IDENTIFICATION AND USE OF CIRCULAR NUCLEAR ACID TUMOR MARKERS

Description

STATEMENT OF GOVERNMENTAL SUPPORT

[0001] This invention was made with government support under grant number W81XWH-12-1-0285 awarded by the Department of Defense. The government has certain rights in the invention.BACKGROUND OF THE INVENTION

[0002] Tumors continually shed DNA into the circulation, where it is readily accessible (Stroun et al. (1987) Eur J Cancer Clin Oncol 23:707-712). Analysis of such cancer-derived cell-free DNA (cfDNA) has the potential to revolutionize detection and monitoring of cancer. Noninvasive access to malignant DNA is particularly attractive for solid tumors, which cannot be repeatedly sampled without invasive procedures. In non-small cell lung cancer (NSCLC), PCR-based assays have been used previously to detect recurrent point mutations in genes such as KRAS or EGFR in plasma DNA (Taniguchi et al. (2011) Clin. Cancer Res. 17:7808-7815; Gautschi et al. (2007) Cancer Lett. 254:265-273; Kuang et al. (2009) Clin. Cancer Res. 15:2630-2636; Rosell et al. (2009) N. Engl. J. Med. 361:958-967), but the majority of patients lack mutations in these genes.

[0003] Other studies have proposed identifying patient-specific chromosomal rearrangements in tumors via whole genome sequencing (WGS), followed by breakpoint qPCR from cfDNA (Leary et al. (2010) Sci. Transl. Med. 2:20ra14; McBride et al. (2010) Genes Chrom. Cancer 49:1062-1069). While sensitive, such methods require optimization of molecular assays for each patient, limiting their widespread clinical application. More recently, several groups have reported amplicon-based deep sequencing methods to detect cfDNA mutations in up to 6 recurrently mutated genes (Forshew et al. (2012) Sci. Transl. Med. 4:136ra168; Narayan et al. (2012) Cancer Res. 72:3492-3498; Kinde et al. (2011) Proc. Natl Acad. Sci. USA 108:9530-9535). While powerful, these approaches are limited by the number of mutations that can be interrogated (Rachlin et al. (2005) BMC Genomics 6:102) and the inability to detect genomic fusions.

[0004] PCT International Patent Publication No. 2011 / 103236 describes methods for identifying personalized tumor markers in a cancer patient using "mate-paired" libraries. The methods are limited to monitoring somatic chromosomal rearrangements, however, and must be personalized for each patient, thus limiting their applicability and increasing their cost.

[0005] U.S. Patent Application Publication No. 2010 / 0041048 A1 describes the quantitation of tumor-specific cell-free DNA in colorectal cancer patients using the "BEAMing" technique (Beads, Emulsion, Amplification, and Magnetics). While this technique provides high sensitivity and specificity, this method is for single mutations and thus any given assay can only be applied to a subset of patients and / or requires patient-specific optimization. U.S. Patent Application Publication No. 2012 / 0183967 A1 describes additional methods to identify and quantify genetic variations, including the analysis of minor variants in a DNA population, using the "BEAMing" technique.

[0006] U.S. Patent Application Publication No. 2012 / 0214678 A1 describes methods and compositions for detecting fetal nucleic acids and determining the fraction of cell-free fetal nucleic acid circulating in a maternal sample. While sensitive, these methods analyze polymorphisms occurring between maternal and fetal nucleic acids rather than polymorphisms that result from somatic mutations in tumor cells. In addition, methods that detect fetal nucleic acids in maternal circulation require much less sensitivity than methods that detect tumor nucleic acids in cancer patient circulation, because fetal nucleic acids are much more abundant than tumor nucleic acids.

[0007] U.S. Patent Application Publication Nos. 2012 / 0237928 A1 and 2013 / 0034546 describe methods for determining copy number variations of a sequence of interest in a test sample comprising a mixture of nucleic acids. While potentially applicable to the analysis of cancer, these methods are directed to measuring major structural changes in nucleic acids, such as translocations, deletions, and amplifications, rather than single nucleotide variations.

[0008] U.S. Patent Application Publication No. 2012 / 0264121 A1 describes methods for estimating a genomic fraction, for example, a fetal fraction, from polymorphisms such as small base variations or insertions-deletions. These methods do not, however, make use of optimized libraries of polymorphisms, such as, for example, libraries containing recurrently-mutated genomic regions.

[0009] U.S. Patent Application Publication No. 2013 / 0024127 A1 describes computer-implemented methods for calculating a percent contribution of cell-free nucleic acids from a major source and a minor source in a mixed sample. The methods do not, however, provide any advantages in identifying or making use of optimized libraries of polymorphisms in the analysis.

[0010] PCT International Publication No. WO 2010 / 141955 A2 describes methods of detecting cancer by analyzing panels of genes from a patient-obtained sample and determining the mutational status of the genes in the panel. The methods rely on a relatively small number of known cancer genes, however, and they do not provide any ranking of the genes according to effectiveness in detection of relevant mutations. In addition, the methods were unable to detect the presence of mutations in the majority of serum samples from actual cancer patients.

[0011] There is thus a need for new and improved methods to detect and monitor tumor-related nucleic acids in cancer patients.

[0012] Frank Diehl et al., (2008, Nature Medicine, Vol. 14, no. 9, pages 958-990) describes assessing tumor dynmacis by circulating mutant DNA.

[0013] J. He et al. (2011, Oncotarget, Vol. 2, no. 3, pages 178-185) studies IgH gene rearrangements as plasma biomarkers in Non-Hodgkin's Lymphoma patients.

[0014] WO 2006 / 047787 discloses methods for monitoring disease progression or recurrence.SUMMARY OF THE INVENTION

[0015] The present invention concerns a method of detecting, diagnosing, prognosing, or therapy selection of a cancer in a subject in need thereof, the method comprising: (i) producing a selector set comprising: (a) obtaining sequence information of a tumor sample from the subject suffering from cancer; (b) comparing the sequencing information of the tumor sample to sequencing information from a non-tumor sample from the subject to identify one or more mutations specific to the sequencing information of the tumor sample; and (c) producing a selector set corresponding to one or more genomic regions comprising the one or more mutations specific to the sequencing information of the tumor sample, wherein the selector set comprises a plurality of oligonucleotides that selectively hybridize the one or more genomic regions; (ii) providing a cell-free DNA (cfDNA) sample obtained from the subject, (iii) performing hybrid selection on the cfDNA sample using the selector set to enrich for cfDNA corresponding to genomic regions known to contain tumor-specific somatic mutations, (iv) sequencing the hybrid selected cfDNA sample to generate sequencing information, and (v) analyzing the sequence information of the hybrid selected cfDNA sample to detect circulating tumor DNA (ctDNA) in the sample, wherein the method is capable of detecting a percentage of ctDNA that is less than or equal to 2% of total cfDNA.

[0016] The present invention is further defined by the appended claims.

[0017] Compositions and methods, including methods of bioinformatic analysis, are provided for the highly sensitive analysis of circulating tumor DNA (ctDNA), e.g. DNA sequences present in the blood of an individual that are derived from tumor cells. The methods may be referred to as CAncer Personalized Profiling by Deep Sequencing (CAPP-Seq). Tumors of particular interest are solid tumors, including without limitation carcinomas, sarcomas, gliomas, lymphomas, melanomas, etc., although hematologic cancers, such as leukemias, are not excluded.

[0018] The methods combine optimized library preparation methods with a multi-phase bioinformatics approach to design a "selector" population of DNA oligonucleotides, which correspond to recurrently mutated regions in the cancer of interest. The selector population of DNA oligonucleotides, which may be referred to as a selector set, comprises probes for a plurality of genomic regions, and is designed such that at least one mutation within the plurality of genomic regions is present in a majority of all subjects with the specific cancer; and preferably multiple mutations are present in a majority of all subjects with the specific cancer.

[0019] In one aspect , methods are provided for the identification of a selector set appropriate for a specific tumor type. Also provided are oligonucleotide compositions of selector sets, which may be provided adhered to a solid substrate, tagged for affinity selection, etc.; and kits containing such selector sets. Included, without limitation, is a selector set suitable for analysis of non-small cell lung carcinoma (NSCLC). Such kits may include executable instructions for bioinformatics analysis of the CAPP-Seq data.

[0020] According to the invention, methods are provided for the use of a selector set in the diagnosis and monitoring of cancer in an individual patient. In some embodiments the selector set is used to enrich, e.g. by hybrid selection, for ctDNA that corresponds to the regions of the genome that are most likely to contain tumor-specific somatic mutations. The "selected" ctDNA is then amplified and sequenced to determine which of the selected genomic regions are mutated in the individual tumor. An initial comparison is optionally made with the individual's germline DNA sequence and / or a tumor biopsy sample from the individual. These somatic mutations provide a means of distinguishing ctDNA from germline DNA, and thus provide useful information about the presence and quantity of tumor cells in the individual.

[0021] In some embodiments, the ctDNA content in an individual's blood, or blood derivative, sample is determined at one or more time points, optionally in conjunction with a therapeutic regimen. The presence of the ctDNA correlates with tumor burden, and is useful in monitoring response to therapy, monitoring residual disease, monitoring for the presence of metastases, monitoring total tumor burden, and the like. Although not required, for some methods CAPP-Seq may be performed in conjunction with tumor imaging methods, e.g. PET / CT scans and the like.

[0022] CAPP-seq may be used for cancer screening and biopsy-free tumor genotyping, where a patient ctDNA sample is analyzed without reference to a biopsy sample. In some such embodiments, where CAPP-Seq identifies a mutation in a clinically actionable target from a ctDNA sample, the methods include providing a therapy appropriate for the target. Such mutations include, without limitation, rearrangements and other mutations involving oncogenes, receptor tyrosine kinases, etc. Actionable targets may include, for example, ALK, ROS1, RET, EGFR, KRAS, and the like.

[0023] The CAPP-Seq methods may include steps of data analysis, which may be provided as a program of instructions executable by computer and performed by means of software components loaded into the computer. Such methods include the design for identification selector set for a cancer of interest. Other bioinformatics methods are provided for determining and quantitating when circulating tumor DNA is detectable above background, e.g. using an approach that integrates information content and classes of mutation into a detection index.

[0024] Disclosed herein is a method for determining the presence of tumor nucleic acids (tNA) in a cell-free nucleic acids (cfNA) sample from an individual by detection of somatic mutations. The method may comprise (a) obtaining a cfNA sample; (b) selecting the cfNA for sequences corresponding to a plurality of regions of mutations in a cancer of interest; (c) sequencing the selected cfNA; (d) determining the presence of somatic mutations, wherein the presence of the somatic mutations may be indicative of tumor cells present in the individual; and (e) providing the individual with an assessment of the presence of tumor cells.

[0025] The cell-free nucleic acid may be cell-free DNA (cfDNA). The cell-free nucleic acid may be cell-free RNA (cfRNA). The cell-free nucleic acids may be a mixture of cell-free DNA (cfDNA) and cell-free RNA (cfRNA). The tumor nucleic acid may be a nucleic acid originating from a tumor cell. The tumor nucleic acid may be tumor-derived DNA (tDNA). The tumor nucleic acid may be a circulating tumor DNA (ctDNA). The tumor nucleic acid may be tumor-derived RNA (tRNA). The tumor nucleic acid may be a circulating tumor RNA (ctRNA). The tumor nucleic acids may be a mixture of tumor-derived DNA and tumor-derived RNA. The tumor nucleic acids may be a mixture of ctDNA and ctRNA.

[0026] Selecting the cfNA may comprise (i) hybridizing the cell-free nucleic acid sample to a plurality of selector set probes comprising a specific binding member; (ii) binding hybridized nucleic acids to a complementary specific binding member; and (iii) washing away unbound DNA.

[0027] The cfNA sample may be compared to a known tumor DNA sequence from the individual.

[0028] The cfNA sample may be de novo analyzed for the presence of somatic mutations.

[0029] The somatic mutations may include single nucleotide variants, insertions, deletions, copy number variations, and rearrangements.

[0030] The plurality of regions of mutations may comprise at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175 or 200 different genomic regions. The plurality of regions of mutations may comprise at least 500 different genomic regions. The plurality of genomic regions of mutations may comprise a total of from 100 to 500 kb of sequence.

[0031] At least one somatic mutation may be present in at least 60%, 65%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% of individuals in a patient population for the cancer of interest.

[0032] The cancer of interest may be a leukemia. The cancer of interest may be a solid tumor. The cancer may be a carcinoma. The carcinoma may be an adenocarcinoma or a squamous cell carcinoma. The carcinoma may be non-small cell lung cancer.

[0033] The individual may be not previously diagnosed with cancer. The individual may be undergoing treatment for cancer.

[0034] Two or more samples may be obtained from the individual over a period of time and compared for residual disease or tumor burden.

[0035] The method may further comprise treating the individual in accordance with the analysis of the presence of tumor cells. The method may further comprise treating the individual based on the detection of the somatic mutations.

[0036] Determining the presence of somatic mutations may comprise: (i) integrating cfDNA fractions across all somatic SNVs; (ii) performing a position-specific background adjustment; and (iii) evaluating statistical significance by Monte Carlo sampling of background alleles across the selector, wherein steps (i) - (iii) are embodied as a program of instructions executable by computer and performed by means of software components loaded into the computer.

[0037] The method may further comprise analysis of insertions and / or deletions by comparing its fractional abundance in a given cfDNA sample against its fractional abundance in a cohort. The method may further comprise combining the fractional abundance into a single Z-score.

[0038] The method may further comprise integrating different mutation types to estimate the significance of tumor burden quantitation.

[0039] Determining the presence of somatic mutations may be identification of genomic fusion events and breakpoints by the method comprising: (i) identification of discordant reads; (ii) detection of breakpoints at base pair-resolution, and (iii) in silico validation of candidate fusions, wherein steps (i) - (iii) are embodied as a program of instructions executable by computer and performed by means of software components loaded into the computer.

[0040] Determining the presence of somatic mutation may comprise the steps of (i) taking allele frequencies from a single cfDNA sample and selecting high quality data; (ii) testing whether a given input cfDNA allele may be significantly different from the corresponding paired germline allele;(iii) assembling a database of cfDNA background allele frequencies by binomial distribution; (iv) testing whether a given input allele differs significantly from cfDNA background at the same position, and selecting those with an average background frequency of a predetermined threshold; and (v) distinguishing tumor-derived SNVs from remaining background noise by outlier analysis, wherein steps (i) - (v) may be embodied as a program of instructions executable by computer and performed by means of software components loaded into the computer.

[0041] The selector set probes may comprise sequences corresponding to a mutated genomic regions identified by the method comprising identifying a plurality of genomic regions from a group of genomic regions that may be mutated in a specific cancer.

[0042] Identifying the plurality of genomic regions may comprise for each genomic region in the plurality of genomic regions, ranking the genomic region to maximize the number of all subjects with the specific cancer having at least one mutation within the genomic region.

[0043] Identifying the plurality of genomic regions may comprise: (i) selecting genes known to be drivers in the cancer of interest to generate a pool of known drivers; (ii) selecting exons from known drivers with the highest recurrence index (RI) that identify at least one new patient compared to step (a); and repeating until no further exons meet these criteria; (iii) identifying remaining exons of known drivers with an RI ≥ 30 and with SNVs covering ≥3 patients in the relevant database that result in the largest reduction in patients with only 1 SNV; and repeating until no further exons meet these criteria; (iv) repeating step (b) using RI ≥ 20; (v) adding in all exons from additional genes previously predicted to harbor driver mutations; and (vi) adding for known recurrent rearrangement the introns most frequently implicated in the fusion event and the flanking exons, wherein steps (i) - (vi) are embodied as a program of instructions executable by computer and performed by means of software components loaded into the computer.

[0044] The plurality of regions of mutations in a cancer of interest may be selected from the regions set forth in Table 2.

[0045] The method of Claim 27, wherein the plurality of regions of mutations may comprise at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 regions set forth in Table 2.

[0046] Further disclosed herein are compositions comprising selector set probes. The composition may comprise a set of selector set probes of at least about 25 nucleotides in length, comprising a specific binding member, and comprising sequences from at least 100 regions set forth in Table 2.

[0047] The set of selector probes may comprise oligonucleotides comprising sequences from at least 300 regions from Table 2. The set of selector probes may comprise oligonucleotides comprising sequences from at least 500 regions from Table 2.

[0048] Further disclosed herein are populations of cell-free DNA (cfDNA). The population of cfDNA may be an enriched population. The enriched population of cfDNA may be produced by hybrid selection. Hybrid selection may comprise of use of one or more selector set probes. The selector set probes may be attached to a solid or semi-solid support. The support may comprise an array. The support may comprise a bead. The bead may be a coated bead. The bead may be a streptavidin bead. The solid support may comprise a flat surface. The solid support may comprise a slide. The solid support may comprise a glass slide.

[0049] Further disclosed herein are methods for detecting, diagnosing, prognosing, or therapy selection for a subject suffering from a disease or condition. The method may comprise: (a) obtaining sequence information of a cell-free DNA (cfDNA) sample derived from the subject; and (b) using sequence information derived from (a) to detect cell-free non-germline DNA (cfNG-DNA) in the sample, wherein the method may be capable of detecting a percentage of cfNG-DNA that may be less than 2% of total cfDNA.

[0050] The method may be capable of detecting a percentage of ctDNA that may be less than 1.5% of the total cfDNA. The method may be capable of detecting a percentage of ctDNA that may be less than 1% of the total cfDNA. The method may be capable of detecting a percentage of ctDNA that may be less than 0.5% of the total cfDNA. The method may be capable of detecting a percentage of ctDNA that may be less than 0.1% of the total cfDNA. The method may be capable of detecting a percentage of ctDNA that may be less than 0.01% of the total cfDNA. The method may be capable of detecting a percentage of ctDNA that may be less than 0.001% of the total cfDNA. The method may be capable of detecting a percentage of ctDNA that may be less than 0.0001% of the total cfDNA.

[0051] The sample may be a plasma or serum sample (sweat, breath, tears, saliva, urine, stool, amniotic fluid). The sample may be a cerebral spinal fluid sample. In some instances, the sample is not a pap smear fluid sample. In some instances, the sample is not a cyst fluid sample. In some instances, the sample is not a pancreatic fluid sample.

[0052] The sequence information may comprise information related to at least 10, 20, 30, 40, 100, 200, or 300 genomic regions. The genomic regions may comprise genes, exonic regions, intronic regions, untranslated regions, non-coding regions or a combination thereof. The genomic regions may comprise two or more of exonic regions, intronic regions, and untranslated regions. The genomic regions may comprise at least one exonic region and at least one intronic region. At least 5% of the genomic regions may comprise intronic regions. At least about 20% of the genomic regions may comprise exonic regions.

[0053] The genomic regions may comprise less than 1.5 megabases (Mb) of the genome. The genomic regions may comprise less than 1 Mb of the genome. The genomic regions may comprise less than 500 kilobases (kb) of the genome. The genomic regions may comprise less than 50, 75, 100 or 350 kb of the genome. The genomic regions may comprise between 100 kb to 300 kb of the genome.

[0054] The sequence information may comprise information pertaining to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20 or more genomic regions from a selector set comprising a plurality of genomic regions. The sequence information may comprise information pertaining to 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions from a selector set comprising a plurality of genomic regions. The sequence information may comprise information pertaining to a plurality of genomic regions.

[0055] The plurality of genomic regions may be based on a selector set comprising genomic regions comprising one or more mutations present in one or more subjects from a population of cancer subjects. At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the plurality of genomic regions may be based on a selector set comprising genomic regions comprising one or more mutations present in one or more subjects from a population of cancer subjects.

[0056] The total size of the genomic regions of the selector set may comprise less than 1.5 megabases (Mb), 1Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of the genome. The total size of the genomic regions of the selector set may be between 100 kb to 300 kb of the genome.

[0057] The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 2. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 6. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 7. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 8. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 9. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 10. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 11. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 12. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 13. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 14. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 15. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 16. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 17. The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 18. In some instances, the subject is not suffering from a pancreatic cancer.

[0058] Obtaining sequence information of the cell-free DNA sample may comprise performing massively parallel sequencing. Massively parallel sequencing may be performed on a subset of a genome of cfDNA from the cfDNA sample. The subset of the genome may comprise less than 1.5 megabases (Mb), 1Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of the genome. The subset of the genome may comprise between 100 kb to 300 kb of the genome.

[0059] Obtaining sequence information of the cell-free DNA sample may comprise using single molecule barcoding. Using single molecule barcoding may comprise attaching barcodes comprising different sequences to nucleic acids from the cfDNA sample.

[0060] The sequence information may comprise sequence information pertaining to the adaptors. The sequence information may comprise sequence information pertaining to the molecular barcodes. The sequence information may comprise sequence information pertaining to the sample indexes.

[0061] The method may comprise obtaining sequencing information of cell-free DNA samples from two or more samples from the subject. The method may comprise obtaining sequencing information of cell-free DNA samples from two or more different subjects. The two or more samples may be the same type of sample. The two or more samples may be two different types of sample. The two or more samples may be obtained from the subject at the same time point. The two or more samples may be obtained from the subject at two or more time points. The samples from two or more different subjects may be indexed and pooled together prior to sequencing.

[0062] Using the sequence information may comprise detecting one or more mutations. The one or more mutations may comprise one or more SNVs, indels, fusions, breakpoints, structural variants, variable number of tandem repeats, hypervariable regions, minisatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, simple sequence repeats, copy number variiants or a combination thereof in selected regions of the subject's genome. Using the sequence information may comprise detecting one or more of SNVs, indels, copy number variants, and rearrangements in selected regions of the subject's genome. Using the sequence information may comprise detecting two or more of SNVs, indels, copy number variants, and rearrangements in selected regions of the subject's genome. Using the sequence information may comprise detecting at least one SNV, indel, copy number variant, and rearrangement in selected regions of the subject's genome.

[0063] In some instances, detecting the one or more mutations does not involve performing digital PCR (dPCR).

[0064] Detecting the one or more mutations may comprise applying an algorithm to the sequence information to determine a quantity of one or more genomic regions from a selector set. The selector set may comprise a plurality of genomic regions comprising one or more mutations present in one or more cancer subjects from a population of cancer subjects. The selector set may comprise a plurality of genomic regions comprising one or more mutations present in at least about 60% of cancer subjects from population of cancer subjects.

[0065] The cfNG-DNA may be derived from a tumor in the subject. The method may further comprise detecting a cancer in the subject based on the detection of the cfNG-DNA. The method may further comprise diagnosing a cancer in the subject based on the detection of the cfNG-DNA. Diagnosing the cancer may have a sensitivity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Diagnosing the cancer may have a specificity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. The method may further comprise prognosing a cancer in the subject based on the detection of the cfNG-DNA. Prognosing the cancer may have a sensitivity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Prognosing the cancer may have a specificity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. The method may further comprise determining a therapeutic regimen for the subject based on the detection of the cfNG-DNA. The method may further comprise administering an anti-cancer therapy to the subject based on the detection of the cfNG-DNA.

[0066] The cfNG-DNA may be derived from a fetus in the subject. The method may further comprise diagnosing a disease or condition in the fetus based on the detection of the cfNG-DNA. Diagnosing the disease or condition in the fetus may have a sensitivity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Diagnosing the disease or condition in the fetus may have a specificity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%.

[0067] The cfNG-DNA may be derived from a transplanted organ, cell or tissue in the subject. The method may further comprise diagnosing an organ transplant rejection in the subject based on the detection of the cfNG-DNA. Diagnosing the organ transplant rejection may have a sensitivity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Diagnosing the organ transplant rejection may have a specificity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. The method may further comprise prognosing a risk of organ transplant rejection in the subject based on the detection of the cfNG-DNA. Prognosing the risk of organ transplant rejection may have a sensitivity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. Prognosing the risk of organ transplant rejection may have a specificity of at least about 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%. The method may further comprise determining an immunosuppresive therapy for the subject based on the detection of the cfNG-DNA. The method may further comprise administering an immunosuppresive therapy to the subject based on the detection of the cfNG-DNA.

[0068] Further disclosed herein are methods of diagnosing a cancer. The method may comprise (a) obtaining sequence information of cell-free genomic DNA derived from a sample from a subject, wherein the sequence information may be derived from regions that are mutated in at least 80% of a population of subjects afflicted with a cancer; and (b) diagnosing a cancer selected from a group consisting of lung cancer, breast cancer, colorectal cancer and prostate cancer in the subject based on the sequence information, wherein the method has a sensitivity of at least 80%.

[0069] The regions that are mutated may comprise a total size of less than 1.5 Mb of the genome. The regions that are mutated may comprise a total size of less than 1 Mb of the genome. The regions that are mutated may comprise a total size of less than 500 kb of the genome. The regions that are mutated may comprise a total size of less than 350 kb of the genome. The regions that are mutated may comprise a total size of less than 300 kb of the genome. The regions that are mutated may comprise a total size of less than 250 kb of the genome. The regions that are mutated may comprise a total size of less than 200 kb of the genome. The regions that are mutated may comprise a total size of less than 150 kb of the genome. The regions that are mutated may comprise a total size of less than 100 kb of the genome. The regions that are mutated may comprise a total size of less than 50 kb of the genome. The regions that are mutated may comprise a total size of less than 40 kb of the genome. The regions that are mutated may comprise a total size of less than 30 kb of the genome. The regions that are mutated may comprise a total size of less than 20 kb of the genome. The regions that are mutated may comprise a total size of less than 10 kb of the genome.

[0070] The regions that are mutated may comprise a total size between 100 kb -300 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -200 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -150 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -100 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -75 kb of the genome. The regions that are mutated may comprise a total size between 1 kb -50 kb of the genome.

[0071] The sequence information may be derived from 2 or more regions. The sequence information may be derived from 3 or more regions. The sequence information may be derived from 4 or more regions. The sequence information may be derived from 5 or more regions. The sequence information may be derived from 6 or more regions. The sequence information may be derived from 7 or more regions. The sequence information may be derived from 8 or more regions. The sequence information may be derived from 9 or more regions. The sequence information may be derived from 10 or more regions. The sequence information may be derived from 20 or more regions. The sequence information may be derived from 30 or more regions. The sequence information may be derived from 40 or more regions. The sequence information may be derived from 50 or more regions. The sequence information may be derived from 60 or more regions. The sequence information may be derived from 70 or more regions. The sequence information may be derived from 80 or more regions. The sequence information may be derived from 90 or more regions. The sequence information may be derived from 100 or more regions.

[0072] The population of subjects afflicted with the cancer may be subjects from one or more databases. The one or more databases may comprise The Cancer Genome Atlas (TCGA).

[0073] The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 60% of the population of subjects afflicted with the cancer. The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 70% of the population of subjects afflicted with the cancer. The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 80% of the population of subjects afflicted with the cancer. The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 90% of the population of subjects afflicted with the cancer. The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 95% of the population of subjects afflicted with the cancer. The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 99% of the population of subjects afflicted with the cancer.

[0074] The sequence information may be derived from regions that may be mutated in at least 65% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 70% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 75% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 80% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 85% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 90% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 95% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 99% of the population of subjects afflicted with the cancer.

[0075] Obtaining the sequence information may comprise sequencing noncoding regions. The noncoding regions may comprise one or more lncRNA, snoRNA, siRNA, miRNA, piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, LSINCTs, or a combination thereof.

[0076] Alternatively, or additionally, obtaining the sequence information may comprise sequencing protein coding regions. The protein coding regions may comprise one or more exons, introns, untranslated regions, or a combination thereof.

[0077] In some instances, at least one of the regions does not comprise KRAS or EGFR. In some instances, at least two of the regions do not comprise KRAS and EGFR. In some instances, at least one of the regions does not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least two of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least three of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least four of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.

[0078] The method may further comprise detecting mutations in the regions based on the sequencing information. Diagnosing the cancer may be based on the detection of the mutations. The detection of at least 3 mutations may be indicative of the cancer. The detection of one or more mutations in three or more regions may be indicative of the cancer.

[0079] The breast cancer may be a BRCA1 cancer.

[0080] The method may have a sensitivity of at least 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0081] The method may have a specificity of at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0082] The method may further comprise providing a computer-generated report comprising the diagnosis of the cancer.

[0083] Further disclosed herein are methods of determining a prognosis of a condition or disease in a subject in need thereof. The method may comprise (a) obtaining sequence information of cell-free genomic DNA derived from a sample from a subject, wherein the sequence information may be derived from regions that are mutated in at least 80% of a population of subjects afflicted with a condition; and (b) determining a prognosis of a condition or disease in the subject based on the sequence information.

[0084] The regions that are mutated may comprise a total size of less than 1.5 Mb of the genome. The regions that are mutated may comprise a total size of less than 1 Mb of the genome. The regions that are mutated may comprise a total size of less than 500 kb of the genome. The regions that are mutated may comprise a total size of less than 350 kb of the genome. The regions that are mutated may comprise a total size of less than 300 kb of the genome. The regions that are mutated may comprise a total size of less than 250 kb of the genome. The regions that are mutated may comprise a total size of less than 200 kb of the genome. The regions that are mutated may comprise a total size of less than 150 kb of the genome. The regions that are mutated may comprise a total size of less than 100 kb of the genome. The regions that are mutated may comprise a total size of less than 50 kb of the genome. The regions that are mutated may comprise a total size of less than 40 kb of the genome. The regions that are mutated may comprise a total size of less than 30 kb of the genome. The regions that are mutated may comprise a total size of less than 20 kb of the genome. The regions that are mutated may comprise a total size of less than 10 kb of the genome.

[0085] The regions that are mutated may comprise a total size between 100 kb -300 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -200 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -150 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -100 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -75 kb of the genome. The regions that are mutated may comprise a total size between 1 kb -50 kb of the genome.

[0086] The sequence information may be derived from 2 or more regions. The sequence information may be derived from 3 or more regions. The sequence information may be derived from 4 or more regions. The sequence information may be derived from 5 or more regions. The sequence information may be derived from 6 or more regions. The sequence information may be derived from 7 or more regions. The sequence information may be derived from 8 or more regions. The sequence information may be derived from 9 or more regions. The sequence information may be derived from 10 or more regions. The sequence information may be derived from 20 or more regions. The sequence information may be derived from 30 or more regions. The sequence information may be derived from 40 or more regions. The sequence information may be derived from 50 or more regions. The sequence information may be derived from 60 or more regions. The sequence information may be derived from 70 or more regions. The sequence information may be derived from 80 or more regions. The sequence information may be derived from 90 or more regions. The sequence information may be derived from 100 or more regions.

[0087] The population of subjects afflicted with the cancer may be subjects from one or more databases. The one or more databases may comprise The Cancer Genome Atlas (TCGA).

[0088] The sequence information may be derived from regions that may be mutated in at least 65% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 70% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 75% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 80% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 85% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 90% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 95% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 99% of the population of subjects afflicted with the cancer.

[0089] Obtaining the sequence information may comprise sequencing noncoding regions. The noncoding regions may comprise one or more lncRNA, snoRNA, siRNA, miRNA, piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, LSINCTs, or a combination thereof.

[0090] Alternatively, or additionally, obtaining the sequence information may comprise sequencing protein coding regions. The protein coding regions may comprise one or more exons, introns, untranslated regions, or a combination thereof.

[0091] In some instances, at least one of the regions does not comprise KRAS or EGFR. In some instances, at least two of the regions do not comprise KRAS and EGFR. In some instances, at least one of the regions does not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least two of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least three of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least four of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.

[0092] The method may further comprise detecting mutations in the regions based on the sequencing information. Prognosing the condition or disease may be based on the detection of the mutations. The detection of at least 3 mutations may be indicative of an outcome of the condition or disease. The detection of one or more mutations in three or more regions may be indicative of an outcome of the condition or disease.

[0093] The condition may be a cancer. The cancer may be a solid tumor. The solid tumor may be non-small cell lung cancer (NSCLC). The cancer may be a breast cancer. The breast cancer may be a BRCA1 cancer. The cancer may be a lung cancer, colorectal cancer, prostate cancer, ovarian cancer, esophageal cancer, breast cancer, lymphoma, or leukemia.

[0094] The method may have a sensitivity of at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0095] The method may have a specificity of at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0096] The method may further comprise providing a computer-generated report comprising the prognosis of the condition.

[0097] Further disclosed herein are methods of diagnosing, prognosing, or determining a therapeutic regimen for a subject afflicted with or susceptible of having a cancer. The method may comprise (a) obtaining sequence information for selected regions of genomic DNA from a cell-free DNA sample from the subject; (b) using the sequence information to determine the presence or absence of one or more mutations in the selected regions, wherein at least 70% of a population of subjects afflicted with the cancer have mutation(s) in the regions; and (c) providing a report with a diagnosis, prognosis or treatment regimen to the subject, based on the presence or absence of the one or more mutations.

[0098] The selected regions may comprise a total size of less than 1.5 Mb of the genome. The selected regions may comprise a total size of less than 1 Mb of the genome. The selected regions may comprise a total size of less than 500 kb of the genome. The selected regions may comprise a total size of less than 350 kb of the genome. The selected regions may comprise a total size of less than 300 kb of the genome. The selected regions may comprise a total size of less than 250 kb of the genome. The selected regions may comprise a total size of less than 200 kb of the genome. The selected regions may comprise a total size of less than 150 kb of the genome. The selected regions may comprise a total size of less than 100 kb of the genome. The selected regions may comprise a total size of less than 50 kb of the genome. The selected regions may comprise a total size of less than 40 kb of the genome. The selected regions may comprise a total size of less than 30 kb of the genome. The selected regions may comprise a total size of less than 20 kb of the genome. The selected regions may comprise a total size of less than 10 kb of the genome.

[0099] The selected regions may comprise a total size between 100 kb -300 kb of the genome. The selected regions may comprise a total size between 5 kb -200 kb of the genome. The selected regions may comprise a total size between 5 kb -150 kb of the genome. The selected regions may comprise a total size between 5 kb -100 kb of the genome. The selected regions may comprise a total size between 5 kb -75 kb of the genome. The selected regions may comprise a total size between 1 kb -50 kb of the genome.

[0100] The sequence information may be derived from 2 or more regions. The sequence information may be derived from 3 or more regions. The sequence information may be derived from 4 or more regions. The sequence information may be derived from 5 or more regions. The sequence information may be derived from 6 or more regions. The sequence information may be derived from 7 or more regions. The sequence information may be derived from 8 or more regions. The sequence information may be derived from 9 or more regions. The sequence information may be derived from 10 or more regions. The sequence information may be derived from 20 or more regions. The sequence information may be derived from 30 or more regions. The sequence information may be derived from 40 or more regions. The sequence information may be derived from 50 or more regions. The sequence information may be derived from 60 or more regions. The sequence information may be derived from 70 or more regions. The sequence information may be derived from 80 or more regions. The sequence information may be derived from 90 or more regions. The sequence information may be derived from 100 or more regions.

[0101] The population of subjects afflicted with the cancer may be subjects from one or more databases. The one or more databases may comprise The Cancer Genome Atlas (TCGA).

[0102] The sequence information may be derived from regions that may be mutated in at least 65% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 70% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 75% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 80% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 85% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 90% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 95% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 99% of the population of subjects afflicted with the cancer.

[0103] Obtaining the sequence information may comprise sequencing noncoding regions. The noncoding regions may comprise one or more lncRNA, snoRNA, siRNA, miRNA, piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, LSINCTs, or a combination thereof.

[0104] Alternatively, or additionally, obtaining the sequence information may comprise sequencing protein coding regions. The protein coding regions may comprise one or more exons, introns, untranslated regions, or a combination thereof.

[0105] In some instances, at least one of the regions does not comprise KRAS or EGFR. In some instances, at least two of the regions do not comprise KRAS and EGFR. In some instances, at least one of the regions does not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least two of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least three of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least four of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.

[0106] Detection of at least 3 mutations may be indicative of an outcome of the cancer. Detection of at least 4 mutations may be indicative of an outcome of the cancer. Detection of at least 5 mutations may be indicative of an outcome of the cancer. Detection of at least 6 mutations may be indicative of an outcome of the cancer.

[0107] Detection of one or more mutations in three or more regions may be indicative of an outcome of the cancer. Detection of one or more mutations in four or more regions may be indicative of an outcome of the cancer. Detection of one or more mutations in five or more regions may be indicative of an outcome of the cancer. Detection of one or more mutations in six or more regions may be indicative of an outcome of the cancer.

[0108] The cancer may be non-small cell lung cancer (NSCLC). The cancer may be a breast cancer. The breast cancer may be a BRCA1 cancer. The cancer may be a lung cancer, colorectal cancer, prostate cancer, ovarian cancer, esophageal cancer, breast cancer, lymphoma, or leukemia.

[0109] The method of diagnosing or prognosing the cancer may have a sensitivity of at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. The method of diagnosing or prognosing the cancer may have a specificity of at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0110] The may further comprise administering a therapeutic drug to the subject. The may further comprise modifying a therapeutic regimen. Modifying the therapeutic regimen may comprise terminating the therapeutic regimen. Modifying the therapeutic regimen may comprise increasing a dosage or frequency of the therapeutic regimen. Modifying the therapeutic regimen may comprise decreasing a dosage or frequency of the therapeutic regimen. Modifying the therapeutic regimen may comprise starting the therapeutic regimen.

[0111] Further disclosed herein are methods of determining a therapeutic region for the treatment of a condition in a subject in need thereof. The method may comprise (a) obtaining sequence information of cell-free genomic DNA derived from a sample from a subject, wherein the sequence information may be derived from regions that are mutated in at least 80% of a population of subjects afflicted with a condition; and (b) determining a therapeutic regimen for a condition in the subject based on the sequence information.

[0112] The regions that are mutated may comprise a total size of less than 1.5 Mb of the genome. The regions that are mutated may comprise a total size of less than 1 Mb of the genome. The regions that are mutated may comprise a total size of less than 500 kb of the genome. The regions that are mutated may comprise a total size of less than 350 kb of the genome. The regions that are mutated may comprise a total size of less than 300 kb of the genome. The regions that are mutated may comprise a total size of less than 250 kb of the genome. The regions that are mutated may comprise a total size of less than 200 kb of the genome. The regions that are mutated may comprise a total size of less than 150 kb of the genome. The regions that are mutated may comprise a total size of less than 100 kb of the genome. The regions that are mutated may comprise a total size of less than 50 kb of the genome. The regions that are mutated may comprise a total size of less than 40 kb of the genome. The regions that are mutated may comprise a total size of less than 30 kb of the genome. The regions that are mutated may comprise a total size of less than 20 kb of the genome. The regions that are mutated may comprise a total size of less than 10 kb of the genome.

[0113] The regions that are mutated may comprise a total size between 100 kb -300 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -200 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -150 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -100 kb of the genome. The regions that are mutated may comprise a total size between 5 kb -75 kb of the genome. The regions that are mutated may comprise a total size between 1 kb -50 kb of the genome.

[0114] The sequence information may be derived from 2 or more regions. The sequence information may be derived from 3 or more regions. The sequence information may be derived from 4 or more regions. The sequence information may be derived from 5 or more regions. The sequence information may be derived from 6 or more regions. The sequence information may be derived from 7 or more regions. The sequence information may be derived from 8 or more regions. The sequence information may be derived from 9 or more regions. The sequence information may be derived from 10 or more regions. The sequence information may be derived from 20 or more regions. The sequence information may be derived from 30 or more regions. The sequence information may be derived from 40 or more regions. The sequence information may be derived from 50 or more regions. The sequence information may be derived from 60 or more regions. The sequence information may be derived from 70 or more regions. The sequence information may be derived from 80 or more regions. The sequence information may be derived from 90 or more regions. The sequence information may be derived from 100 or more regions.

[0115] The population of subjects afflicted with the cancer may be subjects from one or more databases. The one or more databases may comprise The Cancer Genome Atlas (TCGA).

[0116] The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 60% of the population of subjects afflicted with the cancer. The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 70% of the population of subjects afflicted with the cancer. The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 80% of the population of subjects afflicted with the cancer. The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 90% of the population of subjects afflicted with the cancer. The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 95% of the population of subjects afflicted with the cancer. The sequence information may comprise information pertaining to at least one mutation that may be present in at least about 99% of the population of subjects afflicted with the cancer.

[0117] The sequence information may be derived from regions that may be mutated in at least 65% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 70% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 75% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 80% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 85% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 90% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 95% of the population of subjects afflicted with the cancer. The sequence information may be derived from regions that may be mutated in at least 99% of the population of subjects afflicted with the cancer.

[0118] Obtaining the sequence information may comprise sequencing noncoding regions. The noncoding regions may comprise one or more lncRNA, snoRNA, siRNA, miRNA, piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, LSINCTs, or a combination thereof.

[0119] Alternatively, or additionally, obtaining the sequence information may comprise sequencing protein coding regions. The protein coding regions may comprise one or more exons, introns, untranslated regions, or a combination thereof.

[0120] In some instances, at least one of the regions does not comprise KRAS or EGFR. In some instances, at least two of the regions do not comprise KRAS and EGFR. In some instances, at least one of the regions does not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least two of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least three of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1. In some instances, at least four of the regions do not comprise KRAS, EGFR, p53, PIK3CA, BRAF, EZH2, or BRCA1.

[0121] The method may further comprise detecting mutations in the regions based on the sequencing information. Determining the therapeutic regimen may be based on the detection of the mutations.

[0122] The condition may be a cancer. The cancer may be a solid tumor. The solid tumor may be non-small cell lung cancer (NSCLC). The cancer may be a breast cancer. The breast cancer may be a BRCA1 cancer. The cancer may be a lung cancer, colorectal cancer, prostate cancer, ovarian cancer, esophageal cancer, breast cancer, lymphoma, or leukemia.

[0123] Further disclosed herein are methods of assessing tumor burden in a subject in need thereof. The method may comprise (a) obtaining sequence information on cell-free nucleic acids derived from a sample from the subject; (b) using a computer readable medium to determine quantities of circulating tumor DNA (ctDNA) in the sample; (c) assessing tumor burden based on the quantities of ctDNA; and (d) reporting the tumor burden to the subject or a representative of the subject.

[0124] Determining quantities of ctDNA may comprise determining absolute quantities of ctDNA. Determining quantities of ctDNA may comprise determining relative quantities of ctDNA. Determining quantities of ctDNA may be performed by counting sequence reads pertaining to the ctDNA. Determining quantities of ctDNA may be performed by quantitative PCR. Determining quantities of ctDNA may be performed by digital PCR. Determining quantities of ctDNA may comprise counting sequencing reads of the ctDNA.

[0125] Determining quantities of ctDNA may be performed by molecular barcoding of the ctDNA. Molecular barcoding of the ctDNA may comprise attaching adaptors to one or more ends of the ctDNA. The adaptor may comprise a plurality of oligonucleotides. The adaptor may comprise one or more deoxyribonucleotides. The adaptor may comprise ribonucleotides. The adaptor may be single-stranded. The adaptor may be double-stranded. The adaptor may comprise double-stranded and single-stranded portions. For example, the adaptor may be a Y-shaped adaptor. The adaptor may be a linear adaptor. The adaptor may be a circular adaptor. The adaptor may comprise a molecular barcode, sample index, primer sequence, linker sequence or a combination thereof. The molecular barcode may be adjacent to the sample index. The molecular barcode may be adjacent to the primer sequence. The sample index may be adjacent to the primer sequence. A linker sequence may connect the molecular barcode to the sample index. A linker sequence may connect the molecular barcode to the primer sequence. A linker sequence may connect the sample index to the primer sequence.

[0126] The adaptor may comprise a molecular barcode. The molecular barcode may comprise a random sequence. The molecular barcode may comprise a predetermined sequence. Two or more adaptors may comprise two or more different molecular barcodes. The molecular barcodes may be optimized to minimize dimerization. The molecular barcodes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first molecular barcode may introduce a single base error. The first molecular barcode may comprise greater than a single base difference from the other molecular barcodes. Thus, the first molecular barcode with the single base error may still be identified as the first molecular barcode. The molecular barcode may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The molecular barcode may comprise at least 3 nucleotides. The molecular barcode may comprise at least 4 nucleotides. The molecular barcode may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecular barcode may comprise less than 10 nucleotides. The molecular barcode may comprise less than 8 nucleotides. The molecular barcode may comprise less than 6 nucleotides. The molecular barcode may comprise 2 to 15 nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. The molecular barcode may comprise 3 to 10 nucleotides. The molecular barcode may comprise 3 to 8 nucleotides. The molecular barcode may comprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6 nucleotides.

[0127] The adaptor may comprise a sample index. The sample index may comprise a random sequence. The sample index may comprise a predetermined sequence. Two or more sets of adaptors may comprise two or more different sample indexes. Adaptors within a set of adaptors may comprise identical sample indexes. The sample indexes may be optimized to minimize dimerization. The sample indexes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first sample index may introduce a single base error. The first sample index may comprise greater than a single base difference from the other sample indexes. Thus, the first sample index with the single base error may still be identified as the first molecular barcode. The sample index may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The sample index may comprise at least 3 nucleotides. The sample index may comprise at least 4 nucleotides. The sample index may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The sample index may comprise less than 10 nucleotides. The sample index may comprise less than 8 nucleotides. The sample index may comprise less than 6 nucleotides. The sample index may comprise 2 to 15 nucleotides. The sample index may comprise 2 to 12 nucleotides. The sample index may comprise 3 to 10 nucleotides. The sample index may comprise 3 to 8 nucleotides. The sample index may comprise 4 to 8 nucleotides. The sample index may comprise 4 to 6 nucleotides.

[0128] The adaptor may comprise a primer sequence. The primer sequence may be a PCR primer sequence. The primer sequence may be a sequencing primer.

[0129] Adaptors may be attached to one end of a nucleic acid from a sample. The nucleic acids may be DNA. The DNA may be cell-free DNA (cfDNA). The DNA may be circulating tumor DNA (ctDNA). The nucleic acids may be RNA. Adaptors may be attached to both ends of the nucleic acid. Adaptors may be attached to one or more ends of a single-stranded nucleic acid. Adaptors may be attached to one or more ends of a double-stranded nucleic acid.

[0130] Adaptors may be attached to the nucleic acid by ligation. Ligation may be blunt end ligation. Ligation may be sticky end ligation. Adaptors may be attached to the nucleic acid by primer extension. Adaptors may be attached to the nucleic acid by reverse transcription. Adaptors may be attached to the nucleic acids by hybridization. Adaptors may comprise a sequence that is at least partially complementary to the nucleic acid. Alternatively, in some instances, adaptors do not comprise a sequence that is complementary to the nucleic acid.

[0131] The sequence information may comprise information related to one or more genomic regions. The sequence information may comprise information related to at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 100, 200, 300 genomic regions. The genomic regions may comprise genes, exonic regions, intronic regions, untranslated regions, non-coding regions or a combination thereof.

[0132] The genomic regions may comprise two or more of exonic regions, intronic regions, and untranslated regions. The genomic regions may comprise at least one exonic region and at least one intronic region. At least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, or 25% of the genomic regions may comprise intronic regions. At least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, or 25% of the genomic regions may comprise untranslated regions. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may comprise exonic regions. At least less than about 97%, 95%, 93%, 90%, 87%, 85%, 83%, 80%, 75%, 70%, 65%, 60%, 55%, 50% of the genomic regions may comprise exonic regions.

[0133] The genomic regions may comprise less than 1.5 megabases (Mb) of the genome. The genomic regions may comprise less than 1 Mb of the genome.

[0134] The genomic regions may comprise less than 500 kilobases (kb) of the genome.

[0135] The genomic regions may comprise less than 350 kb of the genome. The genomic regions may comprise less than 300 kb of the genome. The genomic regions may comprise less than 250 kb of the genome. The genomic regions may comprise less than 200 kb of the genome. The genomic regions may comprise less than 150 kb of the genome. The genomic regions may comprise less than 100 kb of the genome. The genomic regions may comprise less than 50 kb of the genome. The genomic regions may comprise less than 40 kb, 30kb, 20kb, or 10kb of the genome.

[0136] The genomic regions may comprise between 100 kb to 300 kb of the genome. The genomic regions may comprise between 100 kb to 200 kb of the genome. The genomic regions may comprise between 10 kb to 300 kb of the genome. The genomic regions may comprise between 10 kb to 300 kb of the genome. The genomic regions may comprise between 10 kb to 200 kb of the genome. The genomic regions may comprise between 10 kb to 150 kb of the genome. The genomic regions may comprise between 10 kb to 100 kb of the genome. The genomic regions may comprise between 10 kb to 75 kb of the genome. The genomic regions may comprise between 5 kb to 70 kb of the genome. The genomic regions may comprise between 1 kb to 50 kb of the genome.

[0137] The sequence information may comprise information pertaining to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20 or more genomic regions from a selector set comprising a plurality of genomic regions. The sequence information may comprise information pertaining to 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions from a selector set comprising a plurality of genomic regions.

[0138] The sequence information may comprise information pertaining to a plurality of genomic regions.

[0139] The plurality of genomic regions may be based on a selector set comprising genomic regions comprising one or more mutations present in one or more subjects from a population of cancer subjects. At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the plurality of genomic regions may be based on a selector set comprising genomic regions comprising one or more mutations present in one or more subjects from a population of cancer subjects.

[0140] The total size of the genomic regions of the selector set may comprise less than 1.5 megabases (Mb), 1Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of the genome. The total size of the genomic regions of the selector set may be between 100 kb to 300 kb of the genome.

[0141] The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from Table 2.

[0142] Obtaining sequence information may comprise performing massively parallel sequencing. Massively parallel sequencing may be performed on a subset of a genome of the cell-free nucleic acids from the sample.

[0143] The subset of the genome may comprise less than 1.5 megabases (Mb), 1Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, 150 kb, 100 kb, 75 kb, 50 kb, 40 kb, 30 kb, 20 kb, 10 kb, or 5 kb of the genome. The subset of the genome may comprise between 100 kb to 300 kb of the genome. The subset of the genome may comprise between 100 kb to 200 kb of the genome. The subset of the genome may comprise between 10 kb to 300 kb of the genome. The subset of the genome may comprise between 10 kb to 200 kb of the genome. The subset of the genome may comprise between 10 kb to 100 kb of the genome. The subset of the genome may comprise between 5 kb to 100 kb of the genome. The subset of the genome may comprise between 5 kb to 70 kb of the genome. The subset of the genome may comprise between 1 kb to 50 kb of the genome.

[0144] The method may comprise obtaining sequencing information of cell-free DNA samples from two or more samples from the subject. The method may comprise obtaining sequencing information of cell-free DNA samples from two or more samples from two or more subjects. The two or more samples may be the same type of sample. The two or more samples may be two different types of sample. The two or more samples may be obtained at the same time point. The two or more samples may be obtained at two or more time points.

[0145] Determining the quantities of ctDNA may comprise detecting one or more mutations. Determining the quantities of ctDNA may comprise detecting two or more different types of mutations. The types of mutations include, but are not limited to, SNVs, indels, fusions, breakpoints, structural variants, variable number of tandem repeats, hypervariable regions, minisatellites, dinucleotide repeats, trinucleotide repeats, tetranucleotide repeats, simple sequence repeats, or a combination thereof in selected regions of the subject's genome. Determining the quantities of ctDNA may comprise detecting one or more of SNVs, indels, copy number variants, and rearrangements in selected regions of the subject's genome. Determining the quantities of ctDNA may comprise detecting two or more of SNVs, indels, copy number variants, and rearrangements in selected regions of the subject's genome. Determining the quantities of ctDNA may comprise detecting at least one SNV, indel, copy number variant, and rearrangement in selected regions of the subject's genome.

[0146] In some instances, determining the quantities of ctDNA does comprise performing digital PCR (dPCR). Determining the quantities of ctDNA may comprise applying an algorithm to the sequence information to determine a quantity of one or more genomic regions from a selector set.

[0147] The selector set may comprise a plurality of genomic regions comprising one or more mutations present in one or more cancer subjects from a population of cancer subjects. The selector set may comprise a plurality of genomic regions comprise two or more different types of mutations present in one or more cancer subjects from a population of cancer subjects. The selector set may comprise a plurality of genomic regions comprising one or more mutations present in at least about 60% of cancer subjects from population of cancer subjects.

[0148] The representative of the subject may be a healthcare provider. The healthcare provider may be a nurse, physician, medical technician, or hospital personnel. The representative of the subject may be a family member of the subject. The representative of the subject may be a legal guardian of the subject.

[0149] Further disclosed herein are methods of determining a disease state of a cancer in a subject. The method may comprise (a) obtaining a quantity of circulating tumor DNA (ctDNA) in a sample from the subject; (b) obtaining a volume of a tumor in the subject; and (c) determining a disease state of a cancer in the subject based on a ratio of the quantity of ctDNA to the volume of the tumor. A high ctDNA to volume ratio may be indicative of radiographically occult disease. A low ctDNA to volume ratio may be indicative of non-malignant state.

[0150] The method may further comprise modifying a diagnosis or prognosis of the cancer based on the ratio of the quantity of the ctDNA to the volume of the tumor. The method may comprise diagnosing a stage of the cancer based on the ratio of the quantity of the ctDNA to the volume of the tumor. Modifying the diagnosis may comprise changing the stage of the cancer based on the ratio of the quantity of the ctDNA to the volume of the tumor. For example, a subject may be diagnosed with a stage III cancer. However, a low ratio of the quantity of the ctDNA to the volume of the tumor may result in adjusting the diagnosis of the cancer to a stage I or II cancer. Modifying a prognosis of the cancer may comprise changing the predicted outcome or status of the cancer. For example, a doctor may predict that a cancer in the subject is in remission based on the tumor volume. However, a high ratio of the quantity of the ctDNA to the volume of the tumor may result in a prediction that the cancer is recurrent.

[0151] Obtaining the volume of the tumor may comprise obtaining an image of the tumor. Obtaining the volume of the tumor may comprise obtaining a CT scan of the tumor.

[0152] Obtaining the quantity of ctDNA may comprise PCR. Obtaining the quantity of ctDNA may comprise digital PCR. Obtaining the quantity of ctDNA may comprise quantitative PCR.

[0153] Obtaining the quantity of ctDNA may comprise obtaining sequencing information on the ctDNA. The sequencing information may comprise information relating to one or more genomic regions based on a selector set.

[0154] Obtaining the quantity of ctDNA may comprise hybridization of the ctDNA to an array. The array may comprise a plurality of probes for selective hybridization of one or more genomic regions based on a selector set. The selector set may comprise one or more genomic regions from Table 2. The selector set may comprise one or more genomic regions comprising one or more mutations, wherein the one or more mutations may be present in a population of subjects suffering from a cancer. The selector set may comprise a plurality of genomic regions comprising a plurality of mutations, wherein the plurality of mutations may be present in at least 60% of a population of subjects suffering from a cancer.

[0155] Further disclosed herein are methods of detecting stage I cancer in a subject in need thereof. The method may comprise (a) performing sequencing on cell-free DNA derived from a sample, wherein the cell-free DNA to be sequenced may be based on a selector set comprising a plurality of genomic regions; (b) using a computer readable medium to determine a quantity of the cell-free DNA; and (c) detecting a stage I cancer in the sample based on the quantity of the cell-free DNA.

[0156] Determining the quantity of the cell-free DNA may comprise determining absolute quantities of the cell-free DNA. The quantity of the cell-free DNA may be determined by counting sequencing reads pertaining to the cell-free DNA. The quantity of the cell-free DNA may be determined by quantitative PCR.

[0157] Determining quantities of cell-free DNA (cfDNA) may be performed by molecular barcoding of the cfDNA. Molecular barcoding of the cfDNA may comprise attaching adaptors to one or more ends of the cfDNA. The adaptor may comprise a plurality of oligonucleotides. The adaptor may comprise one or more deoxyribonucleotides. The adaptor may comprise ribonucleotides. The adaptor may be single-stranded. The adaptor may be double-stranded. The adaptor may comprise double-stranded and single-stranded portions. For example, the adaptor may be a Y-shaped adaptor. The adaptor may be a linear adaptor. The adaptor may be a circular adaptor. The adaptor may comprise a molecular barcode, sample index, primer sequence, linker sequence or a combination thereof. The molecular barcode may be adjacent to the sample index. The molecular barcode may be adjacent to the primer sequence. The sample index may be adjacent to the primer sequence. A linker sequence may connect the molecular barcode to the sample index. A linker sequence may connect the molecular barcode to the primer sequence. A linker sequence may connect the sample index to the primer sequence.

[0158] The adaptor may comprise a molecular barcode. The molecular barcode may comprise a random sequence. The molecular barcode may comprise a predetermined sequence. Two or more adaptors may comprise two or more different molecular barcodes. The molecular barcodes may be optimized to minimize dimerization. The molecular barcodes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first molecular barcode may introduce a single base error. The first molecular barcode may comprise greater than a single base difference from the other molecular barcodes. Thus, the first molecular barcode with the single base error may still be identified as the first molecular barcode. The molecular barcode may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The molecular barcode may comprise at least 3 nucleotides. The molecular barcode may comprise at least 4 nucleotides. The molecular barcode may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecular barcode may comprise less than 10 nucleotides. The molecular barcode may comprise less than 8 nucleotides. The molecular barcode may comprise less than 6 nucleotides. The molecular barcode may comprise 2 to 15 nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. The molecular barcode may comprise 3 to 10 nucleotides. The molecular barcode may comprise 3 to 8 nucleotides. The molecular barcode may comprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6 nucleotides.

[0159] The adaptor may comprise a sample index. The sample index may comprise a random sequence. The sample index may comprise a predetermined sequence. Two or more sets of adaptors may comprise two or more different sample indexes. Adaptors within a set of adaptors may comprise identical sample indexes. The sample indexes may be optimized to minimize dimerization. The sample indexes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first sample index may introduce a single base error. The first sample index may comprise greater than a single base difference from the other sample indexes. Thus, the first sample index with the single base error may still be identified as the first molecular barcode. The sample index may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The sample index may comprise at least 3 nucleotides. The sample index may comprise at least 4 nucleotides. The sample index may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The sample index may comprise less than 10 nucleotides. The sample index may comprise less than 8 nucleotides. The sample index may comprise less than 6 nucleotides. The sample index may comprise 2 to 15 nucleotides. The sample index may comprise 2 to 12 nucleotides. The sample index may comprise 3 to 10 nucleotides. The sample index may comprise 3 to 8 nucleotides. The sample index may comprise 4 to 8 nucleotides. The sample index may comprise 4 to 6 nucleotides.

[0160] The adaptor may comprise a primer sequence. The primer sequence may be a PCR primer sequence. The primer sequence may be a sequencing primer.

[0161] Adaptors may be attached to one end of the cfDNA. Adaptors may be attached to both ends of the cfDNA. Adaptors may be attached to one or more ends of a single-stranded cfDNA. Adaptors may be attached to one or more ends of a double-stranded cfDNA.

[0162] Adaptors may be attached to the cfDNA by ligation. Ligation may be blunt end ligation. Ligation may be sticky end ligation. Adaptors may be attached to the cfDNA by primer extension. Adaptors may be attached to the cfDNA by reverse transcription. Adaptors may be attached to the cfDNA by hybridization. Adaptors may comprise a sequence that is at least partially complementary to the cfDNA. Alternatively, in some instances, adaptors do not comprise a sequence that is complementary to the cfDNA.

[0163] Sequencing may comprise massively parallel sequencing. Sequencing may comprise shotgun sequencing.

[0164] The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more genomic regions from Table 2.

[0165] At least 20%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% or more of the genomic regions in the selector set may be based on genomic regions from Table 2.

[0166] The plurality of genomic regions may comprise one or more mutations present in at least 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or 99% or more of a population of subjects suffering from the cancer.

[0167] The total size of the plurality of genomic regions of the selector set may comprise less than 1.5 megabases (Mb), 1Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 100 kb, 90 kb, 80 kb, 70 kb, 60 kb, 50 kb, 40 kb, 30 kb, 20 kb, 10 kb, 5 kb, or 1 kb of a genome.

[0168] The total size of the plurality of genomic regions of the selector set may be between 100 kb to 300 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 100 kb to 200 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 300 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 200 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 100 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 100 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 75 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 50 kb of a genome.

[0169] The method of detecting the stage I cancer may have a sensitivity of at least 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% or more. The method of detecting the stage I cancer may have a sensitivity of at least 60%. The method of detecting the stage I cancer may have a sensitivity of at least 70%. The method of detecting the stage I cancer may have a sensitivity of at least 80%. The method of detecting the stage I cancer may have a sensitivity of at least 90%. The method of detecting the stage I cancer may have a sensitivity of at least 95%.

[0170] The method of detecting the stage I cancer may have a specificity of at least 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% or more. The method of detecting the stage I cancer may have a specificity of at least 60%. The method of detecting the stage I cancer may have a specificity of at least 70%. The method of detecting the stage I cancer may have a specificity of at least 80%. The method of detecting the stage I cancer may have a specificity of at least 90%. The method of detecting the stage I cancer may have a specificity of at least 95%.

[0171] The method may detect at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or more of stage I cancer. The method may detect at least 50% or more of stage I cancer. The method may detect at least 60% or more of stage I cancer. The method may detect at least 70% or more of stage I cancer. The method may detect at least 75% or more of stage I cancer.

[0172] Further disclosed herein are methods of detecting stage II cancer. The method may comprise (a) performing sequencing on cell-free DNA derived from a sample, wherein the cell-free DNA to be sequenced may be based on a selector set comprising a plurality of genomic regions; (b) using a computer readable medium to determine a quantity of the cell-free DNA; and (c) detecting a stage II cancer in the sample based on the quantity of the cell-free DNA.

[0173] Determining the quantity of the cell-free DNA may comprise determining absolute quantities of the cell-free DNA. The quantity of the cell-free DNA may be determined by counting sequencing reads pertaining to the cell-free DNA. The quantity of the cell-free DNA may be determined by quantitative PCR.

[0174] Determining quantities of cell-free DNA (cfDNA) may be performed by molecular barcoding of the cfDNA. Molecular barcoding of the cfDNA may comprise attaching adaptors to one or more ends of the cfDNA. The adaptor may comprise a plurality of oligonucleotides. The adaptor may comprise one or more deoxyribonucleotides. The adaptor may comprise ribonucleotides. The adaptor may be single-stranded. The adaptor may be double-stranded. The adaptor may comprise double-stranded and single-stranded portions. For example, the adaptor may be a Y-shaped adaptor. The adaptor may be a linear adaptor. The adaptor may be a circular adaptor. The adaptor may comprise a molecular barcode, sample index, primer sequence, linker sequence or a combination thereof. The molecular barcode may be adjacent to the sample index. The molecular barcode may be adjacent to the primer sequence. The sample index may be adjacent to the primer sequence. A linker sequence may connect the molecular barcode to the sample index. A linker sequence may connect the molecular barcode to the primer sequence. A linker sequence may connect the sample index to the primer sequence.

[0175] The adaptor may comprise a molecular barcode. The molecular barcode may comprise a random sequence. The molecular barcode may comprise a predetermined sequence. Two or more adaptors may comprise two or more different molecular barcodes. The molecular barcodes may be optimized to minimize dimerization. The molecular barcodes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first molecular barcode may introduce a single base error. The first molecular barcode may comprise greater than a single base difference from the other molecular barcodes. Thus, the first molecular barcode with the single base error may still be identified as the first molecular barcode. The molecular barcode may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The molecular barcode may comprise at least 3 nucleotides. The molecular barcode may comprise at least 4 nucleotides. The molecular barcode may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecular barcode may comprise less than 10 nucleotides. The molecular barcode may comprise less than 8 nucleotides. The molecular barcode may comprise less than 6 nucleotides. The molecular barcode may comprise 2 to 15 nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. The molecular barcode may comprise 3 to 10 nucleotides. The molecular barcode may comprise 3 to 8 nucleotides. The molecular barcode may comprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6 nucleotides.

[0176] The adaptor may comprise a sample index. The sample index may comprise a random sequence. The sample index may comprise a predetermined sequence. Two or more sets of adaptors may comprise two or more different sample indexes. Adaptors within a set of adaptors may comprise identical sample indexes. The sample indexes may be optimized to minimize dimerization. The sample indexes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first sample index may introduce a single base error. The first sample index may comprise greater than a single base difference from the other sample indexes. Thus, the first sample index with the single base error may still be identified as the first molecular barcode. The sample index may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The sample index may comprise at least 3 nucleotides. The sample index may comprise at least 4 nucleotides. The sample index may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The sample index may comprise less than 10 nucleotides. The sample index may comprise less than 8 nucleotides. The sample index may comprise less than 6 nucleotides. The sample index may comprise 2 to 15 nucleotides. The sample index may comprise 2 to 12 nucleotides. The sample index may comprise 3 to 10 nucleotides. The sample index may comprise 3 to 8 nucleotides. The sample index may comprise 4 to 8 nucleotides. The sample index may comprise 4 to 6 nucleotides.

[0177] The adaptor may comprise a primer sequence. The primer sequence may be a PCR primer sequence. The primer sequence may be a sequencing primer.

[0178] Adaptors may be attached to one end of the cfDNA. Adaptors may be attached to both ends of the cfDNA. Adaptors may be attached to one or more ends of a single-stranded cfDNA. Adaptors may be attached to one or more ends of a double-stranded cfDNA.

[0179] Adaptors may be attached to the cfDNA by ligation. Ligation may be blunt end ligation. Ligation may be sticky end ligation. Adaptors may be attached to the cfDNA by primer extension. Adaptors may be attached to the cfDNA by reverse transcription. Adaptors may be attached to the cfDNA by hybridization. Adaptors may comprise a sequence that is at least partially complementary to the cfDNA. Alternatively, in some instances, adaptors do not comprise a sequence that is complementary to the cfDNA.

[0180] Sequencing may comprise massively parallel sequencing. Sequencing may comprise shotgun sequencing.

[0181] The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more genomic regions from Table 2.

[0182] At least 20%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% or more of the genomic regions in the selector set may be based on genomic regions from Table 2.

[0183] The plurality of genomic regions may comprise one or more mutations present in at least 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or 99% or more of a population of subjects suffering from the cancer.

[0184] The total size of the plurality of genomic regions of the selector set may comprise less than 1.5 megabases (Mb), 1Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 100 kb, 90 kb, 80 kb, 70 kb, 60 kb, 50 kb, 40 kb, 30 kb, 20 kb, 10 kb, 5 kb, or 1 kb of a genome.

[0185] The total size of the plurality of genomic regions of the selector set may be between 100 kb to 300 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 100 kb to 200 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 300 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 200 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 100 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 100 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 75 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 50 kb of a genome.

[0186] The method of detecting the stage II cancer may have a sensitivity of at least 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% or more. The method of detecting the stage II cancer may have a sensitivity of at least 60%. The method of detecting the stage II cancer may have a sensitivity of at least 70%. The method of detecting the stage II cancer may have a sensitivity of at least 80%. The method of detecting the stage II cancer may have a sensitivity of at least 90%. The method of detecting the stage II cancer may have a sensitivity of at least 95%.

[0187] The method of detecting the stage II cancer may have a specificity of at least 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% or more. The method of detecting the stage II cancer may have a specificity of at least 60%. The method of detecting the stage II cancer may have a specificity of at least 70%. The method of detecting the stage II cancer may have a specificity of at least 80%. The method of detecting the stage II cancer may have a specificity of at least 90%. The method of detecting the stage II cancer may have a specificity of at least 95%.

[0188] The method may detect at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or more of stage II cancer. The method may detect at least 50% or more of stage II cancer. The method may detect at least 60% or more of stage II cancer. The method may detect at least 70% or more of stage II cancer. The method may detect at least 75% or more of stage II cancer. The method may detect at least 80% or more of stage II cancer. The method may detect at least 85% or more of stage II cancer. The method may detect at least 90% or more stage II cancer.

[0189] Further disclosed herein are methods of detecting stage III cancer in a subject in need thereof. The method may comprise (a) performing sequencing on cell-free DNA derived from a sample, wherein the cell-free DNA to be sequenced may be based on a selector set comprising a plurality of genomic regions; (b) using a computer readable medium to determine a quantity of the cell-free DNA; and (c) detecting a stage III cancer in the sample based on the quantity of the cell-free DNA.

[0190] Determining the quantity of the cell-free DNA may comprise determining absolute quantities of the cell-free DNA. The quantity of the cell-free DNA may be determined by counting sequencing reads pertaining to the cell-free DNA. The quantity of the cell-free DNA may be determined by quantitative PCR.

[0191] Determining quantities of cell-free DNA (cfDNA) may be performed by molecular barcoding of the cfDNA. Molecular barcoding of the cfDNA may comprise attaching adaptors to one or more ends of the cfDNA. The adaptor may comprise a plurality of oligonucleotides. The adaptor may comprise one or more deoxyribonucleotides. The adaptor may comprise ribonucleotides. The adaptor may be single-stranded. The adaptor may be double-stranded. The adaptor may comprise double-stranded and single-stranded portions. For example, the adaptor may be a Y-shaped adaptor. The adaptor may be a linear adaptor. The adaptor may be a circular adaptor. The adaptor may comprise a molecular barcode, sample index, primer sequence, linker sequence or a combination thereof. The molecular barcode may be adjacent to the sample index. The molecular barcode may be adjacent to the primer sequence. The sample index may be adjacent to the primer sequence. A linker sequence may connect the molecular barcode to the sample index. A linker sequence may connect the molecular barcode to the primer sequence. A linker sequence may connect the sample index to the primer sequence.

[0192] The adaptor may comprise a molecular barcode. The molecular barcode may comprise a random sequence. The molecular barcode may comprise a predetermined sequence. Two or more adaptors may comprise two or more different molecular barcodes. The molecular barcodes may be optimized to minimize dimerization. The molecular barcodes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first molecular barcode may introduce a single base error. The first molecular barcode may comprise greater than a single base difference from the other molecular barcodes. Thus, the first molecular barcode with the single base error may still be identified as the first molecular barcode. The molecular barcode may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The molecular barcode may comprise at least 3 nucleotides. The molecular barcode may comprise at least 4 nucleotides. The molecular barcode may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecular barcode may comprise less than 10 nucleotides. The molecular barcode may comprise less than 8 nucleotides. The molecular barcode may comprise less than 6 nucleotides. The molecular barcode may comprise 2 to 15 nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. The molecular barcode may comprise 3 to 10 nucleotides. The molecular barcode may comprise 3 to 8 nucleotides. The molecular barcode may comprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6 nucleotides.

[0193] The adaptor may comprise a sample index. The sample index may comprise a random sequence. The sample index may comprise a predetermined sequence. Two or more sets of adaptors may comprise two or more different sample indexes. Adaptors within a set of adaptors may comprise identical sample indexes. The sample indexes may be optimized to minimize dimerization. The sample indexes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first sample index may introduce a single base error. The first sample index may comprise greater than a single base difference from the other sample indexes. Thus, the first sample index with the single base error may still be identified as the first molecular barcode. The sample index may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The sample index may comprise at least 3 nucleotides. The sample index may comprise at least 4 nucleotides. The sample index may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The sample index may comprise less than 10 nucleotides. The sample index may comprise less than 8 nucleotides. The sample index may comprise less than 6 nucleotides. The sample index may comprise 2 to 15 nucleotides. The sample index may comprise 2 to 12 nucleotides. The sample index may comprise 3 to 10 nucleotides. The sample index may comprise 3 to 8 nucleotides. The sample index may comprise 4 to 8 nucleotides. The sample index may comprise 4 to 6 nucleotides.

[0194] The adaptor may comprise a primer sequence. The primer sequence may be a PCR primer sequence. The primer sequence may be a sequencing primer.

[0195] Adaptors may be attached to one end of the cfDNA. Adaptors may be attached to both ends of the cfDNA. Adaptors may be attached to one or more ends of a single-stranded cfDNA. Adaptors may be attached to one or more ends of a double-stranded cfDNA.

[0196] Adaptors may be attached to the cfDNA by ligation. Ligation may be blunt end ligation. Ligation may be sticky end ligation. Adaptors may be attached to the cfDNA by primer extension. Adaptors may be attached to the cfDNA by reverse transcription. Adaptors may be attached to the cfDNA by hybridization. Adaptors may comprise a sequence that is at least partially complementary to the cfDNA. Alternatively, in some instances, adaptors do not comprise a sequence that is complementary to the cfDNA.

[0197] Sequencing may comprise massively parallel sequencing. Sequencing may comprise shotgun sequencing.

[0198] The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more genomic regions from Table 2.

[0199] At least 20%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% or more of the genomic regions in the selector set may be based on genomic regions from Table 2.

[0200] The plurality of genomic regions may comprise one or more mutations present in at least 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or 99% or more of a population of subjects suffering from the cancer.

[0201] The total size of the plurality of genomic regions of the selector set may comprise less than 1.5 megabases (Mb), 1Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 100 kb, 90 kb, 80 kb, 70 kb, 60 kb, 50 kb, 40 kb, 30 kb, 20 kb, 10 kb, 5 kb, or 1 kb of a genome.

[0202] The total size of the plurality of genomic regions of the selector set may be between 100 kb to 300 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 100 kb to 200 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 300 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 200 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 100 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 100 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 75 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 50 kb of a genome.

[0203] The method of detecting the stage III cancer may have a sensitivity of at least 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% or more. The method of detecting the stage III cancer may have a sensitivity of at least 60%. The method of detecting the stage III cancer may have a sensitivity of at least 70%. The method of detecting the stage III cancer may have a sensitivity of at least 80%. The method of detecting the stage III cancer may have a sensitivity of at least 90%. The method of detecting the stage III cancer may have a sensitivity of at least 95%.

[0204] The method of detecting the stage III cancer may have a specificity of at least 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% or more. The method of detecting the stage III cancer may have a specificity of at least 60%. The method of detecting the stage III cancer may have a specificity of at least 70%. The method of detecting the stage III cancer may have a specificity of at least 80%. The method of detecting the stage III cancer may have a specificity of at least 90%. The method of detecting the stage III cancer may have a specificity of at least 95%.

[0205] The method may detect at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or more of stage III cancer. The method may detect at least 50% or more of stage III cancer. The method may detect at least 60% or more of stage III cancer. The method may detect at least 70% or more of stage III cancer. The method may detect at least 75% or more of stage III cancer. The method may detect at least 80% or more of stage III cancer. The method may detect at least 85% or more of stage III cancer. The method may detect at least 90% or more of stage III cancer.

[0206] Further disclosed herein is a method of detecting stage IV cancer in a subject in need thereof. The method may comprise (a) performing sequencing on cell-free DNA derived from a sample, wherein the cell-free DNA to be sequenced may be based on a selector set comprising a plurality of genomic regions; (b) using a computer readable medium to determine a quantity of the cell-free DNA; and (c) detecting a stage IV cancer in the sample based on the quantity of the cell-free DNA.

[0207] Determining the quantity of the cell-free DNA may comprise determining absolute quantities of the cell-free DNA. The quantity of the cell-free DNA may be determined by counting sequencing reads pertaining to the cell-free DNA. The quantity of the cell-free DNA may be determined by quantitative PCR.

[0208] Determining quantities of cell-free DNA (cfDNA) may be performed by molecular barcoding of the cfDNA. Molecular barcoding of the cfDNA may comprise attaching adaptors to one or more ends of the cfDNA. The adaptor may comprise a plurality of oligonucleotides. The adaptor may comprise one or more deoxyribonucleotides. The adaptor may comprise ribonucleotides. The adaptor may be single-stranded. The adaptor may be double-stranded. The adaptor may comprise double-stranded and single-stranded portions. For example, the adaptor may be a Y-shaped adaptor. The adaptor may be a linear adaptor. The adaptor may be a circular adaptor. The adaptor may comprise a molecular barcode, sample index, primer sequence, linker sequence or a combination thereof. The molecular barcode may be adjacent to the sample index. The molecular barcode may be adjacent to the primer sequence. The sample index may be adjacent to the primer sequence. A linker sequence may connect the molecular barcode to the sample index. A linker sequence may connect the molecular barcode to the primer sequence. A linker sequence may connect the sample index to the primer sequence.

[0209] The adaptor may comprise a molecular barcode. The molecular barcode may comprise a random sequence. The molecular barcode may comprise a predetermined sequence. Two or more adaptors may comprise two or more different molecular barcodes. The molecular barcodes may be optimized to minimize dimerization. The molecular barcodes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first molecular barcode may introduce a single base error. The first molecular barcode may comprise greater than a single base difference from the other molecular barcodes. Thus, the first molecular barcode with the single base error may still be identified as the first molecular barcode. The molecular barcode may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The molecular barcode may comprise at least 3 nucleotides. The molecular barcode may comprise at least 4 nucleotides. The molecular barcode may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecular barcode may comprise less than 10 nucleotides. The molecular barcode may comprise less than 8 nucleotides. The molecular barcode may comprise less than 6 nucleotides. The molecular barcode may comprise 2 to 15 nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. The molecular barcode may comprise 3 to 10 nucleotides. The molecular barcode may comprise 3 to 8 nucleotides. The molecular barcode may comprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6 nucleotides.

[0210] The adaptor may comprise a sample index. The sample index may comprise a random sequence. The sample index may comprise a predetermined sequence. Two or more sets of adaptors may comprise two or more different sample indexes. Adaptors within a set of adaptors may comprise identical sample indexes. The sample indexes may be optimized to minimize dimerization. The sample indexes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first sample index may introduce a single base error. The first sample index may comprise greater than a single base difference from the other sample indexes. Thus, the first sample index with the single base error may still be identified as the first molecular barcode. The sample index may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The sample index may comprise at least 3 nucleotides. The sample index may comprise at least 4 nucleotides. The sample index may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The sample index may comprise less than 10 nucleotides. The sample index may comprise less than 8 nucleotides. The sample index may comprise less than 6 nucleotides. The sample index may comprise 2 to 15 nucleotides. The sample index may comprise 2 to 12 nucleotides. The sample index may comprise 3 to 10 nucleotides. The sample index may comprise 3 to 8 nucleotides. The sample index may comprise 4 to 8 nucleotides. The sample index may comprise 4 to 6 nucleotides.

[0211] The adaptor may comprise a primer sequence. The primer sequence may be a PCR primer sequence. The primer sequence may be a sequencing primer.

[0212] Adaptors may be attached to one end of the cfDNA. Adaptors may be attached to both ends of the cfDNA. Adaptors may be attached to one or more ends of a single-stranded cfDNA. Adaptors may be attached to one or more ends of a double-stranded cfDNA.

[0213] Adaptors may be attached to the cfDNA by ligation. Ligation may be blunt end ligation. Ligation may be sticky end ligation. Adaptors may be attached to the cfDNA by primer extension. Adaptors may be attached to the cfDNA by reverse transcription. Adaptors may be attached to the cfDNA by hybridization. Adaptors may comprise a sequence that is at least partially complementary to the cfDNA. Alternatively, in some instances, adaptors do not comprise a sequence that is complementary to the cfDNA.

[0214] Sequencing may comprise massively parallel sequencing. Sequencing may comprise shotgun sequencing.

[0215] The selector set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more genomic regions from Table 2.

[0216] At least 20%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% or more of the genomic regions in the selector set may be based on genomic regions from Table 2.

[0217] The plurality of genomic regions may comprise one or more mutations present in at least 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or 99% or more of a population of subjects suffering from the cancer.

[0218] The total size of the plurality of genomic regions of the selector set may comprise less than 1.5 megabases (Mb), 1Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 100 kb, 90 kb, 80 kb, 70 kb, 60 kb, 50 kb, 40 kb, 30 kb, 20 kb, 10 kb, 5 kb, or 1 kb of a genome.

[0219] The total size of the plurality of genomic regions of the selector set may be between 100 kb to 300 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 100 kb to 200 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 300 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 200 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 10 kb to 100 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 100 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 75 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 50 kb of a genome.

[0220] The method of detecting the stage IV cancer may have a sensitivity of at least 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% or more. The method of detecting the stage IV cancer may have a sensitivity of at least 60%. The method of detecting the stage IV cancer may have a sensitivity of at least 70%. The method of detecting the stage IV cancer may have a sensitivity of at least 80%. The method of detecting the stage IV cancer may have a sensitivity of at least 90%. The method of detecting the stage IV cancer may have a sensitivity of at least 95%.

[0221] The method of detecting the stage IV cancer may have a specificity of at least 60%, 65%, 70%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97%, or 99% or more. The method of detecting the stage IV cancer may have a specificity of at least 60%. The method of detecting the stage IV cancer may have a specificity of at least 70%. The method of detecting the stage IV cancer may have a specificity of at least 80%. The method of detecting the stage IV cancer may have a specificity of at least 90%. The method of detecting the stage IV cancer may have a specificity of at least 95%.

[0222] The method may detect at least 50%, 52%, 55%, 57%, 60%, 62%, 65%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or more of stage IV cancer. The method may detect at least 50% or more of stage IV cancer. The method may detect at least 60% or more of stage IV cancer. The method may detect at least 70% or more of stage IV cancer. The method may detect at least 75% or more of stage IV cancer. The method may detect at least 80% or more of stage IV cancer. The method may detect at least 85% or more of stage IV cancer. The method may detect at least 90% or more of stage IV cancer.

[0223] Further disclosed herein are methods of producing a selector set. The method may comprise (a) identifying genomic regions comprising mutations in one or more subjects from a population of subjects suffering from the cancer; (b) ranking the genomic regions based on a Recurrence Index (RI), wherein the RI of the genomic region is determined by dividing a number of subjects or tumors with mutations in the genomic region by a size of the genomic region; and (c) producing a selector set comprising one or more genomic regions based on the RI.

[0224] At least a subset of the genomic regions that are ranked may be exon regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions that are ranked may comprise exon regions. At least 30% of the genomic regions that are ranked may comprise exon regions. At least 40% of the genomic regions that are ranked may comprise exon regions. At least 50% of the genomic regions that are ranked may comprise exon regions. At least 60% of the genomic regions that are ranked may comprise exon regions. Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomic regions that are ranked may comprise exon regions. Less than 97% of the genomic regions that are ranked may comprise exon regions. Less than 92% of the genomic regions that are ranked may comprise exon regions. Less than 84% of the genomic regions that are ranked may comprise exon regions. Less than 75% of the genomic regions that are ranked may comprise exon regions. Less than 65% of the genomic regions that are ranked may comprise exon regions.

[0225] At least a subset of the genomic regions of the selector set may comprise exon regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions of the selector set may comprise exon regions. At least 30% of the genomic regions of the selector set may comprise exon regions. At least 40% of the genomic regions of the selector set may comprise exon regions. At least 50% of the genomic regions of the selector set may comprise exon regions. At least 60% of the genomic regions of the selector set may comprise exon regions. Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomic regions of the selector set may comprise exon regions. Less than 97% of the genomic regions of the selector set may comprise exon regions. Less than 92% of the genomic regions of the selector set may comprise exon regions. Less than 84% of the genomic regions of the selector set may comprise exon regions. Less than 75% of the genomic regions of the selector set may comprise exon regions. Less than 65% of the genomic regions of the selector set may comprise exon regions.

[0226] At least a subset of the genomic regions that are ranked may be intron regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions that are ranked may comprise intron regions. At least 30% of the genomic regions that are ranked may comprise intron regions. At least 40% of the genomic regions that are ranked may comprise intron regions. At least 50% of the genomic regions that are ranked may comprise intron regions. At least 60% of the genomic regions that are ranked may comprise intron regions. Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomic regions that are ranked may comprise intron regions. Less than 97% of the genomic regions that are ranked may comprise intron regions. Less than 92% of the genomic regions that are ranked may comprise intron regions. Less than 84% of the genomic regions that are ranked may comprise intron regions. Less than 75% of the genomic regions that are ranked may comprise intron regions. Less than 65% of the genomic regions that are ranked may comprise intron regions.

[0227] At least a subset of the genomic regions of the selector set may comprise intron regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions of the selector set may comprise intron regions. At least 30% of the genomic regions of the selector set may comprise intron regions. At least 40% of the genomic regions of the selector set may comprise intron regions. At least 50% of the genomic regions of the selector set may comprise intron regions. At least 60% of the genomic regions of the selector set may comprise intron regions. Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomic regions of the selector set may comprise intron regions. Less than 97% of the genomic regions of the selector set may comprise intron regions. Less than 92% of the genomic regions of the selector set may comprise intron regions. Less than 84% of the genomic regions of the selector set may comprise intron regions. Less than 75% of the genomic regions of the selector set may comprise intron regions. Less than 65% of the genomic regions of the selector set may comprise intron regions.

[0228] At least a subset of the genomic regions that are ranked may be untranslated regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions that are ranked may comprise untranslated regions. At least 30% of the genomic regions that are ranked may comprise untranslated regions. At least 40% of the genomic regions that are ranked may comprise untranslated regions. At least 50% of the genomic regions that are ranked may comprise untranslated regions. At least 60% of the genomic regions that are ranked may comprise untranslated regions. Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomic regions that are ranked may comprise untranslated regions. Less than 97% of the genomic regions that are ranked may comprise untranslated regions. Less than 92% of the genomic regions that are ranked may comprise untranslated regions. Less than 84% of the genomic regions that are ranked may comprise untranslated regions. Less than 75% of the genomic regions that are ranked may comprise untranslated regions. Less than 65% of the genomic regions that are ranked may comprise untranslated regions.

[0229] At least a subset of the genomic regions of the selector set may comprise untranslated regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions of the selector set may comprise untranslated regions. At least 30% of the genomic regions of the selector set may comprise untranslated regions. At least 40% of the genomic regions of the selector set may comprise untranslated regions. At least 50% of the genomic regions of the selector set may comprise untranslated regions. At least 60% of the genomic regions of the selector set may comprise untranslated regions. Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomic regions of the selector set may comprise untranslated regions. Less than 97% of the genomic regions of the selector set may comprise untranslated regions. Less than 92% of the genomic regions of the selector set may comprise untranslated regions. Less than 84% of the genomic regions of the selector set may comprise untranslated regions. Less than 75% of the genomic regions of the selector set may comprise untranslated regions. Less than 65% of the genomic regions of the selector set may comprise untranslated regions.

[0230] At least a subset of the genomic regions that are ranked may be non-coding regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions that are ranked may comprise non-coding regions. At least 30% of the genomic regions that are ranked may comprise non-coding regions. At least 40% of the genomic regions that are ranked may comprise non-coding regions. At least 50% of the genomic regions that are ranked may comprise non-coding regions. At least 60% of the genomic regions that are ranked may comprise non-coding regions. Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomic regions that are ranked may comprise non-coding regions. Less than 97% of the genomic regions that are ranked may comprise non-coding regions. Less than 92% of the genomic regions that are ranked may comprise non-coding regions. Less than 84% of the genomic regions that are ranked may comprise non-coding regions. Less than 75% of the genomic regions that are ranked may comprise non-coding regions. Less than 65% of the genomic regions that are ranked may comprise non-coding regions.

[0231] At least a subset of the genomic regions of the selector set may comprise non-coding regions. At least 20%, 2%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97% of the genomic regions of the selector set may comprise non-coding regions. At least 30% of the genomic regions of the selector set may comprise non-coding regions. At least 40% of the genomic regions of the selector set may comprise non-coding regions. At least 50% of the genomic regions of the selector set may comprise non-coding regions. At least 60% of the genomic regions of the selector set may comprise non-coding regions. Less than 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50%, 45%, or 40% of the genomic regions of the selector set may comprise non-coding regions. Less than 97% of the genomic regions of the selector set may comprise non-coding regions. Less than 92% of the genomic regions of the selector set may comprise non-coding regions. Less than 84% of the genomic regions of the selector set may comprise non-coding regions. Less than 75% of the genomic regions of the selector set may comprise non-coding regions. Less than 65% of the genomic regions of the selector set may comprise non-coding regions.

[0232] Producing the selector set based on the RI may comprise selecting genomic regions that have a recurrence index in the top 60 th< , 65 th< , 70 th< , 72 nd< , 75 th< , 77 th< , 80 th< , 82 nd< , 85 th< , 87 th< , 90 th< , 92 nd< , 95 th< , or 97 th< or greater percentile. Producing the selector set based on the RI may comprise selecting genomic regions that have a recurrence index in the top 80 th< or greater percentile. Producing the selector set based on the RI may comprise selecting genomic regions that have a recurrence index in the top 70 th< or greater percentile. Producing the selector set based on the RI may comprise selecting genomic regions that have a recurrence index in the top 90 th< or greater percentile.

[0233] Producing the selector set further may comprise selecting genomic regions that result in the largest reduction in a number of subjects with one mutation in the genomic region.

[0234] Producing the selector set may comprise applying an algorithm to a subset of the ranked genomic regions. The algorithm may be applied 2, 3, 4, 5, 6, 7, 8, 9, 10 or more times. The algorithm may be applied two or more times. The algorithm may be applied three or more times.

[0235] Producing the selector set may comprise selecting genomic regions that maximize a median number of mutations per subject of the selector set. Producing the selector set may comprise selecting genomic regions that maximize the number of subjects in the selector set.

[0236] Producing the selector set may comprise selecting genomic regions that minimize the total size of the genomic regions.

[0237] The selector set may comprise information pertaining to a plurality of genomic regions comprising one or more mutations present in at least one subject suffering from a cancer. The selector set may comprise information pertaining to a plurality of genomic regions comprising 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more mutations present in at least one subject suffering from a cancer. The selector set may comprise information pertaining to a plurality of genomic regions comprising 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more mutations present in at least one subject suffering from a cancer.

[0238] The selector set may comprise information pertaining to a plurality of genomic regions comprising one or more mutations present in at least one subject suffering from a cancer. The one or more mutations within the plurality of genomic regions may be present in at least 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more subjects suffering from a cancer. The one or more mutations within the genomic regions may be present in at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more subjects suffering from a cancer.

[0239] The selector set may comprise information pertaining to a plurality of genomic regions comprising one or more mutations present in at least one subject suffering from a cancer. The one or more mutations within the plurality of genomic regions may be present in at least 1%, 2%, 3%, 4%, 5%, 6%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more subjects from a population of subjects suffering from a cancer. The one or more mutations within the plurality of genomic regions may be present in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or more subjects from a population of subjects suffering from a cancer.

[0240] The selector set may comprise sequence information pertaining to a plurality of genomic regions comprising one or more mutations present in at least one subject suffering from a cancer. The selector set may comprise sequence information pertaining to a plurality of genomic regions comprising 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more mutations present in at least one subject suffering from a cancer. The selector set may comprise sequence information pertaining to a plurality of genomic regions comprising 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more mutations present in at least one subject suffering from a cancer.

[0241] The selector set may comprise sequence information pertaining to a plurality of genomic regions comprising one or more mutations present in at least one subject suffering from a cancer. The one or more mutations within the plurality of genomic regions may be present in at least 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more subjects suffering from a cancer. The one or more mutations within the genomic regions may be present in at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more subjects suffering from a cancer.

[0242] The selector set may comprise sequence information pertaining to a plurality of genomic regions comprising one or more mutations present in at least one subject suffering from a cancer. The one or more mutations within the plurality of genomic regions may be present in at least 1%, 2%, 3%, 4%, 5%, 6%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more subjects from a population of subjects suffering from a cancer. The one or more mutations within the plurality of genomic regions may be present in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or more subjects from a population of subjects suffering from a cancer.

[0243] The selector set may comprise genomic coordinates pertaining to a plurality of genomic regions comprising one or more mutations present in at least one subject suffering from a cancer. The selector set may comprise genomic coordinates pertaining to a plurality of genomic regions comprising 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more mutations present in at least one subject suffering from a cancer. The selector set may comprise genomic coordinates pertaining to a plurality of genomic regions comprising 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more mutations present in at least one subject suffering from a cancer.

[0244] The selector set may comprise genomic coordinates pertaining to a plurality of genomic regions comprising one or more mutations present in at least one subject suffering from a cancer. The one or more mutations within the plurality of genomic regions may be present in at least 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more subjects suffering from a cancer. The one or more mutations within the plurality of genomic regions may be present in at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more subjects suffering from a cancer.

[0245] The selector set may comprise genomic coordinates pertaining to a plurality of genomic regions comprising one or more mutations present in at least one subject suffering from a cancer. The one or more mutations within the plurality of genomic regions may be present in at least 1%, 2%, 3%, 4%, 5%, 6%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more subjects from a population of subjects suffering from a cancer. The one or more mutations within the plurality of genomic regions may be present in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or more subjects from a population of subjects suffering from a cancer.

[0246] The selector set may comprise genomic regions comprising one or more types of mutations. The selector set may comprise genomic regions comprising two or more types of mutations. The selector set may comprise genomic regions comprising three or more types of mutations. The selector set may comprise genomic regions comprising four or more types of mutations. The types of mutations may include, but are not limited to, single nucleotide variants (SNVs), insertions / deletions (indels), rearrangements, and copy number variants (CNVs).

[0247] The selector set may comprise genomic regions comprising two or more different types of mutations selected from a group consisting of single nucleotide variants (SNVs), insertions / deletions (indels), rearrangements, and copy number variants (CNVs). The selector set may comprise genomic regions comprising three or more different types of mutations selected from a group consisting of single nucleotide variants (SNVs), insertions / deletions (indels), rearrangements, and copy number variants (CNVs). The selector set may comprise genomic regions comprising four or more different types of mutations selected from a group consisting of single nucleotide variants (SNVs), insertions / deletions (indels), rearrangements, and copy number variants (CNVs).

[0248] The selector set may comprise a genomic region comprising at least one SNV and a genomic region comprising at least one other type of mutation. The selector set may comprise a genomic region comprising at least one SNV and a genomic region comprising at least one indel. The selector set may comprise a genomic region comprising at least one SNV and a genomic region comprising at least one rearrangement. The selector set may comprise a genomic region comprising at least one SNV and a genomic region comprising at least one CNV.

[0249] The selector set may comprise a genomic region comprising at least one indel and a genomic region comprising at least one other type of mutation. The selector set may comprise a genomic region comprising at least one indel and a genomic region comprising at least one SNV. The selector set may comprise a genomic region comprising at least one indel and a genomic region comprising at least one rearrangement. The selector set may comprise a genomic region comprising at least one indel and a genomic region comprising at least one CNV.

[0250] The selector set may comprise a genomic region comprising at least one rearrangement. The selector set may comprise a genomic region comprising at least one rearrangement and a genomic region comprising at least one other type of mutation. The selector set may comprise a genomic region comprising at least one rearrangement and a genomic region comprising at least one SNV. The selector set may comprise a genomic region comprising at least one rearrangement and a genomic region comprising at least one indel. The selector set may comprise a genomic region comprising at least one rearrangement and a genomic region comprising at least one CNV.

[0251] The selector set may comprise a genomic region comprising at least one CNV and a genomic region comprising at least one other type of mutation. The selector set may comprise a genomic region comprising at least one CNV and a genomic region comprising at least one SNV. The selector set may comprise a genomic region comprising at least one CNV and a genomic region comprising at least one indel. The selector set may comprise a genomic region comprising at least one CNV and a genomic region comprising at least one rearrangement.

[0252] At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the genomic regions of the selector set may comprise a SNV. At least about 25%, 30%, 35%, 40%, 45%, 50%, 55%, or 60% of the genomic regions of the selector set may comprise a SNV. At least about 10% of the genomic regions of the selector set may comprise a SNV. At least about 15% of the genomic regions of the selector set may comprise a SNV. At least about 20% of the genomic regions of the selector set may comprise a SNV. At least about 30% of the genomic regions of the selector set may comprise a SNV. At least about 40% of the genomic regions of the selector set may comprise a SNV. At least about 50% of the genomic regions of the selector set may comprise a SNV. At least about 60% of the genomic regions of the selector set may comprise a SNV.

[0253] Less than 99%, 98%, 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50% of the genomic regions of the selector set may comprise a SNV. Less than 97% of the genomic regions of the selector set may comprise a SNV. Less than 95% of the genomic regions of the selector set may comprise a SNV. Less than 90% of the genomic regions of the selector set may comprise a SNV. Less than 85% of the genomic regions of the selector set may comprise a SNV. Less than 77% of the genomic regions of the selector set may comprise a SNV.

[0254] The genomic regions of the selector set may comprise between about 10% to about 95% SNVs. The genomic regions of the selector set may comprise between about 10% to about 90% SNVs. The genomic regions of the selector set may comprise between about 15% to about 95% SNVs. The genomic regions of the selector set may comprise between about 20% to about 95% SNVs. The genomic regions of the selector set may comprise between about 30% to about 95% SNVs. The genomic regions of the selector set may comprise between about 30% to about 90% SNVs. The genomic regions of the selector set may comprise between about 30% to about 85% SNVs. The genomic regions of the selector set may comprise between about 30% to about 80% SNVs.

[0255] At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the genomic regions of the selector set may comprise an indel. At least about 25%, 30%, 35%, 40%, 45%, 50%, 55%, or 60% of the genomic regions of the selector set may comprise an indel. At least about 1% of the genomic regions of the selector set may comprise an indel. At least about 3% of the genomic regions of the selector set may comprise an indel. At least about 5% of the genomic regions of the selector set may comprise an indel. At least about 8% of the genomic regions of the selector set may comprise an indel. At least about 10% of the genomic regions of the selector set may comprise an indel. At least about 15% of the genomic regions of the selector set may comprise an indel. At least about 30% of the genomic regions of the selector set may comprise an indel.

[0256] Less than 99%, 98%, 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50% of the genomic regions of the selector set may comprise an indel. Less than 97% of the genomic regions of the selector set may comprise an indel. Less than 95% of the genomic regions of the selector set may comprise an indel. Less than 90% of the genomic regions of the selector set may comprise an indel. Less than 85% of the genomic regions of the selector set may comprise an indel. Less than 77% of the genomic regions of the selector set may comprise an indel.

[0257] The genomic regions of the selector set may comprise between about 10% to about 95% indels. The genomic regions of the selector set may comprise between about 10% to about 90% indels. The genomic regions of the selector set may comprise between about 10% to about 85% indels. The genomic regions of the selector set may comprise between about 10% to about 80% indels. The genomic regions of the selector set may comprise between about 10% to about 75% indels. The genomic regions of the selector set may comprise between about 10% to about 70% indels. The genomic regions of the selector set may comprise between about 10% to about 60% indels. The genomic regions of the selector set may comprise between about 10% to about 50% indels.

[0258] At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the genomic regions of the selector set may comprise a rearrangement. At least about 1% of the genomic regions of the selector set may comprise a rearrangement. At least about 2% of the genomic regions of the selector set may comprise a rearrangement. At least about 3% of the genomic regions of the selector set may comprise a rearrangement. At least about 4% of the genomic regions of the selector set may comprise a rearrangement. At least about 5% of the genomic regions of the selector set may comprise a rearrangement.

[0259] At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the genomic regions of the selector set may comprise a CNV. At least about 25%, 30%, 35%, 40%, 45%, 50%, 55%, or 60% of the genomic regions of the selector set may comprise a CNV. At least about 1% of the genomic regions of the selector set may comprise a CNV. At least about 3% of the genomic regions of the selector set may comprise a CNV. At least about 5% of the genomic regions of the selector set may comprise a CNV. At least about 8% of the genomic regions of the selector set may comprise a CNV. At least about 10% of the genomic regions of the selector set may comprise a CNV. At least about 15% of the genomic regions of the selector set may comprise a CNV. At least about 30% of the genomic regions of the selector set may comprise a CNV.

[0260] Less than 99%, 98%, 97%, 95%, 92%, 90%, 87%, 85%, 82%, 80%, 77%, 75%, 72%, 70%, 67%, 65%, 62%, 60%, 57%, 55%, 52%, 50% of the genomic regions of the selector set may comprise a CNV. Less than 97% of the genomic regions of the selector set may comprise a CNV. Less than 95% of the genomic regions of the selector set may comprise a CNV. Less than 90% of the genomic regions of the selector set may comprise a CNV. Less than 85% of the genomic regions of the selector set may comprise a CNV. Less than 77% of the genomic regions of the selector set may comprise a CNV.

[0261] The genomic regions of the selector set may comprise between about 5% to about 80% CNVs. The genomic regions of the selector set may comprise between about 5% to about 70% CNVs. The genomic regions of the selector set may comprise between about 5% to about 60% CNVs. The genomic regions of the selector set may comprise between about 5% to about 50% CNVs. The genomic regions of the selector set may comprise between about 5% to about 40% CNVs. The genomic regions of the selector set may comprise between about 5% to about 35% CNVs. The genomic regions of the selector set may comprise between about 5% to about 30% CNVs. The genomic regions of the selector set may comprise between about 5% to about 25% CNVs.

[0262] The selector set may be used to classify a sample from a subject. The selector set may be used to classify 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 or more samples from a subject. The selector set may be used to classify two or more samples from a subject.

[0263] The selector set may be used to classify one or more samples from one or more subjects. The selector set may be used to classify two or more samples from two or more subjects. The selector set may be used to classify a plurality of samples from 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 or more subjects.

[0264] The samples may be the same type of sample. The samples may be two or more different types of samples. The sample may be a plasma sample. The sample may be a tumor sample. The sample may be a germline sample. The sample may comprise tumor-derived molecules. The sample may comprise non-tumor-derived molecules.

[0265] The selector set may classify the sample as tumor-containing. The selector set may classify the sample as tumor-free.

[0266] The selector set may be a personalized selector set. The selector set may be used to diagnose a cancer in a subject in need thereof. The selector set may be used to prognosticate a status or outcome of a cancer in a subject in need thereof. The selector set may be used to determine a therapeutic regimen for treating a cancer in a subject in need thereof.

[0267] Alternatively, the selector set may be a universal selector set. The selector set may be used to diagnose a cancer in a plurality of subjects in need thereof. The selector set may be used to prognosticate a status or outcome of a cancer in a plurality of subjects in need thereof. The selector set may be used to determine a therapeutic regimen for treating a cancer in a plurality of subjects in need thereof.

[0268] The plurality of subjects may comprise 5, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, or 100 or more subjects. The plurality of subjects may comprise 5 or more subjects. The plurality of subjects may comprise 10 or more subjects. The plurality of subjects may comprise 25 or more subjects. The plurality of subjects may comprise 50 or more subjects. The plurality of subjects may comprise 75 or more subjects. The plurality of subjects may comprise 100 or more subjects.

[0269] The selector set may be used to classify one or more subjects based on one or more samples from the one or more subjects. The selector set may be used to classify a subject as a responder to a therapy. The selector set may be used to classify a subject as a non-responder to a therapy.

[0270] The selector set may be used to design a plurality of oligonucleotides. The plurality of oligonucleotides may selectively hybridize to one or more genomic regions identified by the selector set. At least two oligonucleotides may selectively hybridize to one genomic region. At least three oligonucleotides may selectively hybridize to one genomic region. At least four oligonucleotides may selectively hybridize to one genomic region.

[0271] An oligonucleotide of the plurality of oligonucleotides may be at least about 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 nucleotides in length. An oligonucleotide may be at least about 20 nucleotides in length. An oligonucleotide may be at least about 30 nucleotides in length. An oligonucleotide may be at least about 40 nucleotides in length. An oligonucleotide may be at least about 45 nucleotides in length. An oligonucleotide may be at least about 50 nucleotides in length.

[0272] An oligonucleotide of the plurality of oligonucleotides may be less than or equal to 300, 275, 250, 225, 200, 190, 180, 170, 160, 150, 140, 130, 125, 120, 115, 110, 105, 100, 95, 90, 85, 80, 75, or 70 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be less than or equal to 200 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be less than or equal to 150 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be less than or equal to 110 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be less than or equal to 100 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be less than or equal to 80 nucleotides in length.

[0273] An oligonucleotide of the plurality of oligonucleotides may be between about 20 to 200 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be between about 20 to 170 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be between about 20 to 150 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be between about 20 to 130 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be between about 20 to 120 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be between about 30 to 150 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be between about 30 to 120 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be between about 40 to 150 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be between about 40 to 120 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be between about 50 to 150 nucleotides in length. An oligonucleotide of the plurality of oligonucleotides may be between about 50 to 120 nucleotides in length.

[0274] An oligonucleotide of the plurality of oligonucleotides may be attached to a solid support. The solid support may be a bead. The bead may be a coated bead. The bead may be a streptavidin coated bead. The solid support may be an array. The solid support may be a glass slide.

[0275] Further disclosed herein are methods of producing a personalized selector set. The method may comprise (a) obtaining a genotype of a tumor in a subject; (b) identifying genomic regions comprising one or more mutations based on the genotype of the tumor; and (c) producing a selector set comprising at least one genomic region.

[0276] Obtaining the genotype of the tumor in the subject may comprise conducting a sequencing reaction on a sample from the subject. Sequencing may comprise whole genome sequencing. Sequencing may comprise whole exome sequencing.

[0277] Sequencing may comprise use of one or more adaptors. The adaptors may be attached to one or more nucleic acids from the sample. The adaptor may comprise a plurality of oligonucleotides. The adaptor may comprise one or more deoxyribonucleotides. The adaptor may comprise ribonucleotides. The adaptor may be single-stranded. The adaptor may be double-stranded. The adaptor may comprise double-stranded and single-stranded portions. For example, the adaptor may be a Y-shaped adaptor. The adaptor may be a linear adaptor. The adaptor may be a circular adaptor. The adaptor may comprise a molecular barcode, sample index, primer sequence, linker sequence or a combination thereof. The molecular barcode may be adjacent to the sample index. The molecular barcode may be adjacent to the primer sequence. The sample index may be adjacent to the primer sequence. A linker sequence may connect the molecular barcode to the sample index. A linker sequence may connect the molecular barcode to the primer sequence. A linker sequence may connect the sample index to the primer sequence.

[0278] The adaptor may comprise a molecular barcode. The molecular barcode may comprise a random sequence. The molecular barcode may comprise a predetermined sequence. Two or more adaptors may comprise two or more different molecular barcodes. The molecular barcodes may be optimized to minimize dimerization. The molecular barcodes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first molecular barcode may introduce a single base error. The first molecular barcode may comprise greater than a single base difference from the other molecular barcodes. Thus, the first molecular barcode with the single base error may still be identified as the first molecular barcode. The molecular barcode may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The molecular barcode may comprise at least 3 nucleotides. The molecular barcode may comprise at least 4 nucleotides. The molecular barcode may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecular barcode may comprise less than 10 nucleotides. The molecular barcode may comprise less than 8 nucleotides. The molecular barcode may comprise less than 6 nucleotides. The molecular barcode may comprise 2 to 15 nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. The molecular barcode may comprise 3 to 10 nucleotides. The molecular barcode may comprise 3 to 8 nucleotides. The molecular barcode may comprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6 nucleotides.

[0279] The adaptor may comprise a sample index. The sample index may comprise a random sequence. The sample index may comprise a predetermined sequence. Two or more sets of adaptors may comprise two or more different sample indexes. Adaptors within a set of adaptors may comprise identical sample indexes. The sample indexes may be optimized to minimize dimerization. The sample indexes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first sample index may introduce a single base error. The first sample index may comprise greater than a single base difference from the other sample indexes. Thus, the first sample index with the single base error may still be identified as the first molecular barcode. The sample index may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The sample index may comprise at least 3 nucleotides. The sample index may comprise at least 4 nucleotides. The sample index may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The sample index may comprise less than 10 nucleotides. The sample index may comprise less than 8 nucleotides. The sample index may comprise less than 6 nucleotides. The sample index may comprise 2 to 15 nucleotides. The sample index may comprise 2 to 12 nucleotides. The sample index may comprise 3 to 10 nucleotides. The sample index may comprise 3 to 8 nucleotides. The sample index may comprise 4 to 8 nucleotides. The sample index may comprise 4 to 6 nucleotides.

[0280] The adaptor may comprise a primer sequence. The primer sequence may be a PCR primer sequence. The primer sequence may be a sequencing primer.

[0281] Adaptors may be attached to one end of a nucleic acid from a sample. The nucleic acids may be DNA. The DNA may be cell-free DNA (cfDNA). The DNA may be circulating tumor DNA (ctDNA). The nucleic acids may be RNA. Adaptors may be attached to both ends of the nucleic acid. Adaptors may be attached to one or more ends of a single-stranded nucleic acid. Adaptors may be attached to one or more ends of a double-stranded nucleic acid.

[0282] Adaptors may be attached to the nucleic acid by ligation. Ligation may be blunt end ligation. Ligation may be sticky end ligation. Adaptors may be attached to the nucleic acid by primer extension. Adaptors may be attached to the nucleic acid by reverse transcription. Adaptors may be attached to the nucleic acids by hybridization. Adaptors may comprise a sequence that is at least partially complementary to the nucleic acid. Alternatively, in some instances, adaptors do not comprise a sequence that is complementary to the nucleic acid.

[0283] Identifying genomic regions comprising one or more mutations based on the genotype of the tumor may comprise determining a consensus sequence for the genomic region comprising the one or more mutations. Determining the consensus sequence may be based on the adaptors. Determining the consensus sequence may be based on the molecular barcode portion of the adaptor. Determining the consensus sequence may comprise analyzing sequence reads pertaining to a molecular barcode. Determining the consensus sequence may comprise determining a percentage of sequence reads with identical sequences based on the molecular barcode. Identifying genomic regions comprising one or more mutations may comprise producing a list of genomic regions based on a percentage of the consensus sequence. Producing the list of genomic regions may comprise selecting genomic regions with at least 80%, 82%, 85%, 87%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% consensus based on the molecular barcode. For example, sequence information may be arranged into molecular barcode families (e.g., sequences with identical molecular barcodes are grouped together). Analysis of a molecular barcode family may reveal two different sequences. 1000 sequence reads may be associated with a first sequence and 10 sequence reads may be associated with a second sequence. The dominant sequence (e.g., the first sequence) may have a consensus of 99% (e.g., (1000 divided by 1010) times 100%). The list of genomic regions may comprise the dominant sequence of the genomic region. The list of genomic regions may comprise genomic regions with 90% consensus based on the molecular barcode. The list of genomic regions may comprise genomic regions with 95% consensus based on the molecular barcode. The list of genomic regions may comprise genomic regions with 98% consensus based on the molecular barcode. The list of genomic regions may comprise genomic regions with 100% sequence consensus based on the molecular barcode. Identifying genomic regions comprising one or more mutations based on the genotype of the tumor may comprise producing a list of genomic regions ranked by a percentage of their sequence consensus.

[0284] Identifying genomic regions comprising one or more mutations based on the genotype of the tumor may comprise calculating a fractional abundance of the genomic region. Identifying genomic regions comprising one or more mutations based on the genotype of the tumor may comprise calculating a fractional abundance of the genomic region from the list of genomic regions ranked by the percentage of their sequence consensus. The fractional abundance may be calculated by dividing a number of sequence reads that pertain to a genomic region with the one or more mutations by a total number of sequence reads for the genomic regions. For example, a genomic region may comprise exon 2 of gene X. A total number of sequence reads pertaining to the genomic region may be 1000, with 100 of the sequence reads containing an insertion in exon 2 of gene X. The fractional abundance of the genomic region containing the insertion in exon 2 of gene X would be 0.1 (e.g., 100 sequence reads divided by 1000). Identifying genomic regions comprising one or more mutations based on the genotype of the tumor may comprise producing a list of genomic regions ranked by their fractional abundance.

[0285] Producing the selector set may comprise selecting one or more genomic regions from the list of genomic regions ranked by their fractional abundance. Producing the selector set may comprise selecting one or more genomic regions with a fractional abundance of less than 50%, 47%, 45%, 42%, 40%, 37%, 35%, 34%, 33%, 31%, 30%, 29%, 28%, 27%, 26%, 25%, 24%, 23%, 22%, 21%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1%. Producing the selector set may comprise selecting one or more genomic regions with a fractional abundance of less than 37%. Producing the selector set may comprise selecting one or more genomic regions with a fractional abundance of less than 33%. Producing the selector set may comprise selecting one or more genomic regions with a fractional abundance of less than 30%. Producing the selector set may comprise selecting one or more genomic regions with a fractional abundance of less than 27%. Producing the selector set may comprise selecting one or more genomic regions with a fractional abundance of less than 25%. Producing the selector set may comprise selecting one or more genomic regions with a fractional abundance of between about 0.00001% to about 35%. Producing the selector set may comprise selecting one or more genomic regions with a fractional abundance of between about 0.00001% to about 30%. Producing the selector set may comprise selecting one or more genomic regions with a fractional abundance of between about 0.00001% to about 27%.

[0286] The selector set may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more genomic regions. The selector set may comprise one genomic region. The selector set may comprise at least 2 genomic regions. The selector set may comprise at least 3 genomic regions.

[0287] The genomic regions of the selector set may comprise one or more previously unidentified mutations. The genomic regions of the selector set may comprise 2 or more previously unidentified mutations. The genomic regions of the selector set may comprise 3 or more previously unidentified mutations. The genomic regions of the selector set may comprise 4 or more previously unidentified mutations.

[0288] The genomic regions may comprise one or more mutations selected from a group consisting of SNVs, indels, rearrangements, and CNVs. The genomic regions may comprise two or more mutations selected from a group consisting of SNVs, indels, rearrangements, and CNVs. The genomic regions may comprise three or more mutations selected from a group consisting of SNVs, indels, rearrangements, and CNVs. The genomic regions may comprise four or more mutations selected from a group consisting of SNVs, indels, rearrangements, and CNVs.

[0289] The genomic regions may comprise one or more types of mutations selected from a group consisting of SNVs, indels, rearrangements, and CNVs. The genomic regions may comprise two or more types of mutations selected from a group consisting of SNVs, indels, rearrangements, and CNVs. The genomic regions may comprise three or more types of mutations selected from a group consisting of SNVs, indels, rearrangements, and CNVs. The genomic regions may comprise four or more types of mutations selected from a group consisting of SNVs, indels, rearrangements, and CNVs.

[0290] Further disclosed herein are computer readable media for use in the methods disclosed herein. The computer readable medium may comprise sequence information for two or more genomic regions wherein (a) the genomic regions may comprise one or more mutations in greater than 80% of tumors from a population of subjects afflicted with a cancer; (b) the genomic regions represent less than 1.5 Mb of the genome; and (c) one or more of the following (i) the condition may be not hairy cell leukemia, ovarian cancer, Waldenstrom's macroglobulinemia; (ii) a genomic region may comprise at least one mutation in at least one subject afflicted with the cancer; (iii) the cancer includes two or more different types of cancer; (iv) the two or more genomic regions may be derived from two or more different genes; (v) the genomic regions may comprise two or more mutations; or (vi) the two or more genomic regions may comprise at least 10kb.

[0291] In some instances, the condition is not hairy cell leukemia.

[0292] The genomic regions may comprise one or more mutations in greater than 60% of tumors from an additional population of subjects afflicted with another type of cancer.

[0293] The genomic regions may be derived from 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more different genes. The genomic regions may be derived from 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100 or more different genes.

[0294] The genomic regions may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, or 50 kb. The genomic regions may comprise at least 5 kb. The genomic regions may comprise at least 10 kb. The genomic regions may comprise at least 50 kb.

[0295] The sequence information may comprise genomic coordinates pertaining to the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genomic regions. The sequence information may comprise genomic coordinates pertaining to the 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 or more genomic regions. The sequence information may comprise genomic coordinates pertaining to the 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500 or more genomic regions.

[0296] The sequence information may comprise a nucleic acid sequence pertaining to the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genomic regions. The sequence information may comprise a nucleic acid sequence pertaining to the 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 or more genomic regions. The sequence information may comprise a nucleic acid sequence pertaining to the 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500 or more genomic regions.

[0297] The sequence information may comprise a length of the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genomic regions. The sequence information may comprise a length of the 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 or more genomic regions. The sequence information may comprise a length of the 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500 or more genomic regions.

[0298] Further disclosed herein are compositions for use in the methods and systems disclosed herein. The composition may comprise a set of oligonucleotides that selectively hybridize to a plurality of genomic regions, wherein (a) greater than 80% of tumors from a population of cancer subjects include one or more mutations in the genomic regions; (b) the plurality of genomic regions represent less than 1.5 Mb of the genome; and (c) the set of oligonucleotides may comprise 5 or more different oligonucleotides that selectively hybridize to the plurality of genomic regions.

[0299] An oligonucleotide of the set of oligonucleotides may comprise a tag. The tag may be biotin. The tag may be a label. The label may be a fluorescent label or dye. The tag may be an adaptor.

[0300] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 2. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, or 525 regions from those identified in Table 2. The genomic regions may comprise at least 2 regions from those identified in Table 2. The genomic regions may comprise at least 20 regions from those identified in Table 2. The genomic regions may comprise at least 60 regions from those identified in Table 2. The genomic regions may comprise at least 100 regions from those identified in Table 2. The genomic regions may comprise at least 300 regions from those identified in Table 2. The genomic regions may comprise at least 400 regions from those identified in Table 2. The genomic regions may comprise at least 500 regions from those identified in Table 2.

[0301] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 2. At least about 5% of the genomic regions may be regions identified in Table 2. At least about 10% of the genomic regions may be regions identified in Table 2. At least about 20% of the genomic regions may be regions identified in Table 2. At least about 30% of the genomic regions may be regions identified in Table 2. At least about 40% of the genomic regions may be regions identified in Table 2.

[0302] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 6. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, or 830 regions from those identified in Table 6. The genomic regions may comprise at least 2 regions from those identified in Table 6. The genomic regions may comprise at least 20 regions from those identified in Table 6. The genomic regions may comprise at least 60 regions from those identified in Table 6. The genomic regions may comprise at least 100 regions from those identified in Table 6. The genomic regions may comprise at least 300 regions from those identified in Table 6. The genomic regions may comprise at least 600 regions from those identified in Table 6. The genomic regions may comprise at least 800 regions from those identified in Table 6.

[0303] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 6. At least about 5% of the genomic regions may be regions identified in Table 6. At least about 10% of the genomic regions may be regions identified in Table 6. At least about 20% of the genomic regions may be regions identified in Table 6. At least about 30% of the genomic regions may be regions identified in Table 6. At least about 40% of the genomic regions may be regions identified in Table 6.

[0304] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 7. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, or 450 regions from those identified in Table 7. The genomic regions may comprise at least 2 regions from those identified in Table 7. The genomic regions may comprise at least 20 regions from those identified in Table 7. The genomic regions may comprise at least 60 regions from those identified in Table 7. The genomic regions may comprise at least 100 regions from those identified in Table 7. The genomic regions may comprise at least 200 regions from those identified in Table 7. The genomic regions may comprise at least 300 regions from those identified in Table 7. The genomic regions may comprise at least 400 regions from those identified in Table 7.

[0305] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 7. At least about 5% of the genomic regions may be regions identified in Table 7. At least about 10% of the genomic regions may be regions identified in Table 7. At least about 20% of the genomic regions may be regions identified in Table 7. At least about 30% of the genomic regions may be regions identified in Table 7. At least about 40% of the genomic regions may be regions identified in Table 7.

[0306] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 8. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 8. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, or 1050 regions from those identified in Table 8. The genomic regions may comprise at least 2 regions from those identified in Table 8. The genomic regions may comprise at least 20 regions from those identified in Table 8. The genomic regions may comprise at least 60 regions from those identified in Table 8. The genomic regions may comprise at least 100 regions from those identified in Table 8. The genomic regions may comprise at least 300 regions from those identified in Table 8. The genomic regions may comprise at least 600 regions from those identified in Table 8. The genomic regions may comprise at least 800 regions from those identified in Table 8. The genomic regions may comprise at least 1000 regions from those identified in Table 8.

[0307] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 8. At least about 5% of the genomic regions may be regions identified in Table 8. At least about 10% of the genomic regions may be regions identified in Table 8. At least about 20% of the genomic regions may be regions identified in Table 8. At least about 30% of the genomic regions may be regions identified in Table 8. At least about 40% of the genomic regions may be regions identified in Table 8.

[0308] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 9. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 9. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, or 1500 regions from those identified in Table 9. The genomic regions may comprise at least 2 regions from those identified in Table 9. The genomic regions may comprise at least 20 regions from those identified in Table 9. The genomic regions may comprise at least 60 regions from those identified in Table 9. The genomic regions may comprise at least 100 regions from those identified in Table 9. The genomic regions may comprise at least 300 regions from those identified in Table 9. The genomic regions may comprise at least 500 regions from those identified in Table 9. The genomic regions may comprise at least 1000 regions from those identified in Table 9. The genomic regions may comprise at least 1300 regions from those identified in Table 9.

[0309] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 9. At least about 5% of the genomic regions may be regions identified in Table 9. At least about 10% of the genomic regions may be regions identified in Table 9. At least about 20% of the genomic regions may be regions identified in Table 9. At least about 30% of the genomic regions may be regions identified in Table 9. At least about 40% of the genomic regions may be regions identified in Table 9.

[0310] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 10. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 10. The genomic regions may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, or 330 regions from those identified in Table 10. The genomic regions may comprise at least 2 regions from those identified in Table 10. The genomic regions may comprise at least 20 regions from those identified in Table 10. The genomic regions may comprise at least 60 regions from those identified in Table 10.

[0311] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 10. At least about 5% of the genomic regions may be regions identified in Table 10. At least about 10% of the genomic regions may be regions identified in Table 10. At least about 20% of the genomic regions may be regions identified in Table 10. At least about 30% of the genomic regions may be regions identified in Table 10. At least about 40% of the genomic regions may be regions identified in Table 10.

[0312] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 11. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 11. The genomic regions may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 375, 400, 420, 440, or 460 regions from those identified in Table 11. The genomic regions may comprise at least 2 regions from those identified in Table 11. The genomic regions may comprise at least 20 regions from those identified in Table 11. The genomic regions may comprise at least 60 regions from those identified in Table 11. The genomic regions may comprise at least 100 regions from those identified in Table 11. The genomic regions may comprise at least 200 regions from those identified in Table 11. The genomic regions may comprise at least 300 regions from those identified in Table 11. The genomic regions may comprise at least 400 regions from those identified in Table 11.

[0313] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 11. At least about 5% of the genomic regions may be regions identified in Table 11. At least about 10% of the genomic regions may be regions identified in Table 11. At least about 20% of the genomic regions may be regions identified in Table 11. At least about 30% of the genomic regions may be regions identified in Table 11. At least about 40% of the genomic regions may be regions identified in Table 11.

[0314] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 12. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 12. The genomic regions may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 375, 400, 420, 440, 460, 480 or 500 regions from those identified in Table 12. The genomic regions may comprise at least 2 regions from those identified in Table 12. The genomic regions may comprise at least 20 regions from those identified in Table 12. The genomic regions may comprise at least 60 regions from those identified in Table 12. The genomic regions may comprise at least 100 regions from those identified in Table 12. The genomic regions may comprise at least 200 regions from those identified in Table 12. The genomic regions may comprise at least 300 regions from those identified in Table 12. The genomic regions may comprise at least 400 regions from those identified in Table 12. The genomic regions may comprise at least 500 regions from those identified in Table 12.

[0315] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 12. At least about 5% of the genomic regions may be regions identified in Table 12. At least about 10% of the genomic regions may be regions identified in Table 12. At least about 20% of the genomic regions may be regions identified in Table 12. At least about 30% of the genomic regions may be regions identified in Table 12. At least about 40% of the genomic regions may be regions identified in Table 12.

[0316] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 13. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 13. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, or 1450 regions from those identified in Table 13. The genomic regions may comprise at least 2 regions from those identified in Table 13. The genomic regions may comprise at least 20 regions from those identified in Table 13. The genomic regions may comprise at least 60 regions from those identified in Table 13. The genomic regions may comprise at least 100 regions from those identified in Table 13. The genomic regions may comprise at least 300 regions from those identified in Table 13. The genomic regions may comprise at least 500 regions from those identified in Table 13. The genomic regions may comprise at least 1000 regions from those identified in Table 13. The genomic regions may comprise at least 1300 regions from those identified in Table 13.

[0317] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 13. At least about 5% of the genomic regions may be regions identified in Table 13. At least about 10% of the genomic regions may be regions identified in Table 13. At least about 20% of the genomic regions may be regions identified in Table 13. At least about 30% of the genomic regions may be regions identified in Table 13. At least about 40% of the genomic regions may be regions identified in Table 13.

[0318] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 14. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 14. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1210, 1220, 1230, or 1240 regions from those identified in Table 14. The genomic regions may comprise at least 2 regions from those identified in Table 14. The genomic regions may comprise at least 20 regions from those identified in Table 14. The genomic regions may comprise at least 60 regions from those identified in Table 14. The genomic regions may comprise at least 100 regions from those identified in Table 14. The genomic regions may comprise at least 300 regions from those identified in Table 14. The genomic regions may comprise at least 500 regions from those identified in Table 14. The genomic regions may comprise at least 1000 regions from those identified in Table 14. The genomic regions may comprise at least 1100 regions from those identified in Table 14. The genomic regions may comprise at least 1200 regions from those identified in Table 14.

[0319] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 14. At least about 5% of the genomic regions may be regions identified in Table 14. At least about 10% of the genomic regions may be regions identified in Table 14. At least about 20% of the genomic regions may be regions identified in Table 14. At least about 30% of the genomic regions may be regions identified in Table 14. At least about 40% of the genomic regions may be regions identified in Table 14.

[0320] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 15. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, or 170 regions from those identified in Table 15. The genomic regions may comprise at least 2 regions from those identified in Table 15. The genomic regions may comprise at least 20 regions from those identified in Table 15. The genomic regions may comprise at least 60 regions from those identified in Table 15. The genomic regions may comprise at least 100 regions from those identified in Table 15. The genomic regions may comprise at least 120 regions from those identified in Table 15. The genomic regions may comprise at least 150 regions from those identified in Table 15.

[0321] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 15. At least about 5% of the genomic regions may be regions identified in Table 15. At least about 10% of the genomic regions may be regions identified in Table 15. At least about 20% of the genomic regions may be regions identified in Table 15. At least about 30% of the genomic regions may be regions identified in Table 15. At least about 40% of the genomic regions may be regions identified in Table 15.

[0322] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 16. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 16. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, or 2050 regions from those identified in Table 16. The genomic regions may comprise at least 2 regions from those identified in Table 16. The genomic regions may comprise at least 20 regions from those identified in Table 16. The genomic regions may comprise at least 60 regions from those identified in Table 16. The genomic regions may comprise at least 100 regions from those identified in Table 16. The genomic regions may comprise at least 300 regions from those identified in Table 16. The genomic regions may comprise at least 500 regions from those identified in Table 16. The genomic regions may comprise at least 1000 regions from those identified in Table 16. The genomic regions may comprise at least 1200 regions from those identified in Table 16. The genomic regions may comprise at least 1500 regions from those identified in Table 16. The genomic regions may comprise at least 1700 regions from those identified in Table 16. The genomic regions may comprise at least 2000 regions from those identified in Table 16.

[0323] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 16. At least about 5% of the genomic regions may be regions identified in Table 16. At least about 10% of the genomic regions may be regions identified in Table 16. At least about 20% of the genomic regions may be regions identified in Table 16. At least about 30% of the genomic regions may be regions identified in Table 16. At least about 40% of the genomic regions may be regions identified in Table 16.

[0324] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 17. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 17. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1010, 1020, 1030, 1040, 1050, 1060, 1070, or 1080 regions from those identified in Table 17. The genomic regions may comprise at least 2 regions from those identified in Table 17. The genomic regions may comprise at least 20 regions from those identified in Table 17. The genomic regions may comprise at least 60 regions from those identified in Table 17. The genomic regions may comprise at least 100 regions from those identified in Table 17. The genomic regions may comprise at least 300 regions from those identified in Table 17. The genomic regions may comprise at least 500 regions from those identified in Table 17. The genomic regions may comprise at least 1000 regions from those identified in Table 17. The genomic regions may comprise at least 1050 regions from those identified in Table 17.

[0325] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 17. At least about 5% of the genomic regions may be regions identified in Table 17. At least about 10% of the genomic regions may be regions identified in Table 17. At least about 20% of the genomic regions may be regions identified in Table 17. At least about 30% of the genomic regions may be regions identified in Table 17. At least about 40% of the genomic regions may be regions identified in Table 17.

[0326] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 18. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 18. The genomic regions may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 375, 400, 420, 440, 460, 480, 500, 520, 540, or 555 regions from those identified in Table 18. The genomic regions may comprise at least 2 regions from those identified in Table 18. The genomic regions may comprise at least 20 regions from those identified in Table 18. The genomic regions may comprise at least 60 regions from those identified in Table 18. The genomic regions may comprise at least 100 regions from those identified in Table 18. The genomic regions may comprise at least 200 regions from those identified in Table 18. The genomic regions may comprise at least 300 regions from those identified in Table 18. The genomic regions may comprise at least 400 regions from those identified in Table 18. The genomic regions may comprise at least 500 regions from those identified in Table 18.

[0327] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 18. At least about 5% of the genomic regions may be regions identified in Table 18. At least about 10% of the genomic regions may be regions identified in Table 18. At least about 20% of the genomic regions may be regions identified in Table 18. At least about 30% of the genomic regions may be regions identified in Table 18. At least about 40% of the genomic regions may be regions identified in Table 18.

[0328] The set of oligonucleotides may hybridize to less than 1.5, 1.45, 1.4, 1.35, 1.3, 1.25, 1.2, 1.15, 1.1, 1.05, or 1.0 Megabases (Mb) of the genome. The set of oligonucleotides may hybridize to less than 1000, 900, 800, 700, 600, 550, 500, 450, 400, 350, 300, 250, 200, 150, or 100 kb of the genome.The set of oligonucleotides may hybridize to less than 1.5 Megabases (Mb) of the genome. The set of oligonucleotides may hybridize to less than 1.25 Megabases (Mb) of the genome. The set of oligonucleotides may hybridize to less than 1 Megabases (Mb) of the genome. The set of oligonucleotides may hybridize to less than 1000 kb of the genome. The set of oligonucleotides may hybridize to less than 500 kb of the genome. The set of oligonucleotides may hybridize to less than 300 kb of the genome. The set of oligonucleotides may hybridize to less than 100 kb of the genome. The set of oligonucleotides may be capable of hybridizing to greater than 50kb of the genome.

[0329] The set of oligonucleotides may be capable of hybridizing to 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300, 350, 400, 450, or 500 or more different genomic regions. The set of oligonucleotides may be capable of hybridizing to 5 or more different genomic regions. The set of oligonucleotides may be capable of hybridizing to 20 or more different genomic regions. The set of oligonucleotides may be capable of hybridizing to 50 or more different genomic regions. The set of oligonucleotides may be capable of hybridizing to 100 or more different genomic regions.

[0330] The plurality of genomic regions may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100 or more different protein-coding regions. The protein-coding regions may comprise an exon, intron, untranslated region, or a combination thereof.

[0331] The plurality of genomic regions may comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100 or more different non-coding regions. The non-coding regions may comprise a non-coding RNA, ribosomal RNA (rRNA), transfer RNA (tRNA), or a combination thereof.

[0332] The oligonucleotides may be attached to a solid support. The solid support may be a bead. The bead may be a coated bead. The bead may be a streptavidin bead. The solid support may be an array. The solid support may be a glass slide.

[0333] Disclosed herein are populations of circulating tumor DNA (ctDNA) for use in any of the methods or systems disclosed herein. A population of circulating tumor DNA (ctDNA) may comprise ctDNA enriched by hybrid selection using any of the compositions comprising the set of oligonucleotides disclosed herein. A population of ctDNA may comprise ctDNA enriched by selective hybridization of the ctDNA using the set of oligonucleotides based on the selector sets disclosed herein. A population of ctDNA may comprise ctDNA enriched by selective hybridization using a set of oligonucleotides based on any of Tables 2 and 6-18.

[0334] Further disclosed herein are arrays for use in any of the methods and systems disclosed herein. The array may comprise a plurality of oligonucleotides to selectively capture genomic regions, wherein the genomic regions may comprise a plurality of mutations present in greater 60% of a population of subjects suffering from a cancer.

[0335] The plurality of mutations may be present in greater 60% of an additional population of subjects suffering from an additional type of cancer. The plurality of mutations may be present in greater 60% of an additional population of subjects suffering from two or more additional types of cancer. The plurality of mutations may be present in greater 60% of an additional population of subjects suffering from three or more additional types of cancer. The plurality of mutations may be present in greater 60% of an additional population of subjects suffering from four or more additional types of cancer.

[0336] An oligonucleotide of the set of oligonucleotides may comprise a tag. The tag may be biotin. The tag may comprise a label. The label may be a fluorescent label or dye. The tag may be an adaptor. The adaptor may comprise a molecular barcode. The adaptor may comprise a sample index.

[0337] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 2. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, or 525 regions from those identified in Table 2. The genomic regions may comprise at least 2 regions from those identified in Table 2. The genomic regions may comprise at least 20 regions from those identified in Table 2. The genomic regions may comprise at least 60 regions from those identified in Table 2. The genomic regions may comprise at least 100 regions from those identified in Table 2. The genomic regions may comprise at least 300 regions from those identified in Table 2. The genomic regions may comprise at least 400 regions from those identified in Table 2. The genomic regions may comprise at least 500 regions from those identified in Table 2.

[0338] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 2. At least about 5% of the genomic regions may be regions identified in Table 2. At least about 10% of the genomic regions may be regions identified in Table 2. At least about 20% of the genomic regions may be regions identified in Table 2. At least about 30% of the genomic regions may be regions identified in Table 2. At least about 40% of the genomic regions may be regions identified in Table 2.

[0339] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 6. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, or 830 regions from those identified in Table 6. The genomic regions may comprise at least 2 regions from those identified in Table 6. The genomic regions may comprise at least 20 regions from those identified in Table 6. The genomic regions may comprise at least 60 regions from those identified in Table 6. The genomic regions may comprise at least 100 regions from those identified in Table 6. The genomic regions may comprise at least 300 regions from those identified in Table 6. The genomic regions may comprise at least 600 regions from those identified in Table 6. The genomic regions may comprise at least 800 regions from those identified in Table 6.

[0340] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 6. At least about 5% of the genomic regions may be regions identified in Table 6. At least about 10% of the genomic regions may be regions identified in Table 6. At least about 20% of the genomic regions may be regions identified in Table 6. At least about 30% of the genomic regions may be regions identified in Table 6. At least about 40% of the genomic regions may be regions identified in Table 6.

[0341] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 7. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, or 450 regions from those identified in Table 7. The genomic regions may comprise at least 2 regions from those identified in Table 7. The genomic regions may comprise at least 20 regions from those identified in Table 7. The genomic regions may comprise at least 60 regions from those identified in Table 7. The genomic regions may comprise at least 100 regions from those identified in Table 7. The genomic regions may comprise at least 200 regions from those identified in Table 7. The genomic regions may comprise at least 300 regions from those identified in Table 7. The genomic regions may comprise at least 400 regions from those identified in Table 7.

[0342] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 7. At least about 5% of the genomic regions may be regions identified in Table 7. At least about 10% of the genomic regions may be regions identified in Table 7. At least about 20% of the genomic regions may be regions identified in Table 7. At least about 30% of the genomic regions may be regions identified in Table 7. At least about 40% of the genomic regions may be regions identified in Table 7.

[0343] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 8. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 8. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, or 1050 regions from those identified in Table 8. The genomic regions may comprise at least 2 regions from those identified in Table 8. The genomic regions may comprise at least 20 regions from those identified in Table 8. The genomic regions may comprise at least 60 regions from those identified in Table 8. The genomic regions may comprise at least 100 regions from those identified in Table 8. The genomic regions may comprise at least 300 regions from those identified in Table 8. The genomic regions may comprise at least 600 regions from those identified in Table 8. The genomic regions may comprise at least 800 regions from those identified in Table 8. The genomic regions may comprise at least 1000 regions from those identified in Table 8.

[0344] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 8. At least about 5% of the genomic regions may be regions identified in Table 8. At least about 10% of the genomic regions may be regions identified in Table 8. At least about 20% of the genomic regions may be regions identified in Table 8. At least about 30% of the genomic regions may be regions identified in Table 8. At least about 40% of the genomic regions may be regions identified in Table 8.

[0345] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 9. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 9. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, or 1500 regions from those identified in Table 9. The genomic regions may comprise at least 2 regions from those identified in Table 9. The genomic regions may comprise at least 20 regions from those identified in Table 9. The genomic regions may comprise at least 60 regions from those identified in Table 9. The genomic regions may comprise at least 100 regions from those identified in Table 9. The genomic regions may comprise at least 300 regions from those identified in Table 9. The genomic regions may comprise at least 500 regions from those identified in Table 9. The genomic regions may comprise at least 1000 regions from those identified in Table 9. The genomic regions may comprise at least 1300 regions from those identified in Table 9.

[0346] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 9. At least about 5% of the genomic regions may be regions identified in Table 9. At least about 10% of the genomic regions may be regions identified in Table 9. At least about 20% of the genomic regions may be regions identified in Table 9. At least about 30% of the genomic regions may be regions identified in Table 9. At least about 40% of the genomic regions may be regions identified in Table 9.

[0347] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 10. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 10. The genomic regions may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, or 330 regions from those identified in Table 10. The genomic regions may comprise at least 2 regions from those identified in Table 10. The genomic regions may comprise at least 20 regions from those identified in Table 10. The genomic regions may comprise at least 60 regions from those identified in Table 10.

[0348] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 10. At least about 5% of the genomic regions may be regions identified in Table 10. At least about 10% of the genomic regions may be regions identified in Table 10. At least about 20% of the genomic regions may be regions identified in Table 10. At least about 30% of the genomic regions may be regions identified in Table 10. At least about 40% of the genomic regions may be regions identified in Table 10.

[0349] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 11. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 11. The genomic regions may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 375, 400, 420, 440, or 460 regions from those identified in Table 11. The genomic regions may comprise at least 2 regions from those identified in Table 11. The genomic regions may comprise at least 20 regions from those identified in Table 11. The genomic regions may comprise at least 60 regions from those identified in Table 11. The genomic regions may comprise at least 100 regions from those identified in Table 11. The genomic regions may comprise at least 200 regions from those identified in Table 11. The genomic regions may comprise at least 300 regions from those identified in Table 11. The genomic regions may comprise at least 400 regions from those identified in Table 11.

[0350] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 11. At least about 5% of the genomic regions may be regions identified in Table 11. At least about 10% of the genomic regions may be regions identified in Table 11. At least about 20% of the genomic regions may be regions identified in Table 11. At least about 30% of the genomic regions may be regions identified in Table 11. At least about 40% of the genomic regions may be regions identified in Table 11.

[0351] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 12. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 12. The genomic regions may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 375, 400, 420, 440, 460, 480 or 500 regions from those identified in Table 12. The genomic regions may comprise at least 2 regions from those identified in Table 12. The genomic regions may comprise at least 20 regions from those identified in Table 12. The genomic regions may comprise at least 60 regions from those identified in Table 12. The genomic regions may comprise at least 100 regions from those identified in Table 12. The genomic regions may comprise at least 200 regions from those identified in Table 12. The genomic regions may comprise at least 300 regions from those identified in Table 12. The genomic regions may comprise at least 400 regions from those identified in Table 12. The genomic regions may comprise at least 500 regions from those identified in Table 12.

[0352] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 12. At least about 5% of the genomic regions may be regions identified in Table 12. At least about 10% of the genomic regions may be regions identified in Table 12. At least about 20% of the genomic regions may be regions identified in Table 12. At least about 30% of the genomic regions may be regions identified in Table 12. At least about 40% of the genomic regions may be regions identified in Table 12.

[0353] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 13. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 13. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, or 1450 regions from those identified in Table 13. The genomic regions may comprise at least 2 regions from those identified in Table 13. The genomic regions may comprise at least 20 regions from those identified in Table 13. The genomic regions may comprise at least 60 regions from those identified in Table 13. The genomic regions may comprise at least 100 regions from those identified in Table 13. The genomic regions may comprise at least 300 regions from those identified in Table 13. The genomic regions may comprise at least 500 regions from those identified in Table 13. The genomic regions may comprise at least 1000 regions from those identified in Table 13. The genomic regions may comprise at least 1300 regions from those identified in Table 13.

[0354] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 13. At least about 5% of the genomic regions may be regions identified in Table 13. At least about 10% of the genomic regions may be regions identified in Table 13. At least about 20% of the genomic regions may be regions identified in Table 13. At least about 30% of the genomic regions may be regions identified in Table 13. At least about 40% of the genomic regions may be regions identified in Table 13.

[0355] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 14. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 14. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1210, 1220, 1230, or 1240 regions from those identified in Table 14. The genomic regions may comprise at least 2 regions from those identified in Table 14. The genomic regions may comprise at least 20 regions from those identified in Table 14. The genomic regions may comprise at least 60 regions from those identified in Table 14. The genomic regions may comprise at least 100 regions from those identified in Table 14. The genomic regions may comprise at least 300 regions from those identified in Table 14. The genomic regions may comprise at least 500 regions from those identified in Table 14. The genomic regions may comprise at least 1000 regions from those identified in Table 14. The genomic regions may comprise at least 1100 regions from those identified in Table 14. The genomic regions may comprise at least 1200 regions from those identified in Table 14.

[0356] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 14. At least about 5% of the genomic regions may be regions identified in Table 14. At least about 10% of the genomic regions may be regions identified in Table 14. At least about 20% of the genomic regions may be regions identified in Table 14. At least about 30% of the genomic regions may be regions identified in Table 14. At least about 40% of the genomic regions may be regions identified in Table 14.

[0357] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 15. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, or 170 regions from those identified in Table 15. The genomic regions may comprise at least 2 regions from those identified in Table 15. The genomic regions may comprise at least 20 regions from those identified in Table 15. The genomic regions may comprise at least 60 regions from those identified in Table 15. The genomic regions may comprise at least 100 regions from those identified in Table 15. The genomic regions may comprise at least 120 regions from those identified in Table 15. The genomic regions may comprise at least 150 regions from those identified in Table 15.

[0358] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 15. At least about 5% of the genomic regions may be regions identified in Table 15. At least about 10% of the genomic regions may be regions identified in Table 15. At least about 20% of the genomic regions may be regions identified in Table 15. At least about 30% of the genomic regions may be regions identified in Table 15. At least about 40% of the genomic regions may be regions identified in Table 15.

[0359] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 16. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 16. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, or 2050 regions from those identified in Table 16. The genomic regions may comprise at least 2 regions from those identified in Table 16. The genomic regions may comprise at least 20 regions from those identified in Table 16. The genomic regions may comprise at least 60 regions from those identified in Table 16. The genomic regions may comprise at least 100 regions from those identified in Table 16. The genomic regions may comprise at least 300 regions from those identified in Table 16. The genomic regions may comprise at least 500 regions from those identified in Table 16. The genomic regions may comprise at least 1000 regions from those identified in Table 16. The genomic regions may comprise at least 1200 regions from those identified in Table 16. The genomic regions may comprise at least 1500 regions from those identified in Table 16. The genomic regions may comprise at least 1700 regions from those identified in Table 16. The genomic regions may comprise at least 2000 regions from those identified in Table 16.

[0360] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 16. At least about 5% of the genomic regions may be regions identified in Table 16. At least about 10% of the genomic regions may be regions identified in Table 16. At least about 20% of the genomic regions may be regions identified in Table 16. At least about 30% of the genomic regions may be regions identified in Table 16. At least about 40% of the genomic regions may be regions identified in Table 16.

[0361] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 17. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 17. The genomic regions may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1010, 1020, 1030, 1040, 1050, 1060, 1070, or 1080 regions from those identified in Table 17. The genomic regions may comprise at least 2 regions from those identified in Table 17. The genomic regions may comprise at least 20 regions from those identified in Table 17. The genomic regions may comprise at least 60 regions from those identified in Table 17. The genomic regions may comprise at least 100 regions from those identified in Table 17. The genomic regions may comprise at least 300 regions from those identified in Table 17. The genomic regions may comprise at least 500 regions from those identified in Table 17. The genomic regions may comprise at least 1000 regions from those identified in Table 17. The genomic regions may comprise at least 1050 regions from those identified in Table 17.

[0362] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 17. At least about 5% of the genomic regions may be regions identified in Table 17. At least about 10% of the genomic regions may be regions identified in Table 17. At least about 20% of the genomic regions may be regions identified in Table 17. At least about 30% of the genomic regions may be regions identified in Table 17. At least about 40% of the genomic regions may be regions identified in Table 17.

[0363] The genomic regions may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 18. The genomic regions may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 18. The genomic regions may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 375, 400, 420, 440, 460, 480, 500, 520, 540, or 555 regions from those identified in Table 18. The genomic regions may comprise at least 2 regions from those identified in Table 18. The genomic regions may comprise at least 20 regions from those identified in Table 18. The genomic regions may comprise at least 60 regions from those identified in Table 18. The genomic regions may comprise at least 100 regions from those identified in Table 18. The genomic regions may comprise at least 200 regions from those identified in Table 18. The genomic regions may comprise at least 300 regions from those identified in Table 18. The genomic regions may comprise at least 400 regions from those identified in Table 18. The genomic regions may comprise at least 500 regions from those identified in Table 18.

[0364] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions may be regions identified in Table 18. At least about 5% of the genomic regions may be regions identified in Table 18. At least about 10% of the genomic regions may be regions identified in Table 18. At least about 20% of the genomic regions may be regions identified in Table 18. At least about 30% of the genomic regions may be regions identified in Table 18. At least about 40% of the genomic regions may be regions identified in Table 18.

[0365] The oligonucleotides may selectively capture 5, 10, 15, 20, 25, or 30 or more different genomic regions.

[0366] The oligonucleotides may hybridize to less than 1.5, 1.47, 1.45, 1.42, 1.40, 1.37, 1.35, 1.32, 1.30, 1.27, 1.25, 1.22, 1.20, 1.17, 1.15, 1.12, 1.10, 1.07, 1.05, 1.02, or 1.0 Megabases (Mb) of the genome. The oligonucleotides may hybridize to less than 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, or 10 kb of the genome.

[0367] The oligonucleotides may be capable of hybridizing to greater than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, or 50 kb of the genome. The oligonucleotides may be capable of hybridizing to greater than 5 kb of the genome. The oligonucleotides may be capable of hybridizing to greater than 10 kb of the genome. The oligonucleotides may be capable of hybridizing to greater than 30 kb of the genome. The oligonucleotides may be capable of hybridizing to greater than 50 kb of the genome.

[0368] The plurality of genomic regions may comprise 2 or more different protein-coding regions. The plurality of genomic regions may comprise at least 3 different protein-coding regions. The protein-coding regions may comprise an exon, intron, untranslated region, or a combination thereof.

[0369] The plurality of genomic regions may comprise at least one non-coding region. The non-coding region may comprise a non-coding RNA, ribosomal RNA (rRNA), transfer RNA (tRNA), or a combination thereof.

[0370] Further disclosed herein are methods of determining a quantity of circulating tumor DNA (ctDNA). The method may comprise (a) ligating one or more adaptors to cell-free DNA (cfDNA) derived from a sample from a subject to produce one or more adaptor-ligated cfDNA; (b) performing sequencing on the one or more adaptor-ligated cfDNA, wherein the adaptor-ligated cfDNA to be sequenced are based on a selector set comprising a plurality of genomic regions; and (c) using a computer readable medium to determine a quantity of cfDNA originating from a tumor based on the sequencing information obtained from the adaptor-ligated cfDNA.

[0371] In some instances, sequencing does not comprise whole genome sequencing. In some instances, sequencing does not comprise whole exome sequencing. Sequencing may comprise massively parallel sequencing.

[0372] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 2. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, or 525 regions from those identified in Table 2. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 2. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 2. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 2. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 2. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 2. The genomic regions of the selector set may comprise at least 400 regions from those identified in Table 2. The genomic regions of the selector set may comprise at least 500 regions from those identified in Table 2.

[0373] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 2. At least about 5% of the genomic regions of the selector set may be regions identified in Table 2. At least about 10% of the genomic regions of the selector set may be regions identified in Table 2. At least about 20% of the genomic regions of the selector set may be regions identified in Table 2. At least about 30% of the genomic regions of the selector set may be regions identified in Table 2. At least about 40% of the genomic regions of the selector set may be regions identified in Table 2.

[0374] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 6. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, or 830 regions from those identified in Table 6. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 6. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 6. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 6. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 6. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 6. The genomic regions of the selector set may comprise at least 600 regions from those identified in Table 6. The genomic regions of the selector set may comprise at least 800 regions from those identified in Table 6.

[0375] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 6. At least about 5% of the genomic regions of the selector set may be regions identified in Table 6. At least about 10% of the genomic regions of the selector set may be regions identified in Table 6. At least about 20% of the genomic regions of the selector set may be regions identified in Table 6. At least about 30% of the genomic regions of the selector set may be regions identified in Table 6. At least about 40% of the genomic regions of the selector set may be regions identified in Table 6.

[0376] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 7. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, or 450 regions from those identified in Table 7. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 7. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 7. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 7. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 7. The genomic regions of the selector set may comprise at least 200 regions from those identified in Table 7. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 7. The genomic regions of the selector set may comprise at least 400 regions from those identified in Table 7.

[0377] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 7. At least about 5% of the genomic regions of the selector set may be regions identified in Table 7. At least about 10% of the genomic regions of the selector set may be regions identified in Table 7. At least about 20% of the genomic regions of the selector set may be regions identified in Table 7. At least about 30% of the genomic regions of the selector set may be regions identified in Table 7. At least about 40% of the genomic regions of the selector set may be regions identified in Table 7.

[0378] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 8. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 8. The genomic regions of the selector set may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, or 1050 regions from those identified in Table 8. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 8. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 8. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 8. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 8. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 8. The genomic regions of the selector set may comprise at least 600 regions from those identified in Table 8. The genomic regions of the selector set may comprise at least 800 regions from those identified in Table 8. The genomic regions of the selector set may comprise at least 1000 regions from those identified in Table 8.

[0379] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 8. At least about 5% of the genomic regions of the selector set may be regions identified in Table 8. At least about 10% of the genomic regions of the selector set may be regions identified in Table 8. At least about 20% of the genomic regions of the selector set may be regions identified in Table 8. At least about 30% of the genomic regions of the selector set may be regions identified in Table 8. At least about 40% of the genomic regions of the selector set may be regions identified in Table 8.

[0380] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 9. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 9. The genomic regions of the selector set may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, or 1500 regions from those identified in Table 9. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 9. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 9. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 9. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 9. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 9. The genomic regions of the selector set may comprise at least 500 regions from those identified in Table 9. The genomic regions of the selector set may comprise at least 1000 regions from those identified in Table 9. The genomic regions of the selector set may comprise at least 1300 regions from those identified in Table 9.

[0381] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 9. At least about 5% of the genomic regions of the selector set may be regions identified in Table 9. At least about 10% of the genomic regions of the selector set may be regions identified in Table 9. At least about 20% of the genomic regions of the selector set may be regions identified in Table 9. At least about 30% of the genomic regions of the selector set may be regions identified in Table 9. At least about 40% of the genomic regions of the selector set may be regions identified in Table 9.

[0382] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 10. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 10. The genomic regions of the selector set may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, or 330 regions from those identified in Table 10. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 10. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 10. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 10.

[0383] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 10. At least about 5% of the genomic regions of the selector set may be regions identified in Table 10. At least about 10% of the genomic regions of the selector set may be regions identified in Table 10. At least about 20% of the genomic regions of the selector set may be regions identified in Table 10. At least about 30% of the genomic regions of the selector set may be regions identified in Table 10. At least about 40% of the genomic regions of the selector set may be regions identified in Table 10.

[0384] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 11. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 11. The genomic regions of the selector set may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 375, 400, 420, 440, or 460 regions from those identified in Table 11. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 11. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 11. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 11. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 11. The genomic regions of the selector set may comprise at least 200 regions from those identified in Table 11. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 11. The genomic regions of the selector set may comprise at least 400 regions from those identified in Table 11.

[0385] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 11. At least about 5% of the genomic regions of the selector set may be regions identified in Table 11. At least about 10% of the genomic regions of the selector set may be regions identified in Table 11. At least about 20% of the genomic regions of the selector set may be regions identified in Table 11. At least about 30% of the genomic regions of the selector set may be regions identified in Table 11. At least about 40% of the genomic regions of the selector set may be regions identified in Table 11.

[0386] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 12. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 12. The genomic regions of the selector set may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 375, 400, 420, 440, 460, 480 or 500 regions from those identified in Table 12. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 12. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 12. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 12. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 12. The genomic regions of the selector set may comprise at least 200 regions from those identified in Table 12. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 12. The genomic regions of the selector set may comprise at least 400 regions from those identified in Table 12. The genomic regions of the selector set may comprise at least 500 regions from those identified in Table 12.

[0387] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 12. At least about 5% of the genomic regions of the selector set may be regions identified in Table 12. At least about 10% of the genomic regions of the selector set may be regions identified in Table 12. At least about 20% of the genomic regions of the selector set may be regions identified in Table 12. At least about 30% of the genomic regions of the selector set may be regions identified in Table 12. At least about 40% of the genomic regions of the selector set may be regions identified in Table 12.

[0388] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 13. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 13. The genomic regions of the selector set may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1250, 1300, 1350, 1400, or 1450 regions from those identified in Table 13. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 13. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 13. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 13. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 13. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 13. The genomic regions of the selector set may comprise at least 500 regions from those identified in Table 13. The genomic regions of the selector set may comprise at least 1000 regions from those identified in Table 13. The genomic regions of the selector set may comprise at least 1300 regions from those identified in Table 13.

[0389] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 13. At least about 5% of the genomic regions of the selector set may be regions identified in Table 13. At least about 10% of the genomic regions of the selector set may be regions identified in Table 13. At least about 20% of the genomic regions of the selector set may be regions identified in Table 13. At least about 30% of the genomic regions of the selector set may be regions identified in Table 13. At least about 40% of the genomic regions of the selector set may be regions identified in Table 13.

[0390] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 14. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 14. The genomic regions of the selector set may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200, 1210, 1220, 1230, or 1240 regions from those identified in Table 14. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 14. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 14. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 14. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 14. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 14. The genomic regions of the selector set may comprise at least 500 regions from those identified in Table 14. The genomic regions of the selector set may comprise at least 1000 regions from those identified in Table 14. The genomic regions of the selector set may comprise at least 1100 regions from those identified in Table 14. The genomic regions of the selector set may comprise at least 1200 regions from those identified in Table 14.

[0391] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 14. At least about 5% of the genomic regions of the selector set may be regions identified in Table 14. At least about 10% of the genomic regions of the selector set may be regions identified in Table 14. At least about 20% of the genomic regions of the selector set may be regions identified in Table 14. At least about 30% of the genomic regions of the selector set may be regions identified in Table 14. At least about 40% of the genomic regions of the selector set may be regions identified in Table 14.

[0392] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 15. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, or 170 regions from those identified in Table 15. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 15. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 15. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 15. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 15. The genomic regions of the selector set may comprise at least 120 regions from those identified in Table 15. The genomic regions of the selector set may comprise at least 150 regions from those identified in Table 15.

[0393] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 15. At least about 5% of the genomic regions of the selector set may be regions identified in Table 15. At least about 10% of the genomic regions of the selector set may be regions identified in Table 15. At least about 20% of the genomic regions of the selector set may be regions identified in Table 15. At least about 30% of the genomic regions of the selector set may be regions identified in Table 15. At least about 40% of the genomic regions of the selector set may be regions identified in Table 15.

[0394] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, or 2050 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 500 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 1000 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 1200 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 1500 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 1700 regions from those identified in Table 16. The genomic regions of the selector set may comprise at least 2000 regions from those identified in Table 16.

[0395] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 16. At least about 5% of the genomic regions of the selector set may be regions identified in Table 16. At least about 10% of the genomic regions of the selector set may be regions identified in Table 16. At least about 20% of the genomic regions of the selector set may be regions identified in Table 16. At least about 30% of the genomic regions of the selector set may be regions identified in Table 16. At least about 40% of the genomic regions of the selector set may be regions identified in Table 16.

[0396] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 17. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 17. The genomic regions of the selector set may comprise at least 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1010, 1020, 1030, 1040, 1050, 1060, 1070, or 1080 regions from those identified in Table 17. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 17. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 17. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 17. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 17. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 17. The genomic regions of the selector set may comprise at least 500 regions from those identified in Table 17. The genomic regions of the selector set may comprise at least 1000 regions from those identified in Table 17. The genomic regions of the selector set may comprise at least 1050 regions from those identified in Table 17.

[0397] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 17. At least about 5% of the genomic regions of the selector set may be regions identified in Table 17. At least about 10% of the genomic regions of the selector set may be regions identified in Table 17. At least about 20% of the genomic regions of the selector set may be regions identified in Table 17. At least about 30% of the genomic regions of the selector set may be regions identified in Table 17. At least about 40% of the genomic regions of the selector set may be regions identified in Table 17.

[0398] The genomic regions of the selector set may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more regions from those identified in Table 18. The genomic regions of the selector set may comprise at least 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 regions from those identified in Table 18. The genomic regions of the selector set may comprise at least 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 375, 400, 420, 440, 460, 480, 500, 520, 540, or 555 regions from those identified in Table 18. The genomic regions of the selector set may comprise at least 2 regions from those identified in Table 18. The genomic regions of the selector set may comprise at least 20 regions from those identified in Table 18. The genomic regions of the selector set may comprise at least 60 regions from those identified in Table 18. The genomic regions of the selector set may comprise at least 100 regions from those identified in Table 18. The genomic regions of the selector set may comprise at least 200 regions from those identified in Table 18. The genomic regions of the selector set may comprise at least 300 regions from those identified in Table 18. The genomic regions of the selector set may comprise at least 400 regions from those identified in Table 18. The genomic regions of the selector set may comprise at least 500 regions from those identified in Table 18.

[0399] At least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95% of the genomic regions of the selector set may be regions identified in Table 18. At least about 5% of the genomic regions of the selector set may be regions identified in Table 18. At least about 10% of the genomic regions of the selector set may be regions identified in Table 18. At least about 20% of the genomic regions of the selector set may be regions identified in Table 18. At least about 30% of the genomic regions of the selector set may be regions identified in Table 18. At least about 40% of the genomic regions of the selector set may be regions identified in Table 18.

[0400] The plurality of genomic regions may comprise one or more mutations present in at least 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, 97% or 99% or more of a population of subjects suffering from the cancer. The plurality of genomic regions may comprise one or more mutations present in at least 60% or more of a population of subjects suffering from the cancer. The plurality of genomic regions may comprise one or more mutations present in at least 72% or more of a population of subjects suffering from the cancer. The plurality of genomic regions may comprise one or more mutations present in at least 80% or more of a population of subjects suffering from the cancer.

[0401] The total size of the plurality of genomic regions of the selector set may comprise less than 1.5 megabases (Mb), 1Mb, 500 kilobases (kb), 350 kb, 300 kb, 250 kb, 200 kb, or 150 kb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 1.5 Mb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 1 Mb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 500 kb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 300 kb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 100, 90, 80, 70, 60, 50, 40, 30, 20, 10 or 5 kb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 100 kb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 75 kb of a genome. The total size of the plurality of genomic regions of the selector set may comprise less than 50 kb of a genome.

[0402] The total size of the plurality of genomic regions of the selector set may be between 100 kb to 1000 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 100 kb to 500 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 100 kb to 300 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 500 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 300 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 5 kb to 200 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 1 kb to 100 kb of a genome. The total size of the plurality of genomic regions of the selector set may be between 1 kb to 50 kb of a genome.

[0403] Further disclosed herein are methods of preparing a library for sequencing. The method may comprise (a) conducting an amplification reaction on cell-free DNA (cfDNA) derived from a sample to produce a plurality of amplicons, wherein the amplification reaction may comprise 20 or fewer amplification cycles; and (b) producing a library for sequencing, the library comprising the plurality of amplicons.

[0404] The amplification reaction may comprise 19, 18, 17, 16, 15, 14, 13, 12, 11, or 10 or fewer amplification cycles. The amplification reaction may comprise 15 or fewer amplification cycles.

[0405] The method may further comprise attaching adaptors to one or more ends of the cfDNA. The adaptor may comprise a plurality of oligonucleotides. The adaptor may comprise one or more deoxyribonucleotides. The adaptor may comprise ribonucleotides. The adaptor may be single-stranded. The adaptor may be double-stranded. The adaptor may comprise double-stranded and single-stranded portions. For example, the adaptor may be a Y-shaped adaptor. The adaptor may be a linear adaptor. The adaptor may be a circular adaptor. The adaptor may comprise a molecular barcode, sample index, primer sequence, linker sequence or a combination thereof. The molecular barcode may be adjacent to the sample index. The molecular barcode may be adjacent to the primer sequence. The sample index may be adjacent to the primer sequence. A linker sequence may connect the molecular barcode to the sample index. A linker sequence may connect the molecular barcode to the primer sequence. A linker sequence may connect the sample index to the primer sequence.

[0406] The adaptor may comprise a molecular barcode. The molecular barcode may comprise a random sequence. The molecular barcode may comprise a predetermined sequence. Two or more adaptors may comprise two or more different molecular barcodes. The molecular barcodes may be optimized to minimize dimerization. The molecular barcodes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first molecular barcode may introduce a single base error. The first molecular barcode may comprise greater than a single base difference from the other molecular barcodes. Thus, the first molecular barcode with the single base error may still be identified as the first molecular barcode. The molecular barcode may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The molecular barcode may comprise at least 3 nucleotides. The molecular barcode may comprise at least 4 nucleotides. The molecular barcode may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The molecular barcode may comprise less than 10 nucleotides. The molecular barcode may comprise less than 8 nucleotides. The molecular barcode may comprise less than 6 nucleotides. The molecular barcode may comprise 2 to 15 nucleotides. The molecular barcode may comprise 2 to 12 nucleotides. The molecular barcode may comprise 3 to 10 nucleotides. The molecular barcode may comprise 3 to 8 nucleotides. The molecular barcode may comprise 4 to 8 nucleotides. The molecular barcode may comprise 4 to 6 nucleotides.

[0407] The adaptor may comprise a sample index. The sample index may comprise a random sequence. The sample index may comprise a predetermined sequence. Two or more sets of adaptors may comprise two or more different sample indexes. Adaptors within a set of adaptors may comprise identical sample indexes. The sample indexes may be optimized to minimize dimerization. The sample indexes may be optimized to enable identification even with amplification or sequencing errors. For examples, amplification of a first sample index may introduce a single base error. The first sample index may comprise greater than a single base difference from the other sample indexes. Thus, the first sample index with the single base error may still be identified as the first molecular barcode. The sample index may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or more nucleotides. The sample index may comprise at least 3 nucleotides. The sample index may comprise at least 4 nucleotides. The sample index may comprise less than 20, 19, 18, 17, 16, or 15 nucleotides. The sample index may comprise less than 10 nucleotides. The sample index may comprise less than 8 nucleotides. The sample index may comprise less than 6 nucleotides. The sample index may comprise 2 to 15 nucleotides. The sample index may comprise 2 to 12 nucleotides. The sample index may comprise 3 to 10 nucleotides. The sample index may comprise 3 to 8 nucleotides. The sample index may comprise 4 to 8 nucleotides. The sample index may comprise 4 to 6 nucleotides.

[0408] The adaptor may comprise a primer sequence. The primer sequence may be a PCR primer sequence. The primer sequence may be a sequencing primer.

[0409] Adaptors may be attached to one end of a nucleic acid from a sample. The nucleic acids may be DNA. The DNA may be cell-free DNA (cfDNA). The DNA may be circulating tumor DNA (ctDNA). The nucleic acids may be RNA. Adaptors may be attached to both ends of the nucleic acid. Adaptors may be attached to one or more ends of a single-stranded nucleic acid. Adaptors may be attached to one or more ends of a double-stranded nucleic acid.

[0410] Adaptors may be attached to the nucleic acid by ligation. Ligation may be blunt end ligation. Ligation may be sticky end ligation. Adaptors may be attached to the nucleic acid by primer extension. Adaptors may be attached to the nucleic acid by reverse transcription. Adaptors may be attached to the nucleic acids by hybridization. Adaptors may comprise a sequence that is at least partially complementary to the nucleic acid. Alternatively, in some instances, adaptors do not comprise a sequence that is complementary to the nucleic acid.

[0411] The method may further comprise fragmenting the cfDNA. The method may further comprise end-repairing the cfDNA. The method may further comprise A-tailing the cfDNA.

[0412] Further disclosed herein are methods of determining a statistical significance of a selector set. The method may comprise (a) detecting a presence of one or more mutations in one or more samples from a subject, wherein the one or more mutations may be based on a selector set comprising genomic regions comprising the one or more mutations; (b) determining a mutation type of the one or more mutations present in the sample; and (c) determining a statistical significance of the selector set by calculating a ctDNA detection index based on a p-value of the mutation type of mutations present in the one or more samples.

[0413] In some instances, if a rearrangement is observed in two or more samples from the subject, then the ctDNA detection index is 0. At least one of the two or more samples may be a plasma sample. At least one of the two or more samples may be a tumor sample. The rearrangement may be a fusion or a breakpoint.

[0414] In some instances, if one type of mutation is present, then the ctDNA detection index is the p-value of the one type of mutation.

[0415] In some instances, if (i) two or more types of mutations are present in the sample; (ii) the p-values of the two or more types mutations are less than 0.1; and (iii) a rearrangement is not one of the types of mutations, then the ctDNA detection is calculated based on the combined p-values of the two or more mutations. The p-values of the two or more mutations may be combined according to Fisher's method. One of the two or more types of mutations may be a SNV. The p-value of the SNV may be determined by Monte Carlo sampling. One of the two or more types of mutations may be an indel.

[0416] In some instances, if (i) two or more types of mutations are present in the sample; (ii) a p-value of at least one of the two or more types of mutations are greater than 0.1; and (iii) a rearrangement is not one of the types of mutations, then the ctDNA detection is calculated based on the p-value of one of the two or more types mutations. One of the two or more types of mutations may be a SNV. The ctDNA detection index may be calculated based on the p-value of the SNV. One of the two or more types of mutations may be an indel.

[0417] Further disclosed herein are methods of identifying rearrangements in one or more nucleic acids. The method may comprise (a) obtaining sequencing information pertaining to a plurality of genomic regions; (b) producing a list of genomic regions, wherein the genomic regions may be adjacent to one or more candidate rearrangement sites or the genomic regions may comprise one or more candidate rearrangement sites; and (c) applying an algorithm to the list of genomic regions to validate candidate rearrangement sites, thereby identifying rearrangements.

[0418] The sequencing information may comprise an alignment file. The alignment file may comprise an alignment file of pair-end reads, exon coordinates, and a reference genome.

[0419] The sequencing information may be obtained from a database. The database may comprise sequencing information pertaining to a population of subjects suffering from a disease or condition. The disease or condition may be a cancer.

[0420] The sequencing information may be obtained from one or more samples from one or more subjects.

[0421] Producing the list of genomic regions may comprise identifying discordant read pairs based on the sequencing information. The discordant read-pair may refer to a read and its mate, where: (i) the insert size may be not equal to the expected distribution of the dataset; or (ii) the mapping orientation of the reads may be unexpected.

[0422] Producing the list of genomic regions may comprise classifying the discordant read pairs based on the sequencing information. Producing the list of genomic regions further may comprise ranking the genomic regions. The genomic regions may be ranked in decreasing order of discordant read depth.

[0423] Producing the list of genomic regions may comprise selecting genomic regions with a minimum user-defined read depth.

[0424] The minimum user-defined read depth may be at least 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x or more.

[0425] The method may further comprise eliminating duplicate fragments.

[0426] Producing the list of genomic regions may comprise use of one or more algorithms. The algorithm may analyze properly paired reads in which one of the paired reads may be truncated to produce a soft-clipped read. The algorithm may analyze the soft-clipped reads based on a pattern. The pattern may be based on x number of skipped bases (Sx) and on y number of contiguous mapped bases (My). The pattern may be MySx or SxMy.

[0427] Applying the algorithm to validate the candidate rearrangement sites may comprise deleting candidate rearrangements with a read frequency of less than 2. Applying the algorithm to validate the candidate rearrangement sites may comprise ranking the candidate rearrangements based on their read frequency.

[0428] Applying the algorithm to validate the candidate rearrangement sites may comprise comparing two or more reads of the candidate rearrangement. Applying the algorithm to validate the candidate rearrangement sites may comprise identifying the candidate rearrangement as a rearrangement if the two or more reads have a sequence alignment.

[0429] Applying the algorithm to validate the candidate rearrangement sites may comprise evaluating inter-read concordance. Evaluating inter-read concordance may comprise dividing a first sequencing read of the candidate rearrangement site into a plurality of subsequences of length l. Evaluating inter-read concordance may comprise dividing a second sequencing read of the candidate rearrangement site into a plurality of subsequences of length l. Evaluating inter-read concordance may comprise comparing the subsequences of the first sequencing read to the subsequences of the second sequencing read. The first and second sequencing reads may be considered concordant if a minimum matching threshold may be achieved.

[0430] Applying the algorithm to validate the candidate rearrangement sites may comprise in silico validation of the candidate rearrangement sites. In silico validation may comprise aligning sequencing reads of the candidate rearrangement site to a reference rearrangement sequence. The reference rearrangement sequence may be obtained from a reference genome. The candidate rearrangement site may be identified as a rearrangement if the reads map to the reference rearrangement sequence with an identity of at least 70%, 75%, 80%, 85%, 90%, 95%, 97% or more.

[0431] The candidate rearrangement site may be identified as a rearrangement if the length of the aligned sequences may be at least 70%, 75%, 80%, 85%, 90%, or 95% or more of the read length of the candidate rearrangement site.

[0432] Further disclosed herein are methods of identifying tumor-derived single nucleotide variations (SNVs). The method may comprise (a) obtaining a sample from a subject suffering from a cancer or suspected of suffering from a cancer; (b) conducting a sequencing reaction on the sample to produce sequencing information; (c) applying an algorithm to the sequencing information to produce a list of candidate tumor alleles based on the sequencing information from step (b), wherein a candidate tumor allele may comprise a non-dominant base that may be not a germline SNP; and (d) identifying tumor-derived SNVs based on the list of candidate tumor alleles.

[0433] Producing the list of candidate tumor alleles may comprise ranking the tumor alleles by their fractional abundance. Producing the list of candidate tumor alleles may comprise selecting tumor alleles with a fractional abundance in the top 70 th< , 75 th< , 80 th< , 85 th< , 87 th< , 90 th< , 92 nd< , 95 th< , or 97 th< percentile. Producing the list of candidate tumor alleles may comprise selecting tumor alleles with a fractional abundance of less than 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%, 0.1% of the total alleles in the sample from the subject.

[0434] Producing the list of candidate tumor alleles may comprise ranking the tumor alleles based on their sequencing depth. Producing the list of candidate tumor alleles may comprise selecting tumor alleles that meet a minimum sequencing depth. The minimum sequencing depth may be at least 100x, 200x, 300x, 400x, 500x, 600x, 700x, 800x, 900x, 1000x or more.

[0435] Producing the list of candidate tumor alleles may comprise calculating a strand bias percentage of a tumor allele. Producing the list of candidate tumor alleles may comprise ranking the tumor alleles based on their strand bias percentage. Producing the list of candidate tumor alleles may comprise selecting tumor alleles with a user-defined strand bias percentage. The user-defined strand bias percentage may be less than or equal to 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 97%.

[0436] Producing the list of candidate tumor alleles may comprise comparing the sequence of the tumor allele to a reference tumor allele. Producing the list of candidate tumor alleles further may comprise identifying tumor alleles that are different from the reference tumor allele.

[0437] Identifying the tumor alleles that are different from the reference tumor allele may comprise use of one or more statistical analyses. The one or more statistical analyses may comprise using Bonferroni correction to calculate a Bonferroni-adjusted binomial probability for the tumor allele.

[0438] Producing the list of candidate tumor alleles may comprise selecting tumor alleles based on the Bonferroni-adjusted binomial probability. The Bonferroni-adjusted binomial probability of a candidate tumor allele may be less than or equal to 3 x10 -8< , 2.9 x10 -8< , 2.8 x10 -8< , 2.7 x10 -8< , 2.6 x10 -8< , 2.5 x10 -8< , 2.3 x10 -8< , 2.2 x10 -8< , 2.1 x10 -8< , 2.09 x10 -8< , 2.08 x10 -8< , 2.07 x10 -8< , 2.06 x10 -8< , 2.05 x10 -8< , 2.04 x10 -8< , 2.03 x10 -8< , 2.02 x10 -8< , 2.01 x10 -8< or 2 x10 -8< . The Bonferroni-adjusted binomial probability of a candidate tumor allele may be less than or equal to 2.08 x10 -8< .

[0439] Identifying the tumor alleles that are different from the reference tumor allele further may comprise applying a Z-test to the Bonferroni-adjusted binomial probability to produce a Bonferroni-adjusted single-tailed Z-score for the tumor allele. A tumor allele with a Bonferroni-adjusted single-tailed Z-score of greater than or equal to 6, 5.9, 5.8, 5.7, 5.6, 5.5., 5.4, 5.3, 5.2, 5.1, or 5.0 may be considered to be different from the reference tumor allele.

[0440] The sample may be a blood sample. The sample may be a paired sample.

[0441] Further disclosed herein are methods of producing a selector set. The method may comprise (a) obtaining sequencing information of a tumor sample from a subject suffering from a cancer; (b) comparing the sequencing information of the tumor sample to sequencing information from a non-tumor sample from the subject to identify one or more mutations specific to the sequencing information of the tumor sample; and (c) producing a selector set comprising one or more genomic regions comprising the one or more mutations specific to the sequencing information of the tumor sample.

[0442] The selector set may comprise sequencing information pertaining to the one or more genomic regions. The selector set may comprise genomic coordinates pertaining to the one or more genomic regions.

[0443] The selector set may be used to produce a plurality of oligonucleotides that selectively hybridize the one or more genomic regions. The plurality of oligonucleotides may be biotinylated.

[0444] The one or more mutations may comprise SNVs. The one or more mutations may comprise indels. The one or more mutations may comprise rearrangements.

[0445] Producing the selector set may comprise identifying tumor-derived SNVs using the methods disclosed herein.

[0446] Producing the selector set may comprise identifying tumor-derived rearrangements using the method disclosed herein.BRIEF DESCRIPTION OF THE DRAWINGS

[0447] Figure 1: Development of CAncer Personalized Profiling by Deep Sequencing (CAPP-Seq). (a) Schematic depicting design of CAPP-Seq selectors and their application for assessing circulating tumor DNA. (b) Multi-phase design of the NSCLC selector. Phase 1: Genomic regions harboring known / suspected driver mutations in NSCLC are captured. Phases 2-4: Addition of exons containing recurrent SNVs using WES data from lung adenocarcinomas and squamous cell carcinomas from TCGA (n=407). Regions were selected iteratively to maximize the number of mutations per tumor while minimizing selector size. Recurrence index = total unique patients with mutations covered per kb of exon. Phases 5-6: Exons of predicted NSCLC drivers and introns / exons harboring breakpoints in rearrangements involving ALK, ROS1, and RET were added. Bottom: increase of selector length during each design phase. (c) Analysis of the number of SNVs per lung adenocarcinoma covered by the NSCLC selector in the TCGA WES cohort (Training; n=229) and an independent lung adenocarcinoma WES data set (Validation; n=183). Results are compared to selectors randomly sampled from the exome (P < 1.0x10 -6< for the difference between random selectors and the NSCLC selector). (d) Number of SNVs per patient identified by the NSCLC selector in WES data from three adenocarcinomas from TCGA, colon (COAD), rectal (READ), and endometrioid (UCEC) cancers. Figure 2: Analytical performance. (a-c) Quality parameters from a representative CAPP-Seq analysis of plasma cfDNA, including length distribution of sequenced cfDNA fragments (a), and depth of sequencing coverage across all genomic regions in the selector (b). (c) Variation in sequencing depth across cfDNA samples from 4 patients. Orange envelope represents s.e.m. (d) Analysis of background rate for 40 plasma cfDNA samples collected from 13 NSCLC patients and 5 healthy individuals. (e) Analysis of biological background in d focusing on 107 recurrent somatic mutations from a previously reported SNaPshot panel. Mutations found in a given patient's tumor were excluded. The mean frequency over all subjects was ~0.01%. A single outlier mutation (TP53 R175H) is indicated by an orange diamond. (f) Individual mutations from e ranked by most to least recurrent, according to mean frequency across the 40 cfDNA samples. The p-value threshold of 0.01 (horizontal line) corresponds to the 99 th< percentile of global selector background in d. (g) Dilution series analysis of expected versus observed frequencies of mutant alleles using CAPP-Seq. Dilution series were generated by spiking fragmented HCC78 DNA into control cfDNA. (h) Analysis of the effect of the number of SNVs considered on the estimates of fractional abundance (95% confidence intervals shown in gray). (i) Analysis of the effect of the number of SNVs considered on the mean correlation coefficient between expected and observed cancer fractions (blue dashed line) using data from panel h. 95% confidence intervals are shown for e-f. Statistical variation for g is shown as s.e.m. Figure 3: Sensitivity and specificity analysis. (a) Receiver Operating Characteristic (ROC) analysis of cfDNA samples from pre-treatment samples and healthy controls, divided into all stages (n=13 patients) and stages II-IV (n=9 patients). Area Under the Curve (AUC) values are significant at P < 0.0001. Sn, sensitivity; Sp, specificity. (b) Raw data related to a. TP, true positive; FP, false positive; TN, true negative; FN, false negative. (c) Concordance between tumor volume, measured by CT or PET / CT, and pg per mL of ctDNA from pretreatment samples (n=9), measured by CAPP-Seq. Patients P6 and P9 were excluded due to inability to accurately assess tumor volume and differences related to the capture of fusions, respectively. Of note, linear regression was performed in non-log space; the log-log axes and dashed diagonal line are for display purposes only. Figure 4: Noninvasive detection and monitoring of circulating tumor DNA. (a-h) Disease monitoring using CAPP-Seq. (a-b) Disease burden changes in response to treatment in a stage III NSCLC patient using SNVs and an indel (a), and a stage IV NSCLC patient using three rearrangement breakpoints (b). (c) Concordance between different reporters (SNVs and a fusion) in a stage IV NSCLC patient. (d) Detection of a subclonal EGFR T790M resistance mutation in a patient with stage IV NSCLC. The fractional abundance of the dominant clone and T790M-containing clone are shown in the primary tumor (left) and plasma samples (right). (e-f) CAPP-Seq results from post-treatment cfDNA samples are predictive of clinical outcomes in a stage IIB NSCLC patient (e) and Stage IIIB NSCLC patient (f). (g-h) Monitoring of tumor burden following complete tumor resection (g) and Stereotactic Ablative Radiotherapy (SABR) (h) for two stage IB NSCLC patients. (i) Exploratory analysis of the potential application of CAPP-Seq for biopsy-free tumor genotyping or cancer screening. All plasma cfDNA samples from patients in Table 1 were examined for the presence of mutant allele outliers without knowledge of the primary tumor mutations; samples with detectable mutations are shown, along with two samples determined to be cancer-negative (P1-2 and P16-3) and a sample without tumor-derived SNVs (P9-5; see Table 1). The lowest mutant allele fraction detected was ~0.5% (dashed horizontal line). Error bars in d represent s.e.m. Tu, tumor; Ef, pleural effusion; SD, stable disease; PD, progressive disease; PR, partial response; CR, complete response; DOD, dead of disease. Figure 5: Comparison to other methods for detection of ctDNA in plasma. (a) Analytical modeling of CAPP-Seq, WES, and WGS for different detection limits of tumor cfDNA in plasma. Calculations are based on the median number of mutations detected per NSCLC for CAPP-Seq (e.g., 4) and the reported number of mutations in NSCLC exomes and genomes. The vertical dotted line represents the median fraction of tumor-derived cfDNA in plasma from NSCLC patients in this study (see below). (b) Costs for WES and WGS to achieve the same theoretical detection limit as CAPP-Seq (shown as a dark solid line in Figure 5a). Figure 6: CAPP-Seq computational pipeline. Major steps of the bioinformatics pipeline for mutation discovery and quantitation in plasma are schematically illustrated. Figure 7: Statistical enrichment of recurrently mutated NSCLC exons captures known drivers. We employed two metrics to prioritize exons with recurrent mutations for inclusion in the CAPP-Seq NSCLC selector. The first, termed Recurrence Index (RI), is defined as the number of unique patients (e.g. tumors) with somatic mutations per kilobase of a given exon and the second metric is based on the minimum number of unique patients (e.g. tumors) with mutations in a given kb of exon. We analyzed exons containing at least one non-silent SNV genotyped by TCGA (n=47,769) in a combined cohort of 407 lung adenocarcinoma (LUAD) and squamous cell carcinoma (SCC) patients. (a) Known / suspected NSCLC drivers are highly enriched at RI ≥ 30 (inset), comprising 1.8% (n=861) of analyzed exons. (b) Known / suspected NSCLC drivers are highly enriched at ≥ 3 patients with mutations per exon (inset), encompassing 16% of analyzed exons. Figure 8: FACTERA analytical pipeline for breakpoint mapping. Major steps used by FACTERA to precisely identify genomic breakpoints from aligned paired-end sequencing data are anecdotally illustrated using two hypothetical genes, w and v. (a) Improperly paired, or "discordant," reads (indicated in yellow) are used to locate genes involved in a potential fusion (in this case, w and v). (b) Because truncated (e.g., soft-clipped) reads may indicate a fusion breakpoint, any such reads within genomic regions delineated by w and v are also further analyzed. (c) Consider soft-clipped reads, R1 and R2, whose non-clipped segments map to w and v, respectively. If R1 and R2 derive from a fragment encompassing a true fusion between w and v, then the mapped portion of R1 should match the soft-clipped portion of R2, and vice versa. This is assessed by FACTERA using fast k-mer indexing and comparison. (d) Four possible orientations of R1 and R2 are depicted. However, only Cases 1a and 2a can generate valid fusions. Thus, prior to k-mer comparison (panel c), the reverse complement of R1 is taken for Cases 1b and 2b, respectively, converting them into Cases 1a and 2a. (e) In some cases, short sequences immediately flanking the breakpoint are identical, preventing unambiguous determination of the breakpoint. Let iterators i and j denote the first matching sequence positions between R1 and R2. To reconcile sequence overlap, FACTERA arbitrarily adjusts the breakpoint in R2 (e.g., bp2) to match R1 (e.g., bp1) using the sequence offset determined by differences in distance between bp2 and i, and bp1 and j. Two cases are illustrated, corresponding to sequence orientations described in d. Figure 9: Application of FACTERA to NSCLC cell lines NCI-H3122 and HCC78, and Sanger-validation of breakpoints. (a) Pile-up of a subset of soft-clipped reads mapping to the EML4-ALK fusion identified in NCI-H3122 along with the corresponding Sanger chromatogram. (b) Same as a, but for the SLC34A2-ROS1 translocation identified in HCC78. Figure 10: Improvements in CAPP-Seq performance with optimized library preparation procedures. Using 32ng of input cfDNA from plasma, we compared standard versus 'with bead' 5< library preparation methods, as well as two commercially available DNA polymerases (Phusion and KAPA HiFi). We also compared template pre-amplification by Whole Genome Amplification (WGA) using Degenerate Oligonucleotide PCR (DOP). Indices considered for these comparisons included (a) length of the captured cfDNA fragments sequenced, (b) depth and uniformity of sequencing coverage across all genomic regions in the selector, and (c) sequence mapping and capture statistics, including uniqueness. Collectively, these comparisons identified KAPA HiFi polymerase and a "with bead" protocol as having most robust and uniform performance. Figure 11: Optimizing allele recovery from low input cfDNA during Illumina library preparation. Bars reflect the relative yield of CAPP-Seq libraries constructed from 4 ng cfDNA, calculated by averaging quantitative PCR measurements of n=4 pre-selected reporters within CAPP-Seq with pre-defined amplification efficiencies. (a) Sixteen hour ligation at 16°C increases ligation efficiency and reporter recovery. (b) Adapter ligation volume did not have a significant effect on ligation efficiency and reporter recovery. (c) Performing enzymatic reactions "with-bead" to minimize tube transfer steps increases reporter recovery. (d) Increasing adapter concentration during ligation increases ligation efficiency and reporter recovery. Reporter recovery is also higher when using KAPA HiFi DNA polymerase compared to Phusion DNA polymerase (e) and when using the KAPA Library Preparation Kit with the modifications in a - d compared to the NuGEN SP Ovation Ultralow Library System with automation on a Mondrian SP Workstation (f). Relative reporter abundance was determined by qPCR using the 2 -ΔCt< method. A two-sided t test with equal variance was used to test the statistical significance between groups. All values are presented as means ± s.d. N.S., not significant. Based on these results, we estimate that combining the methodological modifications in a and c - e improves yield in NGS libraries by 3.3-fold. Figure 12: CAPP-Seq performance with various amounts of input cfDNA. (a) Length of the captured cfDNA fragments sequenced. (b) Depth of sequencing coverage across all genomic regions in the selector (pre-duplicate removal). (c) Sequence mapping and capture statistics. As expected, more input cfDNA mass correlates with more unique fragments sequenced. Figure 13. Analysis of library complexity and molecule recovery. (a) The expected proportion of additional library complexity present in post-duplicate reads is plotted for all patient and control samples, including plasma cfDNA (n=40) and paired tumor / PBL specimens (n=17 each). Because of the highly stereotyped size of cfDNA fragments occurring naturally in blood plasma, when compared with genomic DNA shorn by sonication, any two fragments of DNA circulating in plasma are inherently more likely by chance to have arisen from different original molecules, whether considering tumor or non-tumor cells as the source of this cfDNA. To estimate this "missing" complexity, we reasoned that two DNA fragments (e.g., paired end reads) with identical start / end coordinates that differ by a single a priori defined germline variant (e.g. one maternal and one paternal allele) represent two unique and independent starting molecules rather than technical artifacts (e.g. PCR duplicates). Therefore, the number of fragments sharing identical start / end coordinates with both maternal and paternal germline alleles of heterozygous SNPs were used to estimate additional library complexity. Library complexity estimates updated to factor in these data are also provided in Tables 3, 20 and 21 and determined as described herein. (b) Empirical assessment of molecule recovery in cfDNA (n=40) by determination of the mass of DNA produced compared to the expected library yield based on mass input, number of PCR cycles, and efficiency (mean = 46%). (a-b) Values are presented as means ± 95% confidence intervals. Figure 14. Analysis of library cross-contamination. Allelic fractions of patient-specific homozygous germline SNPs were assessed in cfDNA samples multiplexed on the same lane. SNPs were called as described in the Methods. The mean "cross-contamination" rate in cfDNA samples was 0.06%, shown by the horizontal dotted line. This level of contamination is too low to affect our estimates of tumor burden given the low fraction of tumor-derived cfDNA in plasma of NSCLC patients (median of ~0.1%; Fig. 5a) (e.g., 0.06 x 0.1 = 0.006% of a given sample would on average represent contamination from ctDNA of another sample). Of note, to minimize the risk of inter-sample contamination, we use aerosol barrier tips, work in hoods, and do not multiplex tumor and plasma libraries in the same lane. Figure 15. Analysis of selector-wide bias in captured sequence. Because the NSCLC selector was designed to target the hg19 reference genome, we reasoned that selector bias for SNVs, if any, should be discernable as a systematically lower ratio of non-reference to reference alleles in heterozygous germline SNPs. Therefore, we analyzed high confidence SNPs detected by VarScan in patient PBL samples, where high confidence was defined as variants with a non-reference fraction >10% present in the common SNPs subset of dbSNP (version 137.0). As shown, we detected a very small skew toward reference (8 of 11 samples have a median non-reference allelic frequency of 49%; the remaining 3 samples are unbiased). Importantly, such bias appears too small to significantly affect our results. Boxes represent the interquartile range, and whiskers encapsulate the 10 th< to 90 th< percentiles. Germline SNPs were identified using VarScan 2. Figure 16: Empirical spiking analysis of CAPP-Seq using two NSCLC cell lines. (a) Expected and observed (by CAPP-Seq) fractions of NCI-H3122 DNA spiked into control HCC78 DNA are linear for all fractions tested (0.1%, 1%, and 10%; R 2< = 1). (b) Using data from a, analysis of the effect of the number of SNVs considered on the estimates of fractional abundance (95% confidence intervals shown in gray). (c) Analysis of the effect of the number of SNVs considered on the mean correlation coefficient and coefficient of variation between expected and observed cancer fractions (blue dashed line) using data from panel a. (d) Expected and observed fractions of the EML4-ALK fusion present in HCC78 are linear (R 2< = 0.995) over all spiking concentrations tested (see Fig. 9b for breakpoint verification). The observed EML4-ALK fractions were normalized based on the relative abundance of the fusion in 100% H3122 DNA. Moreover, both a single heterozygous insertion ('Indel'; chr7: 107416855, +T) and a 4.9kb homozygous deletion ('Deletion', chr17: 29422259-29592392) in NCI-H3122 were concordant with defined concentrations. Values in a are presented as means ± s.e.m. Figure 17: Base-pair resolution breakpoint mapping for all patients and cell lines enumerated by FACTERA. Gene fusions involving ALK (a) and ROS1 (b) are graphically depicted. Schematics in the top panels indicate the exact genomic positions (HG19 NCBI Build 37.1 / GRCh37) of the breakpoints in ALK, ROS1, EML4, KIF5B, SLC34A2, CD74, MKX, and FYN. Bottom panels depict exons flanking the predicted gene fusions with notation indicating the 5' fusion partner gene and last fused exon followed by the 3' fusion partner gene and first fused exon. For example, in S13del37;R34 exons 1-13 of SLC34A2 (excluding the 3' 37 nucleotides of exon 13) are fused to exons 34-43 of ROS1. Exons in FYN are from its 5'UTR and precede the first coding exon. The green dotted line in the predicted FYN-ROS1 fusion indicates the first in-frame methionine in ROS1 exon 33, which preserves an open reading frame encoding the ROS1 kinase domain. All rearrangements were each independently confirmed by PCR and / or FISH. Figure 18: Presence of fusions is inversely related to the number of SNVs detected by CAPP-Seq. For each patient listed in Table 1 the number of identified SNVs versus the presence (n=11) or absence (n=6) of detected genomic fusions is plotted. Statistical significance was determined using a two-sided Wilcoxon rank sum test, and summarized values are presented as means ± s.e.m. Figure 19. Receiver Operating Curve (ROC) analysis of CAPP-Seq performance including both pre- and post-treatment samples. Comparison of sensitivity and specificity achieved for non-deduped (panels a and c) and deduped (post PCR duplicate removal) data (panels b and d). In addition, all stages (panels a and b) are compared with intermediate to advanced stages (stages II-IV, panels c and d). Finally, for all ROC analyses, the effect of the indel / fusion filter on sensitivity / specificity is shown. Reporter fractions for both non-deduped and deduped cfDNA samples are provided in Table 4. Figure 20. CAPP-Seq sensitivity and specificity over all patient reporters and sequenced plasma cfDNA samples. All values shown reflect a ctDNA detection index of 0.03. See Methods for details on detection metrics, and determination of cancer-positive, cancer-negative, and unknown categories. Figure 21. Non-invasive cancer screening with CAPP-Seq, related to Fig. 4i. (a) Steps to identify candidate SNVs in plasma cfDNA demonstrated using a patient sample with NSCLC (P6, see Table 4). Following stepwise filtration, outlier detection is applied. (b) Same as a, but using a plasma cfDNA sample from a patient who had their tumor surgically removed. No SNVs are identified, as expected. (c, d) Three additional representative samples applying retrospective screening to patients analyzed in this study. P2 and P5 samples have confirmed tumor-derived SNVs, while P9 is cancer positive but lacks tumor-derived SNVs. Red points, confirmed tumor-derived SNVs; Green points, background noise. Figure 22. depicts a flow chart of patient analysis. Figure 23. shows a system for implementing the methods of the disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0448] It is characteristic of cancer cells that due to somatic mutation the genome sequence of the cancer cell is changed from the genome sequence of the individual from which it is derived. Most human cancers are relatively heterogeneous for somatic mutations in individual genes. Specifically, in most human tumors, recurrent somatic alterations of single genes account for a minority of patients, and only a minority of tumor types can be defined using a small number of recurrent mutations at predefined positions. The present invention solves this problem by use of enrichment of tumor-derived nucleic acid molecules from total genomic nucleic acids with a selector set. The design of the selector is vital because (1) it dictates which mutations can be detected in with high probability for a patient with a given cancer, and (2) the selector size (in kb) directly impacts the cost and depth of sequence coverage.

[0449] While the specific genetic changes differ from individual to individual and between types of cancer, there are regions of the genome that show recurrent changes. In those regions there is an increased probability that any given individual cancer will show genetic variation. The genetic changes in cancer cells provide a means by which cancer cells can be distinguished from normal (e.g., non-cancer) cells. Cell-free DNA, for example the DNA fragments found in blood samples, can be analyzed for the presence of genetic variation distinctive of tumor cells. However, the absolute levels of tumor DNA in such samples is often small, and the genetic variation may represent only a very small portion of the entire genome. The present invention addresses this issue by providing methods for selective detection of mutated regions associated with cancer, thereby allowing accurate detection of cancer cell DNA or RNA from the background of normal cell DNA or RNA. Although the methods disclosed herein may specifically refer to DNA (e.g., cell-free DNA, circulating tumor DNA), it should be understood that the methods, compositions, and systems disclosed herein are applicable to all types of nucleic acids (e.g., RNA, DNA, RNA / DNA hybrids).

[0450] Provided herein are methods for the ultrasensitive detection of a minority nucleic acid in a heterogeneous sample. The method may comprise (a) obtaining sequence information of a cell-free DNA (cfDNA) sample derived from a subject; and (b) using sequence information derived from (a) to detect cell-free minority nucleic acids in the sample, wherein the method is capable of detecting a percentage of the cell-free minority nucleic acids that is less than 2% of total cfDNA. The minority nucleic acid may refer to a nucleic acid that originated from a cell or tissue that is different from a normal cell or tissue from the subject. For example, the subject may be infected with a pathogen such as a bacteria and the minority nucleic acid may be a nucleic acid from the pathogen. In another example, the subject is a recipient of a cell, tissue or organ from a donor and the minority nucleic acid may be a nucleic acid originating from the cell, tissue or organ from the donor. In another example, the subject is a pregnant subject and the minority nucleic acid may be a nucleic acid originating from a fetus. The method may comprise using the sequence information to detect one or more somatic mutations in the fetus. The method may comprise using the sequence information to detect one or more post-zygotic mutations in the fetus. Alternatively, the subject may be suffering from a cancer and the minority nucleic acid may be a nucleic acid originating from a cancer cell.

[0451] Provided herein are methods for the ultrasensitive detection of circulating tumor DNA in a sample. The method may be called CAncer Personalized Profiling by Deep Sequencing (CAPP-Seq). The method may comprise (a) obtaining sequence information of a cell-free DNA (cfDNA) sample derived from a subject; and (b) using sequence information derived from (a) to detect cell-free tumor DNA (ctDNA) in the sample, wherein the method is capable of detecting a percentage of ctDNA that is less than 2% of total cfDNA. CAPP-Seq may accurately quantify cell-free tumor DNA from early and advanced stage tumors. CAPP-Seq may identify mutant alleles down to 0.025% with a detection limit of <0.01%. Tumor-derived DNA levels often paralleled clinical responses to diverse therapies and CAPP-Seq may identify actionable mutations. CAPP-Seq may be routinely applied to noninvasively detect and monitor tumors, thus facilitating personalized cancer therapy.

[0452] Disclosed herein are methods for determining a quantity of circulating tumor DNA (ctDNA) in a sample. The method may comprise (a) ligating one or more adaptors to cell-free DNA (cfDNA) derived from a sample from a subject to produce one or more adaptor-ligated cfDNA; (b) performing sequencing on the one or more adaptor-ligated cfDNA, wher...

Claims

1. A method of detecting, diagnosing, prognosing, or therapy selection of a cancer in a subject in need thereof, the method comprising: (i) producing a selector set comprising: (a) obtaining sequence information of a tumor sample from the subject suffering from cancer; (b) comparing the sequencing information of the tumor sample to sequencing information from a non-tumor sample from the subject to identify one or more mutations specific to the sequencing information of the tumor sample; and (c) producing a selector set corresponding to one or more genomic regions comprising the one or more mutations specific to the sequencing information of the tumor sample, wherein the selector set comprises a plurality of oligonucleotides that selectively hybridize the one or more genomic regions; (ii) providing a cell-free DNA (cfDNA) sample obtained from the subject, (iii) performing hybrid selection on the cfDNA sample using the selector set to enrich for cfDNA corresponding to genomic regions known to contain tumor-specific somatic mutations, (iv) sequencing the hybrid selected cfDNA sample to generate sequencing information, and (v) analyzing the sequence information of the hybrid selected cfDNA sample to detect circulating tumor DNA (ctDNA) in the sample, wherein the method is capable of detecting a percentage of ctDNA that is less than or equal to 2% of total cfDNA.

2. The method of claim 1, wherein the sequence information of step (iv) comprises information related to at least 2, 3, 5, 8, 10, 20, 30, 40, 100, 200, or 300 genomic regions.

3. The method of claim 2, wherein the genomic regions comprise two or more of exonic regions, intronic regions, and untranslated regions.

4. The method of claim 2, wherein the genomic regions comprise less than 1.5 megabases (Mb), 1 Mb, 500 kb, 350 kb, 100 kb, 75 kb, 50 kb or 25 kb of the genome.

5. The method of claim 1, wherein the sequencing reaction of step (a) is whole genome sequencing or whole exome sequencing.

6. The method of claim 1, wherein the selector set corresponds to at least 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100 or more genomic regions selected from any one of Tables 2 and 6-18.

7. The method of claim 1, further comprising attaching adaptors to the cfDNA prior to step (iii).

8. The method of claim 7, wherein the adaptors comprise a molecular barcode.

9. The method of claim 7, wherein the adaptors comprise a sample index.

10. The method of claim 7, wherein the adaptors comprise a primer sequence.

11. The method of claim 7, wherein the adaptors comprise a Y-shaped adaptor.

12. The method of claim 1, further comprising conducting an amplification reaction on the cfDNA, wherein the amplification reaction comprises 20 or fewer amplification cycles, preferably 15 or fewer amplification cycles, prior to or immediately after step (iii).

13. The method of claim 12, further comprising end-repairing the cfDNA prior to step (iii).

14. The method of claim 12, further comprising A-tailing the cfDNA prior to step (iii).

15. The method of claim 1, wherein analyzing the sequence information in step (v) comprises using a computer readable medium to quantify the ctDNA in the sample.

16. The method of claim 1, further comprising obtaining a second cell-free (cfDNA) sample from the subject following a treatment for the cancer and performing steps (iii)-(v) on the second cfDNA sample.

17. The method of claim 16, further comprising comparing the quantity of the ctDNA in the first cfDNA sample to the quantity of the ctDNA in the second cfDNA sample.

18. The method of any one of claims 1 to 17, wherein the one or more mutations comprise SNVs, indels, rearrangements, or a combination thereof.