Dynamic protein signature to predict non-response to immune checkpoint inhibitor therapy

Abstract

The present disclosure provides methods of identifying a subject with cancer that will not respond or will likely not respond to a treatment (e.g., immune checkpoint inhibitor monotherapy), wherein the cancer has an altered (e.g., increased or decreased) expression level of a panel of biomarkers, and methods of using the same to provide additional cancer treatment to the subject with cancer.

Description

[0001]Attorney Docket: AHY-00325 DYNAMIC PROTEIN SIGNATURE TO PREDICT NON-RESPONSE TO IMMUNE CHECKPOINT INHIBITOR THERAPY RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application Serial No.63 / 536,127 filed September 1, 2023. The entire contents of which are incorporated herein by this reference. BACKGROUND Although advances have been made in the types and number of cancer treatments, many patients fail to respond to such treatments. Clinical readouts to determine whether a patient is responsive to a treatment may take at least 6 months. During this time, the patient may suffer unnecessary financial, social, emotional, and physical hardship should they not respond. Further, awaiting determination of response to a cancer treatment delays the administration of additional treatments that the patient may benefit from to treat the cancer. SUMMARY In some aspects, the disclosure provides a method of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject at two or more different time points, wherein the first time point is prior to administration of an immune checkpoint inhibitor monotherapy, and at least one subsequent time point is from about 3 weeks to about 6 months after administration of the immune checkpoint inhibitor, wherein the panel of biomarkers comprises one or more of CA6, CDNF, MIA, MYOC, NEFL, and TCL1B; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the immune checkpoint inhibitor monotherapy; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some aspects, the disclosure provides a method of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: Attorney Docket: AHY-00325 (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject at two or more different time points, wherein the first time point is prior to administration of an immune checkpoint inhibitor monotherapy, and at least one subsequent time point is from about 3 weeks to about 6 months after administration of the immune checkpoint inhibitor, wherein the panel of biomarkers comprises one or more of CA6, CDNF, MIA, MYOC, NEFL, and TCL1B; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates the subject is unlikely to respond to the immune checkpoint inhibitor monotherapy; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some embodiments, the one or more additional therapies are selected from surgery, radiotherapy, chemotherapy, immunotherapy, endocrine therapy, cryotherapy, and corticosteroids. In some aspects, the disclosure provides a method for identifying a subject with cancer likely to not respond to an immune checkpoint inhibitor monotherapy administered to the subject, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject at two or more different time points, wherein the first time point is prior to administration of an immune checkpoint inhibitor monotherapy, and the at least one subsequent time point is from about 3 weeks to about 6 months after administration of the immune checkpoint inhibitor, the panel of biomarkers comprises one or more of CA6, CDNF, MIA, MYOC, NEFL, and TCL1B; (ii) comparing the amount or level of the panel of biological markers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the immune checkpoint inhibitor monotherapy, thereby identifying a subject likely not to respond to the immune checkpoint inhibitor monotherapy. In some aspects, the disclosure provides a method for identifying a subject with cancer likely to not respond to an immune checkpoint inhibitor monotherapy administered to the subject, comprising: Attorney Docket: AHY-00325 (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject at two or more different time points, wherein the first time point is prior to administration of an immune checkpoint inhibitor monotherapy, and the at least one subsequent time point is from about 3 weeks to about 6 months after administration of the immune checkpoint inhibitor, the panel of biomarkers comprises one or more of CA6, CDNF, MIA, MYOC, NEFL, and TCL1B; (ii) comparing the amount or level of the panel of biological markers from the two or more different time points to determine a non-response score, wherein the non-response score indicates the subject is unlikely to respond to the immune checkpoint inhibitor monotherapy, thereby identifying a subject likely not to respond to the immune checkpoint inhibitor monotherapy. In some embodiments, the immune checkpoint inhibitor monotherapy targets PD-1, PD- L1, CTLA4, LAG3, or any combination thereof. In some embodiments, the immune checkpoint inhibitor monotherapy is an antibody. In some embodiments, the antibody is ipilimumab, nivolumab, relatlimab, pembrolizumab, atezolizumab, durvalumab, avelumab, cemiplimab, or dostarlimab. In some embodiments, the panel of biomarkers comprises CA6, CDNF, MIA, MYOC, NEFL, and TCL1B. In some embodiments, the panel of biomarkers comprises one or more of ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS. In some embodiments, the panel of biomarkers comprises at least six of ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS. In Attorney Docket: AHY-00325 some embodiments, the panel of biomarkers comprises one or more of ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN. In some embodiments, the panel of biomarkers comprises at least six of ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN. In some embodiments, the panel of biomarkers comprises one or more of AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS. In some embodiments, the panel of biomarkers comprises at least six of AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS. In some embodiments, the panel of biomarkers comprises one or more of ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS. In some embodiments, the panel of biomarkers comprises at least six of ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS. In some embodiments, the panel of biomarkers comprises proteins. In some embodiments, (i) comprises detecting the panel of biomarkers in the biological sample by enzyme-linked immunosorbent assay (ELISA) or proximity extension assay (PEA). In some embodiments, the panel of biomarkers comprises nucleic acid molecules. In some embodiments, (i) comprises detecting the panel of biomarkers in the biological sample by a nucleic acid hybridization assay, a nucleic acid amplification assay, or sequencing. Attorney Docket: AHY-00325 In some embodiments, the biological sample is a blood sample, a serum sample, or a plasma sample. In some embodiments, the biological sample is a plasma sample. In some embodiments, the at least one subsequent time point is before clinical readout. In some embodiments, the panel of biomarkers is determined from at least two subsequent time points. In some embodiments, the amount or level of the panel of biomarkers is increased between the first time point and the at least one subsequent time point. In some embodiments, the increased amount or level of the panel of biomarkers between the first time point and the at least one subsequent time point is statistically significant. In some embodiments, the amount or level of the panel of biomarkers is increased by at least 1.5-fold, 2-fold, 5-fold, 10-fold, 15-fold, or 20- fold. In some embodiments, the amount or level of the panel of biomarkers is increased by at least 1.5-fold. In some embodiments, the diagnostic accuracy is at least 0.8. In some embodiments, the non-response score indicated the subject will not respond to the immune checkpoint inhibitory therapy. In some embodiments, the one or more additional therapies improves the subject’s response to the immune checkpoint inhibitor monotherapy. In some aspects, the disclosure provides a kit suitable for performing methods of some or any of the foregoing or related embodiments, comprising (i) one or more reagents for detecting the amount or level of the panel of biomarkers, and (ii) instructions for detecting the amount or level of the panel of biomarkers in a biological sample from a subject obtained from two or more different time points. In some embodiments, the instructions comprise steps for identifying the subject as not likely to respond to an immune checkpoint inhibitor monotherapy. In some embodiments, the biological sample is a blood sample, a serum sample, or a plasma sample. BRIEF DESCRIPTION OF THE DRAWINGS FIGs.1A-1B shows protein signatures of non-responders (NR) at baseline (pre-treatment with one or more immune checkpoint inhibitors (anti-PD-1 or a combination of anti-PD-1 with anti-CTLA-4)). FIG.1A provides a graph showing an ROC curve plotting sensitivity versus specificity for predicting NR. AUC = 0.59. FIG. 1B provides a graph showing the plasma proteins identified using Olink proteomic analysis that comprise the predictive model rank Attorney Docket: AHY-00325 ordered based on their selection frequency in cross validation of the predictive model. ROC = receiver operating characteristic; AUC = area under the ROC curve. FIGs.2A-2B shows dynamic protein signatures of non-responders (NR) at baseline and 6 weeks post-treatment initiation with one or more immune checkpoint inhibitors (anti-PD-1 or a combination of anti-PD-1 with anti-CTLA-4). FIG.2A provides a graph showing a ROC curve plotting sensitivity versus specificity for predicting NR. AUC = 0.71. FIG. 2B provides a graph showing the plasma proteins identified using Olink proteomic analysis that comprise the predictive model rank ordered based on their selection frequency in cross validation of the predictive model. ROC = receiver operating characteristic; AUC = area under the ROC curve. FIGs.3A-3B shows dynamic protein signatures of non-responders (NR) at baseline and 6 months post-treatment initiation with one or more immune checkpoint inhibitors (anti-PD-1 or a combination of anti-PD-1 with anti-CTLA-4). FIG.3A provides a graph showing a ROC curve plotting sensitivity versus specificity for predicting NR. AUC = 0.76. FIG. 3B provides a graph showing the plasma proteins identified using Olink proteomic analysis that comprise the predictive model rank ordered based on their selection frequency in cross validation of the predictive model. ROC = receiver operating characteristic; AUC = area under the ROC curve. FIGs.4A-4B shows dynamic protein signatures of non-responders (NR) at baseline, 6 weeks, and 6-months post-treatment initiation with one or more immune checkpoint inhibitors (anti-PD-1 or a combination of anti-PD-1 with anti-CTLA-4). FIG. 4A provides a graph showing a ROC curve plotting sensitivity versus specificity for predicting NR. AUC = 0.80. FIG.4B provides a graph showing the plasma proteins identified using Olink proteomic analysis that comprise the predictive model rank ordered based on their selection frequency in cross validation of the predictive model. ROC = receiver operating characteristic; AUC = area under the ROC curve. FIGs.5A-5B shows dynamic protein signatures of non-responders (NR) at 6 weeks and 6-months post-treatment initiation with one or more immune checkpoint inhibitors (anti-PD-1 or a combination of anti-PD-1 with anti-CTLA-4). FIG. 5A provides a graph showing a ROC curve plotting sensitivity versus specificity for predicting NR. AUC = 0.74. FIG. 5B provides a graph showing the plasma proteins identified using Olink proteomic analysis that comprise the predictive model rank ordered based on their selection frequency in cross validation of the predictive model. ROC = receiver operating characteristic; AUC = area under the ROC curve. Attorney Docket: AHY-00325 DETAILED DESCRIPTION Overview In some aspects, the disclosure is based, at least in part, on the discovery of a dynamic signature for predicting a subject with cancer is not likely to respond to an immune checkpoint inhibitor (ICI) monotherapy with a diagnostic accuracy of at least 0.7. As demonstrated herein, biological samples from a population of subjects with cancer and treated with ICI monotherapy were analyzed at different time points pre- and post-treatment with ICI monotherapy. Subjects that were identified by clinical read-out as non-responders were found to express a panel of biomarkers that changed over time. As shown herein, the panel of biomarkers had a higher predictive value of non-response to ICI monotherapy when evaluated at multiple time points compared to evaluation at a single time point. Specifically, when the panel of biomarkers was evaluated at baseline (i.e., pre-treatment), 6-weeks post treatment and 6-months post treatment, an AUC of 0.80 was identified. In contrast, evaluating the panel of biomarkers at only baseline provided an AUC of 0.59. Accordingly, without wishing to be bound by theory, the amount or level of the panel of biomarkers determined over time predicts whether a subject will respond to ICI monotherapy. It is believed the dynamic signature described herein predicts whether a subject is not likely to respond to ICI monotherapy before clinical readout, thus providing improved patient care. In some aspects, the present disclosure provides methods for identifying a subject with cancer likely to not respond to an immune checkpoint inhibitor (ICI) monotherapy. In some aspects, the disclosure provides methods for providing one or more additional therapies to a subject with cancer to treat cancer in the subject. In some embodiments, the methods described herein comprise providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points. In some embodiments, the biomarkers are blood-based biomarkers. In some embodiments, the first time point is prior to administration of an immune checkpoint inhibitor monotherapy, and the one or more subsequent time points is from about 3 weeks to about 6 months after administration of the immune checkpoint inhibitor. In some embodiments, the panel of biomarkers comprises one or more of CA6, CDNF, MIA, MYOC, NEFL, and TCL1B. In some embodiments, the methods described herein comprise comparing the amount or level of the panel of biomarkers from the two or more Attorney Docket: AHY-00325 time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that a subject is not likely to respond to the immune checkpoint inhibitor monotherapy. In some embodiments, the methods described herein comprise administering one or more additional therapies to a subject with cancer determined to have a non-response score, thereby treating cancer in the subject. Non-Responder Dynamic Signatures In some aspects, the present disclosure provides a dynamic signature for identifying a subject with cancer not likely to respond to an immune checkpoint inhibitor monotherapy. As used herein, a “dynamic signature” refers to a panel of one or more biomarkers that change levels or amounts over time from pre-treatment to post-treatment with a therapeutic (e.g., an immune checkpoint inhibitor monotherapy). A dynamic signature can be used, for example, to predict non-response to existing standard of care (SOC), or to predict biological mechanisms and specific target(s) that are responsible for the lack of response to SOC. As used herein, the term “responsiveness” refers to the degree to which a diseased tissue (e.g., a tumor) in a subject undergoes a desirable therapeutic change upon exposure to a therapeutic intervention (e.g., ICI monotherapy). In some embodiments, the dynamic signature is based on the amount or level of the panel of biomarkers determined at two more time points. In some embodiments, the two or more time points includes a time point pre-treatment and at least one subsequent time point post-treatment. Panel of Biomarkers In some embodiments, the dynamic signature is based on an amount or level of a panel of biomarkers determined from two or more time points. In some embodiments, the disclosure provides a panel of biomarkers having altered (e.g., increased or decreased) expression level and / or activity in one or more subjects having cancer and treated with an ICI monotherapy. As used herein, a “biomarker” refers to a gene, or a transcriptional or translational product thereof, whose expression level and / or activity can be detected in a biological sample obtained from a subject having a disease or disorder (e.g., cancer), wherein an altered (e.g., increased or decreased) expression level and / or activity of the biomarker functions as an Attorney Docket: AHY-00325 indicator (e.g., diagnostic, predictive, and / or prognostic indicator). In some embodiments, the biomarker is a predictive indicator, wherein an altered expression level and / or activity of the biomarker indicates responsiveness of the disease to a particular therapeutic intervention. In some embodiments, the biomarker is a prognostic indicator, wherein an altered expression level and / or activity of the biomarker indicates an outcome of the disease or disease progression regardless of therapeutic intervention. In some embodiments, the biomarker is a predictive or prognostic indicator when the expression level and / or activity of the biomarker is determined at two or more time points. In some embodiments, the amount or level of the biomarker is compared to a reference sample. As used herein, a “reference sample,” “reference cell”, “reference tissue”, “control sample”, “control cell,” or “control tissue” each refer to a sample, cell, tissue, standard, or level that is used for comparison to establish whether the amount or level of the biomarker in a subject is altered. In some embodiments, the reference is the amount or level of the biomarker in the subject before administration of a therapeutic intervention (e.g., ICI monotherapy). In some embodiments, the reference is a pre-determined amount or level of the biomarker. In some embodiments, the panel of biomarkers comprises one or more biomarkers selected from: ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS. In some embodiments, the panel of biomarkers comprises 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, or 68 biomarkers selected from: ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, Attorney Docket: AHY-00325 PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS. In some embodiments, the panel of biomarkers comprises 2-30, 5-25, 10-20, 15-30, 20- 40, 30-60, 30-50, or 40-68 biomarkers selected from: ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS. In some embodiments, the panel of biomarkers comprises ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS. In some embodiments, the panel of biomarkers comprises ADAM22. In some embodiments, the panel of biomarkers comprises ADAMTS8. In some embodiments, the panel of biomarkers comprises AMTS8. In some embodiments, the panel of biomarkers comprises ANGPT2. In some embodiments, the panel of biomarkers comprises is AOC1. In some embodiments, the panel of biomarkers comprises is BCL2L11. In some embodiments, the panel of biomarkers comprises is BMP4. In some embodiments, the panel of biomarkers comprises is BRK1. In some embodiments, the panel of biomarkers comprises CA6. In some embodiments, a biomarker of the disclosure is CCL13. In some embodiments, the panel of biomarkers comprises CCL25. In some embodiments, the panel of biomarkers comprises CD14. In some embodiments, the panel of biomarkers comprises CD34. In some embodiments, the panel of biomarkers comprises CDH17. In some embodiments, the panel of biomarkers comprises is CDNF. In some embodiments, the panel of biomarkers comprises CERT. In some embodiments, the panel of biomarkers comprises CES3. In some embodiments, the panel of biomarkers comprises CLEC4A. In some embodiments, a biomarker of the disclosure is CPVL. In some embodiments, Attorney Docket: AHY-00325 the panel of biomarkers comprises CSF3. In some embodiments, the panel of biomarkers comprises CTSF. In some embodiments, the panel of biomarkers comprises CTSL. In some embodiments, the panel of biomarkers comprises DKK4. In some embodiments, the panel of biomarkers comprises ECE1. In some embodiments, the panel of biomarkers comprises ENG. In some embodiments, the panel of biomarkers comprises FBP1. In some embodiments, the panel of biomarkers comprises FRZB. In some embodiments, the panel of biomarkers comprises GBP2. In some embodiments, the panel of biomarkers comprises GFBP2. In some embodiments, the panel of biomarkers comprises GLT8D2. In some embodiments, the panel of biomarkers comprises GPR37. In some embodiments, the panel of biomarkers comprises HGF. In some embodiments, the panel of biomarkers comprises HMBS. In some embodiments, the panel of biomarkers comprises IGFBP2. In some embodiments, the panel of biomarkers comprises IL5. In some embodiments, a biomarker of the disclosure is IL6. In some embodiments, the panel of biomarkers comprises ITGB6. In some embodiments, the panel of biomarkers comprises ITM2A. In some embodiments the panel of biomarkers comprises KRT5. In some embodiments, the panel of biomarkers comprises LILRB4. In some embodiments, the panel of biomarkers comprises MIA. In some embodiments, the panel of biomarkers comprises MMP13. In some embodiments, the panel of biomarkers comprises MMP3. In some embodiments, the panel of biomarkers comprises MMP8. In some embodiments, the panel of biomarkers comprises MYOC. In some embodiments, the panel of biomarkers comprises NEFL. In some embodiments, the panel of biomarkers comprises NID1. In some embodiments the panel of biomarkers comprises NOS3. In some embodiments, the panel of biomarkers comprises NRP1. In some embodiments, the panel of biomarkers comprises PAEP. In some embodiments, the panel of biomarkers comprises PAPPA. In some embodiments, the panel of biomarkers comprises PRTG. In some embodiments, the panel of biomarkers comprises PSPN. In some embodiments, the panel of biomarkers comprises PTGDS. In some embodiments, the panel of biomarkers comprises SFTPD. In some embodiments, the panel of biomarkers comprises SMOC1. In some embodiments, the panel of biomarkers comprises TCL1A. In some embodiments, the panel of biomarkers comprises TCL1B. In some embodiments, the panel of biomarkers comprises TCN2. In some embodiments, the panel of biomarkers comprises TDGF1. In some embodiments, the panel of biomarkers comprises TFPI. In some embodiments, the panel of biomarkers comprises TGREM2. In some embodiments, the panel of biomarkers comprises TINAGL1. In some Attorney Docket: AHY-00325 embodiments, the panel of biomarkers comprises TNC. In some embodiments, the panel of biomarkers comprises TNFRSF10B. In some embodiments, the panel of biomarkers comprises TNFSF14. In some embodiments, the panel of biomarkers comprises VASN. In some embodiments, the panel of biomarkers comprises WARS. In some embodiments, the panel of biomarkers comprises one or more biomarkers selected from: CA6, CDNF, MIA, MYOC, NEFL, and TCL1B. In some embodiments, the panel of biomarkers comprises 1, 2, 3, 4, 5, or 6 biomarkers selected from: CA6, CDNF, MIA, MYOC, NEFL, and TCL1B. In some embodiments, the panel of biomarkers comprises 2-5, 2-6, or 3-6 biomarkers selected from: CA6, CDNF, MIA, MYOC, NEFL, and TCL1B. In some embodiments, the panel of biomarkers comprises CA6, CDNF, MIA, MYOC, NEFL, and TCL1B. In some embodiments, the panel of biomarkers comprises one or more biomarkers selected from: ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN. In some embodiments, the panel of biomarkers comprises 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 31 biomarkers selected from: ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN. In some embodiments, the panel of biomarkers comprises 2-30, 5-25, 10-20, 15-30, 20- 31, or 10-31 biomarkers selected from: ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN. In some embodiments, the panel of biomarkers comprises ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN. Attorney Docket: AHY-00325 In some embodiments, the panel of biomarkers comprises one or more biomarkers selected from: AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS. In some embodiments, the panel of biomarkers comprises 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, or 38 biomarkers selected from: AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS. In some embodiments, the panel of biomarkers comprises 2-30, 5-25, 10-20, 15-30, 20- 38, 30-38, or 10-30 biomarkers selected from: AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS. In some embodiments, the panel of biomarkers comprises AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS. In some embodiments, the panel of biomarkers comprises one or more biomarkers selected from: ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS. In some embodiments, the panel of biomarkers comprises 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, or 34 biomarkers selected from: ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS. Attorney Docket: AHY-00325 In some embodiments, the panel of biomarkers comprises 2-30, 5-25, 10-20, 15-30, 20- 30, 25-34 biomarkers selected from: ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS. In some embodiments, the panel of biomarkers comprises ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS. Detection of Biomarkers In some aspects, the disclosure provides methods of detecting one or more biomarkers in a biological sample from a subject. In some embodiments, a biomarker is a protein. In some embodiments, a biomarker is a nucleic acid molecule encoding a protein (e.g., DNA or RNA). In some embodiments, the panel of biomarkers comprises one or more proteins. In some embodiments, the panel of biomarkers comprises one or more nucleic acid molecules. In some embodiments, the panel of biomarkers comprises a combination of proteins and nucleic acid molecules. Methods for detecting proteins and nucleic acid molecules encoding proteins are known to those of skill in the art and described herein. Detection of Protein In some embodiments, the present disclosure provides methods for detecting protein in a biological sample. Methods for detecting proteins and fragments thereof are known to those of skill in the art. Exemplary methods include enzyme-linked immunosorbent assay (ELISA), western blot, proximity extension assay (PEA), mass-spectrometry and immunobead-based formats. In some embodiments, wherein more than one protein biomarker is detected, the same protein detection method is utilized. In some embodiments, wherein more than one protein biomarker is detected, different protein detection methods are utilized. For example, in some embodiments, a first protein biomarker is detected by a first ELISA and a second protein biomarker is detected by a second ELISA. In some embodiments, wherein more than one protein Attorney Docket: AHY-00325 biomarker is detected, the proteins are detected simultaneously. In some embodiments, wherein more than one protein biomarker is detected, the proteins are detected in a multiplex format. For example, in some embodiments, first and second protein biomarkers are detected in the same assay. In some embodiments, one or more protein biomarkers are detected with an antibody. Antibodies include any type of antibody, including antibodies that specifically bind unmodified proteins, glycosylated protein variants, or other post-translationally modified proteins. In some embodiments, these antibodies are used in protein detection methods. Methods for generating antibodies suitable for binding a protein of interest are known those of skill in the art and described herein. In some embodiments, antibodies for detecting one or more protein biomarkers are commercially available. Exemplary immunodetection methods include radioimmunoassay (RIA), ELISA, fluoroimmunoassay, immunoradiometric assay, immunobead-based formats, chemiluminescent assay, and bioluminescent assay. In some embodiments, a method suitable for detecting the presence or amount of one or more protein biomarkers is a proximity extension assay (PEA). In this assay, a pair of antibodies linked to unique oligonucleotides (proximity probes) binds to a protein target. Based on this binding, the probes come in close proximity and hybridize to each other. The method further comprises adding a DNA polymerase to extend the hybridizing oligo and create a DNA amplicon that can subsequently be detected and quantified by quantitative real-time PCR or next generation sequence (NGS). In some embodiments, a method suitable for detecting the presence or amount of one or more protein biomarkers is an ELISA. In this method, one or more antibodies specific for the one or more protein biomarkers are immobilized onto a selected surface exhibiting protein affinity, such as a well in a polystyrene microtiter plate. Then, a test composition suspected of containing the one or more protein biomarkers, such as a diluted clinical sample, is added to the wells. After binding and / or washing to remove non-specifically bound immune complexes, the bound protein biomarker may be detected. Detection can be achieved by contacting the sample with an agent, such as a secondary antibody, that is linked to a detectable label. This type of ELISA is a "sandwich ELISA". Attorney Docket: AHY-00325 Irrespective of the format employed, ELISAs have certain features in common, such as coating, incubating and binding, washing to remove non-specifically bound species, and detecting the bound immune complexes. These are described below. In some embodiments, the method for detecting one or more protein biomarkers is an immunobead-based assay (e.g., Luminex). In some embodiments, the method for detecting one or more protein biomarkers is multiplexed immunobead-based assay. In some embodiments, an antibody targeting the one or more protein biomarker is conjugated to a bead. In some embodiments, upon binding of the antibody to the one or more protein biomarkers, the bead detected using a method known to those of skill in the art or described herein. Detection of Nucleic Acids In some embodiments, the present disclosure provides methods for detecting nucleic acid molecules in a biological sample. Methods for detecting nucleic acid molecules are known to those of skill in the art, including but not limited to nucleic acid sequencing; nucleic acid hybridization; and nucleic acid amplification. In some embodiments, nucleic acid sequencing methods are utilized (e.g., for detection of amplified nucleic acids). In some embodiments, the technology provided herein finds use in a Second Generation (a.k.a. Next Generation or Next-Gen), Third Generation (a.k.a. Next-Next- Gen), or Fourth Generation (a.k.a. N3-Gen) sequencing technology including, but not limited to, pyrosequencing, sequencing-by-ligation, single molecule sequencing, sequence-by-synthesis (SBS), semiconductor sequencing, massive parallel clonal, massive parallel single molecule SBS, massive parallel single molecule real-time, massive parallel single molecule real-time nanopore technology, etc. Morozova and Marra provide a review of some such technologies in Genomics, 92: 255 (2008), herein incorporated by reference in its entirety. Those of skill in the art will recognize that because RNA is less stable in the cell and more prone to nuclease attack experimentally RNA can be reverse transcribed to DNA before sequencing. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^ ^^^^^^^^^!^^"^^^^^#^^^^^$^^^^^^^^^%^&^^^^^^^^^^^^^#^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^'^^(^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^ Attorney Docket: AHY-00325 Illustrative non-limiting examples of nucleic acid hybridization techniques include, but are not limited to, in situ hybridization (ISH), microarray, and Southern or Northern blot. In situ hybridization (ISH) is a type of hybridization that uses a labeled complementary DNA or RNA strand as a probe to localize a specific DNA or RNA sequence in a portion or section of tissue (in situ), or, if the tissue is small enough, the entire tissue (whole mount ISH). DNA ISH can be used to determine the structure of chromosomes. RNA ISH can be used to measure and localize mRNAs and other transcripts (e.g., cancer markers) within tissue sections or whole mounts. Sample cells and tissues can be treated to fix the target transcripts in place and to increase access of the probe. The probe hybridizes to the target sequence at elevated temperature, and then the excess probe is washed away. The probe that was labeled with either radio-, fluorescent- or antigen-labeled bases is localized and quantitated in the tissue using either autoradiography, fluorescence microscopy or immunohistochemistry, respectively. ISH can also use two or more probes, labeled with radioactivity or the other non-radioactive labels, to simultaneously detect two or more transcripts. In some embodiments, the one or more biomarkers are detected by conducting one or more hybridization reactions. In some embodiments, the one or more hybridization reactions comprise one or more hybridization arrays, hybridization reactions, hybridization chain reactions, isothermal hybridization reactions, nucleic acid hybridization reactions, or a combination thereof. In some embodiments, the one or more hybridization arrays comprise hybridization array genotyping, hybridization array proportional sensing, DNA hybridization arrays, macroarrays, microarrays, high-density oligonucleotide arrays, genomic hybridization arrays, comparative hybridization arrays, or a combination thereof. In some embodiments, a microarray is used to determine the amount or level of the one or more biomarkers described herein. Microarrays include but are not limited to: DNA microarrays (e.g., cDNA microarrays and oligonucleotide microarrays); protein microarrays; tissue microarrays; transfection or cell microarrays; chemical compound microarrays; and antibody microarrays. A DNA microarray, commonly known as gene chip, DNA chip, or biochip, is a collection of microscopic DNA spots attached to a solid surface (e.g., glass, plastic or silicon chip) forming an array for the purpose of expression profiling or monitoring expression levels for thousands of genes simultaneously. The affixed DNA segments are known as probes, thousands of which can be used in a single DNA microarray. Microarrays can be fabricated Attorney Docket: AHY-00325 using a variety of technologies, including but not limiting printing with fine-pointed pins onto glass slides; photolithography using pre-made masks; photolithography using dynamic micromirror devices; ink-jet printing; or electrochemistry on microelectrode arrays. Biological Samples The methods described herein are useful for detecting a panel of biomarkers at one or more time points in a biological sample obtained from a subject. Exemplary biological samples include, but are not limited to, plasma, urine, saliva, whole blood, dried blood spot, serum, dried serum spot, stool, and / or hair. In some embodiments, the biological sample is derived from blood. In some embodiments, the biological sample is serum. In some embodiments, the biological is plasma. In some embodiments, the biological sample is whole blood, serum or plasma. In some embodiments, a processed biological sample, e.g., blood plasma or serum, is frozen for transport and / or long-term storage. In some embodiments, the same type of biological sample is obtained at two or more distinct time points. In some embodiments, different biological samples are obtained at two or more distinct time points. For example, a blood sample is obtained at a first time point, and a urine sample is obtained at a second time point. In contrast, a first blood sample is obtained from a first time point, and a second blood sample is obtained from a second time point. In some embodiments, the two or more biological samples obtained from the same subject are processed simultaneously. In some embodiments, the two or more biological samples obtained from the same subject are processed at different time points. In some embodiments, the panel of biomarkers is detected in the two or more biological samples at the same time. In some embodiments, the panel of biomarkers is detected in the two or more biological samples at different times. In some embodiments, one or more biomarkers are detected in a biological sample obtained from a subject, e.g., plasma, urine, saliva, whole blood, dried blood spot, serum, dried serum spot, stool, and / or hair. In some embodiments, one or more biomarkers are detected in plasma. In some embodiments, one or more biomarkers are detected in urine. In some embodiments, one or more biomarkers are detected in saliva. In some embodiments, one or more biomarkers are detected in whole blood. In some embodiments, one or more biomarkers are detected in dried blood spot. In some embodiments, one or more biomarkers are detected in Attorney Docket: AHY-00325 serum. In some embodiments, one or more biomarkers are detected in dried serum spot. In some embodiments, one or more biomarkers are detected in stool. In some embodiments, one or more biomarkers are detected in hair. In some embodiments, the biological sample is processed to allow for detecting of the biomarker. In some embodiments, a biological sample is processed in a manner consistent with methods for detecting protein or nucleic acids. In some embodiments, a sample is processed to isolate the proteins for detection. Methods for isolating proteins are known to those of skill in the art. In some embodiments, a sample is processed to isolate nucleic acid molecules for detection. Methods for isolating nucleic acid molecules are known to those of skill in the art. Determining Panel of Biomarkers and Dynamic Signatures In some embodiments, the disclosure provides methods of determining a dynamic signature suitable for use in the methods disclosed herein. In some embodiments, the dynamic signature is based on a panel of biomarkers described herein. In some embodiments, the panel of biomarkers for the dynamic signature is identified by determining the amount or level of one or more biomarkers from one or more biological samples from a subject. In some embodiments, the amount or level of one or more biomarkers is determined at two or more time points. In some embodiments, the amount or level of one or more biomarkers is determined before a subject receives a treatment (e.g., ICI monotherapy). In some embodiments, the amount or level of one or more biomarkers is determined after a subject receives a treatment (e.g., ICI monotherapy). In some embodiments, the amount or level of one or more biomarkers is determined before a subject receives a treatment and after the subject receives the treatment. In some embodiments, biomarkers are ranked by their frequency of identification. For example, in some embodiments, biomarkers are ranked by their frequency of identification from repeats of cross-validation. In some embodiments, a biomarker has a frequency of identification from 0% to 100%. In some embodiments, a biomarker has a frequency of identification from about 0%, 2%, 4%, 6%, 8%, 10%, 12%, 14%, 16%, 18%, 20%, 22%, 24%, 26%, 28%, 30%, 32%, 34%, 36%, 38%, 40%, 42%, 44%, 46%, 48%, 50%, 52%, 54%, 56%, 58%, 60%, 62%, 64%, 66%, 68%, 70%, 72%, 74%, 76%, 78%, 80%, 82%, 84%, 86%, 88%, 90%, 92%, 94%, 96%, 98%, up to at most 100%. In some embodiments, a biomarker has a frequency of Attorney Docket: AHY-00325 identification from about 1% to about 10%, about 5% to about 20%, about 10% to about 30%, about 20% to about 40%, about 30% to about 50%, about 40% to about 100%, about 50% to about 90%, about 15% to about 40%, about 20% to about 50%, about 10% to about 45%, or about 25% to about 50%. In some embodiments, a biomarker has a frequency of identification of about 25%. In some embodiments, the panel of biomarkers and the dynamic signature is identified using a computational approach. In some embodiments, a predictive algorithm is applied to a dataset compiled from screens to identify a panel of biomarkers and dynamic signature of the disclosure. Predictive algorithms are applied to large datasets to identify correlations between the amount or level of one or more biomarkers detected over time and responsiveness to therapeutic intervention (e.g., ICI monotherapy). In some embodiments, a predictive algorithm comprises performing a statistical test to determine the association of the amount or level of one or more biomarkers detected over time and responsiveness to therapeutic intervention (e.g., ICI monotherapy). In some embodiments, the predictive algorithm is a linear mixed effects model (LMM). In some embodiments, the amount or level of the one or more biomarkers is the dependent variable whereas timepoint, response status and interaction between the two terms are the independent variables. In some embodiments, the significance of the variables is determined with an F-test using Satterthwaite degrees of freedom and type III sum of squares. In some embodiments, biomarkers are analyzed using statistical modeling. Exemplary methods of statistical modeling include, but are not limited to, linear mixed modeling (LMM), analysis of variance (ANOVA), and hierarchical linear modeling (HLM). In some embodiments, biomarkers with altered (e.g., increased or decreased) expression level are analyzed using LMM. In some embodiments, biomarkers with altered (e.g., increased or decreased) expression level are analyzed using ANOVA. In some embodiments, biomarkers with altered (e.g., increased or decreased) expression level are analyzed using HLM. In some embodiments, a statistical modeling method is fit independently to each biomarker, using at least two different time points and their respective biomarkers with altered (e.g., increased or decreased) expression level as inputs. In some embodiments, biomarkers with altered (e.g., increased or decreased) expression level at one or more time points analyzed using statistical modeling generate a model. Attorney Docket: AHY-00325 In some embodiments, a model is assessed for predictive performance. Exemplary measures of predictive performance include, but are not limited to, methods of cross-validation, e.g., k-fold cross-validation (e.g., 5-fold, 10-fold, etc.), leave one out cross-validation (LOOCV), and Monte Carlo, and the like. In some embodiments, a model is assessed for predictive performance using k-fold cross-validation (e.g., 5-fold cross-validation). In some embodiments, a model is assessed using LOOCV. In some embodiments, a model is assessed using Monte Carlo. In some embodiments, a cross-validation method may be repeated any number of times. In some embodiments, a cross-validation method may be repeated about 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or about 1000 times. In some embodiments, a cross-validation method may be repeated from about 1 to about 10, about 5 to about 20, about 10 to about 30, about 20 to about 40, about 30 to about 50, about 40 to about 100, about 50 to about 200, about 100 to about 300, about 200 to about 400, about 300 to about 500, about 400 to about 600, about 500 to about 700, about 600 to about 800, about 700 to about 900, about 800 to about 1000, or about 900 to about 1000 times. In some embodiments, a cross-validation method may be repeated about 100 times. In some embodiments, a dynamic signature is determined from comparing biomarker expression level (e.g., increased or decreased) from two or more different time points. In some embodiments, an expression level of two or more biomarkers is obtained from a sample. In some embodiments, a sample is obtained from a subject pre-treatment (e.g., day -1 post-treatment). In some embodiments, a sample is obtained from a subject post-treatment. In some embodiments, a sample is obtained from a subject concurrently with treatment. In some embodiments, a sample is obtained from a subject at time point day -1, 0, 1, 2, 3, 4, 5, 6, or day 7 post-treatment. In some embodiments, a sample is obtained from a subject at time point week 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or week 24 post-treatment. In some embodiments, a sample is obtained from a subject at time point month 0, 1, 2, 3, 4, 5, or month 6 post-treatment. In some embodiments, a sample is obtained from a subject at time point day -1 post-treatment. In some embodiments, a sample is obtained from a subject at time point week 3 post-treatment. In some embodiments, a sample is obtained from a subject at time point week 6 post-treatment. In some embodiments, a sample is obtained from a subject at time point month 6 post-treatment. Attorney Docket: AHY-00325 In some embodiments, a dynamic signature is obtained by comparing two or biomarker expression levels (e.g., increased or decreased) from two or more different time points. In some embodiments, the two or more time points are selected from: -1, 0, 1, 2, 3, 4, 5, 6, or 7 days post- treatment; 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 weeks post-treatment; and 0, 1, 2, 3, 4, 5, or 6 months post-treatment. In some embodiments, a dynamic signature is obtained by comparing two or more biomarker expression levels (e.g., increased or decreased) from two or more different time points, wherein the time points are selected from day 1, 0, 1, 2, 3, 4, 5, 6, or 7 days post-treatment. In some embodiments, a dynamic signature is obtained by comparing two or more biomarker expression levels (e.g., increased or decreased) from two or more different time points, wherein the time points are selected from 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 weeks post-treatment. In some embodiments, a dynamic signature is obtained by comparing two or more biomarker expression levels (e.g., increased or decreased) from two or more different time points, wherein the time points are selected from 0, 1, 2, 3, 4, 5, or 6 months post-treatment. In some embodiments, a dynamic signature is obtained by comparing two or more biomarker expression levels (e.g., increased or decreased) from two or more different time points, wherein the time points are at least baseline and 3-weeks post-treatment. In some embodiments, a dynamic signature is obtained by comparing two or more biomarker expression levels (e.g., increased or decreased) from two or more different time points, wherein the time points are at least baseline and 6-weeks post-treatment. In some embodiments, a dynamic signature is obtained by comparing two or more biomarker expression levels (e.g., increased or decreased) from two or more different time points, wherein the time points are at least baseline and 6- months post-treatment. In some embodiments, a dynamic signature is obtained by comparing two or more biomarker expression levels (e.g., increased or decreased) from two or more different time points, wherein the time points are at least baseline, 3-weeks post-treatment, and 6-months post treatment. In some embodiments, a dynamic signature is obtained by comparing two or more biomarker expression levels (e.g., increased or decreased) from two or more different time points, wherein the time points are at least baseline, 6-weeks post-treatment, and 6-months post treatment. In some embodiments, a dynamic signature is obtained by comparing two or more biomarker expression levels (e.g., increased or decreased) from two or more different time Attorney Docket: AHY-00325 points, wherein the time points are at least baseline and before clinical read out (e.g., before 6- months). In some embodiments, a dynamic signature is identified using computational methods. Without being bound by theory, computational methods may determine whether a defined set of biomarkers show statistically significant and concordant differences between two biological groups, e.g., altered expression of one or more biomarkers between two or more time points. Exemplary computational methods include, but are not limited to, gene set enrichment analysis (GSEA), gene ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and the like. In some embodiments, a dynamic signature is assessed using GSEA. In some embodiments, a dynamic signature is assessed using GO analysis. In some embodiments, a dynamic signature is assessed using KEGG analysis. In some embodiments, a dynamic signature is assessed using two or more computational methods. In some embodiments, a dynamic signature is assessed using GSEA, GO analysis, and KEGG analysis. Diagnostic Accuracy of Dynamic Signatures Diagnostic accuracy of the methods or kits useful for predicting responsiveness to a therapeutic intervention (e.g., ICI monotherapy) in a subject with cancer can be determined by analyzing the Area Under the Curve (AUC) derived from Receiver Operator Characteristic (ROC) curves. ROC curves are graphical plots that illustrate the ability of a binary classifier system as its discrimination threshold is varied. ROC curves are plotted with true positive rate against the false positive rate, with true positive rate on the y-axis and false positive rate on the x-axis. The true positive rate, also referred to as the sensitivity, is calculated by dividing the number of true positives by the sum of true positives and false negatives. The false positive rate is calculated by either (1) dividing the number of false positives by the sum of true negatives and false positives, or (2) subtracting the specificity from one, wherein specificity is calculated by dividing the number of true negatives by the sum of true negatives and false positives. In some embodiments, ROC curves are generated based on individual amounts of expression of each biomarker. In some embodiments, ROC curves are generated based on a combination of amounts of expression of each biomarker. In some embodiments, the AUC value of the methods or kits described herein is greater than 0.50. In some embodiments, the AUC value of the methods or kits described herein is at Attorney Docket: AHY-00325 least 0.60. In some embodiments, the AUC value of the methods or kits described herein is at least 0.70. In some embodiments, the AUC value of the methods or kits described herein is at least 0.71. In some embodiments, the AUC value of the methods or kits described herein is at least 0.72. In some embodiments, the AUC value of the methods or kits described herein is at least 0.73. In some embodiments, the AUC value the methods or kits described herein is at least 0.74. In some embodiments, the AUC value of the methods or kits described herein is at least 0.75. In some embodiments, the AUC value of the methods or kits described herein is at least 0.76. In some embodiments, the AUC value of the methods or kits described herein is at least 0.77. In some embodiments, the AUC value of the methods or kits described herein is at least 0.78. In some embodiments, the AUC value of the methods or kits described herein is at least 0.79. In some embodiments, the AUC value of the methods or kits described herein is at least 0.80. In some embodiments, the AUC value of the methods or kits described herein is at least 0.81. In some embodiments, the AUC value of the methods or kits described herein is at least 0.82. In some embodiments, the AUC value of the methods or kits described herein is at least 0.83. In some embodiments, the AUC value of the methods or kits described herein is at least 0.84. In some embodiments, the AUC value of the methods or kits described herein is at least 0.85. In some embodiments, the AUC value of the methods or kits described herein is at least 0.86. In some embodiments, the AUC value of the methods or kits described herein is at least 0.87. In some embodiments, the AUC value of the methods or kits described herein is at least 0.88. In some embodiments, the AUC value of the methods or kits described herein is at least 0.89. In some embodiments, the AUC value of the methods or kits described herein is at least 0.90. Diagnostic accuracy of the amount of expression of an individual biomarker or combination of amounts of expression of specific biomarkers can be maximized by implementing a cut-off analysis that takes into account the sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), positive likelihood ratio (PLR) and negative likelihood ratio (NLR) necessary for clinical utility. Results of amounts of expression are analyzed in any of a variety of ways. In some embodiments, the results are analyzed using a univariate, or single-variable analysis (SV). In some embodiments, the results are analyzed using multivariate analysis (MV). Attorney Docket: AHY-00325 The generation of ROC curves and analysis of a population of samples can be used to establish the cutoff value used to distinguish between different subject sub-groups. For example, the cutoff value can be used to distinguish between a high likelihood of responding to a therapeutic intervention (e.g., ICI monotherapy) and a low likelihood of responding to a therapeutic intervention (e.g., ICI monotherapy). In some embodiments, the cutoff value can distinguish between these subjects. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that a subject with cancer will not respond to ICI monotherapy with a diagnostic accuracy of at least 0.70. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that a subject with cancer will not respond to ICI monotherapy with a diagnostic accuracy of at least 0.75. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that a subject with cancer will not respond to ICI monotherapy with a diagnostic accuracy of at least 0.80. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that a subject with cancer will not respond to ICI monotherapy with a diagnostic accuracy of at least 0.85. In some embodiments, the methods or kits described herein provide a score indicating the likelihood that a subject with cancer will not respond to ICI monotherapy with a diagnostic accuracy of at least 0.90. In some embodiments, the methods or kits described herein provide a score indicating a subject with cancer will not respond to ICI monotherapy with a diagnostic accuracy of at least 0.70. In some embodiments, the methods or kits described herein provide a score indicating a subject with cancer will not respond to ICI monotherapy with a diagnostic accuracy of at least 0.75. In some embodiments, the methods or kits described herein provide a score indicating a subject with cancer will not respond to ICI monotherapy with a diagnostic accuracy of at least 0.80. In some embodiments, the methods or kits described herein provide a score indicating a subject with cancer will not respond to ICI monotherapy with a diagnostic accuracy of at least 0.85. In some embodiments, the methods or kits described herein provide a score indicating a subject with cancer will not respond to ICI monotherapy with a diagnostic accuracy of at least 0.90. Attorney Docket: AHY-00325 Exemplary Non-Responder Dynamic Signatures In some embodiments, a dynamic signature of the disclosure comprises detecting a panel of biomarkers disclosed herein at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, a dynamic signature of the disclosure comprises detecting a panel of biomarkers disclosed herein before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising one or more biomarkers selected from: ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising at least six biomarkers selected from: ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising one or more biomarkers selected from: ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, Attorney Docket: AHY-00325 PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising at least six biomarkers selected from: ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising one or more biomarkers selected from: CA6, CDNF, MIA, MYOC, NEFL, and TCL1B, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising CA6, CDNF, MIA, MYOC, NEFL, and TCL1B, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising one or more biomarkers selected from: CA6, CDNF, MIA, MYOC, NEFL, and TCL1B, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising CA6, CDNF, MIA, MYOC, NEFL, and TCL1B, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising one or more biomarkers selected from: ADAM22, BMP4, CA6, CCL25, CDH17, Attorney Docket: AHY-00325 CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising at least six biomarkers selected from: ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising one or more biomarkers selected from: ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising at least six biomarkers selected from: ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, Attorney Docket: AHY-00325 IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising one or more biomarkers selected from: AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising at least six biomarkers selected from: AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising one or more biomarkers selected from: AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising at least six biomarkers selected from: AMTS8, ANGPT2, BCL2L11, BRK1, CA6, Attorney Docket: AHY-00325 CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising one or more biomarkers selected from: ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising at least six biomarkers selected from: ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS, at two or more time points in a subject having cancer that has received an ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS, at two or more time points in a subject having cancer that has received an ICI monotherapy. Attorney Docket: AHY-00325 In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising one or more biomarkers selected from: ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising at least six biomarkers selected from: ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. In some embodiments, the dynamic signature comprises detecting a panel of biomarkers comprising ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS, before a subject having cancer has received an ICI monotherapy, and at one or more time points from about 3 weeks to about 6 months after administration of the ICI monotherapy. Methods of Use In some aspects, the present disclosure provides methods for identifying the panel of biomarkers and / or dynamic signatures in a subject having cancer that has received an ICI monotherapy. In some embodiments, the panel of biomarkers and / or dynamic signatures described herein predict whether a subject will respond to ICI monotherapy. In some embodiments, the panel of biomarkers and / or dynamic signatures described herein predict a patient is not likely to respond to ICI monotherapy. In some embodiments, the present disclosure provides methods for administering additional cancer therapeutics to a subject identified as not likely to respond to ICI monotherapy. Attorney Docket: AHY-00325 In some embodiments, the additional cancer therapeutic is administered in combination with ICI therapy. In some embodiments, the additional cancer therapeutic is administered as monotherapy, i.e., without the ICI therapy. In some embodiments, an ICI monotherapy is an antibody. In some embodiments, an antibody is specific to PD1 (e.g., anti-PD1), PDL1 (e.g., anti-PDL1), LAG3 (e.g., anti-LAG3), or CTLA4 (e.g., anti-CTLA4). In some embodiments, a subject with cancer is administered an anti- PD1 antibody. In some embodiments, a subject with cancer is administered an anti-PDL1 antibody. In some embodiments, a subject with cancer is administered an anti-LAG3 antibody. In some embodiments, a subject with cancer is administered an anti-CTLA4 antibody. In some embodiments, a subject with cancer is administered one or more of an anti-PD1 antibody, an anti-PDL1 antibody, an anti-LAG3 antibody, and an anti-CTLA4 antibody. Exemplary antibodies include, but are not limited to, ipilimumab, nivolumab, relatlimab, pembrolizumab, atezolizumab, durvalumab, avelumab, cemiplimab, and dostarlimab. As used herein, the terms “subject” and “patient” can be used interchangeably. As used herein, a subject can be a mammal such as a non-primate (e.g., cows, pigs, horses, cats, dogs, rats, etc.) or a primate (e.g., monkey and human). In some embodiments, the subject is a human. In some embodiments, a patient to be treated or tested for responsiveness to a treatment according to the methods described herein is one who has been diagnosed with a cancer, such as any cancer described herein. Diagnosis may be performed by any method or technique known in the art, such as x-ray, MRI, or biopsy, and may also be confirmed by a physician. To minimize exposure of a patient to drug treatments that may not be therapeutic, the patient may be determined to be either responsive or non-responsive to a cancer treatment, such as a TEAD therapeutic agent described herein, according to the methods described herein prior to treatment. As used herein, the terms "treat," "treating" and "treatment" refer to an action that occurs while the subject has a disease, disorder or condition described herein. "Treat," "treatment" and "treating" also refer to the reduction or amelioration of the progression, severity, and / or duration of a disease, disorder or condition described herein resulting from the administration of one or more therapeutic agents described herein. As used herein, the terms "cancer" and "cancerous" refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. In some embodiments, the subject has a hematological cancer. Hematological cancer as used herein refers Attorney Docket: AHY-00325 to blood-borne tumors (e.g., multiple myeloma, lymphoma, and leukemia). In some embodiments, the subject has a solid tumor. "Tumor" and "solid tumor" as used herein, refer to all lesions and neoplastic cell growth and proliferation, whether malignant or benign, and all pre- cancerous and cancerous cells and tissues. "Neoplastic," as used herein, refers to any form of dysregulated or unregulated cell growth, whether malignant or benign, resulting in abnormal tissue growth. Thus, "neoplastic cells" include malignant and benign cells having dysregulated or unregulated cell growth. In some embodiments, the subject has a solid tumor. In some embodiments, the solid tumor is a sarcoma (e.g., a solid tumor comprising closely packed cells embedded in a fibrillar or homogeneous substance). Exemplary sarcomas for treatment, prevention, and / or management using the compositions and methods described herein include chondrosarcoma, fibrosarcoma, lymphosarcoma, melanosarcoma, myxosarcoma, osteosarcoma, Abemethy's sarcoma, adipose sarcoma, liposarcoma, alveolar soft part sarcoma, ameloblastic sarcoma, botryoid sarcoma, chloroma sarcoma, chorio carcinoma, embryonal sarcoma, Wilms' tumor sarcoma, endometrial sarcoma, stromal sarcoma, Ewing's sarcoma, fascial sarcoma, fibroblastic sarcoma, giant cell sarcoma, granulocytic sarcoma, Hodgkin's sarcoma, idiopathic multiple pigmented hemorrhagic sarcoma, immunoblastic sarcoma of B cells, lymphoma, immunoblastic sarcoma of T-cells, Jensen's sarcoma, Kaposi's sarcoma, Kupffer cell sarcoma, angiosarcoma, leukosarcoma, malignant mesenchymoma sarcoma, parosteal sarcoma, reticulocytic sarcoma, Rous sarcoma, serocystic sarcoma, synovial sarcoma, and telangiectaltic sarcoma. In some embodiments, the solid tumor is a carcinoma (e.g., a malignant growth comprising epithelial cells that have infiltrated surrounding tissues). Exemplary carcinomas for treatment, prevention, and / or management using the compositions and methods described herein include, adenocarcimonas, colorectal carcinoma, colorectal adenocarcinoma, acinar carcinoma, lung carcinoma, alveolar cell carcinoma, basal cell carcinoma, bronchioalveolar carcinoma, bronchiolar carcinoma, bronchogenic carcinoma, cerebriform carcinoma, chorionic carcinoma, colloid carcinoma, corpus carcinoma, cribriform carcinoma, cylindrical carcinoma, cylindrical cell carcinoma, duct carcinoma, gelatiniforni carcinoma, gelatinous carcinoma, giant cell carcinoma, carcinoma gigantocellulare, glandular carcinoma, hematoid carcinoma, hepatocellular carcinoma, Hurthle cell carcinoma, Krompecher's carcinoma, Kulchitzky-cell carcinoma, large-cell carcinoma, lymphoepithelial carcinoma, nasopharyngeal carcinoma, Attorney Docket: AHY-00325 papillary carcinoma, renal cell carcinoma of kidney, scirrhous carcinoma, small-cell carcinoma, spheroidal cell carcinoma, squamous carcinoma, squamous cell carcinoma, carcinoma telangiectaticum, and verrucous carcinoma. In some embodiments, the solid tumor is oral, lung, gastrointestinal, genitourinary, liver, bone, nervous system, gynecological, skin, thyroid gland, or adrenal gland. In some embodiments, the solid tumor is oral (buccal cavity, lip, tongue, mouth, pharynx); cardiac (sarcoma (angiosarcoma, fibrosarcoma, rhabdomyosarcoma, liposarcoma), myxoma, rhabdomyoma, fibroma, lipoma and teratoma); lung (bronchogenic carcinoma (squamous cell or epidermoid, undifferentiated small cell, undifferentiated large cell, adenocarcinoma), alveolar (bronchiolar) carcinoma, bronchial adenoma, sarcoma, lymphoma, chondromatous hamartoma, mesothelioma); gastrointestinal (esophagus (squamous cell carcinoma, larynx, adenocarcinoma, leiomyosarcoma, lymphoma), stomach (carcinoma, lymphoma, leiomyosarcoma), pancreas (ductal adenocarcinoma, insulinoma, glucagonoma, gastrinoma, carcinoid tumors, vipoma), small bowel or small intestines (adenocarcinoma, lymphoma, carcinoid tumors, Kaposi's sarcoma, leiomyoma, hemangioma, lipoma, neurofibroma, fibroma), large bowel or large intestines (adenocarcinoma, tubular adenoma, villous adenoma, hamartoma, leiomyoma), colon, colon-rectum, colorectal, rectum); genitourinary tract (kidney (adenocarcinoma, Wilm's tumor [nephroblastoma], lymphoma, leukemia), bladder and urethra (squamous cell carcinoma, transitional cell carcinoma, adenocarcinoma), prostate (adenocarcinoma, sarcoma), testis (seminoma, teratoma, embryonal carcinoma, teratocarcinoma, choriocarcinoma, sarcoma, interstitial cell carcinoma, fibroma, fibroadenoma, adenomatoid tumors, lipoma); liver (hepatoma (hepatocellular carcinoma), cholangiocarcinoma, hepatoblastoma, angiosarcoma, hepatocellular adenoma, hemangioma, biliary passages); bone (osteogenic sarcoma (osteosarcoma), fibrosarcoma, malignant fibrous histiocytoma, chondrosarcoma, Ewing's sarcoma, malignant lymphoma (reticulum cell sarcoma), multiple myeloma, malignant giant cell tumor chordoma, osteochronfroma (osteocartilaginous exostoses), benign chondroma, chondroblastoma, chondromyxofibroma, osteoid osteoma and giant cell tumors); nervous system (skull (osteoma, hemangioma, granuloma, xanthoma, osteitis deformans), meninges (meningioma, meningiosarcoma, gliomatosis), brain (astrocytoma, medulloblastoma, glioma, ependymoma, germinoma [pinealoma], glioblastoma multiform, oligodendroglioma, schwannoma, retinoblastoma, congenital tumors), spinal cord neurofibroma, meningioma, Attorney Docket: AHY-00325 glioma, sarcoma); gynecological (uterus (endometrial carcinoma), cervix (cervical carcinoma, pre-tumor cervical dysplasia), ovaries (ovarian carcinoma [serous cystadenocarcinoma, mucinous cystadenocarcinoma, unclassified carcinoma], granulosa-thecal cell tumors, Sertoli- Leydig cell tumors, dysgerminoma, malignant teratoma), vulva (squamous cell carcinoma, intraepithelial carcinoma, adenocarcinoma, fibrosarcoma, melanoma), vagina (clear cell carcinoma, squamous cell carcinoma, botryoid sarcoma (embryonal rhabdomyosarcoma), fallopian tubes (carcinoma), breast; hematologic (blood (myeloid leukemia [acute and chronic], acute lymphoblastic leukemia, chronic lymphocytic leukemia, myeloproliferative diseases, multiple myeloma, myelodysplastic syndrome), Hodgkin's disease, non-Hodgkin's lymphoma [malignant lymphoma] hairy cell, lymphoid disorders); skin (malignant melanoma, basal cell carcinoma, squamous cell carcinoma, Karposi's sarcoma, keratoacanthoma, moles dysplastic nevi, lipoma, angioma, dermatofibroma, keloids, psoriasis), thyroid gland (papillary thyroid carcinoma, follicular thyroid carcinoma, undifferentiated thyroid cancer, medullary thyroid carcinoma, multiple endocrine neoplasia type 2A, multiple endocrine neoplasia type 2B, familial medullary thyroid cancer, pheochromocytoma, paraganglioma); and adrenal glands (neuroblastoma). In some embodiments, the subject has a myeloproliferative disorder (e.g., blood cancer). The term “myeloproliferative disorders” includes disorders such as polycythemia vera, thrombocythemia, myeloid metaplasia with myelofibrosis, hypereosinophilic syndrome, juvenile myelomonocytic leukemia, systemic mast cell disease, and hematopoietic disorders, in particular, acute-myelogenous leukemia (AML), chronic-myelogenous leukemia (CML), acute- promyelocytic leukemia (APL), and acute lymphocytic leukemia (ALL). Methods of Identifying Dynamic Signatures and Predicting Non-Response In some embodiments, the present disclosure provides methods for identifying dynamic signatures in a subject having cancer and administered an ICI monotherapy. In some embodiments, the present disclosure provides methods for identifying a subject having cancer as not likely to respond to ICI monotherapy. In some embodiments, the subject has received an ICI monotherapy and the dynamic signature predicts the subject is not likely to respond to the ICI monotherapy before clinical readout. Attorney Docket: AHY-00325 In some embodiments, the present disclosure provides methods for predicting whether a subject having cancer and receiving ICI monotherapy will respond to the ICI monotherapy, comprising determining the amount or level of a panel of biomarkers disclosed herein at two or more time points. In some embodiments, the amount or level of the panel of biomarkers is altered (e.g., increased or decreased) between the two or more time points (e.g., between baseline and at least one time point after administration of the ICI monotherapy). In some embodiments, the amount or level of the panel of biomarkers is increased between the two or more time points (e.g., between baseline and at least one time point after administration of the ICI monotherapy). In some embodiments, the increase in the amount or level of the panel of biomarkers between the two or more time points is statistically significant. In some embodiments, the amount or level of the panel of biomarkers is decreased between the two or more time points (e.g., between baseline and at least one time point after administration of the ICI monotherapy). In some embodiments, the decrease in the amount or level of the panel of biomarkers between the two or more time points is statistically significant. In some embodiments, the amount or level of the panel of biomarkers is altered relative to a reference sample. In some embodiments, the amount or level of the panel of biomarkers is increased relative to a reference sample. In some embodiments, the amount or level of the panel of biomarkers is decreased relative to a reference sample. In some embodiments, the increase or decrease of the amount or level of the panel of biomarkers indicates the subject is not likely to respond to the therapeutic intervention (e.g., ICI monotherapy). In some embodiments, the disclosure provides methods to identify a subject with cancer likely not to respond to one or more cancer treatments, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject at two or more different time points, wherein the first time point is prior to administration of the one or more cancer treatments, and the second time point is from about 3 weeks to about 6 months after administration of one or more cancer treatments, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, Attorney Docket: AHY-00325 TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS; and (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more cancer treatments. In some embodiments, the disclosure provides methods to identify a subject with cancer likely not to respond to one or more cancer treatments, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of the one or more cancer treatments, and the second time point is from about 3 weeks to about 6 months after administration of one or more cancer treatments, wherein the panel of biomarkers comprises one or more of CA6, CDNF, MIA, MYOC, NEFL, and TCL1B; and (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non- response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more cancer treatments. In some embodiments, the disclosure provides methods to identify a subject with cancer likely not to respond to one or more cancer treatments, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of the one or more cancer treatments, and the second time point is from about 3 weeks to about 6 months after administration of one or more cancer treatments, wherein the panel of biomarkers comprises one or more of ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN; and (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more cancer treatments. In some embodiments, the disclosure provides methods to identify a subject with cancer likely not to respond to one or more cancer treatments, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more Attorney Docket: AHY-00325 different time points, wherein the first time point is prior to administration of the one or more cancer treatments, and the second time point is from about 3 weeks to about 6 months after administration of one or more cancer treatments, wherein the panel of biomarkers comprises one or more of AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS; and (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more cancer treatments. In some embodiments, the disclosure provides methods to identify a subject with cancer likely not to respond to one or more cancer treatments, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of the one or more cancer treatments, and the second time point is from about 3 weeks to about 6 months after administration of one or more cancer treatments, wherein the panel of biomarkers comprises one or more of ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS; and (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more cancer treatments. In some embodiments, the disclosure provides methods to identify a subject with cancer likely not to respond to one or more immune checkpoint inhibitor monotherapies, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject at two or more different time points, wherein the first time point is prior to administration of the one or more immune checkpoint inhibitor monotherapies, and the second time point is from about 3 weeks to about 6 months after administration of the one or more immune checkpoint inhibitor monotherapies, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, Attorney Docket: AHY-00325 CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS; and (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more immune checkpoint inhibitor monotherapies. In some embodiments, the disclosure provides methods to identify a subject with cancer likely not to respond to one or more immune checkpoint inhibitor monotherapies, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of the one or more immune checkpoint inhibitor monotherapies, and the second time point is from about 3 weeks to about 6 months after administration of the one or more immune checkpoint inhibitor monotherapies, wherein the panel of biomarkers comprises one or more of CA6, CDNF, MIA, MYOC, NEFL, and TCL1B; and (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non- response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more immune checkpoint inhibitor monotherapies. In some embodiments, the disclosure provides methods to identify a subject with cancer likely not to respond to one or more immune checkpoint inhibitor monotherapies, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of the one or more immune checkpoint inhibitor monotherapies, and the second time point is from about 3 weeks to about 6 months after administration of the one or more immune checkpoint inhibitor monotherapies, wherein the panel of biomarkers comprises one or more of ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN; and (ii) comparing the Attorney Docket: AHY-00325 amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more immune checkpoint inhibitor monotherapies. In some embodiments, the disclosure provides methods to identify a subject with cancer likely not to respond to one or more immune checkpoint inhibitor monotherapies, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of the one or more immune checkpoint inhibitor monotherapies, and the second time point is from about 3 weeks to about 6 months after administration of the one or more immune checkpoint inhibitor monotherapies, wherein the panel of biomarkers comprises one or more of AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS; and (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more immune checkpoint inhibitor monotherapies. In some embodiments, the disclosure provides methods to identify a subject with cancer likely not to respond to one or more immune checkpoint inhibitor monotherapies, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of the one or more immune checkpoint inhibitor monotherapies, and the second time point is from about 3 weeks to about 6 months after administration of the one or more immune checkpoint inhibitor monotherapies, wherein the panel of biomarkers comprises one or more of ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS; and (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response Attorney Docket: AHY-00325 score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more immune checkpoint inhibitor monotherapies. In some or any of the foregoing embodiments, the one or more immune checkpoint inhibitor monotherapies are selected from an anti-PD1, anti-PDL1, anti-LAG3, and anti-CTLA4 antibodies. Methods of Treating Cancer In some embodiments, the disclosure provides a method of treating cancer in a subject, comprising identifying the subject as not likely to respond to ICI monotherapy based on a dynamic signature disclosed herein, and administering at least one additional cancer therapeutic to the subject. In some embodiments, the additional cancer therapeutic includes but is not limited to surgery, radiotherapy (e.g., gamma-radiation, neutron beam radiotherapy, electron beam radiotherapy, proton therapy, brachytherapy, and / or systemic radioactive isotopes), chemotherapy, immunotherapy, endocrine therapy, hyperthermia, cryotherapy, transplant (e.g., stem cell or bone marrow), agents to attenuate adverse effects, corticosteroids (e.g., triamcinolone, methylprednisolone, budesonide, dexamethasone, triamcinolone, prednisone, hydrocortisone, dexamethasone, betamethasone, prednisolone, deflazacort, aldosterone, and combinations thereof), or a combination thereof. In some embodiments, the cancer therapeutic is radiotherapy (e.g., gamma-radiation, neutron beam radiotherapy, electron beam radiotherapy, proton therapy, brachytherapy, and / or systemic radioactive isotopes). In some embodiments, the cancer therapeutic is chemotherapy. In some embodiments, the cancer therapeutic is immunotherapy. In some embodiments, the cancer therapeutic is endocrine therapy. In some embodiments, the cancer therapeutic is hyperthermia. In some embodiments, the cancer therapeutic is cryotherapy. In some embodiments, the cancer therapeutic is a transplant (e.g., stem cell or bone marrow). In some embodiments, the cancer therapeutic is an agent to attenuate adverse effects. Exemplary immunotherapy treatments include, but are not limited to, immune checkpoint inhibitors (ICI; e.g., antibodies), T-cell transfer therapy (e.g., CAR T-cell therapy and TIL therapy), monoclonal antibodies (also known as therapeutic antibodies), cancer treatment vaccines (e.g., tumor cell-derived or dendritic cell-derived, oncolytic virus therapy), immune Attorney Docket: AHY-00325 system modulators (e.g., cytokines, Bacillus Calmette-Guerin (BCG), and immunomodulatory drugs (also known as biological response modifiers). Exemplary cytokines include, but are not limited to, interferons (INFs) and interleukins (ILs). Exemplary immunomodulatory drugs include, but are not limited to, thalidomide, lenalidomide, pomalidomide, and imiquimod. In some embodiments, a subject with cancer is administered one or more ICI monotherapies (e.g., antibodies). In some embodiments, a subject with cancer is administered T-cell transfer therapy (e.g., CAR T-cell therapy and TIL therapy). In some embodiments, a subject with cancer is administered monoclonal antibodies (also known as therapeutic antibodies). In some embodiments, a subject with cancer is administered cancer treatment vaccines (e.g., tumor cell- derived or dendritic cell-derived, or oncolytic virus therapy). In some embodiments, a subject with cancer is administered immune system modulators (e.g., cytokines, Bacillus Calmette- Guerin (BCG), and immunomodulatory drugs (also known as biological response modifiers)). In some embodiments, a subject with cancer is administered a combination of one or more of ICI monotherapies (e.g., antibodies), T-cell transfer therapy (e.g., CAR T-cell therapy and TIL therapy), monoclonal antibodies (also known as therapeutic antibodies), cancer treatment vaccines (e.g., tumor cell-derived or dendritic cell-derived, oncolytic virus therapy), immune system modulators (e.g., cytokines, Bacillus Calmette-Guerin (BCG), and immunomodulatory drugs (also known as biological response modifiers). In some embodiments, the disclosure provides methods of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of one or more cancer treatments, and the second time point is from about 3 weeks to about 6 months after administration of the one or more cancer treatments, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS; (ii) comparing the amount or level of the panel of Attorney Docket: AHY-00325 biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more cancer treatments; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some embodiments, the disclosure provides methods of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of one or more cancer treatments, and the second time point is from about 3 weeks to about 6 months after administration of the one or more cancer treatments, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: CA6, CDNF, MIA, MYOC, NEFL, and TCL1B; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non- response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more cancer treatments; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some embodiments, the disclosure provides methods of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of one or more cancer treatments, and the second time point is from about 3 weeks to about 6 months after administration of the one or more cancer treatments, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more cancer treatments; and (iii) Attorney Docket: AHY-00325 administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some embodiments, the disclosure provides methods of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of one or more cancer treatments, and the second time point is from about 3 weeks to about 6 months after administration of the one or more cancer treatments, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more cancer treatments; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some embodiments, the disclosure provides methods of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of one or more cancer treatments, and the second time point is from about 3 weeks to about 6 months after administration of the one or more cancer treatments, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or Attorney Docket: AHY-00325 more cancer treatments; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some embodiments, the disclosure provides methods of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of one or more immune checkpoint inhibitor monotherapies, and the second time point is from about 3 weeks to about 6 months after administration of the one or more immune checkpoint inhibitor monotherapies, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more immune checkpoint inhibitor monotherapies; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some embodiments, the disclosure provides methods of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of one or more immune checkpoint inhibitor monotherapies, and the second time point is from about 3 weeks to about 6 months after administration of the one or more immune checkpoint inhibitor monotherapies, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: CA6, CDNF, MIA, MYOC, NEFL, and TCL1B; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic Attorney Docket: AHY-00325 accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more immune checkpoint inhibitor monotherapies; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some embodiments, the disclosure provides methods of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of one or more immune checkpoint inhibitor monotherapies, and the second time point is from about 3 weeks to about 6 months after administration of the one or more immune checkpoint inhibitor monotherapies, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more immune checkpoint inhibitor monotherapies; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some embodiments, the disclosure provides methods of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of one or more immune checkpoint inhibitor monotherapies, and the second time point is from about 3 weeks to about 6 months after administration of the one or more immune checkpoint inhibitor monotherapies, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to Attorney Docket: AHY-00325 determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more immune checkpoint inhibitor monotherapies; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some embodiments, the disclosure provides methods of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject from two or more different time points, wherein the first time point is prior to administration of one or more immune checkpoint inhibitor monotherapies, and the second time point is from about 3 weeks to about 6 months after administration of the one or more immune checkpoint inhibitor monotherapies, wherein the panel of biomarkers comprises one or more of the biomarkers selected from: ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates with a diagnostic accuracy (AUC) of at least 0.7 that the subject is unlikely to respond to the one or more immune checkpoint inhibitor monotherapies; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject. In some or any of the foregoing embodiments, the one or more immune checkpoint inhibitor monotherapies are selected from an anti-PD1, anti-PDL1, anti-LAG3, and anti-CTLA4 antibodies. Kits In some aspects, the disclosure provides a kit comprising reagents and instructions necessary for carrying out the methods described herein. In some embodiments, the kit comprises reagents for detecting a panel of biomarkers described herein, and instructions for identifying a dynamic signature based on the panel of biomarkers. In some embodiments, the kit further comprises instructions for determining whether a subject will respond to an ICI monotherapy based on the dynamic signature. In some Attorney Docket: AHY-00325 embodiments, the kit further comprises instructions for administering an additional cancer therapeutic to a subject identified as not likely to respond to an ICI monotherapy. Definitions While various some embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such some embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It will be understood that various alternatives to some embodiments of the invention described herein may be employed. Whenever the term “at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least,” “greater than” or “greater than or equal to” applies to each of the numerical values in that series of numerical values. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3. Whenever the term “no more than,” “less than,” or “less than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “no more than,” “less than,” or “less than or equal to” applies to each of the numerical values in that series of numerical values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1. The term “about” or “approximately” when immediately preceding a numerical value means a range (e.g., plus or minus 10% of that value). For example, “about 50” can mean 45 to 55, “about 25,000” can mean 22,500 to 27,500, etc., unless the context of the disclosure indicates otherwise, or is inconsistent with such an interpretation. For example, in a list of numerical values such as “about 49, about 50, about 55, …”, “about 50” means a range extending to less than half the interval(s) between the preceding and subsequent values, e.g., more than 49.5 to less than 52.5. Furthermore, the phrases “less than about” a value or “greater than about” a value should be understood in view of the definition of the term “about” provided herein. Similarly, the term “about” when preceding a series of numerical values or a range of values (e.g., “about 10, 20, 30” or “about 10-30”) refers, respectively to all values in the series, or the endpoints of the range. Attorney Docket: AHY-00325 As used herein, the term “dynamic signature” refers to a panel of one or more biomarkers detected at two or more different time points. As used herein, the term “predictive performance” refers to the accuracy and significance of a model (e.g., one made using LMM and training data) to predict an outcome using untested data. As used herein, the term “non-responder” refers to a subject in need thereof (e.g., has cancer) who is treated (e.g., with an ICI monotherapy (e.g., anti-PD1, anti-PDL1, and / or anti- CTLA4 antibodies) and has progressive cancer (e.g., no change in tumor size / number, cancer is resistant to treatment, cancer metastasizes, etc.) As used herein, the term “responder” refers to a subject in need thereof (e.g., has cancer) who is treated (e.g., with an ICI monotherapy (e.g., anti-PD1, anti-PDL1, and / or anti-CTLA4 antibodies) and has a complete response, partial response, or stable disease (e.g., tumor size and / or number stay the same or decrease, cancer does not metastasize). As used herein, the term “immune checkpoint inhibitor” (ICI) refers to a type of immunotherapy that inhibits (e.g., blocks, reduces, slows) immune checkpoint proteins from interacting with (e.g., binding) their cognate ligand (e.g., protein partner). Accordingly, “ICI monotherapy” refers to the use of monoclonal antibodies (mAbs) in cancer treatment. As used herein, the terms “detect,” “detecting” or “detection” may describe either the general act of discovering or discerning or the specific observation of a composition. Detecting a composition may comprise determining the presence or absence of a composition. Detecting may comprise quantifying a composition. For example, detecting comprises determining the expression level of a composition. The composition may comprise a nucleic acid molecule. For example, the composition may comprise at least a portion of the cancer markers disclosed herein. Alternatively, or additionally, the composition may be a detectably labeled composition. The discriminatory ability of biomarkers is evaluated using measures such as sensitivity, specificity, and receiver-operating characteristics (ROC) probability curves. Sensitivity is the ability to detect a disease in patients in whom the disease is truly present (i.e., a true positive), and specificity is the ability to rule out the disease in patients whom the disease is truly absent (i.e., a true negative). To plot ROC probability curves, the false positive rate is plotted on the x- axis against the true positive rate on the y-axis. The area under the ROC curve (AUC, or AUROC) ranges from 0.5, indicating no power to separate cases from non-cases, to 1, indicating Attorney Docket: AHY-00325 perfect discrimination. To be clinically meaningful, biomarkers should have an AUC value as close to 1 as possible. As used herein “sensitivity” refers to the proportion of patients who test positive for the disease among those who actually have the disease; the higher the sensitivity, the lower the proportion of false negative results. As used herein, “specificity” refers to the proportion of patients who test negative for the disease among those who actually are free of the disease; the higher the specificity, the lower the proportion of false positive results. The term “AUC” or “AUROC” is an abbreviation for the area under the receive- operating characteristics (ROC) probability curve. The ROC probability curve is generated by plotting the true positive rate and the false positive rate. The true positive rate is the number of true positives divided by the total number of true positives + false negatives. The false positive rate is the number of false positives divided by the total number false positives + true negatives. The AUC provides an aggregate measure of performance across all possible classification models. AUC ranges in value from 0 to 1. A model whose prediction are 100% correct has an AUC of 1.0. EXAMPLES Example 1. Identification of Dynamic Protein Signature of Non-Responders to Immune Checkpoint Inhibitor Monotherapy The majority of patients treated with an immune checkpoint inhibitor (e.g., anti-PD1, anti-PDL1, and / or anti-CTLA4 antibodies), do not have durable treatment responses. Early prediction of such non-response will prevent patients from being treated unnecessarily and exhibiting potential side effects of such treatment. It will also allow for patients to receive the appropriate treatment for their cancer within a quicker timeframe. To identify a protein signature capable of predicting non-response to immune checkpoint inhibitor monotherapy, plasma proteomic data from a cohort of 116 metastatic melanoma patients treated with standard of care therapy, i.e., immune checkpoint inhibition with either anti- PD-1 or a combination of anti-PD-1 with anti-CTLA-4, was assessed using Olink multiplex proximity extension assay (PEA) identify dynamic precision biomarkers. Attorney Docket: AHY-00325 Patients defined as non-responders (NR) were those with progressive disease, whereas responders included patients with complete response, partial response, or stable disease. NR (n=50) and responder (n=66) patients’ plasma samples were taken at pre-treatment (baseline), 6 weeks, and 6 months post-treatment. Plasma proteomic analysis was performed using the Olink PEA. In brief, for each protein, a pair of oligonucleotide-labeled antibody probes bound to the targeted protein and if the two probes were in close proximity, a PCR target sequence was formed by a proximity-dependent DNA polymerization event and the resulting sequence was subsequently detected and quantified by real-time PCR using the Fluidigm BioMark™ HD real- time PCR platform. The patients’ plasma samples were randomized across four 96-well plates such that each sample was assayed once and normalized for any plate effects using the built-in inter-plate controls according to manufacturer suggestion. The resulting abundance levels were provided in normalized protein expression (NPX; relative expression) that was on log2-scale. Each PEA had an experimentally determined lower limit of detection (LOD) calculated based on negative controls that were included in each run and measurements below this limit were removed from further analysis. Assay characteristics including detection limits, assay performance and validations are available at www.olink.com. Linear mixed effects models (LMMs) were fit independently to each protein using the lme4 package in R with NPX values as the dependent variable and timepoint, response status and the interaction between the two terms as independent variables. Significance of the three model terms was determined with an F-test using Satterthwaite degrees of freedom and type III sum of squares implemented with the lmerTest package in R. P values were adjusted to control the false discovery rate (FDR) at 5% using the Benjamini-Hochberg method. NPX differences between responders and non-responders were estimated at each timepoint for proteins with statistically significant response status or interaction model terms. The group differences and Sidak adjusted p-values were calculated using the emmeans package in R. Differences with adjusted p values less than 0.05 were considered statistically significant. Responder vs. non-responder NPX differences at each timepoint were calculated from the LMMs for all proteins. Predictive models were generated using all available timepoints: Baseline (FIGs.1A- 1B), Baseline and Week 6 (FIGs. 2A-2B), Baseline and Month 6 (FIGs.3A-3B), Baseline and Week 6 and Month 6 (FIGs.4A-4B), and Week 6 and Month 6 (FIGs. 5A-5B). For each combination of data, predictive performance was estimated using 100 repeats of 5-fold cross Attorney Docket: AHY-00325 validation using all proteins. Measures of predictive performance are reported as medians and 95% confidence intervals calculated from the 100 repeats of the cross validation (Table 1). Features (proteins) were ranked by how frequently they were identified and those that were confirmed in 25% of these cross validations were included in the model. Table 1. Performance summary for dynamic signatures for non-responders by time point a average value across all patients b 95% confidence interval (CI) These analyses led to the identification of predictors of non-response to SOC therapy ranging from 15–38 proteins. The predictive model including Baseline, Week 6, and Month 6 data (FIGs.4A-4B) demonstrated the highest level of specificity (0.91) and highest AUC (0.80) in comparison to the other models. Example 2. Prediction of Non-Response to Immune Checkpoint Inhibitor Monotherapy Blood samples are obtained from a patient that is receiving treatment for a cancer with immune checkpoint inhibitor (ICI) monotherapy at various time points, such as: before treatment, and one or more time points after treatment but before clinical readout. The blood is processed to obtain plasma for protein analysis of the dynamic signature. The proteins detailed in Table 2 are exemplary dynamic signatures that are detected in the plasma samples obtained at various time points. Any method suitable for detecting protein in plasma samples is used. For example, ELISA or multiplex PEA. Expression of one or more of the proteins is evaluated relative to a control sample. The control sample is plasma from the same subject at baseline to assess (1) one or more individual protein levels, or (2) a starting signature score. Based on the increased expression of at least six proteins in the dynamic signature, the patient is identified as being predicted to not respond to ICI monotherapy. This information is provided to the patient or Attorney Docket: AHY-00325 the patient’s clinical provider. Based on the protein analysis described above, the patient will pursue an alternative therapy alone or in combination with ICI therapy. Table 2. Exemplary dynamic signatures

Claims

Attorney Docket: AHY-00325 CLAIMS 1. A method of providing one or more additional therapies to a subject with cancer to treat cancer in the subject, comprising: (i) determining an amount or level of a panel of biomarkers in a biological sample obtained from the subject at two or more different time points, wherein the first time point is prior to administration of an immune checkpoint inhibitor monotherapy, and at least one subsequent time point is from about 3 weeks to about 6 months after administration of the immune checkpoint inhibitor, wherein the panel of biomarkers comprises one or more of CA6, CDNF, MIA, MYOC, NEFL, and TCL1B; (ii) comparing the amount or level of the panel of biomarkers from the two or more different time points to determine a non-response score, wherein the non-response score indicates the subject is unlikely to respond to the immune checkpoint inhibitor monotherapy; and (iii) administering one or more additional therapies to the subject determined to have a non-response score, thereby treating cancer in the subject.

2. The method of claim 1, wherein the one or more additional therapies are selected from surgery, radiotherapy, chemotherapy, immunotherapy, endocrine therapy, cryotherapy, and corticosteroids.

3. A method for identifying a subject with cancer likely to not respond to an immune checkpoint inhibitor monotherapy administered to the subject, comprising: (i) providing an amount or level of a panel of biomarkers in a biological sample obtained from the subject at two or more different time points, wherein the first time point is prior to administration of an immune checkpoint inhibitor monotherapy, and the at least one subsequent time point is from about 3 weeks to about 6 months after administration of the immune checkpoint inhibitor, the panel of biomarkers comprises one or more of CA6, CDNF, MIA, MYOC, NEFL, and TCL1B; (ii) comparing the amount or level of the panel of biological markers from the two or more different time points to determine a non-response score, wherein the non-response score indicates the subject is unlikely to respond to the immune checkpoint inhibitor monotherapy,Attorney Docket: AHY-00325 thereby identifying a subject likely not to respond to the immune checkpoint inhibitor monotherapy.

4. The method of any one of claims 1-3, wherein the immune checkpoint inhibitor monotherapy targets PD-1, PD-L1, CTLA4, LAG3, or any combination thereof.

5. The method of any one of claims 1-4, wherein the immune checkpoint inhibitor monotherapy is an antibody.

6. The method of claim 5, wherein the antibody is ipilimumab, nivolumab, relatlimab, pembrolizumab, atezolizumab, durvalumab, avelumab, cemiplimab, or dostarlimab.

7. The method of any one of claims 1-6, wherein the panel of biomarkers comprises CA6, CDNF, MIA, MYOC, NEFL, and TCL1B.

8. The method of any one of claims 1-6, wherein the panel of biomarkers comprises one or more of ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS.

9. The method of any one of claims 1-6, wherein the panel of biomarkers comprises at least six of ADAM22, ADAMTS8, AMTS8, ANGPT2, AOC1, BCL2L11, BMP4, BRK1, CA6, CCL13, CCL25, CD14, CD34, CDH17, CDNF, CERT, CES3, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FBP1, FRZB, GBP2, GFBP2, GLT8D2, GPR37, HGF, HMBS, IGFBP2, IL5, IL6, ITGB6, ITM2A, KRT5, LILRB4, MIA, MMP13, MMP3, MMP8, MYOC, NEFL, NID1, NOS3, NRP1, PAEP, PAPPA, PRTG, PSPN, PTGDS, SFTPD, SMOC1, TCL1A,Attorney Docket: AHY-00325 TCL1B, TCN2, TDGF1, TFPI, TGREM2, TINAGL1, TNC, TNFRSF10B, TNFSF14, VASN, and WARS.

10. The method of any one of claims 1-6, wherein the panel of biomarkers comprises one or more of ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN.

11. The method of any one of claims 1-6, wherein the panel of biomarkers comprises at least six of ADAM22, BMP4, CA6, CCL25, CDH17, CDNF, CES3, GBP2, HGF, HMBS, IL5, IL6, ITGB6, KRT5, MIA, MMP13, MMP3, MYOC, NEFL, NID1, NRP1, PAEP, PTGDS, SFTPD, SMOC1, TCL1B, TGREM2, TNC, TNFRSF10B, TNFSF14, and VASN.

12. The method of any one of claims 1-6, wherein the panel of biomarkers comprises one or more of AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS.

13. The method of any one of claims 1-6, wherein the panel of biomarkers comprises at least six of AMTS8, ANGPT2, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CERT, CLEC4A, CPVL, CSF3, CTSF, CTSL, DKK4, ECE1, ENG, FRZB, GFBP2, GLT8D2, ITGB6, LILRB4, MIA, MMP8, MYOC, NEFL, PAEP, PAPPA, PRTG, PSPN, TCL1A, TCL1B, TCN2, TDGF1, TFPI, TINAGL1, and WARS.

14. The method of any one of claims 1-6, wherein the panel of biomarkers comprises one or more of ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS.Attorney Docket: AHY-00325 15. The method of any one of claims 1-6, wherein the panel of biomarkers comprises at least six of ADAMTS8, ANGPT2, AOC1, BCL2L11, BRK1, CA6, CCL13, CD14, CD34, CDNF, CLEC4A, CPVL, DKK4, ECE1, FBP1, GPR37, IGFBP2, ITM2A, LILRB4, MIA, MMP13, MMP8, MYOC, NEFL, NOS3, PAPPA, PSPN, TCL1A, TCL1B, TCN2, TFPI, TINAGL1, VASN, and WARS.

16. The method of any one of claims 1-15, wherein the panel of biomarkers comprises proteins.

17. The method of claim 16, wherein (i) comprises detecting the panel of biomarkers in the biological sample by enzyme-linked immunosorbent assay (ELISA) or proximity extension assay (PEA).

18. The method of any one of claims 1-15, wherein the panel of biomarkers comprises nucleic acid molecules.

19. The method of claim 18, wherein (i) comprises detecting the panel of biomarkers in the biological sample by a nucleic acid hybridization assay, a nucleic acid amplification assay, or sequencing.

20. The method of any one of claims 1-19, wherein the biological sample is a blood sample, a serum sample, or a plasma sample.

21. The method of claim 30, wherein the biological sample is a plasma sample.

22. The method of any one of claims 1-21, wherein the at least one subsequent time point is before clinical readout.

23. The method of any one of claims 1-22, wherein the panel of biomarkers is determined from at least two subsequent time points.Attorney Docket: AHY-00325 24. The method of any one of claims 1-23, wherein the amount or level of the panel of biomarkers is increased between the first time point and the at least one subsequent time point.

25. The method of claim 24, wherein the increased amount or level of the panel of biomarkers between the first time point and the at least one subsequent time point is statistically significant.

26. The method of any one of claims 1-25, wherein the non-response score has a diagnostic accuracy (AUC) of at least 0.7, optionally wherein the diagnostic accuracy is at least 0.

8.

27. The method of any one of claims 1-26, wherein the non-response score indicated the subject will not respond to the immune checkpoint inhibitory therapy.

28. The method of any one of claims 1-2 and 3-27, wherein the one or more additional therapies improves the subject’s response to the immune checkpoint inhibitor monotherapy.

29. A kit suitable for performing the method of any one of claims 1-28, comprising (i) one or more reagents for detecting the amount or level of the panel of biomarkers, and (ii) instructions for detecting the amount or level of the panel of biomarkers in a biological sample from a subject obtained from two or more different time points.

30. The kit of claim 29, wherein the instructions comprise steps for identifying the subject as not likely to respond to an immune checkpoint inhibitor monotherapy.

31. The kit of claim 29 or 30, wherein the biological sample is a blood sample, a serum sample, or a plasma sample.

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