data identified as combined to indicate or not indicate one or more disease states. In some aspects, using the classifier the data identified as combined to indicate or not indicate one or more disease states include selecting the output of any one classifier in numerous classifiers. In some aspects, using the classifier the data identified as combined to indicate or not indicate one or more disease states include the majority vote across numerous classifiers. In some aspects, identifying the combined data as indicative of or not indicative of one or more disease states using the classifier comprises a majority vote across a subset of the plurality of classifiers. In some aspects, identifying the combined data as indicative of or not indicative of one or more disease states using the classifier comprises a weighted average of the plurality of classifiers. In some aspects, identifying the combined data as indicative of or not indicative of one or more disease states using the classifier comprises a weighted average of the plurality of classifiers.In some aspects, the weights of the weighted average are assigned based on the area under the receiver operating characteristic (ROC) curve. In some aspects, the weights of the weighted average are assigned based on the area under the precision-recall curve. In some aspects, the weights of the weighted average are assigned based on accuracy. In some aspects, the weights of the weighted average are assigned based on precision. In some aspects, the weights of the weighted average are assigned based on recall. In some aspects, the weights of the weighted average are assigned based on sensitivity. In some aspects, the weights of the weighted average are assigned based on F1 score. In some aspects, the weights of the weighted average are assigned based on specificity.

[0016] In some aspects, disclosed herein are methods comprising: obtaining multi-omic data generated from one or more biological fluid samples collected from a subject, the multi-omic data comprising first omic data and second omic data, wherein the first omic data comprises a first omic data type, the first omic data type comprising proteomic data, metabolomic data, transcriptomic data, or genomic data, and wherein the second omic data comprises a second omic data type different from the first omic data type and comprising proteomic data, metabolomic data, transcriptomic data, or genomic data; identifying a first subset of features from the first set of data; identifying a second subset of features from the second set of data; aggregating the first and second subsets of features; and identifying the multi-omic data as indicating or not indicating a disease state based on the aggregated subsets of features. In some aspects, identifying the first or second subset of features from the first or second omic data comprises obtaining univariate data of features of the first or second omic data, and identifying the first or second subset based on the univariate data. In some aspects, the first or second subset of features is identified from features of a classifier used for the first or second omic data. In some aspects, identifying the first or second subset of features from the first or second omics data comprises obtaining a classifier for the first or second omics data, and identifying the first or second subset as top-ranked features of the classifier. In some aspects, identifying the first or second subset of features from the first or second omics data comprises obtaining a classifier for the first or second omics data, removing one or more features at a time from the classifier, and identifying which features, when removed from the classifier, would reduce performance of the classifier.

[0017] In some embodiments, the disease or condition comprises pancreatic cancer. In some aspects, disclosed herein are multi-omics cancer detection methods for detecting pancreatic cancer. In some aspects, disclosed herein are methods for detecting pancreatic cancer in a subject, the methods comprising: identifying a subject at risk of developing pancreatic cancer; obtaining a biological fluid sample from the subject; contacting the biological fluid sample with particles so that the particles adsorb biomolecules comprising proteins onto the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and classifying the proteomic data as indicating pancreatic cancer or not indicating pancreatic cancer. In some aspects, disclosed herein are methods comprising: measuring proteins in a biological fluid sample obtained from a subject identified as having a risk of developing pancreatic cancer to obtain a protein measurement value; and applying a classifier to the protein measurement value, thereby identifying the protein measurement value as indicating that the subject has pancreatic cancer, wherein the classifier is generated using proteomic data, the proteomic data being obtained by contacting a training sample with particles so that the particles adsorb proteins in the training sample and measuring the proteins adsorbed to the particles. In some aspects, disclosed herein are methods of treatment comprising: identifying a mass in a pancreas of a subject; obtaining a biofluid sample from the subject; contacting the biofluid sample with particles such that the particles adsorb biomolecules comprising proteins to the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and classifying the proteomic data as indicating that the mass comprises pancreatic cancer or not indicating that the mass comprises pancreatic cancer. In some aspects, disclosed herein are methods of evaluating a subject suspected of having pancreatic cancer comprising: measuring biomarkers in a biofluid sample from the subject, wherein the biomarkers comprise A2GL, AKR1B1, ANPEP, ANTXR1, ANTXR2, BTK, CALR, CDH1, CDH11, CDH2, CDHR2, CILP2, CLEC3B, COL18A1, CRP, EXT1, F13A1, FAT1, FGL1, FLT4, ICAM1, IDH2, LCN2, LPP, MAPK1, MAP2K1, MYH9, NOTCH1, NOTCH2, PIGR, PPP2R1A, PRKAR1A, PXDN, REL N, RHOA, S100A8, S100A9, S100A12, SAA1, SAA2, SERPINA3, SLAIN2, SND1, SVEP1, TSP2, TUBB, TUBB1 or VCAN.In some aspects, methods are disclosed herein that include: measuring a biomolecule in a biological fluid sample obtained from a subject suspected of having pancreatic cancer to obtain a biomolecule measurement value; and identifying a protein measurement value as indicating that the subject has pancreatic cancer or does not have pancreatic cancer by applying a classifier to the biomolecule measurement value, wherein the classifier is characterized by an area under the curve (AUC) of a receiver operating characteristic (ROC) curve based on the biomolecule measurement value characteristics greater than 0.7, greater than 0.75, greater than 0.8, greater than 0.85, greater than 0.9, greater than 0.91, greater than 0.92, greater than 0.93, or greater than 0.94. In some aspects, the AUC is no greater than 0.75, no greater than 0.8, no greater than 0.85, no greater than 0.9, no greater than 0.91, no greater than 0.92, no greater than 0.93, no greater than 0.94, no greater than 0.95, or no greater than 0.96. In some aspects, the biomolecule comprises a protein, a lipid, or a metabolite, or a combination thereof.

[0018] In some embodiments, the disease or condition comprises liver cancer. In some aspects, disclosed herein is a multi-omics cancer detection method for detecting liver cancer. In some aspects, disclosed herein is a method for detecting liver cancer in a subject, the method comprising: identifying the subject as being at risk of developing liver cancer; obtaining a biological fluid sample from the subject; contacting the biological fluid sample with particles so that the particles adsorb biomolecules comprising proteins onto the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and classifying the proteomic data as indicating liver cancer or not indicating liver cancer. In some aspects, disclosed herein is a method comprising: measuring proteins in a biological fluid sample obtained from a subject identified as having a risk of developing liver cancer to obtain a protein measurement value; and applying a classifier to the protein measurement value, thereby identifying the protein measurement value as indicating that the subject has liver cancer, wherein the classifier is generated using proteomic data, the proteomic data being obtained by contacting a training sample with particles so that the particles adsorb proteins in the training sample and measuring the proteins adsorbed to the particles. In some aspects, disclosed herein are methods of treatment comprising: identifying a mass in a subject's liver; obtaining a biofluid sample from the subject; contacting the biofluid sample with particles such that the particles adsorb biomolecules comprising proteins to the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and classifying the proteomic data as indicating that the mass comprises liver cancer or is not indicative of liver cancer. In some aspects, disclosed herein are methods of detecting liver cancer in a subject comprising: identifying the subject as being at risk for developing liver cancer; obtaining a biofluid sample from the subject; measuring lipids in the biofluid sample to obtain lipid data; and classifying the lipid data as indicating that the liver cancer or is not indicative of liver cancer.

[0019] In some embodiments, the disease or condition includes ovarian cancer. In some aspects, disclosed herein is a multi-omics cancer detection method for detecting ovarian cancer. In some aspects, disclosed herein is a method for detecting ovarian cancer in a subject, the method comprising: identifying the subject as being at risk of developing ovarian cancer; obtaining a biofluid sample from the subject; contacting the biofluid sample with particles so that the particles adsorb biomolecules comprising proteins to the particles; determining the biomolecules adsorbed to the particles to generate proteomic data; and classifying the proteomic data as indicating ovarian cancer or not indicating ovarian cancer. In some aspects, identifying the subject as being at risk of developing ovarian cancer includes identifying the subject as having computed tomography (CT) scans indicating ovarian cancer, having magnetic resonance imaging (MRI) scans indicating ovarian cancer, having positron emission tomography (PET) scans indicating ovarian cancer, having transvaginal ultrasound indicating ovarian cancer, having cancer antigen (CA) -125 levels elevated relative to a control or baseline measurement, or having ovarian cysts, or a combination of these. In some aspects, methods are disclosed herein that include: measuring proteins in a biological fluid sample obtained from a subject identified as at risk for developing ovarian cancer to obtain a protein measurement; and applying a classifier to the protein measurement to identify the protein measurement as indicative of the subject having ovarian cancer, wherein the classifier is generated using proteomic data obtained by contacting a training sample with particles such that the particles adsorb proteins from the training sample and measuring the proteins adsorbed to the particles. In some aspects, the proteins include ANTXR2, BMP1, CILP, EIF2AK2, ENO3, F13B, FGL1, or PEBP4. In some aspects, methods are disclosed herein that include: identifying a mass in the subject's ovary; obtaining a biological fluid sample from the subject; contacting the biological fluid sample with particles such that the particles adsorb biomolecules comprising the proteins to the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and classifying the proteomic data as indicating that the mass contains ovarian cancer or not indicative of ovarian cancer. In some aspects, disclosed herein are methods for detecting ovarian cancer in a subject, comprising: identifying the subject as being at risk for developing ovarian cancer; obtaining a biological fluid sample from the subject; measuring lipids in the biological fluid sample to obtain lipid data; and classifying the lipid data as indicative of ovarian cancer or not indicative of ovarian cancer. In some aspects, the lipids comprise one or more phospholipids.

[0020] In some embodiments, the disease or condition comprises colon cancer. In some aspects, disclosed herein are multi-omics cancer detection methods for detecting colon cancer. In some aspects, disclosed herein are methods for detecting colon cancer in a subject, the methods comprising: identifying the subject as being at risk for developing colon cancer; obtaining a biological fluid sample from the subject; contacting the biological fluid sample with particles such that the particles adsorb biomolecules comprising proteins to the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and classifying the proteomic data as indicating colon cancer or not indicating colon cancer. In some aspects, disclosed herein are methods comprising: measuring proteins in a biological fluid sample obtained from a subject identified as having a risk for developing colon cancer to obtain a protein measurement value; and applying a classifier to the protein measurement value, thereby identifying the protein measurement value as indicating that the subject has colon cancer, wherein the classifier is generated using proteomic data, the proteomic data being obtained by contacting a training sample with particles such that the particles adsorb proteins in the training sample and measuring the proteins adsorbed to the particles. In some aspects, a subject is identified as being at risk for developing colon cancer by identifying the subject as having a computed tomography (CT) scan indicating colon cancer, having a liver function test (LFT) indicating colon cancer, having an elevated carcinoembryonic antigen (CEA) level relative to a control or baseline measurement, having blood in the stool, having a fecal immunochemical test (FIT) indicating colon cancer, or having colon nodules, or a combination thereof. In some aspects, disclosed herein are methods of treatment comprising: identifying a mass in the colon of a subject; obtaining a biological fluid sample from the subject; contacting the biological fluid sample with particles such that the particles adsorb biomolecules comprising proteins to the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and classifying the proteomic data as indicating that the mass comprises colon cancer or as not indicative of colon cancer.

[0021] In some aspects, disclosed herein are methods comprising: determining a protein in a biological fluid sample obtained from a subject identified as having a pulmonary nodule to obtain a protein measurement; and applying a classifier to the protein measurement to evaluate the pulmonary nodule; and (i), (ii), or (iii): (i) wherein the classifier comprises a protein signature of the determined protein, and wherein the classifier comprises a performance characteristic for identifying a pulmonary nodule as cancerous or non-cancerous, the performance characteristic comprising a mean or median area under the curve (AUC) of a receiver operating characteristic (ROC) curve greater than 0.65 (e.g., greater than 0.7), such as in samples derived from more than 20 In some aspects, the classifier comprises a protein feature of the determined proteins and is characterized by a median AUC of greater than 0.7 for the average ROC curve for identifying a pulmonary nodule as cancerous or noncancerous, wherein the AUC greater than 0.7 is determined in a data set derived from a randomized, controlled trial of greater than 20 subjects with cancerous pulmonary nodules and greater than 20 control subjects with noncancerous pulmonary nodules, and as determined in a data set that does not include clinical features in the classifier, (ii) wherein the classifier is generated using proteomic data obtained by contacting a training sample with particles such that the particles adsorb proteins in the training sample and measuring the proteins adsorbed to the particles, or (iii) wherein measuring the proteins comprises contacting a biological fluid sample with particles such that the proteins adsorb to the particles and obtaining protein measurements from the adsorbed proteins. In some aspects, the classifier comprises protein features of the determined proteins and is characterized by a median AUC of greater than 0.7 for the average ROC curve for identifying a pulmonary nodule as cancerous or noncancerous, wherein the AUC greater than 0.7 is determined without including non-protein features in a data set derived from a randomized, controlled trial of greater than 20 subjects with cancerous pulmonary nodules and greater than 20 control subjects with noncancerous pulmonary nodules. In some aspects, classifier is to be generated using proteomics data, and described proteomics data is by making the protein in particle adsorption training sample and measuring the protein adsorbed to particle by contacting training sample with particle.In some aspects, measuring described protein comprises making biofluid sample contact with particle so that protein adsorption is to particle, and obtains protein measurement value from the protein adsorbed.In some aspects, classifier is to be trained using deep learning, hierarchical cluster analysis, principal component analysis, partial least squares discriminant analysis, random forest classification analysis, support vector machine analysis, k nearest neighbor analysis, naive Bayes analysis, K-means cluster analysis or hidden Markov (hidden Markov) analysis.In some aspects, evaluating pulmonary nodules comprises that protein measurement value is identified as indicating that pulmonary nodules are cancerous.In some aspects, comprise applying lung cancer treatment to object based on evaluation.In some aspects, lung cancer treatment comprises chemotherapy, radiotherapy, percutaneous ablation, radiofrequency ablation, cryoablation, microwave ablation, chemoembolization or operation.In some aspects, by using medical imaging equipment, object is identified as suffering from pulmonary nodules. In some aspects, the classifier identifies lung cancer with a sensitivity and specificity greater than 60%.In some aspects, the particle comprises a nanoparticle.In some aspects, the particles include lipid particles, metal particles, silica particles, or polymer particles. In some aspects, the particles include physicochemically distinct groups of nanoparticles. In some aspects, the biological fluid sample includes a blood, serum, or plasma sample. In some aspects, the subject is a human. In some aspects, the protein measurement value includes the measurement value of a protein selected from the group consisting of: APP, IGHG2, SERPING1, SAA2, SERPINF2, GC, IGHA1, HPR, SERPINA3, IGHA1, LTF, SERPINA1, PCSK6, PROS1, BPIF1, C6, CP, A2M, and IGFBP2. In some aspects, disclosed herein are methods comprising: determining proteins in a blood, serum, or plasma sample by mass spectrometry to obtain a protein measurement, the sample being obtained from a subject identified as having a lung nodule using a medical imaging device; applying a classifier to the protein measurement to evaluate the lung nodule; and selecting or administering a lung cancer therapy to the subject based on the evaluation; and (i), (ii), or (iii): (i) wherein the classifier comprises a protein signature of the determined proteins, and wherein the classifier comprises a performance characteristic for identifying a lung nodule as cancerous or non-cancerous, the performance characteristic comprising a median area under the receiver operating characteristic (ROC) curve (AUC) greater than 0.7 , as determined in a holdout data set from a randomized, controlled trial of more than 25 subjects with cancerous pulmonary nodules and more than 25 control subjects with non-cancerous pulmonary nodules, and as determined using only protein features in the classifier, (ii) wherein the classifier is generated using proteomic data obtained by contacting a training sample with nanoparticles such that the nanoparticles adsorb proteins in the training sample and measuring the proteins adsorbed to the nanoparticles, or (iii) wherein measuring the proteins comprises contacting a blood, serum or plasma sample with nanoparticles such that the proteins adsorb to the nanoparticles, and obtaining protein measurements from the adsorbed proteins.

[0022] In some embodiments, the classifier comprises a protein feature of the determined proteins and is characterized in that the average ROC curve for identifying lung nodules as cancerous or non-cancerous has a median AUC greater than 0.7, wherein the AUC greater than 0.7 is determined without including non-protein features in a retained data set from a randomized controlled trial of more than 25 subjects with cancerous lung nodules and more than 25 control subjects with non-cancerous lung nodules. In some embodiments, the classifier is generated using proteomic data obtained by contacting a training sample with nanoparticles such that the nanoparticles adsorb proteins in the training sample and determining the proteins adsorbed to the nanoparticles. In some embodiments, determining the proteins comprises contacting a blood, serum, or plasma sample with nanoparticles such that the proteins adsorb to the nanoparticles, and obtaining protein measurements from the adsorbed proteins.

[0023] In some aspects, disclosed herein is a method comprising: determining the protein in a biological fluid sample obtained from an object identified as having a pulmonary nodule to obtain a protein measurement value; and identifying the protein measurement value as indicating that the pulmonary nodule is cancerous or non-cancerous by applying a classifier to the protein measurement value, wherein the classifier is characterized in that based on the protein measurement value feature, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve is greater than 0.7. In some aspects, an AUC greater than 0.7 is generated without including non-protein clinical features. In some aspects, non-protein clinical features include clinical indicators of lung cancer. In some aspects, the protein includes APP, IGHG2, SERPING1, SAA2, SERPINF2, GC, IGHA1, HPR, SERPINA3, IGHA1, LTF, SERPINA1, PCSK6, PROS1, BPIF1, C6, CP, A2M or IGFBP2.

[0024] In some aspects, a method is disclosed herein, comprising: determining proteins in a biological fluid sample obtained from an object suffering from or suspected of having a pulmonary nodule to obtain a protein measurement value; and applying a classifier to the protein measurement value to evaluate the pulmonary nodule, wherein the classifier is generated using proteomic data obtained by enriching proteins with an affinity reagent. In some aspects, a method is disclosed herein, comprising: determining proteins in a biological fluid sample obtained from an object suffering from or suspected of having a pulmonary nodule to obtain a protein measurement value; and applying a classifier to the protein measurement value, thereby identifying the protein measurement value as indicating that the pulmonary nodule is cancerous or non-cancerous, wherein the classifier is generated using proteomic data, and the proteomic data is obtained by contacting a training sample with particles so that the particles adsorb proteins in the training sample and determining the proteins adsorbed to the particles. Some aspects include obtaining or receiving a biological fluid sample of an object. In some aspects, the object is identified as having a pulmonary nodule by medical imaging. In some aspects, medical imaging includes computed tomography (CT) scanning. Some aspects include performing medical imaging. Some aspects include identifying pulmonary nodules in medical imaging. Some aspects include generating a report based on the protein measurement value being identified as indicating that the lung nodule is cancerous or non-cancerous. In some aspects, the report includes the possibility or indication that the lung nodule is cancerous or non-cancerous. In some aspects, the report includes outputting or transmitting the report. In some aspects, the report is used by medical professionals to diagnose, provide medical advice, or provide treatment for the lung nodule. In some aspects, the protein measurement value is classified as indicating that the lung nodule is cancerous and performing a biopsy on the lung nodule. In some aspects, the biopsy confirms that the lung nodule is cancerous or non-cancerous. In some aspects, the lung nodule is cancerous. In some aspects, the lung nodule includes non-small cell lung cancer (NSCLC). In some aspects, the classifier includes a feature for indicating that the protein measurement value is cancerous or non-cancerous. In some aspects, the feature includes a control protein measurement value, a mass spectrum, an m / z ratio, a chromatographic result, an immunoassay result, or light or fluorescence intensity. In some aspects, the classifier is trained using deep learning, hierarchical cluster analysis, principal component analysis, partial least squares discriminant analysis, random forest classification analysis, support vector machine analysis, k nearest neighbor analysis, naive Bayes analysis, K-means cluster analysis or hidden Markov analysis. In some aspects, the classifier can identify lung cancer with a sensitivity of 50% or higher, 60% or higher, 70% or higher, 80% or higher or 90% or higher. In some aspects, the classifier can identify lung cancer with a specificity of 50% or higher, 60% or higher, 70% or higher, 80% or higher or 90% or higher. Some aspects are included in recommending lung cancer treatment for an object when a protein measurement value is classified as indicating that a lung nodule is cancerous.Some aspects include administering lung cancer treatment to the subject when the protein measurement value is classified as indicating that the lung nodule is cancerous. In some aspects, lung cancer treatment includes chemotherapy, radiotherapy, percutaneous ablation, radiofrequency ablation, cryoablation, microwave ablation, chemoembolization or surgery. In some aspects, the lung nodule is non-cancerous. In some aspects, observing the subject without performing a biopsy includes measuring the protein in a second biological fluid sample obtained from the subject at a later time without performing a biopsy. In some aspects, the particle includes nanoparticles. In some aspects, the particle includes lipid particles, metal particles, silica particles or polymer particles. In some aspects, the particle includes carboxylate particles, polyacrylic acid particles, dextran particles, polystyrene particles, dimethylamine particles, amino particles, silica particles or N-(3-trimethoxysilylpropyl)diethylenetriamine particles. In some aspects, the particles comprise physicochemically distinct groups of nanoparticles. In some aspects, determining the protein comprises contacting a biological fluid sample with particles so that the particles adsorb the protein to the particles. In some aspects, determining the protein comprises measuring a readout indicating the presence, absence, or amount of a biomolecule. In some aspects, determining the protein comprises performing mass spectrometry, chromatography, liquid chromatography, high performance liquid chromatography, solid phase chromatography, lateral flow assay, immunoassay, enzyme-linked immunosorbent assay, Western blotting, dot blot, or immunostaining, or a combination thereof. In some aspects, determining the protein comprises performing mass spectrometry. In some aspects, the protein comprises a secreted protein. In some aspects, the biological fluid comprises blood, plasma, or serum. In some aspects, the diameter of the pulmonary nodule is less than 3 cm. In some aspects, the subject suffers from multiple pulmonary nodules. In some aspects, the subject is a mammal. In some aspects, the subject is a human.

[0025] In some aspects, the present invention discloses a method comprising: obtaining a biofluid sample of an object having a lung nodule; contacting the biofluid sample with particles so that the particles adsorb biomolecules comprising proteins to the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and classifying the proteomic data as indicating whether the lung nodule is cancerous or non-cancerous. In some aspects, the object is identified as having a lung nodule by medical imaging. In some aspects, the medical imaging comprises a computed tomography (CT) scan. In some aspects, the medical imaging is performed. In some aspects, the lung nodule is identified in the medical imaging. In some aspects, a biopsy is performed on the lung nodule when the proteomic data is classified as indicating that the lung nodule is cancerous. In some aspects, the biopsy confirms the possibility that the lung nodule is cancerous or non-cancerous. In some aspects, the lung nodule is cancerous and comprises a tumor. In some aspects, the lung nodule comprises non-small cell lung cancer (NSCLC). In some aspects, classifying the proteomic data as indicating whether the lung nodule is cancerous or non-cancerous comprises applying a classifier to the proteomic data. In some respects, classifier includes the feature for indicating that lung cancer is cancerous or non-cancerous possibility. In some respects, classifier is to use deep learning, hierarchical cluster analysis, principal component analysis, partial least squares discriminant analysis, random forest classification analysis, support vector machine analysis, k nearest neighbor analysis, naive Bayes (naive Bayes) analysis, K-means cluster analysis or hidden Markov (hiddenMarkov) analysis to train. In some respects, proteomic data indicate that lung nodules are cancerous or non-cancerous with about 80% or higher sensitivity or specificity. In some respects, it is recommended that lung cancer treatment be applied to object when proteomic data are classified as indicating that lung nodules are cancerous. In some respects, lung cancer treatment includes chemotherapy, radiotherapy, percutaneous ablation, radiofrequency ablation, cryoablation, microwave ablation, chemoembolization or surgery. In some respects, lung nodules are non-cancerous and benign. Some aspects include observing the subject without performing a biopsy when the proteomic data is classified as indicating that the lung nodule is non-cancerous. Some aspects include monitoring the subject and determining the biomolecule in a second biofluid sample obtained from the subject at a later time. In some aspects, the particles include nanoparticles. In some aspects, the particles include lipid particles, metal particles, silica particles or polymer particles. In some aspects, the particles include carboxylate particles, polyacrylic acid particles, dextran particles, polystyrene particles, dimethylamine particles, amino particles, silica particles or N-(3-trimethoxysilylpropyl)diethylenetriamine particles. In some aspects, the particles include physicochemically different nanoparticle groups. In some aspects, determining the biomolecule includes measuring the readout of the presence, absence or amount of an indicator biomolecule.In some aspects, determining the biomolecule comprises performing mass spectrometry, chromatography, liquid chromatography, high performance liquid chromatography, solid phase chromatography, lateral flow assay, immunoassay, enzyme-linked immunosorbent assay, western blot, dot blot, or immunostaining, or a combination thereof. In some aspects, determining the biomolecule comprises performing mass spectrometry. In some aspects, the protein comprises a secreted protein. In some aspects, the biological fluid comprises blood, plasma, or serum. In some aspects, the diameter of the pulmonary nodule is less than 3 cm. In some aspects, the subject suffers from multiple pulmonary nodules. In some aspects, the subject is a mammal. In some aspects, the subject is a human.

[0026] In some aspects, methods are disclosed herein that include: measuring protein in a biological fluid sample obtained from a subject suspected of having a pulmonary nodule to obtain a protein measurement value; and applying a classifier to the protein measurement value to identify the protein measurement value as indicative of the subject having a pulmonary nodule, wherein the classifier is generated using proteomics data obtained by contacting a training sample with particles so that the particles adsorb proteins from the training sample and measuring the proteins adsorbed to the particles. Some aspects include recommending that the subject undergo medical imaging, such as a CT scan, when the protein measurement value indicates that the subject has a pulmonary nodule, and not recommending that the subject undergo medical imaging when the protein measurement value does not indicate that the subject has a pulmonary nodule. Some aspects include performing medical imaging, such as a CT scan, on the subject when the protein measurement value indicates that the subject has a pulmonary nodule, and not performing medical imaging on the subject when the protein measurement value does not indicate that the subject has a pulmonary nodule. Some aspects include transmitting or receiving a report regarding the medical imaging, such as a CT scan, when the protein measurement value indicates that the subject has a pulmonary nodule, and not transmitting or receiving the report when the protein measurement value does not indicate that the subject has a pulmonary nodule. In some aspects, the protein measurement value indicates that the subject has or may have a pulmonary nodule. In some aspects, the protein measurement indicates that the subject does not have or is unlikely to have a lung nodule.

[0027] In some aspects, methods are disclosed herein that include: measuring protein in a biological fluid sample obtained from a subject suspected of having lung cancer to obtain a protein measurement value; and applying a classifier to the protein measurement value to identify the protein measurement value as indicative of the subject having lung cancer, wherein the classifier is generated using proteomics data obtained by contacting a training sample with particles so that the particles adsorb proteins from the training sample and measuring the proteins adsorbed to the particles. Some aspects include recommending that the subject undergo medical imaging, such as a CT scan, when the protein measurement value indicates that the subject has lung cancer, and not recommending that the subject undergo medical imaging when the protein measurement value does not indicate that the subject has lung cancer. Some aspects include performing medical imaging, such as a CT scan, on the subject when the protein measurement value indicates that the subject has lung cancer, and not performing medical imaging on the subject when the protein measurement value does not indicate that the subject has lung cancer. Some aspects include transmitting or receiving a report regarding the medical imaging, such as a CT scan, when the protein measurement value indicates that the subject has lung cancer, and not transmitting or receiving the report when the protein measurement value does not indicate that the subject has lung cancer. In some aspects, the protein measurement value indicates that the subject has or is likely to have lung cancer. In some aspects, the protein measurement value indicates that the subject does not have or is unlikely to have lung cancer. In some aspects, lung cancer comprises NSCLC.

[0028] In some aspects, methods are disclosed herein that include: obtaining a biofluid sample from a subject suspected of having a pulmonary nodule; contacting the biofluid sample with particles so that the particles adsorb biomolecules comprising proteins to the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and, based on the proteomic data, classifying the proteomic data as indicating that the subject has a pulmonary nodule or not indicating that the subject has a pulmonary nodule. Some aspects include recommending that the subject undergo medical imaging, such as a CT scan, when the proteomic data indicates that the subject has a pulmonary nodule, and not recommending that the subject undergo medical imaging when the proteomic data does not indicate that the subject has a pulmonary nodule. Some aspects include performing medical imaging, such as a CT scan, on the subject when the proteomic data indicates that the subject has a pulmonary nodule, and not performing medical imaging on the subject when the proteomic data does not indicate that the subject has a pulmonary nodule. Some aspects include transmitting or receiving a report regarding the medical imaging, such as a CT scan, when the proteomic data indicates that the subject has a pulmonary nodule, and not transmitting or receiving the report when the proteomic data does not indicate that the subject has a pulmonary nodule. In some aspects, the proteomic data indicates that the subject has or may have a pulmonary nodule. In some aspects, the proteomic data indicates that the subject does not have or is unlikely to have a lung nodule.

[0029] In some aspects, methods are disclosed herein that include: obtaining a biological fluid sample from a subject suspected of having lung cancer; contacting the biological fluid sample with particles so that the particles adsorb biomolecules comprising proteins to the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and, based on the proteomic data, classifying the proteomic data as indicating that the subject has lung cancer or not indicating that the subject has lung cancer. Some aspects include recommending that the subject undergo medical imaging, such as a CT scan, when the proteomic data indicates that the subject has lung cancer, and not recommending that the subject undergo medical imaging when the proteomic data does not indicate that the subject has lung cancer. Some aspects include performing medical imaging, such as a CT scan, on the subject when the proteomic data indicates that the subject has lung cancer, and not performing medical imaging on the subject when the proteomic data does not indicate that the subject has lung cancer. Some aspects include transmitting or receiving a report regarding the medical imaging, such as a CT scan, when the proteomic data indicates that the subject has lung cancer, and not transmitting or receiving the report when the proteomic data does not indicate that the subject has lung cancer. In some aspects, the proteomic data indicates that the subject has or is likely to have lung cancer. In some aspects, the proteomic data indicates that the subject does not have or is unlikely to have lung cancer.

[0030] In some aspects, disclosed herein are monitoring methods that include: obtaining a biological fluid sample from a subject at risk for lung cancer recurrence; contacting the biological fluid sample with particles such that the particles adsorb biomolecules comprising proteins to the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and, based on the proteomic data, classifying the proteomic data as indicating that the subject has lung cancer recurrence or not. Some aspects include recommending that the subject undergo medical imaging, such as a CT scan, when a protein measurement indicates that the subject has lung cancer recurrence, and not recommending that the subject undergo medical imaging when the protein measurement does not indicate that the subject has lung cancer recurrence. Some aspects include performing medical imaging, such as a CT scan, on the subject when the protein measurement indicates that the subject has lung cancer recurrence, and not performing medical imaging on the subject when the protein measurement does not indicate that the subject has lung cancer recurrence. Some aspects include transmitting or receiving a report regarding the medical imaging, such as a CT scan, when the protein measurement indicates that the subject has lung cancer recurrence, and not transmitting or receiving the report when the protein measurement does not indicate that the subject has lung cancer recurrence. In some aspects, the protein measurement indicates that the subject has or is likely to have lung cancer recurrence. In some aspects, the protein measurement indicates that the subject does not have or is unlikely to have lung cancer recurrence. In some aspects, the subject has been treated for lung cancer. In some aspects, the lung cancer treatment includes chemotherapy, radiotherapy, or surgery. In some aspects, the cancer is potentially resectable. In some aspects, the lung cancer includes NSCLC.

[0031] Incorporated by reference

[0032] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent such incorporated-by-reference publications and patents or patent applications conflict with the disclosure contained in this specification, the specification is intended to supersede and / or take precedence over any such conflicting material. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1A Illustration of a multi-omics approach.

[0034] Figure 1B Illustration of combining data sets in a multi-omics approach.

[0035] FIG. 2A illustrates an example of a method for generating and applying a classifier described herein.

[0036] Figure 2B is a flow chart illustrating some aspects that may be used in the methods herein.

[0037] Figure 3A Examples of stages in the screening and treatment of patients having or suspected of having a disease state are shown.

[0038] Figure 3B An example showing the stages of screening and treatment for pancreatic cancer patients.

[0039] Figure 3C An example showing the stages in screening and treatment of liver cancer patients.

[0040] Figure 4 A non-limiting example of a computing device is shown; in this case, a device having one or more processors, memory, storage, and a network interface.

[0041] Figure 5 A simplified diagram showing classifiers and feature information according to some aspects described herein.

[0042] Figure 6 Shown is a graph depicting the differential expression of some proteins that can be used to generate classifiers to diagnose disease states.

[0043] Figure 7 Shown is a simplified graph illustrating the expression of some proteins in diseased subject samples relative to control subjects. Several genes were differentially expressed (either under- or over-expressed) between the groups (NSCLC and healthy samples).

[0044] Figure 8Scatterplot pairs plot predictions are shown for each other. RNASeq: predicted probabilities (affected) based on RNA-Seq data; Proteomics: predicted probabilities (affected) based on proteomics data; and RNA_Prot: predicted probabilities (affected) based on both RNA-Seq and proteomics data.

[0045] Figure 9 Receiver operating characteristic (ROC) curves were included and showed that the area under the curve (AUC) of the combined mRNA transcriptomics data and proteomics data was increased compared to either the mRNA transcriptomics data or the proteomics data alone.

[0046] Figure 10A Additional multi-omics classifications are shown for 30 samples from subjects with a disease state and 30 samples from control subjects, and include mRNA transcriptomics data, proteomics data, and combined mRNA transcriptomics and proteomics data.

[0047] Figure 10B Differential mRNAs and proteins are shown whose abundance in the biological fluid samples was measured and used to generate a classifier.

[0048] Figure 11A Analysis based on proteomics data and microRNA data is shown. The top panel shows the results of a classifier trained on proteomics data alone, the middle panel shows the results of a classifier trained with microRNA data alone, and the bottom panel shows the results of combining the two data types.

[0049] Figure 11B Differentially expressed microRNAs used to generate the classifier are shown.

[0050] Figure 12 Shown is an analysis comparing the case where three omics data types (proteomics, mRNA, and miRNA) are combined with the case where only each of the three data types is used.

[0051] Figure 13A Some aspects that can be used in integrating model classification are shown.

[0052] Figure 13B Some aspects that may be used in transform-based classification are shown.

[0053] Figure 14 Shows the graphical results of the integrated model classification analysis.

[0054] Figure 15Describe some aspects of transformation-based classification analysis.

[0055] Figure 16 Graphical results showing the integrated model classification analysis and transformation-based classification.

[0056] Figure 17 A non-limiting example of a flow chart illustrating a machine training algorithm for improving the sensitivity and specificity of a classifier for predicting the diseases described herein.

[0057] Figure 18A Shown are ROC curves for some protein data and combined protein+lipid data for disease state classification.

[0058] Figure 18B Included are sensitivity aspects of the analysis of protein data, lipid data, and combined protein+lipid data for disease state classification.

[0059] Figure 19 Shows aspects of a two-stage machine learning framework for analyzing and training multiple data types.

[0060] Figure 20A Included are sensitivity aspects of the analysis of protein data, lipid data, and combined protein+lipid data for disease state classification.

[0061] Figure 20B Included are sensitivity aspects of the analysis of protein data, lipid data, and combined protein+lipid data for disease state classification.

[0062] Figure 20C Shown are ROC curves for some protein data, lipid data, and combined protein+lipid data for disease state classification.

[0063] Figure 21 Shown are ROC curves for some protein data and combined protein+lipid+clinical parameter data for disease state classification.

[0064] Figure 22A Shown are information related to some protein data.

[0065] Figure 22B Some classifier performance aspects are shown.

[0066] Figure 22C Some aspects of classifier performance are shown with and without the inclusion of some features.

[0067] Figure 23 Shows some aspects of genetic or transcriptional data that may be used, such as an indication or type of measurement, sample type, quality control aspects, or sequencing depth.

[0068] Figure 24 Various aspects are shown that may be used in some of the methods described herein.

[0069] Figure 25 Included are aspects that may be included in the methods described herein, such as subjects or test results.

[0070] Figure 26A Included are tables showing some proteins, OT scores, and a description of some features in the protein classifier.

[0071] Figure 26B Included are tables showing some proteins, OT scores, and a description of some features in the protein classifier.

[0072] Figure 27 Included are graphs showing the feature importance scores for the lipid classifier.

[0073] Figure 28A Results of the Wilcox test for age comparison and Fisher's exact test for sex ratio are shown.

[0074] Figure 28B Results of the Wilcox test for age comparison and Fisher's exact test for sex ratio are shown.

[0075] Figure 29A Shown are the numbers of proteins detected across subject samples in the analysis of biological fluid samples from controls and cancer patients.

[0076] Figure 29B Shown are the numbers of proteins detected across subject samples in the analysis of biological fluid samples from controls and cancer patients.

[0077] Figure 30A A graph showing some of the top proteins that were differentially detected in biological fluid samples from cancer patients relative to biological fluid samples from control patients.

[0078] Figure 30B : is a graph showing the distribution of OpenTargets (OT) scores. The x-axis includes OT scores (from 0 to 0.8), and the y-axis includes density (0 to 15).

[0079] Figure 31A Included are graphs showing comparisons of median total signal by sample, analyte type, and category.

[0080] Figure 31B Box and whisker plots showing the most significantly different analytes for each omics workflow according to one embodiment; upper left panel: lipids; lower left panel: metabolites; and right panel: proteins).

[0081] Figure 31C Example multi-omics classifier performance combining proteomic, lipidomic, and metabolomic measurements is shown.

[0082] Figure 32A A volcano plot includes the difference in intensities and P-values ​​for proteins adsorbed to nanoparticles and detected in biofluid samples from cancer patients relative to biofluid samples from control patients. The volcano plot displays the magnitude of the difference on the x-axis and the significance on the y-axis, highlighting the most significant analytes.

[0083] Figure 32B Data for protein P35442, which ranked high after particle-based measurements, are included.

[0084] Figure 32C A volcano plot includes the intensity differences and P-values ​​for proteins detected in biofluid samples from cancer patients relative to biofluid samples from control patients. The volcano plot displays the magnitude of the difference on the x-axis and the significance on the y-axis, highlighting the most significant analytes.

[0085] Figure 32D Data for P01011, the top-ranked protein after proteomic measurements, are included.

[0086] Figure 33A A volcano plot includes the intensity differences and P values ​​for lipids detected in biofluid samples from cancer patients relative to biofluid samples from control patients. The volcano plot displays the magnitude of the difference on the x-axis and the significance on the y-axis, highlighting the most significant analytes.

[0087] Figure 33B Data for the top-ranked lipid CERs (d18:1_18:0) following lipidomic measurements were included.

[0088] Figure 34A A volcano plot includes the differences in the intensities of metabolites detected in biofluid samples from cancer patients relative to biofluid samples from control patients and their P values. The volcano plot displays the magnitude of the difference on the x-axis and the significance on the y-axis, highlighting the most significant analytes.

[0089] Figure 34B Data on AICAR of the top ranked metabolites after metabolomics measurements were included.

[0090] Figure 35A Depicts the classification of cancer and healthy samples according to UMAP projection based on the combined data.

[0091] Figure 35BDepicted are the classifications of cancer and healthy samples according to PCA projections based on the combined data.

[0092] Figure 35C Depicts the classification of cancer and healthy samples based on UMAP projection of Proteograph data.

[0093] Figure 35D Depicts the classification of cancer and healthy samples based on PCA projection of the proteograph data.

[0094] Figure 35E Depicts the classification of cancer and healthy samples based on UMAP projections of PiQuant data.

[0095] Figure 35F Depicts the classification of cancer and healthy samples based on PCA projections of the PiQuant data.

[0096] Figure 35G Depicts the classification of cancer and healthy samples based on UMAP projections of lipid data.

[0097] Figure 35H Depicts the classification of cancer and healthy samples based on PCA projection of lipid data.

[0098] Figure 35I Depicts the classification of cancer and healthy samples based on UMAP projections of metabolite data.

[0099] Figure 35J Depicts the classification of cancer and healthy samples based on PCA projection of metabolite data.

[0100] Figure 36 Protein, lipid, and metabolite features included in the classifier.

[0101] Figure 37 Classifier performance in a multi-omics study is shown and includes receiver operating characteristic (ROC) curves for disease state classification. Also included are area under the curve (AUC) values ​​with 90% confidence intervals in parentheses.

[0102] Figure 38A The performance of a classifier trained with data from a genomics assay is shown, and includes ROC curves for disease state classification. The AUC values ​​at the bottom of the graph are shown as ± values ​​based on 90% confidence.

[0103] Figure 38BThe performance of a classifier trained with data from a genomics assay ("Genomics"), a classifier trained with data from a mass spectrometry assay ("Mass Spec"), and a classifier trained with data from both genomics and mass spectrometry ("Combined") is shown. The data shown in the figure include ROC curves for disease state classification. AUC values ​​include ± values ​​based on a 90% confidence level.

[0104] Figure 39A A graphical summary of 18 samples from subjects with liver cancer used in Example 17 is shown.

[0105] Figure 39B Shown are the coefficient of variation (CV) values ​​for some peptides and proteins obtained in the studies described herein.

[0106] Figure 39C Shown are exemplary protein abundance heatmaps for samples from subjects with liver cancer and healthy subjects.

[0107] Figure 39D Shown are examples of differences in the abundance of proteins identified in samples from subjects with liver cancer or from healthy subjects after contacting the samples with various particles described herein.

[0108] Figure 39E Included are plots showing that the lipidomics data obtained from the samples are highly reproducible.

[0109] Figure 39F It is shown that samples from subjects with liver cancer exhibit different lipid profiles than healthy controls. The top 50 lipids based on p-value in this analysis are shown for each patient sample.

[0110] Figure 39G Shown are univariate lipid differences in samples from subjects with liver cancer compared to healthy subjects.

[0111] Figure 40A Shown is a graphical summary of nine samples from ovarian cancer subjects used in Example 19.

[0112] Figure 40B Shown are exemplary protein abundance heatmaps for samples from subjects with ovarian cancer and healthy subjects.

[0113] Figure 40C Shown are univariate lipid differences in samples from subjects with ovarian cancer compared to healthy subjects.

[0114] Figure 41 An example showing the stages in screening and treatment of patients with colon cancer.

[0115] Figure 42Shown are the age and sex breakdown of the 268 subjects in the NSCLC biomarker discovery study.

[0116] Figure 43 Protein counts according to study cohorts (including healthy, comorbidities, NSCLC stage 1 "NSCLC_1," NSCLC stage 2 "NSCLC_2," NSCLC stage 3 "NSCLC_3," and NSCLC stage 4 "NSCLC_4") are shown.

[0117] Figure 44 Protein counts of depleted plasma DP and granule groups are shown.

[0118] Figure 45 Shown is a summary of the fractional detection of proteins across subjects versus their mean abundance for the 10 particle types in the particle panel and depleted plasma (DP).

[0119] Figure 46 The performance of the cross-validated particle group classifier is shown, where the x-axis shows the fraction of classifications that are false positives, and the y-axis shows the fraction of classifications that are true positives.

[0120] Figure 47 Random forest model plots for healthy vs. NSCLC (stages 1, 2, and 3) for depleted plasma (left) and the 10-particle group (right) are shown, with the false positive fraction depicted on the x-axis and the true positive fraction depicted on the y-axis.

[0121] Figure 48 Shown are the performance of the classifier features across the study samples.

[0122] Figure 49 The results of 10 iterations of 10 rounds of 10-fold cross validation with randomization of object class assignment are shown, with the false positive fraction on the x-axis and the true positive fraction on the y-axis.

[0123] Figure 50 Shown are ROC plots for 13 peptides by MRM-MS and 2 proteins by ELISA after removal of proteins found in depleted plasma.

[0124] Figure 51 Random forest models for all study group comparisons are shown.

[0125] Figure 52 Some distinguishing features in the comparison of the study groups are shown.

[0126] Figure 53 Protein counts (eg, number of proteins identified from corona analysis) are shown for panel sizes ranging from 1 to 12 particle types.

[0127] Figure 54 Examples of biomarkers are shown.

[0128] Figure 55 shows a non-limiting example of a web / mobile application providing system (in this case, a system providing a browser-based and / or native mobile user interface); and

[0129] Figure 56 A non-limiting example of a cloud-based web / mobile application provisioning system (in this case, a system including elastic load balancing, auto-scaling web server and application server resources, and synchronously replicated databases) is shown.

[0130] Figure 57 Shown is the ROC curve for the lung nodule classifier, where the sensitivity and corresponding specificity are listed.

[0131] Figure 58 Show Figure 57 Feature information and importance of the lung nodule classifier shown.

[0132] Figure 59 Illustrations illustrate some aspects of the samples used in the research described in this article.

[0133] Figure 60 The number of protein groups observed in the process control samples is shown.

[0134] Figure 61 Some coefficient of variation (CV) values ​​are shown.

[0135] Figure 62 Included are protein abundance heatmaps for samples from subjects with malignant and benign pulmonary nodules.

[0136] Figure 63 Included is a volcano plot that plots the log fold change in protein abundance against the negative log of the p-value.

[0137] Figure 64 Some exemplary proteins from the initial univariate analysis are shown.

[0138] Figure 65A Included are graphs showing some of the proteins that are upregulated in biological fluid samples from subjects with malignant pulmonary nodules.

[0139] Figure 65B Included are graphs showing some of the proteins that are downregulated in biological fluid samples from subjects with malignant lung nodules.

[0140] Figure 66 Included are graphs illustrating the enrichment of differentially expressed proteins in metabolic and phosphorylation pathways.

[0141] Figure 67 Some extrapolated mRNA data showing differentially expressed proteins in metabolic pathways are shown.

[0142] Figure 68 is an image showing where some samples were collected for research.

[0143] Figure 69A Some aspects of the research subjects and proteomics platforms that can be used in the methods described herein are shown.

[0144] Figure 69B Some aspects of a proteomics platform that can be used in the methods described herein are shown.

[0145] Figure 69C Some additional multi-omics aspects are shown.

[0146] Figure 70 Included are graphical depictions of the coefficient of variation (CV) values ​​obtained in the studies described herein.

[0147] Figure 71 Included are empirical power curves for the protein changes in the studies described herein.

[0148] Figure 72 Included are graphical depictions of detected protein groups and peptide counts obtained in the studies described herein.

[0149] Figure 73 Included are graphical depictions of protein concentration versus the natural logarithm of protein intensity data obtained in the studies described herein.

[0150] Figure 74 Included are graphical depictions of protein concentrations of the data obtained in the studies described herein.

[0151] Figure 75A Included are the CVs of the logarithm of the median normalized intensity of proteins detected in 100% of the samples.

[0152] Figure 75B The CV of the log of the median normalized intensity of proteins detected in at least 25% of the samples was included.

[0153] Figure 76 Includes the number of unique protein groups in some sample data.

[0154] Figure 77A Includes relative fluorescence units versus concentration for several standard curves.

[0155] Figure 77B Include some relative fluorescence units for the standard curve.

[0156] Figure 78A Included are peptide yields for some of the nanoparticles used in the experiments described herein.

[0157] Figure 78B Included are peptide yields for some of the nanoparticles used in the experiments described herein.

[0158] Figure 79A Included is a plot of MS1 intensity versus time.

[0159] Figure 79B Includes intraday CV of MS1 intensity.

[0160] Figure 80A Included is a plot of iRT peptides ranked by FWHM.

[0161] Figure 80B A graph showing retention times is included.

[0162] Figure 81A Included are plots showing the distribution of protein group counts for each sample.

[0163] Figure 81B Includes intraday CV of MS1 intensity.

[0164] Figure 82 A volcano plot showing the intensity differences and P-values ​​of peptides detected in a biofluid sample. The volcano plot displays the median peptide-level differences in intensity on the x-axis and the peptide P-values ​​based on the harmonic mean on the y-axis.

[0165] Figure 83 Included are graphs showing some of the transformations of the peptide ANVFVQLPR (SEQ ID NO: 165) from protein P35858 in benign and malignant cohorts.

[0166] Figure 84 Included is a graph illustrating the comparison of lung cancer OpenTarget (OT) scores and peptide difference significance. The graph shows the OpenTarget score on the x-axis and the P value on the y-axis.

[0167] Figure 85 A volcano plot of the intensity differences and P-values ​​of metabolites in lung nodule subjects. The volcano plot displays the median intensity difference on the x-axis and the P-value on the y-axis.

[0168] Figure 86 Included is a diagram illustrating the seer-lung discovery sample cohort. The diagram shows that 186 of the 589 eligible subjects met all criteria.

[0169] Figure 87A simplified diagram illustrating a phased approach to version one, version two, and version three classifier discovery through test development is shown.

[0170] Figure 88 Included are plots showing potency curves for analyte classes. These plots include curves for proteins, metabolites, and lipids.

[0171] Figure 89 A volcano plot of peptide intensity differences and P-values ​​in lung nodule subjects is included. The volcano plot displays the median peptide-level difference in intensity on the x-axis and the peptide P-value based on the harmonic mean on the y-axis.

[0172] Figure 90 Included are graphs showing some of the transformations of the peptide LEYLLLSR (SEQ ID NO: 166) from protein P35858 in benign and malignant cohorts.

[0173] Figure 91 Included are graphs showing some of the transformations of the peptide ANVFVQLPR (SEQ ID NO: 165) from protein P35858 in benign and malignant cohorts.

[0174] Figure 92 Included are graphs showing some of the transformations of the peptide FLNVLSPR (SEQ ID NO: 167) from protein P17936 in benign and malignant cohorts.

[0175] Figure 93 An image depicting StringDB is shown. The image highlights the known interactions of IGFALS and IGFBP3.

[0176] Figure 94 A volcano plot of the intensity differences and P-values ​​of metabolites in lung nodule subjects is shown. The volcano plot displays the median intensity difference on the x-axis and the P-value on the y-axis.

[0177] Figure 95 Included is a graph showing the amount of biopterin metabolites in the benign and malignant cohorts. The graph shows the study cohort type on the x-axis and the amount of metabolite on the y-axis.

[0178] Figure 96 A volcano plot of the intensity difference and P-value for lipids in lung nodule subjects is shown. The volcano plot displays the median intensity difference on the x-axis and the P-value on the y-axis.

[0179] Figure 97 Included is a graph illustrating the comparison of lung cancer OpenTarget (OT) scores and peptide difference significance. The graph shows the OpenTarget score on the x-axis and the P value on the y-axis.

[0180] Figure 98 A simplified diagram illustrating a phased approach to version one, version two, and version three classifier discovery through test development is shown.

[0181] Figure 99 Included are plots of the pre-test probability for subjects having benign nodules and the pre-test and post-test probabilities for subjects having benign nodules. The plot displays probability on the x-axis and the number of subjects on the y-axis.

[0182] Figure 100 Includes a graph comparing sensitivity to specificity. The graph shows specificity on the x-axis and sensitivity on the y-axis.

[0183] Figure 101 Figure 2 shows the ROC curve for 223 subjects with mRNA data in a colorectal cancer (CRC) study. The false positive rate is shown on the x-axis, and the true positive rate is shown on the y-axis. The AUC value is provided.

[0184] Figure 102 Included is a volcano plot illustrating the differential expression of various genes in colorectal cancer studies.

[0185] Figure 103 ROC curves for ProteoGraph, mRNA, and ProteoGraph+mRNA are shown. Corresponding AUC values ​​are provided.

[0186] Figure 104 The ROC curves of ProteoGraph, PiQuant, mRNA, microRNA, and ProteoGraph+PiQuant+mRNA+microRNA are shown. The corresponding AUC values ​​are provided.

[0187] Figure 105 ROC curves for PiQuant, mRNA, and PiQuant+mRNA are shown. Corresponding AUC values ​​are provided.

[0188] Figure 106 ROC curves for classification based on separate or combined biomolecule types are shown.

[0189] Figure 107A Results of the Wilcox test for age comparison and Fisher's exact test for sex ratio are shown.

[0190] Figure 107B Results of the Wilcox test for age comparison and Fisher's exact test for sex ratio are shown.

[0191] Figure 108AShown are the numbers of proteins detected across subject samples in the analysis of biological fluid samples from controls and cancer patients.

[0192] Figure 108B Shown are the numbers of proteins detected across subject samples in the analysis of biological fluid samples from controls and cancer patients.

[0193] Figure 108C This figure shows the platform's reproducibility, demonstrating its ability to detect biological signals. Analysis groups: C = control; S = sample. Left: Only proteins with n > 1 detection / analysis group were retained. For clarity, two features with CV > 300% were removed from the 2,089 features. Right: Only proteins with n > 1 detection / analysis group were retained. For clarity, 48 features with CV > 300% were removed from the 7,672 features.

[0194] Figure 108D Shown is the detection of over 5,000 proteins in a feasibility study of 212 subjects. For proteins present in >25% of the samples, a median of 4 peptides per protein was detected, search parameters: 0.1% peptide / protein FDR, default timsTOF parameters, with the complete UniProt human proteome database containing contaminants (50% reverse bait).

[0195] Figure 108E The results show that a large number of proteins were reproducibly detected across samples. A single nanoparticle produced both complementary and shared protein recognition. Each sample / particle+panel displayed a unique group of proteins, grouped by sample and collection site.

[0196] Figure 108F Figure 2 shows enhanced proteome coverage for detecting known cancer-associated proteins. All detected matching proteins from a sample are plotted on the HPPP curve. GeneCards data uses scores reported using matching gene IDs and the search term "cancer." Detected HPPP1 proteins cover eight orders of magnitude: highest concentration: P00450 - Ceruloplasmin; 830,000 ng / mL; lowest concentration: Q7Z627 - E3 ubiquitin-protein ligase HUWE1; 0.0034 ng / mL.

[0197] Figure 108G Demonstrating large-scale, deep, and efficient plasma proteomics.

[0198] Figure 108H The quantitative performance of Proteograph is shown to be suitable for large-scale studies.

[0199] Figure 108IThis data demonstrates the reproducibility of large-scale protein enrichment using Proteograph. The reproducibility of Proteograph enrichment is well-suited for biomarker discovery. Data were collected from 191 identical sample enrichments across three instruments, three cohorts, five operators, eight months of run time, 121 plates, and over 1,500 subject samples.

[0200] Figure 108J The reproducibility of the platform across time (months) and instrumentation is shown. The median MS1 ​​peak area of ​​the iRT peptides was all below 15%, with the majority below 10%.

[0201] Figure 108K Demonstrates the application of the platform in pancreatic cancer biomarker discovery.

[0202] Figure 109A A graph showing some of the top proteins differentially detected in biological fluid samples from cancer patients relative to biological fluid samples from control patients.

[0203] Figure 109B : is a graph showing the distribution of OpenTargets (OT) scores. The OT score (from 0 to 0.8) is on the x-axis, while the y-axis includes the density (0 to 15).

[0204] Figure 110A Included are graphs showing comparisons of median total signal by sample, analyte type, and category.

[0205] Figure 110B Box and whisker plots showing the most significantly different analytes in each omics workflow (A: lipids; B: metabolites; and C: proteins).

[0206] Figure 110C Example multi-omics classifier performance combining proteomic, lipidomic, and metabolomic measurements is shown.

[0207] Figure 111A A volcano plot includes the difference in intensities and P-values ​​for proteins adsorbed to nanoparticles and detected in biofluid samples from cancer patients relative to biofluid samples from control patients. The volcano plot displays the magnitude of the difference on the x-axis and the significance on the y-axis, highlighting the most significant analytes.

[0208] Figure 111B Data for protein P35442, which ranked high after particle-based measurements, are included.

[0209] Figure 111CA volcano plot includes the intensity differences and P-values ​​for proteins detected in biofluid samples from cancer patients relative to biofluid samples from control patients. The volcano plot displays the magnitude of the difference on the x-axis and the significance on the y-axis, highlighting the most significant analytes.

[0210] Figure 111D Data for P01011, the top-ranked protein after proteomic measurements, are included.

[0211] Figure 112A A volcano plot includes the intensity differences and P values ​​for lipids detected in biofluid samples from cancer patients relative to biofluid samples from control patients. The volcano plot displays the magnitude of the difference on the x-axis and the significance on the y-axis, highlighting the most significant analytes.

[0212] Figure 112B Data for the top-ranked lipid CERs (d18:1_18:0) following lipidomic measurements were included.

[0213] Figure 113A A volcano plot includes the differences in the intensities of metabolites detected in biofluid samples from cancer patients relative to biofluid samples from control patients and their P values. The volcano plot displays the magnitude of the difference on the x-axis and the significance on the y-axis, highlighting the most significant analytes.

[0214] Figure 113B Included are data on AICAR of the top ranked metabolites after metabolomics measurements in biofluid samples.

[0215] Figure 114A Depicted are cancer and healthy control classifications according to UMAP projections based on combined data generated from biological fluid samples.

[0216] Figure 114B Depicted are classifications of cancer and healthy controls according to PCA projections based on the combined data generated from the biological fluid samples.

[0217] Figure 114C Depicts the classification of cancer and healthy controls according to UMAP projections based on Proteograph data generated from biological fluid samples.

[0218] Figure 114D Depicts the classification of cancer and healthy controls according to PCA projection based on Proteograph data generated from biological fluid samples.

[0219] Figure 114E Depicts the classification of cancer and healthy controls according to UMAP projections based on PiQuant data generated from biological fluid samples.

[0220] Figure 114F Depicts the classification of cancer and healthy controls according to PCA projections based on PiQuant data generated from biological fluid samples.

[0221] Figure 114G Depicts the classification of cancer and healthy controls according to UMAP projections based on lipid data generated from biological fluid samples.

[0222] Figure 114H Depicts the classification of cancer and healthy controls according to PCA projections based on lipid data generated from biological fluid samples.

[0223] Figure 114I Depicts the classification of cancer and healthy controls according to UMAP projections based on metabolite data generated from biological fluid samples.

[0224] Figure 114J Depicts the classification of cancer and healthy controls according to PCA projection based on metabolite data generated from biological fluid samples.

[0225] Figure 115 Protein, lipid, and metabolite features included in the classifier.

[0226] Figure 116 Classifier performance in a multi-omics study is shown and includes receiver operating characteristic (ROC) curves for disease state classification. Also included are area under the curve (AUC) values ​​with 90% confidence intervals in parentheses.

[0227] Figure 117A The performance of a classifier trained with data from a genomics assay is shown, and includes ROC curves for disease state classification. The AUC values ​​at the bottom of the graph are shown as ± values ​​based on 90% confidence.

[0228] Figure 117B The performance of a classifier trained with data from a genomics assay ("Genomics"), a classifier trained with data from a mass spectrometry assay ("Mass Spec"), and a classifier trained with data from both genomics and mass spectrometry ("Combined") is shown. The data shown in the figure include ROC curves for disease state classification. AUC values ​​include ± values ​​based on a 90% confidence level.

[0229] Figure 118A Shown is a volcano plot showing intensity differences between biological fluid samples from pancreatic cancer subjects and healthy control biological fluid samples.

[0230] Figure 118B The study comparison groups are shown (H: healthy; PC: pancreatic cancer). 124 of the 3,381 proteins detected were statistically significant.

[0231] Figures 119A-119C Shown is a volcano plot showing the differential abundance of lipid species between biological fluid samples from pancreatic cancer subjects and healthy control biological fluid samples.

[0232] Figure 120A The quantitative performance of Proteograph is shown to be suitable for large-scale studies such as the one in Example 7.

[0233] Figure 120B The reproducibility of large-scale protein enrichment by Proteograph is shown. The reproducibility of Proteograph enrichment is well suited for biomarker discovery. The system provides high-throughput, reproducible, and deep proteome coverage for new discoveries. Proteograph reproducibility enables quantitative, in-depth, non-targeted proteomic biomarker studies. Large-scale protein enrichment by Proteograph is highly reproducible (NP1=0; NP2=0; NP3=2; NP4=0; and NP5=2).

[0234] Figure 121A Figure 2 shows an evaluation of K562 precursor detection using SWATH and Zeno SWATH DIA. The minimum increase in precursor identification detected using Zeno SWATH DIA was 26%. All data were generated from the pr and pg matrices output by DIA-NN (all quantified precursors and proteins were identified). All data were searched in DIA-NN using "Robust LC" and the SCIEX K562 spectral library.

[0235] Figure 121B Figure 2 shows an evaluation of K562 precursor detection using SWATH and Zeno SWATH DIA. The minimum increase in protein group identification detected using Zeno SWATH DIA was 13%. All data were generated from the pr and pg matrices output by DIA-NN (all quantified precursors and proteins were identified). All data were searched in DIA-NN using "Robust LC" and the SCIEX K562 spectral library.

[0236] Figure 122 The results show that the sensitivity increases with the number of low-abundance peptide species detected. Compared with SWATH, the detection of low-abundance peptides is improved by Zenon SWATH DI.

[0237] Figure 123 Graphs generated from all qualified precursors are shown. The data were searched in DIA-NN using "Robust LV" and the SCIEX K562 spectral library.

[0238] Figure 124The results show that the quantitative sensitivity of SWATH and Zeno SWATH DIA increases with increasing mass. The Zeno SWATHDIA MS1 peak area (K562) is distributed among the less abundant peptides.

[0239] Figure 125A The Zeno SWATCH DIA acquisition shows a higher number of K562 MS2-based precursors compared to the SWATH acquisition alone, based on all qualified precursors, at different peptide injection masses. The data were searched in DIA-NN using "Robust LC" and the SCIEX K562 spectral library.

[0240] Figure 125B The CV (5) for the number of K562 precursor levels acquired with Zeno SWATH DIA was shown to be lower compared to SWATH acquisition alone at different peptide injection masses for all quantified precursors. The data were searched in DIA-NN using "Robust LC" and the SCIEX K562 spectral library.

[0241] Figure 126 It was shown that Zeno Swatch DIA MS / MS acquisition resulted in a 53-85% increase in peptide identifications in Proteographs generated from pooled control samples compared to SWATH MS / MSDIA acquisition.

[0242] Figure 127 2,357 protein groups are shown for all five nanoparticles in a representative cohort of subjects, of which 1,077 protein groups were identified in at least 25% of the patient samples.

[0243] Figure 128A A large number of proteins were reproducibly detected across samples, showing that a single nanoparticle produced both complementary and shared protein recognition.

[0244] Figure 126 B shows that the increased sensitivity is equivalent to the detection of more low-abundance peptides in the Proteograph peptide assay.

[0245] Figure 129 The distribution of patients used in the pulmonary nodule evaluation study is shown.

[0246] Figure 130AFigure 3 Assessment of lung nodules. Univariate analysis of each omics was performed using the Wilcoxon test and the Benjamini-Hochberg procedure, corrected for multiple hypothesis testing. 672 lipids, 376 metabolites, 557 miRNAs, 131,603 mRNA transcripts, 555 peptides (targeted), and 9861 peptide-NPs (non-targeted) were detected. Cross-omics analysis failed to generate a univariate molecular signature that was statistically significant after correction for multiple hypothesis testing.

[0247] Figure 130B Figure 3. A lung nodule classifier trained on each omics group separately achieved an AUC of 0.62 from untargeted proteomics data.

[0248] Figure 130C The figure shows that the AUC obtained by training a classifier combining all omics is 0.6. The best performing individual omics (such as untargeted proteomics and mRNA) were used to train the joint model. The AUC of this classifier was also 0.6. Adding clinical covariates associated with Mayo Clinic score to these classifiers did not improve model performance.

[0249] Figure 131 Figure 1 illustrates the development of a high-risk lung cancer screening classifier. Comparison of benign and malignant nodule categories identified features that are more likely to be cancer-specific. Training the high-risk vs. malignant classifier on features selected by the benign vs. malignant comparison increases the likelihood that the model will identify cancer-specific signals rather than differences due to confounding covariates. Careful confounder analysis is required for the trained classifier.

[0250] Figure 132A A volcano plot comparing malignant and high-risk cancers is shown. 725 lipids, 371 metabolites, 480 miRNAs, 111,949 mRNA transcripts, and 509 peptides (targets) were detected. Light gray dots identify features that were significantly different after Benjamini-Hochberg multiple hypothesis testing correction. These features represent a mixture of cancer-specific and nonspecific differences between the groups.

[0251] Figure 132B A volcano plot comparing malignant and high-risk tumors is shown. Light gray dots identify features that are significantly different between malignant and benign tumors (uncorrected for multiple hypothesis testing). These identified features represent cancer-specific signals. Training a classifier on a subset of cancer-specific features can mitigate the effects of confounding factors in the classification of malignant and high-risk tumors.

[0252] Figure 133Figure 3 shows an initial classifier trained using features selected from the comparison of benign and malignant lung nodules, which showed good performance in the comparison of malignant and high risk. A combined set of 831 filtered features including mRNA, lipids, metabolites, peptides and miRNA was used for training.

[0253] Figure 134 Proteomic analysis of biological fluid samples from pancreatic cancer subjects and control subjects is shown.

[0254] Figure 135 Potential multi-omic pancreatic cancer molecular biomarkers spanning the genotype-to-phenotype spectrum are shown. Light grey dots identify molecular features that are differentially abundant and statistically significant in cancers versus controls.

[0255] Figure 136 Included are ROC plots showing the improvement in classifier performance when combining features of different data types.

[0256] Figure 137 Included are diagrams illustrating the initial confounder analysis.

[0257] Figure 138A Metabolomics-based classifier data for PDAC patients and controls are shown.

[0258] Figure 138B Proteograph-based classifier data for PDAC patients and controls are shown.

[0259] Figure 138C PiQuant-based classifier data for PDAC patients and controls are shown.

[0260] Figure 138D Lipidomics-based classifier data for PDAC patients and controls are shown.

[0261] Figure 138E RNA-based classifier data for PDAC patients and controls are shown.

[0262] Figure 138F Classifier data for PDAC patients and controls based on copy number variation are shown.

[0263] Figure 138G Shown are fragmentomic-based classifier data for PDAC patients and controls.

[0264] Figure 138H Shown are CA-19-9 levels in biological fluids of subjects with various stages of pancreatic cancer and control subjects.

[0265] Figure 138IShown are classifier data from PDAC patients and controls based on carbohydrate antigen 19-9 (CA-19-9) alone or in combination with PiQuant data.

[0266] Figure 139 Some features used to train a classifier or apply a classifier to a subject suspected of having cancer, such as pancreatic cancer, are illustrated.

[0267] Figure 140A Illustrated are some copy number variation features used in a classifier for evaluating biological fluid samples from subjects with pancreatic cancer or subjects without cancer.

[0268] Figure 140B Illustrated are some of the mRNA signatures used in a classifier for evaluating biological fluid samples from subjects with pancreatic cancer or subjects without cancer.

[0269] Figure 140C Illustrated are some of the microRNA signatures used in classifiers for evaluating biological fluid samples from subjects with pancreatic cancer or subjects without cancer.

[0270] Figure 140D The diagram shows some protein features used in a classifier for evaluating biological fluid samples from subjects with pancreatic cancer or subjects without cancer. Here, the protein features include measurements obtained using internal standards. Each feature includes a UniProt ID number.

[0271] Figure 140E The figure shows some peptide features used in a classifier for evaluating biofluid samples from subjects with pancreatic cancer or subjects without cancer. Here, the peptide features include measurements obtained using an internal set of nanoparticles (NP1, NP2, NP3, NP4, or NP5). Each feature includes the amino acid sequence and the nanoparticle name.

[0272] Figure 140F The figure shows some protein features used in the classifier for evaluating biological fluid samples from subjects with pancreatic cancer or subjects without cancer. Here, the protein features include measurements obtained using an internal set of nanoparticles (NP1, NP2, NP3, NP4, or NP5). Each feature includes a UniProt ID number and the nanoparticle name.

[0273] Figure 140G Illustrated are some lipid features used in a classifier for evaluating biological fluid samples from subjects with pancreatic cancer or subjects without cancer.

[0274] Figure 140HIllustrated are some metabolite signatures used in a classifier for evaluating biological fluid samples from subjects with pancreatic cancer or subjects without cancer.

[0275] Figure 141A The ROC curve of the combined machine learning model containing PiQuant, metabolomics, lipidomics and CA-19-9 is shown. The respective AUC values ​​are provided.

[0276] Figure 141B The ROC curve of the combined machine learning model containing PiQuant, metabolomics, lipidomics and CA-19-9 is shown. The respective AUC values ​​are provided.

[0277] Figure 142 Schematic representation of the integrated multi-omics approach as described herein.

[0278] Figure 143 Shown are some of the investigated samples and the amounts of protein groups for each sample.

[0279] Figure 144 Some coefficient of variation (CV) values ​​are shown.

[0280] Figure 145 The number of protein groups detected across subject samples is shown.

[0281] Figure 146 The number of unique protein groups detected in groups of nanoparticles and in individual nanoparticles is shown.

[0282] Figure 147 Detected proteins (shown as dots) and their concentrations from the human plasma proteome project (HPPP) are shown. Proteins with the top Open Targets (OT) scores are shown.

[0283] Figure 148A Included are ROC plots showing classifier performance for NSCLC subjects of all stages.

[0284] Figure 148B The sensitivity aspects of the analysis included RNA-seq data, metabolome data, protein data, and combined RNA-seq+metabolome+protein data for NSCLC subjects of all stages.

[0285] Figure 149A Included is a ROC plot showing classifier performance for stage I NSCLC subjects.

[0286] Figure 149BSensitivity aspects of analyses including RNA-seq data, metabolomic data, protein data, and combined RNA-seq+metabolomic+protein data for stage I NSCLC subjects.

[0287] Figure 150 Shows the workflow for generating spectral library data.

[0288] Figures 151A-151D Shown is a comparison of spectral library creation strategies.

[0289] Figure 152 Pairwise Jaccard index comparisons of spectral libraries are shown.

[0290] Figure 153A Pairwise combinations of spectral libraries are shown.

[0291] Figure 153B Comparisons of 3 or more spectral libraries are shown.

[0292] Figure 153C Shows the library construction efficiency.

[0293] Figure 154 Shown is the application of the largest spectral library on 40 clinical samples.

[0294] Figure 155 Shown is the effect of spectral library size on Zeno-SWATH data.

[0295] Figure 156 Shown is the experimental workflow using timTOF from sample processing to data collection and final data analysis.

[0296] Figure 157 Shown are the qualitative performance of the timsTOF HT and timsTOF Pro2 for various plasma peptide loading masses and LC gradients.

[0297] Figure 158 shows a comparison between triplicate measurements of precursors measured under different LC gradients between timsTOF HT and timsTOF Pro2.

[0298] Figure 158A Shown is a comparison between timsTOF HT and timsTOF Pro2 of triplicate measurements of a precursor from pure plasma measured under different LC gradients.

[0299] Figure 158B Shown is a comparison between timsTOF HT and timsTOF Pro2 of triplicate measurements of precursors from proteograph-treated plasma (NP2) measured at different LC gradients.

[0300] FIG159 shows a comparison between the quantitative linear ranges of the timsTOF HT and the timsTOF Pro2.

[0301] Figure 159A Shown are representative total ion chromatograms (TICs) of single PG-NP2 replicates loaded from 100-1200 ng under a 60 SPD gradient between timsTOF HT and timsTOF Pro2.

[0302] Figure 159B Shown are the distributions of the ratios of the precursor MS2 peak areas (triplicate mean) quantified in the timsTOF HT and timsTOF Pro2.

[0303] Figure 159C Shown are the R-squared distributions of precursors quantified in triplicate measurements of each peptide loading mass for PG NP2 peptide loadings ranging from 100 to 600 ng, 900 ng, or 1200 ng in a 60 SPD gradient in timsTOF HT and timsTOF Pro2.

[0304] Figure 160 Shown are the significance of cancer biomarkers detected in plasma of control and case samples between timsTOF HT and timsTOF Pro2.

[0305] Figure 161 Shown is the overall experimental workflow using the ZenoTOF, from sample preparation to sample processing to data analysis.

[0306] Figure 162 A workflow for instrument harmonization for biomarker discovery across >3,000 subjects is shown.

[0307] Figure 163 Shown are the CV% distributions of within-batch reproducibility of precursor quantities (MS2 raw quantities) in the first 1,596 samples.

[0308] Figure 164A Shown are the CV% distributions of the batch-to-batch reproducibility of precursor quantities (MS2 raw quantities) in the first 1,596 samples for each of the four instruments.

[0309] Figure 164B Shown are the CV% distributions of batch-to-batch reproducibility of precursor quantities (MS2 raw quantities) for the first 1,596 samples across four instruments.

[0310] Figure 165 Shown are unique peptide counts as a function of detection frequency across 1,424 subjects.

[0311] Figure 165 Shown are protein group counts as a function of detection frequency across 1,424 subjects. DETAILED DESCRIPTION

[0312] The present disclosure provides non-invasive methods for diagnosing or ruling out the presence of a disease in a subject or the risk of developing a disease in a subject. The disease may include cancer, such as pancreatic cancer, breast cancer, liver cancer, ovarian cancer, or colon cancer. If treatment is provided early, identifying an early stage disease in a subject can prevent the subject from developing the disease. Non-invasive tests can also be used to rule out the presence of a disease, thereby saving the subject from having to undergo invasive tests (such as biopsies), which may cause pain and stress, or may risk damaging the subject.

[0313] The present disclosure also provides non-invasive methods for detecting the presence or risk of developing cancer (such as pancreatic cancer) in a subject. Identifying early-stage cancer in a subject can protect the subject from further development of the cancer if treatment is provided early. Non-invasive tests can also be used to rule out the presence of cancer, thereby sparing the subject from undergoing invasive tests (such as biopsies), which may be painful and stressful, or may risk harming the subject.

[0314] Multi-omics approaches can unlock the ability to detect disease at earlier stages of its development and can improve the accuracy of disease detection. Figure 1A Some aspects of a multi-omics approach for early disease detection are shown, which can combine genomic DNA or DNA methylation information (which may be an example of a general static risk indicator) with molecular phenotypic information from proteomics or metabolomics (which may be a more dynamic functional indicator). Figure 24 Also shown are some aspects that can be included in a multi-omic approach and include some examples of disease states that can be detected or assessed. Figure 1B Examples of integration of multiple omics data types are shown.Any aspect of these figures can be used in the methods described herein.

[0315] FIG2A illustrates a non-limiting example of a method for predicting whether a subject has a disease such as cancer or is at risk of developing the disease. The analysis may include obtaining a biological fluid sample from a subject (200). The sample may be assayed or analyzed. The biological fluid sample may be any of the biological fluids described herein or any combination of the biological fluids described herein. The sample may be directly analyzed to generate data (202), such as proteomic data; or the sample may be contacted with particles described herein to obtain adsorbed biomolecules (203) prior to the analysis of 202. After obtaining data from the analysis of 202, additional analysis (203) may be performed on the sample obtained from 200 or 201 to obtain additional data sets, such as transcriptomic data, genomic data, metabolomic data, or a combination thereof. The data or data sets obtained from the analysis of 202 or 203 may be used to generate a classifier (205). The classifier may be used to identify the likelihood that the subject has the disease or is at risk of developing the disease. The generation or application of the classifier may be further repeated or refined to improve the analysis. Figure 2B Further details are shown that may be used in the methods described herein. Figure 2B Any of the aspects may be used in the methods described herein, such as classification methods.

[0316] In addition, as shown in FIG. 2A or Figure 2B The analysis shown in the figure can be found in Figure 3A The methods described herein may be applied before or during a procedure at any step included in the steps of the present invention. For example, the evaluation or analysis may be done early in the patient's journey before, shortly after, or as part of an invasive procedure. It may be useful to screen high-risk patients before performing an invasive procedure such as a biopsy or invasive treatment. In general, opportunities where the methods described herein may be useful may be in screening high-risk patients for early detection of disease. The methods described herein may be used for such detection with greater accuracy and convenience than other methods. Figure 3A In a non-invasive examination, medical imaging may be included, or an invasive examination may include obtaining a biopsy. A biopsy may be of a suspected tumor. Figure 3B 、 Figure 3C and Figure 41 Similar patient journeys for pancreatic, liver, and colon cancers are shown in . Figure 3B 、 Figure 3C or Figure 41 Completed at or before any point in the

[0317] In some aspects, the cancer detected by the methods described herein may be pancreatic cancer. Pancreatic cancer may be early pancreatic cancer. In other aspects, pancreatic cancer may be late pancreatic cancer. The sample obtained non-invasively can be used for cancer diagnosis by generating data and identifying patterns in the data associated with cancer (e.g., pancreatic cancer). Cancer diagnosis can be improved by obtaining proteomic data. Cancer diagnosis can be improved by combining various types of data (e.g., multiple data sets) into analysis. For example, combining various data types (including proteomics, transcriptomics, genomics, metabolomics, or a combination thereof) can improve the accuracy of predicting whether an object suffers from cancer. In some aspects, the methods described herein include generating or obtaining data and using the data to predict whether an object suffers from cancer. Various methods for combining or analyzing data are described, and further elaborating on the purposes of data for cancer assessment.

[0318] In certain aspects, the method of detecting cancer can include additional screening or diagnostic methods, such as a computed tomography (CT) scan indicative of pancreatic cancer, a magnetic resonance imaging (MRI) scan indicative of pancreatic cancer, a positron emission tomography (PET) scan indicative of pancreatic cancer, an ultrasound indicative of pancreatic cancer, a cholangiopancreatography indicative of pancreatic cancer, an angiography indicative of pancreatic cancer, a liver function test (LFT) indicative of pancreatic cancer, measuring elevated carcinoembryonic antigen (CEA) levels relative to a control or baseline, measuring elevated carbohydrate antigen (CA) 19-9 levels relative to a control or baseline, or a combination thereof. In some aspects, the method of detecting pancreatic cancer can include identifying a symptom in a subject, such as jaundice, abdominal pain, enlargement of the gallbladder or liver, blood clots, digestive problems, or depression, or a combination thereof.

[0319] In some cases where the disease is pancreatic cancer, the opportunity is to screen high-risk patients before biopsy or pancreatoscopy. For example, a major opportunity using the methods described herein includes screening high-risk pancreatic cancer patients for early detection with improved accuracy and convenience. In the journey of liver cancer patients, the opportunity is to screen high-risk liver cancer patients before biopsy. For example, a major opportunity using the methods described herein may include improving the decision-making of uncertain liver nodules to determine whether a biopsy is needed. Another opportunity may include monitoring or diagnosing small low-risk nodules, or following up (e.g., 3-6 months) to track small nodule progression. In the journey of colorectal cancer (CRC) patients, the opportunity may be to screen high-risk patients before colonoscopy. Another opportunity may be to improve the decision-making of imaging or biopsy procedures.

[0320] The sample obtained non-invasively can be used for disease diagnosis by generating omics data and identifying the spectrum associated with the disease in the omics data. The diagnosis of disease can be improved by combining various types of data (e.g., multiple data sets such as omics data sets) into analysis. For example, combining multiple data types can improve the accuracy of predicting whether a subject suffers from a specific disease. If a single data set makes mistakes independently or overlaps incompletely, the combined data may be more accurate than a single data set. The method described herein includes generating or obtaining multi-omics data, and using multi-omics data to make a prediction about whether a subject suffers from a disease. Various methods of combining or analyzing multi-omics data are described. The purposes of multi-omics data and disease assessment are further elaborated.

[0321] Several methods can be used to classify lung nodules. Lung nodules can be either benign or malignant. Malignant lung nodules can rapidly progress to lung cancer, a common and deadly cancer. Improvements are needed in the identification of malignant and benign lung nodules. On the one hand, early diagnosis of malignant lung nodules can lead to earlier treatment options and more favorable prognosis for subjects with malignant lung nodules. On the other hand, non-invasive diagnosis of benign or non-malignant lung nodules can help avoid lung biopsy (which can be expensive and invasive) and therefore also be more beneficial to subjects with non-malignant lung nodules.

[0322] However, there has been little progress in the development of useful clinical tests for diagnosing and interpreting lung nodules as benign or malignant. Imaging methods often result in a high degree of misdiagnosis (e.g., false positive) rate. These imaging methods are generally unable to detect smaller nodules. Other non-invasive methods, such as biomarker screening, also have limitations. Given that plasma is in contact with many tissues in the body, proteins in plasma may be useful biomarker discovery matrices. However, due to multiple factors including a wide range of concentrations (e.g., 10 orders of magnitude), plasma proteins may be problematic. Complex biochemical workflows attempt to circumvent these challenges, but may not be practical for discovery studies with sufficient scale to ensure validation and replication. Alternatively, biomarker studies are limited to evaluating or re-evaluating existing markers without substantial improvement in clinical performance. Therefore, there is still a need for methods for diagnosing or screening for the presence of benign or malignant lung nodules based on the analysis of biomarkers in biological fluid samples. The methods described herein can meet this need.

[0323] Disclosed herein are methods that include obtaining biomolecular data. The biomolecular data may include multi-omics data. The methods may include generating or receiving the data and then evaluating the data using a classifier. The evaluation may include applying the classifier to identify a disease, rule out the presence of a disease, predict the likelihood of a disease, or select a treatment for the disease.

[0324] Disclosed herein are methods for assessing a biological state using multi-omics data. Disclosed herein are methods for assessing a biological state using a combination of protein markers, genetics, and metabolic markers. The biological state may include a disease, such as cancer. The biological state may include a healthy state. The biological state may include a disease-free state.

[0325] Disclosed herein are methods comprising obtaining a multi-omics database comprising multi-omics data generated from a biofluid sample. The sample may be a sample from a population with different disease states and patient characteristics. Some aspects include querying the multi-omics database. The query may identify biomarkers or biomarker panels that can distinguish individuals in a population with a first disease state or patient characteristic from other individuals in the population with a second disease state or patient characteristic. The multi-omics data may comprise a combination of proteomics, metabolomics, lipidomics, transcriptomics, fragmentomics, methylomics, or genomics.

[0326] The method disclosed herein includes obtaining multi-omic data from one or more biofluid samples of a subject identified as having a lung nodule; and applying a classifier to the multi-omic data to evaluate the lung nodule. The evaluation can determine whether the lung nodule is cancerous or non-cancerous. The evaluation can rule out lung cancer.

[0327] The methods disclosed herein include obtaining multi-omic data from one or more biofluid samples of a subject suspected of having pancreatic cancer; and applying a classifier to the multi-omic data to evaluate the subject. The evaluation may include determining or indicating a likelihood that the subject has pancreatic cancer.

[0328] Some aspects relate to sample preparation. Some aspects include preparing samples for the methods disclosed herein. Some methods include preparing multiple samples.

[0329] disease

[0330] The methods described herein can be used to assess a disease state. The methods described herein can be used to predict or identify a disease state. A disease state can include a disease or condition, such as cancer. Examples of cancer include lung cancer, colon cancer, pancreatic cancer, liver cancer, ovarian cancer, breast cancer, prostate cancer, melanoma, bladder cancer, lymphoma, leukemia, kidney cancer, or uterine cancer. In some aspects, the cancer is breast cancer. A disease can include a condition. A disease state can include having a comorbidity associated with the disease or condition. Reference to whether a subject has a disease state can include whether the subject is healthy. A healthy state can exclude a disease state. For example, a healthy state can exclude having cancer. A disease state can exclude being healthy.

[0331] The method may be useful for cancer diagnosis. The method may be useful for cancer screening. The method may be useful for cancer treatment. The method may include measuring proteins in a biological fluid sample obtained from a subject having or suspected of having a nodule, such as a lung nodule, to obtain a protein measurement. The method may include applying a classifier to the protein measurement to identify the protein measurement as indicating that the lung nodule is cancerous or non-cancerous. In some cases, the classifier is generated using proteomic data obtained by contacting a training sample with particles such that the particles adsorb proteins in the training sample and measuring the proteins adsorbed to the particles. Some aspects include obtaining or receiving a biological fluid sample from a subject.

[0332] In some respects, the cancer to be detected by the method described herein can be pancreatic cancer, liver cancer, ovarian cancer or colon cancer.Can improve the diagnosis of cancer by obtaining proteomic data or other group data (such as lipidomics data).Can improve the diagnosis of cancer by combining various types of data (for example, multiple data sets) into analysis.For example, the combination includes the various data types of proteomics, transcriptomics, genomics, metabolomics or their combination and can improve the accuracy rate of predicting whether the object suffers from cancer.In some respects, method described herein includes generating or obtaining data and using these data to predict whether the object suffers from cancer.The method can include distinguishing cancer types (for example, liver cancer versus ovarian cancer).Describe the various ways of combining or analyzing these data, and further elaborate on the purposes of these data for cancer assessment.

[0333] Cancer can be in the early or late stages. Examples of early stage cancer can include stage I. Early stage can include stage I or stage II. Early stage can include stage I, stage II, or stage III. Examples of late stage cancer can include stage IV.

[0334] Cancer can include pancreatic cancer. Pancreatic cancer can be early pancreatic cancer. In other aspects, pancreatic cancer can be late pancreatic cancer. By generating data and identifying the atlas associated with cancer (such as pancreatic cancer) in the data, the sample obtained non-invasively can be used for cancer diagnosis. In some aspects, the method for detecting cancer can include other screening or diagnostic methods, such as indicating the computed tomography (CT) scan of pancreatic cancer, indicating the magnetic resonance imaging (MRI) scan of pancreatic cancer, indicating the positron emission tomography (PET) scan of pancreatic cancer, indicating the ultrasound of pancreatic cancer, indicating the cholangiopancreatography of pancreatic cancer, indicating the angiography of pancreatic cancer, indicating the liver function test (LFT) of pancreatic cancer, the carcinoembryonic antigen (CEA) level that increases relative to a control or baseline measurement value, the carbohydrate antigen (CA) 19-9 level that increases relative to a control or baseline measurement value, or a combination thereof. In some aspects, the method for detecting pancreatic cancer can include the symptoms of identification object, such as jaundice, abdominal pain, gallbladder or liver enlargement, thrombosis, digestive problems or depression or a combination thereof. Any of these aspects can be used to identify subjects at risk for developing pancreatic cancer.

[0335] The cancer may include liver cancer. In some aspects, the cancer to be detected by the methods described herein may be liver cancer. The liver cancer may be early-stage liver cancer. In other aspects, the liver cancer may be late-stage liver cancer. In some cases, the liver cancer may be stage I, II, III, or IV liver cancer. In some examples, the stage of the liver cancer is unknown. By generating data and identifying patterns associated with cancers such as liver cancer in the data, samples obtained non-invasively can be used for cancer diagnosis. In some aspects, the method for detecting cancer can include additional screening or diagnostic methods, such as dynamic contrast computed tomography (CT) scans indicating liver cancer, magnetic resonance imaging (MRI) scans indicating liver cancer, liver function tests (LFTs) indicating liver cancer, bilirubin levels with respect to a control or baseline measurement, aminotransferase levels with respect to a control or baseline measurement, alkaline phosphatase levels with respect to a control or baseline measurement, hypoalbuminemia, prothrombin time with respect to a control or baseline measurement, alpha-fetoprotein levels with respect to a control or baseline measurement, or liver nodules, or a combination thereof. In some aspects, the method for detecting cancer can include identifying symptoms of a subject, such as abdominal discomfort, pain and tenderness, jaundice, white lime-like stools, nausea, vomiting, bruising or easy bleeding, weakness or fatigue, or a combination thereof. Any of these aspects can be used to identify a subject at risk of developing liver cancer.

[0336] Cancer can include ovarian cancer. In some aspects, the cancer to be detected by the methods described herein can be ovarian cancer. Ovarian cancer can be early ovarian cancer. In other aspects, ovarian cancer can be late ovarian cancer. In some cases, the staging of ovarian cancer can be unknown. In some aspects, the staging of ovarian cancer can be Phase I, Phase II, Phase III or Phase IV. By generating data and identifying the atlas associated with cancer (such as ovarian cancer) in the data, the sample obtained non-invasively can be used for cancer diagnosis. In some aspects, the method for detecting cancer can include other screening or diagnostic methods, such as computed tomography (CT) scans indicating ovarian cancer, magnetic resonance imaging (MRI) scans with indications of ovarian cancer, positron emission tomography (PET) scans with indications of ovarian cancer, transvaginal ultrasound examinations with indications of ovarian cancer, cancer antigen (CA)-125 levels with respect to a control or baseline measurement value increase, or ovarian cysts, or combinations thereof. In some aspects, methods of detecting cancer can include identifying symptoms in a subject, such as a feeling of heaviness in the pelvis, lower abdominal pain, vaginal bleeding, weight gain, weight loss, menstrual abnormalities, unexplained back pain that worsens over time, increased urination, flatulence, nausea, vomiting, or loss of appetite, or a combination thereof. Any of these aspects can be used to identify a subject at risk for developing ovarian cancer.

[0337] Cancer can include colon cancer or colorectal cancer (CRC). In some aspects, the cancer to be detected by the method described herein can be colon cancer. Colon cancer can be early stage colon cancer. In other aspects, colon cancer can be late stage colon cancer. By generating data and identifying the collection of figures associated with cancer (such as colon cancer) in the data, the sample obtained non-invasively can be used for cancer diagnosis. The diagnosis of cancer can be improved by obtaining proteomics data. In some aspects, the method for detecting cancer can include other screening or diagnostic methods, such as computed tomography (CT) scans for indicating colon cancer, liver function tests (LFTs) for indicating colon cancer, measuring carcinoembryonic antigen (CEA) relative to the level of control or baseline measurement value, determining fecal blood, performing fecal immunochemical test (FIT) or these combinations. Any one of these aspects can be used to identify the object in the risk of suffering from colon cancer. For example, the object identified as being in the risk of suffering from colon cancer can be identified as being in risk by one of these methods. The non-invasive method described herein can make the patient that does not suffer from colon cancer exempt from undergoing further invasive test or treatment program (such as performing colonoscopy or performing cancer biopsy), or exempt from undergoing colon cancer treatment program.On the other hand, the non-invasive method described herein can be used for identifying the people that may suffer from colon cancer and confirming whether the patient should undergo further test (for example, invasive test) or treatment program.Colon cancer may be the example of colorectal cancer (CRC).The references or teachings relevant to colon cancer herein can be applied to CRC, and vice versa.

[0338] Cancer can include lung cancer. An example of lung cancer is non-small cell lung cancer (NSCLC). An example of lung cancer is small cell lung cancer. A method for diagnosing lung nodules is disclosed. The method may be useful for diagnosing, treating, or screening patients with lung nodules identified from computed tomography (CT) scans who have not undergone a lung biopsy. The method may be useful for informing a medical practitioner about the probability of whether a lung nodule is benign or malignant. Using the test results from this method, a medical practitioner can avoid performing an unnecessary biopsy on a patient. For example, the method can be used as an exclusion test. Using the test results from this method, a medical practitioner can identify an object that should be biopsied. For example, the method can be used as a rule-in test.

[0339] A diagnostic method for identifying candidates for CT imaging is disclosed. The method may be useful for diagnosing, treating, or screening patients who may be candidates for CT imaging. The method may be useful for high-risk patients (e.g., as defined by the USPSTF or another agency) who are candidates for a CT scan for lung cancer screening but have not yet received a CT scan for lung cancer screening. The method may inform a medical practitioner of the likelihood that a patient has lung cancer. Thus, the method may inform a medical practitioner of the urgency or need to obtain a CT scan of the patient's lungs. Such a method may be useful for high-risk patients, such as patients who are not eligible for other CT screening methods. The method may improve patient selection or compliance for CT imaging. The method may improve patient selection or compliance for a biopsy.

[0340] A method for recurrence monitoring is disclosed. The method may be useful for monitoring patients with potentially resectable lung cancer. The method may be useful for monitoring patients undergoing postoperative therapeutic interventions. The method may be useful for monitoring patients undergoing adjuvant chemotherapy or radiotherapy interventions. The method may be useful for detecting cancer recurrence prior to CT scans or other medical imaging. The method may be useful as a surveillance test for recurrence. The method may be customized or developed in conjunction with patient treatment methods.

[0341] Disclosed herein is a method comprising: measuring proteins in a biological fluid sample obtained from a subject having or suspected of having a pulmonary nodule to obtain a protein measurement value; and applying a classifier to the protein measurement value to identify the protein measurement value as indicative of the pulmonary nodule being cancerous or non-cancerous, wherein the classifier is generated using proteomics data obtained by contacting a training sample with particles such that the particles adsorb proteins from the training sample and measuring the proteins adsorbed to the particles. This method may be useful for cancer diagnosis or screening.

[0342] Disclosed herein is a method comprising: obtaining a biological fluid sample from a subject having a pulmonary nodule; contacting the biological fluid sample with particles such that the particles adsorb biomolecules comprising proteins to the particles; measuring the biomolecules adsorbed to the particles to generate proteomic data; and classifying the proteomic data as indicating that the pulmonary nodule is cancerous or non-cancerous. This method may be useful for cancer diagnosis or screening.

[0343] Described herein are methods for determining a lung nodule-associated condition in a sample obtained from a subject. In some embodiments, the lung nodule-associated condition comprises the presence or absence of a lung nodule in the subject. In some embodiments, the lung nodule-associated condition comprises determining whether the lung nodule is benign or malignant. In some embodiments, the method comprises screening for a lung nodule-associated condition by measuring a biomarker in a sample obtained from the subject. In some embodiments, the biomarker comprises at least one protein in the sample. In some embodiments, the sample is a biological fluid sample. In some embodiments, the biological fluid sample is contacted with particles described herein to adsorb proteins from the biological fluid sample. In some embodiments, the method comprises obtaining a protein measurement of the proteins in the sample. In some embodiments, the method comprises applying a classifier to the protein measurement, thereby identifying the protein measurement as indicating whether the lung nodule is cancerous or non-cancerous. In some embodiments, the classifier is generated using proteomics data obtained by contacting a training sample with particles, thereby causing the particles to adsorb proteins from the training sample. The adsorbed proteins can then be measured using the methods described herein. In some embodiments, the subject is suspected of having a lung nodule or has been identified as having a lung nodule using the imaging methods described herein. In some embodiments, a report is generated based on the protein measurement value being identified as indicating that the lung nodule is cancerous or non-cancerous. In some embodiments, the report indicates that the lung nodule is cancerous or non-cancerous. In some embodiments, the report indicates that the lung nodule is cancerous. In some embodiments, the report indicates that the lung nodule includes non-small cell lung cancer (NSCLC). In some embodiments, the method described herein generates a classifier, which includes features for indicating the protein measurement value as indicating that the lung nodule is cancerous or non-cancerous. In some embodiments, the feature includes a control protein measurement value, a mass spectrum, an m / z ratio, a chromatographic result, an immunoassay result, or a light or fluorescence intensity. In some embodiments, a classifier is trained using any of the calculations or machine learning methods described herein.

[0344] In some embodiments, described herein are methods of recommending lung cancer treatment for a subject when the subject is determined to have a malignant lung nodule based on analysis of protein measurements described herein. In some embodiments, the protein measurements are classified as indicating that the lung nodule is cancerous.

[0345] In some aspects, disclosed herein are methods useful for diagnosing, screening, or treating a subject. Some aspects include measuring proteins in a biological fluid sample obtained from a subject suspected of having a pulmonary nodule to obtain a protein measurement. Some aspects include applying a classifier to the protein measurement. Some aspects include identifying the protein measurement as indicating that the subject has a pulmonary nodule. In some aspects, the classifier is generated using proteomic data obtained by contacting a training sample with particles such that the particles adsorb proteins from the training sample and measuring the proteins adsorbed to the particles.

[0346] In some aspects, disclosed herein are methods useful for diagnosing, screening, or treating a subject. Some aspects include measuring proteins in a biological fluid sample obtained from a subject suspected of having lung cancer to obtain a protein measurement value. Some aspects include applying a classifier to the protein measurement value. Some aspects include identifying the protein measurement value as indicating that the subject has lung cancer. In some aspects, the classifier is generated using proteomics data obtained by contacting a training sample with particles such that the particles adsorb proteins from the training sample and measuring the proteins adsorbed to the particles.

[0347] In some aspects, disclosed herein are methods useful for diagnosing, screening, or treating a subject. Some aspects include obtaining a biofluid sample from a subject suspected of having a pulmonary nodule. Some aspects include contacting the biofluid sample with particles such that the particles adsorb biomolecules comprising proteins to the particles. Some aspects include measuring the biomolecules adsorbed to the particles to generate proteomic data. Some aspects include classifying the proteomic data as indicating that the subject has a pulmonary nodule or not indicating that the subject has a pulmonary nodule based on the proteomic data.

[0348] In some aspects, disclosed herein are methods useful for diagnosing, screening, or treating a subject. Some aspects include obtaining a biological fluid sample from a subject suspected of having lung cancer. Some aspects include contacting the biological fluid sample with particles such that the particles adsorb biomolecules comprising proteins to the particles. Some aspects include measuring the biomolecules adsorbed to the particles to generate proteomic data. Some aspects include classifying the proteomic data as indicating that the subject has lung cancer or not indicating that the subject has lung cancer based on the proteomic data.

[0349] In some aspects, methods useful for monitoring a subject are disclosed herein. Some aspects include obtaining a biofluid sample from a subject at risk of lung cancer recurrence. Some aspects include contacting the biofluid sample with particles so that the particles adsorb biomolecules comprising proteins to the particles. Some aspects include measuring the biomolecules adsorbed to the particles to generate proteomic data. Some aspects include classifying the proteomic data based on the proteomic data as indicating that the subject has lung cancer recurrence or not. In some aspects, the subject has received lung cancer treatment, such as chemotherapy, radiotherapy, or surgery. In some aspects, the cancer may be resectable. In some aspects, lung cancer includes NSCLC.

[0350] In some cases, a lung nodule is described as malignant or cancerous. The terms "malignant" and "cancerous" can be used interchangeably. A malignant lung nodule or a cancerous lung nodule can be referred to as a lung cancer, and vice versa. In some cases, a lung nodule is described as benign or noncancerous. The terms "benign" and "noncancerous" can be used interchangeably.

[0351] Samples and objects

[0352] Some aspects relate to subjects. For example, the methods described herein can be used to evaluate a subject or to evaluate a sample from a subject. Multi-omics data can be generated from a sample from a subject.

[0353] The methods described herein can be used to identify a subject as potentially having a disease such as cancer or being at risk of having a disease such as cancer. The subject may have lung cancer, pancreatic cancer, liver cancer, ovarian cancer, or colon cancer. The cancer may include adenocarcinoma, such as pancreatic adenocarcinoma. The subject may have cancer. The subject may not have cancer. The subject may have pancreatic cancer, liver cancer, ovarian cancer, or colon cancer. The subject may not have pancreatic cancer, liver cancer, ovarian cancer, or colon cancer. The subject may be at risk of developing pancreatic cancer, liver cancer, ovarian cancer, or colon cancer. The subject may have a mass (e.g., a nodule or cyst) in the pancreas. The subject may have a mass (e.g., a nodule) in the liver. Liver cancer may include hepatocellular carcinoma (HCC). Liver cancer may include stage I, II, III, or IV liver cancer. The subject may have a mass (e.g., a nodule or cyst) in one or both ovaries. Ovarian cancer may include stage I, II, III, or IV ovarian cancer. Ovarian cancer may include stage III ovarian cancer. Ovarian cancer may include stage IV ovarian cancer. A subject may have a mass (e.g., a nodule) in the colon. A subject may have a lung nodule. Cancer. A subject may be at risk for breast cancer. A subject may have a mass (e.g., a nodule or cyst) in the breast.

[0354] A sample can be obtained from a subject for the purpose of identifying cancer in the subject. The subject may be suspected of having cancer or not having cancer. The method can be used to confirm or deny a suspected cancer.

[0355] The data described herein can be generated from a sample of a subject. The sample can be a biological fluid sample or a mass sample (e.g., an abnormal growth from a biopsy of the subject). Examples of biological fluids include blood, serum, or plasma. The sample can include a blood sample. The sample can include a serum sample. The sample can include a plasma sample. One or more biological fluid samples can include blood, serum, or plasma samples. Other examples of biological fluids include urine, tears, semen, breast milk, vaginal fluid, mucus, saliva, sweat, or cell homogenates.

[0356] The sample can be obtained from the subject for the purpose of identifying the disease state of the subject. The subject may be suspected of having a disease state or not having a disease state. The method can be used to confirm or deny a suspected disease state. In some aspects, the sample from the subject is used to determine whether a lump, nodule (e.g., lung nodule) or cyst is cancerous or noncancerous.

[0357] A biological fluid sample can be obtained from a subject. For example, a blood, serum, or plasma sample can be obtained from a subject by drawing blood. Other ways to obtain a biological fluid sample include aspiration or swabbing.

[0358] The biological fluid sample can be cell-free or substantially cell-free.To obtain a cell-free or substantially cell-free biological fluid sample, the biological fluid can be subjected to a sample preparation method, such as centrifugation and pellet removal.

[0359] Non-biological fluid samples can be obtained from an object or patient. For example, the sample can include a tissue sample. Some examples of organs or tissues that can be sampled include lung, colon, pancreas, liver, breast or ovarian tissue. The sample can include a mass taken from an organ or tissue of the object. The mass may be suspected to be cancerous. The mass may include a nodule (for example, a colon nodule or a liver nodule). The mass may include a cyst (for example, an ovarian cyst). Before performing the method described herein, a doctor may identify a nodule or cyst as being at a high risk or low risk of cancer. The mass can, for example, be biopsied by a needle biopsy procedure. The needle biopsy procedure can include piercing the abdomen of the object with a fine needle and inserting it into the liver to obtain a tissue sample, which can then be examined under a microscope to determine whether there are signs of cancer. The sample can include a cell sample. The sample can include a homogenate of cells or tissue. The sample can include the supernatant of a centrifuged homogenate of cells or tissue.

[0360] The sample may include lung tissue. The sample may include colon tissue. The sample may include pancreatic tissue. The sample may include liver tissue. The sample may include breast tissue. The sample may include ovarian tissue. The tissue may be cancerous. The tissue may be noncancerous. The tissue may be suspected of being cancerous. The tissue may be malignant. The tissue may be nonmalignant. The tissue may be suspected of being malignant.

[0361] Samples (e.g., biological fluid or tissue samples) can be collected at any stage of the screening process such as Figure 3A The sample can be obtained from the subject before, during, or after the stages shown in . The sample can be obtained before or during a stage where the subject is a candidate for biopsy, pancreatoscopy, or colonoscopy for early detection of disease. The sample can be obtained before or during a non-invasive investigation, invasive investigation, treatment, or monitoring stage.

[0362] Data can be generated from a single sample or from multiple samples. Data from multiple samples can be obtained from the same object. In some cases, different data types are obtained from samples collected in different ways or collected in separate containers. Samples can be collected in a container comprising one or more reagents such as preservation reagents or biomolecule separation reagents. Some examples of reagents include heparin, ethylenediaminetetraacetic acid (EDTA), citrate, anti-cleavage agent or a combination of these reagents. Samples from an object can be collected in multiple containers, which contain different reagents such as for preserving or separating the biomolecules of the type separated. Samples can be collected in a container that does not include any reagent therein. Samples can be collected at the same time (for example, the same hour or the same day) or at different times. Samples can be frozen, refrigerated, heated, or stored at room temperature.

[0363] The methods described herein can be used to identify a subject as likely to have a disease state. The disease state can include cancer, including pancreatic cancer, liver cancer, ovarian cancer, or colon cancer. Some aspects of the present disclosure include identifying a subject's lung nodules as cancerous or non-cancerous. The lung nodules can be located in the subject's lungs. The subject may be identified as having lung nodules. In some aspects, the subject has multiple lung nodules. The subject may have lung cancer. The subject may be at risk for developing lung cancer. The subject may have a lung complication. The subject may have a comorbidity described herein. The subject may have difficulty breathing. The subject may have fluid in the lungs.

[0364] In some cases, the subject is monitored. For example, information about the likelihood that the subject has a disease state can be used to determine whether the subject should be monitored without providing treatment to the subject. In other cases, the subject can be monitored while receiving treatment to see if the subject's disease state improves. In some aspects, a subject with a pulmonary nodule can be monitored to determine the progression of the pulmonary nodule. The subject can be monitored for pulmonary nodules. The subject can be treated as described herein.

[0365] The subject can be a vertebrate. The subject can be a mammal. The mammal can include a rat, mouse, gerbil, guinea pig, or hamster. The mammal can include a fox, bear, dog, monkey, cow, pig, or sheep. The subject can be a primate. The primate can include an ape or monkey. The primate can include a chimpanzee, lemur, bonobo, orangutan, or baboon. The subject can be a human. The subject can be an adult (e.g., at least 18 years old). The subject can be male. The subject can be female. The subject can have a disease state. For example, the subject can have a disease or condition, a co-morbidity of the disease or condition, or can be healthy.

[0366] The methods described herein may include using a sample, such as a biological sample. For example, the method may include determining one or more biomarker measurements in a sample. The biological sample may be from a subject, such as a subject with a pulmonary nodule. The biological sample may include a blood sample from which red blood cells have been removed. For example, the biological sample may include a plasma sample. The biological sample may include a serum sample. The biological sample may include blood or a blood component. The biological sample may include a blood sample. The sample described or used herein may be from a subject described herein, such as a subject with an identified pulmonary nodule.

[0367] The sample is consistent with the methods disclosed herein for assessing the presence or absence of one or more biomarkers associated with the presence or malignancy of a pulmonary nodule. The subject can be a human or non-human animal. The biological sample can be a biological fluid. For example, the biological fluid can be plasma, serum, CSF, urine, tears, cell lysate, tissue lysate, cell homogenate, tissue homogenate, nipple aspirate, stool sample, synovial fluid, and whole blood or saliva. The sample can also be a non-biological sample, such as water, milk, solvent, or any substance homogenized into a fluid state. The biological sample can contain a large number of proteins or proteomic data, which can be analyzed after the protein is adsorbed to the surface of various particle types in the group and then digested. The proteomic data can include nucleic acids, peptides, or proteins. Any of the samples herein can contain many different analytes that can be analyzed using the methods disclosed herein. The analyte can be a protein, peptide, small molecule, nucleic acid, metabolite, lipid, or any molecule that can potentially bind to or interact with the surface of a particle type.

[0368] The sample can be a biological fluid. The biological sample can include a biological fluid sample, such as cerebrospinal fluid (CSF), synovial fluid (SF), urine, plasma, serum, tears, fissure fluid, semen, whole blood, milk, nipple aspirate, duct lavage fluid, vaginal fluid, nasal fluid, ear fluid, gastric fluid, pancreatic fluid, trabecular fluid, lung lavage fluid, prostate fluid, sputum, feces, bronchial lavage fluid, fluid from a swab, bronchial aspirate, sweat or saliva. The biological fluid can be a fluidized solid, such as a tissue homogenate, or a fluid extracted from a biological sample. The biological sample can be, for example, a tissue sample or a fine needle aspiration (FNA) sample. The biological sample can be a cell culture sample. For example, the sample that can be used in the method disclosed herein can include cells grown in cell culture or can include a cell-free material taken from a cell culture. The biological fluid can be a fluidized biological sample. For example, the biological fluid can be a fluidized cell culture extract. The sample can be extracted from a fluid sample, or the sample can be extracted from a solid sample. For example, the sample can comprise gaseous molecules extracted from a fluidized solid (eg, volatile organic compounds).In some aspects, the biological fluid comprises blood, plasma, or serum.

[0369] Methods consistent with the present disclosure may include collecting (e.g., separating, enriching, or purifying) species from a biological sample. The species may be a biomolecule (e.g., a protein), a biomacromolecule structure (e.g., a peptide aggregate or a ribosome), a cell, or a tissue. The species may be selectively collected from a biological sample. For example, the method may include separating cancer cells from a tissue (e.g., as a tissue biopsy) or from a biological fluid (e.g., as a liquid biopsy) such as whole blood, plasma, or a buffy coat. The method may include a sample that does not have cancer cells. The species may be processed prior to analysis. For example, proteins may be reduced and degraded, nucleic acids may be separated from histones, or cells may be lysed.

[0370] The biological sample can be obtained or derived from a human subject. The biological sample can be stored under a variety of storage conditions before processing, such as different temperatures (e.g., at room temperature, under refrigerated or frozen conditions, at 25°C, at 4°C, at -18°C, at -20°C, or at -80°C) or different suspensions (e.g., EDTA collection tubes, cell-free RNA collection tubes, or cell-free DNA collection tubes).

[0371] In some cases, the sample can be depleted prior to biomarker analysis. Commercially available kits can be used to deplete the sample. For example, the kit that can be used to deplete the sample can be a spin column-based depletion kit, an albumin depletion kit, an immunodepletion kit, or an abundant protein depletion kit. Non-limiting examples of kits that can be used for sample depletion include PureProteomeTM Human albumin / immunoglobulin depletion kit (EMD Millipore Sigma), Immunoaffinity albumin and IgG depletion kit (MilliporeSigma), Protein depletion kit (Millipore Sigma), spin columns for depletion of the top 12 most abundant proteins (Pierce), or Proteome Purify TM Immunodepletion Kit (R&D Systems). Depletion can remove high concentrations of biomolecules from a sample. For example, the method can include removing albumin from a plasma sample prior to low concentration biomarker analysis. The sample can include depleted plasma.

[0372] Data Generation and Use

[0373] The methods disclosed herein may include obtaining data, such as multi-omics data generated from one or more biological fluid samples collected from a subject. The data may include biomolecule measurements, such as protein measurements, transcript measurements, genetic material measurements, or metabolite measurements. Omics data may include any of the following: proteomic data, genomics data, transcriptomic data, or metabolomic data. This section includes some methods for generating each of these types of omics data. Methods for generating or analyzing omics data may also be applied to methods for generating or analyzing a single biomolecule or a collection of biomolecules. Other types of omics data may also be generated. The description of generating or analyzing omics data may be applied to methods for generating or analyzing a single biomolecule or a collection of biomolecules that do not necessarily include omics data. Aspects described for biomolecule data may be related to biomolecule measurements, and vice versa. The data may be labeled or identified as indicating a disease or not indicating a disease. The data may be labeled or identified as indicating pancreatic cancer, liver cancer, ovarian cancer, or colon cancer or not indicating pancreatic cancer, liver cancer, ovarian cancer, or colon cancer. The methods described herein may include obtaining multi-omics measurements, such as by performing an assay.

[0374] The methods described herein can include generating or using omics data. Omics data can include data about all biomolecules of a certain type (such as proteins, transcripts, genetic material, or metabolites). Omics data can include data about a subset of biomolecules. For example, omics data can include data about 500 or more, 750 or more, 1000 or more, 2500 or more, 5000 or more, 10,000 or more, 25,000 or more biomolecules of a certain type. The methods described herein can comprise obtaining measurements of more than 10, more than 20, more than 30, more than 40, more than 50, more than 75, more than 100, more than 250, more than 500, more than 750, more than 1000, more than 1250, more than 2500, more than 5000, more than 7500, more than 10,000, more than 12,500, more than 15,000, more than 17,500, more than 20,000, more than 22,500, or more than 25,000 biomolecules of a certain type. The methods described herein can include obtaining less than 10, less than 20, less than 30, less than 40, less than 50, less than 75, less than 100, less than 250, less than 500, less than 750, less than 1000, less than 1250, less than 2500, less than 5000, less than 7500, less than 10,000, less than 12,500, less than 15,000, less than 17,500, less than 20,000, less than 22,500, or less than 25,000 measurements of a certain type of biomolecule. For each of the multiple data types, any number of biomolecules in the aforementioned numbers can be measured. Polyomics includes at least 100 measurements of each of at least two omics data types. Polyomics includes at least 500 measurements of each of at least two omics data types. Multi-omics includes at least 1000 measurements of each of at least two omics data types. The data can relate to the presence, absence, or amount of a given biomolecule. Examples of data types can include lipid, protein, peptide, transcript, mRNA, miRNA, DNA sequence, methylation, or metabolite data.

[0375] Deep proteome coverage facilitates multi-omics approaches. New technologies and sample availability address historical challenges in scaling proteomics. Some of these challenges include access to large, well-collected, annotated sample cohorts for specific clinical questions, technical challenges associated with plasma proteomics (such as reproducibility, throughput, and depth of coverage that may limit translation to the clinic), and reproducible measurement and integration of multi-omics data sets to provide new insights into cancer biology.

[0376] The concepts described herein may help address some of these challenges. For example, the use of particles or the inclusion of additional omics types may address these issues.

[0377] Disclosed herein are methods for multi-omic analysis. "Multi-omic(s) / multiomic(s)" may include analytical methods for large-scale analysis of biomolecules, wherein the data set is a plurality of groups, such as a proteome, a genome, a transcriptome, a lipidome, and a metabolome. Non-limiting examples of multi-omic data may include proteomic data, genomic data, lipidomic data, glycomic data, transcriptomic data, or metabolomic data. The "biomolecule" in the "biomolecule corona" may refer to any molecule or biological component that can be produced by or present in a biological organism. Non-limiting examples of biomolecules include proteins (protein corona), polypeptides, polysaccharides, sugars, lipids, lipoproteins, metabolites, oligonucleotides, nucleic acids (DNA, RNA, microRNA, plasmids, single-stranded nucleic acids, double-stranded nucleic acids), metabolomes, and small molecules (such as primary metabolites, secondary metabolites, and other natural products), or any combination thereof. In some embodiments, the biomolecule is selected from the group of proteins, nucleic acids, lipids, and metabolites.

[0378] Some aspects that could be included in a multi-omics strategy include well-defined disease biobanks with diverse sample types optimized for multi-omics measurements, the development and optimization of novel proteomic technologies to increase proteome coverage and throughput without compromising reproducibility, or unbiased multi-omics platforms that deploy state-of-the-art instrumentation and advanced machine learning analyses to transform complex early disease detection.

[0379] Proteomics data

[0380] The data described herein, such as multi-omics data, may include protein data or proteomic data. Proteomic data may relate to data on proteins, peptides, or protein variants. The data may include only peptides or proteins, or a combination of the two. An example of a peptide is an amino acid chain. An example of a protein is a peptide or a combination of peptides. For example, a protein may include one, two, or more peptides bound together. A protein may be a secreted protein. Proteomic data may include data on various protein variants. Protein variants may include different forms of proteins produced by genomes with any type of sequence variation, splicing isoforms, or post-translational modifications. Proteomic data may be generated using unbiased, non-targeted methods, or may include a specific protein set. The aspects described for proteomic data may be related to protein data, and vice versa.

[0381] Proteomic data can include information about the presence, absence, or amount of various proteins or peptides. For example, proteomic data can include the amount of a protein. The amount of a protein can be expressed as the concentration or quantity of the protein, such as the concentration of the protein in a biological fluid. The amount of a protein can be relative to another protein or another biomolecule. Proteomic data can include information about the presence of a protein or peptide. Proteomic data can include information about the absence of a protein or peptide. Proteomic data can be distinguished by subtypes, where each subtype includes different types of proteins, peptides, or protein variants.

[0382] Proteomic data typically includes data about many proteins or peptides. For example, proteomic data may include information about the presence, absence, or amount of 1000 or more proteins or peptides. In some cases, proteomic data may include information about the presence, absence, or amount of 5000, 10,000, 20,000, or more peptides, proteins, or protein variants. Proteomic data may even include up to about 1 million protein variants. Proteomic data may include a range of proteins, peptides, or protein variants defined by any of the aforementioned numbers of proteins, peptides, or protein variants. Some examples of proteins or peptides that may be included in proteomic data are shown in Figure 6 、 Figure 7 、 Figure 10B or Figure 15 middle.

[0383] Proteomic data can include protein information, such as protein measurements in biological fluids. Figure 140DExamples of some protein biomarkers that can be used in the methods disclosed herein (such as evaluating cancers, such as pancreatic cancer) are included. Protein measurements can be obtained using internal standards. Any combination or number of such biomarkers can be included. In some cases, a biomarker is useful when its feature importance score is greater than 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.12, or 0.14.These features may include any of the following proteins: coagulation factor XIII A chain (F13A_human; UniProt ID P00488), aminopeptidase N (AMPN_human; UniProt ID P15144), polymeric immunoglobulin receptor (PIGR_human; UniProt ID P01833), anthrax toxin receptor 2 (ANTR2_human; UniProt ID P58335), protein S100-A8 (S10A8_human; UniProt ID P05109), leucine-rich alpha-2-glycoprotein (A2GL_human; UniProt ID P02750), apolipoprotein M (APOM_human; UniProt ID DO95445), apolipoprotein CI (APOC1_human; UniProt ID P02654), protein S100-A9 (S10A9_human; UniProt ID P06702), neuropilin-1 (NRP1_human; UniProt ID O14786), low affinity immunoglobulin gamma Fc region receptor III-A (FCG3A_human; UniProt ID P08637), transthyretin (TTHY_human; UniProtID P02766), cartilage acidic protein 1 (CRAC1_human; UniProt ID Q9NQ79), intercellular adhesion molecule 1 (ICAM1_human; UniProt ID P05362), CD166 antigen (CD166_human; UniProt ID Q13740), tenascin (TENA_human; UniProt ID P24821), gelsolin (GELS_human; UniProt ID P06396), tetranectin (TETN_human; UniProt ID P05452), insulin-like growth factor binding protein 2 (IBP2_human; UniProt ID

[00155] Biomarkers may include factor XIII A chain (F13A_human; UniProt ID P00488). Biomarkers may include aminopeptidase N (AMPN_human; UniProt IDP15144). Biomarkers may include polymeric immunoglobulin receptor (PIGR_human; UniProt ID P01833).Biomarkers may include anthrax toxin receptor 2 (ANTR2_human; UniProt ID P58335). Biomarkers may include protein S100-A8 (S10A8_human; UniProt ID P05109). Biomarkers may include leucine-rich alpha-2-glycoprotein (A2GL_human; UniProt ID P02750). Biomarkers may include apolipoprotein M (APOM_human; UniProt ID 095445). Biomarkers may include apolipoprotein CI (APOC1_human; UniProt ID P02654). Biomarkers may include protein S100-A9 (S10A9_human; UniProt ID P06702). Biomarkers may include neuropilin-1 (NRP1_human; UniProt ID 014786). Biomarkers may include low affinity immunoglobulin gamma Fc region receptor III-A (FCG3A_human; UniProt ID P08637). Biomarkers may include transthyretin (TTHY_human; UniProt ID P02766). Biomarkers may include cartilage acidic protein 1 (CRAC1_human; UniProtID Q9NQ79). Biomarkers may include intercellular adhesion molecule 1 (ICAM1_human; UniProt ID P05362). Biomarkers may include CD166 antigen (CD166_human; UniProt ID Q13740). Biomarkers may include tenascin (TENA_human; UniProt ID P24821). Biomarkers may include gelsolin (GELS_human; UniProt IDP06396). Biomarkers may include tetranectin (TETN_human; UniProt ID P05452). The biomarker may include insulin-like growth factor binding protein 2 (IBP2_human; UniProt ID P18065). The biomarker may include endoclin-1 (ITLN1_human; UniProt ID Q8WWA0). The biomarker may include inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3_human; UniProt ID Q06033). The biomarker may include vascular cell adhesion protein 1 (VCAM1_human; UniProt ID P19320). The biomarker may include complement component C9 (C09_human; UniProt ID P02748). The biomarker may include apolipoprotein C-III (APOC3_human; UniProt ID P02656).

[0384] Proteomic data can include protein information, such as protein measurements in biological fluids. Figure 140FExamples of some protein biomarkers that can be used in the methods disclosed herein (such as evaluating cancer, such as pancreatic cancer) are included. Protein measurements can be obtained using particles such as those described herein. Any combination or number of such biomarkers can be included. In some cases, a biomarker is useful when its feature importance score is greater than 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, or 0.07. These features may include any of the following proteins: interferon-induced transmembrane protein 3 (IFM3_human; UniProt ID Q01628), aminopeptidase N (AMPN_human; UniProt ID P15144), leucine-rich alpha-2-glycoprotein (A2GL_human; UniProt ID P02750), alpha-1-antichymotrypsin (AACT_human; UniProt ID P01011), SPARC-associated module calcium-binding protein 1 (SMOC1_human; UniProt ID Q9H4F8-2), alpha-1-antitrypsin (A1AT_human; UniProt ID P01009), pentraxin-related protein PTX3 (PTX3_human; UniProt ID P26022), cadherin-associated family member 2 (CDHR2_human; UniProt ID Q9BYE9), histone H2A 2-C type (H2A2C_human; UniProt ID ID Q16777), anthrax toxin receptor 2 (ANTR2_human; UniProt ID P58335-4), stromelysin (MMP7_human; UniProt ID P09237), complement component C7 (CO7_human; UniProt ID P10643), annexin A2 (ANXA2_human; UniProt ID P07355-2), fibrinogen-like protein 1 (FGL1_human; UniProt ID Q08830), histone H4 (H4_human; UniProt ID P62805), or very long chain-specific acyl-CoA dehydrogenase (mitochondrial) (ACADV_human; UniProt ID P49748-3). Biomarkers may include interferon-induced transmembrane protein 3 (IFM3_human; UniProt ID Q01628). Biomarkers may include aminopeptidase N (AMPN_human; UniProt ID P15144). Biomarkers may include leucine-rich alpha-2-glycoprotein (A2GL_human; UniProt ID P02750). Biomarkers may include alpha-1-antichymotrypsin (AACT_human; UniProt IDP01011).Biomarkers may include SPARC-associated module calcium binding protein 1 (SMOC1_Human; UniProt ID Q9H4F8) (e.g., Q9H4F8-2). Biomarkers may include alpha-1-antitrypsin (A1AT_Human; UniProt IDP01009). Biomarkers may include pentraxin-related protein PTX3 (PTX3_Human; UniProt IDP26022). Biomarkers may include cadherin-associated family member 2 (CDHR2_Human; UniProt ID Q9BYE9). Biomarkers may include histone H2A 2-type C (H2A2C_Human; UniProt ID Q16777). Biomarkers may include anthrax toxin receptor 2 (ANTR2_Human; UniProt ID P58335) (e.g., P58335-4). Biomarkers may include stromelysin (MMP7_Human; UniProt ID P09237). Biomarkers may include complement component C7 (C07_human; UniProt ID P10643). Biomarkers may include annexin A2 (ANXA2_human; UniProt ID P07355) (e.g., P07355-2). Biomarkers may include fibrinogen-like protein 1 (FGL1_human; UniProt IDQ08830). Biomarkers may include histone H4 (H4_human; UniProt ID P62805). Biomarkers may include very long chain specific acyl-CoA dehydrogenase (mitochondrial) (ACADV_human; UniProt ID P49748) (e.g., P49748-3).

[0385] Proteomics data can include protein information, such as protein measurements in a biological fluid. Examples of some protein biomarkers can be used with the methods disclosed herein. Protein measurements can be obtained using particles such as those described herein. Any combination or number of such biomarkers can be included. These features may include any of the following proteins: complement component C9 (CO9_human; UniProt ID P02748), complement C2 (CO2_human; UniProt IDP06681), CSC1-like protein 1 (CSCL1_human; UniProt ID 094886), cathepsin F (H0YD65_human; UniProt ID H0YD65), cartilage intermediate layer protein 2 (K7EPJ4_human; UniProt ID K7EPJ4), cathepsin B (E9PHZ5_human; UniProt ID E9PHZ5), progranulin (GRN_human; UniProt ID P28799), inter alpha-trypsin inhibitor heavy chain H4 (ITIH4_human; UniProt ID Q14624), phospholipid transfer protein (PLTP_human; UniProt ID P55058), peptidase inhibitor 16 (PI16_human; UniProt ID Q6UXB8) or plasma serine protease inhibitor (IPSP_human; UniProt ID P05154). Biomarkers may include complement C2 (C02_human; UniProt ID P06681). Biomarkers may include CSC1-like protein 1 (CSCL1_human; UniProt ID 094886). Biomarkers may include cathepsin F (HOYD65_human; UniProt ID HOYD65). Biomarkers may include cartilage intermediate layer protein 2 (K7EPJ4_human; UniProt ID K7EPJ4). Biomarkers may include cathepsin B (E9PHZ5_human; UniProt IDE9PHZ5). Biomarkers may include progranulin (GRN_human; UniProt ID P28799). Biomarkers may include inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4_human; UniProt ID Q14624). Biomarkers may include phospholipid transfer protein (PLTP_human; UniProt ID P55058).

[0386] Table 15E shows some examples of proteins that can be used as biomarkers. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 of these proteins can be used as biomarkers, for example, for lung nodule assessment.Any of the following proteins can be used for this purpose (as indicated by their UniProt ID numbers): coagulation factor XIII A chain (F13A_human; UniProt ID P00488), endoglin C receptor (EPCR_human; UniProt ID Q9UNN8), insulin-like growth factor binding protein 2 (IBP2_human; UniProt ID P18065), phosphatidylcholinesterol acyltransferase (LCAT_human; UniProt ID P04180), polymeric immunoglobulin receptor (PIGR_human; UniProt ID P01833), tenascin-X (TENX_human; UniProt ID P22105), attractin (ATRN_human; UniProt ID O75882), intectin-1 (ITLN1_human; UniProt ID Q8WWA0), Integrin beta-1 (ITB1_human; UniProt ID P05556), Immunoglobulin heavy chain constant region gamma 2 (IGHG2_human; UniProt ID P01859), α-N-acetylglucosaminidase (ANAG_human; UniProt ID P54802), Hepatocyte growth factor activator (HGFA_human; UniProt ID Q04756), β-Ala-His dipeptidase (CNDP1_human; UniProt ID Q96KN2), Lumican (LUM_human; UniProt ID P51884), Neurogenic locus Notch homologous protein 2 (NOTC2_human; UniProt ID Q04721), Synaptophysin-like protein 1 (SYPL1_human; UniProt ID

[00155] Biomarkers may include factor XIIIA chain (F13A_human; UniProt ID P00488). Biomarkers may include endoglin C receptor (EPCR_human; UniProt ID Q9UNN8).Biomarkers may include insulin-like growth factor binding protein 2 (IBP2_human; UniProt ID P18065). Biomarkers may include phosphatidylcholinesterol acyltransferase (LCAT_human; UniProt ID P04180). Biomarkers may include polymeric immunoglobulin receptor (PIGR_human; UniProt ID P01833). Biomarkers may include tenascin-X (TENX_human; UniProt ID P22105). Biomarkers may include attractin (ATRN_human; UniProt ID O75882). Biomarkers may include inner lectin-1 (ITLN1_human; UniProt ID Q8WWA0). Biomarkers may include integrin beta-1 (ITB1_human; UniProt ID P05556). Biomarkers may include immunoglobulin heavy chain constant region gamma 2 (IGHG2_human; UniProt ID P01859). Biomarkers may include alpha-N-acetylglucosaminidase (ANAG_human; UniProt ID P54802). Biomarkers may include hepatocyte growth factor activator (HGFA_human; UniProt ID Q04756). Biomarkers may include beta-Ala-His dipeptidase (CNDP1_human; UniProt ID Q96KN2). Biomarkers may include hyaluronic acid (LUM_human; UniProt IDP51884). Biomarkers may include neurogenic locus Notch homologous protein 2 (NOTC2_human; UniProt ID Q04721). Biomarkers may include synaptobrevin-like protein 1 (SYPL1_human; UniProt ID Q16563). Biomarkers may include complement factor H-related protein 1 (FHR1_human; UniProt ID Q03591). Biomarkers may include coagulation factor VII (FA7_human; UniProt ID P08709). Biomarkers can include extracellular matrix protein 1 (ECM1_human; UniProt ID Q16610). Biomarkers can include GDH / 6PGL endoplasmic bifunctional protein (G6PE_human; UniProt ID 095479). Fragments of any of these proteins can be used. Any of these biomarkers can be used alone or in combination to assess lung nodules (e.g., to determine the likelihood that a lung nodule is cancerous). Protein measurements can be obtained using internal standards.

[0387] Table 15F shows some examples of proteins that can be used as biomarkers. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15 of these proteins can be used as biomarkers, for example, for lung nodule assessment. Any of the following proteins can be used for this purpose (as indicated by their UniProt ID numbers): alpha-2-HS-glycoprotein (FETUA_human; UniProt ID P02765), fetuin-B (FETUB_human; UniProt ID Q9UGM5), Src kinase-associated phosphoprotein 2 (SKAP2_human; UniProt ID O75563), complement C5 (CO5_human; UniProt ID P01031), collagen alpha-3 (VI) chain (CO6A3_human; UniProt ID P12111), dehydrogenase / reductase SDR family member 7 (DHRS7_human; UniProt ID Q9Y394), UDP-glucuronide decarboxylase 1 (UXS1_human; UniProt ID Q8NBZ7-2), complement C1s subcomponent (C1S; A0A087X232), complement C1s subcomponent (C1S_human; UniProt ID P09871), thrombospondin-1 (TSP1_human; UniProt ID P07996), tryptophan-tRNA ligase (cytoplasmic) (SYWC_human; UniProt IDP23381), alpha-2-macroglobulin (A2MG_human; UniProt ID P01023), alpha-actinin-1 (ACTN1_human; UniProt ID P12814), septin-2 (SEPT2_human; UniProt ID Q15019-2), apolipoprotein B-100 (APOB_human; UniProt ID

[00155] Biomarkers may include complement component C8 beta chain (CO8B_human; UniProt ID P04114) or complement component C8 beta chain (CO8B_human; UniProt ID DP07358). Biomarkers may include alpha-2-HS-glycoprotein (FETUA_human; UniProt ID P02765). Biomarkers may include fetuin-B (FETUB_human; UniProt ID Q9UGM5). Biomarkers may include Src kinase-associated phosphoprotein 2 (SKAP2_human; UniProt ID 075563). Biomarkers may include complement C5 (CO5_human; UniProt ID P01031). Biomarkers may include collagen alpha-3 (VI) chain (CO6A3_human; UniProt IDP12111).Biomarkers may include dehydrogenase / reductase SDR family member 7 (DHRS7_Human; UniProt ID Q9Y394). Biomarkers may include UDP-glucuronate decarboxylase 1 (UXS1_Human; UniProt ID Q8NBZ7) (e.g., Q8NBZ7-2). Biomarkers may include complement C1s subcomponent (C1S; A0A087X232). Biomarkers may include complement C1s subcomponent (C1S_Human; UniProt ID P09871). Biomarkers may include thrombospondin-1 (TSP1_Human; UniProt ID P07996). Biomarkers may include tryptophan-tRNA ligase (cytoplasmic) (SYWC_Human; UniProt ID P23381). Biomarkers may include alpha-2-macroglobulin (A2MG_Human; UniProt IDP01023). Biomarkers may include alpha-actinin-1 (ACTN1_human; UniProt ID P12814). Biomarkers may include mitogen-2 (SEPT2_human; UniProt ID Q15019) (e.g., Q15019-2). Biomarkers may include apolipoprotein B-100 (APOB_human; UniProt ID P04114). Biomarkers may include complement component C8 beta chain (CO8B_human; UniProt ID P07358). Fragments of any of these proteins may be used. Any of these biomarkers may be used alone or in combination to assess lung nodules (e.g., to determine the likelihood that a lung nodule is cancerous). In some cases, any of these peptides may be used as a biomarker when measured by adsorption onto particles from a biological fluid sample.

[0388] Some proteins can be used as biomarkers. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15 of these proteins can be used as biomarkers, for example, for the assessment of lung cancer such as non-small cell lung cancer. Any of the following proteins can be used for this purpose (as indicated by their UniProt ID numbers): Sushi, von Willebrand factor type A, EGF and pentraxin domain-containing protein 1 (SVEP1_Human; UniProt ID Q4LDE5), polymeric immunoglobulin receptor (PIGR_Human; UniProt ID P01833), band 3 anion transporter (B3AT_Human; UniProt ID P02730), cysteine-rich protein 1 (CRIP1_Human; UniProt IDP50238), fibrinogen-like protein 1 (FGL1_Human; UniProt ID Q08830), Cas scaffold protein family member 4 (CASS4_Human; UniProt ID Q9NQ75), complement component C9 (CO9_Human; UniProt ID P02748), plasminogen activator inhibitor 1 (PAI1_Human; UniProt ID P05121), alpha-1-acid glycoprotein 1 (A1AG1_human; UniProt ID DP02763), apolipoprotein B-100 (APOB_human; UniProt ID P04114), leukotriene A-4 hydrolase (LKHA4_human; UniProt ID P09960), beta-Ala-His dipeptidase (CNDP1_human; UniProt ID Q96KN2), histone H4 (H4_human; UniProt ID P62805), bone morphogenetic protein 1 (BMP1_human; UniProt ID P13497), beta-enolase (ENOB_human; UniProt ID P13929), histone H2A 2-C type (H2A2C_human; UniProt ID Q16777), protein S100-A12 (S10AC_human; UniProt ID P80511), insulin-like growth factor binding protein 2 (IBP2_human; UniProt ID P18065), protein S100-A8 (S10A8_human; UniProt ID P05109), complement C4-B (CO4B_human; UniProt ID P0C0L5), pleckstrin (PLEK_human; UniProt ID P08567), adipocyte plasma membrane-associated protein (APMAP_human;UniProt ID Q9HDC9), apolipoprotein (a) (APOA_human; UniProt ID P08519), integrin-linked protein kinase (ILK_human; UniProt ID Q13418), cytoplasmic dynein 1 heavy chain 1 (DYHC1_human; UniProt ID Q14204), myosin light chain 12A (J3QRS3_human; UniProt ID J3QRS3), hepcidin (HEPC_human; UniProt ID P81172), transforming growth factor beta-1-induced transcript 1 protein (TGFI1_human; UniProt ID O43294), latent transforming growth factor beta binding protein 2 (LTBP2_human; UniProt ID Q14767), activated RNA polymerase II transcription coactivator p15 (TCP4_human; UniProt ID P53999), alpha-2-macroglobulin (A2MG_human; UniProt ID P01023), apolipoprotein A-IV (APOA4_human; UniProt ID P06727), ribonuclease inhibitor (RINI_human; UniProt ID P13489), neutrophil defensin 1 (DEF1_human; UniProtID P59665), CXC motif chemokine 17 (CXL17_human; UniProt ID Q6UXB2), histone H1.4 (H14_human; UniProt ID P10412), protein disulfide isomerase A3 (PDIA3_human; UniProt ID P30101), PDZ and LIM domain protein 1 (PDLI1_human; UniProt ID O00151), α-actinin-1 (ACTN1_human; UniProt IDP12814), serum amyloid A-1 (SAA1_human; UniProt ID P0DJI8), desmoglein-1 (DSC1_human; UniProt ID Q08554), coagulation factor V (FA5_human; UniProt ID P12259), alpha-1-antichymotrypsin (AACT_human; UniProt ID P01011), myosin-9 (MYH9_human; UniProt ID P35579), basement membrane-specific heparan sulfate proteoglycan core protein (PGBM_human; UniProt ID P98160), tyrosine protein kinase SYK (KSYK_human; UniProt ID P43405), fibrinogen alpha chain (FIBA_human; UniProt ID P02671), tubulin beta-1 chain (TBB1_human; UniProt ID P02672), and mitochondrial isomerase (MIT) protein.UniProt ID Q9H4B7), heparin cofactor 2 (HEP2_human; UniProt ID P05546), apolipoprotein AI (APOA1_human; UniProt ID P02647), complement C3 (CO3_human; UniProt ID P01024), tropomodulin-3 (TMOD3_human; UniProt ID Q9NYL9), high-mobility group protein B2 (HMGB2_human; UniProt ID P26583), pulmonary surfactant-associated protein B (PSPB_human; UniProt ID P07988), interleukin enhancer binding factor 2 (ILF2_human; UniProt ID Q12905), serine protease inhibitor H1 (Serpin H1) (SERPH_human; UniProt ID P50454), Reelin (J3KQ66_human; UniProt ID J3KQ66), WD repeat-containing protein 1 (WDR1_human; UniProt ID), flavin reductase (BLVRB_human; UniProt ID P30043), inter-alpha-trypsin inhibitor heavy chain H1 (ITIH1_human; UniProt ID P19827), glyceraldehyde-3-phosphate dehydrogenase (G3P_human; UniProt ID P04406), stomatin-like protein 2 (mitochondrial) (STML2_human; UniProt ID Q9UJZ1), asporin (ASPN_human; UniProt ID Q9BXN1), leucine-rich repeat protein 47 (LRC47_human; UniProt ID Q8N1G4), POSTN-5_human, SMD3-2_human, FINC-1_human, MASP1-2_human, AMD-3_human, ILF3-7_human, VINC-2_human, ITIH3-2_human, or FLNA-2_human. Biomarkers may include protein 1 containing Sushi, von Willebrand factor type A, EGF, and pentraxin domains (SVEP1_human; UniProt ID Q4LDE5). Biomarkers may include polymeric immunoglobulin receptor (PIGR_human; UniProt ID P01833). Biomarkers may include band 3 anion transporter (B3AT_human; UniProt ID P02730). Biomarkers may include cysteine-rich protein 1 (CRIP1_human; UniProt ID P50238). Biomarkers may include fibrinogen-like protein 1 (FGL1_human;UniProt ID Q08830). Biomarkers may include Cas scaffold protein family member 4 (CASS4_Human; UniProt ID Q9NQ75). Biomarkers may include complement component C9 (C09_Human; UniProt ID P02748). Biomarkers may include plasminogen activator inhibitor 1 (PAI1_Human; UniProt ID P05121). Biomarkers may include alpha-1-acid glycoprotein 1 (A1AG1_Human; UniProt ID P02763). Biomarkers may include apolipoprotein B-100 (APOB_Human; UniProt ID P04114). Biomarkers may include leukotriene A-4 hydrolase (LKHA4_Human; UniProt ID P09960). Biomarkers may include beta-Ala-His dipeptidase (CNDP1_Human; UniProt ID Q96KN2). Biomarkers may include histone H4 (H4_human; UniProt ID P62805). Biomarkers may include bone morphogenetic protein 1 (BMP1_human; UniProt ID P13497). Biomarkers may include beta-enolase (ENOB_human; UniProt ID P13929). Biomarkers may include histone H2A 2-type C (H2A2C_human; UniProt ID Q16777). Biomarkers may include protein S100-A12 (S10AC_human; UniProt IDP80511). Biomarkers may include insulin-like growth factor binding protein 2 (IBP2_human; UniProt IDP18065). Biomarkers may include protein S100-A8 (S10A8_human; UniProt ID P05109). The biomarker may include complement C4-B (CO4B_human; UniProt ID P0C0L5). The biomarker may include pleckstrin (PLEK_human; UniProt ID P08567). The biomarker may include adipocyte plasma membrane associated protein (APMAP_human; UniProt ID Q9HDC9). The biomarker may include apolipoprotein (a) (APOA_human; UniProt IDP08519). The biomarker may include integrin linked protein kinase (ILK_human; UniProt ID Q13418). The biomarker may include cytoplasmic dynein 1 heavy chain 1 (DYHC1_human; UniProt ID Q14204). The biomarker may include myosin light chain 12A (J3QRS3_human; UniProt ID J3QRS3). The biomarker may include hepcidin (HEPC_human;UniProt ID P81172). Biomarkers may include transforming growth factor beta-1 induced transcript 1 protein (TGFI1_human; UniProt ID O43294). Biomarkers may include latent transforming growth factor beta binding protein 2 (LTBP2_human; UniProt ID Q14767). Biomarkers may include activated RNA polymerase II transcription coactivator p15 (TCP4_human; UniProt ID P53999). Biomarkers may include alpha-2-macroglobulin (A2MG_human; UniProt IDP01023). Biomarkers may include apolipoprotein A-IV (APOA4_human; UniProt ID P06727). Biomarkers may include ribonuclease inhibitor (RINI_human; UniProt ID P13489). Biomarkers may include neutrophil defensin 1 (DEF1_human; UniProt ID P59665). Biomarkers may include CXC motif chemokine 17 (CXL17_human; UniProt ID Q6UXB2). Biomarkers may include histone H1.4 (H14_human; UniProt IDP10412). Biomarkers may include protein disulfide isomerase A3 (PDIA3_human; UniProt ID P30101). Biomarkers may include PDZ and LIM domain protein 1 (PDLI1_human; UniProt ID O00151). Biomarkers may include alpha-actin-1 (ACTN1_human; UniProt ID P12814). Biomarkers may include serum amyloid A-1 (SAA1_human; UniProt ID P0DJ18). Biomarkers may include desmoglein-1 (DSC1_human; UniProt ID Q08554). Biomarkers may include coagulation factor V (FA5_human; UniProt ID P12259). Biomarkers may include alpha-1-antichymotrypsin (AACT_human; UniProt ID P01011). Biomarkers may include myosin-9 (MYH9_human; UniProt ID P35579). Biomarkers may include basement membrane-specific heparin sulfate proteoglycan core protein (PGBM_human; UniProt ID P98160). Biomarkers may include tyrosine protein kinase SYK (KSYK_human; UniProt ID P43405). Biomarkers may include fibrinogen alpha chain (FIBA_human; UniProt ID P02671). Biomarkers may include tubulin beta-1 chain (TBB1_human;UniProt IDQ9H4B7). Biomarkers may include heparin cofactor 2 (HEP2_human; UniProt ID P05546). Biomarkers may include apolipoprotein AI (APOA1_human; UniProt ID P02647). Biomarkers may include complement C3 (CO3_human; UniProt ID P01024). Biomarkers may include promodulin-3 (TMOD3_human; UniProt IDQ9NYL9). Biomarkers may include high mobility group protein B2 (HMGB2_human; UniProt ID P26583). Biomarkers may include pulmonary surfactant-associated protein B (PSPB_human; UniProt ID P07988). Biomarkers may include interleukin enhancer binding factor 2 (ILF2_human; UniProt ID Q12905). The biomarker may include serine protease inhibitor H1 (SERPH_human; UniProt ID P50454). The biomarker may include Reelin (J3KQ66_human; UniProt ID J3KQ66). The biomarker may include WD repeat-containing protein 1 (WDR1_human; UniProt ID). The biomarker may include flavin reductase (BLVRB_human; UniProt ID P30043). The biomarker may include inter-alpha-trypsin inhibitor heavy chain H1 (ITIH1_human; UniProt ID P19827). The biomarker may include glyceraldehyde-3-phosphate dehydrogenase (G3P_human; UniProt ID P04406). The biomarker may include Stomatin-like protein 2 (mitochondrial) (STML2_human; UniProt ID Q9UJZ1). The biomarker may include Asporin (ASPN_human; UniProt ID Q9BXN1). Biomarkers can include leucine-rich repeat 47 (LRC47_human;UniProt ID Q8N1G4). ​​Biomarkers may include POSTN-5_human. Biomarkers may include SMD3-2_human. Biomarkers may include FINC-1_human. Biomarkers may include MASP1-2_human. Biomarkers may include AMD-3_human. Biomarkers may include ILF3-7_human. Biomarkers may include VINC-2_human. Biomarkers may include ITIH3-2_human. Biomarkers may include FLNA-2_human. Fragments of any of these proteins may be used. Any of these biomarkers may be used alone or in combination to assess lung cancer (e.g., non-small cell lung cancer). In some cases, any of these peptides may be used as biomarkers when measured by adsorption onto particles from a biological fluid sample.

[0389] Any of the following protein biomarkers can be used to detect, identify, or assess the presence, absence, or likelihood of cancer: SVEP1, PIGR, B3AT, CRIP1, FGL1, CASS4, CO9, PAI1, A1AG1, APOB, LKHA4, CNDP1, H4, BMP1, ENOB, H2A2C, S10AC, IBP2, S10A8, CO4B, PLEK, APMAP, APOA, ILK, DYHC1, J3QRS3, HEPC, TGFI1, LTBP2, TCP4, A2MG, APOA4, RINI, DEF1, C XL17, H14, PDIA3, PDLI1, ACTN1, SAA1, DSC1, FA5, AACT, MYH9, PGBM, KSYK, FIBA, TBB1, HEP2, APOA1, CO3, TMOD3, HMGB2, PSPB, ILF2, SERP H, J3KQ66, WDR1, BLVRB, ITIH1, G3P, STML2, ASPN, LRC47, POSTN-5, SMD3-2, FINC-1, MASP1-2, AMD-3, ILF3-7, VINC-2, ITIH3-2, or FLNA-2.Any of the following protein biomarkers can be used to detect, identify, or assess the presence, absence, or likelihood of cancer: SVEP1_human, PIGR_human, B3AT_human, CRIP1_human, FGL1_human, CASS4_human, CO9_human, PAI1_human, A1AG1_human, APOB_human, LKHA4_human, CNDP1_human, H4_human, BMP1_human, ENOB_human, H2A2C_human, S10AC_human, IBP2_human, S10A8_human, CO4B_human, PLEK_human, APMAP_human, APOA_human, ILK_human, DYHC1_human, J3QRS3_human, HEPC_human, TGFI1_human, LTBP2_human, TCP4_human, A2MG_human, APOA4_human, RINI_human, DEF1_human, CXL17_human Human, H14_human, PDIA3_human, PDLI1_human, ACTN1_human, SAA1_human, DSC1_human, FA5_human, AACT_human, MYH9_human, PGBM_human, KSYK_human, FIBA_human, TBB1_human, HEP2_human, APOA1_human, CO3_human, TMOD3_human, HMGB2_human, PSPB_human, ILF2_human, SERPH_human, J3KQ66_human, WDR1_human, BLVRB_human, ITIH1_human, G3P_human, STML2_human, ASPN_human, LRC47_human, POSTN-5_human, SMD3-2_human, FINC-1_human, MASP1-2_human, AMD-3_human, ILF3-7_human, VINC-2_human, ITIH3-2_human, or FLNA-2_human. Cancer can include lung cancer, such as NSCLC. Any number or combination of the above biomarkers can be used. For example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or more of the above proteins can be used.

[0390] Proteomic data can include peptide information, such as peptide measurements in biological fluids. Figure 140EExamples of peptide biomarkers useful in the methods disclosed herein, such as for evaluating cancers such as pancreatic cancer, are included. Protein measurements can be obtained using particles such as those described herein. Any combination or number of such biomarkers can be included. In some cases, a biomarker is useful when its feature importance score is greater than 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, or 0.10. These features can include any of the following peptides (represented using the single-letter amino acid code): TELVEPTEYLVVHLK (SEQ ID NO: 1), TFVIIPELVLPNR (SEQ ID NO: 2), LQELHLSSNGLESLSPEFLRPVPQLR (SEQ ID NO: 3), ITLLSALVETR (SEQ ID NO: 4), VVATTQMQAADAR (SEQ ID NO: 5), TFVIIPELVLPNR (SEQ ID NO: 6), LQHLENELTHDIITK (SEQ ID NO: 7), FLENEDRR (SEQ ID NO: 8), LWYENPGVFSPAQLTQIK (SEQ ID NO: 9), QWMENPNNNPIHPNLR (SEQ ID NO: 10), or LEIYQEDQIHFMCPLAR (SEQ ID NO: 11). Biomarkers can include TELVEPTEYLVVHLK (SEQ ID NO: 1). Biomarkers can include TFVIIPELVLPNR (SEQ ID NO: 2). Biomarkers may include LQELHLSSNGLESLSPEFLRPVPQLR (SEQ ID NO: 3). Biomarkers may include ITLLSALVETR (SEQ ID NO: 4). Biomarkers may include VVATTQMQAADAR (SEQ ID NO: 5). Biomarkers may include TFVIIPELVLPNR (SEQ ID NO: 6). Biomarkers may include LQHLENELTHDIITK (SEQ ID NO: 7). Biomarkers may include FLENEDRR (SEQ ID NO: 8). Biomarkers may include LWYENPGVFSPAQLTQIK (SEQ ID NO: 9). Biomarkers may include QWMENPNNNPIHPNLR (SEQ ID NO: 10). Biomarkers may include LEIYQEDQIHFMCPLAR (SEQ ID NO: 11). Fragments of any of these peptides may be used.Any of these biomarkers can be used alone or in combination to evaluate a sample from a subject suspected of having cancer (e.g., pancreatic cancer) (e.g., to determine the likelihood that the subject has cancer). In some cases, any of these peptides can be used as a biomarker when measured in conjunction with an internal standard.

[0391] Table 15E shows some examples of peptides that can be used as biomarkers. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 of these peptides can be used as biomarkers, for example, for lung nodule assessment. Any of the following peptides may be used for this purpose (using the single-letter amino acid code): STVLTIPEIIIK (SEQ ID NO: 12), TLAFPLTIR (SEQ ID NO: 13), LIQGAPTIR (SEQ ID NO: 14), SSGLVSNAPGVQIR (SEQ ID NO: 15), DGSFSVVITGLR (SEQ ID NO: 16), LGPISADSTTAPLEK (SEQ ID NO: 17), SEAACLAAGPGIR (SEQ ID NO: 18), TDTGFLQTLGHNLFGIYQK (SEQ ID NO: 19), LKPEDITQIQPQQLVLR (SEQ ID NO: 20), GLPAPIEK (SEQ ID NO: 21), LLGPGPAADFSVSVER (SEQ ID NO: 22), YEYLEGGDR (SEQ ID NO: 23), HLEDVFSK (SEQ ID NO: 24), ILGPLSYSK (SEQ ID NO: 25), NCQTVLAPCSPNPCENAAVCK (SEQ ID NO: 26). NO: 26), TVTATFGYPFR (SEQ ID NO: 27), STDTSCVNPPTVQNAHILSR (SEQ ID NO: 28), FSLVSGWGQLLDR (SEQ ID NO: 29), ELLALIQLER (SEQ ID NO: 30), or DAHSVLLSHIFHGR (SEQ ID NO: 31), or fragments thereof. Biomarkers may include STVLTIPEIIIK (SEQ ID NO: 12). Biomarkers may include TLAFPLTIR (SEQ ID NO: 13). Biomarkers may include LIQGAPTIR (SEQ ID NO: 14). Biomarkers may include SSGLVSNAPGVQIR (SEQ ID NO: 15). Biomarkers may include DGSFSVVITGLR (SEQ ID NO: 16). Biomarkers may include LGPISADSTTAPLEK (SEQ ID NO: 17). Biomarkers may include SEAACLAAGPGIR (SEQ ID NO: 18). A biomarker may include TDTGFLQTLGHNLFGIYQK (SEQ ID NO: 19).Biomarkers may include LKPEDITQIQPQQLVLR (SEQ ID NO: 20). Biomarkers may include GLPAPIEK (SEQ ID NO: 21). Biomarkers may include LLGPGPAADFSVSVER (SEQ ID NO: 22). Biomarkers may include YEYLEGGDR (SEQ ID NO: 23). Biomarkers may include HLEDVFSK (SEQ ID NO: 24). Biomarkers may include ILGPLSYSK (SEQ ID NO: 25). Biomarkers may include NCQTVLAPCSPNPCENAAVCK (SEQ ID NO: 26). Biomarkers may include TVTATFGYPFR (SEQ ID NO: 27). Biomarkers may include STDTSCVNPPTVQNAHILSR (SEQ ID NO: 28). Biomarkers may include FSLVSGWGQLLDR (SEQ ID NO: 29). Biomarkers may include ELLALIQLER (SEQ ID NO: 30). Biomarkers may include DAHSVLLSHIFHGR (SEQ ID NO: 31). Fragments of any of these peptides may be used. Any of these biomarkers may be used alone or in combination to assess lung nodules (e.g., to determine the likelihood that a lung nodule is cancerous). In some cases, any of these peptides may be used as a biomarker when measured in conjunction with an internal standard.

[0392] Biomarkers may include STVLTIPEIIIK (SEQ ID NO: 12) and may be associated with coagulation factor XIII A chain (F13A_human; UniProt ID P00488). Biomarkers may include TLAFPLTIR (SEQ ID NO: 13) and may be associated with endothelial protein C receptor (EPCR_human; UniProt ID Q9UNN8). Biomarkers may include LIQGAPTIR (SEQ ID NO: 14) and may be associated with insulin-like growth factor binding protein 2 (IBP2_human; UniProt ID P18065). Biomarkers may include SSGLVSNAPGVQIR (SEQ ID NO: 15) and may be associated with phosphatidylcholinesterol acyltransferase (LCAT_human; UniProt ID P04180). Biomarkers may include DGSFSVVITGLR (SEQ ID NO: 16) and may be associated with polymeric immunoglobulin receptor (PIGR_human; UniProt IDP01833). Biomarkers may include LGPISADSTTAPLEK (SEQ ID NO: 17) and may be associated with tenascin-X (TENX_human; UniProt ID P22105). Biomarkers may include SEAACLAAGPGIR (SEQ ID NO: 18) and may be associated with attractin (ATRN_human; UniProt ID 075882). Biomarkers may include TDTGFLQTLGHNLFGIYQK (SEQ ID NO: 19) and may be associated with inner condensin-1 (ITLN1_human; UniProt IDQ8WWA0). Biomarkers may include LKPEDITQIQPQQLVLR (SEQ ID NO: 20) and may be associated with Integrin beta-1 (ITB1_human; UniProt P05556). Biomarkers may include GLPAPIEK (SEQ ID NO: 21) and may be associated with Immunoglobulin Heavy Chain Constant Gamma 2 (IGHG2_human; UniProt P01859). Biomarkers may include LLGPGPAADFSVSVER (SEQ ID NO: 22) and may be associated with alpha-N-acetylglucosaminidase (ANAG_human; UniProt P54802). Biomarkers may include YEYLEGGDR (SEQ ID NO: 23) and may be associated with Hepatocyte Growth Factor Activator (HGFA_human; UniProt Q04756).Biomarkers may include HLEDVFSK (SEQ ID NO: 24) and may be associated with beta-Ala-His dipeptidase (CNDP1_human; UniProt Q96KN2). Biomarkers may include ILGPLSYSK (SEQ ID NO: 25) and may be associated with hyaluronic acid (LUM_human; UniProt P51884). Biomarkers may include NCQTVLAPCSPNPCENAAVCK (SEQ ID NO: 26) and may be associated with neurogenic locus Notch homologous protein 2 (NOTC2_human; UniProt Q04721). Biomarkers may include TVTATFGYPFR (SEQ ID NO: 27) and may be associated with synaptobrevin-like protein 1 (SYPL1_human; UniProt Q16563). Biomarkers may include STDTSCVNPPTVQNAHILSR (SEQ ID NO: 28) and may be associated with complement factor H-related protein 1 (FHR1_Human; UniProt Q03591). Biomarkers may include FSLVSGWGQLLDR (SEQ ID NO: 29) and may be associated with coagulation factor VII (FA7_Human; UniProt P08709). Biomarkers may include ELLALIQLER (SEQ ID NO: 30) and may be associated with extracellular matrix protein 1 (ECM1_Human; UniProt Q16610). Biomarkers may include DAHSVLLSHIFHGR (SEQ ID NO: 31) and may be associated with GDH / 6PGL endoplasmic bifunctional protein (G6PE_Human; UniProt O95479). Fragments of any of these peptides may be used. Any of the above peptide or protein biomarkers (or combinations of such biomarkers) may be used to identify the presence, absence, or likelihood of a cancer as described herein.

[0393] Table 15F shows some examples of peptides that can be used as biomarkers. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 of these peptides can be used as biomarkers, for example, for lung nodule assessment. Any of the following peptides may be used for this purpose (using the single-letter amino acid code): EHAVEGDCDFQLLK (SEQ ID NO: 32), SQASSCSLQSSDSVPVGLCK (SEQ ID NO: 33), GEFAIDGYSVR (SEQ ID NO: 34), ALVEGVDQLFTDYQIK (SEQ ID NO: 35), LLPYIVGVAQR (SEQ ID NO: 36), HTLNQIDEVK (SEQ ID NO: 37), IDILVNNGGMSQR (SEQ ID NO: 38), LMMDGHEVTVVDNFFTGR (SEQ ID NO: 39), MYGEILSPNYPQAYPSEVEK (SEQ ID NO: 40), NNEEWTVDSCTECHCQNSVTICK (SEQ ID NO: 41), IDTQDIEASHYR (SEQ ID NO: 42), TFIFSDLDYMGMSSGFYK (SEQ ID NO: 43), PDAELSASSVYNLLPEK (SEQ ID NO: 44), ID NO:44), ASIHEAWTDGK (SEQ ID NO:45), LYPWGVVEVENPEHNDFLK (SEQ ID NO:46), YHWEHTGLTLR (SEQ ID NO:47), or IGGAIEEVYVSLGVSVGK (SEQ ID NO:48), or fragments thereof. Biomarkers may include EHAVEGDCDFQLLK (SEQ ID NO:32). Biomarkers may include SQASSCSLQSSDSVPVGLCK (SEQ ID NO:33). Biomarkers may include GEFAIDGYSVR (SEQ ID NO:34). Biomarkers may include ALVEGVDQLFTDYQIK (SEQ ID NO:35). Biomarkers may include LLPYIVGVAQR (SEQ ID NO:36). Biomarkers may include HTLNQIDEVK (SEQ ID NO:37). Biomarkers may include IDILVNNGGMSQR (SEQ ID NO:38). Biomarkers may include LMMDGHEVTVVDNFFTGR (SEQ ID NO: 39). Biomarkers may include MYGEILSPNYPQAYPSEVEK (SEQ ID NO: 40).Biomarkers may include NNEEWTVDSCTECHCQNSVTICK (SEQ ID NO: 41). Biomarkers may include IDTQDIEASHYR (SEQ ID NO: 42). Biomarkers may include TFIFSDLDYMGMSSGFYK (SEQ ID NO: 43). Biomarkers may include PDAELSASSVYNLLPEK (SEQ ID NO: 44). Biomarkers may include ASIHEAWTDGK (SEQ ID NO: 45). Biomarkers may include LYPWGVVEVENPEHNDFLK (SEQ ID NO: 46). Biomarkers may include YHWEHTGLTLR (SEQ ID NO: 47). Biomarkers may include IGGAIEEVYVSLGVSVGK (SEQ ID NO: 48). Fragments of any of these peptides may be used. Any of these biomarkers may be used alone or in combination to assess lung nodules (e.g., to determine the likelihood that a lung nodule is cancerous). In some cases, any of these peptides may be used as a biomarker when measured by adsorption onto particles from a biological fluid sample.

[0394] Biomarkers may include EHAVEGDCDFQLLK (SEQ ID NO: 32) and may be associated with alpha-2-HS-glycoprotein (FETUA_human; UniProt ID P02765). Biomarkers may include SQASSCSLQSSDSVPVGLCK (SEQ ID NO: 33) and may be associated with fetuin-B (FETUB_human; UniProt ID Q9UGM5). Biomarkers may include GEFAIDGYSVR (SEQ ID NO: 34) and may be associated with Src kinase-associated phosphoprotein 2 (SKAP2_human; UniProt ID 075563). Biomarkers may include ALVEGVDQLFTDYQIK (SEQ ID NO: 35) and may be associated with complement C5 (C05_human; UniProt ID P01031). Biomarkers may include LLPYIVGVAQR (SEQ ID NO: 36) and may be associated with collagen alpha-3 (VI) chain (CO6A3_human; UniProt ID P12111). Biomarkers may include HTLNQIDEVK (SEQ ID NO: 37) and may be associated with alpha-2-HS-glycoprotein (FETUA_human; UniProt IDP02765). Biomarkers may include IDILVNNGGMSQR (SEQ ID NO: 38) and may be associated with dehydrogenase / reductase SDR family member 7 (DHRS7_human; UniProt ID Q9Y394). Biomarkers may include LMMDGHEVTVVDNFFTGR (SEQ ID NO: 39) and may be associated with UDP-glucuronic acid decarboxylase 1 (UXS1_human; UniProt ID Q8NBZ7-2). Biomarkers may include MYGEILSPNYPQAYPSEVEK (SEQ ID NO: 40) and may be associated with complement C1s subcomponent (UniProt ID A0A087X232). Biomarkers may include NNEEWTVDSCTECHCQNSVTICK (SEQ ID NO: 41) and may be associated with thrombospondin-1 (TSP1_human; UniProt ID P07996). Biomarkers may include IDTQDIEASHYR (SEQ ID NO: 42) and may be associated with complement C5 (C05_human; UniProt ID P01031). Biomarkers may include TFIFSDLDYMGMSSGFYK (SEQ ID NO: 43) and may be associated with tryptophan-tRNA ligase (cytoplasmic) (SYWC_human; UniProt ID P23381).Biomarkers may include PDAELSASSVYNLLPEK (SEQ ID NO: 44) and may be associated with alpha-2-macroglobulin (A2MG_human; UniProt ID P01023). Biomarkers may include ASIHEAWTDGK (SEQ ID NO: 45) and may be associated with alpha-actinin-1 (ACTN1_human; UniProt ID P12814). Biomarkers may include LYPWGVVEVENPEHNDFLK (SEQ ID NO: 46) and may be associated with mitogen-2 (SEPT2_human; UniProt ID Q15019-2). Biomarkers may include YHWEHTGLTLR (SEQ ID NO: 47) and may be associated with apolipoprotein B-100 (APOB_human; UniProt ID P04114). Biomarkers may include IGGAIEEVYVSLGVSVGK (SEQ ID NO: 48) and may be associated with complement component C8 beta chain (CO8B_human; UniProt ID P07358). Fragments of any of these peptides may be used. Any of the above peptide or protein biomarkers (or combinations of such biomarkers) may be used to identify the presence, absence, or likelihood of a cancer as described herein.

[0395] Proteomics data can include peptide information, such as peptide measurements in biological fluids. Examples of some peptide biomarkers can be used in methods disclosed herein, such as evaluating cancer, such as lung cancer (e.g., non-small cell lung cancer). Protein measurements can be obtained using particles such as those described herein. Such biomarkers of any combination or quantity can be included. In some cases, when the feature importance score of a biomarker is higher than 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, or 0.10, the biomarker is useful. These features can include any of the following peptides (represented using the single-letter amino acid code): LC(UniMod:4)PSGMYTEYIHSR (SEQ ID NO:49), LCPSGMYTEYIHSR (SEQ ID NO: 139), NADLQVLKPEPELVYEDLR (SEQ ID NO:50), ASTPGAAAQIQEVK (SEQ ID NO:51), PYC(UniMod:4)NHPC(UniMod:4)YAAMFGPK (SEQ ID NO:52), PYCNHPCYAAMFGPK (SEQ ID NO: 140), QLLQENEVQFLDK (SEQ ID NO:53), AISAFHGSLSSSQPAEIITQSK (SEQ ID NO:54), FEGIAC(UniMod:4)EISK (SEQ ID NO:55), FEGIACEISK (SEQ ID NO: 141), FIINDWVK (SEQ ID NO:56). NO: 56), YVGGQEHFAHLLILRDTK (SEQ ID NO: 57), SVGFHLPSR (SEQ ID NO: 58), GSPMEISLPIALSK (SEQ ID NO: 59), M (UniMod: 35) NO:142), TVTAM(UniMod:35)DVVYALK(SEQ ID NO:61), TVTAMDVVYALK(SEQ ID NO:143), C(UniMod:4)SC(UniMod:4)DPGYELAPDKR(SEQ ID NO:62), CSCDPGYELAPDKR(SEQ ID NO:144), GNPTVEVDLHTAK(SEQ ID NO:63), HLQLAIRNDEELNK(SEQ ID NO:64), FQDGDLTLYQSNTILR(SEQ IDNO:65)、IRPNDFIPNVI(SEQ ID NO:66)、TKLEEHLEGIVNIFHQYSVRK(SEQ ID NO:67)、GDPEC(UniMod:4)HLFYNEQQEAR(SEQ ID NO:68)、GDPECHLFYNEQQEAR(SEQ ID NO:145)、ALNSIIDVYHK(SEQ ID NO:69)、DDPDAPLQPVTPLQLFEGR(SEQ ID NO:70)、KSEEENLFEIITADEVHYFLQAATPK(SEQ ID NO:71)、FPNGVQLSPAEDFVLVAETTMAR(SEQ ID NO:72)、LYFMHFNLESSYLC(UniMod:4)EYDYVK(SEQ IDNO:73)、LYFMHFNLESSYLCEYDYVK(SEQ ID NO:146)、LFDYC(UniMod:4)DIPLC(UniMod:4)ASSSFDC(UniMod:4)GK(SEQ ID NO:74)、LFDYCDIPLCASSSFDCGK(SEQ ID NO:147)、AEQC(UniMod:4)C(UniMod:4)EETASSISLHGK(SEQ ID NO:75)、AEQCCEETASSISLHGK(SEQ ID NO:148)、VALEGLRPTIPPGISPHVC(UniMod:4)K(SEQ ID NO:76)、VALEGLRPTIPPGISPHVCK(SEQ IDNO:149)、VWEQIDQMK(SEQ ID NO:77)、FTDEEVDELYREAPIDK(SEQ ID NO:78)、DTHFPIC(UniMod:4)IFC(UniMod:4)C(UniMod:4)GC(UniMod:4)C(UniMod:4)HR(SEQ ID NO:79)、DTHFPICIFCCGCCHR(SEQ ID NO:150)、RQDNEILIFWSK(SEQ ID NO:80)、QDNEILIFWSK(SEQ IDNO:81)、EVGTVLSQVYSK(SEQ ID NO:82)、MVTALGTHWHPEHFC(UniMod:4)C(UniMod:4)VSC(UniMod:4)GEPFGDEGFHER(SEQ ID NO:83)、MVTALGTHWHPEHFCCVSCGEPFGDEGFHER(SEQIDNO:151)、EVTFHC(UniMod:4)HEGYILHGAPK(SEQ ID NO:84)、EVTFHCHEGYILHGAPK(SEQ IDNO:152)、GAGGQSMSEAPTGDHAPAPTR(SEQ ID NO:85)、DGSFSVVITGLR(SEQ ID NO:86)、GISLNPEQWSQLK(SEQ ID NO:87)、LVHVEEPHTETVR(SEQ ID NO:88)、RVEPYGENFNK(SEQ IDNO:89)、LDDC(UniMod:4)GLTEAR(SEQ ID NO:90)、LDDCGLTEAR(SEQ ID NO:153)、LVQAAQMLQSDPYSVPAR(SEQ ID NO:91)、DFLGFYVVDSHR(SEQ ID NO:92)、YGTC(UniMod:4)IYQGR(SEQ ID NO:93)、YGTCIYQGR(SEQ ID NO:154)、WLQEGGQEC(UniMod:4)EC(UniMod:4)K(SEQ ID NO:94)、WLQEGGQECECK(SEQ ID NO:155)、ASGPPVSELITK(SEQ ID NO:95)、ELSDFISYLQR(SEQ ID NO:96)、EGHVLQGPSVLK(SEQ ID NO:97)、MNLASEPQEVLHIGSAHNR(SEQID) NO:98)、FLILPDMLK(SEQ ID NO:99)、GISQEQMNEFR(SEQ ID NO:100)、DPNHFRPAGLPEK(SEQ ID NO:101)、VPSHLQAETLVGK(SEQ ID NO:102)、NLHFLTTQEDYTLK(SEQ ID NO:103)、SEAYNTFSER(SEQ ID NO:104)、AVLDVFEEGTEASAATAVK(SEQ ID NO:105)、VIQYLAYVASSHK(SEQ ID NO:106)、ASYAQQPAESR(SEQ ID NO:107)、YLEESNFVHR(SEQ ID NO:108)、GSFTYFAPSNEAWDNLDSDIR(SEQ ID NO:109)、ALTDMPQM(UniMod:35)R(SEQ ID) NO:110)、LAVNM(UniMod:35)VPFPR(SEQ ID)NO:111)、TSC(UniMod:4)LLFMGR(SEQ ID NO:112)、QQQHLFGSNVTDC(UniMod:4)SGNFC(UniMod:4)LFR(SEQ ID NO:113)、ALTDMPQMR(SEQ ID NO:156)、LAVNMVPFPR(SEQ ID NO:157)、TSCLLFMGR(SEQ ID NO:158)、QQQHLFGSNVTDCSGNFCLFR(SEQ ID NO:159)、DYVSQFEGSALGK(SEQ ID NO:114)、DSITTWEILAVSMSDK(SEQ ID NO:115)、FC(UniMod:4)NIMGSSNGVDQEHFSNVVK(SEQ ID NO:116)、FCNIMGSSNGVDQEHFSNVVK(SEQ IDNO:160)、SEHPGLSIGDTAK(SEQ ID NO:117)、QFVEQHTPQLLTLVPR(SEQ ID NO:118)、NQDLAPNSAEQASILSLVTK(SEQ ID NO:119)、TDGALLVNAMFFK(SEQ ID NO:120)、DDFEGQLESDRFLLMSGGK(SEQ ID NO:121)、SIQC(UniMod:4)LTVHK(SEQ ID NO:122)、SIQCLTVHK(SEQ ID NO:161)、EDITQSAQHALR(SEQ ID NO:123)、VVAC(UniMod:4)TSAFLLWDPTK(SEQ ID NO:124)、VVACTSAFLLWDPTK(SEQ ID NO:162)、NYPMHVFAYR(SEQ IDNO:125)、MEEVEAMLLPETLK(SEQ ID NO:126)、ADVQAHGEGQEFSITC(UniMod:4)LVDEEEM(UniMod:35)K(SEQ ID NO:127)、ADVQAHGEGQEFSITCLVDEEEMK(SEQ ID NO:163)、DFALLSLQVPLK(SEQ ID NO:128)、LLIYAVLPTGDVIGDSAK(SEQ ID NO:129)、VDIVAINDPFIDLNYMVYMFQYDSTHGK(SEQ ID NO:130)、AEQINQAAGEASAVLAK(SEQ ID NO:131)、TPAYYPNAGLIK(SEQ IDNO: 132), QGENGQMM(UniMod: 35)SC(UniMod: 4)TC(UniMod: 4)LGNGK(SEQ ID NO: 133), QGENGQMMSCTCLGNGK(SEQ ID NO: 164), YWEMQPATFR(SEQ ID NO: 134), HGEYWLGNK(SEQ ID NO: 135), FVPAEMGTHTVSVK(SEQ ID NO: 136), NALGPGLSPELGPLPALR(SEQ ID NO: 137), or TKLEEHLEGIVNIFHQYSVR(SEQ ID NO: 138). Fragments of any of these peptides can be used. Any of these biomarkers can be used alone or in combination to assess lung nodules (e.g., to determine the likelihood that a lung nodule is cancerous). In some cases, any of these peptides can be used as a biomarker when measured by adsorption onto particles from a biological fluid sample. Biomarkers may include LC(UniMod:4)PSGMYTEYIHSR (SEQ ID NO:49). Biomarkers may include NADLQVLKPEPELVYEDLR (SEQ ID NO:50). Biomarkers may include ASTPGAAAQIQEVK (SEQ ID NO:51). Biomarkers may include PYC(UniMod:4)NHPC(UniMod:4)YAAMFGPK (SEQ ID NO:52). Biomarkers may include QLLQENEVQFLDK (SEQ ID NO:53). Biomarkers may include AISAFHGSLSSSQPAEIITQSK (SEQ ID NO:54). Biomarkers may include FEGIAC(UniMod:4)EISK (SEQ ID NO:55). Biomarkers may include FIINDWVK (SEQ ID NO:56). Biomarkers may include YVGGQEHFAHLLILRDTK (SEQ ID NO:57). Biomarkers may include SVGFHLPSR (SEQ ID NO: 58). Biomarkers may include GSPMEISLPIALSK (SEQ ID NO: 59). Biomarkers may include M(UniMod: 35)VVSMTLGLHPWIANIDDTQYLAAK (SEQ ID NO: 60). Biomarkers may include TVTAM(UniMod: 35)DVVYALK (SEQ ID NO: 61).

[0396] Biomarkers may include C(UniMod:4)SC(UniMod:4)DPGYELAPDKR (SEQ ID NO:62). Biomarkers may include GNPTVEVDLHTAK (SEQ ID NO:63). Biomarkers may include HLQLAIRNDEELNK (SEQ ID NO:64). Biomarkers may include FQDGDLTLYQSNTILR (SEQ ID NO:65). Biomarkers may include IRPNDFIPNVI (SEQ ID NO:66). Biomarkers may include TKLEEHLEGIVNIFHQYSVRK (SEQ ID NO:67). Biomarkers may include GDPEC(UniMod:4)HLFYNEQQEAR (SEQ ID NO:68). Biomarkers may include ALNSIIDVYHK (SEQ ID NO:69). Biomarkers may include DDPDAPLQPVTPLQLFEGR (SEQ ID NO:70). Biomarkers may include KSEEENLFEIITADEVHYFLQAATPK (SEQ ID NO: 71). Biomarkers may include FPNGVQLSPAEDFVLVAETTMAR (SEQ ID NO: 72). Biomarkers may include LYFMHFNLESSYLC (UniMod: 4) EYDYVK (SEQ ID NO: 73). Biomarkers may include LFDYC (UniMod: 4) DIPLC (UniMod: 4) ASSSFDC (UniMod: 4) GK (SEQ ID NO: 74). Biomarkers may include AEQC (UniMod: 4) C (UniMod: 4) EETASSISLHGK (SEQ ID NO: 75). Biomarkers may include VALEGLRPTIPPGISPHVC (UniMod: 4) K (SEQ ID NO: 76). Biomarkers may include VWEQIDQMK (SEQ ID NO: 77). Biomarkers may include FTDEEVDELYREAPIDK (SEQ ID NO: 78). Biomarkers may include DTHFPIC (UniMod: 4) IFC (UniMod: 4) C (UniMod: 4) GC (UniMod: 4) C (UniMod: 4) HR (SEQ ID NO: 79). Biomarkers may include RQDNEILIFWSK (SEQ ID NO: 80). Biomarkers may include QDNEILIFWSK (SEQ ID NO: 81). Biomarkers may include EVGTVLSQVYSK (SEQ ID NO: 82).Biomarkers may include MVTALGTHWHPEHFC(UniMod:4)C(UniMod:4)VSC(UniMod:4)GEP FGDEGFHER(SEQ ID NO:83). Biomarkers may include EVTFHC(UniMod:4)HEGYILHGAPK(SEQ ID NO:84). Biomarkers may include GAGGQSMSEAPTGDHAPAPTR(SEQ ID NO:85). Biomarkers may include DGSFSVVITGLR(SEQ ID NO:86). Biomarkers may include GISLNPEQWSQLK(SEQ ID NO:87). Biomarkers may include LVHVEEPHTETVR(SEQ ID NO:88). Biomarkers may include RVEPYGENFNK(SEQ ID NO:89). Biomarkers may include LDDC(UniMod:4)GLTEAR(SEQ ID NO:90). Biomarkers may include LVQAAQMLQSDPYSVPAR(SEQ ID NO:91). Biomarkers may include DFLGFYVVDSHR (SEQ ID NO: 92). Biomarkers may include YGTC (UniMod: 4) IYQGR (SEQ ID NO: 93). Biomarkers may include WLQEGGQEC (UniMod: 4) EC (UniMod: 4) K (SEQ ID NO: 94). Biomarkers may include ASGPPVSELITK (SEQ ID NO: 95). Biomarkers may include ELSDFISYLQR (SEQ ID NO: 96). Biomarkers may include EGHVLQGPSVLK (SEQ ID NO: 97). Biomarkers may include MNLASEPQEVLHIGSAHNR (SEQ ID NO: 98). Biomarkers may include FLILPDMLK (SEQ ID NO: 99). Biomarkers may include GISQEQMNEFR (SEQ ID NO: 100). Biomarkers may include DPNHFRPAGLPEK (SEQ ID NO: 101). Biomarkers may include VPSHLQAETLVGK (SEQ ID NO: 102). Biomarkers may include NLHFLTTQEDYTLK (SEQ ID NO: 103). Biomarkers may include SEAYNTFSER (SEQ ID NO: 104). Biomarkers may include AVLDVFEEGTEASAATAVK (SEQ ID NO: 105). Biomarkers may include VIQYLAYVASSHK (SEQ ID NO: 106).Biomarkers may include ASYAQQPAESR (SEQ ID NO: 107). Biomarkers may include YLEESNFVHR (SEQ ID NO: 108). Biomarkers may include GSFTYFAPSNEAWDNLDSDIR (SEQ ID NO: 109). Biomarkers may include ALTDMPQM (UniMod: 35) R (SEQ ID NO: 110). Biomarkers may include LAVNM (UniMod: 35) VPFPR (SEQ ID NO: 111). Biomarkers may include TSC (UniMod: 4) LLFMGR (SEQ ID NO: 112). Biomarkers may include QQQHLFGSNVTDC (UniMod: 4) SGNFC (UniMod: 4) LFR (SEQ ID NO: 113). Biomarkers may include DYVSQFEGSALGK (SEQ ID NO: 114). Biomarkers may include DSITTWEILAVSMSDK (SEQ ID NO: 115). Biomarkers may include FC(UniMod:4)NIMGSSNGVDQEHFSNVVK (SEQ ID NO: 116). Biomarkers may include SEHPGLSIGDTAK (SEQ ID NO: 117). Biomarkers may include QFVEQHTPQLLTLVPR (SEQ ID NO: 118). Biomarkers may include NQDLAPNSAEQASILSLVTK (SEQ ID NO: 119). Biomarkers may include TDGALLVNAMFFK (SEQ ID NO: 120). Biomarkers may include DDFEGQLESDRFLLMSGGK (SEQ ID NO: 121). Biomarkers may include SIQC(UniMod:4)LTVHK (SEQ ID NO: 122). Biomarkers may include EDITQSAQHALR (SEQ ID NO: 123). Biomarkers may include VVAC(UniMod:4)TSAFLLWDPTK (SEQ ID NO: 124). Biomarkers may include NYPMHVFAYR (SEQ ID NO: 125). Biomarkers may include MEEVEAMLLPETLK (SEQ ID NO: 126). Biomarkers may include ADVQAHGEGQEFSITC (UniMod: 4) LVDEEEM (UniMod: 35) K (SEQ ID NO: 127). Biomarkers may include DFALLSLQVPLK (SEQ ID NO: 128).The biomarker may include LLIYAVLPTGDVIGDSAK (SEQ ID NO: 129). The biomarker may include VDIVAINDPFIDLNYMVYMFQYDSTHGK (SEQ ID NO: 130). The biomarker may include AEQINQAAGEASAVLAK (SEQ ID NO: 131). The biomarker may include TPAYYPNAGLIK (SEQ ID NO: 132).

[0397] Biomarkers may include QGENGQMM(UniMod:35)SC(UniMod:4)TC(UniMod:4)LGNGK(SEQ ID NO:133). Biomarkers may include YWEMQPATFR(SEQ ID NO:134). Biomarkers may include HGEYWLGNK(SEQ ID NO:135). Biomarkers may include FVPAEMGTHTVSVK(SEQ ID NO:136). Biomarkers may include NALGPGLSPELGPLPALR(SEQ ID NO:137). Biomarkers may include TKLEEHLEGIVNIFHQYSVR(SEQ ID NO:138). Biomarkers may include LCPSGMYTEYIHSR(SEQ ID NO:139). Biomarkers may include PYCNHPCYAAMFGPK(SEQ ID NO:140). Biomarkers may include FEGIACEISK(SEQ ID NO:141). Biomarkers may include MVVSMTLGLHPWIANIDDTQYLAAK (SEQ ID NO: 142). Biomarkers may include TVTAMDVVYALK (SEQ ID NO: 143). Biomarkers may include CSCDPGYELAPDKR (SEQ ID NO: 144). Biomarkers may include GDPECHLFYNEQQEAR (SEQ ID NO: 145). Biomarkers may include LYFMHFNLESSYLCEYDYVK (SEQ ID NO: 146). Biomarkers may include LFDYCDIPLCASSSFDCGK (SEQ ID NO: 147). Biomarkers may include AEQCCEETASSISLHGK (SEQ ID NO: 148). Biomarkers may include VALEGLRPTIPPGISPHVCK (SEQ ID NO: 149). Biomarkers may include DTHFPICIFCCGCCHR (SEQ ID NO: 150). Biomarkers may include MVTALGTHWHPEHFCCVSCGEPFGDEGFHER (SEQ ID NO: 151). Biomarkers may include EVTFHCHEGYILHGAPK (SEQ ID NO: 152). Biomarkers may include LDDCGLTEAR (SEQ ID NO: 153). Biomarkers may include YGTCIYQGR (SEQ ID NO: 154). Biomarkers may include WLQEGGQECECK (SEQ ID NO: 155). Biomarkers may include ALTDMPQMR (SEQ ID NO: 156).Biomarkers may include LAVNMVPFPR (SEQ ID NO: 157). Biomarkers may include TSCLLFMGR (SEQ ID NO: 158). Biomarkers may include QQQHLFGSNVTDCSGNFCLFR (SEQ ID NO: 159). Biomarkers may include FCNIMGSSNGVDQEHFSNVVK (SEQ ID NO: 160). Biomarkers may include SIQCLTVHK (SEQ ID NO: 161). Biomarkers may include VVACTSAFLLWDPTK (SEQ ID NO: 162). Biomarkers may include ADVQAHGEGQEFSITCLVDEEEMK (SEQ ID NO: 163). Biomarkers may include QGENGQMMSCTCLGNGK (SEQ ID NO: 164). Any of the above peptide or protein biomarkers (or combinations of such biomarkers) may be used to identify the presence, absence, or likelihood of a cancer as described herein.

[0398] Some aspects include peptide conversion. Some aspects include using multiple peptide conversions. For example, measuring multiple peptide conversions from a biological fluid sample may be useful in diagnostic methods or any multi-omics method.

[0399] In some aspects, the multi-omics data comprises measurements of greater than 10 groups of peptides or proteins, greater than 15 groups of peptides or proteins, greater than 20 groups of peptides or proteins, greater than 25 groups of peptides or proteins, greater than 30 groups of peptides or proteins, greater than 35 groups of peptides or proteins, greater than 40 groups of peptides or proteins, greater than 45 groups of peptides or proteins, greater than 50 groups of peptides or proteins, greater than 75 groups of peptides or proteins, greater than 100 groups of peptides or proteins, greater than 250 groups of peptides or proteins, greater than 500 groups of peptides or proteins, greater than 1,000 groups of peptides or proteins, greater than 2,500 groups of peptides or proteins, greater than 5,000 groups of peptides or proteins, greater than 10,000 groups of peptides or proteins, greater than 15,000 groups of peptides or proteins, or greater than 20,000 groups of peptides or proteins. In some aspects, the multi-omics data comprises measurements of at least about 10 groups of peptides or proteins, at least about 15 groups of peptides or proteins, at least about 20 groups of peptides or proteins, at least about 25 groups of peptides or proteins, at least about 30 groups of peptides or proteins, at least about 35 groups of peptides or proteins, at least about 40 groups of peptides or proteins, at least about 45 groups of peptides or proteins, at least about 50 groups of peptides or proteins, at least about 75 groups of peptides or proteins, at least about 100 groups of peptides or proteins, at least about 250 groups of peptides or proteins, at least about 500 groups of peptides or proteins, at least about 1,000 groups of peptides or proteins, at least about 2,500 groups of peptides or proteins, at least about 5,000 groups of peptides or proteins, at least about 10,000 groups of peptides or proteins, at least about 15,000 groups of peptides or proteins, or at least about 20,000 groups of peptides or proteins. In some aspects, the protein data comprises measurements of no more than 10 peptide or protein groups, no more than 15 peptide or protein groups, no more than 20 peptide or protein groups, no more than 25 peptide or protein groups, no more than 30 peptide or protein groups, no more than 35 peptide or protein groups, no more than 40 peptide or protein groups, no more than 45 peptide or protein groups, no more than 50 peptide or protein groups, no more than 75 peptide or protein groups, no more than 100 peptide or protein groups, no more than 250 peptide or protein groups, no more than 500 peptide or protein groups, no more than 1,000 peptide or protein groups, no more than 2,500 peptide or protein groups, no more than 5,000 peptide or protein groups, no more than 10,000 peptide or protein groups, no more than 15,000 peptide or protein groups, or no more than 20,000 peptide or protein groups. The peptide or protein groups may comprise or consist of peptides. The peptide or protein groups may comprise or consist of protein groups.

[0400] Proteins may also include post-translational modifications (PTMs). Examples of PTMs include glycosylation. A protein or peptide may include a glycoprotein or a glycopeptide. A protein may include a glycoprotein. A peptide may include a glycopeptide. Examples of PTMs include phosphorylation. A protein or peptide may include a phosphoprotein or a phosphopeptide. A protein may include a phosphoprotein. A peptide may include a phosphopeptide. Examples of PTMs include carboxamidomethylation. A protein or peptide may include a carbamidomethyl protein or a carbamidomethyl peptide. A protein may include a carbamidomethyl protein. A peptide may include a carbamidomethyl peptide. Examples of PTMs include oxidation or hydroxylation. A protein or peptide may include an oxidized or hydroxylated protein or an oxidized or hydroxylated peptide. A protein may include an oxidized protein. A protein may include a hydroxylated protein. A peptide may include an oxidized peptide. A peptide may include a hydroxylated peptide.

[0401] Proteomic data can be generated by any of a variety of methods. Generating proteomic data can include using a detection reagent that binds to a peptide or protein and produces a detectable signal. Following use of a detection reagent that binds to a peptide or protein and produces a detectable signal, a readout can be obtained that indicates the presence, absence, or amount of the protein or peptide. Generating proteomic data can include concentrating, filtering, or centrifuging a sample.

[0402] Proteomic data can be generated using mass spectrometry, chromatography, liquid chromatography, high performance liquid chromatography, solid phase chromatography, lateral flow assay, immunoassay, enzyme-linked immunosorbent assay, western blot, dot blot or immunostaining or a combination thereof. Some examples of methods for generating proteomic data include using mass spectrometry, protein chips or reversed-phase protein microarrays. Proteomic data can also be generated using immunoassays, such as enzyme-linked immunosorbent assay, western blot, dot blot or immunohistochemical assays. Generating proteomic data can involve using immunoassay groups.

[0403] A way to obtain proteomics data includes using mass spectrometry. The example of mass spectrometry includes using high-resolution two-dimensional electrophoresis to separate proteins from different samples in parallel, and then the differentially expressed proteins to be identified by mass spectrometry are selected or dyed. Another method uses stable isotope labels to differentially mark proteins from two different complex mixtures. The protein in the complex mixture can be isotopically labeled, and then digested to produce labeled peptides. The labeled mixture can then be combined, and the peptide can be separated by multidimensional liquid chromatography and analyzed by tandem mass spectrometry. Mass spectrometry can include using liquid chromatography-mass spectrometry (LC-MS), i.e. a technology that can combine liquid chromatography (e.g., HPLC) with the physical separation ability of mass spectrum.

[0404] Proteins can be enriched before being assayed or measured. The enrichment can be for one protein pool without enriching for another protein pool, or can be for enriching for a single protein without enriching for another protein. Enrichment can be achieved by using an affinity reagent, for example, by incubating the sample with the affinity reagent before measuring the protein in the sample. The affinity reagent can include antibodies. The affinity reagent can include particles such as nanoparticles. The protein can be adsorbed to the affinity reagent, separated from the rest of the sample, and then assayed using the proteomic assays described herein.

[0405] Generating proteomic data can include contacting a sample with particles such that the particles adsorb biomolecules including proteins. The adsorbed proteins can be part of a biomolecule corona. The adsorbed proteins can be measured or identified when generating proteomic data.

[0406] Generating proteomic data can include using an internal reference protein of known quantity. The reference protein can be labeled. The label can include an isotope label. Generating proteomic data can include using an internal reference protein of known quantity (called "PiQuant") labeled with an isotope. The internal reference protein can be spiked into the sample. The internal reference protein can be used to identify the mass spectrum of a single endogenous protein. The internal reference protein can be used as a standard for determining the amount of a single endogenous protein. Proteomic measurements can be generated based on the amount of the protein in the sample added to the one or more biological fluid samples. Proteomic measurements can be generated based on the amount of the labeled protein in the sample added to the one or more biological fluid samples. In some aspects, proteomic data can include spatial proteomic data, wherein spatial proteomic data can include detecting and quantifying the subcellular localization of proteins in cells. Spatial proteomic data can be obtained by microscope, mass spectrometry and machine learning applications for data analysis. In some aspects, spatial proteomic data are in situ proteomic data.

[0407] Transcriptomics data

[0408] Data described herein such as multi-omics data can include transcript data or transcriptomics data. Transcriptomics data can relate to data about nucleotide transcripts (such as RNA). Examples of RNA include messenger RNA (mRNA), ribosomal RNA (rRNA), signal recognition particle (SRP) RNA, transfer RNA (tRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), long non-coding RNA (lncRNA), microRNA (miRNA), non-coding RNA (ncRNA) or piwi interaction RNA (piRNA) or a combination thereof. The RNA can include mRNA. RNA can include miRNA. Transcriptomics data can be distinguished by subtypes, wherein each subtype includes different types of RNA or transcripts. For example, mRNA data can be included in a subtype, and one or more types of small non-coding RNA such as miRNA or piRNA data can be included in another subtype. MiRNA can include 5p miRNA or 3p miRNA.

[0409] Transcriptomic data can include information about the presence, absence or amount of various RNAs. For example, transcriptomic data can include the amount of RNA. The amount of RNA can be expressed as the concentration or number of RNA molecules, such as the concentration of RNA in a biological fluid. The amount of RNA can be relative to another RNA or another biomolecule. Transcriptomic data can include information about the presence of RNA. Transcriptomic data can include information about the absence of RNA. Aspects described for transcriptomic data can be related to transcripts or RNA data, and vice versa.

[0410] Transcriptomic data typically includes data about many RNAs. For example, transcriptomic data can include information about the presence, absence, or amount of 1000 or more RNAs. In some cases, transcriptomic data can include information about the presence, absence, or amount of 5000, 10,000, 20,000, or more RNAs. Transcript quantity can include copy number. Transcriptomic data can even include up to about 200,000 transcripts. Transcriptomic data can include a range of transcripts defined by any of the aforementioned RNA or transcript numbers. Some examples of mRNAs that can be included in transcriptomic data are shown in Figure 10B or Figure 15 Some examples of microRNAs that can be included in transcriptomics data are shown in Figure 11B or Figure 15 middle.

[0411] Some examples of mRNAs that can be used as biomarkers are shown in Figure 10B middle. Figure 10BOne, two, three, four, five, six, seven, eight, nine or ten of the mRNAs included in can be used as biomarkers, for example, for determining whether a lung nodule is cancerous or for determining the likelihood that a lung nodule is cancerous. Some examples of microRNAs that can be used as biomarkers are shown in Figure 11B middle. Figure 11B 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 microRNAs included therein can be used as biomarkers, for example, for determining whether a lung nodule is cancerous or for determining the likelihood that a lung nodule is cancerous.

[0412] Table 15B shows some examples of RNA that can be used as biomarkers, and includes mRNA. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 of these RNAs can be used as biomarkers, for example, for lung nodule assessment. Any of the following RNAs can be used for this purpose (represented by Ensembl reference number): ENSG00000224067.2 (description: pseudogene similar to a portion of HLA-B associated transcript 2 (BAT2)), ENSG00000196735.13 (description: major histocompatibility complex, class II, DQα1; HLA-DQA1), ENSG00000287647.1 (description: antisense to AK5), ...224067.2 (description: pseudogene similar to a portion of HLA-B associated transcript 2 (BAT2)), ENSG00000224067.2 (description: pseudogene similar to a portion of HLA-B associated transcript 2 (BAT2)), ENSG00000224067.2 (description: 000230797.3 (description: YY2 transcription factor; YY2), ENSG00000287219.1 (description: novel human transcript from chromosome 22 at positions 38,675,876 to 38,677,800 from the forward strand of genome build GRCh38), ENSG00000271543.1 (description: ribosomal protein L6 (RPL6) pseudogene), ENSG00000223711.1 (description: AC091633 .3 (based on the cloned (Vega) gene), located on chromosome 3, forward strand of human genome build GRCh37, positions 195,270,871 to 195,277,400), ENSG00000223711.2 (description: novel transcript located on chromosome 3, forward strand of genome build GRCh38, positions 195,543,418 to 195,550,581), ENSG00000177602.5 (description: histone H3 ENSG00000144671.11 (Description: Solute carrier family 22 member 14; SLC22A14), ENSG00000129673.10 (Description: Arylalkylamine N-acetyltransferase; AANAT), ENSG00000265817.4 (Description: Fibrinogen silencer binding protein; FSBP), ENSG00000108924.14 (Description: HLF transcription factor, PAR bZIP family member; HLF), ENSG00000232125.5 (Description: Dystrotelin; DYTN), ENSG00000252800.1 (description: human transcript from positions 63,479,272 to 63,479,413 on chromosome 14 from the forward strand of genome construct GRCh38); this RNA biomarker may correspond to Small Cajal body-specific RNA20 (SCARNA20), ENSG00000287537.1 (description: novel human transcript from positions 49,536,677 to 49,538,894 on chromosome 12 from the reverse strand of genome construct GRCh38), ENSG00000196405.13 (description: Enah / Vasp-like; EVL), ENSG00000250893.1 (description: novel human transcript from positions 40,426,119 to 40,427,585 on chromosome 4 from the forward strand of genome construct GRCh38), ENSG00000287537.1 (description: novel human transcript from positions 49,536,677 to 49,538,894 on chromosome 12 from the reverse strand of genome construct GRCh38), ENSG00000196405.13 (description: Enah / Vasp-like; EVL), Biomarkers may include ENSG00000224067.2 (description: pseudogene similar to a portion of HLA-B associated transcript 2 (BAT2)). Biomarkers may include ENSG00000196735.13 (Description: Major histocompatibility complex, class II, DQα1; HLA-DQA1). Biomarkers may include ENSG00000287647.1 (Description: Antisense to AK5). Biomarkers may include ENSG00000230797.3 (Description: YY2 transcription factor; YY2). Biomarkers may include ENSG00000287219.1 (Description: A novel human transcript at positions 38,675,876 to 38,677,800 on chromosome 22 from the forward strand of genomic construct GRCh38). Biomarkers may include ENSG00000271543.1 (Description: Ribosomal protein L6 (RPL6) pseudogene). Biomarkers may include ENSG00000223711.1 (Description: AC091633.3 (based on the cloned (Vega) gene), located on chromosome 3, position 195,270,871 to 195,277,400, of the human genome construction GRCh37 forward chain). Biomarkers may include ENSG00000223711.2 (description: novel transcript at position 195,543,418 to 195,550,581, of chromosome 3, of the genome construction GRCh38 forward chain). Biomarkers may include ENSG00000177602.5 (description: histone H3-associated protein kinase; HASPIN). Biomarkers may include ENSG00000144671.11 (description: solute carrier family 22 member 14; SLC22A14). Biomarkers may include ENSG00000129673.10 (description: arylalkylamine N-acetyltransferase; AANAT). Biomarkers may include ENSG00000265817.4 (description: fibrinogen silencer binding protein; FSBP). Biomarkers may include ENSG00000108924.14 (description: HLF transcription factor, PAR bZIP family member; HLF). Biomarkers may include ENSG00000232125.5 (description: dystrophin-related protein; DYTN). Biomarkers may include ENSG00000252800.1 (description: human transcript from chromosome 14 at positions 63,479,272 to 63,479,413 from the forward strand of genome build GRCh38); this RNA biomarker may correspond to small Cajal body-specific RNA 20 (SCARNA20). Biomarkers may include ENSG00000287537.1 (Description: A novel human transcript at positions 49,536,677 to 49,538,894 of chromosome 12 from the reverse strand of genomic construct GRCh38). Biomarkers may include ENSG00000196405.13 (Description: Enah / Vasp-like; EVL). Biomarkers may include ENSG00000250893.1 (Description: A novel human transcript at positions 40,426,119 to 40,427,585 of chromosome 4 from the forward strand of genomic construct GRCh38). Biomarkers may include ENSG00000153446.16 (Description: Chromosome 16 open reading frame 89; C16orf89). Biomarkers may include ENSG00000284630.1 (Description: Novel human transcript from chromosome 22 at positions 21,657,811 to 21,661,021 from genomic construct GRCh38 forward strand). Biomarkers may include ENSG00000284687.1 (Description: RNA binding protein, human fox-1 homolog (C. elegans) 1 (RBFOX1) pseudogene from chromosome 12, positions 8,390,270 to 8,390,488, reverse strand of genomic construct GRCh38). Any of these biomarkers can be used alone or in combination to evaluate lung nodules (e.g., to determine the likelihood that a lung nodule is cancerous).

[0413] RNA can be used as a biomarker and includes mRNA. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 of these RNAs can be used as biomarkers, for example, in lung cancer (such as non-small cell lung cancer). Any of the following RNAs can be used for this purpose (indicated by Ensembl reference number): ENSG00000155744.10 (Description: Hyccin PI4KA lipid kinase complex subunit 2; HYCC2), ENSG00000081052.14 (description: type IV collagen α4 chain; COL4A4), ENSG00000173726.11 (description: mitochondrial outer membrane translocase 20; TOMM20), ENSG00000143995.20 (description: Meis homeobox 1; MEIS1), ENSG00000108528.14 (description: solute carrier family 25 member 11; SLC25A11), ENSG00000 177427.13 (Description: Mitochondrial elongation factor 2; MIEF2), ENSG00000163961.4 (Description: RING finger protein 168; RNF168), ENSG00000049130.16 (Description: KIT ligand; KITLG), ENSG00000008405.12 (Description: Cryptochrome circadian regulator 1; CRY1), ENSG00000135090.14 (Description: TAO kinase 3; TAOK3), ENSG00000151778.11 (Description: stress-related endoplasmic reticulum protein family member 2; SERP2), ENSG00000172116.23 (Description: CD8b molecule; CD8B), ENSG00000144218.21 (Description: ALF transcription elongation factor 3; AFF3), ENSG00000131196.18 (Description: nuclear factor of activated T cells 1; NFATC1), ENSG00000129351.18 (Description: interleukin enhancer binding factor 3; ILF3), ENSG00000105518 .14 ​​(description: transmembrane protein 205; TMEM205), ENSG00000182162.11 (description: P2Y receptor family member 8; P2RY8), ENSG00000126368.6 (description: nuclear receptor subfamily 1 group D member 1; NR1D1), ENSG00000176358.16 (description: tachykinin precursor 4; TAC4), ENSG00000112599.9 (description: guanylate cyclase activator 1B; GUCA1B), ENSG00000142864.15 (description: SERPINE1 mRNA binding protein 1; SERBP1), ENSG00000163159.15 (description: vacuolar protein sorting 72 homolog; VPS72), ENSG00000165661.17, ENSG00000165661.18 (description: Quiescin sulfhydryl oxidase 2; QSOX2), ENSG00000007923.17 (description: DnaJ heat shock protein family (Hsp40) member C11; DNAJC11), ENSG00000054116.12 (description: transport protein particle complex subunit 3; TRAPPC3), ENSG00000113811.12 (description: selenoprotein K; SELENOK), ENSG00000100 644.17 (description: hypoxia-inducible factor 1 subunit alpha; HIF1A), ENSG00000133997.12 (description: mediator complex subunit 6; MED6), ENSG00000120925.16 (description: ring finger protein 170; RNF170), ENSG00000110048.12 (description: oxysterol-binding protein; OSBP), ENSG00000197863.9 (description: zinc finger protein 790; ZNF790), ENSG00000174307.7 (description: pleckstrin homology domain family A member 3; PHLDA3), or ENSG00000109381.21 (description: E74-like ETS transcription factor 2; ELF2). Biomarkers may include ENSG00000155744.10 (Description: Hyccin PI4KA lipid kinase complex subunit 2; HYCC2). Biomarkers may include ENSG00000081052.14 (Description: Type IV collagen alpha 4 chain; COL4A4). Biomarkers may include ENSG00000173726.11 (Description: Mitochondrial outer membrane translocase 20; TOMM20). Biomarkers may include ENSG00000143995.20 (Description: Meis homeobox 1; MEIS1). Biomarkers may include ENSG00000108528.14 (Description: Solute carrier family 25 member 11; SLC25A11). Biomarkers may include ENSG00000177427.13 (Description: Mitochondrial elongation factor 2; MIEF2). Biomarkers may include ENSG00000163961.4 (Description: RING finger protein 168; RNF168). Biomarkers may include ENSG00000049130.16 (Description: KIT ligand; KITLG). Biomarkers may include ENSG00000008405.12 (Description: Cryptochrome circadian rhythm regulator 1; CRY1). Biomarkers may include ENSG00000135090.14 (Description: TAO kinase 3; TAOK3). Biomarkers may include ENSG00000151778.11 (Description: Stress-related endoplasmic reticulum protein family member 2; SERP2). Biomarkers may include ENSG00000172116.23 (Description: CD8b molecule; CD8B). Biomarkers may include ENSG00000144218.21 (Description: ALF transcription elongation factor 3; AFF3). Biomarkers may include ENSG00000131196.18 (Description: Nuclear factor of activated T cells 1; NFATC1). Biomarkers may include ENSG00000129351.18 (Description: Interleukin enhancer binding factor 3; ILF3). Biomarkers may include ENSG00000105518.14 (Description: Transmembrane protein 205; TMEM205). Biomarkers may include ENSG00000182162.11 (Description: P2Y receptor family member 8; P2RY8). Biomarkers may include ENSG00000126368.6 (Description: Nuclear receptor subfamily 1 group D member 1; NR1D1). Biomarkers may include ENSG00000176358.16 (Description: Tachykinin precursor 4; TAC4). Biomarkers may include ENSG00000112599.9 (Description: Guanylate cyclase activator 1B; GUCA1B). Biomarkers may include ENSG00000142864.15 (Description: SERPINE1 mRNA binding protein 1; SERBP1). Biomarkers may include ENSG00000163159.15 (Description: Vacuolar protein sorting 72 homolog; VPS72). Biomarkers may include ENSG00000165661.17. Biomarkers may include ENSG00000165661.18 (Description: Quiescin sulfhydryl oxidase 2; QSOX2). Biomarkers may include ENSG00000007923.17 (Description: DnaJ heat shock protein family (Hsp40) member C11; DNAJC11). Biomarkers may include ENSG00000054116.12 (Description: Transport protein particle complex subunit 3; TRAPPC3). Biomarkers may include ENSG00000113811.12 (Description: Selenoprotein K; SELENOK), ENSG00000100644.17 (Description: Hypoxia-inducible factor 1 subunit alpha; HIF1A). Biomarkers may include ENSG00000133997.12 (Description: Mediator complex subunit 6; MED6). Biomarkers may include ENSG00000120925.16 (Description: Ring finger protein 170; RNF170). Biomarkers may include ENSG00000110048.12 (Description: Oxysterol binding protein; OSBP). Biomarkers may include ENSG00000197863.9 (Description: Zinc finger protein 790; ZNF790). Biomarkers may include ENSG00000174307.7 (Description: Pleckstrin homology domain family A member 3; PHLDA3). Biomarkers may include ENSG00000109381.21 (Description: E74-like ETS transcription factor 2; ELF2). Any of the above-mentioned nucleic acid biomarkers (or combinations of such biomarkers) can be used to identify the presence, absence, or likelihood of a cancer as described herein. Any of these biomarkers can be used alone or in combination to assess lung cancer (e.g., non-small cell lung cancer).

[0414] Transcriptomic data can be generated by any of a variety of methods. Generating transcriptomic data can include using a detection reagent that binds to the RNA and produces a detectable signal. After using a detection reagent that binds to the RNA and produces a detectable signal, a readout indicating the presence, absence, or amount of the RNA can be obtained. Generating transcriptomic data can include concentrating, filtering, or centrifuging the sample.

[0415] Transcriptomics data can include RNA sequence data. Some examples of the method for generating RNA sequence data include using order-checking, microarray analysis, hybridization, polymerase chain reaction (PCR) or electrophoresis or their combination. Microarray can be used to generate transcriptomics data. PCR can be used to generate transcriptomics data. PCR can include quantitative PCR (qPCR). Such methods can include using a detectable probe (e.g., a fluorescent probe) that is chimeric or bonded to a target nucleotide sequence with a double-stranded nucleotide. PCR can include reverse transcriptase quantitative PCR (RT-qPCR). Generating transcriptomics data can involve using a PCR group.

[0416] RNA sequence data can be generated by sequencing the RNA of the subject or by first converting the RNA of the subject into DNA (e.g., complementary DNA (cDNA)) and sequencing the DNA. Sequencing can include massively parallel sequencing. Examples of massively parallel sequencing technologies include pyrophosphate sequencing, sequencing by reversible terminator chemistry, sequencing by ligase-mediated edge-linking, or fluorescent nucleotides or real-time sequencing linked by phosphates. Generating transcriptomics data can include preparing samples or templates for sequencing. Reverse transcriptase can be used to convert RNA into cDNA. Some template preparation methods include using amplified templates derived from single RNA or cDNA molecules, or single RNA or cDNA molecule templates. Examples of amplification methods include emulsion PCR, rolling circle or solid phase amplification.

[0417] In addition to any of the above methods, generating transcriptomic data can include contacting a sample with particles such that the particles adsorb biomolecules comprising RNA. The adsorbed RNA can be part of a biomolecule corona. The adsorbed RNA can be measured or identified when generating transcriptomic data.

[0418] In some embodiments, transcriptomic data may include spatial transcriptomics. For example, a biopsy or tissue sample may be permeabilized and contacted with a probe to determine an in situ transcriptome, including transcriptomic data and its positional context in tissue cells. In some embodiments, spatial transcriptomics may be obtained by microdissection (e.g., laser capture microdissection, RNA sequencing of a single frozen section, TIVA, tomo-seq, LCM-seq, Geo-seq, NICHE-seq, or ProximID); fluorescence in situ hybridization (e.g., smFISH, RNAscope, seqFISH, MERFISH, smHCR, osmFISH, seqFISH+, or DNA microscopy); in situ sequencing (e.g., ISS, FISSEQ, Barista-seq, or STARmap using a padlock probe); in situ capture (e.g., GeoMx, Slide-seq, APEX-seq, HDST, or 10X Visium); computer construction (e.g., reconstruction using ISH or DistMap).

[0419] Genomics data

[0420] Data described herein, such as multi-omics data, can include data or genomics data about genetic material. Genomics data can include data about genetic material such as nucleic acids or histones. Nucleic acids can include DNA. Genomics data can include information about the presence, absence or amount of genetic material. The amount of genetic material can be expressed as concentration, absolute number, or can be relative. Aspects described for genomics data can be relevant to nucleic acid or DNA data, and vice versa. Nucleic acid data can include RNA data, or genomics data can include transcriptomics data.

[0421] Genomics data can include DNA sequence data. The sequence data can include gene sequences. For example, genomics data can include sequence data for up to about 20,000 genes. Genomics data can also include sequence data for non-coding DNA regions. DNA sequence data can include information about the presence, absence, or amount of a DNA sequence. DNA sequence data can include information about the presence or absence of a mutation (such as a single nucleotide polymorphism). DNA sequence data can include DNA measurements of the amount of mutated DNA, such as measurements from mutated DNA of cancer cells.

[0422] The amount of DNA can include copy number. Similarly, genomic data can include the copy number of various sequences. The copy number variation of circulating cell-free DNA (cfDNA) can be determined. The...

Claims

1. A method comprising: acquiring a multi-omics database comprising multi-omics data generated from biofluid samples of a population with different disease states and patient characteristics, wherein the multi-omics data comprises proteomics, metabolomics, lipidomics, transcriptomics, fragmentomics, methylomics, and genomics; and The multi-omics database is queried to identify a biomarker or panel of biomarkers that can distinguish individuals of the population having a first disease state or patient characteristic from other individuals of the population having a second disease state or patient characteristic.

2. The method of claim 1, wherein the query comprises identifying the biomarker or biomarker group as being useful for identifying a third disease state or patient characteristic, and determining that the biomarker or biomarker group is also useful for identifying the first disease state or patient characteristic or a second first disease state or patient characteristic.

3. The method of claim 1, wherein the query comprises identifying other biomarkers or groups of biomarkers that can be used to distinguish individuals in the population having the first disease state or patient characteristic from other individuals in the population having the second disease state or patient characteristic, and determining that the biomarker or group of biomarkers is associated with the other biomarker or group of biomarkers among individuals in the population. The method of claim 1 , wherein the query comprises comparing or correlating measurements of the multi-omics data. 5 . The method of claim 1 , wherein the querying the multi-omics database comprises associating a value of the multi-omics data with the first disease state or patient characteristic or a second disease state or patient characteristic.

6. The method of claim 1, wherein the querying comprises using machine learning.

7. The method of claim 1, wherein the multi-omic data is generated from biological fluid samples of more than 500, more than 1000, more than 1500, more than 2000, more than 2500, or more than 3000 population members.

8. The method of claim 1, wherein the multi-omics data is generated from biological fluid samples of no more than 500, no more than 1000, no more than 1500, no more than 2000, no more than 2500, or no more than 3000 population members.

9. The method of claim 1, wherein the multi-omics data is generated using a non-targeted omics measurement approach.

10. The method of claim 1, wherein at least some of the multi-omics data is generated after enrichment using nanoparticles.

11. The method of claim 1, wherein the biomarker or panel of biomarkers comprises a secretory biomarker.

12. The method of claim 1, wherein the biomarker or biomarker panel comprises a protein, a lipid, a nucleic acid, a metabolite, or a combination thereof.

13. The method of claim 1, wherein the biomarker panel corresponds to a metabolic pathway.

14. The method of claim 1, wherein the first disease state or patient characteristic comprises a cancer state.

15. The method of claim 1, wherein the first disease state or patient characteristic or the second disease state or patient characteristic comprises a co-morbid condition.

16. The method of claim 1, wherein the second disease state or patient characteristic comprises a health state.

17. The method of claim 1, wherein the first patient characteristic or the second patient characteristic comprises age, sex, race, weight, height, dietary consumption, exercise habits, activity level, or smoking status.

18. The method of claim 1, further comprising using the biomarker or biomarker panel to classify a subject as having the first disease state or patient characteristic or as having the second disease state or patient characteristic.

19. The method of claim 1, further comprising identifying, recommending, or administering a disease treatment based on the use of the biomarker or biomarker panel.

20. The method of claim 1, wherein the biological fluid sample comprises a blood, serum, or plasma sample.

21. The method of claim 1, wherein the population comprises human subjects.

22. A method comprising: acquiring multi-omics data from one or more biofluid samples of a subject identified as having a pulmonary nodule; as well as A classifier is applied to the multi-omics data to assess whether a lung nodule is cancerous or non-cancerous.

23. The method of claim 22, wherein the multi-omics data comprises metabolomics data, lipidomics data, proteomics data, or transcriptomics data.

24. The method of claim 23, wherein the proteomics data comprises targeted proteomics data.

25. The method of claim 23, wherein the proteomics data comprises untargeted proteomics data.

26. The method of claim 23, wherein the transcriptomic data comprises mRNA data.

27. The method of claim 23, wherein the transcriptomic data comprises microRNA data.

28. The method of claim 22, wherein the classifier exhibits an area under the curve determined in a receiver operating characteristic curve of at least about 0.6 when distinguishing a biological fluid sample to indicate whether a lung nodule is cancerous.

29. The method of claim 22, wherein the multi-omic data comprises measurements of a biomarker selected from the group consisting of: STVLTIPEIIIK (SEQ ID NO: 12), TLAFPLTIR (SEQ ID NO: 13), LIQGAPTIR (SEQ ID NO: 14), SSGLVSNAPGVQIR (SEQ ID NO: 15), DGSFSVVITGLR (SEQ ID NO: 16), LGPISADSTTAPLEK (SEQ ID NO: 17), SEAACLAAGPGIR (SEQ ID NO: 18), TDTGFLQTLGHNLFGIYQK (SEQ ID NO: 19), LKPEDITQIQPQQLVLR (SEQ ID NO: 20), GLPAPIEK (SEQ ID NO: 21), LLGPGPAADFSVSVER (SEQ ID NO: 22), YEYLEGGDR (SEQ ID NO: 23), HLEDVFSK (SEQ ID NO: 24), ILGPLSYSK (SEQ ID NO: 25). NO:25),NCQTVLAPCSPNPCENAAVCK(SEQ ID NO:26),TVTATFGYPFR(SEQ ID NO:27),STDTSVNPPTVQNAHILSR(SEQ ID NO:28),FSLVSGWGQLLDR(SEQ ID NO:29),ELLALIQLER(SEQID NO:30),DAHSVLLSHIFHGR(SEQ ID NO:31), EHAVEGDCDFQLLK (SEQ ID NO:32), SQASSCSLQSSDSVPVGLCK (SEQ ID NO:33), GEFAIDGYSVR (SEQ ID NO:34), ALVEGVDQLFTDYQIK (SEQ ID NO:35), LLPYIVGVAQR (SEQ ID NO:36), HTLNQIDEVK (SEQ ID NO:37), IDILVNNGGMSQR (SEQ ID NO:38), LMMDGHEVTVVDNFFTGR (SEQ ID NO:39), MYGEILSPNYPQAYPSEVEK (SEQ ID NO:40), NNEEWTVDSCTECHCQNSVTICK (SEQ ID NO:41), IDTQDIEASHYR (SEQ ID NO:42), TFIFSLDDYMGMSSGFYK (SEQ ID NO:43), PDAELSASSVYNLLPEK (SEQ IDNO:44), ASIHEAWTDGK (SEQ ID NO:45), LYPWGVVEVENPEHNDFLK (SEQ ID NO:46), YHWEHTGLTLR (SEQ ID NO:47) or IGGAIEEVYVSLGVSVGK (SEQ IDNO:48), BAT2, HLA-DQA1, antisense to AK5, YY2, ENSG00000287219.1, ribosomal protein L6 (RPL6) pseudogene, ENSG00000223711.1, HASPIN, SLC22A14, AANAT, FSBP, HLF, DYTN, transcripts from chromosome 14 at positions 63,479,272 to 63,479,413 from the forward strand of genome build GRCh38, novel human transcripts from chromosome 12 at positions 49,536,677 to 49,538,894 from the reverse strand of genome build GRCh38, EVL, from novel human transcripts from chromosome 4 at positions 40,426,119 to 40,427,585 from the forward strand of genome construction GRCh38, C16orf89), novel human transcripts from chromosome 22 at positions 21,657,811 to 21,661,021 from the forward strand of genome construction GRCh38, RNA binding protein, human FOX-1 homologous pseudogene from chromosome 12 at positions 8,390,270 to 8,390,488 from the reverse strand of genome construction GRCh38, PC(20:3_20:4)+AcO, DAG(18:2_20:2)+NH4, PC(18:2_20: 5)+AcO, LPE(18:1)-H, LPE(16:0)-H, TAG(58:6_FA18:0)+NH4, DAG(20:1_20:5)+NH4, PC(14:0_20:2)+AcO, PC(18:2_20:3)+AcO, PE(18:1_22 :4)-H, PE(18:0_20:1)-H, CER(d18:1 / 26:1)+H, PC(14:0_18:2)+AcO, PE(18:0_22:4)-H, PI(15:0_22:5)-H, PE(P-18:1_18:0)+H, TAG(54:5_F A18:3)+NH4, TAG(58:5_FA18:1)+NH4, DAG(20:5_22:4)+NH4, LPE(20:3)-H, biomarkers can include PC(20:3_20:4)+AcO, sedoheptulose 1,7-bisphosphate, glucuronate, biopterin, reduced glutathione, N-acetyl-arginine, cotinine, indole-3-lactic acid, 13C4-ketoglutarate, propionyl-CoA, AICAR, 3-methyl-3-hydroxyglutarate, imidazoleacetic acid, shikimic acid, 1-methyladenosine, dopamine, carnosine, homocitrulline, indolepyruvate, 2-phosphoglycerate or glutaric acid, or a combination thereof.

30. A method comprising: obtaining multi-omic data from one or more biofluid samples from a subject suspected of having pancreatic cancer; as well as A classifier is applied to the multi-omics data to assess the likelihood that the subject has pancreatic cancer.

31. The method of claim 30, wherein the classifier exhibits an area under the curve determined in a receiver operating characteristic curve of at least 0.85, at least 0.86, at least 0.87, at least 0.88, at least 0.89, at least 0.90, at least 0.91, at least 0.92, at least 0.93, at least 0.94, at least 0.95, at least 0.96, at least 0.97, or at least 0.98 when distinguishing whether a biological fluid sample is indicative of pancreatic cancer.

32. The method of claim 30, wherein the pancreatic cancer comprises stage 1 or stage 2 pancreatic cancer.

33. The method of claim 30, wherein the pancreatic cancer comprises stage 3 or stage 4 pancreatic cancer.

34. The method of claim 30, wherein the multi-omics data comprises data of copy number variation, fragmentomics, mRNA, protein, metabolite, or lipid.

35. The method of claim 30, wherein the multi-omics data comprises copy number variation data, fragmentomic data, transcriptomic data, proteomic data, metabolomic data, and lipidomic data.

36. The method of claim 30, wherein the multi-omics data comprises measurements of a biomarker selected from the group consisting of: F13A_human, AMPN_human, PIGR_human ANTR2_human, S10A8_human, A2GL_human, APOM_human, APOC1_human, S10A9_human, NRP1_human, FCG3A_human, TTHY_human, CRAC1_human, ICAM1_human, CD166_human, TENA_human, GELS_human, TETN_human , IBP2_human, ITLN1_human, ITIH3_human, VCAM1_human, APOC3_human, , IFM3_human, AMPN_human, A2GL_human, AACT_human, SMOC1_human, A1AT_human, PTX3_human, CDHR2_human, H2A2C_human, ANTR2_human, MMP7_human, CO7_human, ANXA2_human, FGL1_human, H4_human or ACADV_human, TELVEPTEYLVVHLK (SEQ ID NO: 11 ID NO:1), TFVIIPELVLPNR (SEQ ID NO:2), LQELHLSSNGLESLSPEFLRPVPQLR (SEQ ID NO:3), ITLLSALVETR (SEQ ID NO:4), VVATTQMQAADAR (SEQ ID NO:5), TFVIIPELVLPNR (SEQ ID NO:6), LQHLENELTHDIITK (SEQ ID NO:7), FLENEDRR (SEQ ID NO:8), LWYENPGVFSPAQLTQIK (SEQ ID NO:9), QWMENPNNNPIHPNLR (SEQ ID NO:10), LEIYQEDQIHFMCPLAR (SEQ ID NO:11), TMEM192H2BC17)GAPDHP60, ENSG00000271270.7, ZBED3, TEC Chromosome 19: 4,246,339-4,247,358 Reverse strand GRCh38, MIR5187, MIR6739, MIR3162, MIR4772, MIR877, MIR744, MIR3909, MIR6842, MIR101-1, MIR206) MIR1225, MIR193B, MIR200A, MIR26B, MIR4446, MIR7108, MIR23B, MIR365B, MIR362, MIR134, MIRLET7F2, MIR6852 , MIR5009, MIR6736, MIR6850, MIR1180, MIR5584, MIR3121, MIR429, MIR320A, MIR93, MIR4747, MIR320C1, MIR95 or MIR221, CER(d18:1 / 16:0)+H, CER(d18:1 / 18:0)+H, PA(18:0_20:5)-H, DAG(18:1_20:0)+NH4, PC(18:2_20:5)+AcO, PC(20:3_20:4)+AcO, PE(O-18:0_22 :5)-H, PE(14:0_22:5)-H, PC(16:0_20:2)+AcO, PI(18:3+20:4)-H, PA(20:2+20:3)-H, 17:0-18:1PE-d5-H_USPLASH.IS, PC(16:0_1 6:0)+AcO, PC(17:0_20:1)+AcO, CER(d18:0 / 24:0)+H, PE(P=16:0+22:5)+H, PE(18:2+20:1)-H, PE(P-16:0+20:5)+H, TAG(48:0+FA16 :0)+NH4, PC(16:0+18:1)+AcO, PE(18:0+20:2)=H, PE(18:1+20:1)-H, AICAR, CMP, dimethylglycine, epinephrine, sorbitol, 5-thymidylic acid (dTMP), taurocholic acid, glycocholate, fructose-6-phosphate, farnesyl pyrophosphate, ATP, cystamine, taurocholate, glycine, choline, hydroxyphenyllactic acid, inosine, glutarylcarnitine, 1-methylimidazole acetate, AMP, gluconate, reduced glutathione, glutamic acid, creatine, L-dihydroorotic acid, thymidine, imidazoleacetic acid and UMP, or a combination thereof. .

37. A method for detecting pancreatic cancer, comprising: (a) obtaining a biomarker from a biological fluid sample of a subject; as well as (b) applying a classifier to the biomarkers to assess pancreatic cancer, wherein the classifier distinguishes between biological fluid samples of subjects with and without pancreatic cancer by performance characterized by a receiver operating characteristic (ROC) curve having a mean or median area under the curve (AUC) of at least 0.7, and wherein the biomarkers include any one of the following chromosome regions: ThXX chr10:113000001-113100000, chr7:45200001-45300000, chr9:104900001-105000000, chr18:58600001-58700000, chr17:17400001-17500000, chr2:150700001-150800000, chr7:149300001-149400000, chr4:88700001-8880000 0, chr20:28900001-29000000 or chr8:55300001-55400000; any of the following mRNA transcripts: TMEM192, H2BC17, GAPDHP60, ENSG00000271270.7, ZBED3, or GRCh38; any of the following microRNAs: MIR5187, MIR6739, MIR3162, MIR4772, MIR877, MIR744, MIR3909, MIR6842, MIR101-1, MIR206, MIR1225, MIR193B, MIR200A, MIR26B, MIR4446, MIR7108, MIR23B, MIR365B, MIR362, MIR134, MIRL ET7F2, MIR6852, MIR5009, MIR6736, MIR6850, MIR1180, MIR5584, MIR3121, MIR429, MIR320A, MIR93, MIR4747, MIR320 C1 or MIR221; any of the following proteins: F13A_human, AMPN_human, PIGR_human, ANTR2_human, S10A8_human, A2GL_human, APOM_human, APOC1_human, S10A9_human, NRP1_human, FCG3A_human, TTHY_human, CRAC1_human, ICAM1_human, CD166_human, TENA_human, GELS_human, TETN_human, IBP2_human, ITLN1_human, ITIH3_human, VCAM1_human, or APOC3_human;Any of the following peptides: TELVEPTEYLVVHLK (SEQ ID NO: 1), TFVIIPELVLPNR (SEQ ID NO: 2), LQELHLSSNGLESLSPEFLRPVPQLR (SEQ ID NO: 3), ITLLSALVETR (SEQ ID NO: 4), VVATTQMQAADAR (SEQ ID NO: 5), TFVIIPELVLPNR (SEQ ID NO: 6), LQHLENELTHDIITK (SEQ ID NO: 7), FLENEDRR (SEQ ID NO: 8), LWYENPGVFSPAQLTQIK (SEQ ID NO: 9), QWMENPNNNPIHPNLR (SEQ ID NO: 10), or LEIYQEDQIHFMCPLAR (SEQ ID NO: 11). NO:11); any of the following proteins: IFM3_human, AMPN_human, A2GL_human, AACT_human, SMOC1_human, A1AT_human, PTX3_human, CDHR2_human, H2A2C_human, ANTR2_human, MMP7_human, CO7_human, ANXA2_human, FGL1_human, H4_human or ACADV_human; any of the following lipids: CER(d18:1 / 16:0)+H, CER(d18:1 / 18:0)+H, PA(18:0_20:5)-H, DAG(18:1_20:0)+NH4, PC(18:2_20:5)+AcO, PC(20:3_20:4)+AcO, PE(O-18:0_22:5)-H, P E(14:0_22:5)-H, PC(16:0_20:2)+AcO, PI(18:3+20:4)-H, PA(20:2+20:3)-H, 17 :0-18:1PE-d5-H_USPLASH.IS、PC(16:0_16:0)+AcO、PC(17:0_20:1)+AcO、CER(d1 8:0 / 24:0)+H, PE(P=16:0+22:5)+H, PE(18:2+20:1)-H, PE(P-16:0+20:5)+H, TAG (48:0+FA16:0)+NH4, PC(16:0+18:1)+AcO or PE(18:0+20:2)=H, PE(18:1+20:1)-H;or any of the following metabolites: AICAR, CMP, dimethylglycine, epinephrine, sorbitol, 5-thymidylic acid (dTMP), taurocholic acid, glycocholate, fructose-6-phosphate, farnesyl pyrophosphate, ATP, cystamine, taurocholate, glycine, choline, hydroxyphenyllactic acid, inosine, glutarylcarnitine, 1-methylimidazole acetate, AMP, gluconate, reduced glutathione, glutamate, creatine, L-dihydroorotic acid, thymidine, imidazoleacetic acid, or UMP.; 38. The method of claim 37, wherein the biomarker comprises any one of the following chromosomal regions: chr10: 113000001-113100000, chr7: 45200001-45300000, chr9: 104900001-105000000, chr18: 58600001-58700000, chr 17:17400001-17500000, chr2:150700001-150800000, chr7:149300001-149400000, chr4:88700001-88800000, chr20:28900001-29000000 or chr8:55300001-55400000.

39. The method of claim 38, wherein the biomarkers comprise 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the chromosomal regions.

40. The method of claim 37, wherein the biomarker comprises any of the following mRNA transcripts: TMEM192, H2BC17, GAPDHP60, ENSG00000271270.7, ZBED3, or GRCh38.

41. The method of claim 40, wherein the biomarkers comprise 1, 2, 3, 4, 5 or 6 of the mRNA transcripts.

42. The method of claim 37, wherein the biomarker comprises any one of the following microRNAs: MIR5187, MIR6739, MIR3162, MIR4772, MIR877, MIR744, MIR3909, MIR6842, MIR101-1, MIR206, MIR1225, MIR193B, MIR200A, MIR26B, MIR4 446, MIR7108, MIR23B, MIR365B, MIR362, MIR134, MIRLET7F2, MIR6852, MIR5009, MIR6736, MIR6850, MIR1180, MIR5584, MIR3121, MIR429, MIR320A, MIR93, MIR4747, MIR320C1, or MIR221.

43. The method of claim 42, wherein the biomarkers comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, or 33 of the microRNAs.

44. The method of claim 37, wherein the biomarkers include any one of the following proteins: F13A_human, AMPN_human, PIGR_human, ANTR2_human, S10A8_human, A2GL_human, APOM_human, APOC1_human, S10A9_human, NRP1_human, FCG3A_human, TTHY_human, CRAC1_human, ICAM1_human, CD166_human, TENA_human, GELS_human, TETN_human, IBP2_human, ITLN1_human, ITIH3_human, VCAM1_human, or APOC3_human.

45. The method of claim 44, wherein the biomarkers comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23 of the proteins.

46. ​​The method of claim 37, wherein the biomarker comprises any one of the following peptides: TELVEPTEYLVVHLK (SEQ ID NO: 1), TFVIIPELVLPNR (SEQ ID NO: 2), LQELHLSSNGLESLSPEFLRPVPQLR (SEQ ID NO: 3), ITLLSALVETR (SEQ ID NO: 4), VVATTQMQAADAR (SEQ ID NO: 5), TFVIIPELVLPNR (SEQ ID NO: 6), LQHLENELTHDIITK (SEQ ID NO: 7), FLENEDRR (SEQ ID NO: 8), LWYENPGVFSPAQLTQIK (SEQ ID NO: 9), QWMENPNNNPIHPNLR (SEQ ID NO: 10), or LEIYQEDQIHFMCPLAR (SEQ ID NO: 11).

47. The method of claim 46, wherein the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or 11 of the peptides.

48. The method of claim 37, wherein the biomarkers include any one of the following proteins: IFM3_human, AMPN_human, A2GL_human, AACT_human, SMOC1_human, A1AT_human, PTX3_human, CDHR2_human, H2A2C_human, ANTR2_human, MMP7_human, CO7_human, ANXA2_human, FGL1_human, H4_human, or ACADV_human.

49. The method of claim 48, wherein the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 of the proteins.

50. The method of claim 37, wherein the biomarker comprises any one of the following lipids: CER (d18:1 / 16:0) + H, CER (d18:1 / 18:0) + H, PA (18:0-20:5) -H, DAG (18:1-20:0) + NH4, PC (18:2-20:5) + AcO, PC (20:3-20:4) + AcO, PE (O-18:0-22:5) -H, PE (14:0-22:5) -H, PC (16:0-20:2) + AcO, PI (18:3 + 20:4) -H, PA (2 0:2+20:3)-H, 17:0-18:1PE-d5-H_USPLASH.IS, PC(16:0_16:0)+AcO, PC(17:0_20:1)+AcO, CER(d18:0 / 24:0)+H, PE(P=16:0+22:5)+H, PE(18:2+20:1)-H, PE(P-16:0+20:5)+H, TAG(48:0+FA16:0)+NH4, PC(16:0+18:1)+AcO, PE(18:0+20:2)=H or PE(18:1+20:1)-H.

51. The method of claim 50, wherein the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, or 22 of the lipids.

52. The method of claim 37, wherein the biomarker comprises any one of the following metabolites: AICAR, CMP, dimethylglycine, epinephrine, sorbitol, 5-thymidylic acid (dTMP), taurocholic acid, glycocholate, fructose-6-phosphate, farnesyl pyrophosphate, ATP, cystamine, taurocholate, glycine, choline, hydroxyphenyllactic acid, inosine, glutarylcarnitine, 1-methylimidazole acetate, AMP, gluconate, reduced glutathione, glutamate, creatine, L-dihydroorotic acid, thymidine, imidazoleacetic acid, or UMP.

53. The method of claim 52, wherein the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, or 28 of the metabolites.

54. The method of claim 37, wherein the biomarkers include any one of the following biomarkers: APOM_human, G6PE_human, F13A_human, A1AT_human, AACT_human, A2MG_human, CO5_human, IGHG2_human, APOC1_human, APOC3_human, APOB_human, ICAM1_human, ITB1_human, GELS_human, S10A9_human, CO8B_human, TSP1_human, MMP7_human, or CO7_human.

55. The method of claim 37, wherein the classifier comprises performance characterized by a receiver operating characteristic (ROC) curve having a mean or median area under the curve (AUC) of at least 0.

8.

56. The method of claim 37, wherein the subject is suspected of having pancreatic cancer.

57. The method of claim 37, wherein the evaluating comprises identifying the biomarker as indicative of pancreatic cancer.

58. The method of claim 37, further comprising administering a pancreatic cancer treatment to the subject when the subject has pancreatic cancer.

59. The method of claim 37, further comprising monitoring the subject when the subject does not have pancreatic cancer.

60. A method for detecting lung cancer, the method comprising: (a) identifying a biomarker from a biological fluid sample of a subject; as well as (b) applying a classifier to the biomarker to assess lung cancer, wherein the classifier distinguishes between biological fluid samples of subjects with and without lung cancer by performance characterized by a receiver operating characteristic (ROC) curve having a mean or median area under the curve (AUC) of at least 0.7, and wherein the biomarkers include any of the following RNAs: BAT2, HLA-DQA1, antisense to AK5, YY2, ENSG00000287219.1, RPL6 pseudogene, ENSG00000223711.1, HASPIN, SLC22A14, AANAT, FSBP, HLF, DYTN, ENSG00000252800.1, novel human transcript from chromosome 12 at positions 49,536,677 to 49,538,894 from the reverse strand of genome build GRCh38, EVL, from gene A novel human transcript from chromosome 4 at positions 40,426,119 to 40,427,585 of the forward chain of genome construct GRCh38, C16orf89, a novel human transcript from chromosome 22 at positions 21,657,811 to 21,661,021 of the forward chain of genome construct GRCh38, or RBFOX1; any of the following lipids: PC(20:3_20:4)+AcO, DAG(18:2_20:2)+NH4, PC(18:2_20:5)+AcO, LPE(18:1)-H, LPE(16:0) -H, TAG(58:6_FA18:0)+NH4, DAG(20:1_20:5)+NH4, PC(14:0_20:2)+AcO, PC(18:2_20:3)+AcO, PE(18:1_22:4)-H, PE(18:0_2 0:1)-H, CER(d18:1 / 26:1)+H, PC(14:0_18:2)+AcO, PE(18:0_22:4)-H, PI(15:0_22:5)-H, PE(P-18:1_18:0)+H, TAG(54:5_FA1 8:3)+NH4, TAG(58:5_FA18:1)+NH4, DAG(20:5_22:4)+NH4 or LPE(20:3)-H; any of the following metabolites: sedoheptulose 1,7-bisphosphate, glucuronate, biopterin, reduced glutathione, N-acetylarginine, cotinine, indole-3-lactic acid, 13C4-ketoglutarate, propionyl-CoA, AICAR, 3-methyl-3-hydroxyglutarate, imidazoleacetic acid, shikimic acid, 1-methyladenosine, dopamine, carnosine, homocitrulline, indolepyruvate, 2-phosphoglycerate or glutaric acid;Any of the following peptides: STVLTIPEIIIK (SEQ ID NO: 12), TLAFPLTIR (SEQ ID NO: 13), LIQGAPTIR (SEQ ID NO: 14), SSGLVSNAPGVQIR (SEQ ID NO: 15), DGSFSVVITGLR (SEQ ID NO: 16), LGPISADSTTAPLEK (SEQ ID NO: 17), SEAACLAAGPGIR (SEQ ID NO: 18), TDTGFLQTLGHNLFGIYQK (SEQ ID NO: 19), LKPEDITQIQPQQLVLR (SEQ ID NO: 20), GLPAPIEK (SEQ ID NO: 21), LLGPGPAADFSVSVER (SEQ ID NO: 22), YEYLEGGDR (SEQ ID NO: 23), HLEDVFSK (SEQ ID NO: 24), ILGPLSYSK (SEQ ID NO: 25), NCQTVLAPCSPNPCENAAVCK (SEQ ID NO: 26). NO:26), TVTATFGYPFR (SEQ ID NO:27), STDTSCVNPPTVQNAHILSR (SEQ ID NO:28), FSLVSGWGQLLDR (SEQ ID NO:29), ELLALIQLER (SEQ ID NO:30) or DAHSVLLSHIFHGR (SEQ ID NO:31) or fragments thereof; Or any of the following peptides: EHAVEGDCDFQLLK (SEQ ID NO:32), SQASSCSLQSSDSVPVGLCK (SEQ ID NO:33), GEFAIDGYSVR (SEQ ID NO:34), ALVEGVDQLFTDYQIK (SEQ ID NO:35), LLPYIVGVAQR (SEQ IDNO:36), HTLNQIDEVK (SEQ ID NO:37), IDILVNNGGMSQR (SEQ ID NO:38), LMMDGHEVTVVDNFFTGR (SEQ ID NO:39), MYGEILSPNYPQAYPSEVEK (SEQ ID NO:40), NNEEWTVDSCTECHCQNSVTICK (SEQ ID NO:41), IDTQDIEASHYR (SEQ ID NO:42), TFIFSDLDYMGMSSGFYK (SEQ ID NO:43), PDAELSASSVYNLLPEK (SEQ ID NO:44), ASIHEAWTDGK (SEQ ID NO:45), LYPWGVVEVENPEHNDFLK (SEQ ID NO:46), YHWEHTGLTLR (SEQ ID NO:47) or IGGAIEEVYVSLGVSVGK (SEQ ID NO:48), or fragments thereof.

61. The method of claim 60, wherein the biomarkers include any of the following RNAs: BAT2, HLA-DQA1, antisense to AK5, YY2, ENSG00000287219.1, RPL6 pseudogene, ENSG00000223711.1, HASPIN, SLC22A14, AANAT, FSBP, HLF, DYTN, ENSG00000252800.1, from genome build GRCh38 a novel human transcript at positions 49,536,677 to 49,538,894 on chromosome 12 of the reverse strand, EVL, a novel human transcript at positions 40,426,119 to 40,427,585 on chromosome 4 from the forward strand of genome build GRCh38, C16orf89, a novel human transcript at positions 21,657,811 to 21,661,021 on chromosome 22 from the forward strand of genome build GRCh38, or RBFOX1.

62. The method of claim 61, wherein the biomarkers comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 of the RNAs.

63. The method of claim 60, wherein the biomarker comprises any one of the following lipids: PC (20:3-20:4) + AcO, DAG (18:2-20:2) + NH4, PC (18:2-20:5) + AcO, LPE (18:1) -H, LPE (16:0) -H, TAG (58:6-FA18:0) + NH4, DAG (20:1-20:5) + NH4, PC (14:0-20:2) + AcO, PC (18:2-20:3) + AcO , PE(18:1_22:4)-H, PE(18:0_20:1)-H, CER(d18:1 / 26:1)+H, PC(14:0_18:2)+AcO, PE(18:0_22:4)-H, PI(15:0_22:5)-H, PE(P-18:1_18:0)+H, TAG(54:5_FA18:3)+NH4, TAG(58:5_FA18:1)+NH4, DAG(20:5_22:4)+NH4 or LPE(20:3)-H.

64. The method of claim 63, wherein the biomarkers comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 of the lipids.

65. The method of claim 60, wherein the biomarker comprises any one of the following metabolites: sedoheptulose 1,7-bisphosphate, glucuronate, biopterin, reduced glutathione, N-acetylarginine, cotinine, indole-3-lactic acid, 13C4-ketoglutarate, propionyl-CoA, AICAR, 3-methyl-3-hydroxyglutarate, imidazoleacetic acid, shikimic acid, 1-methyladenosine, dopamine, carnosine, homocitrulline, indolepyruvate, 2-phosphoglycerate, or glutaric acid.

66. The method of claim 65, wherein the biomarkers comprise wherein the biomarkers comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 of the metabolites.

67. The method of claim 60, wherein the biomarker comprises any one of the following peptides: STVLTIPEIIIK (SEQ ID NO: 12), TLAFPLTIR (SEQ ID NO: 13), LIQGAPTIR (SEQ ID NO: 14), SSGLVSNAPGVQIR (SEQ ID NO: 15), DGSFSVVITGLR (SEQ ID NO: 16), LGPISADSTTAPLEK (SEQ ID NO: 17), SEAACLAAGPGIR (SEQ ID NO: 18), TDTGFLQTLGHNLFGIYQK (SEQ ID NO: 19), LKPEDITQIQPQQLVLR (SEQ ID NO: 20), GLPAPIEK (SEQ ID NO: 21), LLGPGPAADFSVSVER (SEQ ID NO: 22), YEYLEGGDR (SEQ ID NO: 23), HLEDVFSK (SEQ ID NO: 24), ILGPLSYSK (SEQ ID NO: 25), NCQTVLAPCSPNPCENAAVCK (SEQ ID NO: 26). NO:26), TVTATFGYPFR (SEQ ID NO:27), STDTSCVNPPTVQNAHILSR (SEQ ID NO:28), FSLVSGWGQLLDR (SEQ ID NO:29), ELLALIQLER (SEQ ID NO:30) or DAHSVLLSHIFHGR (SEQ ID NO:31), or fragments thereof.

68. The method of claim 67, wherein the biomarkers comprise wherein the biomarkers comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 of the peptides.

69. The method of claim 60, wherein the biomarkers include any of the following peptides: EHAVEGDCDFQLLK (SEQ ID NO:32), SQASSCSLQSSDSVPVGLCK (SEQ ID NO:33), GEFAIDGYSVR (SEQ ID NO:34), ALVEGVDQLFTDYQIK (SEQ ID NO:35), LLPYIVGVAQR (SEQ ID NO:36), HTLNQIDEVK (SEQ ID NO:37), IDILVNNGGMSQR (SEQ ID NO:38), LMMDGHEVTVVDNFFTGR (SEQ ID NO:39), MYGEILSPNYPQAYPSEVEK (SEQ ID NO:40), NNEEWTVDSCTECHCQNSVTICK (SEQ ID NO:41), IDTQDIEASHYR (SEQ ID NO:42), TFIFSDLDYMGMSSGFYK (SEQ ID NO:43), PDAELSASSVYNLLPEK (SEQ ID NO:44), ID NO:44), ASIHEAWTDGK (SEQ ID NO:45), LYPWGVVEVENPEHNDFLK (SEQ ID NO:46), YHWEHTGLTLR (SEQ ID NO:47) or IGGAIEEVYVSLGVSVGK (SEQ ID NO:48), or fragments thereof.

70. The method of claim 69, wherein the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17 of the peptides.

71. The method of claim 60, wherein the classifier comprises performance characterized by a receiver operating characteristic (ROC) curve having a mean or median area under the curve (AUC) of at least 0.

8.

72. The method of claim 60, wherein the subject is suspected of having lung cancer.

73. The method of claim 60, wherein the evaluating comprises identifying the biomarker as indicative of lung cancer.

74. The method of claim 60, further comprising administering a lung cancer treatment to the subject or obtaining a lung nodule biopsy from the subject when the subject has lung cancer.

75. The method of claim 60, further comprising monitoring the subject when the subject does not have lung cancer.

76. The method of claim 60, wherein the lung cancer comprises non-small cell lung cancer (NSCLC).

77. The method of claim 60, wherein the biological fluid sample is obtained from a subject identified as having a lung nodule.

78. The method of claim 60, further comprising identifying the subject as having a lung nodule by performing medical imaging.

79. The method of claim 60, wherein the classifier distinguishes between cancerous and non-cancerous lung nodules.

80. A method for detecting lung cancer, comprising: (a) obtaining a biomarker from a biological fluid sample of a subject; as well as (b) applying a classifier to the biomarker to assess lung cancer, wherein the classifier distinguishes between biological fluid samples of subjects with and without lung cancer by performance characterized by a receiver operating characteristic (ROC) curve having a mean or median area under the curve (AUC) of at least 0.7 and wherein the biomarkers include any of the following mRNA transcripts: ENSG00000155744.10, ENSG00000081052.14, ENSG00000173726.11, ENSG00000143995.20, ENSG00000108528.14, ENSG00000177427.13, ENSG00000163961.4, ENSG00000049130.16 , ENSG00000008405.12, ENSG00000135090.14, ENSG00000151778.11, ENSG00000172116.23, ENSG0000014 4218.21, ENSG00000131196.18, ENSG00000129351.18, ENSG00000105518.14, ENSG00000182162.11, ENSG 00000126368.6, ENSG00000176358.16, ENSG00000112599.9, ENSG00000142864.15, ENSG00000163159.15 , ENSG00000165661.17, ENSG00000165661.18, ENSG00000007923.17, ENSG00000054116.12, ENSG0000011 3811.12, ENSG00000100644.17, ENSG00000133997.12, ENSG00000120925.16, ENSG00000110048.12, ENSG00000197863.9, ENSG00000174307.7 or ENSG00000109381.21; any of the following peptides: LC (UniMod: 4) PSGMYTEYIHSR (SEQ ID NO:49),LCPSGMYTEYIHSR(SEQ ID NO:139),NADLQVLKPEPELVYEDLR(SEQ ID NO:50),ASTPGAAAQIQEVK(SEQ ID NO:51),PYC(UniMod:4)NHPC(UniMod:4)YAAMFGPK(SEQ ID NO:52),PYCNHPCYAAMFGPK(SEQ ID NO:140), QLLQENEVQFLDK (SEQ ID NO:53), AISAFHGSLSSQPAEIITQSK (SEQ ID NO:54), FEGIAC (UniMod:4)EISK (SEQ ID NO:55),FEGIACEISK(SEQ ID NO:141)、FIINDWVK(SEQ ID NO:56)、YVGGQEHFAHLLILRDTK(SEQ IDNO:57)、SVGFHLPSR(SEQ ID NO:58)、GSPMEISLPIALSK(SEQ ID NO:59)、M(UniMod:35)VVSMTLGLHPWIANIDDTQYLAAK(SEQ ID NO:60)、MVVSMTLGLHPWIANIDDTQYLAAK(SEQ ID NO:142)、TVTAM(UniMod:35)DVVYALK(SEQ ID NO:61)、TVTAMDVVYALK(SEQ ID NO:143)、C(UniMod:4)SC(UniMod:4)DPGYELAPDKR(SEQ ID NO:62)、CSCDPGYELAPDKR(SEQ ID NO:144)、GNPTVEVDLHTAK(SEQ ID NO:63)、HLQLAIRNDEELNK(SEQ ID NO:64)、FQDGDLTLYQSNTILR(SEQ ID NO:65)、IRPNDFIPNVI(SEQ ID NO:66)、TKLEEHLEGIVNIFHQYSVRK(SEQ ID NO:67)、GDPEC(UniMod:4)HLFYNEQQEAR(SEQ ID NO:68)、GDPECHLFYNEQQEAR(SEQ ID NO:145)、ALNSIIDVYHK(SEQ ID NO:69)、DDPDAPLQPVTPLQLFEGR(SEQ ID NO:70)、KSEEENLFEIITADEVHYFLQAATPK(SEQ ID NO:71)、FPNGVQLSPAEDFVLVAETTMAR(SEQ ID NO:72)、LYFMHFNLESSYLC(UniMod:4)EYDYVK(SEQ IDNO:73)、LYFMHFNLESSYLCEYDYVK(SEQ ID NO:146)、LFDYC(UniMod:4)DIPLC(UniMod:4)ASSSFDC(UniMod:4)GK(SEQ ID NO:74)、LFDYCDIPLCASSSFDCGK(SEQ ID NO:147)、AEQC(UniMod:4)C(UniMod:4)EETASSISLHGK(SEQ ID NO:75)、AEQCCEETASSISLHGK(SEQ ID NO:148)、VALEGLRPTIPPGISPHVC(UniMod:4)K(SEQ ID NO:76)、VALEGLRPTIPPGISPHVCK(SEQ IDNO:149)、VWEQIDQMK(SEQ ID NO:77)、FTDEEVDELYREAPIDK(SEQ ID NO:78)、DTHFPIC(UniMod:4)IFC(UniMod:4)C(UniMod:4)GC(UniMod:4)C(UniMod:4)HR(SEQ ID NO:79)、DTHFPICIFCCGCCHR(SEQ ID NO:150)、RQDNEILIFWSK(SEQ ID NO:80)、QDNEILIFWSK(SEQ IDNO:81)、EVGTVLSQVYSK(SEQ ID NO:82)、MVTALGTHWHPEHFC(UniMod:4)C(UniMod:4)VSC(UniMod:4)GEPFGDEGFHER(SEQ ID NO:83)、MVTALGTHWHPEHFCCVSCGEPFGDEGFHER(SEQ IDNO:151)、EVTFHC(UniMod:4)HEGYILHGAPK(SEQ ID NO:84)、EVTFHCHEGYILHGAPK(SEQ IDNO:152)、GAGGQSMSEAPTGDHAPAPTR(SEQ ID NO:85)、DGSFSVVITGLR(SEQ ID NO:86)、GISLNPEQWSQLK(SEQ ID NO:87)、LVHVEEPHTETVR(SEQ ID NO:88)、RVEPYGENFNK(SEQ IDNO:89)、LDDC(UniMod:4)GLTEAR(SEQ ID NO:90)、LDDCGLTEAR(SEQ ID NO:153)、LVQAAQMLQSDPYSVPAR(SEQ ID NO:91)、DFLGFYVVDSHR(SEQ ID NO:92)、YGTC(UniMod:4)IYQGR(SEQ ID NO:93)、YGTCIYQGR(SEQ ID NO:154)、WLQEGGQEC(UniMod:4)EC(UniMod:4)K(SEQ ID NO:94)、WLQEGGQECECK(SEQ ID NO:155)、ASGPPVSELITK(SEQ ID NO:95)、ELSDFISYLQR(SEQ ID NO:96)、<h2 style=";text-align:left;direction:ltr">EGHVLQGPSVLK(SEQ ID NO:97)、MNLASEPQEVLHIGSAHNR(SEQ ID NO:98)、FLILPDMLK(SEQ ID NO:99)、GISQEQMNEFR(SEQ ID NO:100)、DPNHFRPAGLPEK(SEQ ID NO:101)、VPSHLQAETLVGK(SEQ ID NO:102)、NLHFLTTQEDYTLK(SEQ ID NO:103)、SEAYNTFSER(SEQ ID NO:104)、AVLDVFEEGTEASAATAVK(SEQ ID NO:105)、VIQYLAYVASSHK(SEQ ID NO:106)、ASYAQQPAESR(SEQ ID NO:107)、YLEESNFVHR(SEQ ID NO:108)、GSFTYFAPSNEAWDNLDSDIR(SEQ ID NO:109)、ALTDMPQM(UniMod:35)R(SEQ ID NO:110)、LAVNM(UniMod:35)VPFPR(SEQ ID NO:111)、TSC(UniMod:4)LLFMGR(SEQ ID NO:112)、QQQHLFGSNVTDC(UniMod:4)SGNFC(UniMod:4)LFR(SEQ ID NO:113)、ALTDMPQMR(SEQ ID NO:156)、LAVNMVPFPR(SEQ ID NO:157)、TSCLLFMGR(SEQ ID NO:158)、QQQHLFGSNVTDCSGNFCLFR(SEQ ID NO:159)、DYVSQFEGSALGK(SEQ ID NO:114)、DSITTWEILAVSMSDK(SEQ ID NO:115)、FC(UniMod:4)NIMGSSNGVDQEHFSNVVK(SEQ ID NO:116)、FCNIMGSSNGVDQEHFSNVVK(SEQ ID NO:160)、SEHPGLSIGDTAK(SEQ ID NO:117)、QFVEQHTPQLLTLVPR(SEQ ID NO:118)、NQDLAPNSAEQASILSLVTK(SEQ ID NO:119)、TDGALLVNAMFFK(SEQ ID NO:120)、DDFEGQLESDRFLLMSGGK(SEQ ID NO:120)、DDFEGQLESDRFLLMSGGK(SEQ ID NO:120) NO:121)、SIQC(UniMod:4)LTVHK(SEQ ID NO:122)、SIQCLTVHK(SEQ ID NO:161),EDITQSAQHALR(SEQ ID NO:123),VVAC(UniMod:4)TSAFLLWDPTK(SEQ ID NO:124),VVACTSAFLLWDPTK(SEQ ID NO:162),NYPMHVFAYR(SEQ IDNO:125),MEEVEAMLLPETLK(SEQ ID NO:126), ADVQAHGEGQEFSITC(UniMod:4)LVDEEEM(UniMod:35)K(SEQ ID NO:127), ADVQAHGEGQEFSITCLVDEEEMK(SEQ ID NO:163), DFALLSLQVPLK(SEQ ID NO:128), LLIYAVLPTGDVIGDSAK(SEQ ID NO:129), VDIVAINDPFIDLNYMVYMFQYDSTHGK (SEQ ID NO:130), AEQINQAAGEASAVLAK (SEQ ID NO:131), TPAYYPNAGLIK(SEQ ID NO:132), QGENGQMM(UniMod:35)SC(UniMod:4)TC(UniMod:4)LGNGK(SEQ ID NO:133), QGENGQMMSCTCLGNGK(SEQ ID NO:164), YWEMQPATFR(SEQ ID NO:134), HGEYWLGNK(SEQ ID NO:135), FVPAEMGTHTVSVK (SEQ ID NO:136), NALGPGLSPELGPLPALR (SEQ ID NO:137) or TKLEEHLEGIVNIFHQYSVR (SEQ ID NO:138; any of the following lipids: 1-palmitoyl-GPE (16:0), phosphatidylcholine (18:0 / 20:2, 20:0 / 18:2), linoleamide (18:2n6), linolenamide (18:3), 2-aminocaprylate, 1-linoleoyl-2-arachidonoyl-GPC (18:2 / 20:4n6), 1-palmitoylglycerol (16:0), 1-oleoyl-G PC (18:1), 1-linolenoyl-GPC (18:3), pregnanolone / isopregnanolone sulfate, sphingomyelin (d18:2 / 24:1, d18:1 / 24:2), myristic acid amide (14:1), 1-linoleoylglycerol (18:2), 11β-hydroxyandrosterone glucuronide, 2S,3R-dihydroxybutyrate, glycosyl-N-behenyl sphingosine (d18:1 / 22:0),1-palmitoyl-2-linoleoyl-GPC (16:0 / 18:2), 1-stearoyl-2-arachidonoyl-GPS (18:0 / 20:4), 1-tetracosyl-GPC (24:0), 3β-hydroxy-5-cholestenol ester, 5α-androstane-3α,17β-diol monosulfate (2), hexadecenoic acid ester (C16:1-DC), myristamide (14:0), 1-stearoyl-GPE (18:0), 1-myristoyl-2-arachidonoyl-GPC (14:0 / 20:4), 1-arachidonoyl-GPC (20:0), 4-hydroxy-2-oxoglutarate, nicotinic acid ester (24:6n3), sphingomyelin (d17:1 / 16:0, d1 8:1 / 15:0, d16:1 / 17:0), 3-hydroxyoctanoate, 1-arachidonic acid glycerol (20:4), 1-stearoyl-2-oleoyl-GPS (18:0 / 18:1), 1-eicosenoyl-GPE (20:1), sphingosine, glycoursodeoxycholic acid sulfate (1), 1-stearoyl-2-linoleoyl-GPC (18:0 / 18:2), erucate (22:1n9), phosphoethanolamine, androstenone glucuronide, behenyl dihydrosphingomyelin (d18:0 / 22:0), androstenediol (3α,17α) monosulfate (2), isoursodeoxycholate, N-stearoyl-sphingosine (d18:1 / 18:0), heptadecanamide (17:0), 1-eicosenoyl or any of the following metabolites: N-acetylcarnosine, indole lactate, lanthionine, 3-(4-hydroxyphenyl) lactate, hydantoin-5-propionate, urea, homoarginine, β-citrolglutamate, S-1-pyrroline-5-carboxylate, aspartic acid, isovalerylcarnitine (C5), creatine, N-acetylglucosamine / N-acetylgalactosamine, galactonic acid, N-acetylneuraminic acid, 3-phosphoglyceric acid, bilirubin (E, Z or Z, E), retinol (vitamin B), creatine, N-acetylglucosamine / N-acetylgalactosamine, galactonic acid, N-acetylneuraminic acid, 3-phosphoglyceric acid, bilirubin (E, Z or Z, E), retinol (vitamin B), creatine, 1-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2-hydroxy-1-hydroxy-2 Vitamin A), heme, nicotinamide, carotene diol (1), bilirubin (Z, Z), 1-methylnicotinamide, α-ketoglutaric acid, xanthine, phenylacetylcarnitine, HWESASXX, 5-acetylamino-6-formylamino-3-methyluracil, 2-keto-3-deoxy-gluconate, iminodiacetate (IDA), 4-acetaminophen sulfate, caffeic acid sulfate, 2-hydroxyacetaminophen sulfate, 3-formyl indole, X-18779, X-24473, X-23593, X-24307, X-24027, X-14939, X-12456, X-25790, X-17146, X-15220, X-12740, X-17765,or any of the following proteins: SVEP1, PIGR, B3AT, CRIP1, FGL1, CASS4, CO9, PAI1, A1AG1, APOB, LKHA4, CNDP1, H4, BMP1, ENOB, H2A2C, S10AC, IBP2, S10A8, CO4B, PLEK, APMAP, APOA, ILK, DYHC1, J3QRS3, HEPC, TGFI1, LTBP2, TCP4, A2MG, APOA4, RINI, DEF1, CXL17, H14, PDIA3, PDLI1, ACTN1, SAA1, DSC1, FA5, AACT, MYH9, PGBM, KSYK, FIBA, TBB1, HEP2, APOA1, CO3, TMOD3, HMGB2, PSPB, ILF2, S ERPH, J3KQ66, WDR1, BLVRB, ITIH1, G3P, STML2, ASPN, LRC47, POSTN-5, SMD3-2, FINC-1, MASP1-2, AMD-3, ILF3-7, VINC-2, ITIH3-2, or FLNA-2. , 81. The method of claim 80, wherein the biomarkers include any of the following mRNA transcripts: ENSG00000155744.10, ENSG00000081052.14, ENSG00000173726.11, ENSG00000143995.20, ENSG00000108528.14, ENSG00000177427.13, ENSG0000016396 1.4, ENSG00000049130.16, ENSG00000008405.12, ENSG00000135090.14, ENSG00000151778.11, ENSG0 0000172116.23, ENSG00000144218.21, ENSG00000131196.18, ENSG00000129351.18, ENSG00000105518 .14, ENSG00000182162.11, ENSG00000126368.6, ENSG00000176358.16, ENSG00000112599.9, ENSG000 00142864.15, ENSG00000163159.15, ENSG00000165661.17, ENSG00000165661.18, ENSG00000007923.1 7. ENSG00000054116.12, ENSG00000113811.12, ENSG00000100644.17, ENSG00000133997.12, ENSG00000120925.16, ENSG00000110048.12, ENSG00000197863.9, ENSG00000174307.7 or ENSG00000109381.

21.

82. The method of claim 81 , wherein the biomarkers comprise 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, or 28 of the mRNA transcripts.

83. The method of claim 80, wherein the biomarker comprises any one of the following peptides: LC(UniMod:4)PSGMYTEYIHSR (SEQ ID NO:49), LCPSGMYTEYIHSR (SEQ ID NO: 139), NADLQVLKPEPELVYEDLR (SEQ ID NO:50), ASTPGAAAQIQEVK (SEQ ID NO:51), PYC(UniMod:4)NHPC(UniMod:4)YAAMFGPK (SEQ ID NO:52), PYCNHPCYAAMFGPK (SEQ ID NO: 140), QLLQENEVQFLDK (SEQ ID NO:53), AISAFHGSLSSSQPAEIITQSK (SEQ ID NO:54), FEGIAC(UniMod:4)EISK (SEQ ID NO:55), FEGIACEISK (SEQ ID NO: 141), FIINDWVK (SEQ ID NO:56), NO: 56), YVGGQEHFAHLLILRDTK (SEQ ID NO: 57), SVGFHLPSR (SEQ ID NO: 58), GSPMEISLPIALSK (SEQ ID NO: 59), M (UniMod: 35) NO:142), TVTAM(UniMod:35)DVVYALK(SEQ ID NO:61), TVTAMDVVYALK(SEQ ID NO:143), C(UniMod:4)SC(UniMod:4)DPGYELAPDKR(SEQ ID NO:62), CSCDPGYELAPDKR(SEQ ID NO:144), GNPTVEVDLHTAK(SEQ ID NO:63), HLQLAIRNDEELNK(SEQ ID NO:64), FQDGDLTLYQSNTILR(SEQ ID NO:65), IRPNDFIPNVI(SEQ ID NO:66), TKLEEHLEGIVNIFHQYSVRK(SEQ ID NO:67), GDPEC(UniMod:4)HLFYNEQQEAR(SEQ ID NO:68), GDPECHLFYNEQQEAR(SEQ ID NO:145), ALNSIIDVYHK (SEQ ID NO:69), DDPDAPLQPVTPLQLFEGR (SEQ IDNO:70)、KSEEENLFEIITADEVHYFLQAATPK(SEQ ID NO:71)、FPNGVQLSPAEDFVLVAETTMAR(SEQ ID NO:72)、LYFMHFNLESSYLC(UniMod:4)EYDYVK(SEQ IDNO:73)、LYFMHFNLESSYLCEYDYVK(SEQ ID NO:146)、LFDYC(UniMod:4)DIPLC(UniMod:4)ASSSFDC(UniMod:4)GK(SEQ ID NO:74)、LFDYCDIPLCASSSFDCGK(SEQ ID NO:147)、AEQC(UniMod:4)C(UniMod:4)EETASSISLHGK(SEQ ID NO:75)、AEQCCEETASSISLHGK(SEQ ID NO:148)、VALEGLRPTIPPGISPHVC(UniMod:4)K(SEQ ID NO:76)、VALEGLRPTIPPGISPHVCK(SEQ IDNO:149)、VWEQIDQMK(SEQ ID NO:77)、FTDEEVDELYREAPIDK(SEQ ID NO:78)、DTHFPIC(UniMod:4)IFC(UniMod:4)C(UniMod:4)GC(UniMod:4)C(UniMod:4)HR(SEQ ID NO:79)、DTHFPICIFCCGCCHR(SEQ ID NO:150)、RQDNEILIFWSK(SEQ ID NO:80)、QDNEILIFWSK(SEQ IDNO:81)、EVGTVLSQVYSK(SEQ ID NO:82)、MVTALGTHWHPEHFC(UniMod:4)C(UniMod:4)VSC(UniMod:4)GEPFGDEGFHER(SEQ ID NO:83)、MVTALGTHWHPEHFCCVSCGEPFGDEGFHER(SEQ IDNO:151)、EVTFHC(UniMod:4)HEGYILHGAPK(SEQ ID NO:84)、EVTFHCHEGYILHGAPK(SEQ IDNO:152)、GAGGQSMSEAPTGDHAPAPTR(SEQ ID NO:85)、DGSFSVVITGLR(SEQ ID NO:86)、GISLNPEQWSQLK(SEQ ID NO:87)、LVHVEEPHTETVR(SEQ IDNO:88)、RVEPYGENFNK(SEQ IDNO:89)、LDDC(UniMod:4)GLTEAR(SEQ ID NO:90)、LDDCGLTEAR(SEQ ID NO:153)、LVQAAQMLQSDPYSVPAR(SEQ ID NO:91)、DFLGFYVVDSHR(SEQ ID NO:92)、YGTC(UniMod:4)IYQGR(SEQ ID NO:93)、YGTCIYQGR(SEQ ID NO:154)、WLQEGGQEC(UniMod:4)EC(UniMod:4)K(SEQ ID NO:94)、WLQEGGQECECK(SEQ ID NO:155)、ASGPPVSELITK(SEQ ID NO:95)、ELSDFISYLQR(SEQ ID NO:96)、EGHVLQGPSVLK(SEQ ID NO:97)、MNLASEPQEVLHIGSAHNR(SEQID NO:98)、FLILPDMLK(SEQ ID NO:99)、GISQEQMNEFR(SEQ ID NO:100)、DPNHFRPAGLPEK(SEQ ID NO:101)、VPSHLQAETLVGK(SEQ ID NO:102)、NLHFLTTQEDYTLK(SEQ ID NO:103)、SEAYNTFSER(SEQ ID NO:104)、AVLDVFEEGTEASAATAVK(SEQ ID NO:105)、VIQYLAYVASSHK(SEQ ID NO:106)、ASYAQQPAESR(SEQ ID NO:107)、YLEESNFVHR(SEQ ID NO:108)、GSFTYFAPSNEAWDNLDSDIR(SEQ ID NO:109)、ALTDMPQM(UniMod:35)R(SEQ ID NO:110)、LAVNM(UniMod:35)VPFPR(SEQ ID NO:111)、TSC(UniMod:4)LLFMGR(SEQ ID NO:112)、QQQHLFGSNVTDC(UniMod:4)SGNFC(UniMod:4)LFR(SEQ ID NO:113)、ALTDMPQMR(SEQ ID NO:156)、LAVNMVPFPR(SEQ ID NO:157)、TSCLLFMGR(SEQ ID NO:158)、QQQHLFGSNVTDCSGNFCLFR(SEQ IDNO:159)、DYVSQFEGSALGK(SEQ ID NO:114)、DSITTWEILAVSMSDK(SEQ ID NO:115)、FC(UniMod:4)NIMGSSNGVDQEHFSNVVK(SEQ ID NO:116)、FCNIMGSSNGVDQEHFSNVVK(SEQ IDNO:160)、SEHPGLSIGDTAK(SEQ ID NO:117)、QFVEQHTPQLLTLVPR(SEQ ID NO:118)、NQDLAPNSAEQASILSLVTK(SEQ ID NO:119)、TDGALLVNAMFFK(SEQ ID NO:120)、DDFEGQLESDRFLLMSGGK(SEQ ID NO:121)、SIQC(UniMod:4)LTVHK(SEQ ID NO:122)、SIQCLTVHK(SEQ ID NO:161)、EDITQSAQHALR(SEQ ID NO:123)、VVAC(UniMod:4)TSAFLLWDPTK(SEQ ID NO:124)、VVACTSAFLLWDPTK(SEQ ID NO:162)、NYPMHVFAYR(SEQ IDNO:125)、MEEVEAMLLPETLK(SEQ ID NO:126)、ADVQAHGEGQEFSITC(UniMod:4)LVDEEEM(UniMod:35)K(SEQ ID NO:127)、ADVQAHGEGQEFSITCLVDEEEMK(SEQ ID NO:163)、DFALLSLQVPLK(SEQ ID NO:128)、LLIYAVLPTGDVIGDSAK(SEQ ID NO:129)、VDIVAINDPFIDLNYMVYMFQYDSTHGK(SEQ ID NO:130)、AEQINQAAGEASAVLAK(SEQ ID NO:131)、TPAYYPNAGLIK(SEQ ID NO:132)、QGENGQMM(UniMod:35)SC(UniMod:4)TC(UniMod:4)LGNGK(SEQ ID NO:133)、QGENGQMMSCTCLGNGK(SEQ ID NO:164)、YWEMQPATFR(SEQ ID NO:134)、HGEYWLGNK(SEQ ID NO:135)、FVPAEMGTHTVSVK(SEQ ID NO:136)、NALGPGLSPELGPLPALR(SEQIDNO: 137) or TKLEEHLEGIVNIFHQYSVR (SEQ ID NO: 138).

84. The method of claim 83, wherein the biomarkers comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, or 28 of the peptides.

85. The method of claim 80, wherein the biomarkers include any of the following lipids: 1-palmitoyl-GPE (16:0), phosphatidylcholine (18:0 / 20:2, 20:0 / 18:2), linoleamide (18:2n6), linolenamide (18:3), 2-aminocaprylate, 1-linoleoyl-2-arachidonoyl-GPC (18:2 / 20:4n6), 1-palmitoylglycerol (16:0), 1-oleoyl-GPC (18:1), 1-linolenoyl-GPC (18:3), pregnanolone / isopregnanolone sulfate, sphingomyelin (d18:2 / 24:1, d18:1 / 24:2), myristic acid amide (14:1), 1-linoleoylglycerol (18:2), 11β-hydroxyandrosterone glucuronide, 2S,3R-dihydroxybutyrate, glycosyl-N-behenyl sphingosine (d18:1 / 22:0), 1-palmitoyl-2-linoleoyl-GPC (16:0 / 18:2), 1-stearoyl-2-arachidonoyl-GPS (18:0 / 20:4), 1-lignosyl-GPC (24:0), 3β-hydroxy-5-cholestenol ester, 5α-androstane-3α,17β-diol monosulfate (2), hexadecenedioate (C16:1-DC), myristic acid amide (1 4:0), 1-stearoyl-GPE (18:0), 1-myristoyl-2-arachidonoyl-GPC (14:0 / 20:4), 1-arachidonoyl-GPC (20:0), 4-hydroxy-2-oxoglutarate, nicotinic acid ester (24:6n3), sphingomyelin (d17:1 / 16:0, d18:1 / 15:0, d16:1 / 17:0), 3-hydroxyoctanoate, 1-arachidonylglycerol (20:4), 1-stearoyl-2-oleoyl-GPS (18:0 / 18:1), 1-eicosenoyl-GPE (20:1), sphingosine, glycoursodeoxycholic acid sulfate (1), 1-stearoyl yl-2-linoleoyl-GPC (18:0 / 18:2), erucate (22:1n9), phosphoethanolamine, androstenone glucuronide, behenyl dihydrosphingomyelin (d18:0 / 22:0), androstenediol (3α,17α) monosulfate (2), isoursodeoxycholate, N-stearoyl sphingosine (d18:1 / 18:0), heptadecanamide (17:0), 1-eicosenoyl-GPC (20:1), tetrahydrocortisone glucuronide (5), linoleoylcarnitine (C18:2), hydroxypalmitoyl sphingomyelin (d18:1 / 16:0(OH)), or 1-eicosapentaenoyl-GPC (20:5).

86. The method of claim 85, wherein the biomarkers comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, or 28 of the lipids.

87. The method of claim 80, wherein the biomarker comprises any one of the following metabolites: N-acetylcarnosine, indole lactate, lanthionine, 3-(4-hydroxyphenyl) lactate, hydantoin-5-propionate, urea, homoarginine, β-citrolglutamate, S-1-pyrroline-5-carboxylate, aspartic acid, isovalerylcarnitine (C5), creatine, N-acetylglucosamine / N-acetylgalactosamine, galactonic acid, N-acetylneuraminic acid, 3-phosphoglyceric acid, bilirubin (E, Z or Z, E), retinol (vitamin A), heme, niacinamide, carotene glycol (1), bilirubin (Z, Z), 1-methylnicotinamide, α-ketoglutarate, xanthine azine, phenylacetylcarnitine, HWESASXX, 5-acetamido-6-formylamino-3-methyluracil, 2-keto-3-deoxygluconate, iminodiacetate (IDA), 4-acetaminophen sulfate, caffeic acid sulfate, 2-hydroxyacetaminophen sulfate, 3-formyl indole, X-18779, X-24473, X-23593, X-24307, X-24027, X-14939, X-12456, X-25790, X-17146, X-15220, X-12740, X-17765, X-25420, X-23639, X-12462, X-15728 or X-25422.

88. The method of claim 87, wherein the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, or 28 of the metabolites.

89. The method of claim 80, wherein the biomarkers include any one of the following proteins: SVEP1_human, PIGR_human, B3AT_human, CRIP1_human, FGL1_human, CASS4_human, CO9_human, PAI1_human, A1AG1_human, APOB_human, LKHA4_human, CNDP1_human, H4_human, BMP1_human, ENOB_human, H2A2C_human, S10AC_human, IBP2_human, S10A8_human, CO4B_human, PLEK_human, APMAP_human, APOA_human, ILK_human, DYHC1_human, J3QRS3_human, HEPC_human, TGFI1_human, LTBP2_human, TCP4_human, A2MG_human, APOA4_human, RINI_human, DEF1_human, CXL17_human , H14_human, PDIA3_human, PDLI1_human, ACTN1_human, SAA1_human, DSC1_human, FA5_human, AACT_human, MYH9_human, PGBM_human, KSYK_human, FIBA_human, TBB1_human, HEP2_human, APOA1_human, CO3_human, TMOD3_human, HMGB2_human, PSPB_human, ILF2_human, SERPH_human, J3KQ66_human, WDR1_human, BLVRB_human, ITIH1_human, G3P_human, STML2_human, ASPN_human, LRC47_human, POSTN-5_human, SMD3-2_human, FINC-1_human, MASP1-2_human, AMD-3_human, ILF3-7_human, VINC-2_human, ITIH3-2_human or FLNA-2_human.

90. The method of claim 87, wherein the biomarkers include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, or 28 of the proteins.

91. The method of claim 80, wherein the classifier comprises performance characterized by a receiver operating characteristic (ROC) curve having a mean or median area under the curve (AUC) of at least 0.

8.

92. The method of claim 80, wherein the subject is suspected of having lung cancer.

93. The method of claim 80, wherein the evaluating comprises identifying the biomarker as indicative of lung cancer.

94. The method of claim 80, further comprising administering a lung cancer treatment to the subject when the subject has lung cancer.

95. The method of claim 80, further comprising monitoring the subject when the subject does not have lung cancer.

96. The method of claim 80, wherein the lung cancer comprises non-small cell lung cancer.

97. The method of claim 80, wherein the lung cancer comprises stage 1, stage 2, or stage 3 lung cancer.

98. The method of claim 80, wherein the lung cancer comprises stage 4 lung cancer.

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