US20210198747A1

US20210198747A1 - Cell-free dna for assessing and/or treating cancer

Info

Publication number: US20210198747A1
Application number: US17/056,726
Authority: US
Inventors: Victor E. Velculescu; Stephen Cristiano; Alessandro Leal; Jillian A. Phallen; Jacob Fiksel; Vilmos Adleff; Robert B. Scharpf
Original assignee: Johns Hopkins University
Current assignee: Johns Hopkins University
Priority date: 2018-05-18
Filing date: 2019-05-17
Publication date: 2021-07-01
Also published as: EP3794348A1; BR112020023587A2; US20200149118A1; JP2021525069A; US20210254152A1; EP3794348A4; KR20210045953A; US20220325343A1; US10975431B2; CN112805563A; CA3100345A1; AU2019269679A1; JP2024069295A; WO2019222657A1; US10982279B2; US20200131571A1

Abstract

This document relates to methods and materials for assessed, monitored, and/or treated mammals (e.g., humans) having cancer. For example, methods and materials for identifying a mammal as having cancer (e.g., a localized cancer) are provided. For example, methods and materials for assessing, monitoring, and/or treating a mammal having cancer are provided.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Patent Application Ser. No. 62/673,516, filed on May 18, 2018, and claims the benefit of U.S. Patent Application Ser. No. 62/795,900, filed on Jan. 23, 2019. The disclosure of the prior applications are considered part of (and are incorporated by reference in) the disclosure of this application.

STATEMENT REGARDING FEDERAL FUNDING

This invention was made with U.S. government support under grant No. CA121113 from the National Institutes of Health. The U.S. government has certain rights in the invention.

BACKGROUND

1. Technical Field

This document relates to methods and materials for assessing and/or treating mammals (e.g., humans) having cancer. For example, this document provides methods and materials for identifying a mammal as having cancer (e.g., a localized cancer). For example, this document provides methods and materials for monitoring and/or treating a mammal having cancer.

2. Background Information

Much of the morbidity and mortality of human cancers world-wide is a result of the late diagnosis of these diseases, where treatments are less effective (Torre et al., 2015 CA Cancer J Clin 65:87; and World Health Organization, 2017 Guide to Cancer Early Diagnosis). Unfortunately, clinically proven biomarkers that can be used to broadly diagnose and treat patients are not widely available (Mazzucchelli, 2000 Advances in clinical pathology 4:111; Ruibal Morell, 1992 The International journal of biological markers 7:160; Galli et al., 2013 Clinical chemistry and laboratory medicine 51:1369; Sikaris, 2011 Heart, lung & circulation 20:634; Lin et al., 2016 in Screening for Colorectal Cancer: A Systematic Review for the U.S. Preventive Services Task Force. (Rockville, Md.); Wanebo et al., 1978 N Engl J Med 299:448; and Zauber, 2015 Dig Dis Sci 60:681).

SUMMARY

Recent analyses of cell-free DNA suggests that such approaches may provide new avenues for early diagnosis (Phallen et al., 2017 Sci Transl Med 9; Cohen et al., 2018 Science 359:926; Alix-Panabieres et al., 2016 Cancer discovery 6:479; Siravegna et al., 2017 Nature reviews. Clinical oncology 14:531; Haber et al., 2014 Cancer discovery 4:650; Husain et al., 2017 JAMA 318:1272; and Wan et al., 2017 Nat Rev Cancer 17:223).
This document provides methods and materials for determining a cell free DNA (cfDNA) fragmentation profile in a mammal (e.g., in a sample obtained from a mammal). In some cases, determining a cfDNA fragmentation profile in a mammal can be used for identifying a mammal as having cancer. For example, cfDNA fragments obtained from a mammal (e.g., from a sample obtained from a mammal) can be subjected to low coverage whole-genome sequencing, and the sequenced fragments can be mapped to the genome (e.g., in non-overlapping windows) and assessed to determine a cfDNA fragmentation profile. This document also provides methods and materials for assessing and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile. In some cases, this document provides methods and materials for monitoring and/or treating a mammal having cancer. For example, one or more cancer treatments can be administered to a mammal identified as having cancer (e.g., based, at least in part, on a cfDNA fragmentation profile) to treat the mammal.
Described herein is a non-invasive method for the early detection and localization of cancer. cfDNA in the blood can provide a non-invasive diagnostic avenue for patients with cancer. As demonstrated herein, DNA Evaluation of Fragments for early Interception (DELFI) was developed and used to evaluate genome-wide fragmentation patterns of cfDNA of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers as well as 245 healthy individuals. These analyses revealed that cfDNA profiles of healthy individuals reflected nucleosomal fragmentation patterns of white blood cells, while patients with cancer had altered fragmentation profiles. DELFI had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity and identified the tissue of origin of the cancers to a limited number of sites in 75% of cases. Assessing cfDNA (e.g., using DELFI) can provide a screening approach for early detection of cancer, which can increase the chance for successful treatment of a patient having cancer. Assessing cfDNA (e.g., using DELFI) can also provide an approach for monitoring cancer, which can increase the chance for successful treatment and improved outcome of a patient having cancer. In addition, a cfDNA fragmentation profile can be obtained from limited amounts of cfDNA and using inexpensive reagents and/or instruments.
In general, one aspect of this document features methods for determining a cfDNA fragmentation profile of a mammal. The methods can include, or consist essentially of, processing cfDNA fragments obtained from a sample obtained from the mammal into sequencing libraries, subjecting the sequencing libraries to whole genome sequencing (e.g., low-coverage whole genome sequencing) to obtain sequenced fragments, mapping the sequenced fragments to a genome to obtain windows of mapped sequences, and analyzing the windows of mapped sequences to determine cfDNA fragment lengths. The mapped sequences can include tens to thousands of windows. The windows of mapped sequences can be non-overlapping windows. The windows of mapped sequences can each include about 5 million base pairs. The cfDNA fragmentation profile can be determined within each window. The cfDNA fragmentation profile can include a median fragment size. The cfDNA fragmentation profile can include a fragment size distribution. The cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments in the windows of mapped sequences. The cfDNA fragmentation profile can be over the whole genome. The cfDNA fragmentation profile can be over a subgenomic interval (e.g., an interval in a portion of a chromosome).
In another aspect, this document features methods for identifying a mammal as having cancer. The methods can include, or consist essentially of, determining a cfDNA fragmentation profile in a sample obtained from a mammal, comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile, and identifying the mammal as having cancer when the cfDNA fragmentation profile in the sample obtained from the mammal is different from the reference cfDNA fragmentation profile. The reference cfDNA fragmentation profile can be a cfDNA fragmentation profile of a healthy mammal. The reference cfDNA fragmentation profile can be generated by determining a cfDNA fragmentation profile in a sample obtained from the healthy mammal. The reference DNA fragmentation pattern can be a reference nucleosome cfDNA fragmentation profile. The cfDNA fragmentation profiles can include a median fragment size, and a median fragment size of the cfDNA fragmentation profile can be shorter than a median fragment size of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include a fragment size distribution, and a fragment size distribution of the cfDNA fragmentation profile can differ by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include position dependent differences in fragmentation patterns, including a ratio of small cfDNA fragments to large cfDNA fragments, where a small cfDNA fragment can be 100 base pairs (bp) to 150 bp in length and a large cfDNA fragments can be 151 bp to 220 bp in length, and where a correlation of fragment ratios in the cfDNA fragmentation profile can be lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include sequence coverage of small cfDNA fragments, large cfDNA fragments, or of both small and large cfDNA fragments, across the genome. The cancer can be colorectal cancer, lung cancer, breast cancer, bile duct cancer, pancreatic cancer, gastric cancer, or ovarian cancer. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile in windows across the whole genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over a subgenomic interval (e.g., an interval in a portion of a chromosome). The mammal can have been previously administered a cancer treatment to treat the cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof. The method also can include administering to the mammal a cancer treatment (e.g., surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof). The mammal can be monitored for the presence of cancer after administration of the cancer treatment.
In another aspect, this document features methods for treating a mammal having cancer. The methods can include, or consist essentially of, identifying the mammal as having cancer, where the identifying includes determining a cfDNA fragmentation profile in a sample obtained from the mammal, comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile, and identifying the mammal as having cancer when the cfDNA fragmentation profile obtained from the mammal is different from the reference cfDNA fragmentation profile; and administering a cancer treatment to the mammal. The mammal can be a human. The cancer can be colorectal cancer, lung cancer, breast cancer, gastric cancers, pancreatic cancers, bile duct cancers, or ovarian cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or combinations thereof. The reference cfDNA fragmentation profile can be a cfDNA fragmentation profile of a healthy mammal. The reference cfDNA fragmentation profile can be generated by determining a cfDNA fragmentation profile in a sample obtained from a healthy mammal. The reference DNA fragmentation pattern can be a reference nucleosome cfDNA fragmentation profile. The cfDNA fragmentation profile can include a median fragment size, where a median fragment size of the cfDNA fragmentation profile is shorter than a median fragment size of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include a fragment size distribution, where a fragment size distribution of the cfDNA fragmentation profile differs by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments in the windows of mapped sequences, where a small cfDNA fragment is 100 bp to 150 bp in length, where a large cfDNA fragments is 151 bp to 220 bp in length, and where a correlation of fragment ratios in the cfDNA fragmentation profile is lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include the sequence coverage of small cfDNA fragments in windows across the genome. The cfDNA fragmentation profile can include the sequence coverage of large cfDNA fragments in windows across the genome. The cfDNA fragmentation profile can include the sequence coverage of small and large cfDNA fragments in windows across the genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over the whole genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over a subgenomic interval. The mammal can have previously been administered a cancer treatment to treat the cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or combinations thereof. The method also can include monitoring the mammal for the presence of cancer after administration of the cancer treatment.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used to practice the invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.
The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.

DESCRIPTION OF THE DRAWINGS

FIG. 1. Schematic of an exemplary DELFI approach. Blood is collected from a cohort of healthy individuals and patients with cancer. Nucleosome protected cfDNA is extracted from the plasma fraction, processed into sequencing libraries, examined through whole genome sequencing, mapped to the genome, and analyzed to determine cfDNA fragment profiles in different windows across the genome. Machine learning approaches are used to categorize individuals as healthy or as having cancer and to identify the tumor tissue of origin using genome-wide cfDNA fragmentation patterns.

FIG. 2. Simulations of non-invasive cancer detection based on number of alterations analyzed and tumor-derived cfDNA fragment distributions. Monte Carlo simulations were performed using different numbers of tumor-specific alterations to evaluate the probability of detecting cancer alterations in cfDNA at the indicated fraction of tumor-derived molecules. The simulations were performed assuming an average of 2000 genome equivalents of cfDNA and the requirement of five or more observations of any alteration. These analyses indicate that increasing the number of tumor-specific alterations improves the sensitivity of detection of circulating tumor DNA.

FIG. 3. Tumor-derived cfDNA fragment distributions. Cumulative density functions of cfDNA fragment lengths of 42 loci containing tumor-specific alterations from 30 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands (blue). Lengths of mutant cfDNA fragments were significantly different in size compared to wild-type cfDNA fragments (red) at these loci.

FIGS. 4A and 4B. Tumor-derived cfDNA GC content and fragment length. A, GC content was similar for mutated and non-mutated fragments. B, GC content was not correlated to fragment length.

FIG. 5. Germline cfDNA fragment distributions. Cumulative density functions of fragment lengths of 44 loci containing germline alterations (non-tumor derived) from 38 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands. Fragments with germline mutations (blue) were comparable in length to wild-type cfDNA fragment lengths (red).

FIG. 6. Hematopoietic cfDNA fragment distributions. Cumulative density functions of fragment lengths of 41 loci containing hematopoietic alterations (non-tumor derived) from 28 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands. After correction for multiple testing, there were no significant differences (α=0.05) in the size distributions of mutated hematopoietic cfDNA fragments (blue) and wild-type cfDNA fragments (red).

FIGS. 7A-7F. cfDNA fragmentation profiles in healthy individuals and patients with cancer. A, Genome-wide cfDNA fragmentation profiles (defined as the ratio of short to long fragments) from ˜9× whole genome sequencing are shown in 5 Mb bins for 30 healthy individuals (top) and 8 lung cancer patients (bottom). B, An analysis of healthy cfDNA (top), lung cancer cfDNA (middle), and healthy lymphocyte (bottom) fragmentation profiles and lymphocyte profiles from chromosome 1 at 1 Mb resolution. The healthy lymphocyte profiles were scaled with a standard deviation equal to that of the median healthy cfDNA profiles. Healthy cfDNA patterns closely mirrored those in healthy lymphocytes while lung cancer cfDNA profiles were more varied and differed from both healthy and lymphocyte profiles. C, Smoothed median distances between adjacent nucleosome centered at zero using 100 kb bins from healthy cfDNA (top) and nuclease-digested healthy lymphocytes (middle) are depicted together with the first eigenvector for the genome contact matrix obtained through previously reported Hi-C analyses of lymphoblastoid cells (bottom). Healthy cfDNA nucleosome distances closely mirrored those in nuclease-digested lymphocytes as well as those from lymphoblastoid Hi-C analyses. cfDNA fragmentation profiles from healthy individuals (n=30) had high correlations while patients with lung cancer had lower correlations to median fragmentation profiles of lymphocytes (D), healthy cfDNA (E), and lymphocyte nucleosome (F) distances.

FIG. 8. Density of cfDNA fragment lengths in healthy individuals and patients with lung cancer. cfDNA fragments lengths are shown for healthy individuals (n=30, gray) and patients with lung cancer (n=8, blue).

FIGS. 9A and 9B. Subsampling of whole genome sequence data for analysis of cfDNA fragmentation profiles. A, High coverage (9×) whole-genome sequencing data were subsampled to 2×, 1×, 0.5×, 0.2×, and 0.1× fold coverage. Mean centered genome-wide fragmentation profiles in 5 Mb bins for 30 healthy individuals and 8 patients with lung cancer are depicted for each subsampled fold coverage with median profiles shown in blue. B, Pearson correlation of subsampled profiles to initial profile at 9× coverage for healthy individuals and patients with lung cancer.

FIG. 10. cfDNA fragmentation profiles and sequence alterations during therapy. Detection and monitoring of cancer in serial blood draws from NSCLC patients (n=19) undergoing treatment with targeted tyrosine kinase inhibitors (black arrows) was performed using targeted sequencing (top) and genome-wide fragmentation profiles (bottom). For each case, the vertical axis of the lower panel displays −1 times the correlation of each sample to the median healthy cfDNA fragmentation profile. Error bars depict confidence intervals from binomial tests for mutant allele fractions and confidence intervals calculated using Fisher transformation for genome-wide fragmentation profiles. Although the approaches analyze different aspects of cfDNA (whole genome compared to specific alterations) the targeted sequencing and fragmentation profiles were similar for patients responding to therapy as well as those with stable or progressive disease. As fragmentation profiles reflect both genomic and epigenomic alterations, while mutant allele fractions only reflect individual mutations, mutant allele fractions alone may not reflect the absolute level of correlation of fragmentation profiles to healthy individuals.

FIGS. 11A-11C. cfDNA fragmentation profiles in healthy individuals and patients with cancer. A, Fragmentation profiles (bottom) in the context of tumor copy number changes (top) in a colorectal cancer patient where parallel analyses of tumor tissue were performed. The distribution of segment means and integer copy numbers are shown at top right in the indicated colors. Altered fragmentation profiles were present in regions of the genome that were copy neutral and were further affected in regions with copy number changes. B, GC adjusted fragmentation profiles from 1-2× whole genome sequencing for healthy individuals and patients with cancer are depicted per cancer type using 5 Mb windows. The median healthy profile is indicated in black and the 98% confidence band is shown in gray. For patients with cancer, individual profiles are colored based on their correlation to the healthy median. C, Windows are indicated in orange if more than 10% of the cancer samples had a fragment ratio more than three standard deviations from the median healthy fragment ratio. These analyses highlight the multitude of position dependent alterations across the genome in cfDNA of individuals with cancer.

FIGS. 12A and 12B. Profiles of cfDNA fragment lengths in copy neutral regions in healthy individuals and one patient with colorectal cancer. A, The fragmentation profile in 211 copy neutral windows in chromosomes 1-6 for 25 randomly selected healthy individuals (gray). For a patient with colorectal cancer (CGCRC291) with an estimated mutant allele fraction of 20%, the cancer fragment length profile was diluted to an approximate 10% tumor contribution (blue). A and B, While the marginal densities of the fragment profiles for the healthy samples and cancer patient show substantial overlap (A, right), the fragmentation profiles are different as can be seen visualization of the fragmentation profiles (A, left) and by the separation of the colorectal cancer patient from the healthy samples in a principal component analysis (B).

FIGS. 13A and 13B. Genome-wide GC correction of cfDNA fragments. To estimate and control for the effects of GC content on sequencing coverage, coverage in non-overlapping 100 kb genomic windows was calculated across the autosomes. For each window, the average GC of the aligned fragments was calculated. A, Loess smoothing of raw coverage (top row) for two randomly selected healthy subjects (CGPLH189 and CGPLH380) and two cancer patients (CGPLLU161 and CGPLBR24) with undetectable aneuploidy (PA score <2.35). After subtracting the average coverage predicted by the loess model, the residuals were rescaled to the median autosomal coverage (bottom row). As fragment length may also result in coverage biases, this GC correction procedure was performed separately for short (≤150 bp) and long (≥151 bp) fragments. While the 100 kb bins on chromosome 19 (blue points) consistently have less coverage than predicted by the loess model, we did not implement a chromosome-specific correction as such an approach would remove the effects of chromosomal copy number on coverage. B, Overall, a limited correlation was found between short or long fragment coverage and GC content after correction among healthy subjects and cancer patients with a PA score <3.

FIG. 14. Schematic of machine learning model. Gradient tree boosting machine learning was used to examine whether cfDNA can be categorized as having characteristics of a cancer patient or healthy individual. The machine learning model included fragmentation size and coverage characteristics in windows throughout the genome, as well as chromosomal arm and mitochondrial DNA copy numbers. A 10-fold cross validation approach was employed in which each sample is randomly assigned to a fold and 9 of the folds (90% of the data) are used for training and one fold (10% of the data) is used for testing. The prediction accuracy from a single cross validation is an average over the 10 possible combinations of test and training sets. As this prediction accuracy can reflect bias from the initial randomization of patients, the entire procedure was repeat, including the randomization of patients to folds, 10 times. For all cases, feature selection and model estimation were performed on training data and were validated on test data and the test data were never used for feature selection. Ultimately, a DELFI score was obtained that could be used to classify individuals as likely healthy or having cancer.

FIG. 15. Distribution of AUCs across the repeated 10-fold cross-validation. The 25^th, 50^th, and 75^thpercentiles of the 100 AUCs for the cohort of 215 healthy individuals and 208 patients with cancer are indicated by dashed lines.

FIGS. 16A and 16B. Whole-genome analyses of chromosomal arm copy number changes and mitochondrial genome representation. A, Z scores for each autosome arm are depicted for healthy individuals (n=215) and patients with cancer (n=208). The vertical axis depicts normal copy at zero with positive and negative values indicating arm gains and losses, respectively. Z scores greater than 50 or less than −50 are thresholded at the indicated values. B, The fraction of reads mapping to the mitochondrial genome is depicted for healthy individuals and patients with cancer.

FIGS. 17A and 17B. Detection of cancer using DELFI. A, Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and 208 patients with cancer (DELFI, AUC=0.94), with ≥95% specificity shaded in blue. Machine learning analyses of chromosomal arm copy number (Chr copy number (ML)), and mitochondrial genome copy number (mtDNA), are shown in the indicated colors. B, Analyses of individual cancers types using the DELFI-combined approach had AUCs ranging from 0.86 to >0.99.

FIG. 18. DELFI detection of cancer by stage. Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and each stage of 208 patients with cancer with >95% specificity shaded in blue.

FIG. 19. DELFI tissue of origin prediction. Receiver operator characteristics for DELFI tissue prediction of bile duct, breast, colorectal, gastric, lung, ovarian, and pancreatic cancers are depicted. In order to increase sample sizes within cancer type classes, cases detected with a 90% specificity were included, and the lung cancer cohort was supplemented with the addition of baseline cfDNA data from 18 lung cancer patients with prior treatment (see, e.g., Shen et al., 2018 Nature, 563:579-583).

FIG. 20. Detection of cancer using DELFI and mutation-based cfDNA approaches. DELFI (green) and targeted sequencing for mutation identification (blue) were performed independently in a cohort of 126 patients with breast, bile duct, colorectal, gastric, lung, or ovarian cancers. The number of individuals detected by each approach and in combination are indicated for DELFI detection with a specificity of 98%, targeted sequencing specificity at >99%, and a combined specificity of 98%. ND indicates not detected.

DETAILED DESCRIPTION

This document provides methods and materials for determining a cfDNA fragmentation profile in a mammal (e.g., in a sample obtained from a mammal). As used herein, the terms “fragmentation profile,” “position dependent differences in fragmentation patterns,” and “differences in fragment size and coverage in a position dependent manner across the genome” are equivalent and can be used interchangeably. In some cases, determining a cfDNA fragmentation profile in a mammal can be used for identifying a mammal as having cancer. For example, cfDNA fragments obtained from a mammal (e.g., from a sample obtained from a mammal) can be subjected to low coverage whole-genome sequencing, and the sequenced fragments can be mapped to the genome (e.g., in non-overlapping windows) and assessed to determine a cfDNA fragmentation profile. As described herein, a cfDNA fragmentation profile of a mammal having cancer is more heterogeneous (e.g., in fragment lengths) than a cfDNA fragmentation profile of a healthy mammal (e.g., a mammal not having cancer). As such, this document also provides methods and materials for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the presence and, optionally, the tissue of origin of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for monitoring a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the presence of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying a mammal as having cancer, and administering one or more cancer treatments to the mammal to treat the mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal, and one or more cancer treatments can be administered to the mammal.
A cfDNA fragmentation profile can include one or more cfDNA fragmentation patterns. A cfDNA fragmentation pattern can include any appropriate cfDNA fragmentation pattern. Examples of cfDNA fragmentation patterns include, without limitation, median fragment size, fragment size distribution, ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In some cases, a cfDNA fragmentation pattern includes two or more (e.g., two, three, or four) of median fragment size, fragment size distribution, ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In some cases, cfDNA fragmentation profile can be a genome-wide cfDNA profile (e.g., a genome-wide cfDNA profile in windows across the genome). In some cases, cfDNA fragmentation profile can be a targeted region profile. A targeted region can be any appropriate portion of the genome (e.g., a chromosomal region). Examples of chromosomal regions for which a cfDNA fragmentation profile can be determined as described herein include, without limitation, a portion of a chromosome (e.g., a portion of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and/or 14q) and a chromosomal arm (e.g., a chromosomal arm of 8q, 13q, 11q, and/or 3p). In some cases, a cfDNA fragmentation profile can include two or more targeted region profiles.
In some cases, a cfDNA fragmentation profile can be used to identify changes (e.g., alterations) in cfDNA fragment lengths. An alteration can be a genome-wide alteration or an alteration in one or more targeted regions/loci. A target region can be any region containing one or more cancer-specific alterations. Examples of cancer-specific alterations, and their chromosomal locations, include, without limitation, those shown in Table 3 (Appendix C) and those shown in Table 6 (Appendix F). In some cases, a cfDNA fragmentation profile can be used to identify (e.g., simultaneously identify) from about 10 alterations to about 500 alterations (e.g., from about 25 to about 500, from about 50 to about 500, from about 100 to about 500, from about 200 to about 500, from about 300 to about 500, from about 10 to about 400, from about 10 to about 300, from about 10 to about 200, from about 10 to about 100, from about 10 to about 50, from about 20 to about 400, from about 30 to about 300, from about 40 to about 200, from about 50 to about 100, from about 20 to about 100, from about 25 to about 75, from about 50 to about 250, or from about 100 to about 200, alterations).
In some cases, a cfDNA fragmentation profile can be used to detect tumor-derived DNA. For example, a cfDNA fragmentation profile can be used to detect tumor-derived DNA by comparing a cfDNA fragmentation profile of a mammal having, or suspected of having, cancer to a reference cfDNA fragmentation profile (e.g., a cfDNA fragmentation profile of a healthy mammal and/or a nucleosomal DNA fragmentation profile of healthy cells from the mammal having, or suspected of having, cancer). In some cases, a reference cfDNA fragmentation profile is a previously generated profile from a healthy mammal. For example, methods provided herein can be used to determine a reference cfDNA fragmentation profile in a healthy mammal, and that reference cfDNA fragmentation profile can be stored (e.g., in a computer or other electronic storage medium) for future comparison to a test cfDNA fragmentation profile in mammal having, or suspected of having, cancer. In some cases, a reference cfDNA fragmentation profile (e.g., a stored cfDNA fragmentation profile) of a healthy mammal is determined over the whole genome. In some cases, a reference cfDNA fragmentation profile (e.g., a stored cfDNA fragmentation profile) of a healthy mammal is determined over a subgenomic interval.
In some cases, a cfDNA fragmentation profile can be used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer).
A cfDNA fragmentation profile can include a cfDNA fragment size pattern. cfDNA fragments can be any appropriate size. For example, cfDNA fragment can be from about 50 base pairs (bp) to about 400 bp in length. As described herein, a mammal having cancer can have a cfDNA fragment size pattern that contains a shorter median cfDNA fragment size than the median cfDNA fragment size in a healthy mammal. A healthy mammal (e.g., a mammal not having cancer) can have cfDNA fragment sizes having a median cfDNA fragment size from about 166.6 bp to about 167.2 bp (e.g., about 166.9 bp). In some cases, a mammal having cancer can have cfDNA fragment sizes that are, on average, about 1.28 bp to about 2.49 bp (e.g., about 1.88 bp) shorter than cfDNA fragment sizes in a healthy mammal. For example, a mammal having cancer can have cfDNA fragment sizes having a median cfDNA fragment size of about 164.11 bp to about 165.92 bp (e.g., about 165.02 bp).
A cfDNA fragmentation profile can include a cfDNA fragment size distribution. As described herein, a mammal having cancer can have a cfDNA size distribution that is more variable than a cfDNA fragment size distribution in a healthy mammal. In some case, a size distribution can be within a targeted region. A healthy mammal (e.g., a mammal not having cancer) can have a targeted region cfDNA fragment size distribution of about 1 or less than about 1. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is longer (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 or more bp longer, or any number of base pairs between these numbers) than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is shorter (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 or more bp shorter, or any number of base pairs between these numbers) than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is about 47 bp smaller to about 30 bp longer than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution of, on average, a 10, 11, 12, 13, 14, 15, 15, 17, 18, 19, 20 or more bp difference in lengths of cfDNA fragments. For example, a mammal having cancer can have a targeted region cfDNA fragment size distribution of, on average, about a 13 bp difference in lengths of cfDNA fragments. In some case, a size distribution can be a genome-wide size distribution. A healthy mammal (e.g., a mammal not having cancer) can have very similar distributions of short and long cfDNA fragments genome-wide. In some cases, a mammal having cancer can have, genome-wide, one or more alterations (e.g., increases and decreases) in cfDNA fragment sizes. The one or more alterations can be any appropriate chromosomal region of the genome. For example, an alteration can be in a portion of a chromosome. Examples of portions of chromosomes that can contain one or more alterations in cfDNA fragment sizes include, without limitation, portions of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and 14q. For example, an alteration can be across a chromosome arm (e.g., an entire chromosome arm).
A cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments and a correlation of fragment ratios to reference fragment ratios. As used herein, with respect to ratios of small cfDNA fragments to large cfDNA fragments, a small cfDNA fragment can be from about 100 bp in length to about 150 bp in length. As used herein, with respect to ratios of small cfDNA fragments to large cfDNA fragments, a large cfDNA fragment can be from about 151 bp in length to 220 bp in length. As described herein, a mammal having cancer can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) that is lower (e.g., 2-fold lower, 3-fold lower, 4-fold lower, 5-fold lower, 6-fold lower, 7-fold lower, 8-fold lower, 9-fold lower, 10-fold lower, or more) than in a healthy mammal. A healthy mammal (e.g., a mammal not having cancer) can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) of about 1 (e.g., about 0.96). In some cases, a mammal having cancer can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) that is, on average, about 0.19 to about 0.30 (e.g., about 0.25) lower than a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) in a healthy mammal.
A cfDNA fragmentation profile can include coverage of all fragments. Coverage of all fragments can include windows (e.g., non-overlapping windows) of coverage. In some cases, coverage of all fragments can include windows of small fragments (e.g., fragments from about 100 bp to about 150 bp in length). In some cases, coverage of all fragments can include windows of large fragments (e.g., fragments from about 151 bp to about 220 bp in length).
In some cases, a cfDNA fragmentation profile can be used to identify the tissue of origin of a cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, or an ovarian cancer). For example, a cfDNA fragmentation profile can be used to identify a localized cancer. When a cfDNA fragmentation profile includes a targeted region profile, one or more alterations described herein (e.g., in Table 3 (Appendix C) and/or in Table 6 (Appendix F)) can be used to identify the tissue of origin of a cancer. In some cases, one or more alterations in chromosomal regions can be used to identify the tissue of origin of a cancer.
A cfDNA fragmentation profile can be obtained using any appropriate method. In some cases, cfDNA from a mammal (e.g., a mammal having, or suspected of having, cancer) can be processed into sequencing libraries which can be subjected to whole genome sequencing (e.g., low-coverage whole genome sequencing), mapped to the genome, and analyzed to determine cfDNA fragment lengths. Mapped sequences can be analyzed in non-overlapping windows covering the genome. Windows can be any appropriate size. For example, windows can be from thousands to millions of bases in length. As one non-limiting example, a window can be about 5 megabases (Mb) long. Any appropriate number of windows can be mapped. For example, tens to thousands of windows can be mapped in the genome. For example, hundreds to thousands of windows can be mapped in the genome. A cfDNA fragmentation profile can be determined within each window. In some cases, a cfDNA fragmentation profile can be obtained as described in Example 1. In some cases, a cfDNA fragmentation profile can be obtained as shown in FIG. 1.
In some cases, methods and materials described herein also can include machine learning. For example, machine learning can be used for identifying an altered fragmentation profile (e.g., using coverage of cfDNA fragments, fragment size of cfDNA fragments, coverage of chromosomes, and mtDNA).
In some cases, methods and materials described herein can be the sole method used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer). For example, determining a cfDNA fragmentation profile can be the sole method used to identify a mammal as having cancer.
In some cases, methods and materials described herein can be used together with one or more additional methods used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer). Examples of methods used to identify a mammal as having cancer include, without limitation, identifying one or more cancer-specific sequence alterations, identifying one or more chromosomal alterations (e.g., aneuploidies and rearrangements), and identifying other cfDNA alterations. For example, determining a cfDNA fragmentation profile can be used together with identifying one or more cancer-specific mutations in a mammal's genome to identify a mammal as having cancer. For example, determining a cfDNA fragmentation profile can be used together with identifying one or more aneuploidies in a mammal's genome to identify a mammal as having cancer.
In some aspects, this document also provides methods and materials for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying the location (e.g., the anatomic site or tissue of origin) of a cancer in a mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the tissue of origin of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying a mammal as having cancer, and administering one or more cancer treatments to the mammal to treat the mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal, and administering one or more cancer treatments to the mammal. In some cases, this document provides methods and materials for treating a mammal having cancer. For example, one or more cancer treatments can be administered to a mammal identified as having cancer (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal) to treat the mammal. In some cases, during or after the course of a cancer treatment (e.g., any of the cancer treatments described herein), a mammal can undergo monitoring (or be selected for increased monitoring) and/or further diagnostic testing. In some cases, monitoring can include assessing mammals having, or suspected of having, cancer by, for example, assessing a sample (e.g., a blood sample) obtained from the mammal to determine the cfDNA fragmentation profile of the mammal as described herein, and changes in the cfDNA fragmentation profiles over time can be used to identify response to treatment and/or identify the mammal as having cancer (e.g., a residual cancer).
Any appropriate mammal can be assessed, monitored, and/or treated as described herein. A mammal can be a mammal having cancer. A mammal can be a mammal suspected of having cancer. Examples of mammals that can be assessed, monitored, and/or treated as described herein include, without limitation, humans, primates such as monkeys, dogs, cats, horses, cows, pigs, sheep, mice, and rats. For example, a human having, or suspected of having, cancer can be assessed to determine a cfDNA fragmentation profiled as described herein and, optionally, can be treated with one or more cancer treatments as described herein.
Any appropriate sample from a mammal can be assessed as described herein (e.g., assessed for a DNA fragmentation pattern). In some cases, a sample can include DNA (e.g., genomic DNA). In some cases, a sample can include cfDNA (e.g., circulating tumor DNA (ctDNA)). In some cases, a sample can be fluid sample (e.g., a liquid biopsy). Examples of samples that can contain DNA and/or polypeptides include, without limitation, blood (e.g., whole blood, serum, or plasma), amnion, tissue, urine, cerebrospinal fluid, saliva, sputum, broncho-alveolar lavage, bile, lymphatic fluid, cyst fluid, stool, ascites, pap smears, breast milk, and exhaled breath condensate. For example, a plasma sample can be assessed to determine a cfDNA fragmentation profiled as described herein.
A sample from a mammal to be assessed as described herein (e.g., assessed for a DNA fragmentation pattern) can include any appropriate amount of cfDNA. In some cases, a sample can include a limited amount of DNA. For example, a cfDNA fragmentation profile can be obtained from a sample that includes less DNA than is typically required for other cfDNA analysis methods, such as those described in, for example, Phallen et al., 2017 Sci Transl Med 9; Cohen et al., 2018 Science 359:926; Newman et al., 2014 Nat Med 20:548; and Newman et al., 2016 Nat Biotechnol 34:547).
In some cases, a sample can be processed (e.g., to isolate and/or purify DNA and/or polypeptides from the sample). For example, DNA isolation and/or purification can include cell lysis (e.g., using detergents and/or surfactants), protein removal (e.g., using a protease), and/or RNA removal (e.g., using an RNase). As another example, polypeptide isolation and/or purification can include cell lysis (e.g., using detergents and/or surfactants), DNA removal (e.g., using a DNase), and/or RNA removal (e.g., using an RNase).
A mammal having, or suspected of having, any appropriate type of cancer can be assessed (e.g., to determine a cfDNA fragmentation profile) and/or treated (e.g., by administering one or more cancer treatments to the mammal) using the methods and materials described herein. A cancer can be any stage cancer. In some cases, a cancer can be an early stage cancer. In some cases, a cancer can be an asymptomatic cancer. In some cases, a cancer can be a residual disease and/or a recurrence (e.g., after surgical resection and/or after cancer therapy). A cancer can be any type of cancer. Examples of types of cancers that can be assessed, monitored, and/or treated as described herein include, without limitation, colorectal cancers, lung cancers, breast cancers, gastric cancers, pancreatic cancers, bile duct cancers, and ovarian cancers.
When treating a mammal having, or suspected of having, cancer as described herein, the mammal can be administered one or more cancer treatments. A cancer treatment can be any appropriate cancer treatment. One or more cancer treatments described herein can be administered to a mammal at any appropriate frequency (e.g., once or multiple times over a period of time ranging from days to weeks). Examples of cancer treatments include, without limitation adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors), targeted therapy such as administration of kinase inhibitors (e.g., kinase inhibitors that target a particular genetic lesion, such as a translocation or mutation), (e.g. a kinase inhibitor, an antibody, a bispecific antibody), signal transduction inhibitors, bispecific antibodies or antibody fragments (e.g., BiTEs), monoclonal antibodies, immune checkpoint inhibitors, surgery (e.g., surgical resection), or any combination of the above. In some cases, a cancer treatment can reduce the severity of the cancer, reduce a symptom of the cancer, and/or to reduce the number of cancer cells present within the mammal.
In some cases, a cancer treatment can include an immune checkpoint inhibitor. Non-limiting examples of immune checkpoint inhibitors include nivolumab (Opdivo), pembrolizumab (Keytruda), atezolizumab (tecentriq), avelumab (bavencio), durvalumab (imfinzi), ipilimumab (yervoy). See, e.g., Pardoll (2012) Nat. Rev Cancer 12: 252-264; Sun et al. (2017) Eur Rev Med Pharmacol Sci 21(6): 1198-1205; Hamanishi et al. (2015) J. Clin. Oncol. 33(34): 4015-22; Brahmer et al. (2012) N Engl J Med 366(26): 2455-65; Ricciuti et al. (2017) J. Thorac Oncol. 12(5): e51-e55; Ellis et al. (2017) Clin Lung Cancer pii: 51525-7304(17)30043-8; Zou and Awad (2017) Ann Oncol 28(4): 685-687; Sorscher (2017) N Engl J Med 376(10: 996-7; Hui et al. (2017) Ann Oncol 28(4): 874-881; Vansteenkiste et al. (2017) Expert Opin Biol Ther 17(6): 781-789; Hellmann et al. (2017) Lancet Oncol. 18(1): 31-41; Chen (2017) J. Chin Med Assoc 80(1): 7-14.
In some cases, a cancer treatment can be an adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors). See, e.g., Rosenberg and Restifo (2015) Science 348(6230): 62-68; Chang and Chen (2017) Trends Mol Med 23(5): 430-450; Yee and Lizee (2016) Cancer J. 23(2): 144-148; Chen et al. (2016) Oncoimmunology 6(2): e1273302; US 2016/0194404; US 2014/0050788; US 2014/0271635; U.S. Pat. No. 9,233,125; incorporated by reference in their entirety herein.
In some cases, a cancer treatment can be a chemotherapeutic agent. Non-limiting examples of chemotherapeutic agents include: amsacrine, azacitidine, axathioprine, bevacizumab (or an antigen-binding fragment thereof), bleomycin, busulfan, carboplatin, capecitabine, chlorambucil, cisplatin, cyclophosphamide, cytarabine, dacarbazine, daunorubicin, docetaxel, doxifluridine, doxorubicin, epirubicin, erlotinib hydrochlorides, etoposide, fiudarabine, floxuridine, fludarabine, fluorouracil, gemcitabine, hydroxyurea, idarubicin, ifosfamide, irinotecan, lomustine, mechlorethamine, melphalan, mercaptopurine, methotrxate, mitomycin, mitoxantrone, oxaliplatin, paclitaxel, pemetrexed, procarbazine, all-trans retinoic acid, streptozocin, tafluposide, temozolomide, teniposide, tioguanine, topotecan, uramustine, valrubicin, vinblastine, vincristine, vindesine, vinorelbine, and combinations thereof. Additional examples of anti-cancer therapies are known in the art; see, e.g. the guidelines for therapy from the American Society of Clinical Oncology (ASCO), European Society for Medical Oncology (ESMO), or National Comprehensive Cancer Network (NCCN).
When monitoring a mammal having, or suspected of having, cancer as described herein (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal), the monitoring can be before, during, and/or after the course of a cancer treatment. Methods of monitoring provided herein can be used to determine the efficacy of one or more cancer treatments and/or to select a mammal for increased monitoring. In some cases, the monitoring can include identifying a cfDNA fragmentation profile as described herein. For example, a cfDNA fragmentation profile can be obtained before administering one or more cancer treatments to a mammal having, or suspected or having, cancer, one or more cancer treatments can be administered to the mammal, and one or more cfDNA fragmentation profiles can be obtained during the course of the cancer treatment. In some cases, a cfDNA fragmentation profile can change during the course of cancer treatment (e.g., any of the cancer treatments described herein). For example, a cfDNA fragmentation profile indicative that the mammal has cancer can change to a cfDNA fragmentation profile indicative that the mammal does not have cancer. Such a cfDNA fragmentation profile change can indicate that the cancer treatment is working. Conversely, a cfDNA fragmentation profile can remain static (e.g., the same or approximately the same) during the course of cancer treatment (e.g., any of the cancer treatments described herein). Such a static cfDNA fragmentation profile can indicate that the cancer treatment is not working. In some cases, the monitoring can include conventional techniques capable of monitoring one or more cancer treatments (e.g., the efficacy of one or more cancer treatments). In some cases, a mammal selected for increased monitoring can be administered a diagnostic test (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to a mammal that has not been selected for increased monitoring. For example, a mammal selected for increased monitoring can be administered a diagnostic test at a frequency of twice daily, daily, bi-weekly, weekly, bi-monthly, monthly, quarterly, semi-annually, annually, or any at frequency therein. In some cases, a mammal selected for increased monitoring can be administered a one or more additional diagnostic tests compared to a mammal that has not been selected for increased monitoring. For example, a mammal selected for increased monitoring can be administered two diagnostic tests, whereas a mammal that has not been selected for increased monitoring is administered only a single diagnostic test (or no diagnostic tests). In some cases, a mammal that has been selected for increased monitoring can also be selected for further diagnostic testing. Once the presence of a tumor or a cancer (e.g., a cancer cell) has been identified (e.g., by any of the variety of methods disclosed herein), it may be beneficial for the mammal to undergo both increased monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and/or to assess the development of one or more cancer biomarkers such as mutations), and further diagnostic testing (e.g., to determine the size and/or exact location (e.g., tissue of origin) of the tumor or the cancer). In some cases, one or more cancer treatments can be administered to the mammal that is selected for increased monitoring after a cancer biomarker is detected and/or after the cfDNA fragmentation profile of the mammal has not improved or deteriorated. Any of the cancer treatments disclosed herein or known in the art can be administered. For example, a mammal that has been selected for increased monitoring can be further monitored, and a cancer treatment can be administered if the presence of the cancer cell is maintained throughout the increased monitoring period. Additionally or alternatively, a mammal that has been selected for increased monitoring can be administered a cancer treatment, and further monitored as the cancer treatment progresses. In some cases, after a mammal that has been selected for increased monitoring has been administered a cancer treatment, the increased monitoring will reveal one or more cancer biomarkers (e.g., mutations). In some cases, such one or more cancer biomarkers will provide cause to administer a different cancer treatment (e.g., a resistance mutation may arise in a cancer cell during the cancer treatment, which cancer cell harboring the resistance mutation is resistant to the original cancer treatment).
When a mammal is identified as having cancer as described herein (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal), the identifying can be before and/or during the course of a cancer treatment. Methods of identifying a mammal as having cancer provided herein can be used as a first diagnosis to identify the mammal (e.g., as having cancer before any course of treatment) and/or to select the mammal for further diagnostic testing. In some cases, once a mammal has been determined to have cancer, the mammal may be administered further tests and/or selected for further diagnostic testing. In some cases, methods provided herein can be used to select a mammal for further diagnostic testing at a time period prior to the time period when conventional techniques are capable of diagnosing the mammal with an early-stage cancer. For example, methods provided herein for selecting a mammal for further diagnostic testing can be used when a mammal has not been diagnosed with cancer by conventional methods and/or when a mammal is not known to harbor a cancer. In some cases, a mammal selected for further diagnostic testing can be administered a diagnostic test (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to a mammal that has not been selected for further diagnostic testing. For example, a mammal selected for further diagnostic testing can be administered a diagnostic test at a frequency of twice daily, daily, bi-weekly, weekly, bi-monthly, monthly, quarterly, semi-annually, annually, or any at frequency therein. In some cases, a mammal selected for further diagnostic testing can be administered a one or more additional diagnostic tests compared to a mammal that has not been selected for further diagnostic testing. For example, a mammal selected for further diagnostic testing can be administered two diagnostic tests, whereas a mammal that has not been selected for further diagnostic testing is administered only a single diagnostic test (or no diagnostic tests). In some cases, the diagnostic testing method can determine the presence of the same type of cancer (e.g., having the same tissue or origin) as the cancer that was originally detected (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal). Additionally or alternatively, the diagnostic testing method can determine the presence of a different type of cancer as the cancer that was original detected. In some cases, the diagnostic testing method is a scan. In some cases, the scan is a computed tomography (CT), a CT angiography (CTA), a esophagram (a Barium swallom), a Barium enema, a magnetic resonance imaging (MM), a PET scan, an ultrasound (e.g., an endobronchial ultrasound, an endoscopic ultrasound), an X-ray, a DEXA scan. In some cases, the diagnostic testing method is a physical examination, such as an anoscopy, a bronchoscopy (e.g., an autofluorescence bronchoscopy, a white-light bronchoscopy, a navigational bronchoscopy), a colonoscopy, a digital breast tomosynthesis, an endoscopic retrograde cholangiopancreatography (ERCP), an ensophagogastroduodenoscopy, a mammography, a Pap smear, a pelvic exam, a positron emission tomography and computed tomography (PET-CT) scan. In some cases, a mammal that has been selected for further diagnostic testing can also be selected for increased monitoring. Once the presence of a tumor or a cancer (e.g., a cancer cell) has been identified (e.g., by any of the variety of methods disclosed herein), it may be beneficial for the mammal to undergo both increased monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and/or to assess the development of one or more cancer biomarkers such as mutations), and further diagnostic testing (e.g., to determine the size and/or exact location of the tumor or the cancer). In some cases, a cancer treatment is administered to the mammal that is selected for further diagnostic testing after a cancer biomarker is detected and/or after the cfDNA fragmentation profile of the mammal has not improved or deteriorated. Any of the cancer treatments disclosed herein or known in the art can be administered. For example, a mammal that has been selected for further diagnostic testing can be administered a further diagnostic test, and a cancer treatment can be administered if the presence of the tumor or the cancer is confirmed. Additionally or alternatively, a mammal that has been selected for further diagnostic testing can be administered a cancer treatment, and can be further monitored as the cancer treatment progresses. In some cases, after a mammal that has been selected for further diagnostic testing has been administered a cancer treatment, the additional testing will reveal one or more cancer biomarkers (e.g., mutations). In some cases, such one or more cancer biomarkers (e.g., mutations) will provide cause to administer a different cancer treatment (e.g., a resistance mutation may arise in a cancer cell during the cancer treatment, which cancer cell harboring the resistance mutation is resistant to the original cancer treatment).
The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES

Example 1: Cell-Free DNA Fragmentation in Patients with Cancer

Analyses of cell free DNA have largely focused on targeted sequencing of specific genes. Such studies permit detection of a small number of tumor-specific alterations in patients with cancer and not all patients, especially those with early stage disease, have detectable changes. Whole genome sequencing of cell-free DNA can identify chromosomal abnormalities and rearrangements in cancer patients but detection of such alterations has been challenging in part due to the difficulty in distinguishing a small number of abnormal from normal chromosomal changes (Leary et al., 2010 Sci Transl Med 2:20ra14; and Leary et al., 2012 Sci Transl Med 4:162ra154). Other efforts have suggested nucleosome patterns and chromatin structure may be different between cancer and normal tissues, and that cfDNA in patients with cancer may result in abnormal cfDNA fragment size as well as position (Snyder et al., 2016 Cell 164:57; Jahr et al., 2001 Cancer Res 61:1659; Ivanov et al., 2015 BMC Genomics 16(Suppl 13):S1). However, the amount of sequencing needed for nucleosome footprint analyses of cfDNA is impractical for routine analyses.
The sensitivity of any cell-free DNA approach depends on the number of potential alterations examined as well as the technical and biological limitations of detecting such changes. As a typical blood sample contains 2000 genome equivalents of cfDNA per milliliter of plasma (Phallen et al., 2017 Sci Transl Med 9), the theoretical limit of detection of a single alteration can be no better than one in a few thousand mutant to wild-type molecules. An approach that detects a larger number of alterations in the same number of genome equivalents would be more sensitive for detecting cancer in the circulation. Monte Carlo simulations show that increasing the number of potential abnormalities detected from only a few to tens or hundreds can potentially improve the limit of detection by orders of magnitude, similar to recent probability analyses of multiple methylation changes in cfDNA (FIG. 2).
This study presents a novel method called DELFI for detection of cancer and further identification of tissue of origin using whole genome sequencing (FIG. 1). The approach uses cfDNA fragmentation profiles and machine learning to distinguish patterns of healthy blood cell DNA from tumor-derived DNA and to identify the primary tumor tissue. DELFI was used for a retrospective analysis of cfDNA from 245 healthy individuals and 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers, with most patients exhibiting localized disease. Assuming this approach had sensitivity ≥0.80 for discriminating cancer patients from healthy individuals while maintaining a specificity of 0.95, a study of at least 200 cancer patients would enable estimation of the true sensitivity with a margin of error of 0.06 at the desired specificity of 0.95 or greater.

Materials and Methods

Patient and Sample Characteristics

Plasma samples from healthy individuals and plasma and tissue samples from patients with breast, lung, ovarian, colorectal, bile duct, or gastric cancer were obtained from ILSBio/Bioreclamation, Aarhus University, Herlev Hospital of the University of Copenhagen, Hvidovre Hospital, the University Medical Center of the University of Utrecht, the Academic Medical Center of the University of Amsterdam, the Netherlands Cancer Institute, and the University of California, San Diego. All samples were obtained under Institutional Review Board approved protocols with informed consent for research use at participating institutions. Plasma samples from healthy individuals were obtained at the time of routine screening, including for colonoscopies or Pap smears. Individuals were considered healthy if they had no previous history of cancer and negative screening results.
Plasma samples from individuals with breast, colorectal, gastric, lung, ovarian, pancreatic, and bile duct cancer were obtained at the time of diagnosis, prior to tumor resection or therapy. Nineteen lung cancer patients analyzed for change in cfDNA fragmentation profiles across multiple time points were undergoing treatment with anti-EGFR or anti-ERBB2 therapy (see, e.g., Phallen et al., 2019 Cancer Research 15, 1204-1213). Clinical data for all patients included in this study are listed in Table 1 (Appendix A). Gender was confirmed through genomic analyses of X and Y chromosome representation. Pathologic staging of gastric cancer patients was performed after neoadjuvant therapy. Samples where the tumor stage was unknown were indicated as stage X or unknown.

Nucleosomal DNA Purification

Viably frozen lymphocytes were elutriated from leukocytes obtained from a healthy male (C0618) and female (D0808-L) (Advanced Biotechnologies Inc., Eldersburg, Md.). Aliquots of 1×10⁶cells were used for nucleosomal DNA purification using EZ Nucleosomal DNA Prep Kit (Zymo Research, Irvine, Calif.). Cells were initially treated with 100 μl of Nuclei Prep Buffer and incubated on ice for 5 minutes. After centrifugation at 200 g for 5 minutes, supernatant was discarded and pelleted nuclei were treated twice with 100 μl of Atlantis Digestion Buffer or with 100 μl of micrococcal nuclease (MN) Digestion Buffer. Finally, cellular nucleic DNA was fragmented with 0.5U of Atlantis dsDNase at 42° C. for 20 minutes or 1.5U of MNase at 37° C. for 20 minutes. Reactions were stopped using 5×MN Stop Buffer and DNA was purified using Zymo-Spin™ IIC Columns. Concentration and quality of eluted cellular nucleic DNA were analyzed using the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.).
Sample Preparation and Sequencing of cfDNA
Whole blood was collected in EDTA tubes and processed immediately or within one day after storage at 4° C., or was collected in Streck tubes and processed within two days of collection for three cancer patients who were part of the monitoring analysis. Plasma and cellular components were separated by centrifugation at 800 g for 10 min at 4° C. Plasma was centrifuged a second time at 18,000 g at room temperature to remove any remaining cellular debris and stored at −80° C. until the time of DNA extraction. DNA was isolated from plasma using the Qiagen Circulating Nucleic Acids Kit (Qiagen GmbH) and eluted in LoBind tubes (Eppendorf AG). Concentration and quality of cfDNA were assessed using the Bioanalyzer 2100 (Agilent Technologies).
NGS cfDNA libraries were prepared for whole genome sequencing and targeted sequencing using 5 to 250 ng of cfDNA as described elsewhere (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415). Briefly, genomic libraries were prepared using the NEBNext DNA Library Prep Kit for Illumina [New England Biolabs (NEB)] with four main modifications to the manufacturer's guidelines: (i) The library purification steps used the on-bead AMPure XP approach to minimize sample loss during elution and tube transfer steps (see, e.g., Fisher et al., 2011 Genome Biol 12:R1); (ii) NEBNext End Repair, A-tailing, and adapter ligation enzyme and buffer volumes were adjusted as appropriate to accommodate the on-bead AMPure XP purification strategy; (iii) a pool of eight unique Illumina dual index adapters with 8-base pair (bp) barcodes was used in the ligation reaction instead of the standard Illumina single or dual index adapters with 6- or 8-bp barcodes, respectively; and (iv) cfDNA libraries were amplified with Phusion Hot Start Polymerase.
Whole genome libraries were sequenced directly. For targeted libraries, capture was performed using Agilent SureSelect reagents and a custom set of hybridization probes targeting 58 genes (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415) per the manufacturer's guidelines. The captured library was amplified with Phusion Hot Start Polymerase (NEB). Concentration and quality of captured cfDNA libraries were assessed on the Bioanalyzer 2100 using the DNA1000 Kit (Agilent Technologies). Targeted libraries were sequenced using 100-bp paired-end runs on the Illumina HiSeq 2000/2500 (Illumina).
Analyses of Targeted Sequencing Data from cfDNA
Analyses of targeted NGS data for cfDNA samples was performed as described elsewhere (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415). Briefly, primary processing was completed using Illumina CASAVA (Consensus Assessment of Sequence and Variation) software (version 1.8), including demultiplexing and masking of dual-index adapter sequences. Sequence reads were aligned against the human reference genome (version hg18 or hg19) using NovoAlign with additional realignment of select regions using the Needleman-Wunsch method (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53). The positions of the sequence alterations have not been affected by the different genome builds. Candidate mutations, consisting of point mutations, small insertions, and deletions, were identified using VariantDx (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53) (Personal Genome Diagnostics, Baltimore, Md.) across the targeted regions of interest.
To analyze the fragment lengths of cfDNA molecules, each read pair from a cfDNA molecule was required to have a Phred quality score ≥30. All duplicate ctDNA fragments, defined as having the same start, end, and index barcode were removed. For each mutation, only fragments for which one or both of the read pairs contained the mutated (or wild-type) base at the given position were included. This analysis was done using the R packages Rsamtools and GenomicAlignments.
For each genomic locus where a somatic mutation was identified, the lengths of fragments containing the mutant allele were compared to the lengths of fragments of the wild-type allele. If more than 100 mutant fragments were identified, Welch's two-sample t-test was used to compare the mean fragment lengths. For loci with fewer than 100 mutant fragments, a bootstrap procedure was implemented. Specifically, replacement N fragments containing the wild-type allele, where N denotes the number of fragments with the mutation, were sampled. For each bootstrap replicate of wild type fragments their median length was computed. The p-value was estimated as the fraction of bootstrap replicates with a median wild-type fragment length as or more extreme than the observed median mutant fragment length.
Analyses of whole genome sequencing data from cfDNA Primary processing of whole genome NGS data for cfDNA samples was performed using Illumina CASAVA (Consensus Assessment of Sequence and Variation) software (version 1.8.2), including demultiplexing and masking of dual-index adapter sequences. Sequence reads were aligned against the human reference genome (version hg19) using ELAND.
Read pairs with a MAPQ score below 30 for either read and PCR duplicates were removed. hg19 autosomes were tiled into 26,236 adjacent, non-overlapping 100 kb bins. Regions of low mappability, indicated by the 10% of bins with the lowest coverage, were removed (see, e.g., Fortin et al, 2015 Genome Biol 16:180), as were reads falling in the Duke blacklisted regions (see, e.g., hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeMapability/). Using this approach, 361 Mb (13%) of the hg19 reference genome was excluded, including centromeric and telomeric regions. Short fragments were defined as having a length between 100 and 150 bp and long fragments were defined has having a length between 151 and 220 bp.
To account for biases in coverage attributable to GC content of the genome, the locally weighted smoother loess with span 3/4 was applied to the scatterplot of average fragment GC versus coverage calculated for each 100 kb bin. This loess regression was performed separately for short and long fragments to account for possible differences in GC effects on coverage in plasma by fragment length (see, e.g., Benjamini et al., 2012 Nucleic Acids Res 40:e72). The predictions for short and long coverage explained by GC from the loess model were subtracted, obtaining residuals for short and long that were uncorrelated with GC. The residuals were returned to the original scale by adding back the genome-wide median short and long estimates of coverage. This procedure was repeated for each sample to account for possible differences in GC effects on coverage between samples. To further reduce the feature space and noise, the total GC-adjusted coverage in 5 Mb bins was calculated.
To compare the variability of fragment lengths from healthy subjects to fragments in patients with cancer, the standard deviation of the short to long fragmentation profiles for each individual was calculated. The standard deviations in the two groups were compared by a Wilcoxon rank sum test.

Analyses of Chromosome Arm Copy Number Changes

To develop arm-level statistics for copy number changes, an approach for aneuploidy detection in plasma as described elsewhere (see, e.g., Leary et al., 2012 Sci Transl Med 4:162ra154) was adopted. This approach divides the genome into non-overlapping 50 KB bins for which GC-corrected log 2 read depth was obtained after correction by loess with span 3/4. This loess-based correction is comparable to the approach outlined above, but is evaluated on a log 2 scale to increase robustness to outliers in the smaller bins and does not stratify by fragment length. To obtain an arm-specific Z-score for copy number changes, the mean GC-adjusted read depth for each arm (GR) was centered and scaled by the average and standard deviation, respectively, of GR scores obtained from an independent set of 50 healthy samples.
Analyses of Mitochondrial-Aligned Reads from cfDNA
Whole genome sequence reads that initially mapped to the mitochondrial genome were extracted from bam files and realigned to the hg19 reference genome in end-to-end mode with Bowtie2 as described elsewhere (see, e.g., Langmead et al., 2012 Nat Methods 9:357-359). The resulting aligned reads were filtered such that both mates aligned to the mitochondrial genome with MAPQ >=30. The number of fragments mapping to the mitochondrial genome was counted and converted to a percentage of the total number of fragments in the original bam files.

Prediction Model for Cancer Classification

To distinguish healthy from cancer patients using fragmentation profiles, a stochastic gradient boosting model was used (gbm; see, e.g., Friedman et al., 2001 Ann Stat 29:1189-1232; and Friedman et al., 2002 Comput Stat Data An 38:367-378). GC-corrected total and short fragment coverage for all 504 bins were centered and scaled for each sample to have mean 0 and unit standard deviation. Additional features included Z-scores for each of the 39 autosomal arms and mitochondrial representation (log 10-transformed proportion of reads mapped to the mitochondria). To estimate the prediction error of this approach, 10-fold cross-validation was used as described elsewhere (see, e.g., Efron et al., 1997 J Am Stat Assoc 92, 548-560). Feature selection, performed only on the training data in each cross-validation run, removed bins that were highly correlated (correlation >0.9) or had near zero variance. Stochastic gradient boosted machine learning was implemented using the R package gbm package with parameters n.trees=150, interaction. depth=3, shrinkage=0.1, and n.minobsinside=10. To average over the prediction error from the randomization of patients to folds, the 10-fold cross validation procedure was repeated 10 times. Confidence intervals for sensitivity fixed at 98% and 95% specificity were obtained from 2000 bootstrap replicates.

Prediction Model for Tumor Tissue of Origin Classification

For samples correctly classified as cancer patients at 90% specificity (n=174), a separate stochastic gradient boosting model was trained to classify the tissue of origin. To account for the small number of lung samples used for prediction, 18 cfDNA baseline samples from late stage lung cancer patients were included from the monitoring analyses. Performance characteristics of the model were evaluated by 10-fold cross-validation repeated 10 times. This gbm model was trained using the same features as in the cancer classification model. As previously described, features that displayed correlation above 0.9 to each other or had near zero variance were removed within each training dataset during cross-validation. The tissue class probabilities were averaged across the 10 replicates for each patient and the class with the highest probability was taken as the predicted tissue.
Analyses of Nucleosomal DNA from Human Lymphocytes and cfDNA
From the nuclease treated lymphocytes, fragment sizes were analyzed in 5 Mb bins as described for whole genome cfDNA analyses. A genome-wide map of nucleosome positions was constructed from the nuclease treated lymphocyte cell-lines. This approach identified local biases in the coverage of circulating fragments, indicating a region protected from degradation. A “Window positioning score” (WPS) was used to score each base pair in the genome (see, e.g., Snyder et al., 2016 Cell 164:57). Using a sliding window of 60 bp centered around each base, the WPS was calculated as the number of fragments completely spanning the window minus the number of fragments with only one end in the window. Since fragments arising from nucleosomes have a median length of 167 bp, a high WPS indicated a possible nucleosomic position. WPS scores were centered at zero using a running median and smoothed using a Kolmogorov-Zurbenko filter (see, e.g., Zurbenko, The spectral analysis of time series. North-Holland series in statistics and probability; Elsevier, New York, N Y, 1986). For spans of positive WPS between 50 and 450 bp, a nucleosome peak was defined as the set of base pairs with a WPS above the median in that window. The calculation of nucleosome positions for cfDNA from 30 healthy individuals with sequence coverage of 9× was determined in the same manner as for lymphocyte DNA. To ensure that nucleosomes in healthy cfDNA were representative, a consensus track of nucleosomes was defined consisting only of nucleosomes identified in two or more individuals. Median distances between adjacent nucleosomes were calculated from the consensus track.

Monte Carlo Simulation of Detection Sensitivity

A Monte Carlo simulation was used to estimate the probability of detecting a molecule with a tumor-derived alteration. Briefly, 1 million molecules were generated from a multinomial distribution. For a simulation with m alterations, wild-type molecules were simulated with probability p and each of the m tumor alterations were simulated with probability (1−p)/m. Next, g*m molecules were sampled randomly with replacement, where g denotes the number of genome equivalents in 1 ml of plasma. If a tumor alteration was sampled s or more times, the sample was classified as cancer-derived. The simulation was repeated 1000 times, estimating the probability that the in silico sample would be correctly classified as cancer by the mean of the cancer indicator. Setting g=2000 and s=5, the number of tumor alterations was varied by powers of 2 from 1 to 256 and the fraction of tumor-derived molecules from 0.0001% to 1%.

Statistical Analyses

All statistical analyses were performed using R version 3.4.3. The R packages caret (version 6.0-79) and gbm (version 2.1-4) were used to implement the classification of healthy versus cancer and tissue of origin. Confidence intervals from the model output were obtained with the pROC (version 1.13) R package (see, e.g., Robin et al., 2011 BMC bioinformatics 12:77). Assuming the prevalence of undiagnosed cancer cases in this population is high (1 or 2 cases per 100 healthy), a genomic assay with a specificity of 0.95 and sensitivity of 0.8 would have useful operating characteristics (positive predictive value of 0.25 and negative predictive value near 1). Power calculations suggest that an analysis of more than 200 cancer patients and an approximately equal number of healthy controls, enable an estimation of the sensitivity with a margin of error of 0.06 at the desired specificity of 0.95 or greater.

Data and Code Availability

Sequence data utilized in this study have been deposited at the European Genome-phenome Archive under study accession nos. EGAS00001003611 and EGAS00001002577. Code for analyses is available at github.com/Cancer-Genomics/delfi scripts.

Results

DELFI allows simultaneous analysis of a large number of abnormalities in cfDNA through genome-wide analysis of fragmentation patterns. The method is based on low coverage whole genome sequencing and analysis of isolated cfDNA. Mapped sequences are analyzed in non-overlapping windows covering the genome. Conceptually, windows may range in size from thousands to millions of bases, resulting in hundreds to thousands of windows in the genome. 5 Mb windows were used for evaluating cfDNA fragmentation patterns as these would provide over 20,000 reads per window even at a limited amount of 1-2× genome coverage. Within each window, the coverage and size distribution of cfDNA fragments was examined. This approach was used to evaluate the variation of genome-wide fragmentation profiles in healthy and cancer populations (Table 1; Appendix A). The genome-wide pattern from an individual can be compared to reference populations to determine if the pattern is likely healthy or cancer-derived. As genome-wide profiles reveal positional differences associated with specific tissues that may be missed in overall fragment size distributions, these patterns may also indicate the tissue source of cfDNA.
The fragmentation size of cfDNA was focused on as it was found that cancer-derived cfDNA molecules may be more variable in size than cfDNA derived from non-cancer cells. cfDNA fragments from targeted regions that were captured and sequenced at high coverage (43,706 total coverage, 8,044 distinct coverage) from patients with breast, colorectal, lung or ovarian cancer (Table 1 (Appendix A), Table 2 (Appendix B), and Table 3 (Appendix C)) were initially examined. Analyses of loci containing 165 tumor-specific alterations from 81 patients (range of 1-7 alterations per patient) revealed an average absolute difference of 6.5 bp (95% CI, 5.4-7.6 bp) between lengths of median mutant and wild-type cfDNA fragments (FIG. 3, Table 3 (Appendix C)). The median size of mutant cfDNA fragments ranged from 30 bases smaller at chromosome 3 position 41, 266, 124 to 47 bases larger at chromosome 11 position 108, 117, 753 than the wild-type sequences at these regions (Table 3; Appendix C). GC content was similar for mutated and non-mutated fragments (FIG. 4a ), and there was no correlation between GC content and fragment length (FIG. 4b ). Similar analyses of 44 germline alterations from 38 patients identified median cfDNA size differences of less than 1 bp between fragment lengths of different alleles (FIG. 5, Table 3 (Appendix C)). Additionally, 41 alterations related to clonal hematopoiesis were identified through a previous sequence comparison of DNA from plasma, buffy coat, and tumors of the same individuals. Unlike tumor-derived fragments, there were no significant differences between fragments with hematopoietic alterations and wild type fragments (FIG. 6, Table 3 (Appendix C)). Overall, cancer-derived cfDNA fragment lengths were significantly more variable compared to non-cancer cfDNA fragments at certain genomic regions (p<0.001, variance ratio test). It was hypothesized that these differences may be due to changes in higher-order chromatin structure as well as other genomic and epigenomic abnormalities in cancer and that cfDNA fragmentation in a position-specific manner could therefore serve as a unique biomarker for cancer detection.
As targeted sequencing only analyzes a limited number of loci, larger-scale genome-wide analyses to detect additional abnormalities in cfDNA fragmentation were investigated. cfDNA was isolated from ˜4 ml of plasma from 8 lung cancer patients with stage I-III disease, as well as from 30 healthy individuals (Table 1 (Appendix A), Table 4 (Appendix D), and Table 5 (Appendix E)). A high efficiency approach was used to convert cfDNA to next generation sequencing libraries and performed whole genome sequencing at −9× coverage (Table 4; Appendix D). Overall cfDNA fragment lengths of healthy individuals were larger, with a median fragment size of 167.3 bp, while patients with cancer had median fragment sizes of 163.8 (p<0.01, Welch's t-test) (Table 5; Appendix E). To examine differences in fragment size and coverage in a position dependent manner across the genome, sequenced fragments were mapped to their genomic origin and fragment lengths were evaluated in 504 windows that were 5 Mb in size, covering ˜2.6 Gb of the genome. For each window, the fraction of small cfDNA fragments (100 to 150 bp in length) to larger cfDNA fragments (151 to 220 bp) as well as overall coverage were determined and used to obtain genome-wide fragmentation profiles for each sample.
Healthy individuals had very similar fragmentation profiles throughout the genome (FIG. 7 and FIG. 8). To examine the origins of fragmentation patterns normally observed in cfDNA, nuclei were isolated from elutriated lymphocytes of two healthy individuals and treated with DNA nucleases to obtain nucleosomal DNA fragments. Analyses of cfDNA patterns in observed healthy individuals revealed a high correlation to lymphocyte nucleosomal DNA fragmentation profiles (FIGS. 7b and 7d ) and nucleosome distances (FIGS. 7c and 7f ). Median distances between nucleosomes in lymphocytes were correlated to open (A) and closed (B) compartments of lymphoblastoid cells as revealed using the Hi-C method (see, e.g., Lieberman-Aiden et al., 2009 Science 326:289-293; and Fortin et al., 2015 Genome Biol 16:180) for examining the three-dimensional architecture of genomes (FIG. 7c ). These analyses suggest that the fragmentation patterns of normal cfDNA are the result of nucleosomal DNA patterns that largely reflect the chromatin structure of normal blood cells.
In contrast to healthy cfDNA, patients with cancer had multiple distinct genomic differences with increases and decreases in fragment sizes at different regions (FIGS. 7a and 7b ). Similar to our observations from targeted analyses, there was also greater variation in fragment lengths genome-wide for patients with cancer compared to healthy individuals.
To determine whether cfDNA fragment length patterns could be used to distinguish patients with cancer from healthy individuals, genome-wide correlation analyses were performed of the fraction of short to long cfDNA fragments for each sample compared to the median fragment length profile calculated from healthy individuals (FIGS. 7a, 7b, and 7e ). While the profiles of cfDNA fragments were remarkably consistent among healthy individuals (median correlation of 0.99), the median correlation of genome-wide fragment ratios among cancer patients was 0.84 (0.15 lower, 95% CI 0.07-0.50, p<0.001, Wilcoxon rank sum test; Table 5 (Appendix E)). Similar differences were observed when comparing fragmentation profiles of cancer patients to fragmentation profiles or nucleosome distances in healthy lymphocytes (FIGS. 7c, 7d, and 7f ). To account for potential biases in the fragmentation profiles attributable to GC content, a locally weighted smoother was applied independently to each sample and found that differences in fragmentation profiles between healthy individuals and cancer patients remained after this adjustment (median correlation of cancer patients to healthy=0.83) (Table 5; Appendix E).
Subsampling analyses of whole genome sequence data was performed at 9× coverage from cfDNA of patients with cancer at −2×, ˜1×, ˜0.5×, ˜0.2×, and −0.1× genome coverage, and it was determined that altered fragmentation profiles were readily identified even at 0.5× genome coverage (FIG. 9). Based on these observations, whole genome sequencing was performed with coverage of 1-2× to evaluate whether fragmentation profiles may change during the course of targeted therapy in a manner similar to monitoring of sequence alterations. cfDNA from 19 non-small cell lung cancer patients including 5 with partial radiographic response, 8 with stable disease, 4 with progressive disease, and 2 with unmeasurable disease, during the course of anti-EGFR or anti-ERBB2 therapy was evaluated (Table 6; Appendix F). As shown in FIG. 10, the degree of abnormality in the fragmentation profiles during therapy closely matched levels of EGFR or ERBB2 mutant allele fractions as determined using targeted sequencing (Spearman correlation of mutant allele fractions to fragmentation profiles=0.74). This correlation is remarkable as genome-wide and mutation-based methods are orthogonal and examine different cfDNA alterations that may be suppressed in these patients due to prior therapy. Notably all cases that had progression free survival of six or more months displayed a drop of or had extremely low levels of ctDNA after initiation of therapy as determined by fragmentation profiles, while cases with poor clinical outcome had increases in ctDNA. These results demonstrate the feasibility of fragmentation analyses for detecting the presence of tumor-derived cfDNA, and suggests that such analyses may also be useful for quantitative monitoring of cancer patients during treatment.
The fragmentation profiles were examined in the context of known copy number changes in a patient where parallel analyses of tumor tissue were obtained. These analyses demonstrated that altered fragmentation profiles were present in regions of the genome that were copy neutral and that these may be further affected in regions with copy number changes (FIG. 11a and FIG. 12a ). Position dependent differences in fragmentation patterns could be used to distinguish cancer-derived cfDNA from healthy cfDNA in these regions (FIG. 12a, b ), while overall cfDNA fragment size measurements would have missed such differences (FIG. 12a ).
These analyses were extended to an independent cohort of cancer patients and healthy individuals. Whole genome sequencing of cfDNA at 1-2× coverage from a total of 208 patients with cancer, including breast (n=54), colorectal (n=27), lung (n=12), ovarian (n=28), pancreatic (n=34), gastric (n=27), or bile duct cancers (n=26), as well as 215 individuals without cancer was performed (Table 1 (Appendix A) and Table 4 (Appendix D)). All cancer patients were treatment naïve and the majority had resectable disease (n=183). After GC adjustment of short and long cfDNA fragment coverage (FIG. 13a ), coverage and size characteristics of fragments in windows throughout the genome were examined (FIG. 11b , Table 4 (Appendix D) and Table 7 (Appendix G)). Genome-wide correlations of coverage to GC content were limited and no differences in these correlations between cancer patients and healthy individuals were observed (FIG. 13b ). Healthy individuals had highly concordant fragmentation profiles, while patients with cancer had high variability with decreased correlation to the median healthy profile (Table 7; Appendix G). An analysis of the most commonly altered fragmentation windows in the genome among cancer patients revealed a median of 60 affected windows across the cancer types analyzed, highlighting the multitude of position dependent alterations in fragmentation of cfDNA in individuals with cancer (FIG. 11c ).
To determine if position dependent fragmentation changes can be used to detect individuals with cancer, a gradient tree boosting machine learning model was implemented to examine whether cfDNA can be categorized as having characteristics of a cancer patient or healthy individual and estimated performance characteristics of this approach by ten-fold cross validation repeated ten times (FIGS. 14 and 15). The machine learning model included GC-adjusted short and long fragment coverage characteristics in windows throughout the genome. A machine learning classifier for copy number changes from chromosomal arm dependent features rather than a single score was also developed (FIG. 16a and Table 8 (Appendix H)) and mitochondrial copy number changes were also included (FIG. 16b ) as these could also help distinguish cancer from healthy individuals. Using this implementation of DELFI, a score was obtained that could be used to classify patients as healthy or having cancer. 152 of the 208 cancer patients were detected (73% sensitivity, 95% CI 67%-79%) while four of the 215 healthy individuals were misclassified (98% specificity) (Table 9). At a threshold of 95% specificity, 80% of patients with cancer were detected (95% CI, 74%-85%), including 79% of resectable (stage I-III) patients (145 of 183) and 82% of metastatic (stage IV) patients (18 out of 22) (Table 9). Receiver operator characteristic analyses for detection of patients with cancer had an AUC of 0.94 (95% CI 0.92-0.96), ranged among cancer types from 0.86 for pancreatic cancer to ≥0.99 for lung and ovarian cancers (FIGS. 17a and 17b ), and had AUCs ≥0.92 across all stages (FIG. 18). The DELFI classifier score did not differ with age among either cancer patients or healthy individuals (Table 1; Appendix A).

TABLE 9

DELFI performance for cancer detection.

95% specificity

98% specificity

Individuals	Individuals			Individuals
analyzed	detected	Sensitivity	95% CI	detected	Sensitivity	95% CI

Healthy	215	10	—	—	4	—	—
Cancer	208	166	80%	74%-85%	152	73%	67%-79%

Type	Breast	54	38	70%	56%-82%	31	57%	43%-71%
	Bile duct	26	23	88%	70%-98%	21	81%	61%-93%
	Colorectal	27	22	81%	62%-94%	19	70%	50%-86%
	Gastric	27	22	81%	62%-94%	22	81%	62%-94%
	Lung	12	12	100%	74%-100%	12	100%	74%-100%
	Ovarian	28	25	89%	72%-98%	25	89%	72%-98%
	Pancreatic	34	24	71%	53%-85%	22	65%	46%-80%
Stage	I	41	30	73%	53%-86%	28	68%	52%-82%
	II	109	85	78%	69%-85%	78	72%	62%-80%
	III	33	30	91%	76%-98%	26	79%	61%-91%
	IV	22	18	82%	60%-95%	17	77%	55%-92%
	0, X	3	3	100%	29%-100%	3	100%	29%-100%

To assess the contribution of fragment size and coverage, chromosome arm copy number, or mitochondrial mapping to the predictive accuracy of the model, the repeated 10-fold cross-validation procedure was implemented to assess performance characteristics of these features in isolation. It was observed that fragment coverage features alone (AUC=0.94) were nearly identical to the classifier that combined all features (AUC=0.94) (FIG. 17a ). In contrast, analyses of chromosomal copy number changes had lower performance (AUC=0.88) but were still more predictive than copy number changes based on individual scores (AUC=0.78) or mitochondrial mapping (AUC=0.72) (FIG. 17a ). These results suggest that fragment coverage is the major contributor to our classifier. Including all features in the prediction model may contribute in a complementary fashion for detection of patients with cancer as they can be obtained from the same genome sequence data.
As fragmentation profiles reveal regional differences in fragmentation that may differ between tissues, a similar machine learning approach was used to examine whether cfDNA patterns could identify the tissue of origin of these tumors. It was found that this approach had a 61% accuracy (95% CI 53%-67%), including 76% for breast, 44% for bile duct, 71% for colorectal, 67% for gastric, 53% for lung, 48% for ovarian, and 50% for pancreatic cancers (FIG. 19, Table 10). The accuracy increased to 75% (95% CI 69%-81%) when considering assigning patients with abnormal cfDNA to one of two sites of origin (Table 10). For all tumor types, the classification of the tissue of origin by DELFI was significantly higher than determined by random assignment (p<0.01, binomial test, Table 10).

TABLE 10

DELFI tissue of origin prediction

Cancer

Patients

Top Prediction

Top Two Predictions

Random Assignment

Type	Detected*	Patients	Accuracy (95% CI)	Patients	Accuracy (95% CI)	Patients	Accuracy

Breast

	42	32	76% (61%-88%)	38	91% (77%-97%)	9	22%
Bile Duct
	23	10	44% (23%-66%)	15	65% (43%-84%)	3	12%
Colorectal	24	17	71% (49%-87%)	19	79% (58%-93%)	3	12%
Gastric	24	16	67% (45%-84%)	19	79% (58%-93%)	3	12%
Lung
	30	16	53% (34%-72%)	23	77% (58%-90%)	2	6%
Ovarian	27	13	48% (29%-68%)	16	59% (38%-78%)	4	14%
Pancreatic	24	12	50% (29%-71%)	16	67% (45%-84%)	3	12%
Total	194	116	61% (53%-67%)	146	75% (69%-81%)	26	13%

*Patients detected are based on DELFI detection at 90% specificity. Lung cohort includes additional lung cancer patients with prior therapy.

As cancer-specific sequence alterations can be used to identify patients with cancer, it was evaluated whether combining DELFI with this approach could increase the sensitivity of cancer detection (FIG. 20). An analysis of cfDNA from a subset of the treatment naïve cancer patients using both DELFI and targeted sequencing revealed that 82% (103 of 126) of patients had fragmentation profile alterations, while 66% (83 of 126) had sequence alterations. Over 89% of cases with mutant allele fractions >1% were detected by DELFI while for cases with mutant allele fractions <1% the fraction detected by DELFI was 80%, including for cases that were undetectable using targeted sequencing (Table 7; Appendix G). When these approaches were used together, the combined sensitivity of detection increased to 91% (115 of 126 patients) with a specificity of 98% (FIG. 20).
Overall, genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals. The variability in fragment lengths and coverage in a position dependent manner throughout the genome may explain the apparently contradictory observations of previous analyses of cfDNA at specific loci or of overall fragment sizes. In patients with cancer, heterogeneous fragmentation patterns in cfDNA appear to be a result of mixtures of nucleosomal DNA from both blood and neoplastic cells. These studies provide a method for simultaneous analysis of tens to potentially hundreds of tumor-specific abnormalities from minute amounts of cfDNA, overcoming a limitation that has precluded the possibility of more sensitive analyses of cfDNA. DELFI analyses detected a higher fraction of cancer patients than previous cfDNA analysis methods that have focused on sequence or overall fragmentation sizes (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415; Cohen et al., 2018 Science 359:926; Newman et al., 2014 Nat Med 20:548; Bettegowda et al., 2014 Sci Transl Med 6:224ra24; Newman et al., 2016 Nat Biotechnol 34:547). As demonstrated in this Example, combining DELFI with analyses of other cfDNA alterations may further increase the sensitivity of detection. As fragmentation profiles appear related to nucleosomal DNA patterns, DELFI may be used for determining the primary source of tumor-derived cfDNA. The identification of the source of circulating tumor DNA in over half of patients analyzed may be further improved by including clinical characteristics, other biomarkers, including methylation changes, and additional diagnostic approaches (Ruibal Morell, 1992 The International journal of biological markers 7:160; Galli et al., 2013 Clinical chemistry and laboratory medicine 51:1369; Sikaris, 2011 Heart, lung & circulation 20:634; Cohen et al., 2018 Science 359:926). Finally, this approach requires only a small amount of whole genome sequencing, without the need for deep sequencing typical of approaches that focus on specific alterations. The performance characteristics and limited amount of sequencing needed for DELFI suggests that our approach could be broadly applied for screening and management of patients with cancer.
These results demonstrate that genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals. As such, cfDNA fragmentation profiles can have important implications for future research and applications of non-invasive approaches for detection of human cancer.

Other Embodiments

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

TABLE 1

APPENDIX A: Summary of patients and samples analyzed

				Age at
		Sample		Diag-	Gen-		TNM	Site of
Patient	Patient Type	Type	Timepoint	nosis	der	Stage	Staging	Primary Tumor

CGCRC291	Colorectal Cancer	cfDNA	Preoperative treatment naïve	69	F	IV	T3N2M1	Coecum
CGCRC292	Colorectal Cancer	cfDNA	Preoperative treatment naïve	51	M	IV	T3N2M1	Sigmoid Colon
CGCRC293	Colorectal Cancer	cfDNA	Preoperative treatment naïve	55	M	IV	T3N2M1	Rectum
CGCRC294	Colorectal Cancer	cfDNA	Preoperative treatment naïve	67	F	II	T3N0M0	Sigmoid Colon
CGCRC296	Colorectal Cancer	cfDNA	Preoperative treatment naïve	76	F	II	T4N0M0	Coecum
CGCRC299	Colorectal Cancer	cfDNA	Preoperative treatment naïve	71	M	I	T1N0M0	Rectum
CGCRC300	Colorectal Cancer	cfDNA	Preoperative treatment naïve	65	M	I	T2N0M0	Rectum
CGCRC301	Colorectal Cancer	cfDNA	Preoperative treatment naïve	76	F	I	T2N0M0	Rectum
CGCRC302	Colorectal Cancer	cfDNA	Preoperative treatment naïve	73	M	II	T3N0M0	Transverse Colon
CGCRC304	Colorectal Cancer	cfDNA	Preoperative treatment naïve	86	F	II	T3N0M0	Rectum
CGCRC305	Colorectal Cancer	cfDNA	Preoperative treatment naïve	83	F	II	T3N0M0	Transverse Colon
CGCRC306	Colorectal Cancer	cfDNA	Preoperative treatment naïve	80	F	II	T4N0M0	Ascending Colon
CGCRC307	Colorectal Cancer	cfDNA	Preoperative treatment naïve	78	F	II	T3N0M0	Ascending Colon
CGCRC308	Colorectal Cancer	cfDNA	Preoperative treatment naïve	72	F	III	T4N2M0	Ascending Colon
CGCRC311	Colorectal Cancer	cfDNA	Preoperative treatment naïve	59	M	I	T2N0M0	Sigmoid Colon
CGCRC315	Colorectal Cancer	cfDNA	Preoperative treatment naïve	74	M	III	T3N1M0	Sigmoid Colon
CGCRC316	Colorectal Cancer	cfDNA	Preoperative treatment naïve	80	M	III	T3N2M0	Transverse Colon
CGCRC317	Colorectal Cancer	cfDNA	Preoperative treatment naïve	74	M	III	T3N2M0	Descending Colon
CGCRC318	Colorectal Cancer	cfDNA	Preoperative treatment naïve	81	M	I	T2N0M0	Coecum
CGCRC319	Colorectal Cancer	cfDNA	Preoperative treatment naïve	80	F	III	T2N1M0	Descending Colon
CGCRC320	Colorectal Cancer	cfDNA	Preoperative treatment naïve	73	F	I	T2N0M0	Ascending Colon
CGCRC321	Colorectal Cancer	cfDNA	Preoperative treatment naïve	68	M	I	T2N0M0	Rectum
CGCRC333	Colorectal Cancer	cfDNA	Preoperative treatment naïve	NA	F	IV	NA	Colon/Rectum
CGCRC336	Colorectal Cancer	cfDNA	Preoperative treatment naïve	NA	M	IV	NA	Colon/Rectum
CGCRC338	Colorectal Cancer	cfDNA	Preoperative treatment naïve	NA	F	IV	NA	Colon/Rectum
CGCRC341	Colorectal Cancer	cfDNA	Preoperative treatment naïve	NA	F	IV	NA	Colon/Rectum
CGCRC342	Colorectal Cancer	cfDNA	Preoperative treatment naïve	NA	M	IV	NA	Colon/Rectum
CGLU316	Lung Cancer	cfDNA	Pre-treatment, Day −53	50	F	IV	T3N2M0	Left Upper Lobe of Lung
CGLU316	Lung Cancer	cfDNA	Pre-treatment, Day −4	50	F	IV	T3N2M0	Left Upper Lobe of Lung
CGLU316	Lung Cancer	cfDNA	Post-treatment, Day 18	50	F	IV	T3N2M0	Left Upper Lobe of Lung
CGLU316	Lung Cancer	cfDNA	Post-treatment, Day 87	50	F	IV	T3N2M0	Left Upper Lobe of Lung
CGLU344	Lung Cancer	cfDNA	Pre-treatment, Day −21	65	F	IV	T2N2M1	Right Upper Lobe of Lung
CGLU344	Lung Cancer	cfDNA	Pre-treatment, Day 0	65	F	IV	T2N2M1	Right Upper Lobe of Lung
CGLU344	Lung Cancer	cfDNA	Post-treatment, Day 0.1875	65	F	IV	T2N2M1	Right Upper Lobe of Lung
CGLU344	Lung Cancer	cfDNA	Post-treatment, Day 59	65	F	IV	T2N2M1	Right Upper Lobe of Lung
CGLU369	Lung Cancer	cfDNA	Pre-treatment, Day −2	48	F	IV	T2NxM1	Right Upper Lobe of Lung
CGLU369	Lung Cancer	cfDNA	Post-treatment, Day 12	48	F	IV	T2NxM1	Right Upper Lobe of Lung
CGLU369	Lung Cancer	cfDNA	Post-treatment, Day 68	48	F	IV	T2NxM1	Right Upper Lobe of Lung
CGLU369	Lung Cancer	cfDNA	Post-treatment, Day 110	48	F	IV	T2NxM1	Right Upper Lobe of Lung
CGLU373	Lung Cancer	cfDNA	Pre-treatment, Day −2	56	F	IV	T3N1M0	Right Upper Lobe of Lung
CGLU373	Lung Cancer	cfDNA	Post-treatment, Day 0.125	56	F	IV	T3N1M0	Right Upper Lobe of Lung
CGLU373	Lung Cancer	cfDNA	Post-treatment, Day 7	56	F	IV	T3N1M0	Right Upper Lobe of Lung
CGLU373	Lung Cancer	cfDNA	Post-treatment, Day 47	56	F	IV	T3N1M0	Right Upper Lobe of Lung
CGPLBR100	Breast Cancer	cfDNA	Preoperative treatment naïve	44	F	III	T2N2M0	Left Breast
CGPLBR101	Breast Cancer	cfDNA	Preoperative treatment naïve	46	F	II	T2N1M0	Left Breast
CGPLBR102	Breast Cancer	cfDNA	Preoperative treatment naïve	47	F	II	T2N1M0	Right Breast
CGPLBR103	Breast Cancer	cfDNA	Preoperative treatment naïve	48	F	II	T2N1M0	Left Breast
CGPLBR104	Breast Cancer	cfDNA	Preoperative treatment naïve	68	F	II	T2N0M0	Right Breast
CGPLBR12	Breast Cancer	cfDNA	Preoperative treatment naïve	NA	F	III	NA	Breast
CGPLBR18	Breast Cancer	cfDNA	Preoperative treatment naïve	NA	F	III	NA	Breast
CGPLBR23	Breast Cancer	cfDNA	Preoperative treatment naïve	53	F	II	NA	Breast
CGPLBR24	Breast Cancer	cfDNA	Preoperative treatment naïve	52	F	II	NA	Breast
CGPLBR28	Breast Cancer	cfDNA	Preoperative treatment naïve	59	F	III	NA	Breast
CGPLBR30	Breast Cancer	cfDNA	Preoperative treatment naïve	61	F	II	NA	Breast
CGPLBR31	Breast Cancer	cfDNA	Preoperative treatment naïve	54	F	II	NA	Breast
CGPLBR32	Breast Cancer	cfDNA	Preoperative treatment naïve	NA	F	II	NA	Breast
CGPLBR33	Breast Cancer	cfDNA	Preoperative treatment naïve	47	F	II	NA	Breast
CGPLBR34	Breast Cancer	cfDNA	Preoperative treatment naïve	60	F	II	NA	Breast
CGPLBR35	Breast Cancer	cfDNA	Preoperative treatment naïve	43	F	II	NA	Breast
CGPLBR36	Breast Cancer	cfDNA	Preoperative treatment naïve	36	F	II	NA	Breast
CGPLBR37	Breast Cancer	cfDNA	Preoperative treatment naïve	58	F	II	NA	Breast
CGPLBR38	Breast Cancer	cfDNA	Preoperative treatment naïve	54	F	I	T1N0M0	Left Breast
CGPLBR40	Breast Cancer	cfDNA	Preoperative treatment naïve	66	F	III	T2N2M0	Left Breast
CGPLBR41	Breast Cancer	cfDNA	Preoperative treatment naïve	51	F	III	T3N1M0	Left Breast
CGPLBR45	Breast Cancer	cfDNA	Preoperative treatment naïve	57	F	II	NA	Breast
CGPLBR46	Breast Cancer	cfDNA	Preoperative treatment naïve	54	F	III	NA	Breast
CGPLBR47	Breast Cancer	cfDNA	Preoperative treatment naïve	54	F	I	NA	Breast
CGPLBR48	Breast Cancer	cfDNA	Preoperative treatment naïve	47	F	II	T2N1M0	Left Breast
CGPLBR49	Breast Cancer	cfDNA	Preoperative treatment naïve	37	F	II	T2N1M0	Left Breast
CGPLBR50	Breast Cancer	cfDNA	Preoperative treatment naïve	51	F	I	NA	Breast
CGPLBR51	Breast Cancer	cfDNA	Preoperative treatment naïve	53	F	II	NA	Breast
CGPLBR52	Breast Cancer	cfDNA	Preoperative treatment naïve	68	F	III	NA	Breast
CGPLBR55	Breast Cancer	cfDNA	Preoperative treatment naïve	53	F	III	T3N1M0	Right Breast
CGPLBR56	Breast Cancer	cfDNA	Preoperative treatment naïve	56	F	II	NA	Breast
CGPLBR57	Breast Cancer	cfDNA	Preoperative treatment naïve	54	F	III	T2N2M0	Left Breast
CGPLBR59	Breast Cancer	cfDNA	Preoperative treatment naïve	42	F	I	T1N0M0	Left Breast
CGPLBR60	Breast Cancer	cfDNA	Preoperative treatment naïve	61	F	II	NA	Left Breast
CGPLBR61	Breast Cancer	cfDNA	Preoperative treatment naïve	67	F	II	T2N1M0	Left Breast
CGPLBR63	Breast Cancer	cfDNA	Preoperative treatment naïve	48	F	II	T2N1M0	Left Breast
CGPLBR65	Breast Cancer	cfDNA	Preoperative treatment naïve	50	F	II	NA	Left Breast
CGPLBR68	Breast Cancer	cfDNA	Preoperative treatment naïve	64	F	III	T4N1M0	Breast
CGPLBR69	Breast Cancer	cfDNA	Preoperative treatment naïve	43	F	II	T2N0M0	Breast
CGPLBR70	Breast Cancer	cfDNA	Preoperative treatment naïve	60	F	II	T2N1M0	Breast
CGPLBR71	Breast Cancer	cfDNA	Preoperative treatment naïve	65	F	II	T2N0M0	Breast
CGPLBR72	Breast Cancer	cfDNA	Preoperative treatment naïve	67	F	II	T2N0M0	Breast
CGPLBR73	Breast Cancer	cfDNA	Preoperative treatment naïve	60	F	II	T2N1M0	Breast
CGPLBR76	Breast Cancer	cfDNA	Preoperative treatment naïve	53	F	II	T2N0M0	Right Breast
CGPLBR81	Breast Cancer	cfDNA	Preoperative treatment naïve	54	F	II	NA	Breast
CGPLBR82	Breast Cancer	cfDNA	Preoperative treatment naïve	70	F	I	T1N0M0	Right Breast
CGPLBR83	Breast Cancer	cfDNA	Preoperative treatment naïve	53	F	II	T2N1M0	Right Breast
CGPLBR84	Breast Cancer	cfDNA	Preoperative treatment naïve	NA	F	III	NA	Breast
CGPLBR87	Breast Cancer	cfDNA	Preoperative treatment naïve	80	F	II	T2N1M0	Right Breast
CGPLBR88	Breast Cancer	cfDNA	Preoperative treatment naïve	48	F	II	T1N1M0	Left Breast
CGPLBR90	Breast Cancer	cfDNA	Preoperative treatment naïve	51	F	II	NA	Right Breast
CGPLBR91	Breast Cancer	cfDNA	Preoperative treatment naïve	62	F	III	T2N2M0	Breast
CGPLBR92	Breast Cancer	cfDNA	Preoperative treatment naïve	58	F	II	T2N1M0	Breast
CGPLBR93	Breast Cancer	cfDNA	Preoperative treatment naïve	59	F	II	T1N0M0	Breast
CGPLH189	Healthy	cfDNA	Preoperative treatment naïve	74	M	NA	NA	NA
CGPLH190	Healthy	cfDNA	Preoperative treatment naïve	67	M	NA	NA	NA
CGPLH192	Healthy	cfDNA	Preoperative treatment naïve	74	M	NA	NA	NA
CGPLH193	Healthy	cfDNA	Preoperative treatment naïve	72	F	NA	NA	NA
CGPLH194	Healthy	cfDNA	Preoperative treatment naïve	75	F	NA	NA	NA
CGPLH196	Healthy	cfDNA	Preoperative treatment naïve	64	M	NA	NA	NA
CGPLH197	Healthy	cfDNA	Preoperative treatment naïve	74	M	NA	NA	NA
CGPLH198	Healthy	cfDNA	Preoperative treatment naïve	66	M	NA	NA	NA
CGPLH199	Healthy	cfDNA	Preoperative treatment naïve	75	F	NA	NA	NA
CGPLH200	Healthy	cfDNA	Preoperative treatment naïve	51	M	NA	NA	NA
CGPLH201	Healthy	cfDNA	Preoperative treatment naïve	68	F	NA	NA	NA
CGPLH202	Healthy	cfDNA	Preoperative treatment naïve	73	M	NA	NA	NA
CGPLH203	Healthy	cfDNA	Preoperative treatment naïve	59	M	NA	NA	NA
CGPLH205	Healthy	cfDNA	Preoperative treatment naïve	68	F	NA	NA	NA
CGPLH208	Healthy	cfDNA	Preoperative treatment naïve	75	F	NA	NA	NA
CGPLH209	Healthy	cfDNA	Preoperative treatment naïve	74	M	NA	NA	NA
CGPLH210	Healthy	cfDNA	Preoperative treatment naïve	75	M	NA	NA	NA
CGPLH211	Healthy	cfDNA	Preoperative treatment naïve	75	F	NA	NA	NA
CGPLH300	Healthy	cfDNA	Preoperative treatment naïve	72	F	NA	NA	NA
CGPLH307	Healthy	cfDNA	Preoperative treatment naïve	53	M	NA	NA	NA
CGPLH308	Healthy	cfDNA	Preoperative treatment naïve	60	M	NA	NA	NA
CGPLH309	Healthy	cfDNA	Preoperative treatment naïve	61	F	NA	NA	NA
CGPLH310	Healthy	cfDNA	Preoperative treatment naïve	55	F	NA	NA	NA
CGPLH311	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH314	Healthy	cfDNA	Preoperative treatment naïve	59	M	NA	NA	NA
CGPLH314	Healthy	cfDNA,	Preoperative treatment naïve	59	M	NA	NA	NA
		technical
		replicate
CGPLH315	Healthy	cfDNA	Preoperative treatment naïve	59	F	NA	NA	NA
CGPLH316	Healthy	cfDNA	Preoperative treatment naïve	64	M	NA	NA	NA
CGPLH317	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH319	Healthy	cfDNA	Preoperative treatment naïve	60	F	NA	NA	NA
CGPLH320	Healthy	cfDNA	Preoperative treatment naïve	75	F	NA	NA	NA
CGPLH322	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH324	Healthy	cfDNA	Preoperative treatment naïve	59	F	NA	NA	NA
CGPLH325	Healthy	cfDNA	Preoperative treatment naïve	54	M	NA	NA	NA
CGPLH326	Healthy	cfDNA	Preoperative treatment naïve	67	F	NA	NA	NA
CGPLH327	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH328	Healthy	cfDNA	Preoperative treatment naïve	68	F	NA	NA	NA
CGPLH328	Healthy	cfDNA,	Preoperative treatment naïve	68	F	NA	NA	NA
		technical
		replicate
CGPLH329	Healthy	cfDNA	Preoperative treatment naïve	59	M	NA	NA	NA
CGPLH330	Healthy	cfDNA	Preoperative treatment naïve	75	M	NA	NA	NA
CGPLH331	Healthy	cfDNA	Preoperative treatment naïve	55	M	NA	NA	NA
CGPLH331	Healthy	cfDNA,	Preoperative treatment naïve	55	M	NA	NA	NA
		technical
		replicate
CGPLH333	Healthy	cfDNA	Preoperative treatment naïve	60	M	NA	NA	NA
CGPLH335	Healthy	cfDNA	Preoperative treatment naïve	74	M	NA	NA	NA
CGPLH336	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH337	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH338	Healthy	cfDNA	Preoperative treatment naïve	75	M	NA	NA	NA
CGPLH339	Healthy	cfDNA	Preoperative treatment naïve	70	M	NA	NA	NA
CGPLH340	Healthy	cfDNA	Preoperative treatment naïve	62	M	NA	NA	NA
CGPLH341	Healthy	cfDNA	Preoperative treatment naïve	61	F	NA	NA	NA
CGPLH342	Healthy	cfDNA	Preoperative treatment naïve	49	F	NA	NA	NA
CGPLH343	Healthy	cfDNA	Preoperative treatment naïve	58	M	NA	NA	NA
CGPLH344	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH345	Healthy	cfDNA	Preoperative treatment naïve	55	F	NA	NA	NA
CGPLH346	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH35	Healthy	cfDNA	Preoperative treatment naïve	48	F	NA	NA	NA
CGPLH350	Healthy	cfDNA	Preoperative treatment naïve	65	M	NA	NA	NA
CGPLH351	Healthy	cfDNA	Preoperative treatment naïve	71	M	NA	NA	NA
CGPLH352	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH353	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH354	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH355	Healthy	cfDNA	Preoperative treatment naïve	70	M	NA	NA	NA
CGPLH356	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH357	Healthy	cfDNA	Preoperative treatment naïve	52	F	NA	NA	NA
CGPLH358	Healthy	cfDNA	Preoperative treatment naïve	55	M	NA	NA	NA
CGPLH36	Healthy	cfDNA	Preoperative treatment naïve	36	F	NA	NA	NA
CGPLH360	Healthy	cfDNA	Preoperative treatment naïve	60	M	NA	NA	NA
CGPLH361	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH362	Healthy	cfDNA	Preoperative treatment naïve	72	F	NA	NA	NA
CGPLH363	Healthy	cfDNA	Preoperative treatment naïve	68	F	NA	NA	NA
CGPLH364	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH365	Healthy	cfDNA	Preoperative treatment naïve	68	F	NA	NA	NA
CGPLH366	Healthy	cfDNA	Preoperative treatment naïve	61	M	NA	NA	NA
CGPLH367	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH368	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH369	Healthy	cfDNA	Preoperative treatment naïve	55	F	NA	NA	NA
CGPLH369	Healthy	cfDNA,	Preoperative treatment naïve	55	F	NA	NA	NA
		technical
		replicate
CGPLH37	Healthy	cfDNA	Preoperative treatment naïve	39	F	NA	NA	NA
CGPLH370	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH371	Healthy	cfDNA	Preoperative treatment naïve	57	F	NA	NA	NA
CGPLH380	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH381	Healthy	cfDNA	Preoperative treatment naïve	56	F	NA	NA	NA
CGPLH382	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH383	Healthy	cfDNA	Preoperative treatment naïve	62	F	NA	NA	NA
CGPLH384	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH385	Healthy	cfDNA	Preoperative treatment naïve	69	M	NA	NA	NA
CGPLH386	Healthy	cfDNA	Preoperative treatment naïve	62	M	NA	NA	NA
CGPLH386	Healthy	cfDNA	Preoperative treatment naïve	62	M	NA	NA	NA
		technical
		replicate
CGPLH387	Healthy	cfDNA	Preoperative treatment naïve	71	F	NA	NA	NA
CGPLH388	Healthy	cfDNA	Preoperative treatment naïve	57	F	NA	NA	NA
CGPLH389	Healthy	cfDNA	Preoperative treatment naïve	73	F	NA	NA	NA
CGPLH390	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH391	Healthy	cfDNA	Preoperative treatment naïve	58	M	NA	NA	NA
CGPLH391	Healthy	cfDNA	Preoperative treatment naïve	58	M	NA	NA	NA
		technical
		replicate
CGPLH392	Healthy	cfDNA	Preoperative treatment naïve	57	F	NA	NA	NA
CGPLH393	Healthy	cfDNA	Preoperative treatment naïve	54	M	NA	NA	NA
CGPLH394	Healthy	cfDNA	Preoperative treatment naïve	55	F	NA	NA	NA
CGPLH395	Healthy	cfDNA	Preoperative treatment naïve	56	F	NA	NA	NA
CGPLH395	Healthy	cfDNA	Preoperative treatment naïve	56	F	NA	NA	NA
		technical
		replicate
CGPLH396	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH398	Healthy	cfDNA	Preoperative treatment naïve	68	M	NA	NA	NA
CGPLH399	Healthy	cfDNA	Preoperative treatment naïve	62	F	NA	NA	NA
CGPLH400	Healthy	cfDNA	Preoperative treatment naïve	64	M	NA	NA	NA
CGPLH400	Healthy	cfDNA	Preoperative treatment naïve	64	M	NA	NA	NA
		technical
		replicate
CGPLH401	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH401	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
		technical
		replicate
CGPLH402	Healthy	cfDNA	Preoperative treatment naïve	57	F	NA	NA	NA
CGPLH403	Healthy	cfDNA	Preoperative treatment naïve	64	M	NA	NA	NA
CGPLH404	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH405	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH406	Healthy	cfDNA	Preoperative treatment naïve	57	M	NA	NA	NA
CGPLH407	Healthy	cfDNA	Preoperative treatment naïve	75	F	NA	NA	NA
CGPLH408	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH409	Healthy	cfDNA	Preoperative treatment naïve	53	M	NA	NA	NA
CGPLH410	Healthy	cfDNA	Preoperative treatment naïve	52	M	NA	NA	NA
CGPLH411	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH412	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH413	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
CGPLH414	Healthy	cfDNA	Preoperative treatment naïve	56	M	NA	NA	NA
CGPLH415	Healthy	cfDNA	Preoperative treatment naïve	59	M	NA	NA	NA
CGPLH416	Healthy	cfDNA	Preoperative treatment naïve	58	F	NA	NA	NA
CGPLH417	Healthy	cfDNA	Preoperative treatment naïve	70	M	NA	NA	NA
CGPLH418	Healthy	cfDNA	Preoperative treatment naïve	70	F	NA	NA	NA
CGPLH419	Healthy	cfDNA	Preoperative treatment naïve	65	F	NA	NA	NA
CGPLH42	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
CGPLH420	Healthy	cfDNA	Preoperative treatment naïve	51	M	NA	NA	NA
CGPLH422	Healthy	cfDNA	Preoperative treatment naïve	68	F	NA	NA	NA
CGPLH423	Healthy	cfDNA	Preoperative treatment naïve	54	M	NA	NA	NA
CGPLH424	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH425	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH426	Healthy	cfDNA	Preoperative treatment naïve	68	M	NA	NA	NA
CGPLH427	Healthy	cfDNA	Preoperative treatment naïve	68	M	NA	NA	NA
CGPLH428	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH429	Healthy	cfDNA	Preoperative treatment naïve	63	F	NA	NA	NA
CGPLH43	Healthy	cfDNA	Preoperative treatment naïve	49	F	NA	NA	NA
CGPLH430	Healthy	cfDNA	Preoperative treatment naïve	69	F	NA	NA	NA
CGPLH431	Healthy	cfDNA	Preoperative treatment naïve	59	F	NA	NA	NA
CGPLH432	Healthy	cfDNA	Preoperative treatment naïve	59	F	NA	NA	NA
CGPLH434	Healthy	cfDNA	Preoperative treatment naïve	59	M	NA	NA	NA
CGPLH435	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH436	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH437	Healthy	cfDNA	Preoperative treatment naïve	56	M	NA	NA	NA
CGPLH438	Healthy	cfDNA	Preoperative treatment naïve	69	M	NA	NA	NA
CGPLH439	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH440	Healthy	cfDNA	Preoperative treatment naïve	72	M	NA	NA	NA
CGPLH441	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH442	Healthy	cfDNA	Preoperative treatment naïve	59	F	NA	NA	NA
CGPLH443	Healthy	cfDNA	Preoperative treatment naïve	52	F	NA	NA	NA
CGPLH444	Healthy	cfDNA	Preoperative treatment naïve	60	F	NA	NA	NA
CGPLH445	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH446	Healthy	cfDNA	Preoperative treatment naïve	51	F	NA	NA	NA
CGPLH447	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH448	Healthy	cfDNA	Preoperative treatment naïve	51	F	NA	NA	NA
CGPLH449	Healthy	cfDNA	Preoperative treatment naïve	51	F	NA	NA	NA
CGPLH45	Healthy	cfDNA	Preoperative treatment naïve	58	F	NA	NA	NA
CGPLH450	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH451	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
CGPLH452	Healthy	cfDNA	Preoperative treatment naïve	69	M	NA	NA	NA
CGPLH453	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH455	Healthy	cfDNA	Preoperative treatment naïve	55	F	NA	NA	NA
CGPLH455	Healthy	cfDNA	Preoperative treatment naïve	55	F	NA	NA	NA
		technical
		replicate
CGPLH456	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
CGPLH457	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
CGPLH458	Healthy	cfDNA	Preoperative treatment naïve	52	F	NA	NA	NA
CGPLH459	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH46	Healthy	cfDNA	Preoperative treatment naïve	35	F	NA	NA	NA
CGPLH460	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH463	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH464	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH465	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH466	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH466	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
		technical
		replicate
CGPLH467	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH468	Healthy	cfDNA	Preoperative treatment naïve	53	M	NA	NA	NA
CGPLH469	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH47	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH470	Healthy	cfDNA	Preoperative treatment naïve	68	F	NA	NA	NA
CGPLH471	Healthy	cfDNA	Preoperative treatment naïve	70	F	NA	NA	NA
CGPLH472	Healthy	cfDNA	Preoperative treatment naïve	69	F	NA	NA	NA
CGPLH473	Healthy	cfDNA	Preoperative treatment naïve	62	M	NA	NA	NA
CGPLH474	Healthy	cfDNA	Preoperative treatment naïve	63	M	NA	NA	NA
CGPLH475	Healthy	cfDNA	Preoperative treatment naïve	67	F	NA	NA	NA
CGPLH476	Healthy	cfDNA	Preoperative treatment naïve	65	F	NA	NA	NA
CGPLH477	Healthy	cfDNA	Preoperative treatment naïve	61	F	NA	NA	NA
CGPLH478	Healthy	cfDNA	Preoperative treatment naïve	51	F	NA	NA	NA
CGPLH479	Healthy	cfDNA	Preoperative treatment naïve	52	M	NA	NA	NA
CGPLH48	Healthy	cfDNA	Preoperative treatment naïve	38	F	NA	NA	NA
CGPLH480	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH481	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH482	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH483	Healthy	cfDNA	Preoperative treatment naïve	66	M	NA	NA	NA
CGPLH484	Healthy	cfDNA	Preoperative treatment naïve	72	M	NA	NA	NA
CGPLH485	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH486	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH487	Healthy	cfDNA	Preoperative treatment naïve	50	M	NA	NA	NA
CGPLH488	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH49	Healthy	cfDNA	Preoperative treatment naïve	39	F	NA	NA	NA
CGPLH490	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH491	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH492	Healthy	cfDNA	Preoperative treatment naïve	51	F	NA	NA	NA
CGPLH493	Healthy	cfDNA	Preoperative treatment naïve	64	M	NA	NA	NA
CGPLH494	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH495	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH496	Healthy	cfDNA	Preoperative treatment naïve	74	M	NA	NA	NA
CGPLH497	Healthy	cfDNA	Preoperative treatment naïve	68	F	NA	NA	NA
CGPLH497	Healthy	cfDNA	Preoperative treatment naïve	68	F	NA	NA	NA
		technical
		replicate
CGPLH498	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
CGPLH499	Healthy	cfDNA	Preoperative treatment naïve	52	F	NA	NA	NA
CGPLH50	Healthy	cfDNA	Preoperative treatment naïve	55	F	NA	NA	NA
CGPLH500	Healthy	cfDNA	Preoperative treatment naïve	51	F	NA	NA	NA
CGPLH501	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH502	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH503	Healthy	cfDNA	Preoperative treatment naïve	67	M	NA	NA	NA
CGPLH504	Healthy	cfDNA	Preoperative treatment naïve	57	F	NA	NA	NA
CGPLH504	Healthy	cfDNA	Preoperative treatment naïve	57	F	NA	NA	NA
		technical
		replicate
CGPLH505	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH506	Healthy	cfDNA	Preoperative treatment naïve	51	F	NA	NA	NA
CGPLH507	Healthy	cfDNA	Preoperative treatment naïve	56	M	NA	NA	NA
CGPLH508	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
CGPLH508	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
		technical
		replicate
CGPLH509	Healthy	cfDNA	Preoperative treatment naïve	60	M	NA	NA	NA
CGPLH51	Healthy	cfDNA	Preoperative treatment naïve	48	F	NA	NA	NA
CGPLH510	Healthy	cfDNA	Preoperative treatment naïve	67	M	NA	NA	NA
CGPLH511	Healthy	cfDNA	Preoperative treatment naïve	75	M	NA	NA	NA
CGPLH512	Healthy	cfDNA	Preoperative treatment naïve	52	M	NA	NA	NA
CGPLH513	Healthy	cfDNA	Preoperative treatment naïve	57	M	NA	NA	NA
CGPLH514	Healthy	cfDNA	Preoperative treatment naïve	55	F	NA	NA	NA
CGPLH515	Healthy	cfDNA	Preoperative treatment naïve	68	F	NA	NA	NA
CGPLH516	Healthy	cfDNA	Preoperative treatment naïve	65	F	NA	NA	NA
CGPLH517	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
CGPLH517	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
		technical
		replicate
CGPLH518	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH519	Healthy	cfDNA	Preoperative treatment naïve	54	M	NA	NA	NA
CGPLH522	Healthy	cfDNA	Preoperative treatment naïve	40	F	NA	NA	NA
CGPLH520	Healthy	cfDNA	Preoperative treatment naïve	51	F	NA	NA	NA
CGPLH54	Healthy	cfDNA	Preoperative treatment naïve	47	F	NA	NA	NA
CGPLH55	Healthy	cfDNA	Preoperative treatment naïve	46	F	NA	NA	NA
CGPLH56	Healthy	cfDNA	Preoperative treatment naïve	42	F	NA	NA	NA
CGPLH57	Healthy	cfDNA	Preoperative treatment naïve	39	F	NA	NA	NA
CGPLH59	Healthy	cfDNA	Preoperative treatment naïve	34	F	NA	NA	NA
CGPLH625	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH625	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH626	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
		technical
		replicate
CGPLH63	Healthy	cfDNA	Preoperative treatment naïve	47	F	NA	NA	NA
CGPLH639	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH64	Healthy	cfDNA	Preoperative treatment naïve	55	F	NA	NA	NA
CGPLH640	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH642	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
CGPLH643	Healthy	cfDNA	Preoperative treatment naïve	55	F	NA	NA	NA
CGPLH644	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH646	Healthy	cfDNA	Preoperative treatment naïve	50	F	NA	NA	NA
CGPLH75	Healthy	cfDNA	Preoperative treatment naïve	46	F	NA	NA	NA
CGPLH76	Healthy	cfDNA	Preoperative treatment naïve	53	F	NA	NA	NA
CGPLH77	Healthy	cfDNA	Preoperative treatment naïve	46	F	NA	NA	NA
CGPLH78	Healthy	cfDNA	Preoperative treatment naïve	34	F	NA	NA	NA
CGPLH79	Healthy	cfDNA	Preoperative treatment naïve	37	F	NA	NA	NA
CGPLH80	Healthy	cfDNA	Preoperative treatment naïve	37	F	NA	NA	NA
CGPLH81	Healthy	cfDNA	Preoperative treatment naïve	54	F	NA	NA	NA
CGPLH82	Healthy	cfDNA	Preoperative treatment naïve	38	F	NA	NA	NA
CGPLH83	Healthy	cfDNA	Preoperative treatment naïve	60	F	NA	NA	NA
CGPLH84	Healthy	cfDNA	Preoperative treatment naïve	45	F	NA	NA	NA
CGPLLU13	Lung Cancer	cfDNA	Pre treatment, Day 2	72	F	IV	T1BN2bM1a	Right Lung
CGPLLU13	Lung Cancer	cfDNA	Post-treatment, Day 5	72	F	IV	T1BN2bM1a	Right Lung
CGPLLU13	Lung Cancer	cfDNA	Post-treatment, Day 28	72	F	IV	T1BN2bM1a	Right Lung
CGPLLU13	Lung Cancer	cfDNA	Post-treatment, Day 91	72	F	IV	T1BN2bM1a	Right Lung
CGPLLU14	Lung Cancer	cfDNA	Pre-treatment, Day −38	55	F	IV	T1N1M0	Right Lower Lobe of Lung
CGPLLU14	Lung Cancer	cfDNA	Pre-treatment, Day −16	55	F	IV	T1N1M0	Right Lower Lobe of Lung
CGPLLU14	Lung Cancer	cfDNA	Pre-treatment, Day −3	55	F	IV	T1N1M0	Right Lower Lobe of Lung
CGPLLU14	Lung Cancer	cfDNA	Pre-treatment, Day 0	55	F	IV	T1N1M0	Right Lower Lobe of Lung
CGPLLU14	Lung Cancer	cfDNA	Post-treatment, Day 0.33	55	F	IV	T1N1M0	Right Lower Lobe of Lung
CGPLLU14	Lung Cancer	cfDNA	Post-treatment, Day 7	55	F	IV	T1N1M0	Right Lower Lobe of Lung
CGPLLU144	Lung Cancer	cfDNA	Preoperative treatment naïve	52	M	II	T2aN1M0	Lung
CGPLLU147	Lung Cancer	cfDNA	Preoperative treatment naïve	60	M	III	T3N2M0	Lung
CGPLLU161	Lung Cancer	cfDNA	Preoperative treatment naïve	41	F	II	T3N0M0	Lung
CGPLLU162	Lung Cancer	cfDNA	Preoperative treatment naïve	38	M	II	T1N1M0	Right Lung
CGPLLU163	Lung Cancer	cfDNA	Preoperative treatment naïve	66	M	II	T1N1M0	Left Lung
CGPLLU165	Lung Cancer	cfDNA	Preoperative treatment naïve	68	F	II	T1N1M0	Right Lung
CGPLLU168	Lung Cancer	cfDNA	Preoperative treatment naïve	70	F	I	T2aN0M0	Lung
CGPLLU169	Lung Cancer	cfDNA	Preoperative treatment naïve	64	M	I	T1bN0M0	Lung
CGPLLU175	Lung Cancer	cfDNA	Preoperative treatment naïve	47	M	I	T2N0M0	Lung
CGPLLU176	Lung Cancer	cfDNA	Preoperative treatment naïve	58	M	I	T2N0M0	Lung
CGPLLU177	Lung Cancer	cfDNA	Preoperative treatment naïve	45	M	II	T3N0M0	Right Lung
CGPLLU180	Lung Cancer	cfDNA	Preoperative treatment naïve	57	M	I	T2N0M0	Right Lung
CGPLLU198	Lung Cancer	cfDNA	Preoperative treatment naïve	49	F	I	T2N0M0	Left Lung
CGPLLU202	Lung Cancer	cfDNA	Preoperative treatment naïve	68	M	I	T2N0M0	Right Lung
CGPLLU203	Lung Cancer	cfDNA	Preoperative treatment naïve	66	M	II	T3N0M0	Right Lung
CGPLLU205	Lung Cancer	cfDNA	Preoperative treatment naïve	65	M	II	T3N0M0	Left Lung
CGPLLU206	Lung Cancer	cfDNA	Preoperative treatment naïve	55	M	III	T3N1M0	Right Lung
CGPLLU207	Lung Cancer	cfDNA	Preoperative treatment naïve	60	F	II	T2N1M0	Lung
CGPLLU208	Lung Cancer	cfDNA	Preoperative treatment naïve	56	F	II	T2N1M0	Lung
CGPLLU209	Lung Cancer	cfDNA	Preoperative treatment naïve	65	M	II	T2aN0M0	Lung
CGPLLU244	Lung Cancer	cfDNA	Pre-treatment, Day −7	66	F	IV	NA	Right Upper Lobe of Lung
CGPLLU244	Lung Cancer	cfDNA	Pre-treatment, Day −1	66	F	IV	NA	Right Upper Lobe of Lung
CGPLLU244	Lung Cancer	cfDNA	Post-treatment, Day 6	66	F	IV	NA	Right Upper Lobe of Lung
CGPLLU244	Lung Cancer	cfDNA	Post-treatment, Day 62	66	F	IV	NA	Right Upper Lobe of Lung
CGPLLU245	Lung Cancer	cfDNA	Pre-treatment, Day −32	49	M	IV	T2aN2M1B	Left Upper Lobe of Lung
CGPLLU245	Lung Cancer	cfDNA	Pre-treatment, Day 0	49	M	IV	T2aN2M1B	Left Upper Lobe of Lung
CGPLLU245	Lung Cancer	cfDNA	Post-treatment, Day 7	49	M	IV	T2aN2M1B	Left Upper Lobe of Lung
CGPLLU245	Lung Cancer	cfDNA	Post-treatment, Day 21	49	M	IV	T2aN2M1B	Left Upper Lobe of Lung
CGPLLU246	Lung Cancer	cfDNA	Pre-treatment, Day −21	65	F	IV	NA	Right Lower Lobe of Lung
CGPLLU246	Lung Cancer	cfDNA	Pre-treatment, Day 0	65	F	IV	NA	Right Lower Lobe of Lung
CGPLLU246	Lung Cancer	cfDNA	Post-treatment, Day 9	65	F	IV	NA	Right Lower Lobe of Lung
CGPLLU246	Lung Cancer	cfDNA	Post-treatment, Day 42	65	F	IV	NA	Right Lower Lobe of Lung
CGPLLU264	Lung Cancer	cfDNA	Pre-treatment, Day −1	84	M	IV	T1N2BM1	Left Middle Lung
CGPLLU264	Lung Cancer	cfDNA	Post-treatment, Day 6	84	M	IV	T1N2BM1	Left Middle Lung
CGPLLU264	Lung Cancer	cfDNA	Post-treatment, Day 27	84	M	IV	T1N2BM1	Left Middle Lung
CGPLLU264	Lung Cancer	cfDNA	Post-treatment, Day 69	84	M	IV	T1N2BM1	Left Middle Lung
CGPLLU265	Lung Cancer	cfDNA	Pre-treatment, Day 0	71	F	IV	T1N0Mx	Left Lower Lobe of Lung
CGPLLU265	Lung Cancer	cfDNA	Post-treatment, Day 3	71	F	IV	T1N0Mx	Left Lower Lobe of Lung
CGPLLU265	Lung Cancer	cfDNA	Post-treatment, Day 7	71	F	IV	T1N0Mx	Left Lower Lobe of Lung
CGPLLU265	Lung Cancer	cfDNA	Post-treatment, Day 84	71	F	IV	T1N0Mx	Left Lower Lobe of Lung
CGPLLU266	Lung Cancer	cfDNA	Pre-treatment, Day 0	78	M	IV	T2aN1	Left Lower Lobe of Lung
CGPLLU266	Lung Cancer	cfDNA	Post-treatment, Day 16	78	M	IV	T2aN1	Left Lower Lobe of Lung
CGPLLU266	Lung Cancer	cfDNA	Post-treatment, Day 83	78	M	IV	T2aN1	Left Lower Lobe of Lung
CGPLLU266	Lung Cancer	cfDNA	Post-treatment, Day 328	78	M	IV	T2aN1	Left Lower Lobe of Lung
CGPLLU267	Lung Cancer	cfDNA	Pre-treatment, Day −1	55	F	IV	T3NxM1a	Right Upper Lobe of Lung
CGPLLU267	Lung Cancer	cfDNA	Post-treatment, Day 34	55	F	IV	T3NxM1a	Right Upper Lobe of Lung
CGPLLU267	Lung Cancer	cfDNA	Post-treatment, Day 90	55	F	IV	T3NxM1a	Right Upper Lobe of Lung
CGPLLU269	Lung Cancer	cfDNA	Pre-treatment, Day 0	52	F	IV	T1CNxM1C	Right Paratracheal Lesion
CGPLLU269	Lung Cancer	cfDNA	Post-treatment, Day 9	52	F	IV	T1CNxM1C	Right Paratracheal Lesion
CGPLLU269	Lung Cancer	cfDNA	Post-treatment, Day 28	52	F	IV	T1CNxM1C	Right Paratracheal Lesion
CGPLLU271	Lung Cancer	cfDNA	Post-treatment, Day 259	73	M	IV	T1aNxM1	Left Upper Lobe of Lung
CGPLLU271	Lung Cancer	cfDNA	Pre-treatment, Day 0	73	M	IV	T1aNxM1	Left Upper Lobe of Lung
CGPLLU271	Lung Cancer	cfDNA	Post-treatment, Day 6	73	M	IV	T1aNxM1	Left Upper Lobe of Lung
CGPLLU271	Lung Cancer	cfDNA	Post-treatment, Day 20	73	M	IV	T1aNxM1	Left Upper Lobe of Lung
CGPLLU271	Lung Cancer	cfDNA	Post-treatment, Day 104	73	M	IV	T1aNxM1	Left Upper Lobe of Lung
CGPLLU43	Lung Cancer	cfDNA	Pre-treatment, Day −1	57	F	IV	T1BN0M0	Right Lower Lobe of Lung
CGPLLU43	Lung Cancer	cfDNA	Post-treatment, Day 6	57	F	IV	T1BN0M0	Right Lower Lobe of Lung
CGPLLU43	Lung Cancer	cfDNA	Post-treatment, Day 27	57	F	IV	T1BN0M0	Right Lower Lobe of Lung
CGPLLU43	Lung Cancer	cfDNA	Post-treatment, Day 83	57	F	IV	T1BN0M0	Right Lower Lobe of Lung
CGPLLU86	Lung Cancer	cfDNA	Pre-treatment, Day 0	55	M	IV	NA	Left Upper Lobe of Lung
CGPLLU86	Lung Cancer	cfDNA	Post-treatment, Day 0.5	55	M	IV	NA	Left Upper Lobe of Lung
CGPLLU86	Lung Cancer	cfDNA	Post-treatment, Day 7	55	M	IV	NA	Left Upper Lobe of Lung
CGPLLU86	Lung Cancer	cfDNA	Post-treatment, Day 17	55	M	IV	NA	Left Upper Lobe of Lung
CGPLLU88	Lung Cancer	cfDNA	Pre-treatment, Day 0	59	M	IV	NA	Right Middle Lobe of Lung
CGPLLU88	Lung Cancer	cfDNA	Post-treatment, Day 7	59	M	IV	NA	Right Middle Lobe of Lung
CGPLLU88	Lung Cancer	cfDNA	Post-treatment, Day 297	59	M	IV	NA	Right Middle Lobe of Lung
CGPLLU89	Lung Cancer	cfDNA	Pre-treatment, Day 0	54	F	IV	NA	Right Upper Lobe of Lung
CGPLLU89	Lung Cancer	cfDNA	Post-treatment, Day 7	54	F	IV	NA	Right Upper Lobe of Lung
CGPLLU89	Lung Cancer	cfDNA	Post-treatment, Day 22	54	F	IV	NA	Right Upper Lobe of Lung
CGPLOV11	Ovarian Cancer	cfDNA	Preoperative treatment naïve	51	F	IV	T3cN0M1	Right Ovary
CGPLOV12	Ovarian Cancer	cfDNA	Preoperative treatment naïve	45	F	I	T1aN0MX	Ovary
CGPLOV13	Ovarian Cancer	cfDNA	Preoperative treatment naïve	62	F	IV	T1bN0M1	Right Ovary
CGPLOV15	Ovarian Cancer	cfDNA	Preoperative treatment naïve	54	F	III	T3N1M0	Ovary
CGPLOV16	Ovarian Cancer	cfDNA	Preoperative treatment naïve	40	F	III	T3aN0M0	Ovary
CGPLOV19	Ovarian Cancer	cfDNA	Preoperative treatment naïve	52	F	II	T2aN0M0	Ovary
CGPLOV20	Ovarian Cancer	cfDNA	Preoperative treatment naïve	52	F	II	T2aN0M0	Left Ovary
CGPLOV21	Ovarian Cancer	cfDNA	Preoperative treatment naïve	51	M	IV	TanyN1M1	Ovary
CGPLOV22	Ovarian Cancer	cfDNA	Preoperative treatment naïve	54	F	III	T1cNXMX	Left Ovary
CGPLOV23	Ovarian Cancer	cfDNA	Preoperative treatment naïve	47	F	I	T1aN0M0	Ovary
CGPLOV24	Ovarian Cancer	cfDNA	Preoperative treatment naïve	14	F	I	T1aN0M0	Ovary
CGPLOV25	Ovarian Cancer	cfDNA	Preoperative treatment naïve	18	F	I	T1aN0M0	Ovary
CGPLOV26	Ovarian Cancer	cfDNA	Preoperative treatment naïve	35	F	I	T1aN0M0	Ovary
CGPLOV28	Ovarian Cancer	cfDNA	Preoperative treatment naïve	63	F	I	T1aNxM0	Right Ovary
CGPLOV31	Ovarian Cancer	cfDNA	Preoperative treatment naïve	45	F	III	T3aNxM0	Right Ovary
CGPLOV32	Ovarian Cancer	cfDNA	Preoperative treatment naïve	53	F	I	T1aNxM0	Left Ovary
CGPLOV37	Ovarian Cancer	cfDNA	Preoperative treatment naïve	40	F	I	T1cN0M0	Ovary
CGPLOV38	Ovarian Cancer	cfDNA	Preoperative treatment naïve	46	F	I	T1cN0M0	Ovary
CGPLOV40	Ovarian Cancer	cfDNA	Preoperative treatment naïve	53	F	IV	T3aN0M0	Ovary
CGPLOV41	Ovarian Cancer	cfDNA	Preoperative treatment naïve	57	F	IV	T3N0M1	Ovary
CGPLOV42	Ovarian Cancer	cfDNA	Preoperative treatment naïve	52	F	I	T3N0M1	Ovary
CGPLOV43	Ovarian Cancer	cfDNA	Preoperative treatment naïve	30	F	I	T3aN0M0	Ovary
CGPLOV44	Ovarian Cancer	cfDNA	Preoperative treatment naïve	69	F	I	T1aN0M0	Ovary
CGPLOV46	Ovarian Cancer	cfDNA	Preoperative treatment naïve	58	F	I	T1bN0M0	Ovary
CGPLOV47	Ovarian Cancer	cfDNA	Preoperative treatment naïve	41	F	I	T1aN0M0	Ovary
CGPLOV48	Ovarian Cancer	cfDNA	Preoperative treatment naïve	52	F	I	T1bN0M0	Ovary
CGPLOV49	Ovarian Cancer	cfDNA	Preoperative treatment naïve	68	F	III	T3bN0M0	Ovary
CGPLOV50	Ovarian Cancer	cfDNA	Preoperative treatment naïve	30	F	III	T3cN0M0	Ovary
CGPLPA112	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	58	M	II	NA	Intra Pancreatic Bile Duct
CGPLPA113	Duodenal Cancer	cfDNA	Preoperative treatment naïve	71	M	I	NA	Intra Pancreatic Bile Duct
CGPLPA114	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	NA	F	II	NA	Intra Pancreatic Bile Duct
CGPLPA115	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	NA	M	IV	NA	Intra Hepatic Bile Duct
CGPLPA117	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	NA	M	II	NA	Intra Pancreatic Bile Duct
CGPLPA118	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	68	F	I	NA	Bile Duct
CGPLPA122	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	62	F	II	NA	Bile Duct
CGPLPA124	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	83	F	II	NA	Bile Duct
CGPLPA125	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	58	M	II	NA	Bile Duct
CGPLPA126	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	68	M	II	NA	Bile Duct
CGPLPA127	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	71	F	IV	NA	Bile Duct
CGPLPA128	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	67	M	II	NA	Bile Duct
CGPLPA129	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	56	F	II	NA	Bile Duct
CGPLPA130	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	82	F	II	NA	Bile Duct
CGPLPA131	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	71	M	II	NA	Bile Duct
CGPLPA134	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	68	M	II	NA	Bile Duct
CGPLPA135	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	67	F	I	NA	Bile Duct
CGPLPA136	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	69	F	II	NA	Bile Duct
CGPLPA137	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	NA	M	II	NA	Bile Duct
CGPLPA139	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	NA	M	IV	NA	Bile Duct
CGPLPA14	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	68	M	II	NA	Pancreas
CGPLPA140	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	52	M	II	NA	Extra Hepatic Bile Duct
CGPLPA141	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	68	F	II	NA	Extra Hepatic Bile Duct
CGPLPA15	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	70	F	II	NA	Pancreas
CGPLPA155	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	NA	F	II	NA	NA
CGPLPA156	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	73	F	II	NA	Pancreas
CGPLPA165	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	42	M	I	NA	Bile Duct
CGPLPA168	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	58	M	II	NA	Bile Duct
CGPLPA17	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	65	M	II	NA	Pancreas
CGPLPA184	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	75	F	II	NA	Bile Duct
CGPLPA187	Bile Duct Cancer	cfDNA	Preoperative treatment naïve	67	F	II	NA	Bile Duct
CGPLPA23	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	58	F	II	NA	Pancreas
CGPLPA25	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	65	F	II	NA	Pancreas
CGPLPA26	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	64	M	II	NA	Pancreas
CGPLPA28	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	79	F	II	NA	Pancreas
CGPLPA33	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	67	F	II	NA	Pancreas
CGPLPA34	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	73	M	II	NA	Pancreas
CGPLPA37	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	67	F	II	NA	Pancreas
CGPLPA38	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	65	M	II	NA	Pancreas
CGPLPA39	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	67	F	II	NA	Pancreas
CGPLPA40	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	64	M	II	NA	Pancreas
CGPLPA42	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	73	M	II	NA	Pancreas
CGPLPA46	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	59	F	II	NA	Pancreas
CGPLPA47	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	67	M	II	NA	Pancreas
CGPLPA48	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	72	F	I	NA	Pancreas
CGPLPA52	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	63	M	II	NA	Pancreas
CGPLPA53	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	46	M	I	NA	Pancreas
CGPLPA58	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	74	F	II	NA	Pancreas
CGPLPA59	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	59	F	II	NA	Pancreas
CGPLPA67	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	55	M	III	NA	Pancreas
CGPLPA69	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	70	M	I	NA	Pancreas
CGPLPA71	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	64	M	II	NA	Pancreas
CGPLPA74	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	71	F	II	NA	Pancreas
CGPLPA76	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	69	M	II	NA	Pancreas
CGPLPA85	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	77	F	II	NA	Pancreas
CGPLPA86	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	66	M	II	NA	Pancreas
CGPLPA92	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	72	M	II	NA	Pancreas
CGPLPA93	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	48	M	II	NA	Pancreas
CGPLPA94	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	72	F	II	NA	Pancreas
CGPLPA95	Pancreatic Cancer	cfDNA	Preoperative treatment naïve	64	F	II	NA	Pancreas
CGST102	Gastric cancer	cfDNA	Preoperative treatment naïve	76	F	II	T3N0M0	Stomach
CGST11	Gastric cancer	cfDNA	Preoperative treatment naïve	49	M	IV	TXNXM1	Stomach
CGST110	Gastric cancer	cfDNA	Preoperative treatment naïve	77	M	III	T4AN3aM0	Stomach
CGST114	Gastric cancer	cfDNA	Preoperative treatment naïve	65	M	III	T4AN1M0	Stomach
CGST13	Gastric cancer	cfDNA	Preoperative treatment naïve	72	F	II	T1AN2M0	Stomach
CGST131	Gastric cancer	cfDNA	Preoperative treatment naïve	63	M	III	T2N3aM0	Stomach
CGST141	Gastric cancer	cfDNA	Preoperative treatment naïve	38	F	III	T3N2M0	Stomach
CGST16	Gastric cancer	cfDNA	Preoperative treatment naïve	78	M	III	T4AN3aM0	Stomach
CGST18	Gastric cancer	cfDNA	Preoperative treatment naïve	56	M	II	T3N0M0	Stomach
CGST21	Gastric cancer	cfDNA	Preoperative treatment naïve	39	M	II	T2N1 (mi)M0	Stomach
CGST26	Gastric cancer	cfDNA	Preoperative treatment naïve	51	M	IV	TXNXM1	Stomach
CGST28	Gastric cancer	cfDNA	Preoperative treatment naïve	55	M	X	TXNXMx	Stomach
CGST30	Gastric cancer	cfDNA	Preoperative treatment naïve	64	F	III	T3N2M0	Stomach
CGST32	Gastric cancer	cfDNA	Preoperative treatment naïve	67	M	II	T3N1M0	Stomach
CGST33	Gastric cancer	cfDNA	Preoperative treatment naïve	61	M	I	T2N0M0	Stomach
CGST38	Gastric cancer	cfDNA	Preoperative treatment naïve	71	F	0	T0N0M0	Stomach
CGST39	Gastric cancer	cfDNA	Preoperative treatment naïve	51	M	IV	TXNXM1	Stomach
CGST41	Gastric cancer	cfDNA	Preoperative treatment naïve	66	F	IV	TXNXM1	Stomach
CGST45	Gastric cancer	cfDNA	Preoperative treatment naïve	41	F	II	T3N0M0	Stomach
CGST47	Gastric cancer	cfDNA	Preoperative treatment naïve	74	F	I	T1AN0M0	Stomach
CGST48	Gastric cancer	cfDNA	Preoperative treatment naïve	62	M	IV	TXNXM1	Stomach
CGST53	Gastric cancer	cfDNA	Preoperative treatment naïve	70	M	0	T0N0M0	Stomach
CGST58	Gastric cancer	cfDNA	Preoperative treatment naïve	58	M	III	T4AN3bM0	Stomach
CGST67	Gastric cancer	cfDNA	Preoperative treatment naïve	69	M	I	T1BN0M0	Stomach
CGST77	Gastric cancer	cfDNA	Preoperative treatment naïve	70	M	IV	TXNXM1	Stomach
CGST80	Gastric cancer	cfDNA	Preoperative treatment naïve	58	M	III	T3N3aM0	Stomach
CGST81	Gastric cancer	cfDNA	Preoperative treatment naïve	64	F	I	T2N0Mx	Stomach
CGH14	Healthy	Human adult	NA	NA	M	NA	NA	NA
		elutriated
		lymophocytes
CGH15	Healthy	Human adult	NA	NA	F	NA	NA	NA
		elutriated
		lymophocytes

							Whole	Tar-	Tar-
							Genome	geted	geted
				Volume	cfDNA		Fragment	Fragment	Muta-
		Degree of	Location of	of	Ex-	cfDNA	Profile	Profile	tion
	Histopathological	Differen-	Metastases at	Plasma	tracted	Input	Anal-	Anal-	Anal-
Patient	Diagnosis	tiation	Diagnosis	(ml)	(ng/ml)	(ng/ml)	ysis	ysis	ysis

CGCRC291	Adenocarcinoma	Moderate	Synchronous Liver	7.9	7.80	7.80	Y	Y	Y
CGCRC292	Adenocarcinoma	Moderate	Synchronous	7.9	6.73	6.73	Y	Y	Y
			liver, Lung
CGCRC293	Adenocarcinoma	Moderate	Synchronous Liver	7.2	3.83	3.83	Y	Y	Y
CGCRC294	Adenocarcinoma	Moderate	None	8.4	18.87	18.87	Y	Y	Y
CGCRC296	Adenocarcinoma	Poor	None	4.3	31.24	31.24	Y	Y	Y
CGCRC299	Adenocarcinoma	Moderate	None	8.8	10.18	10.18	Y	Y	Y
CGCRC300	Adenocarcinoma	Moderate	None	4.3	10.48	10.48	Y	Y	Y
CGCRC301	Adenocarcinoma	Moderate	None	4.1	6.51	6.51	Y	Y	Y
CGCRC302	Adenocarcinoma	Moderate	None	4.3	52.13	52.13	Y	Y	Y
CGCRC304	Adenocarcinoma	Moderate	None	4.1	30.19	30.19	Y	Y	Y
CGCRC305	Adenocarcinoma	Moderate	None	8.6	9.10	9.10	Y	Y	Y
CGCRC306	Adenocarcinoma	Moderate	None	4.5	24.31	24.31	Y	Y	Y
CGCRC307	Adenocarcinoma	Moderate	None	8.5	14.26	14.26	Y	Y	Y
CGCRC308	Adenocarcinoma	Moderate	None	4.3	46.87	46.87	Y	Y	Y
CGCRC311	Adenocarcinoma	Moderate	None	8.5	3.91	3.91	Y	Y	Y
CGCRC315	Adenocarcinoma	Moderate	None	8.6	9.67	9.67	Y	Y	Y
CGCRC316	Adenocarcinoma	Moderate	None	4.9	52.16	52.16	Y	Y	Y
CGCRC317	Adenocarcinoma	Moderate	None	8.8	16.08	16.08	Y	Y	Y
CGCRC318	Adenocarcinoma	Moderate	None	9.8	18.24	18.24	Y	Y	Y
CGCRC319	Adenocarcinoma	Moderate	None	4.2	53.54	53.54	Y	N	Y
CGCRC320	Adenocarcinoma	Moderate	None	4.5	30.37	30.37	Y	Y	Y
CGCRC321	Adenocarcinoma	Moderate	None	9.3	4.25	4.25	Y	Y	Y
CGCRC333	Adenocarcinoma	NA	Liver	4.0	113.88	113.88	Y	Y	Y
CGCRC336	Adenocarcinoma	NA	Liver	4.4	211.74	211.74	Y	Y	Y
CGCRC338	Adenocarcinoma	NA	Liver	2.3	109.76	109.76	Y	Y	Y
CGCRC341	Adenocarcinoma	NA	Liver	4.6	156.62	156.62	Y	N	Y
CGCRC342	Adenocarcinoma	NA	Liver	3.9	56.09	56.09	Y	N	Y
CGLU316	Adeno, Squamous,	Poor	Lung	5.0	2.38	2.38	Y	N	Y
	Small Cell Carcinoma
CGLU316	Adeno, Squamous,	Poor	Lung	5.0	2.11	2.11	Y	N	Y
	Small Cell Carcinoma
CGLU316	Adeno, Squamous,	Poor	Lung	5.0	0.87	1.07	Y	N	Y
	Small Cell Carcinoma
CGLU316	Adeno, Squamous,	Poor	Lung	2.0	8.74	8.75	Y	N	Y
	Small Cell Carcinoma
CGLU344	Adenocarcinoma	NA	Pleura, Liver,	5.0	34.77	25.00	Y	N	Y
			Peritoneum
CGLU344	Adenocarcinoma	NA	Pleura, Liver,	5.0	15.63	15.64	Y	N	Y
			Peritoneum
CGLU344	Adenocarcinoma	NA	Pleura, Liver,	5.0	9.22	9.22	Y	N	Y
			Peritoneum
CGLU344	Adenocarcinoma	NA	Pleura, Liver,	5.0	5.31	5.32	Y	N	Y
			Peritoneum
CGLU369	Adenocarcinoma	NA	Brain	2.0	11.28	11.28	Y	N	Y
CGLU369	Adenocarcinoma	NA	Brain	5.0	10.09	10.09	Y	N	Y
CGLU369	Adenocarcinoma	NA	Brain	5.0	6.69	6.70	Y	N	Y
CGLU369	Adenocarcinoma	NA	Brain	5.0	8.41	8.42	Y	N	Y
CGLU373	Adenocarcinoma	Moderate	None	5.0	6.35	6.35	Y	N	Y
CGLU373	Adenocarcinoma	Moderate	None	5.0	6.28	6.28	Y	N	Y
CGLU373	Adenocarcinoma	Moderate	None	5.0	3.82	3.82	Y	N	Y
CGLU373	Adenocarcinoma	Moderate	None	3.5	5.55	5.55	Y	N	Y
CGPLBR100	Infiltration Ductal Carcinoma	NA	None	4.0	4.25	4.25	Y	N	Y
CGPLBR101	Infiltration Lobular Carcinoma	Moderate	None	4.0	37.88	37.88	Y	N	Y
CGPLBR102	Infiltration Ductal Carcinoma	Moderate	None	3.6	13.87	13.67	Y	N	Y
CGPLBR103	Infiltration Ductal Carcinoma	Moderate	None	3.6	7.11	7.11	Y	N	Y
CGPLBR104	Infiltration Lobular Carcinoma	Moderate	None	4.7	19.89	19.89	Y	N	Y
CGPLBR12	Ductal Carcinoma insitu	NA	NA	4.3	4.21	4.21	Y	N	N
	with Microinvasion
CGPLBR18	Infiltration Lobular Carcinoma	NA	NA	4.1	40.39	30.49	Y	N	N
CGPLBR23	Infiltration Ductal Carcinoma	NA	None	4.7	20.09	20.09	Y	N	N
CGPLBR24	Infiltration Ductal Carcinoma	NA	None	3.6	58.33	34.72	Y	N	N
CGPLBR28	Infiltration Ductal Carcinoma	NA	None	4.2	12.86	12.86	Y	N	N
CGPLBR30	Infiltration Ductal Carcinoma	NA	None	4.1	59.73	30.49	Y	N	N
CGPLBR31	Infiltration Ductal Carcinoma	NA	None	3.4	23.94	23.94	Y	N	N
CGPLBR32	Infiltration Ductal Carcinoma	NA	None	4.4	71.23	28.41	Y	N	N
CGPLBR33	Infiltration Lobular Carcinoma	NA	None	4.4	11.00	11.00	Y	N	N
CGPLBR34	Infiltration Lobular Carcinoma	NA	None	4.4	23.61	23.61	Y	N	N
CGPLBR35	Ductal Carcinoma insitu	NA	None	4.5	22.58	22.58	Y	N	N
	with Microinvasion
CGPLBR36	Infiltration Ductal Carcinoma	NA	None	4.4	17.23	17.73	Y	N	N
CGPLBR37	Infiltration Ductal Carcinoma	NA	None	4.4	9.39	9.39	Y	N	N
CGPLBR38	Infiltration Ductal Carcinoma	Moderate	None	4.0	5.77	5.77	Y	Y	Y
CGPLBR40	Infiltration Ductal Carcinoma	Poor	None	4.6	15.69	15.69	Y	Y	Y
CGPLBR41	Infiltration Ductal Carcinoma	Moderate	None	4.5	11.56	11.56	Y	N	Y
CGPLBR45	Infiltration Ductal Carcinoma	NA	None	4.5	20.36	20.36	Y	N	N
CGPLBR46	Infiltration Ductal Carcinoma	NA	None	3.5	20.17	20.17	Y	N	N
CGPLBR47	Infiltration Ductal Carcinoma	NA	None	4.5	13.89	13.89	Y	N	N
CGPLBR48	Infiltration Ductal Carcinoma	Poor	None	3.9	7.07	7.07	Y	Y	Y
CGPLBR49	Infiltration Ductal Carcinoma	Poor	None	4.0	5.74	5.74	Y	N	Y
CGPLBR50	Infiltration Ductal Carcinoma	NA	None	4.5	45.58	27.78	Y	N	N
CGPLBR51	Infiltration Ductal Carcinoma	NA	None	4.0	8.83	8.83	Y	N	N
CGPLBR52	Infiltration Ductal Carcinoma	NA	None	4.5	80.71	27.78	Y	N	N
CGPLBR55	Infiltration Ductal Carcinoma	Poor	None	4.3	4.57	4.57	Y	Y	Y
CGPLBR56	Infiltration Ductal Carcinoma	NA	None	4.5	22.16	22.16	Y	N	N
CGPLBR57	Infiltration Ductal Carcinoma	NA	None	4.3	4.02	4.02	Y	N	Y
CGPLBR59	Infiltration Ductal Carcinoma	Moderate	None	4.1	8.24	8.24	Y	N	Y
CGPLBR60	Infiltration Ductal Carcinoma	NA	None	4.5	11.09	11.09	Y	N	N
CGPLBR61	Infiltration Ductal Carcinoma	Moderate	None	4.1	13.25	13.25	Y	N	Y
CGPLBR63	Infiltration Ductal Carcinoma	Moderate	None	4.0	6.19	6.19	Y	Y	Y
CGPLBR65	Infiltration Ductal Carcinoma	NA	None	3.5	41.75	35.71	Y	N	N
CGPLBR68	Infiltration Ductal Carcinoma	Poor	None	3.4	10.41	10.41	Y	N	Y
CGPLBR69	Infiltration Ductal Carcinoma	Moderate	None	4.4	4.07	4.07	Y	Y	Y
CGPLBR70	Infiltration Ductal Carcinoma	Moderate	None	3.4	11.94	11.94	Y	Y	Y
CGPLBR71	Infiltration Ductal Carcinoma	Poor	None	3.1	7.64	7.64	Y	Y	Y
CGPLBR72	Infiltration Ductal Carcinoma	Well	None	3.9	4.43	4.43	Y	Y	Y
CGPLBR73	Infiltration Ductal Carcinoma	Moderate	None	3.3	14.69	14.69	Y	Y	Y
CGPLBR76	Infiltration Ductal Carcinoma	Well	None	4.9	8.71	8.71	Y	Y	Y
CGPLBR81	Infiltration Ductal Carcinoma	NA	None	2.5	83.14	50.00	Y	N	N
CGPLBR82	Infiltration Lobular Carcinoma	Moderate	None	4.8	23.39	23.39	Y	N	Y
CGPLBR83	Infiltration Ductal Carcinoma	Moderate	None	3.7	100.17	100.17	Y	Y	Y
CGPLBR84	Infiltration Ductal Carcinoma	NA	NA	3.6	16.95	16.95	Y	N	N
CGPLBR87	Papillary Carcinoma	Well	None	3.6	277.39	69.44	Y	Y	Y
CGPLBR88	Infiltration Ductal Carcinoma	Poor	None	3.6	49.75	49.75	Y	Y	Y
CGPLBR90	Infiltration Ductal Carcinoma	NA	None	3.0	14.24	14.24	Y	N	N
CGPLBR91	Infiltration Lobular Carcinoma	Poor	None	3.2	22.41	22.41	Y	N	Y
CGPLBR92	Infiltration Medullary Carcinoma	Poor	None	3.1	81.00	81.00	Y	Y	Y
CGPLBR93	Infiltration Ductal Carcinoma	Moderate	None	3.3	27.94	27.94	Y	N	Y
CGPLH189	NA	NA	NA	5.0	5.84	5.84	Y	N	N
CGPLH190	NA	NA	NA	4.7	18.07	18.07	Y	N	N
CGPLH192	NA	NA	NA	4.7	12.19	12.19	Y	N	N
CGPLH193	NA	NA	NA	5.0	5.47	5.47	Y	N	N
CGPLH194	NA	NA	NA	5.0	9.98	9.98	Y	N	N
CGPLH196	NA	NA	NA	5.0	11.69	11.69	Y	N	N
CGPLH197	NA	NA	NA	5.0	5.69	5.69	Y	N	N
CGPLH198	NA	NA	NA	5.0	4.36	4.36	Y	N	N
CGPLH199	NA	NA	NA	5.0	9.77	9.77	Y	N	N
CGPLH200	NA	NA	NA	5.0	5.60	5.60	Y	N	N
CGPLH201	NA	NA	NA	5.0	8.82	8.82	v	N	N
CGPLH202	NA	NA	NA	5.0	5.54	5.54	Y	N	N
CGPLH203	NA	NA	NA	5.0	9.03	9.03	Y	N	N
CGPLH205	NA	NA	NA	5.0	4.74	4.74	Y	N	N
CGPLH208	NA	NA	NA	5.0	4.67	4.67	Y	N	N
CGPLH209	NA	NA	NA	5.0	5.15	5.15	Y	N	N
CGPLH210	NA	NA	NA	5.0	5.41	5.41	Y	N	N
CGPLH211	NA	NA	NA	5.0	6.24	6.24	Y	N	N
CGPLH300	NA	NA	NA	4.4	6.75	6.75	Y	N	N
CGPLH307	NA	NA	NA	4.5	3.50	3.50	Y	N	N
CGPLH308	NA	NA	NA	4.5	6.01	6.01	Y	N	N
CGPLH309	NA	NA	NA	4.5	5.21	5.21	Y	N	N
CGPLH310	NA	NA	NA	4.5	15.25	15.25	Y	N	N
CGPLH311	NA	NA	NA	4.5	4.47	4.47	Y	N	N
CGPLH314	NA	NA	NA	4.5	9.62	9.62	Y	N	N
CGPLH314	NA	NA	NA	4.4	16.24	16.24	Y	N	N
CGPLH315	NA	NA	NA	4.2	11.55	11.55	Y	N	N
CGPLH316	NA	NA	NA	4.5	28.92	27.78	Y	N	N
CGPLH317	NA	NA	NA	4.5	7.62	7.62	Y	N	N
CGPLH319	NA	NA	NA	4.2	4.41	4.41	Y	N	N
CGPLH320	NA	NA	NA	4.5	6.93	6.93	Y	N	N
CGPLH322	NA	NA	NA	4.2	8.17	8.17	Y	N	N
CGPLH324	NA	NA	NA	5.0	6.63	6.63	Y	N	N
CGPLH325	NA	NA	NA	4.6	4.15	4.15	Y	N	N
CGPLH326	NA	NA	NA	4.5	6.06	6.06	Y	N	N
CGPLH327	NA	NA	NA	1.8	1.24	1.24	Y	N	N
CGPLH328	NA	NA	NA	4.4	3.42	3.42	Y	N	N
CGPLH328	NA	NA	NA	4.9	5.47	5.47	Y	N	N
CGPLH329	NA	NA	NA	4.5	5.27	5.27	Y	N	N
CGPLH330	NA	NA	NA	4.3	10.21	10.21	Y	N	N
CGPLH331	NA	NA	NA	4.6	2.63	2.63	Y	N	N
CGPLH331	NA	NA	NA	4.3	4.15	4.15	Y	N	N
CGPLH333	NA	NA	NA	4.7	4.06	4.06	Y	N	N
CGPLH335	NA	NA	NA	4.4	9.39	9.39	Y	N	N
CGPLH336	NA	NA	NA	4.6	6.64	6.64	Y	N	N
CGPLH337	NA	NA	NA	4.2	4.48	4.48	Y	N	N
CGPLH338	NA	NA	NA	4.5	59.44	27.78	Y	N	N
CGPLH339	NA	NA	NA	4.5	12.27	12.27	Y	N	N
CGPLH340	NA	NA	NA	4.5	4.86	4.86	Y	N	N
CGPLH341	NA	NA	NA	4.1	7.62	7.62	Y	N	N
CGPLH342	NA	NA	NA	4.2	18.29	18.29	Y	N	N
CGPLH343	NA	NA	NA	4.5	3.49	3.49	Y	N	N
CGPLH344	NA	NA	NA	4.2	8.41	8.41	Y	N	N
CGPLH345	NA	NA	NA	4.5	9.73	9.73	Y	N	N
CGPLH346	NA	NA	NA	4.5	7.86	7.86	Y	N	N
CGPLH35	NA	NA	NA	4.0	13.15	13.15	Y	N	Y
CGPLH350	NA	NA	NA	3.5	6.09	6.09	Y	N	N
CGPLH351	NA	NA	NA	4.0	15.91	15.91	Y	N	N
CGPLH352	NA	NA	NA	4.2	6.47	6.47	Y	N	N
CGPLH353	NA	NA	NA	4.2	4.47	4.47	Y	N	N
CGPLH354	NA	NA	NA	4.2	17.49	17.49	Y	N	N
CGPLH355	NA	NA	NA	4.2	11.58	11.58	Y	N	N
CGPLH356	NA	NA	NA	4.5	3.94	3.94	Y	N	N
CGPLH357	NA	NA	NA	4.2	11.79	11.79	Y	N	N
CGPLH358	NA	NA	NA	4.2	21.06	21.06	Y	N	N
CGPLH36	NA	NA	NA	4.0	13.00	13.00	Y	N	Y
CGPLH360	NA	NA	NA	4.2	3.48	3.48	Y	N	N
CGPLH361	NA	NA	NA	4.3	6.98	6.98	Y	N	N
CGPLH362	NA	NA	NA	4.4	8.49	8.49	Y	N	N
CGPLH363	NA	NA	NA	4.5	4.44	4.44	Y	N	N
CGPLH364	NA	NA	NA	4.5	17.31	17.31	Y	N	N
CGPLH365	NA	NA	NA	4.5	0.55	0.55	Y	N	N
CGPLH366	NA	NA	NA	4.5	4.88	4.88	Y	N	N
CGPLH367	NA	NA	NA	4.4	6.48	6.48	Y	N	N
CGPLH368	NA	NA	NA	4.3	2.63	2.63	Y	N	N
CGPLH369	NA	NA	NA	4.3	10.18	10.18	Y	N	N
CGPLH369	NA	NA	NA	4.4	10.71	10.71	Y	N	N
CGPLH37	NA	NA	NA	4.0	9.73	9.73	Y	N	Y
CGPLH370	NA	NA	NA	4.5	7.22	7.22	Y	N	N
CGPLH371	NA	NA	NA	4.6	5.62	5.62	Y	N	N
CGPLH380	NA	NA	NA	4.2	6.61	6.61	Y	N	N
CGPLH381	NA	NA	NA	4.2	27.38	27.33	Y	N	N
CGPLH382	NA	NA	NA	4.5	11.58	11.58	Y	N	N
CGPLH383	NA	NA	NA	4.5	25.50	25.50	Y	N	N
CGPLH384	NA	NA	NA	4.5	15.66	15.66	Y	N	N
CGPLH385	NA	NA	NA	4.5	19.35	19.35	Y	N	N
CGPLH386	NA	NA	NA	4.5	6.46	6.46	Y	N	N
CGPLH386	NA	NA	NA	4.6	6.54	6.54	Y	N	N
CGPLH387	NA	NA	NA	4.5	6.19	6.19	Y	N	N
CGPLH388	NA	NA	NA	4.5	6.62	6.62	Y	N	N
CGPLH389	NA	NA	NA	4.6	14.78	14.78	Y	N	N
CGPLH390	NA	NA	NA	4.5	12.14	12.14	Y	N	N
CGPLH391	NA	NA	NA	4.5	8.88	8.88	Y	N	N
CGPLH391	NA	NA	NA	4.5	8.37	8.37	Y	N	N
CGPLH392	NA	NA	NA	4.5	8.39	8.39	Y	N	N
CGPLH393	NA	NA	NA	4.5	5.27	5.27	Y	N	N
CGPLH394	NA	NA	NA	4.4	3.79	3.79	Y	N	N
CGPLH395	NA	NA	NA	4.4	9.56	9.56	Y	N	N
CGPLH395	NA	NA	NA	4.4	5.40	5.40	Y	N	N
CGPLH396	NA	NA	NA	4.4	20.31	20.31	Y	N	N
CGPLH398	NA	NA	NA	4.3	13.01	13.01	Y	N	N
CGPLH399	NA	NA	NA	4.4	4.79	4.79	Y	N	N
CGPLH400	NA	NA	NA	4.4	7.70	7.70	Y	N	N
CGPLH400	NA	NA	NA	4.4	6.26	6.26	Y	N	N
CGPLH401	NA	NA	NA	4.3	13.01	13.01	Y	N	N
CGPLH401	NA	NA	NA	4.4	11.13	11.13	Y	N	N
CGPLH402	NA	NA	NA	4.5	2.89	2.89	Y	N	N
CGPLH403	NA	NA	NA	4.3	4.41	4.41	Y	N	N
CGPLH404	NA	NA	NA	4.2	6.38	6.38	Y	N	N
CGPLH405	NA	NA	NA	4.4	7.28	7.28	Y	N	N
CGPLH406	NA	NA	NA	4.2	5.40	5.40	Y	N	N
CGPLH407	NA	NA	NA	4.0	13.30	13.30	Y	N	N
CGPLH408	NA	NA	NA	4.2	5.18	5.18	Y	N	N
CGPLH409	NA	NA	NA	3.7	3.98	3.98	Y	N	N
CGPLH410	NA	NA	NA	4.1	6.91	6.91	Y	N	N
CGPLH411	NA	NA	NA	4.1	3.30	3.30	Y	N	N
CGPLH412	NA	NA	NA	4.1	5.55	5.55	Y	N	N
CGPLH413	NA	NA	NA	4.5	8.18	8.18	Y	N	N
CGPLH414	NA	NA	NA	3.8	5.85	5.85	Y	N	N
CGPLH415	NA	NA	NA	4.7	10.20	10.20	Y	N	N
CGPLH416	NA	NA	NA	4.5	11.73	11.73	Y	N	N
CGPLH417	NA	NA	NA	4.2	10.98	10.98	Y	N	N
CGPLH418	NA	NA	NA	4.5	10.96	10.96	Y	N	N
CGPLH419	NA	NA	NA	4.5	10.17	10.17	Y	N	N
CGPLH42	NA	NA	NA	4.0	14.30	14.30	Y	N	Y
CGPLH420	NA	NA	NA	4.2	12.32	12.32	Y	N	N
CGPLH422	NA	NA	NA	4.6	5.42	5.42	Y	N	N
CGPLH423	NA	NA	NA	4.2	2.85	2.85	Y	N	N
CGPLH424	NA	NA	NA	4.7	1.66	1.66	Y	N	N
CGPLH425	NA	NA	NA	4.4	5.98	5.98	Y	N	N
CGPLH426	NA	NA	NA	4.4	2.84	2.84	Y	N	N
CGPLH427	NA	NA	NA	4.4	10.86	10.86	Y	N	N
CGPLH428	NA	NA	NA	4.5	6.27	6.27	Y	N	N
CGPLH429	NA	NA	NA	4.5	3.89	3.89	Y	N	N
CGPLH43	NA	NA	NA	4.0	8.50	8.50	Y	N	Y
CGPLH430	NA	NA	NA	4.2	10.33	10.33	Y	N	N
CGPLH431	NA	NA	NA	4.8	12.81	12.81	Y	N	N
CGPLH432	NA	NA	NA	4.8	2.42	2.42	Y	N	N
CGPLH434	NA	NA	NA	4.6	8.83	8.83	Y	N	N
CGPLH435	NA	NA	NA	4.5	8.95	8.95	Y	N	N
CGPLH436	NA	NA	NA	4.5	4.29	4.29	Y	N	N
CGPLH437	NA	NA	NA	4.6	18.07	18.07	Y	N	N
CGPLH438	NA	NA	NA	4.8	16.62	16.62	Y	N	N
CGPLH439	NA	NA	NA	4.7	4.38	4.38	Y	N	N
CGPLH440	NA	NA	NA	4.7	4.32	4.32	Y	N	N
CGPLH441	NA	NA	NA	4.7	7.80	7.80	Y	N	N
CGPLH442	NA	NA	NA	4.5	6.15	6.15	Y	N	N
CGPLH443	NA	NA	NA	4.4	3.44	3.44	Y	N	N
CGPLH444	NA	NA	NA	4.4	4.12	4.12	Y	N	N
CGPLH445	NA	NA	NA	4.4	4.38	4.38	Y	N	N
CGPLH446	NA	NA	NA	4.4	2.92	2.92	Y	N	N
CGPLH447	NA	NA	NA	4.6	3.87	3.87	Y	N	N
CGPLH448	NA	NA	NA	4.4	5.29	5.29	Y	Y	N
CGPLH449	NA	NA	NA	4.5	3.77	3.77	Y	N	N
CGPLH45	NA	NA	NA	4.0	10.85	10.85	Y	N	Y
CGPLH450	NA	NA	NA	4.5	5.62	5.62	Y	N	N
CGPLH451	NA	NA	NA	4.6	7.24	7.24	Y	N	N
CGPLH452	NA	NA	NA	4.4	2.54	2.54	Y	N	N
CGPLH453	NA	NA	NA	4.6	9.11	9.11	Y	N	N
CGPLH455	NA	NA	NA	4.4	2.64	2.64	Y	N	N
CGPLH455	NA	NA	NA	4.5	2.42	2.42	Y	N	N
CGPLH456	NA	NA	NA	4.5	3.11	3.11	Y	N	N
CGPLH457	NA	NA	NA	4.4	5.92	5.92	Y	N	N
CGPLH458	NA	NA	NA	4.5	16.04	16.04	Y	N	N
CGPLH459	NA	NA	NA	4.4	6.52	6.52	Y	N	N
CGPLH46	NA	NA	NA	4.0	8.25	8.25	Y	N	Y
CGPLH460	NA	NA	NA	4.6	5.24	5.24	Y	N	N
CGPLH463	NA	NA	NA	4.5	22.77	22.77	Y	N	N
CGPLH464	NA	NA	NA	4.4	2.90	2.90	Y	N	N
CGPLH465	NA	NA	NA	4.5	4.76	4.76	Y	N	N
CGPLH466	NA	NA	NA	4.6	5.68	5.68	Y	N	N
CGPLH466	NA	NA	NA	4.5	6.75	6.75	Y	N	N
CGPLH467	NA	NA	NA	4.5	4.59	4.59	Y	N	N
CGPLH468	NA	NA	NA	4.5	11.19	11.19	Y	N	N
CGPLH469	NA	NA	NA	4.5	3.25	3.25	Y	N	N
CGPLH47	NA	NA	NA	4.0	7.43	7.43	Y	N	Y
CGPLH470	NA	NA	NA	4.5	13.64	13.64	Y	N	N
CGPLH471	NA	NA	NA	4.3	13.00	13.00	Y	N	N
CGPLH472	NA	NA	NA	4.2	10.17	10.17	Y	N	N
CGPLH473	NA	NA	NA	4.3	2.98	2.98	Y	N	N
CGPLH474	NA	NA	NA	4.3	29.15	29.15	Y	N	N
CGPLH475	NA	NA	NA	4.0	7.26	7.26	Y	N	N
CGPLH476	NA	NA	NA	4.3	6.16	6.16	Y	N	N
CGPLH477	NA	NA	NA	4.3	15.21	15.21	Y	N	N
CGPLH478	NA	NA	NA	4.4	7.29	7.29	Y	N	N
CGPLH479	NA	NA	NA	4.5	8.73	8.73	Y	N	N
CGPLH48	NA	NA	NA	4.0	6.38	6.38	Y	N	Y
CGPLH480	NA	NA	NA	4.4	10.62	10.62	Y	N	N
CGPLH481	NA	NA	NA	4.3	6.75	6.75	Y	N	N
CGPLH482	NA	NA	NA	4.3	23.58	23.58	Y	N	N
CGPLH483	NA	NA	NA	4.4	14.44	14.44	Y	N	N
CGPLH484	NA	NA	NA	4.2	14.32	14.32	Y	N	N
CGPLH485	NA	NA	NA	4.3	9.64	9.64	Y	N	N
CGPLH486	NA	NA	NA	4.3	10.16	10.16	Y	N	N
CGPLH487	NA	NA	NA	4.4	6.11	6.11	Y	N	N
CGPLH488	NA	NA	NA	4.5	7.88	7.88	Y	N	N
CGPLH49	NA	NA	NA	4.0	6.60	6.60	Y	N	Y
CGPLH490	NA	NA	NA	4.5	4.18	4.18	Y	N	N
CGPLH491	NA	NA	NA	4.5	13.16	13.16	Y	N	N
CGPLH492	NA	NA	NA	4.5	3.83	3.83	Y	N	N
CGPLH493	NA	NA	NA	4.5	25.06	25.06	Y	N	N
CGPLH494	NA	NA	NA	4.4	5.24	5.24	Y	N	N
CGPLH495	NA	NA	NA	4.4	5.03	5.03	Y	N	N
CGPLH496	NA	NA	NA	4.5	34.01	27.78	Y	N	N
CGPLH497	NA	NA	NA	4.5	8.24	8.24	Y	N	N
CGPLH497	NA	NA	NA	4.4	5.88	5.88	Y	N	N
CGPLH498	NA	NA	NA	4.4	5.33	5.33	Y	N	N
CGPLH499	NA	NA	NA	4.5	7.85	7.85	Y	N	N
CGPLH50	NA	NA	NA	4.0	7.05	7.05	Y	N	Y
CGPLH500	NA	NA	NA	4.5	3.49	3.49	Y	N	N
CGPLH501	NA	NA	NA	4.3	6.29	6.29	Y	N	N
CGPLH502	NA	NA	NA	4.5	2.74	2.24	Y	N	N
CGPLH503	NA	NA	NA	4.5	11.01	11.01	Y	N	N
CGPLH504	NA	NA	NA	4.3	6.60	6.60	Y	N	N
CGPLH504	NA	NA	NA	4.2	10.02	10.02	Y	N	N
CGPLH505	NA	NA	NA	4.1	5.23	5.23	Y	N	N
CGPLH506	NA	NA	NA	4.5	12.23	12.23	Y	N	N
CGPLH507	NA	NA	NA	4.1	9.89	9.89	Y	N	N
CGPLH508	NA	NA	NA	4.5	8.88	8.88	Y	N	N
CGPLH508	NA	NA	NA	4.4	9.55	9.55	Y	N	N
CGPLH509	NA	NA	NA	4.0	9.79	9.79	Y	N	N
CGPLH51	NA	NA	NA	4.0	7.85	7.85	Y	N	Y
CGPLH510	NA	NA	NA	4.2	14.20	14.20	Y	N	N
CGPLH511	NA	NA	NA	4.5	12.94	12.94	Y	N	N
CGPLH512	NA	NA	NA	4.3	8.60	8.60	Y	N	N
CGPLH513	NA	NA	NA	4.3	6.54	6.54	Y	N	N
CGPLH514	NA	NA	NA	4.4	10.94	10.94	Y	N	N
CGPLH515	NA	NA	NA	4.5	8.71	8.71	Y	N	N
CGPLH516	NA	NA	NA	4.5	7.32	7.32	Y	N	N
CGPLH517	NA	NA	NA	4.6	5.16	5.16	Y	N	N
CGPLH517	NA	NA	NA	4.5	9.74	9.74	Y	N	N
CGPLH518	NA	NA	NA	4.4	5.92	5.92	Y	N	N
CGPLH519	NA	NA	NA	4.4	6.96	6.96	Y	N	N
CGPLH522	NA	NA	NA	4.0	9.90	9.90	Y	N	Y
CGPLH520	NA	NA	NA	4.3	8.27	8.27	Y	N	N
CGPLH54	NA	NA	NA	4.0	14.18	14.18	Y	N	Y
CGPLH55	NA	NA	NA	4.0	7.35	7.35	Y	N	Y
CGPLH56	NA	NA	NA	4.0	5.20	5.20	Y	N	Y
CGPLH57	NA	NA	NA	4.0	7.15	7.15	Y	N	Y
CGPLH59	NA	NA	NA	4.0	6.03	6.03	Y	N	Y
CGPLH625	NA	NA	NA	4.5	2.64	2.64	Y	N	N
CGPLH625	NA	NA	NA	4.5	2.69	2.69	Y	N	N
CGPLH626	NA	NA	NA	4.0	11.12	11.12	Y	N	N
CGPLH63	NA	NA	NA	4.0	10.10	10.10	Y	N	Y
CGPLH639	NA	NA	NA	4.5	2.00	2.00	Y	N	N
CGPLH64	NA	NA	NA	4.0	8.03	8.03	Y	N	Y
CGPLH640	NA	NA	NA	4.5	9.36	9.36	Y	N	N
CGPLH642	NA	NA	NA	4.5	4.99	4.99	Y	N	N
CGPLH643	NA	NA	NA	4.4	7.12	7.12	Y	N	N
CGPLH644	NA	NA	NA	4.4	5.06	5.06	Y	N	N
CGPLH646	NA	NA	NA	4.4	6.75	6.75	Y	N	N
CGPLH75	NA	NA	NA	4.0	3.87	3.87	Y	N	Y
CGPLH76	NA	NA	NA	4.0	4.03	4.03	Y	N	Y
CGPLH77	NA	NA	NA	4.0	5.89	5.89	Y	N	Y
CGPLH78	NA	NA	NA	4.0	2.51	2.51	Y	N	Y
CGPLH79	NA	NA	NA	4.0	3.68	3.68	Y	N	Y
CGPLH80	NA	NA	NA	4.0	1.94	1.94	Y	N	Y
CGPLH81	NA	NA	NA	4.0	5.16	5.16	Y	N	Y
CGPLH82	NA	NA	NA	4.0	3.30	3.30	Y	N	Y
CGPLH83	NA	NA	NA	4.0	5.04	5.04	Y	N	Y
CGPLH84	NA	NA	NA	4.0	3.33	3.33	Y	N	Y
CGPLLU13	Adenocarcinoma	NA	Bone	5.0	7.67	7.67	Y	N	Y
CGPLLU13	Adenocarcinoma	NA	Bone	4.5	8.39	8.39	Y	N	Y
CGPLLU13	Adenocarcinoma	NA	Bone	3.2	8.66	8.66	Y	N	Y
CGPLLU13	Adenocarcinoma	NA	Bone	5.0	5.97	5.97	Y	N	Y
CGPLLU14	Adenocarcinoma	Moderate	NA	2.0	2.55	2.55	Y	N	Y
CGPLLU14	Adenocarcinoma	Moderate	NA	2.0	2.55	2.55	Y	N	Y
CGPLLU14	Adenocarcinoma	Moderate	NA	2.0	2.55	2.55	Y	N	Y
CGPLLU14	Adenocarcinoma	Moderate	NA	2.0	2.55	2.55	Y	N	Y
CGPLLU14	Adenocarcinoma	Moderate	NA	2.0	2.55	2.55	Y	N	Y
CGPLLU14	Adenocarcinoma	Moderate	NA	2.0	2.55	2.55	Y	N	Y
CGPLLU144	Adenocarcinoma	Poor	None	3.5	31.51	31.51	Y	Y	Y
CGPLLU147	Adenosquamous Carcinoma	Poor	None	3.8	6.72	6.72	Y	Y	Y
CGPLLU161	Adenocarcinoma	Well	None	4.0	83.04	83.04	Y	N	Y
CGPLLU162	Adenocarcinoma	Moderate	None	3.1	40.32	40.32	Y	Y	Y
CGPLLU163	Adenocarcinoma	Poor	None	5.0	54.03	54.03	Y	Y	Y
CGPLLU165	Adenocarcinoma	Well	None	4.5	20.13	20.13	Y	Y	Y
CGPLLU168	Adenocarcinoma	Poor	None	4.3	19.38	19.38	Y	Y	Y
CGPLLU169	Squamous Cell Carcinoma	Moderate	None	4.2	13.70	13.70	Y	N	Y
CGPLLU175	Squamous Cell Carcinoma	Moderate	None	4.4	16.84	16.84	Y	Y	Y
CGPLLU176	Adenosquamous Carcinoma	Moderate	None	3.2	7.86	7.86	Y	Y	Y
CGPLLU177	Adenocarcinoma	NA	None	3.9	19.07	19.07	Y	Y	Y
CGPLLU180	Large Cell Carcinoma	Poor	None	3.2	19.31	19.31	Y	Y	Y
CGPLLU198	Adenocarcinoma	Moderate	None	4.2	14.09	14.09	Y	Y	Y
CGPLLU202	Adenocarcinoma	NA	None	4.4	24.72	24.72	Y	Y	Y
CGPLLU203	Squamous Cell Carcinoma	Well	None	4.2	26.24	26.24	Y	N	Y
CGPLLU205	Adenocarcinoma	Poor	None	4.0	18.56	18.55	Y	Y	Y
CGPLLU206	Squamous Cell Carcinoma	Poor	None	3.5	18.24	18.24	Y	Y	Y
CGPLLU207	Adenocarcinoma	Well	None	4.0	17.29	17.29	Y	Y	Y
CGPLLU208	Adenocarcinoma	Moderate	None	3.0	24.34	24.34	Y	Y	Y
CGPLLU209	Large Cell Carcinoma	Poor	None	5.5	53.95	53.95	Y	Y	Y
CGPLLU244	Adenocarcinoma	Moderate/	Liver, Rib,	4.5	17.84	17.48	Y	N	Y
		Poor	Brain, Pleura
CGPLLU244	Adenocarcinoma	Moderate/	Liver, Rib,	4.5	17.84	17.84	Y	N	Y
		Poor	Brain, Pleura
CGPLLU244	Adenocarcinoma	Moderate/	Liver, Rib,	4.5	17.84	17.84	Y	N	Y
		Poor	Brain, Pleura
CGPLLU244	Adenocarcinoma	Moderate/	Liver, Rib,	4.5	17.84	17.84	Y	N	Y
		Poor	Brain, Pleura
CGPLLU245	Adenocarcinoma	NA	Brain	4.7	19.42	19.42	Y	N	Y
CGPLLU245	Adenocarcinoma	NA	Brain	4.7	19.42	19.42	Y	N	Y
CGPLLU245	Adenocarcinoma	NA	Brain	4.7	19.42	19.42	Y	N	Y
CGPLLU245	Adenocarcinoma	NA	Brain	4.7	19.42	19.42	Y	N	Y
CGPLLU246	Adenocarcinoma	Poor	Pleura	5.5	18.51	18.51	Y	N	Y
CGPLLU246	Adenocarcinoma	Poor	Pleura	5.5	18.51	18.51	Y	N	Y
CGPLLU246	Adenocarcinoma	Poor	Pleura	5.5	18.51	18.51	Y	N	Y
CGPLLU246	Adenocarcinoma	Poor	Pleura	5.5	18.51	18.51	Y	N	Y
CGPLLU264	Adenocarcinoma	NA	Lung	4.0	22.97	22.97	Y	N	Y
CGPLLU264	Adenocarcinoma	NA	Lung	4.5	10.53	10.53	Y	N	Y
CGPLLU264	Adenocarcinoma	NA	Lung	3.0	7.15	7.15	Y	N	Y
CGPLLU264	Adenocarcinoma	NA	Lung	4.0	9.60	9.60	Y	N	Y
CGPLLU265	Adenocarcinoma	NA	Lung	4.2	7.16	7.16	Y	N	Y
CGPLLU265	Adenocarcinoma	NA	None	4.0	8.11	8.11	Y	N	Y
CGPLLU265	Adenocarcinoma	NA	None	4.2	7.53	7.53	Y	N	Y
CGPLLU265	Adenocarcinoma	NA	None	5.0	16.17	16.17	Y	N	Y
CGPLLU266	Adenocarcinoma	Moderate	None	5.0	5.32	5.32	Y	N	Y
CGPLLU266	Adenocarcinoma	Moderate	None	3.5	6.31	6.31	Y	N	Y
CGPLLU266	Adenocarcinoma	Moderate	None	5.0	7.64	7.64	Y	N	Y
CGPLLU266	Adenocarcinoma	Moderate	None	5.0	14.39	14.39	Y	N	Y
CGPLLU267	Squamous Cell Carcinoma	Poor	Lung	4.5	2.87	2.87	Y	N	Y
CGPLLU267	Squamous Cell Carcinoma	Poor	Lung	4.5	3.34	3.34	Y	N	Y
CGPLLU267	Squamous Cell Carcinoma	Poor	Lung	3.5	3.00	3.00	Y	N	Y
CGPLLU269	Adenocarcinoma	NA	Brain, Liver,	5.0	11.40	11.40	Y	N	Y
			Bone, Pleura
CGPLLU269	Adenocarcinoma	NA	Brain, Liver,	5.0	8.35	8.35	Y	N	Y
			Bone, Pleura
CGPLLU269	Adenocarcinoma	NA	Brain, Liver,	3.5	17.79	17.79	Y	N	Y
			Bone, Pleura
CGPLLU271	Adenocarcinoma	NA	Pleura	4.0	4.70	4.70	Y	N	Y
CGPLLU271	Adenocarcinoma	NA	Pleura	5.0	18.86	18.86	Y	N	Y
CGPLLU271	Adenocarcinoma	NA	Pleura	4.5	13.84	13.84	Y	N	Y
CGPLLU271	Adenocarcinoma	NA	Pleura	3.5	13.46	13.46	Y	N	Y
CGPLLU271	Adenocarcinoma	NA	Pleura	4.0	13.77	13.77	Y	N	Y
CGPLLU43	Adenocarcinoma	Moderate	None	4.9	2.17	2.17	Y	N	Y
CGPLLU43	Adenocarcinoma	Moderate	None	3.7	3.26	3.26	Y	N	Y
CGPLLU43	Adenocarcinoma	Moderate	None	4.0	4.12	4.12	Y	N	Y
CGPLLU43	Adenocarcinoma	Moderate	None	3.7	8.20	8.20	Y	N	Y
CGPLLU86	Adenocarcinoma	NA	Lung	4.0	7.90	7.90	Y	N	Y
CGPLLU86	Adenocarcinoma	NA	Lung	4.C	7.90	7.90	Y	N	Y
CGPLLU86	Adenocarcinoma	NA	Lung	4.0	7.90	7.90	Y	N	Y
CGPLLU86	Adenocarcinoma	NA	Lung	4.0	7.90	7.90	Y	N	Y
CGPLLU88	Adenocarcinoma	NA	None	5.0	27.66	27.66	Y	N	Y
CGPLLU88	Adenocarcinoma	NA	None	5.0	6.49	6.49	Y	N	Y
CGPLLU88	Adenocarcinoma	NA	None	4.0	3.04	3.04	Y	N	Y
CGPLLU89	Adenocarcinoma	NA	Brain, Bone, Lung	8.0	8.43	8.43	Y	N	Y
CGPLLU89	Adenocarcinoma	NA	Brain, Bone, Lung	8.0	8.43	8.43	Y	N	Y
CGPLLU89	Adenocarcinoma	NA	Brain, Bone, Lung	8.0	8.43	8.43	Y	N	Y
CGPLOV11	Endometrioid Adenocarcinoma	Moderate	Omentum	3.4	17.35	17.35	Y	Y	Y
CGPLOV12	Endometrioid Adenocarcinoma	NA	None	3.2	12.44	12.44	Y	N	Y
CGPLOV13	Endometrioid Adenocarcinoma	Poor	Omentum	3.8	27.00	27.00	Y	Y	Y
CGPLOV15	Adenocarcinoma	Poor	None	5.0	4.77	4.77	Y	Y	Y
CGPLOV16	Serous Adenocarcinoma	Moderate	None	4.5	27.28	27.28	Y		Y
CGPLOV19	Endometrioid Adenocarcinoma	Moderate	None	5.0	23.46	23.46	Y	Y	Y
CGPLOV20	Endometrioid Adenocarcinoma	Poor	None	4.2	5.67	5.67	Y	Y	Y
CGPLOV21	Serous Adenocarcinoma	Poor	Omentum,	4.3	56.32	56.32	Y	Y	Y
			Appendix
CGPLOV22	Serous Adenocarcinoma	Well	None	4.6	17.42	17.42	Y	Y	Y
CGPLOV23	Serous Adenocarcinoma	Poor	None	5.0	26.73	26.73	Y	N	Y
CGPLOV24	Germ Cell Tumor	Poor	None	4.2	10.71	10.71	Y	N	Y
CGPLOV25	Germ Cell Tumor	Poor	None	4.8	6.78	6.78	Y	N	Y
CGPLOV26	Germ Cell Tumor	Poor	None	4.5	27.90	27.90	Y	N	Y
CGPLOV28	Serous Carcinoma	NA	None	3.2	10.74	10.74	Y	N	Y
CGPLOV31	Clear Cell adenocarcinoma	NA	None	4.0	14.45	14.45	Y	N	Y
CGPLOV32	Mucinous Cystadenoma	NA	None	3.2	27.36	27.36	Y	N	Y
CGPLOV37	Serous Carcinoma	NA	None	3.2	46.88	46.88	Y	N	Y
CGPLOV38	Serous Carcinoma	NA	None	2.4	34.29	34.29	Y	N	Y
CGPLOV40	Serous Carcinoma	NA	Omentum, Uterus,	1.6	193.60	156.25	Y	N	Y
			Appendix
CGPLOV41	Serous Carcinoma	NA	Omentum, Uterus,	4.4	10.03	10.03	Y	N	Y
			Cervix
CGPLOV42	Serous Carcinoma	NA	None	4.2	49.51	49.51	Y	N	Y
CGPLOV43	Mucinous Cystadenocarcinoma	NA	None	4.4	9.09	9.09	Y	N	Y
CGPLOV44	Mucinous Adenocarcinoma	NA	None	4.5	8.79	8.79	Y	N	Y
CGPLOV46	Serous Carcinoma	NA	None	4.1	8.97	8.97	Y	N	Y
CGPLOV47	Serous Cystadenoma	NA	None	4.5	19.35	19.35	Y	N	Y
CGPLOV48	Serous Carcinoma	NA	None	3.5	22.80	22.80	Y	N	Y
CGPLOV49	Serous Carcinoma	NA	None	4.2	16.48	16.48	Y	N	Y
CGPLOV50	Serous Carcinoma	NA	None	4.5	8.89	8.89	Y	N	Y
CGPLPA112	NA	NA	None	35	18.52	18.52	Y	N	N
CGPLPA113	NA	NA	None	4.8	8.24	8.24	Y	N	N
CGPLPA114	NA	NA	None	4.8	26.43	26.43	Y	N	N
CGPLPA115	NA	NA	NA	5.0	31.41	31.41	Y	N	N
CGPLPA117	NA	NA	None	3.4	2.29	2.29	Y	N	N
CGPLPA118	Intra-Ampullary Bile Duct	NA	None	3.8	9.93	9.93	Y	N	Y
CGPLPA122	Intra-Pancreatic Bile Duct	NA	None	3.8	66.54	32.89	Y	N	Y
CGPLPA124	Intra-Ampullary Bile Duct	moderate	None	4.6	29.24	27.17	Y	N	Y
CGPLPA125	Intra-Pancreatic Bile Duct	poor	None	2.7	8.31	8.31	Y	N	N
CGPLPA126	Intra-Pancreatic Bile Duct	NA	None	4.3	80.56	29.07	Y	N	Y
CGPLPA127	Extra-Pancreatic Bile Duct	NA	NA	3.0	20.60	20.60	Y	N	N
CGPLPA128	Intra-Pancreatic Bile Duct	NA	None	3.9	5.91	5.91	Y	N	Y
CGPLPA129	Intra-Pancreatic Bile Duct	NA	None	4.6	27.07	27.07	Y	N	Y
CGPLPA130	Intra-Ampullary Bile Duct	well	None	4.0	4.34	4.34	Y	N	Y
CGPLPA131	Intra-Pancreatic Bile Duct	NA	None	3.9	68.95	32.05	Y	N	Y
CGPLPA134	Intra-Pancreatic Bile Duct	NA	None	4.1	58.08	30.49	Y	N	Y
CGPLPA135	Intra-Pancreatic Bile Duct	NA	NA	3.9	4.22	4.22	Y	N	N
CGPLPA136	Intra-Pancreatic Bile Duct	NA	None	4.1	20.23	20.23	Y	N	Y
CGPLPA137	NA	NA	NA	4.0	5.75	5.75	Y	N	N
CGPLPA139	NA	NA	NA	4.0	14.89	14.89	Y	N	N
CGPLPA14	Ductal Adenocarcinoma	Poor	None	4.0	1.30	1.30	Y	N	N
CGPLPA140	Intra Pancreatic Bile Duct	Poor	None	4.7	29.34	26.60	Y	N	Y
CGPLPA141	Intra Pancreatic Bile Duct	Moderate	None	2.8	53.67	44.64	Y	N	N
CGPLPA15	Ductal Acenocarcinoma	Well	Lymph Node	4.0	1.92	1.92	Y	N	N
CGPLPA155	NA	NA	NA	4.0	25.72	25.72	Y	N	N
CGPLPA156	Ductal Adenocarcinoma	Poor	Lymph Node	4.5	7.54	7.54	Y	N	N
CGPLPA165	Intra-Pancreatic Bile Duck	well	None	3.9	10.48	10.48	Y	N	N
	with Medullary Features
CGPLPA168	Extra-Pancreatic Bile Duct	NA	NA	3.0	139.12	34.72	Y	N	N
CGPLPA17	Ductal Adenocarcinoma	Well	Lymph Node	4.0	13.08	13.08	Y	N	N
CGPLPA184	Intra-Pancreatic Bile Duct	NA	None	NA	NA	NA	Y	N	N
CGPLPA187	Intra-Pancreatic Bile Duct	NA	None	NA	NA	NA	Y	N	N
CGPLPA23	Ductal Adenocarcinoma	Moderate	Lymph Node	4.0	16.62	16.62	Y	N	N
CGPLPA25	Ductal Adenocarcinoma	Poor	Lymph Node	4.0	8.71	8.71	Y	N	N
CGPLPA26	Ductal Adenocarcinoma	Well	Lymph Node	4.0	6.97	6.97	Y	N	N
CGPLPA28	Ductal Adenocarcinoma	Well	Lymph Node	4.0	18.13	18.13	Y	N	N
CGPLPA33	Ductal Adenocarcinoma	Well	Lymph Node	4.0	1.80	1.80	Y	N	N
CGPLPA34	Ductal Adenocarcinoma	Well	Lymph Node	4.0	3.36	3.36	Y	N	N
CGPLPA37	Ductal Adenocarcinoma	NA	Lymph Node	4.0	21.83	21.83	Y	N	N
CGPLPA38	Ductal Adenocarcinoma	Moderate	None	4.0	5.29	5.29	Y	N	N
CGPLPA39	Ductal Adenocarcinoma	Well	Lymph Node	4.0	11.73	11.73	Y	N	N
CGPLPA40	Ductal Adenocarcinoma	Well	Lymph Node	4.0	4.78	4.78	Y	N	N
CGPLPA42	Ductal Adenocarcinoma	Moderate	Lymph Node	4.0	3.41	3.41	Y	N	N
CGPLPA46	Ductal Adenocarcinoma	Poor	Lymph Node	4.0	0.74	0.74	Y	N	N
CGPLPA47	Ductal Adenocarcinoma	Well	Lymph Node	4.0	6.01	6.01	Y	N	N
CGPLPA48	Ductal Adenocarcinoma	Well	None	NA	NA	NA	Y	N	N
CGPLPA52	Ductal Adenocarcinoma	Moderate	None	2.5	9.86	9.86	Y	N	N
CGPLPA53	Ductal Adenocarcinoma	Poor	Lymph Node	3.0	14.48	14.48	Y	N	N
CGPLPA58	Ductal Adenocarcinoma	NA	None	3.0	6.87	6.87	Y	N	N
CGPLPA59	Ductal Adenocarcinoma or	Well	Lymph Node	NA	NA	NA	Y	N	N
	Adenome
CGPLPA67	Ductal Adenocarcinoma	Well	Lymph Node	3.2	9.72	9.72	Y	N	N
CGPLPA69	Ductal Adenocarcinoma	Well	None	2.0	1.72	1.72	Y	N	N
CGPLPA71	Ductal Adenocarcinoma	Well	Lymph Node	2.2	39.07	39.07	Y	N	N
CGPLPA74	Ductal Adenocarcinoma	Moderate	Lymph Node	2.5	4.99	4.99	Y	N	N
CGPLPA76	Ductal Adenocarcinoma	Poor	None	2.5	23.19	23.19	Y	N	N
CGPLPA85	Ductal Adenocarcinoma	Poor	Lymph Node	3.0	152.46	41.67	Y	N	N
CGPLPA86	Ductal Adenocarcinoma	Moderate	Lymph Node	2.5	11.02	11.02	Y	N	N
CGPLPA92	Ductal Adenocarcinoma	NA	Lymph Node	2.0	5.34	5.34	Y	N	N
CGPLPA93	Ductal Adenocarcinoma	Poor	None	3.0	96.28	41.67	Y	N	N
CGPLPA94	Ductal Adenocarcinoma	NA	Lymph Node	3.0	29.66	29.66	Y	N	N
CGPLPA95	Ductal Adenocarcinoma	Well	Lymph Node	NA	NA	NA	Y	N	N
CGST102	Tubular Adenocarcinoma	Moderate	None	4.1	8.03	8.03	Y	N	Y
CGST11	Mixed Carcinoma	Moderate	None	3.8	3.57	3.57	Y	N	N
CGST110	Tubular Adenocarcinoma	Moderate	None	3.8	5.00	5.00	Y	N	Y
CGST114	Tubular Adenocarcinoma	Poor	None	4.4	10.35	10.35	Y	N	Y
CGST13	Signet Ring Cell Carcinoma	Poor	None	4.4	24.33	24.33	Y	N	Y
CGST131	Signet ring cel carcinoma	Poor	None	4.0	4.28	4.28	Y	N	N
CGST141	Signet Cell Carcinoma	Poor	None	4.4	10.84	10.84	Y	N	Y
CGST16	Tubular Adenocarcinoma	Poor	None	4.0	40.69	40.69	Y	N	Y
CGST18	Mucinous Adenocarcinoma	Well	None	4.3	9.78	9.78	Y	N	Y
CGST21	Papillary Adenocarcinoma	Moderate	None	4.0	0.83	0.83	Y	N	N
CGST26	Signet ring cel carcinoma	Poor	None	3.5	5.56	5.56	Y	N	N
CGST28	Undifferentiated Carcinoma	Poor	None	4.0	5.86	5.86	Y	N	Y
CGST30	Signet Ring Cell Carcinoma	Poor	None	3.0	4.22	4.22	Y	N	Y
CGST32	Tubular Adenocarcinoma	Moderate	None	4.0	11.49	11.49	Y	N	Y
CGST33	Tubular Adenocarcinoma	Moderate	None	3.5	5.71	5.71	Y	N	Y
CGST38	Mucinous adenocarcinoma	NA	None	4.0	NA	NA	Y	N	N
CGST39	Signet Ring Cell Carcinoma	Poor	None	3.5	20.69	20.69	Y	N	Y
CGST41	Signet Ring Cell Carcinoma	Poor	None	3.5	7.83	7.83	Y	N	Y
CGST45	Signet Ring Cell Carcinoma	Poor	None	3.8	7.14	7.14	Y	N	Y
CGST47	Tubular Adenocarcinoma	Moderate	None	4.0	4.55	4.55	Y	N	Y
CGST48	Tubular Adenocarcinoma	Poor	None	4.5	8.79	8.79	Y	N	Y
CGST53	NA	NA	None	3.8	15.82	15.82	V	N	N
CGST58	Signet Ring Cell Carcinoma	Poor	None	3.8	19.81	19.81	Y	N	Y
CGST67	Tubular adenocarcinoma	Moderate	None	3.0	23.01	23.01	Y	N	N
CGST77	Tubular adenocarcinoma	Moderate	None	4.5	15.09	15.09	Y	N	N
CGST80	Mucinous Adenocarcinoma	Poor	None	4.5	8.56	8.56	Y	N	Y
CGST81	Signet Ring Cell Carcinoma	Poor	None	3.5	37.32	37.32	Y	N	Y
CGH14	NA	NA	NA	NA	NA	NA	Y	N	N
CGH15	NA	NA	NA	NA	NA	NA	Y	N	N

*NA denotes data not available or not applicable for healthy individuals.

TABLE 2

APPENDIX B: Summary of targeted cfDNA analyses

								Bases Mapped	Percent Mapped
			Fragment Profile	Mutation		Bases in	Bases Mapped	to Target	to Target	Total	Distinct
Patient	Patient Type	Timepoint	Analysis	Analysis	Read Length	Target Region	to Genome	Regions	Regions	Coverage	Coverage

CGCRC291	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7501485600	3771359756	50%	44345	10359
CGCRC292	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	6736035200	3098886973	46%	36448	8603
CGCRC233	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	6300244000	2818734206	45%	33117	5953
CGCRC294	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7786872600	3911796709	50%	46016	12071
CGCRC295	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	8240660200	3478059753	42%	40787	5826
CGCRC296	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	5718556500	2898549356	51%	33912	10180
CGCRC297	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	7550826100	3717222432	49%	43545	5870
CGCRC298	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	12501036400	6096393764	49%	71196	9617
CGCRC299	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7812602900	4121569690	53%	48098	10338
CGCRC300	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	8648090300	3962285136	46%	46364	5756
CGCRC301	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7538758100	3695480348	49%	43024	6618
CGCRC302	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	8573658300	4349420574	51%	51006	13799
CGCRC303	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	5224046400	2505714343	48%	29365	8372
CGCRC304	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	5762112600	2942170530	51%	34462	10208
CGCRC305	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7213384100	3726953480	52%	43516	8589
CGCRC306	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7075579700	3552441899	50%	41507	7372
CGCRC307	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7572687100	3492191519	46%	40793	9680
CGCRC308	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7945738000	3895908986	49%	45224	11809
CGCRC309	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	8487455800	3921079811	46%	45736	10739
CGCRC310	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	9003580500	4678812441	52%	54713	11139
CGCRC311	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	6528162700	3276653864	50%	38324	6044
CGCRC312	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	7683294300	3316719187	43%	38652	4622
CGCRC313	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	5874099200	2896148722	49%	33821	6506
CGCRC314	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	6883148500	3382767492	49%	39414	6664
CGCRC315	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7497252500	3775556051	50%	44034	8666
CGCRC315	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	10684720400	5533857153	52%	64693	14289
CGCRC317	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7086877600	3669434216	52%	43538	10944
CGCRC318	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	6880041100	3326357413	48%	39077	11571
CGCRC319	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7485342900	3982677483	53%	47327	10502
CGCRC320	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7056703200	3450648135	49%	40888	10198
CGCRC321	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7203625900	3633396892	50%	43065	6499
CGCRC332	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	7202969100	3758323705	52%	44580	3243
CGCRC333	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	8767144700	4199126827	48%	49781	8336
CGCRC334	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	7771869100	3944578280	51%	46518	5014
CGCRC335	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	7972524600	4064901201	51%	48308	6151
CGCRC336	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	8597346400	4333410573	50%	51390	7551
CGCRC337	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	7399611700	3800666199	51%	45083	8092
CGGRC338	Colorectal Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	8029493700	4179383804	52%	49380	5831
CGCRC339	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	7938963600	4095555110	52%	48397	3808
CGCRC340	Colorectal Cancer	Preoperative, Treatment naïve	Y	N	100	80930	7214889500	3706643098	51%	43805	3014
CGCRC341	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	8803159200	3668208527	42%	43106	11957
CGCRC342	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	8478811500	3425540889	40%	40328	9592
CGCRC344	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6942167800	3098232737	45%	36823	2300
CGCRC345	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	8182868200	2383173431	29%	28233	7973
CGGRC346	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7448272300	3925056341	53%	46679	5582
CGCRC347	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	5804744500	2986809912	51%	35490	4141
CGCRC349	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6943451600	3533145275	51%	41908	5762
CGCRC350	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7434818400	3848923016	52%	45678	4652
CGCRC351	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7306546400	3636910409	50%	43162	5205
CGCRC352	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7864655000	3336939252	42%	39587	4502
CGCRC353	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7501674800	3642919375	49%	43379	4666
CGCRC354	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7938270200	2379068977	30%	28256	4858
CGCRC356	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6013175900	3046754994	51%	36127	3425
CGGRC357	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6013464600	3022035300	50%	35813	4259
CGCRC358	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7227212400	3188723303	44%	37992	5286
CGCRC359	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7818567700	425110101	5%	5040	2566
CGCRC367	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6582043200	3363063597	51%	39844	5839
CGCRC368	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	8042242400	4101646000	51%	48636	11471
CGCRC370	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6940330100	3198954121	46%	38153	4826
CGCRC373	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6587201700	3120088035	47%	37234	5190
CGCRC376	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6727983100	3162416807	47%	37735	3445
CGCRC377	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6716339200	3131415570	47%	37160	4524
CGCRC378	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6523969900	2411096720	37%	28728	3239
CGCRC379	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6996252100	3371081103	48%	39999	2891
CGCRC380	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7097496300	2710244446	38%	32020	3261
CGCRC381	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6951936100	3287050681	47%	38749	9357
CGCRC382	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6959048700	2552325859	37%	30040	5148
CGCRC384	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7012798900	3293884583	47%	39158	3653
CGCRC385	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7542017900	3356570505	45%	39884	3686
CGCRC386	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6876059600	3064412286	45%	36431	2787
CGCRC387	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7399564700	3047254560	41%	36141	6675
CGCRC388	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6532692900	3137284885	48%	37285	5114
CGCRC389	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6651206300	3102100941	47%	36764	6123
CGCRC390	Colorectal Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7260616800	3376667585	47%	40048	4368
CGCRC331	Colorectal Cancer	Preoperative, Treatment naïve	N	Y	100	80930	6883624500	3202877881	47%	37978	5029
CGLU316	Lung Cancer	Pre-treatment, Day −53	Y	N	100	80930	7864415100	1991331171	25%	23601	3565
CGLU316	Lung Cancer	Pre-treatment, Day −53	Y	N	100	80930	7502591600	3730963390	50%	44262	3966
CGLU316	Lung Cancer	Pre-treatment, Day −53	Y	N	100	80930	6582515900	3187059470	48%	37813	3539
CGLU316	Lung Cancer	Pre-treatment, Day −53	Y	N		100	80930	6587281800	1947630979	30%	23094	4439
CGLU344	Lung Cancer	Pre-treatment, Day −21	Y	N	100	80930	6151628500	2748983603	45%	32462	8063
CGLU344	Lung Cancer	Pre-treatment, Day −21	Y	N		100	80930	7842910900	1147703178	15%	13565	4303
CGLU344	Lung Cancer	Pre-treatment, Day −21	Y	N		100	80930	5838083100	2291108925	39%	27067	4287
CGLU344	Lung Cancer	Pre-treatment, Day −21	Y	N	100	80930	7685989200	3722274529	48%	43945	3471
CGLU369	Lung Cancer	Pre-treatment, Day −2	Y	N		100	80930	7080245300	1271457982	18%	15109	2364
CGLU369	Lung Cancer	Pre-treatment, Day −2	Y	N		100	80930	7078131900	1482448715	21%	17583	4275
CGLU369	Lung Cancer	Pre-treatment, Day −2	Y	N	100	80930	6904701700	2124660124	31%	25230	5278
CGLU369	Lung Cancer	Pre-treatment, Day −2	Y	N		100	80930	7003462200	3162195578	45%	37509	6062
CGLU373	Lung Cancer	Pre-treatment, Day −2	Y	N		100	80930	6346267200	3053520676	48%	36137	6251
CGLU373	Lung Cancer	Pre-treatment, Day −2	Y	N		100	80930	6517189900	3192984468	49%	38066	8040
CGLU373	Lung Cancer	Pre-treatment, Day −2	Y	N		100	80930	7767146300	3572598842	46%	42378	5306
CGLU373	Lung Cancer	Pre-treatment, Day −2	Y	N		100	80930	7190999100	3273648804	46%	38784	4454
CGPLBR100	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7299964400	3750278051	51%	44794	3249
CGPLBR101	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7420822800	3810365416	51%	45565	9784
CGPLBR102	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6679304900	3269688319	49%	38679	7613
CGPLBR103	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7040304400	3495542468	50%	41786	6748
CGPLBR104	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7188389200	3716096781	52%	44316	9448
CGPLBR38	Breast Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7810293900	4057576306	52%	48098	9868
CGPLBR39	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7745701500	3805623239	49%	45084	11065
CGPLBR40	Breast Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7558990500	3652442341	48%	43333	12948
CGPLBR41	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7900994600	3836600101	49%	45535	10847
CGPLBR44	Breast Cancer	Preoperative, Treatment naïve	Y	N	100	80930	7017744200	3269110569	47%	38672	8344
CGPLBR48	Breast Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	5629044200	2611554623	46%	30860	8652
CGPLBR49	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	5784711600	2673457893	46%	31274	10429
CGPLBR55	Breast Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	8309154900	4306956261	52%	51143	8328
CGPLBR57	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8636181000	4391502618	51%	52108	5857
CGPLBR59	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8799457700	4152328555	47%	49281	5855
CGPLBR61	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8163706700	3952010628	48%	46755	8522
CGPLBR63	Breast Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7020533100	3542447304	50%	41956	4773
CGPLBR67	Breast Cancer	Preoperative, Treatment naïve	Y	N		100	80930	8254353900	3686093696	45%	43516	7752
CGPLBR68	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7629312300	4078969547	53%	48389	7402
CGPLBR69	Breast Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7571501500	3857354512	51%	45322	7047
CGPLBR70	Breast Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7251760700	3641333708	50%	43203	8884
CGPLBR71	Breast Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	8515402600	4496696391	53%	53340	6805
CGPLBR72	Breast Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	8556946900	4389761697	51%	52081	5632
CGPLBR73	Breast Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7959392300	4006933338	50%	47555	8791
CGPLBR74	Breast Cancer	Preoperative, Treatment naïve	Y	N		100	80930	8524536400	4063900599	48%	48252	7013
CGPLBR75	Breast Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	8250379100	3960599885	48%	46955	6319
CGPLBP76	Breast Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7774235200	3893522420	50%	46192	9628
CGPLBR77	Breast Cancer	Preoperative, Treatment naïve	Y	N		100	80930	7572797600	3255963429	43%	38568	8263
CGPLBR80	Breast Cancer	Preoperative, Treatment naïve	Y	N		100	80930	6845325800	3147476693	46%	37201	5595
CGPLBR82	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8236705200	4170465005	51%	49361	12319
CGPLBR83	Breast Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7434568100	3676855019	49%	43628	5458
CGPLBR86	Breast Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7616282500	3644791327	48%	43490	7048
CGPLBR87	Breast Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	6194021300	3004882010	49%	35765	5306
CGPLBR88	Breast Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	6071567200	2847926237	47%	33945	10319
CGPLBR91	Breast Cancer	Preoperative, Treatment naïve	N	Y	100	80930	7192457700	3480203404	48%	41570	9912
CGPLBP92	Breast Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7678981800	3600279233	47%	42975	13580
CGPLBR93	Breast Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7605717800	3998713397	53%	47866	10329
CGPLBR96	Breast Cancer	Preoperative, Treatment naïve	Y	N		100	80930	6297446700	2463064737	39%	29341	7937
CGPLBR97	Breast Cancer	Preoperative, Treatment naïve	Y	N		100	80930	7114921600	3557069027	50%	42488	10712
CGPLH35	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	6919126300	2312758764	33%	25570	1989
CGPLH36	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	6089923400	2038548115	33%	22719	1478
CGPLH37	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	5557270200	1935301929	35%	21673	2312
CGPLH42	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	5792045400	2388036949	41%	27197	2523
CGPLH43	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	5568321700	2017813329	36%	23228	1650
CGPLH45	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	8485593200	2770176078	33%	32829	3114
CGPLH46	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	5083171100	1899395790	37%	21821	1678
CGPLH47	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	6016388500	2062392156	34%	23459	1431
CGPLH48	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	4958945900	1809825992	36%	20702	1698
CGPLH49	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	7953812200	2511365904	32%	27006	1440
CGPLH50	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	6989407600	2561288100	37%	29177	2591
CGPLH51	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	7862073200	2525091396	32%	29999	1293
CGPLH52	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	6939636800	2397922699	35%	27029	2501
CGPLH54	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	10611934700	2290823134	22%	27175	3306
CGPLH55	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	9912569200	2521962244	25%	27082	3161
CGPLH56	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	5777591900	2023874863	35%	22916	1301
CGPLH57	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	9234904800	1493926244	16%	15843	1655
CGPLH59	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	9726052100	2987875484	31%	35427	2143
CGPLH63	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	8696405000	2521574759	29%	26689	1851
CGPLH64	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	5438852600	996198502	18%	11477	1443
CGPLH75	Healthy	Preoperative, Treatment naïve	Y	N		100	80930	3446444000	1505718480	44%	17805	3016
CGPLH76	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	7499116400	3685762725	49%	43682	4643
CGPLH77	Healthy	Preoperative, Treatment naïve	Y	N		100	80930	6512408400	2537359345	39%	30280	3131
CGPLH78	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	7642949300	3946069680	52%	46316	5358
CGPLH79	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	7785475700	3910639227	50%	45280	6714
CGPLH80	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	7918361500	3558236955	45%	42171	5062
CGPLH81	Healthy	Preoperative, Treatment naïve	Y	N		100	80930	6646268900	3112369850	47%	37119	3678
CGPLH82	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	7744065000	3941700596	51%	46820	5723
CGPLH83	Healthy	Preoperative, Treatment naïve	Y	N		100	80930	6957686000	1447503106	21%	17280	2875
CGPLH84	Healthy	Preoperative, Treatment naïve	Y	N		100	80930	8326493200	3969908122	48%	47464	3647
CGPLH86	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	8664194700	4470145091	52%	53398	5094
CGPLH90	Healthy	Preoperative, Treatment naïve	N	Y		100	80930	7516078800	3841504088	51%	45907	4414
CGPLLU13	Lung Cancer	Pre-treatment, Day −2	Y	N		100	80930	5659546100	1721618955	30%	20587	6025
CGPLLU13	Lung Cancer	Pre-treatment, Day −2	Y	N		100	80930	6199049700	2563659840	41%	30728	6514
CGPLLU13	Lung Cancer	Pre-treatment, Day −2	Y	N	100	80930	5864396500	1194237002	20%	14331	3952
CGPLLU13	Lung Cancer	Pre-treatment, Day −2	Y	N	100	80930	5080197700	1373550586	27%	16480	5389
CGPLLU14	Lung Cancer	Pre-treatment, Day −38	N	Y	100	80930	8668655700	3980731089	46%	48628	3148
CGPLLU14	Lung Cancer	Pre-treatment, Day −16	N	Y		100	80930	8271043600	4105092738	50%	50152	4497
CGPLLU14	Lung Cancer	Pre-treatment, Day −3	N	Y	100	80930	7149809200	3405754720	48%	40382	6170
CGPLLU14	Lung Cancer	Pre-treatment, Day 0	N	Y		100	80930	6556332200	3289504484	50%	39004	4081
CGPLLU14	Lung Cancer	Post-treatment, Day 0.33	N	Y		100	80930	7410378300	3464236558	47%	41108	4259
CGPLLU14	Lung Cancer	Post-treatment, Day 7	N	Y	100	80930	7530190700	3752054349	50%	45839	2469
CGPLLU144	Lung Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	8716827400	4216576624	48%	49370	10771
CGPLLU146	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	8506844200	4195033049	49%	49084	6968
CGPLLU147	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	7416300600	3530746046	48%	41302	4691
CGPLLU161	Lung Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7789148700	3280139772	42%	38568	12229
CGPLLU162	Lung Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7625462000	3470147667	46%	40918	10099
CGPLLU163	Lung Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	8019293200	3946533983	49%	46471	12108
CGPLLU164	Lung Cancer	Preoperative, Treatment naïve	Y	N	100	80930	8110030900	3592748235	44%	42161	6947
CGPLLU165	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	8389514600	4147501817	49%	48770	8996
CGPLLU168	Lung Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7690630000	3868237773	50%	45625	9711
CGPLLU169	Lung Cancer	Preoperative, Treatment naïve	N	Y		100	80930	9378353000	4800407624	51%	56547	10261
CGPLLU174	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	7481844600	3067532518	41%	36321	6137
CGPLLU175	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	8532324200	4002541569	47%	47084	7862
CGPLLU176	Lung Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	8143905000	4054098929	50%	47708	5588
CGPLLU177	Lung Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	8421611300	4197108809	50%	49476	8780
CGPLLU178	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	8483124700	4169577489	49%	48580	6445
CGPLLU179	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	7774358700	3304915738	43%	38768	6862
CGPLLU180	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	8192813800	3937552475	48%	46498	6568
CGPLLU197	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	7996779200	3082397881	39%	36381	5388
CGPLLU198	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	7175247200	3545719100	49%	42008	6817
CGPLLU202	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	6840112800	3427820669	50%	40670	7951
CGPLLU203	Lung Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7468749900	3762726574	50%	44500	9917
CGPLLU204	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	7445026400	3703545153	50%	44317	6856
CGPLLU205	Lung Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	9205429100	4350573991	47%	51627	9810
CGPLLU206	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	7397914600	3635210205	49%	43016	7124
CGPLLU207	Lung Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7133043900	3736258011	52%	44291	8499
CGPLLU208	Lung Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7346976400	3855814032	52%	45782	8940
CGPLLU209	Lung Cancer	Preoperative, Treatment naïve	Y	N		100	80930	6723337800	3362944595	50%	39531	11946
CGPLLU244	Lung Cancer	Pre-treatment, Day −7	N	Y		100	80930	8305560600	4182616104	50%	50851	7569
CGPLLU244	Lung Cancer	Pre-treatment, Day −1	N	Y		100	80930	7739951100	3788487116	49%	45925	8552
CGPLLU244	Lung Cancer	Post-treatment, Day 6	N	Y		100	80930	8061928000	4225322272	52%	51279	8646
CGPLLU244	Lung Cancer	Post-treatment, Day 62	N	Y		100	80930	8894936700	4437962639	50%	53862	7361
CGPLLU245	Lung Cancer	Pre-treatment, Day −32	N	Y	100	80930	7679235200	3935822054	51%	47768	7266
CGPLLU245	Lung Cancer	Pre-treatment, Day 0	N	Y	100	80930	8985252500	4824268339	54%	58338	10394
CGPLLU245	Lung Cancer	Post-treatment, Day 7	N	Y	100	80930	8518229300	4480236927	53%	54083	10125
CGPLLU245	Lung Cancer	Post-treatment, Day 21	N	Y	100	80930	9031131000	4824738475	53%	58313	10598
CGPLLU246	Lung Cancer	Pre-treatment, Day −21	N	Y	100	80930	8520360800	3509660305	41%	42349	8086
CGPLLU246	Lung Cancer	Pre-treatment, Day 0	N	Y		100	80930	5451467800	2826351657	52%	34243	8256
CGPLLU246	Lung Cancer	Post-treatment, Day 9	N	Y	100	80930	8137616600	4135036174	51%	50121	6466
CGPLLU246	Lung Cancer	Post-treatment, Day 42	N	Y		100	80930	8385724600	4413323333	53%	53495	7303
CGPLLU264	Lung Cancer	Pre-treatment, Day −1	Y	N		100	80930	3254777700	3016326208	48%	36164	12138
CGPLLU264	Lung Cancer	Pre-treatment, Day −1	Y	N		100	80930	6185331000	8087883231	50%	37003	8388
CGPLLU264	Lung Cancer	Pre-treatment, Day −1	Y	N		100	80930	6274540300	2861143666	46%	34308	6817
CGPLLU264	Lung Cancer	Pre-treatment, Day −1	Y	N		100	80930	5701274000	1241270938	22%	14886	4273
CGPLLU265	Lung Cancer	Pre-treatment, Day 0	Y	N		100	80930	6091276800	2922585558	48%	35004	7742
CGPLLU265	Lung Cancer	Pre-treatment, Day 0	Y	N		100	80930	8430107900	2945953499	46%	35219	8574
CGPLLU265	Lung Cancer	Pre-treatment, Day 0	Y	N		100	80930	5869510300	2792208995	48%	33423	8423
CGPLLU265	Lung Cancer	Pre-treatment, Day 0	Y	N		100	80930	5884330900	2588386038	44%	30977	9803
CGPLLU266	Lung Cancer	Pre-treatment, Day 0	Y	N		100	80930	5807524900	2347651479	40%	28146	5793
CGPLLU266	Lung Cancer	Pre-treatment, Day 0	Y	N	100	80930	6064269800	2086938782	34%	24994	6221
CGPLLU266	Lung Cancer	Pre-treatment, Day 0	Y	N		100	80930	6785913900	3458588505	51%	41432	7765
CGPLLU266	Lung Cancer	Pre-treatment, Day 0	Y	N	100	80930	6513702000	2096370387	32%	25142	6598
CGPLLU267	Lung Cancer	Pre-treatment, Day −1	Y	N		100	80930	6610761200	2576886619	39%	31095	4485
CGPLLU267	Lung Cancer	Pre-treatment, Day −1	Y	N		100	80930	6156102000	2586081726	42%	30714	5309
CGPLLU267	Lung Cancer	Pre-treatment, Day −1	Y	N		100	80930	6180799700	2013434756	33%	23902	3885
CGPLLU269	Lung Cancer	Pre-treatment, Day 0	Y	N		100	80930	6221168600	1499602843	24%	17799	6098
CGPLLU269	Lung Cancer	Pre-treatment, Day 0	Y	N		100	80930	5353961600	1698331125	32%	20094	5252
CGPLLU269	Lung Cancer	Pre-treatment, Day 0	Y	N		100	80930	5831612800	1521114956	26%	18067	6210
CGPLLU271	Lung Cancer	Post-treatment, Day 259	Y	N		100	80930	6229704000	1481468974	24%	17608	4633
CGPLLU271	Lung Cancer	Post-treatment, Day 259	Y	N		100	80930	6134366400	1351029627	22%	16170	7024
CGPLLU271	Lung Cancer	Post-treatment, Day 259	Y	N		100	80930	6491884900	1622578435	25%	19433	5792
CGPLLU271	Lung Cancer	Post-treatment, Day 259	Y	N		100	80930	5742881200	2349421128	41%	28171	5723
CGPLLU271	Lung Cancer	Post-treatment, Day 259	Y	N		100	80930	5503999300	1695782705	31%	20320	5907
CGPLLU43	Lung Cancer	Pre-treatment, Day −1	Y	N		100	80930	6575907000	3002048491	46%	35997	5445
CGPLLU43	Lung Cancer	Pre-treatment, Day −1	Y	N		100	80930	6204350900	3016077187	49%	36162	5704
CGPLLU43	Lung Cancer	Pre-treatment, Day −1	Y	N		100	80930	5997724300	2989608757	50%	35873	6228
CGPLLU43	Lung Cancer	Pre-treatment, Day −1	Y	N		100	80930	6026261500	2881177658	48%	34568	7221
CGPLLU86	Lung Cancer	Pre-treatment, Day 0	N	Y	100	80930	8222093400	3523035056	43%	41165	3614
CGPLLU86	Lung Cancer	Post-treatment, Day 0.5	N	Y	100	80930	8305719500	4271264008	51%	49508	6681
CGPLLU86	Lung Cancer	Post-treatment, Day 7	N	Y	100	80930	6787785300	3443658418	51%	40132	3643
CGPLLU86	Lung Cancer	Post-treatment, Day 17	N	Y	100	80930	6213229400	3120325926	50%	36413	3560
CGPLLU88	Lung Cancer	Pre-treatment, Day 0	Y	N		100	80930	7252433900	3621678746	50%	42719	8599
CGPLLU88	Lung Cancer	Pre-treatment, Day 0	Y	N		100	80930	7679995800	4004738253	52%	46951	6387
CGPLLU88	Lung Cancer	Pre-treatment, Day 0	Y	N	100	80930	6509178000	3316053733	51%	39274	2661
CGPLLU89	Lung Cancer	Pre-treatment, Day 0	N	Y	100	80930	7662496600	3781536306	49%	44097	7909
CGPLLU89	Lung Cancer	Post-treatment, Day 7	N	Y	100	80930	7005599500	3339612564	48%	38977	5034
CGPLLU89	Lung Cancer	Post-treatment, Day 22	N	Y	100	80930	8325998600	3094796789	37%	36061	2822
CGPLOV10	Ovarian Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7073534200	3402308123	48%	39820	4059
CGPLOV11	Ovarian Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	6924062200	3324593050	48%	38796	7185
CGPLOV12	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6552080100	3181854993	49%	37340	6114
CGPLOV13	Ovarian Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	6796755500	3264897084	48%	38340	7931
CGPLOV14	Ovarian Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7856573900	3408425065	43%	39997	7712
CGPLOV15	Ovarian Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7239201500	3322285607	46%	38953	6644
CGPLOV16	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8570755900	4344288233	51%	51009	11947
CGPLOV17	Ovarian Cancer	Preoperative, Treatment naïve	Y	N		100	80930	6910310400	2805243492	41%	32828	4307
CGPLOV18	Ovarian Cancer	Preoperative, Treatment naïve	Y	N		100	80930	8173037600	4064432407	50%	47714	5182
CGPLOV19	Ovarian Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	7732198900	3672564399	47%	43020	11127
CGPLOV20	Ovarian Cancer	Preoperative, Treatment naïve	Y	Y	100	80930	7559602000	3678700179	49%	43230	4872
CGPLOV21	Ovarian Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	8949032900	4616255499	52%	54012	12777
CGPLOV22	Ovarian Cancer	Preoperative, Treatment naïve	Y	Y		100	80930	8680136500	4049934586	47%	46912	9715
CGPLOV23	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6660696600	3422631774	51%	40810	9460
CGPLOV24	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8634287200	4272258165	49%	50736	8689
CGPLOV25	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6978295000	3390206388	49%	40188	5856
CGPLOV26	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7041038300	3728879661	53%	44341	8950
CGPLOV28	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7429236900	3753051715	51%	45430	4155
CGPLOV31	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8981384000	4621838729	51%	55429	5458
CGPLOV32	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	9344536800	4737698323	51%	57234	6165
CGPLOV37	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8158083200	4184432898	51%	50648	6934
CGPLOV38	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8654435400	4492987085	52%	53789	6124
CGPLOV40	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	9868640700	4934400809	50%	59049	7721
CGPLOV41	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7689013600	3861448829	50%	46292	4469
CGPLOV42	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	9836516300	4864154366	49%	58302	7632
CGPLOV43	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8756507100	4515479918	52%	54661	4310
CGPLOV44	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7576310800	4120933322	54%	49903	4969
CGPLOV46	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	9346036300	5037820346	54%	61204	3927
CGPLOV47	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	10880620200	5491357828	50%	66363	6895
CGPLOV48	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7658787800	3335991337	44%	40332	4066
CGPLOV49	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	10076208000	5519656698	55%	67117	5097
CGPLOV50	Ovarian Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8239290400	4472380276	54%	54150	3836
CGPLPA118	Bile Duct Cancer	Preoperative, Treatment naïve	N	Y		100	80930	9094827600	4828332902	53%	57021	4802
CGPLPA122	Bile Duct Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7303323100	3990160379	55%	47240	7875
CGPLPA124	Bile Duct Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7573482800	3965807442	52%	46388	8658
CGPLPA126	Bile Duct Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7904953600	4061463168	51%	47812	10498
CGPLPA128	Bile Duct Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7249238300	2244188735	31%	26436	3413
CGPLPA129	Bile Duct Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7559858900	4003725804	53%	47182	5733
CGPLPA130	Bile Duct Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6973946500	1247144905	18%	14691	1723
CGPLPA131	Bile Duct Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7226237900	3370664342	47%	39661	5054
CGPLPA134	Bile Duct Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7268866100	3754945844	52%	44306	7023
CGPLPA136	Bile Duct Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7476690700	4073978408	54%	48134	5244
CGPLPA140	Bile Duct Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7364654600	3771765342	51%	44479	7080
CGST102	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	5715504500	2644902854	46%	31309	4503
CGST110	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	9179291500	4298269268	47%	51666	3873
CGST114	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7151572200	3254967293	46%	38496	4839
CGST13	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6449701500	3198545984	50%	38515	6731
CGST141	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6781001300	3440927391	51%	40762	5404
CGST16	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6396470600	2931380289	46%	35354	8148
CGST18	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6647324000	3138967777	47%	37401	4992
CGST28	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6288486100	2884997993	46%	34538	2586
CGST30	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6141213100	3109994564	51%	37194	2555
CGST32	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6969139300	3099120469	44%	36726	3935
CGST33	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6560309400	3168371917	48%	37916	4597
CGST39	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	7043791400	2992801875	42%	35620	6737
CGST41	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6975053100	3224065662	46%	38300	4016
CGST45	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6130812200	2944524278	48%	35264	4745
CGST47	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	5961400000	3083523351	52%	37008	3112
CGST48	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6418652700	1497230327	23%	17782	2410
CGST58	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	5818344500	1274708429	22%	15281	2924
CGST80	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	6368064600	3298497188	52%	39692	5280
CGST81	Gastric Cancer	Preoperative, Treatment naïve	N	Y		100	80930	8655691400	1519121452	18%	17988	6419

TABLE 3

APPENDIX C: Targeted cfDNA fragment analyses in cancer patients

	Patient	Stage at			Amino Acid		Mutation
Patient	Type	Diagnosis	Alteration Type	Gene	(Protein)	Nucleotide	Type

CGCRC291	Colorectal	IV	Tumor-derived	STK11	39R>C	chr19_1207027-1207027_C_T	Substitution
	Cancer
CGCRC291	Colorectal	IV	Tumor-derived	TP53	272V>M	chr17_7577124-7577124_C_T	Substitution
	Cancer
CGCRC291	Colorectal	IV	Tumor-derived	TP53	167Q>X	chr17_7578431-7578431_G_A	Substitution
	Cancer
CGCRC291	Colorectal	IV	Tumor-derived	KRAS	12G>A	chr12_25398284-25398284_C_G	Substitution
	Cancer
CGCRC291	Colorectal	IV	Tumor-derived	APC	1260Q>X	chr5_112175069-112175069_C_T	Substitution
	Cancer
CGCRC291	Colorectal	IV	Tumor-derived	APC	1450R>X	chr5_112175639-112175639_C_T	Substitution
	Cancer
CGCRC291	Colorectal	IV	Tumor-derived	PIK3CA	542E>K	chr3_178936082-178936082_G_A	Substitution
	Cancer
CGCRC292	Colorectal	IV	Tumor-derived	KRAS	146A>V	chr12_25378561-25378561_G_A	Substitution
	Cancer
CGCRC292	Colorectal	IV	Tumor-derived	CTNNB1	41T>A	chr3_41266124-41266124_A_G	Substitution
	Cancer
CGCRC292	Colorectal	IV	Germline	EGFR	2284−4C>G	chr7_55248982-55248982_C_G	Substitution
	Cancer
CGCRC293	Colorectal	IV	Tumor-derived	TP53	176C>S	chr17_7578404-7578404_A_T	Substitution
	Cancer
CGCRC294	Colorectal	II	Tumor-derived	APC	213R>X	chr5_112116592-112116592_C_T	Substitution
	Cancer
CGCRC294	Colorectal	II	Tumor-derived	APC	1367Q>X	chr5_112175390-112175390_C_T	Substitution
	Cancer
CGCRC295	Colorectal	IV	Tumor-derived	PDGFRA	49+4C>T	chr4_55124988-55124988_C_T	Substitution
	Cancer
CGCRC295	Colorectal	IV	Hematopoietic	IDH1	104G>V	chr2_209113196-209113196_C_A	Substitution
	Cancer
CGCRC296	Colorectal	II	Germline	EGFR	922E>K	chr7_55266472-55266472_G_A	Substitution
	Cancer
CGCRC297	Colorectal	III	Germline	KIT	18L>F	chr4_55524233-55524233_C_T	Substitution
	Cancer
CGCRC298	Colorectal	II	Hematopoietic	DNMT3A	882R>H	chr2_25457242-25457242_C_T	Substitution
	Cancer
CGCRC298	Colorectal	II	Hematopoietic	DNMT3A		714S>C	chr2_25463541-25463541_G_C	Substitution
	Cancer
CGCRC298	Colorectal	II	Tumor-derived	PIK3CA	414G>V	chr3_178927478-178927478_G_T	Substitution
	Cancer
CGCRC299	Colorectal	I	Hematopoietic	DNMT3A	735Y>C	chr2_25463289-25463289_T_C	Substitution
	Cancer
CGCRC299	Colorectal	I	Hematopoietic	DNMT3A	710C>S	chr2_25463553-25463553_C_G	Substitution
	Cancer
CGCRC300	Colorectal	I	Hematopoietic	DNMT3A		720R>G	chr2_25463524-25463524_G_C	Substitution
	Cancer
CGCRC301	Colorectal	I	Tumor-derived	ATM	2397Q>X	chr11_108199847-108199847_C_T	Substitution
	Cancer
CGCRC302	Colorectal	II	Tumor-derived	TP53	141C>Y	chr17_7578508-7578508_C_T	Substitution
	Cancer
CGCRC302	Colorectal	II	Tumor-derived	BRAF	600V>E	chr7_140453136-140453136_A_T	Substitution
	Cancer
CGCRC303	Colorectal	III	Tumor-derived	TP53	173V>L	chr17_7578413-7578413_C_A	Substitution
	Cancer
CGCRC303	Colorectal	III	Hematopoietic	DNMT3A	755F>3	chr2_25463229-25463229_A_G	Substitution
	Cancer
CGCRC303	Colorectal	III	Hematopoietic	DNMT3A	2173+1G>A	chr2_25463508-25463508_C_T	Substitution
	Cancer
CGCRC304	Colorectal	II	Tumor-derived	EGFR	1131T>S	chr7_55273068-55273068_A_T	Substitution
	Cancer
CGCRC304	Colorectal	II	Tumor-derived	ATM	3077+1G>A	chr11_108142134-108142134_G_A	Substitution
	Cancer
CGCRC304	Colorectal	II	Hematopoietic	ATM	3008R>C	chr11_108236086-108236086_C_T	Substitution
	Cancer
CGCRC305	Colorectal	II	Tumor-derived	GNA11	213R>Q	chr19_3118954-3118954_G_A	Substitution
	Cancer
CGCRC305	Colorectal	II	Tumor-derived	TP53	273R>H	chr17_7577120-7577120_C_T	Substitution
	Cancer
CGCRC306	Colorectal	II	Tumor-derived	TP53	196R>X	chr17_7578263-7578263_G_A	Substitution
	Cancer
CGCRC306	Colorectal	II	Tumor-derived	CDKN2A	107R>C	chr9_21971039-21971039_G_A	Substitution
	Cancer
CGCRC306	Colorectal	II	Tumor-derived	KRAS	61Q>K	chr12_25380277-25380277_G_T	Substitution
	Cancer
CGCRC306	Colorectal	II	Germline	PDGFRA	200T>S	chr4_55130065-55130065_C_G	Substitution
	Cancer
CGCRC306	Colorectal	II	Tumor-derived	EGFR	618H>R	chr7_55233103-55233103_A_G	Substitution
	Cancer
CGCRC306	Colorectal	II	Tumor-derived	PIK3CA	545E>A	chr3_178936092-178936092_A_C	Substitution
	Cancer
CGCRC306	Colorectal	II	Germline	ERBB4	1155R>X	chr2_212251596-212251596_G_A	Substitution
	Cancer
CGCRC307	Colorectal	II	Tumor-derived	JAK2	805L>V	chr9_5080662-5080662_C_G	Substitution
	Cancer
CGCRC307	Colorectal	II	Tumor-derived	SMARCB1	501−2A>G	chr22_24145480-24145480_A_G	Substitution
	Cancer
CGCRC307	Colorectal	II	Tumor-derived	GNAS	201R>C	chr20_57484420-57484420_C_T	Substitution
	Cancer
CGCRC307	Colorectal	II	Tumor-derived	BRAF	600V>E	chr7_140453136-140453136_A_T	Substitution
	Cancer
CGCRC307	Colorectal	II	Tumor-derived	FBXW7	465R>C	chr4_153249365-153249385_G_A	Substitution
	Cancer
CGCRC307	Colorectal	II	Tumor-derived	ER8B4	17A>V	chr2_213403205-213403205_G_A	Substitution
	Cancer
CGCRC308	Colorectal	III	Hematopoietic	DNMT3A		882R>H	chr2_25457242-25457242_C_T	Substitution
	Cancer
CGCRC308	Colorectal	III	Germline	EGFR	848P>L	chr7_55259485-55259485_C_T	Substitution
	Cancer
CGCRC308	Colorectal	III	Tumor-derived	APC	1480Q>X	chr5_112175729-112175729_C_T	Substitution
	Cancer
CGCRC309	Colorectal	III	Tumor-derived	AKT1	17E>K	chr14_105246551-105246551_C_T	Substitution
	Cancer
CGCRC309	Colorectal	III	Tumor-derived	BRAF	600V>E	chr7_140453136-140453136_A_T	Substitution
	Cancer
CGCRC310	Colorectal	II	Tumor-derived	KRAS	12G>V	chr12_25398284-25398284_C_A	Substitution
	Cancer
CGCRC310	Colorectal	II	Tumor-derived	APC	1513E>X	chr5_112175828-112175828_G_T	Substitution
	Cancer
CGCRC310	Colorectal	II	Tumor-derived	APC	1521E>X	chr5_112175852-112175352_G_T	Substitution
	Cancer
CGCRC311	Colorectal	I	Hematopoietic	DNMT3A	882R>H	chr2_25457242-25457242_C_T	Substitution
	Cancer
CGCRC312	Colorectal	III	Tumor-derived	APC	960S>X	chr5_112174170-112174170_C_G	Substitution
	Cancer
CGCRC312	Colorectal	III	Tumor-derived	NRAS	61Q>K	chr1_115256530-115256530_G_T	Substitution
	Cancer
CGCRC313	Colorectal	III	Tumor-derived	KRAS	12G>S	chr12_25398285-25398285_C_T	Substitution
	Cancer
CGCRC313	Colorectal	III	Tumor-derived	APC	876R>X	chr5_112173917-112173917_C_T	Substitution
	Cancer
CGCRC314	Colorectal	I	Tumor-derived	KRAS	12G>D	chr12_25398284-25398284_C_T	Substitution
	Cancer
CGCRC314	Colorectal	I	Hematopoietic	DNMT3A	738L>Q	chr2_25463280-25463280_A_T	Substitution
	Cancer
CGCRC314	Colorectal	I	Tumor-derived	APC	1379E>X	chr5_112175426-112175426_G_T	Substitution
	Cancer
CGCRC315	Colorectal	III	Tumor-derived	NRAS	12G>D	chr1_115258747-115258747_C_T	Substitution
	Cancer
CGCRC315	Colorectal	III	Tumor-derived	FBXW7	505R>C	chr4_153247289-153247289_G_A	Substitution
	Cancer
CGCRC316	Colorectal	III	Tumor-derived	TP53	245G>S	chr17_7577548-7577548_C_T	Substitution
	Cancer
CGCRC316	Colorectal	III	Tumor-derived	CDKN2A	1M>R	chr9_21974825-21974825_A_C	Substitution
	Cancer
CGCRC316	Colorectal	III	Tumor-derived	CTNNB1	37S>C	chr3_41266113-41266113_C_G	Substitution
	Cancer
CGCRC316	Colorectal	III	Tumor-derived	EGFR	2702−3C>T	chr7_55266407-55266407_C_T	Substitution
	Cancer
CGCRC316	Colorectal	III	Hematopoietic	ATM	3008R>P	chr11_108236087-108236087_G_C	Substitution
	Cancer
CGCRC317	Colorectal	III	Tumor-derived	TP53	220Y>C	chr17_7578190-7578190_T_C	Substitution
	Cancer
CGCRC317	Colorectal	III	Tumor-derived	ATM	1026W>R	chr11_108142132-108142132_T_C	Substitution
	Cancer
CGCRC317	Colorectal	III	Tumor-derived	APC	216R>X	chr5_112128143-112128143_C_T	Substitution
	Cancer
CGCRC318	Colorectal	I	Hematopoietic	DNMT3A	698W>X	chr2_25463589-25463589_C_T	Substitution
	Cancer
CGCRC320	Colorectal	I	Germline	KIT	18L>F	chr4_55524233-55524233_C_T	Substitution
	Cancer
CGCRC320	Colorectal	I	Tumor-derived	ERBB4	78R>W	chr2_212989479-212989479_G_A	Substitution
	Cancer
CGCRC321	Colorectal	I	Tumor-derived	CDKN2A	12S>L	chr9_21974792-21974792_G_A	Substitution
	Cancer
CGCRC321	Colorectal	I	Hematopoietic	DNMT3A		882R>H	chr2_25457242-25457242_C_T	Substitution
	Cancer
CGCRC321	Colorectal	I	Germline	EGFR	511S>Y	chr7_55229225-55229225_C_A	Substitution
	Cancer
CGCRC332	Colorectal	IV	Tumor-derived	TP53	125T>R	chr17_7579313-7579313_G_C	Substitution
	Cancer
CGCRC333	Colorectal	IV	Tumor-derived	TP53	673−2A>G	chr17_7577610-7577610_T_C	Substitution
	Cancer
CGCRC333	Colorectal	IV	Tumor-derived	BRAF	600V>E	chr7_140453136-140453136_A_T	Substitution
	Cancer
CGCRC333	Colorectal	IV	Tumor-derived	ERBB4	691E>A	chr2_212495194-212495194_T_G	Substitution
	Cancer
CGCRC334	Colorectal	IV	Tumor-derived	TP53	245G>S	chr17_7577548-7577548_C_T	Substitution
	Cancer
CGCRC334	Colorectal	IV	Germline	EGFR	638T>M	chr7_55238900-55238900_C_T	Substitution
	Cancer
CGCRC334	Colorectal	IV	Tumor-derived	PIK3CA	104P>R	chr3_178916924-178916924_C_G	Substitution
	Cancer
CGCRC335	Colorectal	IV	Tumor-derived	BRAF	600V>E	chr7_140453136-140453136_A_T	Substitution
	Cancer
CGCRC336	Colorectal	IV	Tumor-derived	TP53	175R>H	chr17_7578406-7578406_C_T	Substitution
	Cancer
CGCRC336	Colorectal	IV	Tumor-derived	KRAS	12G>V	chr12_25398284-25398284_C_A	Substitution
	Cancer
CGCRC336	Colorectal	IV	Turner-derived	APC	1286E>X	chr5_112175147-112175147_G_T	Substitution
	Cancer
CGCRC337	Colorectal	IV	Tumor-derived	STK11	734+2T>A	chr19_1220718-1220718_T_A	Substitution
	Cancer
CGCRC337	Colorectal	IV	Germline	APC	485M>I	chr5_112162851-112162851_G_A	Substitution
	Cancer
CGCRC338	Colorectal	IV	Tumor-derived	KRAS	12G>D	chr12_25398284-25398284_C_T	Substitution
	Cancer
CGCRC339	Colorectal	IV	Tumor-derived	KRAS	13G>D	chr12_25393281-25398281_C_T	Substitution
	Cancer
CGCRC339	Colorectal	IV	Tumor-derived	APC	876R>X	chr5_112173917-112173917_C_T	Substitution
	Cancer
CGCRC339	Colorectal	IV	Tumor-derived	PIK3CA	407C>F	chr3_178927457-178927457_G_T	Substitution
	Cancer
CGCRC339	Colorectal	IV	Tumor-derived	PIK3CA	1047H>L	chr3_178952085-178952085_A_T	Substitution
	Cancer
CGCRC340	Colorectal	IV	Tumor-derived	TP53	196R>X	chr17_7578263-7578263_G_A	Substitution
	Cancer
CGCRC340	Colorectal	IV	Tumor-derived	APC	1306E>X	chr5_112175207-112175207_G_T	Substitution
	Cancer
CGPLBR38	Breast	I	Tumor-derived	TP53	241S>P	chr17_7577560-7577560_A_G	Substitution
	Cancer
CGPLBR40	Breast	III	Germline	AR	392P>R	chrX_66766163-66766163_C_G	Substitution
	Cancer
CGPLBR44	Breast	III	Hematopoietic	DNMT3A		882R>H	chr2_25457242-25457242_C_T	Substitution
	Cancer
CGPLBR44	Breast	III	Hematopoietic	DNMT3A	705I>T	chr2_25463568-25463568_A_G	Substitution
	Cancer
CGPLBR44	Breast	III	Tumor-derived	PDGFRA	859V>M	chr4_55153609-55153609_G_A	Substitution
	Cancer
CGPLBR48	Breast	II	Germline	ALK	1231R>Q	chr2_29436901-29436901_C_T	Substitution
	Cancer
CGPLBR48	Breast	II	Tumor-derived	EGFR	669R>Q	chr7_55240762-55240762_G_A	Substitution
	Cancer
CGPLBR55	Breast	III	Hematopoietic	DNMT3A	743P>S	chr2_25463266-25463266_G_A	Substitution
	Cancer
CGPLBR55	Breast	III	Tumor-derived	GNAS	201R>H	chr20_57484421-57484421_G_A	Substitution
	Cancer
CGPLBR55	Breast	III	Tumor-derived	PIK3CA	345N>K	chr3_178921553-178921553_T_A	Substitution
	Cancer
CGPLBR63	Breast	II	Germline	FGFR3	403K>E	chr4_1806188-1806188_A_G	Substitution
	Cancer
CGPLBR67	Breast	III	Hematopoietic	DNMT3A		882R>H	chr2_25457242-25457242_C_T	Substitution
	Cancer
CGPLBR67	Breast	III	Tumor-derived	PIK3CA	545E>K	chr3_178936091-178936091_G_A	Substitution
	Cancer
CGPLBR67	Breast	III	Tumor-derived	ERBB4	1000D>A	chr2_212285302-212285302_T_G	Substitution
	Cancer
CGPLBR69	Breast	II	Hematopoietic	DNMT3A		774E>V	chr2_25463172-25463172_T_A	Substitution
	Cancer
CGPLBR69	Breast	II	Germline	CTNNB1	30Y>S	chr3_41266092-41266092_A_C	Substitution
	Cancer
CGPLBR69	Breast	II	Germline	IDH1	231Y>N	chr2_209108158-209108158_A_T	Substitution
	Cancer
CGPLBR70	Breast	II	Tumor-derived	ATM	2832R>H	chr11_108216546-108216546_G_A	Substitution
	Cancer
CGRLBR70	Breast	II	Germline	APC	1577E>D	chr5_112176022-112176022_A_C	Substitution
	Cancer
CGPLBR71	Breast	II	Tumor-derived	TP53	273R>H	chr17_7577120-7577120_C_T	Substitution
	Cancer
CGPLBR72	Breast	II	Germline	APC	1532D>G	chr5_112175886-112175886_A_G	Substitution
	Cancer
CGPLBR73	Breast	II	Tumor-derived	ALK	708S>P	chr2_29474053-29474053_A_G	Substitution
	Cancer
CGPLBR73	Breast	II	Germline	ERBB4	158A>E	chr2_212652833-212652833_G_T	Substitution
	Cancer
CGPLBR74	Breast	II	Germline	AR	20+1G>T	chrX_66788865-66788865_G_T	Substitution
	Cancer
CGPLBR75	Breast	II	Tumor-derived	PIK3CA	1047H>R	chr3_178952085-178352085_A_G	Substitution
	Cancer
CGPLBR76	Breast	II	Germline	KDR	1290S>N	chr4_55946310-55946310_C_T	Substitution
	Cancer
CGPLBR76	Breast	II	Tumor-derived	PIK3CA	1047H>R	chr3_178952085-178952085_A_G	Substitution
	Cancer
CGPLBR77	Breast	III	Tumor-derived	PTEN	170S>I	chr10_89711891-89711891_G_T	Substitution
	Cancer
CGPLBR80	Breast	II	Tumor-derived	CDKN2A	12S>L	chr9_21974792-21974792_G_A	Substitution
	Cancer
CGPLBR83	Breast	II	Germline	AR	728N>D	chrX_66937328-66937328_A_G	Substitution
	Cancer
GGPLBR83	Breast	II	Tumor-derived	ATM	322E>K	chr11_108117753-108117753_G_A	Substitution
	Cancer
CGPLBR83	Breast	II	Germline	ERBB4	539Y>S	chr2_212543783 212543783_T_G	Substitution
	Cancer
CGPLBR86	Breast	II	Germline	STK11	354F>L	chr19_1223125-1223125_C_G	Substitution
	Cancer
CGPLBR86	Breast	II	Germline	SMARCB1	795+3A>G	chr22_24159126-24159126_A_G	Substitution
	Cancer
CGPLBR87	Breast	II	Tumor-derived	JAK2	215R>X	chr9_5054591-5054591_C_T	Substitution
	Cancer
CGPLBR87	Breast	II	Hematopoietic	DNMT3A		882R>H	chr2_25457242-25457242_C_T	Substitution
	Cancer
CGPLBR87	Breast	II	Tumor-derived	SMAD4	496R>C	chr18_48304664-48604664_C_T	Substitution
	Cancer
CGPLBR87	Breast	II	Germline	AR	651S>N	chrX_66931310-66931310_G_A	Substitution
	Cancer
CGPLBR88	Breast	II	Tumor-derived	CDK6	51E>K	chr7_92462487-92462487_G_T	Substitution
	Cancer
CGPLBR88	Breast	II	Germline	APC	1125V>A	chr5_112174665-112174665_T_C	Substitution
	Cancer
CGPLBR92	Breast	II	Tumor-derived	TP53	257L>P	chr17_7577511-7577511_A_G	Substitution
	Cancer
CGPLBR96	Breast	II	Tumor-derived	TP53	213R>X	chr17.fa:7578212-7576212_G_A	Substitution
	Cancer
CGPLBR96	Breast	II	Hematopoietic	DNMT3A		531D>G	chr2_25467484-25467484_T_G	Substitution
	Cancer
CGPLBR96	Breast	II	Tumor-derived	AR	13R>Q	chrX_66765026-66765026_G_A	Substitution
	Cancer
CGPLBR97	Breast	II	Hematopoietic	DNMT3A		882R>H	chr2_25457242-25457242_C_T	Substitution
	Cancer
CGPLBR97	Breast	II	Germline	PDGFRA	401A>D	chr4_55136880-55136880_C_A	Substitution
	Cancer
CGPLBR97	Breast	II	Tumor-derived	GNAS	201R>H	chr20_57484421-57484421_G_A	Substitution
	Cancer
CGPLLU144	Lung	II	Tumor-derived	TP53	241S>F	chr17_7577559-7577559_G_A	Substitution
	Cancer
CGPLLU144	Lung	II	Tumor-derived	KRAS	12G>C	chr12_25398285-25398285_C_A	Substitution
	Cancer
CGPLLU144	Lung	II	Tumor-derived	EGFR	373P>S	chr7_55224336-55224336_C_T	Substitution
	Cancer
CGPLLU144	Lung	II	Tumor-derived	ATM	292P>L	chr11_108115727-108115727_C_T	Substitution
	Cancer
CGPLLU144	Lung	II	Tumor-derived	PIK3CA	545E>K	chr3_178936091 178936091_G_A	Substitution
	Cancer
CGPLLU144	Lung	II	Tumor-derived	ERBB4	426R>K	chr2_212568841-212568841_C_T	Substitution
	Cancer
CGPLLU146	Lung	II	Hematopoietic	JAK2	617V>F	chr9_5073770-5073770_G_T	Substitution
	Cancer
CGPLLU146	Lung	II	Tumor-derived	TP53	282R>P	chr17_7577093-7577093_C_G	Substitution
	Cancer
CGPLLU146	Lung	II	Hematopoietic	DNMT3A	737L>H	chr2_25463283-25463283_A_T	Substitution
	Cancer
CGPLLU146	Lung	II	Tumor-derived	RB1	861+2T>C	chr13_48937095-48937095_T_C	Substitution
	Cancer
CGPLLU146	Lung	II	Tumor-derived	ATM	581L>F	chr11_108122699-108122699_A_T	Substitution
	Cancer
CGPLLU147	Lung	III	Tumor-derived	TP53	248R>Q	chr17_7577538-7577538_C_T	Substitution
	Cancer
CGPLLU147	Lung	III	Tumor-derived	TP53	201L>X	chr17_7573247-7578247_A_T	Substitution
	Cancer
CGPLLU147	Lung	III	Tumor-derived	ALK	1537G>E	chr2_29416343-29416343_C_T	Substitution
	Cancer
CGPLLU147	Lung	III	Germline	PDGFRA	200T>S	chr4_55130065-55130065_C_G	Substitution
	Cancer
CGPLLU162	Lung	II	Tumor-derived	CDKN2A	12S>L	chr9_21974792-21974792_G_A	Substitution
	Cancer
CGPLLU162	Lung	II	Tumor-derived	EGFR	858L>R	chr7_55259515-55259515_T_G	Substitution
	Cancer
CGPLLU162	Lung	II	Tumor-derived	BRAF	354R>Q	chr7_140494187-140494187_C_T	Substitution
	Cancer
CGPLLU163	Lung	II	Tumor-derived	CDKN2A	12S>L	chr9_21974792-21974792_G_A	Substitution
	Cancer
CGPLLU163	Lung	II	Hematopoietic	DNMT3A		528Y>D	chr2_25467494-25467494_A_C	Substitution
	Cancer
CGPLLU164	Lung	II	Tumor-derived	STK11	216S>Y	chr19_1220629-1220629_C_A	Substitution
	Cancer
CGPLLU164	Lung	II	Germline	STK11	354F>L	chr19_1223125-1223125_C_G	Substitution
	Cancer
CGPLLU164	Lung	II	Tumor-derived	GNA11	606−3C>T	chr19_3118919-3118919_C_T	Substitution
	Cancer
CGPLLU164	Lung	II	Tumor-derived	TP53	278P>S	chr17_7577106-7577106_G_A	Substitution
	Cancer
CGPLLU164	Lung	II	Tumor-derived	TP53	161A>S	chr17_7578449-7578449_C_A	Substitution
	Cancer
CGPLLU164	Lung	II	Tumor-derived	TP53	160M>I	chr17_7578450-7578450_C_A	Substitution
	Cancer
CGPLLU164	Lung	II	Tumor-derived	ERBB4	1299P>L	chr2_212248371-212248371_G_A	Substitution
	Cancer
CGPLLU164	Lung	II	Tumor-derived	ERBB4	253N>S	chr2_212587243-212587243_T_C	Substitution
	Cancer
CGPLLU165	Lung	II	Germline	STK11	354F>L	chr19_1223125-1223125_C_G	Substitution
	Cancer
CGPLLU165	Lung	II	Tumor-derived	GNAS	201R>H	chr20_57484421-57484421_G_A	Substitution
	Cancer
CGPLLU168	Lung	I	Tumor-derived	TP53	136Q>X	chr17.fa:7578524-7578524_G_A	Substitution
	Cancer
CGPLLU168	Lung	I	Hematopoietic	DNMT3A		736R>S	chr2_25463287-25463287_G_T	Substitution
	Cancer
CGPLLU168	Lung	I	Tumor-derived	EGFR	858L>R	chr7.fa:55259515-55259515_T_G	Substitution
	Cancer
CGPLLU174	Lung	I	Tumor-derived	STK11	597+1G>T	chr19_1220505-1220505_G_T	Substitution
	Cancer
CGPLLU174	Lung	I	Tumor-derived	JAK2	160D>Y	chr9_5050695-5050695_G_T	Substitution
	Cancer
CGPLLU174	Lung	I	Tumor-derived	KRAS	12G>C	chr12_25398285-25398285_C_A	Substitution
	Cancer
CGPLLU174	Lung	I	Hematopoietic	DNMT3A		891R>W	chr2_25457216-25457216_G_A	Substitution
	Cancer
CGPLLU174	Lung	I	Hematopoietic	DNMT3A	715I>M	chr2_25463537-25463537_G_C	Substitution
	Cancer
CGPLLU175	Lung	I	Tumor-derived	TP53	179H>R	chr17_7578394-7578394_T_C	Substitution
	cancer
CGPLLU175	Lung	I	Hematopoietic	DNMT3A	2598−1G>A	chr2_25457290-25457290_C_T	Substitution
	Cancer
CGPLLU175	Lung	I	Hematopoietic	DNMT3A		755F>L	chr2_25463230-25463230_A_G	Substitution
	Cancer
CGPLLU175	Lung	I	Germline	ATM	337R>C	chr11_108117798-108117798_C_T	Substitution
	Cancer
CGPLLU175	Lung	I	Tumor-derived	ERBB4	941Q>X	chr2_212288925-212288925_G_A	Substitution
	Cancer
CGPLLU176	Lung	I	Hematopoietic	DNMT3A		750P>S	chr2_25463245-25463245_G_A	Substitution
	Cancer
CGPLLU176	Lung	I	Hematopoietic	DNMT3A	735Y>C	chr2_25463289-25463289_T_C	Substitution
	Cancer
CGPLLU177	Lung	II	Tumor-derived	KRAS	12G>V	chr12_25398284-25398284_C_A	Substitution
	Cancer
CGPLLU177	Lung	II	Hematopoietic	DNMT3A		897V>G	chr2_25457197-25457197_A_C	Substitution
	Cancer
CGPLLU177	Lung	II	Hematopoietic	DNMT3A		882R>C	chr2_25457243-25457243_G_A	Substitution
	Cancer
CGPLLU177	Lung	II	Hematopoietic	DNMT3A		2173+1G>A	chr2_25463508-25463508_C_T	Substitution
	Cancer
CGPLLU178	Lung	I	Tumor-derived	CDH1	251T>M	chr16_68844164-68844164_C_T	Substitution
	Cancer
CGPLLU178	Lung	I	Tumor-derived	PIK3CA	861Q>X	chr3_178947145-178947145_C_T	Substitution
	Cancer
CGPLLU179	Lung	I	Hematopoietic	DNMT3A	879N>D	chr2_25457252-25457252_T_C	Substitution
	Cancer
CGPLLU179	Lung	I	Germline	APC	2611T>I	chr5_112179123-112179123_C_T	Substitution
	Cancer
CGPLLU180	Lung	I	Tumor-derived	STK11	237D>Y	chr19_1220691-1220691_G_T	Substitution
	Cancer
CGPLLU180	Lung	I	Tumor-derived	TP53	293G>V	chr17_7577060-7577060_C_A	Substitution
	Cancer
CGPLLU180	Lung	I	Tumor-derived	TP53	282R>P	chr17_7577093-7577093_C_G	Substitution
	Cancer
CGPLLU180	Lung	I	Tumor-derived	TP53	177P>L	chr17.fa:7578400-7578400_G_A	Substitution
	Cancer
CGPLLU180	Lung	I	Tumor-derived	RB1	565S>X	chr13_48955578-48955578_C_G	Substitution
	Cancer
CGPLLU197	Lung	I	Hematopoietic	DNMT3A		882R>C	chr2_25457243-25457243_G_A	Substitution
	Cancer
CGPLLU197	Lung	I	Hematopoietic	DNMT3A		879N>D	chr2_25457252-25457252_T_C	Substitution
	Cancer
CGPLLU198	Lung	I	Tumor-derived	TP53	162I>N	chr17_7578445-7578445_A_T	Substitution
	Cancer
CGPLLU198	Lung	I	Tumor-derived	EGFR	858L>R	chr7_55259515_55259515_T_G	Substitution
	Cancer
CGPLLU202	Lung	I	Tumor-derived	EGFR	790T>M	chr7.fa:55249071-55249071_C_T	Substitution
	Cancer
CGPLLU202	Lung	I	Tumor-derived	EGFR	868E>X	chr7_55259544-55259544_G_T	Substitution
	Cancer
CGPLLU204	Lung	I	Tumor-derived	KIT	956R>Q	chr4_55604659-55604659_G_A	Substitution
	Cancer
CGPLLU205	Lung	II	Hematopoietic	DNMT3A		736R>C	chr2_25463287-25463287_G_A	Substitution
	Cancer
CGPLLU205	Lung	II	Hematopoietic	DNMT3A		696Q>X	chr2_25463596-25463596_G_A	Substitution
	Cancer
CGPLLU206	Lung	III	Tumor-derived	TP53	672+1G>A	chr17_7578176-7578176_C_T	Substitution
	Cancer
CGPLLU206	Luna	III	Tumor-derived	TP53	131N>S	chr17_7573538-7578538_T_C	Substitution
	Cancer
CGPLLU207	Lung	II	Tumor-derived	TP53	376−1G>A	chr17_7578555-7578555_C_T	Substitution
	Cancer
CGPLLU207	Lung	II	Germline	ALK	419F>L	chr2_29606625-29606625_A_G	Substitution
	Cancer
CGPLLU207	Lung	II	Tumor-derived	EGFR	790T>M	chr7.fa:55249071-55249071_C_T	Substitution
	Cancer
CGPLLU208	Lung	II	Tumor-derived	TP53	250P>L	chr17_7577532-7577532_G_A	Substitution
	Cancer
CGPLLU208	Lung	II	Germline	EGFR	224R>H	chr7_55220281-55220281_G_A	Substitution
	Cancer
CGPLLU208	Lung	II	Tumor-derived	EGFR	858L>R	chr7_55259515_55259515_T_G	Substitution
	Cancer
CGPLLU208	Lung	II	Tumor-derived	MYC	98R>W	chr8_128750755-128750755_C_T	Substitution
	Cancer
CGPLLU209	Lung	II	Germline	STK11	354F>L	chr19_1223125-1223125_C_G	Substitution
	Cancer
CGPLLU209	Lung	II	Tumor-derived	TP53	100Q>X	chr17_7579389-7579389_G_A	Substitution
	Cancer
CGPLLU209	Lung	II	Tumor-derived	CDKN2A	88E>X	chr9_21971096-21971096_C_A	Substitution
	Cancer
CGPLLU209	Lung	II	Tumor-derived	PDGFRA	921A>T	chr4_55155052_55155052_G_A	Substitution
	Cancer
CGPLLU209	Lung	II	Germline	EGFR	567M>V	chr7_55231493-55231493_A_G	Substitution
	Cancer
CGPLOV10	Ovarian	I	Tumor-derived	TP53	342R>X	chr17_7574003-7574003_G_A	Substitution
	Cancer
CGPLOV11	Ovarian	IV	Tumor-derived	TP53	248R>Q	chr17_7577538-7577538_C_T	Substitution
	Cancer
CGPLOV11	Ovarian	IV	Germline	TP53	63A>V	chr17_7579499-7579499_G_A	Substitution
	Cancer
CGPLOV13	Ovarian	IV	Tumor-derived	ALK	444W>C	chr2_29551298-29551298_C_A	Substitution
	Cancer
CGPLOV13	Ovarian	IV	Germline	PDGFRA	401A>D	chr4_55136880-55136880_C_A	Substitution
	Cancer
CGPLOV13	Ovarian	IV	Tumor-derived	KIT	135R>H	chr4_55564516-55564516_G_A	Substitution
	Cancer
CGPLOV14	Ovarian	I	Tumor-derived	HNF1A	230E>K	chr12_12143484-121431484_G_A	Substitution
	Cancer
CGPLOV15	Ovarian	III	Tumor-derived	TP53	278P>S	chr17_7577106-7577106_G_A	Substitution
	Cancer
CGPLOV15	Ovarian	III	Tumor-derived	EGFR	433H>D	chr7_55225445_55225445_C_G	Substitution
	Cancer
CGPLOV17	Ovarian	I	Tumor-derived	TP53	248R>Q	chr17_7577538-7577538_C_T	Substitution
	Cancer
CGPLOV17	Ovarian	I	Germline	PDGFRA	1071D>N	chr4_55161380-55161380_G_A	Substitution
	Cancer
CGPLOV18	Ovarian	I	Germline	AFC	1125V>A	chr5_112174665-112174665_T_C	Substitution
	Cancer
CGPLOV19	Ovarian	II	Germline	FGFR3	403K>E	chr4_1806188-1806188_A_G	Substitution
	Cancer
CGPLOV19	Ovarian	II	Tumor-derived	TP53	273R>H	chr17_7577120-7577120_C_T	Substitution
	Cancer
CGPLOV19	Ovarian	II	Germline	AR	176S>R	chrX_66765516-66765516_C_A	Substitution
	Cancer
CGPLOV19	Ovarian	II	Tumor-derived	APC	1378Q>X	chr5_112175423-112175423_C_T	Substitution
	Cancer
CGPLOV20	Ovarian	II	Tumor-derived	TP53	195I>T	chr17_7578265-7578265_A_G	Substitution
	Cancer
CGPLOV20	Ovarian	II	Germline	EGFR	253K>R	chr7_55221714-55221714_A_G	Substitution
	Cancer
CGPLOV21	Ovarian	IV	Germline	STK11	354F>L	chr19_1223125-1223125_C_G	Substitution
	Cancer
CGPLOV21	Ovarian	IV	Tumor-derived	TP53	275C>Y	chr17_7577114-7577114_C_T	Substitution
	Cancer
CGPLOV21	Ovarian	IV	Tumor-derived	ERBB4	602S>T	chr2_212530114_212530114_C_G	Substitution
	Cancer
CGPLOV22	Ovarian	III	Tumor-derived	TP53	193H>P	chr17_7578271-7578271_T_G	Substitution
	Cancer
CGPLOV22	Ovarian	III	Tumor-derived	CTNNB1	41T>A	chr3_41266124-41266124_A_G	Substitution
	Cancer

Wild-type Fragments

						25th
					Minimum	Percentile	Mode	Median
		Alteration	Mutant		cfDNA	cfDNA	cfDNA	cfDNA
	Hotspot	Detected	Allele	Distinct	Fragment	Fragment	Fragment	Fragment
Patient	Alteration	in Tissue	Fraction	Coverage	Size (bp)	Size (bp)	Size (bp)	Size (bp)

CGCRC291	No	No	0.14%	11688	100	151	167	169
CGCRC291	Yes	No	0.10%	11779	100	155	171	169
CGCRC291	Yes	Yes	22.85%	11026	100	156	166	169
CGCRC291	Yes	Yes	14.85%	7632	97	152	169	167
CGCRC291	No	Yes	11.23%	7218	101	155	167	169
CGCRC291	Yes	Yes	11.05%	10757	86	154	166	167
CGCRC291	Yes	Yes	18.11%	5429	100	151	171	167
CGCRC292	Yes	No	1.41%	6120	101	157	167	169
CGCRC292	Yes	Yes	0.13%	10693	100	155	169	168
CGCRC292	NA	Yes	31.99%	7587	97	158	166	171
CGCRC293	No	No	0.35%	7672	95	159	168	170
CGCRC294	Yes	Yes	0.14%	7339	84	155	166	167
CGCRC294	Yes	Yes	0.13%	12054	89	159	167	170
CGCRC295	No	No	0.45%	5602	101	157	164	170
CGCRC295	No	Yes	0.34%	8330	100	157	166	169
CGCRC296	NA	Yes	30.48%	8375	89	161	166	172
CGCRC297	NA	Yes	41.39%	3580	102	159	164	170
CGCRC298	Yes	Yes	0.08%	13032	100	159	168	171
CGCRC298	No	No	0.11%	13475	93	158	169	170
CGCRC298	No	No	0.55%	5815	100	156	168	169
CGCRC299	No	Yes	0.30%	11995	100	154	164	165
CGCRC299	No	Yes	0.12%	15363	96	151	166	164
CGCRC300	No	No	0.15%	7487	100	162	179	173
CGCRC301	No	No	0.21%	5881	100	156	169	169
CGCRC302	Yes	Yes	0.05%	24784	84	153	165	164
CGCRC302	Yes	Yes	0.12%	11763	95	154	165	165
CGCRC303	Yes	Yes	0.08%	13967	95	159	169	171
CGCRC303	No	No	0.21%	10167	81	160	169	172
CGCRC303	No	No	0.17%	10845	100	160	169	172
CGCRC304	No	No	0.22%	16168	90	153	167	164
CGCRC304	No	No	0.27%	10502	100	152	165	163
CGCRC304	No	Yes	0.43%	12987	101	154	165	165
CGCRC305	No	Yes	0.11%	12507	100	159	169	171
CGCRC305	Yes	No	0.19%	10301	100	156	168	166
CGCRC306	Yes	No	0.12%	8594	101	157	165	169
CGCRC306	No	Yes	8.02%	9437	90	159	167	171
CGCRC306	Yes	Yes	7.30%	6090	100	152	163	166
CGCRC306	NA	Yes	34.78%	4585	103	158	167	179
CGCRC306	No	Yes	6.32%	7395	81	160	166	171
CGCRC306	Yes	No	0.96%	4885	100	152	170	167
CGCRC306	NA	Yes	38.70%	3700	100	159	168	171
CGCRC307	No	No	0.56%	6860	100	158	170	170
CGCRC307	No	Yes	0.34%	10065	95	157	168	169
CGCRC307	Yes	Yes#	0.24%	7520	102	156	167	168
CGCRC307	Yes	Yes	0.38%	8623	76	157	169	168
CGCRC307	Yes	Yes	0.31%	10606	100	155	167	168
CGCRC307	No	No	0.15%	13189	90	158	168	171
CGCRC308	Yes	No	0.06%	16287	90	159	168	169
CGCRC308	NA	Yes	27.69%	7729	100	160	164	170
CGCRC308	No	Yes	0.11%	14067	92	157	170	169
CGCRC309	Yes	Yes	2.70%	13036	85	157	170	169
CGCRC309	Yes	Yes	3.00%	9084	101	157	166	168
CGCRC310	Yes	Yes	0.13%	7393	100	153	165	164
CGCRC310	No	Yes	0.11%	11689	100	152	166	164
CGCRC310	No	Yes	0.15%	10273	100	153	166	164
CGCRC311	Yes	No	0.86%	8456	94	160	171	172
CGCRC312	No	Yes	0.59%	4719	100	160	165	173
CGCRC312	Yes	Yes	0.47%	3391	101	157	172	170
CGCRC313	Yes	Yes	0.17%	5013	100	163	166	174
CGCRC313	Yes	Yes	0.07%	8150	72	161	171	174
CGCRC314	Yes	Yes	0.30%	4684	100	158	165	169
CGCRC314	No	Yes	2.50%	6902	85	159	165	170
CGCRC314	Yes	Yes	0.38%	7229	102	158	167	170
CGCRC315	Yes	Yes	0.27%	8733	94	155	167	169
CGCRC315	Yes	Yes	0.25%	9623	101	158	166	170
CGCRC316	Yes	Yes	6.52%	12880	100	150	166	163
CGCRC316	No	Yes	5.74%	7479	93	157	164	168
CGCRC316	Yes	Yes	5.47%	13682	100	149	165	162
CGCRC316	No	No	0.11%	16716	85	153	166	156
CGCRC316	No	Yes	0.13%	17060	100	150	166	153
CGCRC317	Yes	Yes	0.36%	14587	84	152	166	154
CGCRC317	No	Yes	0.23%	10483	100	152	164	155
CGCRC317	Yes	No	0.29%	3497	101	149	166	153
CGCRC318	No	Yes	0.25%	16436	98	158	170	170
CGCRC320	NA	Yes	34.76%	6521	100	163	170	175
CGCRC320	No	No	0.12%	11633	100	162	174	174
CGCRC321	No	No	0.20%	6916	88	161	167	174
CGCRC321	Yes	No	0.08%	9559	94	159	171	170
CGCRC321	NA	Yes	41.86%	5545	100	159	172	172
CGCRC332	No	Yes	19.98%	605	104	154	170	176
CGCRC333	No	Yes	43.03%	1265	89	159	165	171
CGCRC333	Yes	Yes	22.26%	3338	102	153	165	169
CGCRC333	No	No	1.00%	3008	102	153	169	169
CGCRC334	Yes	Yes	13.44%	1725	105	160	170	175
CGCRC334	NA	Yes	35.28%	1168	100	159	164	174
CGCRC334	No	No	3.85%	1798	103	159	166	173
CGCRC335	Yes	Yes	0.32%	2411	99	155	167	157
CGCRC336	Yes	Yes	75.26%	757	104	156	171	170
CGCRC336	Yes	Yes	42.87%	1080	102	150	166	167
CGCRC336	No	Yes	81.61%	391	102	161	165	171
CGCRC337	No	No	0.12%,	6497	72	153	169	177
CGCRC337	NA	Yes	46.26%	1686	100	147	170	163
CGCRC338	Yes	Yes	27.03%	1408	105	153	164	166
CGCRC339	Yes	Yes	1.94%	1256	105	158	168	169
CGCRC339	Yes	Yes	2.35%	1639	101	158	165	172
CGCRC339	No	Yes	3.14%	1143	100	154	170	167
CGCRC339	Yes	Yes	1.71%	1584	108	161	171	173
CGCRC340	Yes	Yes	18.26%	876	101	162	170	175
CGCRC340	Yes	Yes	22.57%	796	105	159	164	174
CGPLBR38	No	Yes	0.53%	9684	95	156	166	168
CGPLBR40	NA	Yes	28.99%	10277	78	162	168	173
CGPLBR44	Yes	Yes	1.82%	10715	99	162	171	173
CGPLBR44	No	Yes	0.41%	10837	100	159	169	171
CGPLBR44	No	Yes	0.13%	12640	100	159	168	171
CGPLBR48	NA	Yes	34.61%	5631	100	164	170	179
CGPLBR48	No	No	0.18%	12467	101	167	174	180
CGPLBR55	No	No	0.18%	10527	101	158	169	169
CGPLBR55	Yes	Yes	0.68%	6011	101	153	166	167
CGPLBR55	Yes	Yes	0.42%	3973	101	153	166	166
CGPLBR63	NA	Yes	34.82%	3405	97	165	170	176
CGPLBR67	Yes	Yes	0.11%	10259	87	157	168	168
CGPLBR67	Yes	Yes	0.68%	5163	100	151	167	166
CGPLBR67	No	No	0.28%	6250	100	155	166	167
CGPLBR69	No	No	0.29%	7558	100	159	166	170
CGPLBR69	NA	Yes	41.74%	3938	101	154	169	166
CGPLBR69	NA	Yes	41.66%	2387	101	157	166	168
CGPLBR70	No	No	0.36%	6916	100	158	171	169
CGRLBR70	NA	Yes	40.28%	3580	107	160	169	173
CGPLBR71	Yes	Yes	0.10%	7930	85	156	166	158
CGPLBR72	NA	Yes	44.03%	2389	100	157	160	170
CGPLBR73	No	No	0.27%	11348	95	161	173	174
CGPLBR73	NA	Yes	35.58%	3422	102	157	168	169
CGPLBR74	NA	Yes	36.23%	3784	101	163	175	174
CGPLBR75	Yes	Yes	0.14%	7290	103	162	173	172
CGPLBR76	NA	Yes	36.57%	4342	104	166	171	179
CGPLBR76	Yes	Yes	0.12%	11785	100	165	168	177
CGPLBR77	No	Yes	2.29%	6161	100	158	166	169
CGPLBR80	No	No	0.54%	3643	96	165	166	185
CGPLBR83	NA	Yes	42.66%	3479	105	162	164	174
GGPLBR83	No	No	0.28%	3496	103	165	170	177
CGPLBR83	NA	Yes	44.91%	1748	100	164	173	175
CGPLBR86	NA	Yes	42.32%	4241	98	160	168	175
CGPLBR86	NA	Yes	43.38%	3096	88	160	167	174
CGPLBR87	No	No	0.35%	3680	101	162	168	175
CGPLBR87	Yes	No	0.31%	6180	101	163	164	175
CGPLBR87	No	No	0.40%	7746	86	160	167	175
CGPLBR87	NA	Yes	42.94%	2266	106	160	166	172
CGPLBR88	No	No	0.13%	17537	89	185	200	223
CGPLBR88	NA	Yes	31.19%	5919	101	162	172	173
CGPLBR92	No	Yes	0.20%	15530	77	150	164	152
CGPLBR96	Yes	No	0.10%	9893	100	159	164	171
CGPLBR96	No	Yes	5.81%	8620	95	162	167	173
CGPLBR96	No	No	0.60%	8036	85	162	169	175
CGPLBR97	Yes	Yes	0.11%	14856	93	160	168	170
CGPLBR97	NA	Yes	34.12%	5329	100	161	165	171
CGPLBR97	Yes	Yes	0.13%	7010	97	158	169	170
CGPLLU144	Yes	Yes	1.95%	11371	100	156	165	167
CGPLLU144	Yes	Yes	5.10%	7641	100	155	167	166
CGPLLU144	No	Yes	0.16%	9996	100	158	168	169
CGPLLU144	No	No	0.22%	4956	101	159	166	169
CGPLLU144	Yes	Yes	2.94%	8540	100	153	170	166
CGPLLU144	No	No	0.18%	7648	101	156	164	166
CGPLLU146	Yes	No	0.25%	5920	100	155	164	168
CGPLLU146	No	Yes	1.30%	9356	100	155	166	168
CGPLLU146	No	Yes	0.84%	7284	101	158	165	170
CGPLLU146	No	Yes	0.87%	4183	103	160	166	170
CGPLLU146	No	No	0.20%	6778	100	157	166	168
CGPLLU147	Yes	No	0.15%	4807	100	155	166	170
CGPLLU147	No	Yes	0.55%	5282	100	156	167	171
CGPLLU147	No	Yes	0.94%	7122	100	158	174	173
CGPLLU147	NA	Yes	43.47%	2825	101	160	165	173
CGPLLU162	No	No	0.22%	9940	95	161	164	174
CGPLLU162	Yes	Yes	0.22%	13855	87	160	174	173
CGPLLU162	No	No	0.14%	11251	100	153	167	166
CGPLLU163	No	No	0.21%	10805	85	159	165	173
CGPLLU163	No	Yes	0.15%	20185	83	158	166	170
CGPLLU164	No	Yes	1.23%	6795	91	156	161	169
CGPLLU164	NA	Yes	42.52%	4561	92	157	164	169
CGPLLU164	No	No	0.20%	8097	100	158	170	170
CGPLLU164	Yes	No	0.10%	9241	100	155	165	157
CGPLLU164	No	Yes	1.78%	10806	100	157	168	159
CGPLLU164	No	Yes	1.86%	10919	100	157	168	159
CGPLLU164	No	Yes	0.96%	5412	103	159	175	171
CGPLLU164	No	No	0.22%	5151	101	160	166	169
CGPLLU165	NA	Yes	36.62%	7448	95	155	167	167
CGPLLU165	Yes	Yes	0.16%	5822	102	154	166	166
CGPLLU168	Yes	Yes	0.06%	15985	97	152	165	166
CGPLLU168	No	No	0.39%	11070	100	156	165	168
CGPLLU168	Yes	Yes	0.07%	11063	83	157	166	169
CGPLLU174	No	Yes	0.33%	5881	88	162	165	174
CGPLLU174	No	Yes	0.40%	3696	100	162	167	172
CGPLLU174	Yes	Yes	0.16%	4941	101	162	167	172
CGPLLU174	No	Yes	0.29%	7527	100	163	168	173
CGPLLU174	No	Yes	0.26%	8353	101	162	168	173
CGPLLU175	Yes	Yes	8.03%	10214	100	160	166	170
CGPLLU175	No	No	0.21%	9739	100	157	168	168
CGPLLU175	No	Yes	0.15%	9509	100	157	165	168
CGPLLU175	NA	Yes	43.84%	2710	101	157	165	167
CGPLLU175	No	Yes	3.64%	6565	100	158	166	168
CGPLLU176	No	Yes	0.92%	6513	101	164	168	175
CGPLLU176	No	Yes	0.21%	5962	100	164	174	175
CGPLLU177	Yes	Yes	2.49%	7044	102	160	165	170
CGPLLU177	No	Yes	1.53%	9950	88	160	169	171
CGPLLU177	Yes	No	0.29%	11233	100	160	168	171
CGPLLU177	No	No	0.13%	10966	75	160	169	172
CGPLLU178	No	No	0.29%	8378	100	162	176	172
CGPLLU178	No	No	0.17%	7235	101	159	167	170
CGPLLU179	No	Yes	0.38%	8350	103	161	169	171
CGPLLU179	NA	Yes	39.91%	2609	103	162	171	173
CGPLLU180	No	Yes	2.43%	6085	91	158	165	170
CGPLLU180	No	Yes	2.07%	6680	92	158	164	169
CGPLLU180	No	Yes	1.94%	7790	92	158	167	168
CGPLLU180	Yes	No	0.08%	9036	101	160	169	171
CGPLLU180	No	Yes	1.01%	4679	100	157	169	168
CGPLLU197	Yes	No	0.16%	7196	102	162	166	172
CGPLLU197	No	No	0.38%	7147	100	161	166	172
CGPLLU198	No	Yes	0.87%	9322	97	157	165	168
CGPLLU198	Yes	Yes	0.52%	8303	100	160	173	172
CGPLLU202	Yes	Yes	0.05%	14197	90	151	165	166
CGPLLU202	No	No	0.13%	9279	51	150	168	167
CGPLLU204	No	No	0.26%	7185	100	157	165	168
CGPLLU205	No	Yes	0.70%	10739	96	156	165	166
CGPLLU205	No	Yes	3.47%	12065	100	154	165	165
CGPLLU206	Yes	Yes	26.13%	6746	94	148	165	164
CGPLLU206	No	No	0.21%	11225	100	147	167	164
CGPLLU207	Yes	Yes	0.32%	11224	100	159	165	170
CGPLLU207	NA	Yes	34.58%	4960	101	160	166	170
CGPLLU207	Yes	No	0.09%	13216	85	161	165	172
CGPLLU208	Yes	Yes	1.33%	5211	101	156	166	168
CGPLLU208	NA	Yes	39.34%	5253	100	159	164	170
CGPLLU208	Yes	Yes	0.86%	10233	100	160	170	171
CGPLLU208	No	No	0.17%	11421	100	158	165	171
CGPLLU209	NA	Yes	26.84%	11695	96	153	166	169
CGPLLU209	No	Yes	9.97%	12771	94	155	163	168
CGPLLU209	Yes	Yes	9.13%	16557	92	157	169	170
CGPLLU209	No	Yes	9.32%	13057	97	158	167	171
CGPLLU209	NA	Yes	30.41%	8521	100	155	167	169
CGPLOV10	Yes	Yes	3.14%	4421	101	161	165	172
CGPLOV11	Yes	Yes	0.87%	7987	100	157	164	169
CGPLOV11	NA	Yes	37.77%	3782	97	160	166	171
CGPLOV13	No	Yes	0.12%	12072	88	157	165	169
CGPLOV13	NA	Yes	37.98%	4107	103	159	166	169
CGPLOV13	No	Yes	0.35%	8427	100	161	165	171
CGPLOV14	No	No	0.14%	11418	92	154	167	171
CGPLOV15	Yes	Yes	3.54%	7689	102	157	164	169
CGPLOV15	No	No	0.19%	7617	101	159	167	171
CGPLOV17	Yes	Yes	0.32%	4463	96	155	168	163
CGPLOV17	NA	Yes	44.10%	2884	110	157	170	170
CGPLOV18	NA	Yes	40.81%	2945	101	159	164	169
CGPLOV19	NA	Yes	23.80%	9727	95	158	167	172
CGPLOV19	Yes	Yes	36.83%	4387	100	158	165	169
CGPLOV19	NA	Yes	65.29%	2775	93	161	171	171
CGPLOV19	Yes	Yes	46.35%	3818	102	156	170	170
CGPLOV20	Yes	Yes	0.21%	5404	94	159	165	170
CGPLOV20	NA	Yes	44.05%	3744	102	158	166	169
CGPLOV21	NA	Yes	7.68%	21823	81	158	166	169
CGPLOV21	No	Yes	2.04%	18806	101	159	165	169
CGPLOV21	No	No	14.36%	10801	89	160	166	169
CGPLOV22	No	Yes	0.49%	11952	100	155	165	167
CGPLOV22	Yes	Yes	0.34%	12399	92	150	165	164

Wild-type Fragments

Mutant Fragments

	75th				25th
Mean	Percentile	Maximum		Minimum	Percentile	Mode	Median
cfDNA	cfDNA	cfDNA		cfDNA	cfDNA	cfDNA	cfDNA
Fragment	Fragment	Fragment	Distinct	Fragment	Fragment	Fragment	Fragment
Size (bp)	Size (bp)	Size (bp)	Coverage	Size (bp)	Size (bp)	Size (bp)	Size (bp)

179	188	400	19	100	142	233	165
182	185	400	21	132	166	182	176
180	183	400	5411	92	152	167	169
177	182	400	1903	100	148	166	166
184	185	400	1344	108	155	167	170
181	182	400	2108	100	153	166	168
176	180	400	1951	101	149	175	167
176	183	399	75	123	162	167	172
177	182	400	23	101	130	130	139
183	188	399	6863	100	160	168	173
188	186	400	34	77	154	171	170
175	179	396	9	138	147	176	171
184	185	400	21	115	145	155	159
179	185	397	30	137	149	181	162
179	182	397	44	125	155	155	169
185	188	400	8167	101	160	166	171
187	188	400	3562	102	158	168	170
184	187	399	15	93	137	127	174
183	185	400	26	137	163	166	167
181	182	397	35	118	147	176	163
172	175	400	71	133	152	170	165
169	174	400	55	130	153	165	164
189	187	399	17	149	155	326	170
176	183	400	18	156	170	174	174
169	175	397	51	108	143	268	152
166	173	397	26	118	147	153	156
184	186	400	45	116	151	168	163
185	186	400	25	157	165	191	175
185	187	400	25	124	168	180	180
167	175	394	86	121	155	169	166
167	173	397	45	124	143	197	162
170	175	398	108	126	147	162	162
190	189	400	23	131	148	145	166
182	182	399	42	138	155	155	174
189	187	399	25	126	153	176	176
192	193	400	977	101	149	189	170
173	179	391	525	102	140	168	159
181	185	399	4010	100	158	166	170
178	184	399	625	100	140	167	162
175	179	398	37	111	143	142	166
181	186	396	3184	102	159	168	172
180	183	399	47	111	148	144	169
133	184	397	39	111	146	182	162
185	184	400	24	110	146	309	182
176	180	400	32	117	146	154	157
180	184	399	43	111	143	144	177
185	187	400	29	109	140	204	159
179	182	399	20	128	152	180	163
176	184	398	7515	101	160	170	171
182	182	399	31	85	146	137	166
181	182	395	428	100	135	138	149
175	180	397	352	97	136	132	147
165	172	397	15	131	137	132	144
170	173	398	25	107	138	159	161
171	173	400	27	122	147	161	161
189	189	400	91	112	165	168	173
189	189	400	27	124	144	154	154
178	184	399	24	105	143	132	159
188	189	399	8	122	143	122	161
194	192	400	17	144	163	173	173
180	183	394	15	132	159	186	166
183	185	399	233	131	162	167	172
186	186	398	27	136	155	183	163
192	195	399	23	137	144	175	152
182	184	399	29	131	157	177	171
166	172	396	1616	100	146	164	159
175	180	400	806	96	158	169	169
165	172	399	1410	102	140	149	154
170	177	397	49	99	153	143	182
166	173	398	33	140	155	154	170
180	178	400	73	95	140	140	155
172	177	400	38	115	160	164	167
171	174	386	6	124	137	170	156
180	183	400	70	124	151	151	164
194	199	399	6586	96	162	168	175
184	188	400	41	112	172	176	177
194	198	399	35	146	168	175	175
182	184	399	20	166	180	185	191
183	186	397	5338	102	159	175	171
202	203	393	178	101	150	168	171
195	195	397	1350	104	153	163	171
185	189	400	1257	100	153	168	170
185	189	396	30	117	163	164	172
203	210	391	336	105	153	141	171
188	194	399	741	101	161	169	176
193	193	396	89	100	145	171	171
172	179	396	12	129	143	143	153
186	188	387	3559	91	155	164	173
177	183	392	873	102	149	163	164
194	200	377	1909	100	158	167	176
202	259	400	27	122	157	164	179
171	178	395	1818	103	147	169	162
178	182	374	546	102	151	166	166
179	184	397	26	132	142	138	171
195	194	400	53	117	157	166	169
176	179	397	40	124	150	169	166
188	191	390	38	107	153	180	174
205	207	399	217	102	146	144	163
196	195	397	266	111	147	150	166
186	184	400	76	123	157	171	169
179	186	400	9832	93	161	166	172
191	190	400	277	104	162	160	176
191	189	400	65	123	165	166	172
187	189	400	31	136	163	171	167
202	202	400	5286	102	166	168	181
196	201	400	102	138	166	161	179
181	182	397	30	138	158	189	185
181	181	400	64	113	158	163	167
176	179	398	27	121	163	200	171
191	192	398	2943	100	165	176	176
179	181	399	25	138	153	138	167
171	177	399	60	110	136	147	147
172	179	399	26	139	147	180	176
186	184	398	35	121	149	360	161
176	178	397	4000	103	155	166	167
176	178	385	2390	99	157	164	168
182	184	400	28	131	160	168	167
194	193	400	3545	100	161	169	173
179	180	398	15	121	146	166	166
188	187	400	2587	103	158	162	169
189	192	400	86	121	165	183	177
178	184	399	3339	101	157	165	169
179	187	391	3193	101	163	178	173
183	186	398	13	111	153	153	161
197	201	400	4140	102	166	169	179
191	194	400	16	130	143	143	157
183	183	400	209	125	154	175	170
211	230	400	41	158	176	197	186
193	193	400	3445	94	162	175	174
197	199	400	23	123	182	248	224
193	195	399	1787	100	163	163	176
204	207	400	4100	100	159	164	173
196	195	400	3096	79	159	161	173
202	203	400	73	142	178	178	184
205	203	400	23	161	168	168	171
195	196	400	170	125	158	173	173
195	192	400	2086	101	162	169	176
238	280	400	125	84	192	194	207
197	194	400	5715	108	163	154	174
172	173	398	109	78	148	149	158
196	191	399	35	119	161	172	171
189	190	400	826	102	162	166	171
194	195	400	95	135	160	161	170
184	184	400	27	128	150	150	169
179	184	399	4771	103	161	168	171
187	185	399	7	147	154	154	167
179	179	395	330	106	152	165	166
172	177	399	536	106	151	167	163
179	183	400	45	138	163	175	172
182	182	397	16	138	146	146	155
172	177	397	293	101	152	169	164
171	177	399	23	130	152	162	162
180	183	399	54	104	161	154	176
184	184	400	154	96	149	157	163
186	187	399	79	102	163	177	174
183	185	400	44	118	149	163	163
182	184	400	35	136	164	204	181
192	191	400	13	138	164	169	169
199	205	400	50	128	155	161	171
191	193	400	81	108	150	108	173
190	191	389	2597	101	159	165	172
192	197	400	58	92	173	192	192
183	189	400	74	90	147	142	167
175	178	400	37	144	163	185	172
194	202	400	61	93	164	181	181
184	186	400	66	104	158	194	174
191	190	396	101	126	155	176	176
188	185	394	4718	100	156	164	168
186	186	399	30	134	161	175	175
180	180	397	34	139	163	155	170
182	182	400	262	101	150	152	165
182	182	400	277	101	150	147	166
180	182	395	65	121	158	161	167
177	182	400	16	144	172	179	179
185	184	399	7186	100	154	167	166
181	179	394	21	108	164	164	173
177	180	400	18	111	127	127	158
179	181	400	72	121	156	173	166
177	182	400	30	106	160	174	174
200	199	399	36	131	147	143	177
184	185	392	20	144	173	266	178
182	184	395	16	147	156	156	164
186	187	399	34	159	168	168	176
186	186	396	5	116	182	182	185
185	183	399	1073	100	142	164	152
179	180	400	46	109	151	143	175
181	181	400	30	146	154	146	168
176	179	392	2742	102	154	164	166
174	180	399	298	103	140	148	150
197	194	399	67	115	164	250	173
195	194	399	19	156	165	165	185
178	182	395	189	105	138	141	150
183	185	398	227	123	160	168	169
185	184	397	53	78	161	175	175
190	188	395	50	130	161	168	168
186	187	396	28	139	150	173	170
179	184	400	24	130	153	176	170
185	185	394	48	111	154	170	168
189	187	398	2337	100	163	166	172
198	200	396	172	83	152	160	166
190	188	400	215	123	151	159	163
184	184	400	207	121	151	157	161
191	189	397	17	143	170	217	214
181	182	398	52	122	152	167	164
191	189	399	17	109	161	173	171
191	189	399	40	136	164	166	171
180	181	399	127	88	149	131	162
181	186	400	68	141	166	175	176
169	179	398	10	81	167	167	167
170	181	398	33	107	162	167	167
175	181	391	23	112	156	190	164
175	177	400	109	130	153	169	166
172	176	400	684	105	153	167	166
179	178	398	2946	100	138	157	155
175	178	399	30	121	165	165	176
187	186	400	63	140	155	154	167
181	184	400	4754	101	160	170	170
182	187	400	31	131	162	162	174
181	183	400	150	110	144	166	162
179	184	400	5290	95	159	167	169
181	186	400	140	101	155	175	167
187	190	397	20	92	141	241	168
190	192	400	8065	85	156	164	169
174	182	400	2586	101	147	165	165
185	188	400	2808	100	150	158	167
182	187	400	2227	100	154	162	171
176	183	396	8425	100	155	165	169
186	188	399	142	112	146	140	159
186	185	399	104	132	158	159	167
183	185	392	3462	101	160	173	172
182	183	399	25	94	140	140	158
177	181	399	3789	101	159	168	169
181	184	400	57	131	152	170	170
183	191	400	36	118	154	201	182
187	185	399	362	110	152	143	180
182	188	400	20	158	153	311	174
186	187	397	23	126	151	184	168
188	189	400	2980	100	158	169	170
183	183	391	2793	91	158	167	170
185	189	395	7357	100	158	175	171
184	184	398	5186	101	157	165	170
182	187	400	15595	64	159	167	170
186	185	400	6749	101	158	167	170
193	190	400	23	127	148	148	194
182	185	394	3901	101	160	167	171
179	180	400	4633	100	158	169	170
175	179	400	734	101	151	155	165
175	180	394	4022	101	159	167	168
184	182	400	117	116	156	156	172
172	176	395	65	109	145	177	167

		Adjusted
Difference	Difference	P Value of
between	between	Difference

Wild-type

Mutant Fragments

Median

Mean

between

Fragments		75th		Mutant and	Mutant and	Mutant and
Mean	Mean	Percentile	Maximum	Wild-type	Wild-Type	Wild-type
cfDNA	cfDNA	cfDNA	cfDNA	cfDNA	cfDNA	cfDNA
Fragment	Fragment	Fragment	Fragment	Fragment	Fragment	Fragment
Size (bp)	Size (bp)	Size (bp)	Size (bp)	Sizes (bp)	Sizes (bp)	Sizes

179	180	230	305	−4.0	1.54	0.475
182	191	198	309	7.0	8.33	0.250
180	186	191	399	0.0	5.89	0.000
177	177	183	383	−1.0	−0.25	0.874
184	189	131	398	1.0	5.37	0.009
181	165	187	386	1.0	3.30	0.025
176	179	182	397	0.0	2.65	0.148
176	182	190	370	3.0	5.31	0.368
177	164	155	345	−29.5	−12.79	0.000
183	186	189	400	2.0	3.13	0.002
188	177	192	335	−0.5	−11.46	0.571
175	177	176	290	4.0	1.22	0.475
184	176	175	368	−11.0	−7.99	0.052
179	182	181	369	−8.0	3.49	0.061
179	185	194	338	0.0	5.78	0.023
185	184	187	400	−1.0	−1.27	0.212
187	185	185	399	0.0	−2.62	0.114
184	173	193	261	3.0	−11.00	0.507
183	179	180	364	−3.0	−4.34	0.430
181	172	176	336	−6.0	−9.35	0.166
172	169	173	301	0.0	3.57	0.668
169	166	168	325	0.0	−2.15	0.630
189	221	301	387	−3.0	32.43	0.453
176	210	219	372	5.0	33.84	0.368
169	164	178	268	−12.0	−5.12	0.000
166	174	158	327	−9.5	8.37	0.036
184	175	177	346	−8.0	−8.84	0.057
185	207	199	350	3.0	22.93	0.465
185	189	191	338	8.0	4.06	0.154
167	168	175	309	2.0	0.46	0.445
167	166	168	377	−1.0	−0.91	0.482
170	164	174	302	−3.0	6.74	0.064
190	189	205	333	−5.0	−0.80	0.297
182	177	187	343	5.5	−4.51	0.171
189	188	229	305	7.0	−0.19	0.234
192	182	192	380	−1.0	−9.76	0.000
173	168	176	382	−7.0	−5.57	0.052
181	181	185	398	0.0	0.37	0.773
178	172	181	380	−9.0	−6.68	0.009
175	172	186	321	−1.0	−2.38	0.572
181	182	187	400	0.5	0.95	0.564
180	176	183	353	−1.0	−4.83	0.598
133	182	185	337	−7.0	−0.44	0.064
185	208	284	355	14.0	22.31	0.031
176	167	166	298	−11.0	−8.94	0.013
180	187	212	319	9.0	7.22	0.062
185	188	204	387	−12.0	3.32	0.031
179	166	180	219	−6.5	−13.04	0.155
176	177	185	400	1.0	1.08	0.166
182	167	176	316	−3.0	−14.62	0.469
181	158	166	340	−20.0	−23.47	0.000
175	149	159	326	−21.0	−26.04	0.000
165	163	171	323	−20.0	−1.73	0.000
170	175	190	299	−3.0	4.33	0.384
171	173	171	342	−3.0	2.54	0.354
189	196	192	379	1.0	6.83	0.571
189	167	172	320	−19.0	22.39	0.000
178	183	190	367	−11.0	4.97	0.054
188	168	195	241	−13.0	−19.21	0.100
194	213	261	372	−1.0	19.22	0.587
180	174	185	265	−3.0	−5.62	0.461
183	190	187	394	2.0	7.27	0.137
186	170	178	262	−7.0	−16.03	0.131
192	190	212	327	−17.0	−1.76	0.018
182	183	179	319	1.0	0.74	0.564
166	163	170	354	−3.5	−3.57	0.000
175	179	184	366	1.0	3.80	0.054
165	164	170	398	−8.0	−0.35	0.816
170	206	284	333	16.0	36.25	0.000
166	180	180	296	7.0	14.38	0.104
180	173	178	324	−9.0	−6.66	0.000
172	182	179	329	1.5	10.09	0.479
171	153	168	178	−7.5	18.98	0.411
180	182	183	385	−6.0	1.71	0.064
194	193	196	399	0.0	−1.79	0.166
184	195	195	373	3.0	11.02	0.397
194	181	186	312	1.0	−13.40	0.587
182	205	219	357	21.0	23.48	0.013
183	183	185	394	−1.0	0.03	0.984
202	198	240	357	−5.0	−4.34	0.571
195	201	258	400	0.0	5.94	0.066
185	189	202	392	1.0	4.37	0.064
185	175	179	372	3.0	−10.29	0.463
203	200	240	399	−4.0	−3.10	0.571
188	190	194	400	2.0	1.96	0.571
193	197	229	393	−2.0	3.42	0.479
172	163	166	275	−14.0	−8.99	0.084
186	195	211	398	3.0	8.92	0.001
177	177	181	400	−3.0	−0.39	0.880
194	202	242	398	5.0	7.98	0.061
202	199	231	350	2.0	−3.82	0.685
171	173	180	396	−1.0	1.92	0.372
178	180	182	381	0.0	2.87	0.416
179	183	188	351	1.5	3.29	0.572
195	192	198	336	−3.0	−2.86	0.451
176	181	176	309	−1.0	4.53	0.539
188	185	210	326	0.5	−2.59	0.576
205	188	212	360	−12.0	−17.11	0.004
196	188	204	379	−8.0	−7.53	0.208
186	182	182	346	1.0	−3.64	0.479
179	180	186	399	−1.0	1.04	0.155
191	201	200	384	3.0	9.95	0.061
191	198	192	371	1.0	7.08	0.560
187	201	199	387	−4.0	14.14	0.341
202	201	203	400	2.0	−0.88	0.587
196	199	209	372	−1.5	2.90	0.679
181	191	191	311	16.0	9.25	0.000
181	179	176	318	0.0	−2.85	0.679
176	187	190	392	5.0	10.89	0.314
191	187	192	398	0.0	−3.83	0.015
179	181	184	340	−1.0	2.00	0.571
171	161	159	327	−19.0	−9.77	0.000
172	176	184	344	9.0	3.52	0.015
186	197	195	360	−9.0	10.77	0.314
176	176	178	397	0.5	0.65	0.610
176	178	180	400	0.0	1.78	0.314
182	177	179	338	−2.0	−5.83	0.463
194	194	192	399	0.0	0.40	0.825
179	172	204	221	−2.0	−7.32	0.564
188	189	186	399	−1.0	1.12	0.598
189	189	193	373	3.0	−0.01	0.293
178	177	184	400	0.0	−1.73	0.598
179	180	186	389	−1.0	0.22	0.839
183	171	179	323	−11.0	−12.36	0.061
197	197	200	400	0.0	−0.32	0.839
191	173	173	325	−20.0	−18.40	0.000
183	196	233	357	1.0	12.55	0.025
211	215	220	374	1.0	3.72	0.603
193	194	194	399	0.0	0.65	0.714
197	232	260	359	47.0	34.97	0.000
193	192	194	400	1.0	−0.85	0.718
204	200	202	400	−2.0	−3.65	0.062
196	194	191	397	−1.0	−2.45	0.251
202	237	338	377	9.0	35.30	0.114
205	189	186	380	−4.0	−16.38	0.435
195	188	190	400	−2.0	−6.17	0.293
195	203	203	400	4.5	8.80	0.000
238	243	324	400	−16.0	5.51	0.574
197	200	196	400	1.0	2.87	0.065
172	166	173	302	−4.0	−5.94	0.190
196	191	180	390	0.0	−4.34	0.627
189	187	187	395	−2.0	−1.94	0.475
194	182	184	400	−5.0	−11.54	0.155
184	174	185	319	−1.0	−9.68	0.571
179	179	183	400	0.0	0.15	0.880
187	164	174	177	−3.0	−22.90	0.155
179	178	178	361	−1.0	−1.35	0.685
172	172	175	363	−3.0	−0.34	0.880
179	185	191	380	3.0	6.52	0.368
182	162	170	224	−14.0	−19.82	0.007
172	170	174	392	−2.0	−1.37	0.646
171	163	177	232	−4.0	−7.62	0.252
180	195	206	383	7.5	14.58	0.064
184	176	185	347	−5.5	−7.87	0.154
186	200	203	372	4.0	14.61	0.270
183	185	188	338	−7.0	1.98	0.039
182	194	203	369	13.0	11.80	0.039
192	198	173	333	−1.0	6.05	0.610
199	216	301	360	0.0	17.02	0.623
191	198	224	385	0.0	6.48	0.624
190	185	187	397	−1.0	−5.17	0.005
192	202	200	397	18.0	9.79	0.007
183	176	182	391	−6.5	−6.78	0.061
175	192	186	375	6.0	17.15	0.005
194	197	211	370	8.0	3.34	0.169
184	189	194	379	3.5	4.60	0.270
191	194	213	331	7.0	2.50	0.718
188	190	187	393	−1.0	2.54	0.113
186	190	208	339	5.0	4.07	0.302
180	178	175	349	3.0	−1.65	0.407
182	181	186	393	−4.0	−0.65	0.876
182	182	185	393	−3.0	0.36	0.926
180	186	188	338	−4.0	6.15	0.234
177	187	180	376	10.0	9.98	0.130
185	183	181	396	−1.0	−1.73	0.154
181	196	200	357	7.0	14.95	0.213
177	189	186	352	−8.0	12.47	0.179
179	183	179	396	−2.0	4.31	0.427
177	180	186	282	5.0	3.09	0.252
200	196	227	298	2.5	−4.24	0.479
184	199	215	269	6.0	15.13	0.252
182	177	169	302	−8.0	−4.82	0.119
186	206	196	365	3.0	20.55	0.415
186	201	192	329	12.0	14.52	0.263
185	157	164	346	−18.0	−27.67	0.000
179	174	183	325	7.0	−5.22	0.054
181	186	181	367	−0.5	5.19	0.568
176	176	178	387	−1.0	−0.24	0.874
174	152	162	288	−18.0	−22.25	0.000
197	187	201	366	−2.0	−9.89	0.425
195	197	199	361	10.0	2.20	0.154
178	164	175	348	−20.0	−14.58	0.000
183	185	184	396	−2.0	1.68	0.706
185	189	188	392	4.0	3.80	0.241
190	184	175	377	−4.5	−5.86	0.234
186	170	173	354	−2.5	−15.88	0.416
179	193	199	359	0.0	13.13	0.598
185	173	183	295	−3.0	−11.80	0.270
189	187	185	394	−1.0	−1.27	0.564
198	193	226	396	−4.0	−4.93	0.490
190	188	196	365	−6.0	−1.72	0.735
184	181	179	365	−7.0	−3.01	0.571
191	198	217	294	43.0	7.08	0.000
181	179	173	372	−4.5	−2.07	0.137
191	181	174	293	−1.0	−9.24	0.576
191	185	185	335	−1.0	−5.86	0.571
180	168	178	311	−6.0	−11.80	0.005
181	198	207	387	4.0	17.11	0.184
169	159	176	182	1.0	−10.20	0.589
170	174	185	322	0.0	4.57	0.636
175	175	190	349	−4.0	−0.92	0.308
175	175	178	382	0.0	−0.09	0.987
172	172	175	385	1.0	0.00	0.999
179	172	174	398	−9.0	−7.28	0.000
175	198	219	325	12.0	22.37	0.007
187	201	215	372	−3.0	13.70	0.286
181	179	181	393	0.0	−1.72	0.154
182	180	185	352	2.0	−2.26	0.494
181	176	173	385	−6.0	−5.86	0.314
179	179	184	400	−1.0	0.11	0.909
181	179	180	352	−4.5	−2.77	0.589
187	178	209	283	−3.0	−9.82	0.479
190	190	190	399	0.0	−0.08	0.942
174	169	179	386	−3.5	−4.59	0.000
185	189	200	399	−3.0	4.17	0.007
182	183	190	398	0.0	1.00	0.564
176	176	184	400	0.0	0.54	0.568
186	180	193	352	−13.0	−5.41	0.463
186	189	180	331	−2.0	3.05	0.657
183	184	187	396	1.0	0.82	0.576
182	159	163	341	−11.0	−23.47	0.027
177	176	181	395	0.0	−0.66	0.576
181	179	184	327	−1.0	−2.41	0.568
183	187	201	328	11.0	3.60	0.114
187	207	268	389	11.0	20.70	0.000
182	198	209	311	3.0	15.25	0.475
185	185	185	328	−1.0	−1.49	0.571
188	187	189	398	0.0	−0.84	0.637
183	181	182	389	1.0	−2.30	0.171
185	182	187	399	−1.0	−2.37	0.008
184	185	186	400	1.0	1.72	0.240
182	181	185	397	−1.0	−1.39	0.245
186	185	187	400	0.0	−0.52	0.702
193	222	292	378	24.0	29.58	0.027
182	182	185	398	2.0	0.32	0.821
179	185	187	400	1.0	6.16	0.000
175	176	178	366	−4.0	0.48	0.823
175	172	178	399	−1.0	−2.84	0.000
184	199	184	399	5.0	15.08	0.084
172	181	181	306	3.0	9.11	0.293

TABLE 4

APPENDIX - D: Summary of whole genome cfDNA analyses

		Analysis	Patient	Read	Total Bases	High Quality
Patient	Timepoint	type	Type	Length	Sequenced	Bases Analyzed	Coverage

CGCRC291	Preoperative	WGS	Colorectal	100	7232125000	4695396600	1.86
	treatment naïve		Cancer
CGCRC292	Preoperative	WGS	Colorectal	100	6794092800	4471065400	1.77
	treatment naïve		Cancer
CGCRC293	Preoperative	WGS	Colorectal	100	8373899600	5686176000	2.26
	treatment naïve		Cancer
CGCRC294	Preoperative	WGS	Colorectal	100	8081312000	5347045800	2.12
	treatment naïve		Cancer
CGCRC296	Preoperative	WGS	Colorectal	100	10072029200	6770998200	2.69
	treatment naïve		Cancer
CGCRC299	Preoperative	WGS	Colorectal	100	10971591600	7632723200	3.03
	treatment naïve		Cancer
CGCRC300	Preoperative	WGS	Colorectal	100	9894332600	6699951000	2.66
	treatment naïve		Cancer
CGCRC301	Preoperative	WGS	Colorectal	100	7857346200	5021002000	1.99
	treatment naïve		Cancer
CGCRC302	Preoperative	WGS	Colorectal	100	11671913000	8335275800	3.31
	treatment naïve		Cancer
CGCRC304	Preoperative	WGS	Colorectal	100	19011739200	12957614200	5.14
	treatment naïve		Cancer
CGCRC305	Preoperative	WGS	Colorectal	100	7177341400	4809957200	1.91
	treatment naïve		Cancer
CGCRC306	Preoperative	WGS	Colorectal	100	8302233200	5608043600	2.23
	treatment naïve		Cancer
CGCRC307	Preoperative	WGS	Colorectal	100	8034729400	5342620000	2.12
	treatment naïve		Cancer
CGCRC308	Preoperative	WGS	Colorectal	100	8670084800	5934037200	2.35
	treatment naïve		Cancer
CGCRC311	Preoperative	WGS	Colorectal	100	6947634400	4704601800	1.87
	treatment naïve		Cancer
CGCRC315	Preoperative	WGS	Colorectal	100	5205544000	3419565400	1.36
	treatment naïve		Cancer
CGCRC316	Preoperative	WGS	Colorectal	100	6405388600	4447534800	1.76
	treatment naïve		Cancer
CGCRC317	Preoperative	WGS	Colorectal	100	6060390400	4104616600	1.63
	treatment naïve		Cancer
CGCRC318	Preoperative	WGS	Colorectal	100	6848768600	4439404800	1.76
	treatment naïve		Cancer
CGCRC319	Preoperative	WGS	Colorectal	100	10545294400	7355181600	2.92
	treatment naïve		Cancer
CGCRC320	Preoperative	WGS	Colorectal	100	5961999200	3945054000	1.57
	treatment naïve		Cancer
CGCRC321	Preoperative	WGS	Colorectal	100	8248095400	5614355000	2.23
	treatment naïve		Cancer
CGCRC333	Preoperative	WGS	Colorectal	100	10540267600	6915490600	2.74
	treatment naïve		Cancer
CGCRC336	Preoperative	WGS	Colorectal	100	10675581800	7087691800	2.81
	treatment naïve		Cancer
CGCRC338	Preoperative	WGS	Colorectal	100	13788172600	3970308600	3.56
	treatment naïve		Cancer
CGCRC341	Preoperative	WGS	Colorectal	100	10753467600	7311539200	2.90
	treatment naïve		Cancer
CGCRC342	Preoperative	WGS	Colorectal	100	11836966000	7552793200	3.00
	treatment naïve		Cancer
CGH14	Human adult elutriated	WGS	Healthy	100	36525427600	24950300200	9.90
	lymphocytes
CGH15	Human adult elutriated	WGS	Healthy	100	29930855000	23754049400	9.43
	lymphocytes
CGLU316	Pre-treatment, Day −53	WGS	Lung	100	10354123200	6896471400	2.74
			Cancer
CGLU316	Pre-treatment, Day −4	WGS	Lung	100	7870039200	5254938800	2.09
			Cancer
CGLU316	Post-treatment, Day 18	WGS	Lung	100	8155322000	5416262400	2.15
			Cancer
CGLU316	Post-treatment, Day 87	WGS	Lung	100	9442310400	6087893400	2.42
			Cancer
CGLU344	Pre-treatment, Day −21	WGS	Lung	100	8728318600	5769097200	2.29
			Cancer
CGLU344	Pre-treatment, Day 0	WGS	Lung	100	11710249400	7826902600	3.11
			Cancer
CGLU344	Post-treatment, Day 0.1875	WGS	Lung	100	11569683000	7654701600	3.04
			Cancer
CGLU344	Post-treatment, Day 59	WGS	Lung	100	11042459200	6320138800	2.51
			Cancer
CGLU369	Pre-treatment, Day −2	WGS	Lung	100	8630932800	5779595800	2.29
			Cancer
CGLU369	Post-treatment, Day 12	WGS	Lung	100	9227709600	6136755200	2.44
			Cancer
CGLU369	Post-treatment, Day 68	WGS	Lung	100	7995282600	5239077200	2.08
			Cancer
CGLU369	Post-treatment, Day 110	WGS	Lung	100	8750541000	5626139000	2.23
			Cancer
CGLU373	Pre-treatment, Day −2	WGS	Lung	100	11746059600	7547485800	3.00
			Cancer
CGLU373	Post-treatment, Day 0.125	WGS	Lung	100	13801136800	9255579400	3.67
			Cancer
CGLU373	Post-treatment, Day 7	WGS	Lung	100	11537896800	7654111200	3.04
			Cancer
CGLU373	Post-treatment, Day 47	WGS	Lung	100	8046326400	5397702400	2.14
			Cancer
CGPLBR100	Preoperative	WGS	Breast	100	8440532400	5729474800	2.27
	treatment naïve		Cancer
CGPLBR101	Preoperative	WGS	Breast	100	9786253600	6673495200	2.65
	treatment naïve		Cancer
CGPLBR102	Preoperative	WGS	Breast	100	8664980400	5669781600	2.25
	treatment naïve		Cancer
CGPLBR103	Preoperative	WGS	Breast	100	9846936200	6662883400	2.64
	treatment naïve		Cancer
CGPLBR104	Preoperative	WGS	Breast	100	9443375400	6497061000	2.58
	treatment naïve		Cancer
CGPLBR12	Preoperative	WGS	Breast	100	7017577800	4823327400	1.91
	treatment naïve		Cancer
CGPLBR18	Preoperative	WGS	Breast	100	10309652800	7130386000	2.83
	treatment naïve		Cancer
CGPLBR23	Preoperative	WGS	Breast	100	9034484800	6219625800	2.47
	treatment naïve		Cancer
CGPLBR24	Preoperative	WGS	Breast	100	9891454200	6601857400	2.62
	treatment naïve		Cancer
CGPLBR28	Preoperative	WGS	Breast	100	7997607200	5400803200	2.14
	treatment naïve		Cancer
CGPLBR30	Preoperative	WGS	Breast	100	8502597200	5885822400	2.34
	treatment naïve		Cancer
CGPLBR31	Preoperative	WGS	Breast	100	12660085600	8551995600	3.39
	treatment naïve		Cancer
CGPLBR32	Preoperative	WGS	Breast	100	8773498600	5839034600	2.32
	treatment naïve		Cancer
CGPLBR33	Preoperative	WGS	Breast	100	10931742800	6967030600	2.76
	treatment naïve		Cancer
CGPLBR34	Preoperative	WGS	Breast	100	10861398600	7453225800	2.96
	treatment naïve		Cancer
CGPLBR35	Preoperative	WGS	Breast	100	9180193600	6158440200	2.44
	treatment naïve		Cancer
CGPLBR36	Preoperative	WGS	Breast	100	9159948400	6091817800	2.42
	treatment naïve		Cancer
CGPLBR37	Preoperative	WGS	Breast	100	10307505800	6929530600	2.75
	treatment naïve		Cancer
CGPLBR38	Preoperative	WGS	Breast	100	9983824000	6841725400	2.71
	treatment naïve		Cancer
CGPLBR40	Preoperative	WGS	Breast	100	10148823800	7024345400	2.79
	treatment naïve		Cancer
CGPLBR41	Preoperative	WGS	Breast	100	11168192000	7562945800	3.00
	treatment naïve		Cancer
CGPLBR45	Preoperative	WGS	Breast	100	8793780600	6011109400	2.39
	treatment naïve		Cancer
CGPLBR46	Preoperative	WGS	Breast	100	7228607600	4706130000	1.87
	treatment naïve		Cancer
CGPLBR47	Preoperative	WGS	Breast	100	7906911400	5341655000	2.12
	treatment naïve		Cancer
CGPLBR48	Preoperative	WGS	Breast	100	6992032000	4428636200	1.76
	treatment naïve		Cancer
CGPLBR49	Preoperative	WGS	Breast	100	7311195000	4559460200	1.81
	treatment naïve		Cancer
CGPLBR50	Preoperative	WGS	Breast	100	11107960600	7582776600	3.01
	treatment naïve		Cancer
CGPLBR51	Preoperative	WGS	Breast	100	8393547400	5102069000	2.02
	treatment naïve		Cancer
CGPLBR52	Preoperative	WGS	Breast	100	9491894800	6141729000	2.44
	treatment naïve		Cancer
CGPLBR55	Preoperative	WGS	Breast	100	9380109800	6518855200	2.59
	treatment naïve		Cancer
CGPLBR56	Preoperative	WGS	Breast	100	12191816800	8293011200	3.29
	treatment naïve		Cancer
CGPLBR57	Preoperative	WGS	Breast	100	9847584400	6713638000	2.66
	treatment naïve		Cancer
CGPLBR59	Preoperative	WGS	Breast	100	7476477000	5059878200	2.01
	treatment naïve		Cancer
CGPLBR60	Preoperative	WGS	Breast	100	6531354600	4331253800	1.72
	treatment naïve		Cancer
CGPLBR61	Preoperative	WGS	Breast	100	9311029200	6430920800	2.55
	treatment naïve		Cancer
CGPLBR63	Preoperative	WGS	Breast	100	8971949000	6044009600	2.40
	treatment naïve		Cancer
CGPLBR65	Preoperative	WGS	Breast	100	7197301400	4835015200	1.92
	treatment naïve		Cancer
CGPLBR68	Preoperative	WGS	Breast	100	10003774000	6974918800	2.77
	treatment naïve		Cancer
CGPLBR69	Preoperative	WGS	Breast	100	10080881800	6903459200	2.74
	treatment naïve		Cancer
CGPLBR70	Preoperative	WGS	Breast	100	8824002800	6002533800	2.38
	treatment naïve		Cancer
CGPLBR71	Preoperative	WGS	Breast	100	10164136800	6994668600	2.78
	treatment naïve		Cancer
CGPLBR72	Preoperative	WGS	Breast	100	18416841400	12328783000	4.89
	treatment naïve		Cancer
CGPLBR73	Preoperative	WGS	Breast	100	10281460200	7078613200	2.81
	treatment naïve		Cancer
CGPLBR76	Preoperative	WGS	Breast	100	10105270400	6800705000	2.70
	treatment naïve		Cancer
CGPLBR81	Preoperative	WGS	Breast	100	5087126000	3273367200	1.30
	treatment naïve		Cancer
CGPLBR82	Preoperative	WGS	Breast	100	10576496600	7186662600	2.85
	treatment naïve		Cancer
CGPLBR83	Preoperative	WGS	Breast	100	8977124400	5947525000	2.36
	treatment naïve		Cancer
CGPLBR84	Preoperative	WGS	Breast	100	6272538600	4066870600	1.61
	treatment naïve		Cancer
CGPLBR87	Preoperative	WGS	Breast	100	8460954800	5375710200	2.13
	treatment naïve		Cancer
CGPLBR88	Preoperative	WGS	Breast	100	8665810400	5499898200	2.18
	treatment naïve		Cancer
CGPLBR90	Preoperative	WGS	Breast	100	6663469200	4392442400	1.74
	treatment naïve		Cancer
CGPLBR91	Preoperative	WGS	Breast	100	10933002400	7647842000	3.03
	treatment naïve		Cancer
CGPLBR92	Preoperative	WGS	Breast	100	10392674000	6493598000	2.58
	treatment naïve		Cancer
CGPLBR93	Preoperative	WGS	Breast	100	5659836000	3931106800	1.56
	treatment naïve		Cancer
CGPLH189	Preoperative	WGS	Healthy	100	11400610400	7655568800	3.04
	treatment naïve
CGPLH190	Preoperative	WGS	Healthy	100	11444671600	7581175200	3.01
	treatment naïve
CGPLH192	Preoperative	WGS	Healthy	100	12199010800	8126804800	3.22
	treatment naïve
CGPLH193	Preoperative	WGS	Healthy	100	10201897600	6635285400	2.63
	treatment naïve
CGPLH194	Preoperative	WGS	Healthy	100	11005087400	7081652600	2.81
	treatment naïve
CGPLH196	Preoperative	WGS	Healthy	100	12891462800	8646881800	3.43
	treatment naïve
CGPLH197	Preoperative	WGS	Healthy	100	11961841600	8052855200	3.20
	treatment naïve
CGPLH198	Preoperative	WGS	Healthy	100	13605489000	8885716000	3.53
	treatment naïve
CGPLH199	Preoperative	WGS	Healthy	100	1818090200	5615316000	2.23
	treatment naïve
CGPLH200	Preoperative	WGS	Healthy	100	14400027600	9310342000	3.69
	treatment naïve
CGPLH201	Preoperative	WGS	Healthy	100	6208766800	4171848400	1.66
	treatment naïve
CGPLH202	Preoperative	WGS	Healthy	100	11282922800	7363530600	2.92
	treatment naïve
CGPLH203	Preoperative	WGS	Healthy	100	13540689600	9068747600	3.60
	treatment naïve
CGPLH205	Preoperative	WGS	Healthy	100	10343537800	6696988600	2.66
	treatment naïve
CGPLH208	Preoperative	WGS	Healthy	100	12796300000	8272073400	3.28
	treatment naïve
CGPLH209	Preoperative	WGS	Healthy	100	13123035400	8531813600	3.39
	treatment naïve
CGPLH210	Preoperative	WGS	Healthy	100	10184218800	6832204600	2.71
	treatment naïve
CGPLH211	Preoperative	WGS	Healthy	100	14655260200	8887067600	3.53
	treatment naïve
CGPLH300	Preoperative	WGS	Healthy	100	7062083400	4553351200	1.81
	treatment naïve
CGPLH307	Preoperative	WGS	Healthy	100	7239128200	4547697200	1.80
	treatment naïve
CGPLH308	Preoperative	WGS	Healthy	100	8512551400	5526653600	2.19
	treatment naïve
CGPLH309	Preoperative	WGS	Healthy	100	11664474200	7431836600	2.95
	treatment naïve
CGPLH310	Preoperative	WGS	Healthy	100	11045691000	7451506200	2.96
	treatment naïve
CGPLH311	Preoperative	WGS	Healthy	100	10406803200	6786479600	2.69
	treatment naïve
CGPLH314	Preoperative	WGS	Healthy	100	10371343800	6925866600	2.75
	treatment naïve
CGPLH315	Preoperative	WGS	Healthy	100	9508538400	6208744600	2.46
	treatment naïve
CGPLH316	Preoperative	WGS	Healthy	100	10131063600	6891181000	2.73
	treatment naïve
CGPLH317	Preoperative	WGS	Healthy	100	8364314400	5302232600	2.10
	treatment naïve
CGPLH319	Preoperative	WGS	Healthy	100	8780528200	5585897000	2.22
	treatment naïve
CGPLH320	Preoperative	WGS	Healthy	100	8956232600	5784619200	2.30
	treatment naïve
CGPLH322	Preoperative	WGS	Healthy	100	9563837800	6445517800	2.56
	treatment naïve
CGPLH324	Preoperative	WGS	Healthy	100	6765038600	4469201600	1.77
	treatment naïve
CGPLH325	Preoperative	WGS	Healthy	100	8008213400	5099262800	2.02
	treatment naïve
CGPLH326	Preoperative	WGS	Healthy	100	9554226200	6112544800	2.43
	treatment naïve
CGPLH327	Preoperative	WGS	Healthy	100	8239168800	5351280200	2.12
	treatment naïve
CGPLH328	Preoperative	WGS	Healthy	100	7197086800	4516894800	1.79
	treatment naïve
CGPLH329	Preoperative	WGS	Healthy	100	8921554800	5493709800	2.18
	treatment naïve
CGPLH330	Preoperative	WGS	Healthy	100	10693603400	7077793600	2.81
	treatment naïve
CGPLH331	Preoperative	WGS	Healthy	100	8982792000	5538096200	2.20
	treatment naïve
CGPLH333	Preoperative	WGS	Healthy	100	7856985400	5178829600	2.06
	treatment naïve
CGPLH335	Preoperative	WGS	Healthy	100	9370663400	6035739400	2.40
	treatment naïve
CGPLH336	Preoperative	WGS	Healthy	100	8002498200	5340331400	2.12
	treatment naïve
CGPLH337	Preoperative	WGS	Healthy	100	7399022000	4954467600	1.97
	treatment naïve
CGPLH338	Preoperative	WGS	Healthy	100	8917121600	6170927200	2.45
	treatment naïve
CGPLH339	Preoperative	WGS	Healthy	100	8591130800	5866411400	2.33
	treatment naïve
CGPLH340	Preoperative	WGS	Healthy	100	8046351000	5368062000	2.13
	treatment naïve
CGPLH341	Preoperative	WGS	Healthy	100	7914788600	5200304800	2.06
	treatment naïve
CGPLH342	Preoperative	WGS	Healthy	100	8633473000	5701972400	2.26
	treatment naïve
CGPLH343	Preoperative	WGS	Healthy	100	6694769800	4410670800	1.75
	treatment naïve
CGPLH344	Preoperative	WGS	Healthy	100	7628192400	4961476600	1.97
	treatment naïve
CGPLH345	Preoperative	WGS	Healthy	100	7121569400	4747223000	1.88
	treatment naïve
CGPLH346	Preoperative	WGS	Healthy	100	7707924600	4873321600	1.93
	treatment naïve
CGPLH35	Preoperative	WGS	Healthy	100	47305985200	4774186200	12.63
	treatment naïve
CGPLH350	Preoperative	WGS	Healthy	100	9745839800	6054055200	2.40
	treatment naïve
CGPLH351	Preoperative	WGS	Healthy	100	13317435800	6714465000	3.46
	treatment naïve
CGPLH352	Preoperative	WGS	Healthy	100	7659351600	4752309400	1.89
	treatment naïve
CGPLH353	Preoperative	WGS	Healthy	100	8435782400	5275098200	2.09
	treatment naïve
CGPLH354	Preoperative	WGS	Healthy	100	8018644000	4857577600	1.93
	treatment naïve
CGPLH355	Preoperative	WGS	Healthy	100	8624675800	5709726400	2.27
	treatment naïve
CGPLH356	Preoperative	WGS	Healthy	100	8817952800	5729595200	2.27
	treatment naïve
CGPLH357	Preoperative	WGS	Healthy	100	11931696200	7690004400	3.05
	treatment naïve
CGPLH358	Preoperative	WGS	Healthy	100	12802561200	8451274800	3.35
	treatment naïve
CGPLH36	Preoperative	WGS	Healthy	100	40173545600	3974810400	10.52
	treatment naïve
CGPLH360	Preoperative	WGS	Healthy	100	7280078400	4918566200	1.95
	treatment naïve
CGPLH361	Preoperative	WGS	Healthy	100	7493498400	4966813800	1.97
	treatment naïve
CGPLH362	Preoperative	WGS	Healthy	100	11345644200	7532133600	2.99
	treatment naïve
CGPLH363	Preoperative	WGS	Healthy	100	6117382800	3965952400	1.57
	treatment naïve
CGPLH364	Preoperative	WGS	Healthy	100	10823498400	7195657000	2.86
	treatment naïve
CGPLH365	Preoperative	WGS	Healthy	100	5938367400	3954556200	1.57
	treatment naïve
CGPLH366	Preoperative	WGS	Healthy	100	7063168600	4731853000	1.88
	treatment naïve
CGPLH367	Preoperative	WGS	Healthy	100	7119631800	4627888200	1.84
	treatment naïve
CGPLH368	Preoperative	WGS	Healthy	100	7726718400	4975233400	1.97
	treatment naïve
CGPLH369	Preoperative	WGS	Healthy	100	10967584200	7130956800	2.83
	treatment naïve
CGPLH37	Preoperative	WGS	Healthy	100	45970545400	4591328800	12.15
	treatment naïve
CGPLH370	Preoperative	WGS	Healthy	100	9237170600	6106373800	2.42
	treatment naïve
CGPLH371	Preoperative	WGS	Healthy	100	8077798800	5237070600	2.08
	treatment naïve
CGPLH380	Preoperative	WGS	Healthy	100	14049589200	8614241200	3.42
	treatment naïve
CGPLH381	Preoperative	WGS	Healthy	100	16743792000	10767882800	4.27
	treatment naïve
CGPLH382	Preoperative	WGS	Healthy	100	18474025200	12276437200	4.87
	treatment naïve
CGPLH383	Preoperative	WGS	Healthy	100	13215954000	8430420600	3.35
	treatment naïve
CGPLH384	Preoperative	WGS	Healthy	100	8481814000	5463636200	2.17
	treatment naïve
CGPLH385	Preoperative	WGS	Healthy	100	9596118800	6445445600	2.56
	treatment naïve
CGPLH386	Preoperative	WGS	Healthy	100	7399540400	4915484800	1.95
	treatment naïve
CGPLH387	Preoperative	WGS	Healthy	100	6860332600	4339724400	1.72
	treatment naïve
CGPLH388	Preoperative	WGS	Healthy	100	8679705600	5463945400	2.17
	treatment naïve
CGPLH389	Preoperative	WGS	Healthy	100	7266863600	4702386000	1.87
	treatment naïve
CGPLH390	Preoperative	WGS	Healthy	100	7509035600	4913901800	1.95
	treatment naïve
CGPLH391	Preoperative	WGS	Healthy	100	7252286000	4702404800	1.87
	treatment naïve
CGPLH392	Preoperative	WGS	Healthy	100	7302618200	4722407000	1.87
	treatment naïve
CGPLH393	Preoperative	WGS	Healthy	100	8879138000	5947871800	2.36
	treatment naïve
CGPLH394	Preoperative	WGS	Healthy	100	8737031000	5599777400	2.22
	treatment naïve
CGPLH395	Preoperative	WGS	Healthy	100	7783904800	4907146000	1.95
	treatment naïve
CGPLH396	Preoperative	WGS	Healthy	100	7585567200	5076638200	2.01
	treatment naïve
CGPLH398	Preoperative	WGS	Healthy	100	13001418200	8607025000	3.42
	treatment naïve
CGPLH399	Preoperative	WGS	Healthy	100	9867699200	5526646000	2.19
	treatment naïve
CGPLH400	Preoperative	WGS	Healthy	100	10573939000	6290438200	2.50
	treatment naïve
CGPLH401	Preoperative	WGS	Healthy	100	9415150000	6139638000	2.44
	treatment naïve
CGPLH402	Preoperative	WGS	Healthy	100	5541458000	2972027800	1.18
	treatment naïve
CGPLH403	Preoperative	WGS	Healthy	100	6470913200	3549772600	1.41
	treatment naïve
CGPLH404	Preoperative	WGS	Healthy	100	7369651800	4120205000	1.64
	treatment naïve
CGPLH405	Preoperative	WGS	Healthy	100	7360239000	4293522600	1.70
	treatment naïve
CGPLH406	Preoperative	WGS	Healthy	100	6028125400	3426007400	1.36
	treatment naïve
CGPLH407	Preoperative	WGS	Healthy	100	7073375200	4079286800	1.62
	treatment naïve
CGPLH408	Preoperative	WGS	Healthy	100	8006103200	5121285600	2.03
	treatment naïve
CGPLH409	Preoperative	WGS	Healthy	100	7343124600	4432335600	1.76
	treatment naïve
CGPLH410	Preoperative	WGS	Healthy	100	7551842000	4818779600	1.91
	treatment naïve
CGPLH411	Preoperative	WGS	Healthy	100	6119676400	3636478400	1.44
	treatment naïve
CGPLH412	Preoperative	WGS	Healthy	100	7960821200	4935752200	1.96
	treatment naïve
CGPLH413	Preoperative	WGS	Healthy	100	7623405400	4827888400	1.92
	treatment naïve
CGPLH414	Preoperative	WGS	Healthy	100	7381312400	4743337200	1.88
	treatment naïve
CGPLH415	Preoperative	WGS	Healthy	100	7240754200	4162208800	1.65
	treatment naïve
CGPLH416	Preoperative	WGS	Healthy	100	7745658600	4670226000	1.85
	treatment naïve
CGPLH417	Preoperative	WGS	Healthy	100	7627498600	4403085600	1.75
	treatment naïve
CGPLH418	Preoperative	WGS	Healthy	100	9090285000	5094814000	2.02
	treatment naïve
CGPLH419	Preoperative	WGS	Healthy	100	7914120200	5078389800	2.02
	treatment naïve
CGPLH42	Preoperative	WGS	Healthy	100	39492040600	3901039400	10.32
	treatment naïve
CGPLH420	Preoperative	WGS	Healthy	100	7014307800	4711393600	1.87
	treatment naïve
CGPLH422	Preoperative	WGS	Healthy	100	9103972800	6053559800	2.40
	treatment naïve
CGPLH423	Preoperative	WGS	Healthy	100	10154714200	6128800200	2.43
	treatment naïve
CGPLH424	Preoperative	WGS	Healthy	100	11002394000	6573756000	2.61
	treatment naïve
CGPLH425	Preoperative	WGS	Healthy	100	14681352600	9272557000	3.68
	treatment naïve
CGPLH426	Preoperative	WGS	Healthy	100	8336731000	5177430800	2.05
	treatment naïve
CGPLH427	Preoperative	WGS	Healthy	100	8242924400	5632991800	2.24
	treatment naïve
CGPLH428	Preoperative	WGS	Healthy	100	8512550400	5604756600	2.22
	treatment naïve
CGPLH429	Preoperative	WGS	Healthy	100	8369802800	5477121400	2.17
	treatment naïve
CGPLH43	Preoperative	WGS	Healthy	100	38513193400	3815698400	10.10
	treatment naïve
CGPLH430	Preoperative	WGS	Healthy	100	10357365400	6841611000	2.71
	treatment naïve
CGPLH431	Preoperative	WGS	Healthy	100	7599875800	5006909000	1.99
	treatment naïve
CGPLH432	Preoperative	WGS	Healthy	100	7932532400	4932304200	1.96
	treatment naïve
CGPLH434	Preoperative	WGS	Healthy	100	10417028600	6965998800	2.76
	treatment naïve
CGPLH435	Preoperative	WGS	Healthy	100	8747793800	5677115200	2.25
	treatment naïve
CGPLH436	Preoperative	WGS	Healthy	100	7990589400	5228737800	2.07
	treatment naïve
CGPLH437	Preoperative	WGS	Healthy	100	10156991200	6935537200	2.75
	treatment naïve
CGPLH438	Preoperative	WGS	Healthy	100	9473604000	6445455600	2.56
	treatment naïve
CGPLH439	Preoperative	WGS	Healthy	100	8303723400	5439877200	2.16
	treatment naïve
CGPLH440	Preoperative	WGS	Healthy	100	9055233800	6018631400	2.39
	treatment naïve
CGPLH441	Preoperative	WGS	Healthy	100	10290682000	6896415200	2.74
	treatment naïve
CGPLH442	Preoperative	WGS	Healthy	100	9876551600	6591249800	2.62
	treatment naïve
CGPLH443	Preoperative	WGS	Healthy	100	9837225800	6360740800	2.52
	treatment naïve
CGPLH444	Preoperative	WGS	Healthy	100	9199271400	5755941600	2.28
	treatment naïve
CGPLH445	Preoperative	WGS	Healthy	100	8089236400	5218259800	2.07
	treatment naïve
CGPLH446	Preoperative	WGS	Healthy	100	7890664200	5181606000	2.06
	treatment naïve
CGPLH447	Preoperative	WGS	Healthy	100	7775775000	5120239800	2.03
	treatment naïve
CGPLH448	Preoperative	WGS	Healthy	100	8686964800	5605079200	2.22
	treatment naïve
CGPLH449	Preoperative	WGS	Healthy	100	8604545400	5527726600	2.19
	treatment naïve
CGPLH45	Preoperative	WGS	Healthy	100	39029653000	3771601200	9.98
	treatment naïve
CGPLH450	Preoperative	WGS	Healthy	100	8428254800	5439950000	2.16
	treatment naïve
CGPLH451	Preoperative	WGS	Healthy	100	8128977600	5186265600	2.06
	treatment naïve
CGPLH452	Preoperative	WGS	Healthy	100	6474313400	4216316400	1.67
	treatment naïve
CGPLH453	Preoperative	WGS	Healthy	100	9831832800	6224917600	2.47
	treatment naïve
CGPLH455	Preoperative	WGS	Healthy	100	7373753000	4593473600	1.82
	treatment naïve
CGPLH456	Preoperative	WGS	Healthy	100	8455416200	5457148200	2.17
	treatment naïve
CGPLH457	Preoperative	WGS	Healthy	100	8647618000	5534503800	2.20
	treatment naïve
CGPLH458	Preoperative	WGS	Healthy	100	6633156400	4415186000	1.75
	treatment naïve
CGPLH459	Preoperative	WGS	Healthy	100	8361048200	5497193800	2.18
	treatment naïve
CGPLH46	Preoperative	WGS	Healthy	100	35361484600	3516232800	9.30
	treatment naïve
CGPLH460	Preoperative	WGS	Healthy	100	6788835400	4472282800	1.77
	treatment naïve
CGPLH463	Preoperative	WGS	Healthy	100	8534880800	5481759200	2.18
	treatment naïve
CGPLH464	Preoperative	WGS	Healthy	100	6692520000	4184463400	1.66
	treatment naïve
CGPLH465	Preoperative	WGS	Healthy	100	7772884600	4878430800	1.94
	treatment naïve
CGPLH466	Preoperative	WGS	Healthy	100	9056275000	5830877400	2.31
	treatment naïve
CGPLH467	Preoperative	WGS	Healthy	100	9331419200	4585861000	1.82
	treatment naïve
CGPLH468	Preoperative	WGS	Healthy	100	9334067400	6314830400	2.51
	treatment naïve
CGPLH469	Preoperative	WGS	Healthy	100	7376691000	4545246600	1.80
	treatment naïve
CGPLH47	Preoperative	WGS	Healthy	100	38485647600	3534883600	9.35
	treatment naïve
CGPLH470	Preoperative	WGS	Healthy	100	7899727600	5221650600	2.07
	treatment naïve
CGPLH471	Preoperative	WGS	Healthy	100	9200430600	6102371000	2.42
	treatment naïve
CGPLH472	Preoperative	WGS	Healthy	100	8143742400	5399946600	2.14
	treatment naïve
CGPLH473	Preoperative	WGS	Healthy	100	8123924600	5419825400	2.15
	treatment naïve
CGPLH474	Preoperative	WGS	Healthy	100	8853071400	6084059400	2.41
	treatment naïve
CGPLH475	Preoperative	WGS	Healthy	100	8115374000	5291718000	2.10
	treatment naïve
CGPLH476	Preoperative	WGS	Healthy	100	8163162600	5096869600	2.02
	treatment naïve
CGPLH477	Preoperative	WGS	Healthy	100	8350093200	5465468600	2.17
	treatment naïve
CGPLH478	Preoperative	WGS	Healthy	100	8259642200	5406516200	2.15
	treatment naïve
CGPLH479	Preoperative	WGS	Healthy	100	8027598600	5417376800	2.15
	treatment naïve
CGPLH48	Preoperative	WGS	Healthy	100	42232410000	4165893400	11.02
	treatment naïve
CGPLH480	Preoperative	WGS	Healthy	100	7832983200	5020127000	1.99
	treatment naïve
CGPLH481	Preoperative	WGS	Healthy	100	7578518800	4883280800	1.94
	treatment naïve
CGPLH482	Preoperative	WGS	Healthy	100	8279364800	5652263600	2.24
	treatment naïve
CGPLH483	Preoperative	WGS	Healthy	100	8660338800	5823859200	2.31
	treatment naïve
CGPLH484	Preoperative	WGS	Healthy	100	8445420000	5794328000	2.30
	treatment naïve
CGPLH485	Preoperative	WGS	Healthy	100	8371255400	5490207800	2.18
	treatment naïve
CGPLH486	Preoperative	WGS	Healthy	100	8216712200	5506871000	2.19
	treatment naïve
CGPLP487	Preoperative	WGS	Healthy	100	7936294200	5309250200	2.11
	treatment naïve
CGPLH488	Preoperative	WGS	Healthy	100	8355603600	5453160000	2.16
	treatment naïve
CGPLH49	Preoperative	WGS	Healthy	100	33912191800	3310056000	8.76
	treatment naïve
CGPLH490	Preoperative	WGS	Healthy	100	7768712400	5175567800	2.05
	treatment naïve
CGPLH491	Preoperative	WGS	Healthy	100	9070904000	6011275000	2.39
	treatment naïve
CGPLH492	Preoperative	WGS	Healthy	100	7208727200	4753213800	1.89
	treatment naïve
CGPLH493	Preoperative	WGS	Healthy	100	10542882600	7225870800	2.87
	treatment naïve
CGPLH494	Preoperative	WGS	Healthy	100	10908197600	7046645000	2.80
	treatment naïve
CGPLH495	Preoperative	WGS	Healthy	100	8945040400	5891697800	2.34
	treatment naïve
CGPLH496	Preoperative	WGS	Healthy	100	10859723400	7549608000	3.00
	treatment naïve
CGPLH497	Preoperative	WGS	Healthy	100	9630507400	6473162800	2.57
	treatment naïve
CGPLH498	Preoperative	WGS	Healthy	100	10060232600	6744622800	2.68
	treatment naïve
CGPLH499	Preoperative	WGS	Healthy	100	10221293600	6951282800	2.76
	treatment naïve
CGPLH50	Preoperative	WGS	Healthy	100	41243860600	4073272800	10.78
	treatment naïve
CGPLH500	Preoperative	WGS	Healthy	100	9703168200	6239893800	2.48
	treatment naïve
CGPLH501	Preoperative	WGS	Healthy	100	9104779800	6161602800	2.45
	treatment naïve
CGPLH502	Preoperative	WGS	Healthy	100	8514467400	5290881400	2.10
	treatment naïve
CGPLH503	Preoperative	WGS	Healthy	100	9019992200	6100383400	2.42
	treatment naïve
CGPLH504	Preoperative	WGS	Healthy	100	9320330200	6199750200	2.46
	treatment naïve
CGPLH505	Preoperative	WGS	Healthy	100	7499497400	4914559000	1.95
	treatment naïve
CGPLH506	Preoperative	WGS	Healthy	100	10526142000	6963312600	2.76
	treatment naïve
CGPLH507	Preoperative	WGS	Healthy	100	9091018400	6146678600	2.44
	treatment naïve
CGPLH508	Preoperative	WGS	Healthy	100	10989315600	7360201400	2.92
	treatment naïve
CGPLH509	Preoperative	WGS	Healthy	100	9729084600	6702691600	2.66
	treatment naïve
CGPLH51	Preoperative	WGS	Healthy	100	35967451400	3492833200	9.24
	treatment naïve
CGPLH510	Preoperative	WGS	Healthy	100	11162691600	7626795400	3.03
	treatment naïve
CGPLH511	Preoperative	WGS	Healthy	100	11888619600	8110427600	3.22
	treatment naïve
CGPLH512	Preoperative	WGS	Healthy	100	10726438400	7110078000	2.82
	treatment naïve
CGPLH513	Preoperative	WGS	Healthy	100	10701564200	7155271400	2.84
	treatment naïve
CGPLH514	Preoperative	WGS	Healthy	100	8822067000	5958773800	2.36
	treatment naïve
CGPLH515	Preoperative	WGS	Healthy	100	7792074800	5317464600	2.11
	treatment naïve
CGPLH516	Preoperative	WGS	Healthy	100	8642620000	5846439400	2.32
	treatment naïve
CGPLH517	Preoperative	WGS	Healthy	100	11915929600	8013937000	3.18
	treatment naïve
CGPLH518	Preoperative	WGS	Healthy	100	12804517400	8606661600	3.42
	treatment naïve
CGPLH519	Preoperative	WGS	Healthy	100	11513222200	7922798400	3.14
	treatment naïve
CGPLH52	Preoperative	WGS	Healthy	100	49247304200	4849631400	12.83
	treatment naïve
CGPLH520	Preoperative	WGS	Healthy	100	8942102400	6030683400	2.39
	treatment naïve
CGPLH54	Preoperative	WGS	Healthy	100	45399346400	4466164600	11.82
	treatment naïve
CGPLH55	Preoperative	WGS	Healthy	100	42547725000	4283337600	11.33
	treatment naïve
CGPLH56	Preoperative	WGS	Healthy	100	33460308000	3226338000	8.53
	treatment naïve
CGPLH57	Preoperative	WGS	Healthy	100	36504735200	3509125000	9.28
	treatment naïve
CGPLH59	Preoperative	WGS	Healthy	100	39642810600	3820011000	10.11
	treatment naïve
CGPLH625	Preoperative	WGS	Healthy	100	6408225000	4115487600	1.63
	treatment naïve
CGPLH626	Preoperative	WGS	Healthy	100	9915193600	6391657000	2.54
	treatment naïve
CGPLH63	Preoperative	WGS	Healthy	100	37447047600	3506737000	9.28
	treatment naïve
CGPLH639	Preoperative	WGS	Healthy	100	8158965800	5216049600	2.07
	treatment naïve
CGPLH64	Preoperative	WGS	Healthy	100	34275506800	3264508000	8.63
	treatment naïve
CGPLH640	Preoperative	WGS	Healthy	100	8058876800	5333551800	2.12
	treatment naïve
CGPLH642	Preoperative	WGS	Healthy	100	7545555600	4909732800	1.95
	treatment naïve
CGPLH643	Preoperative	WGS	Healthy	100	7865776800	5254772000	2.09
	treatment naïve
CGPLH644	Preoperative	WGS	Healthy	100	6890139000	4599387400	1.83
	treatment naïve
CGPLH646	Preoperative	WGS	Healthy	100	7757219400	5077408200	2.01
	treatment naïve
CGPLH75	Preoperative	WGS	Healthy	100	23882926000	2250344400	5.95
	treatment naïve
CGPLH76	Preoperative	WGS	Healthy	100	30631483600	3086042200	8.16
	treatment naïve
CGPLH77	Preoperative	WGS	Healthy	100	31651741400	3041290200	8.04
	treatment naïve
CGPLH78	Preoperative	WGS	Healthy	100	31165831200	3130079800	8.28
	treatment naïve
CGPLH79	Preoperative	WGS	Healthy	100	31935043000	3128408200	8.27
	treatment naïve
CGPLH80	Preoperative	WGS	Healthy	100	32965093000	3311371800	8.76
	treatment naïve
CGPLH81	Preoperative	WGS	Healthy	100	27035311200	2455084400	6.49
	treatment naïve
CGPLH82	Preoperative	WGS	Healthy	100	28447051200	2893358200	7.65
	treatment naïve
CGPLH83	Preoperative	WGS	Healthy	100	26702240200	2459494000	6.50
	treatment naïve
CGPLH84	Preoperative	WGS	Healthy	100	25176861400	2524467400	6.68
	treatment naïve
CGPLLU13	Pre-treatment, Day −2	WGS	Lung	100	9126585600	5915061800	2.35
			Cancer
CGPLLU13	Post-treatment, Day 5	WGS	Lung	100	7739120200	5071745800	2.01
			Cancer
CGPLLU13	Post-treatment, Day 28	WGS	Lung	100	9081585400	5764371600	2.29
			Cancer
CGPLLU13	Post-treatment, Day 91	WGS	Lung	100	9576557000	6160760200	2.44
			Cancer
CGPLLU14	Pre-treatment, Day −38	WGS	Lung	100	13659198400	9033455800	3.58
			Cancer
CGPLLU14	Pre-treatment, Day −16	WGS	Lung	100	7178855800	4856648600	1.93
			Cancer
CGPLLU14	Pre-treatment, Day −3	WGS	Lung	100	7653473000	4816193600	1.91
			Cancer
CGPLLU14	Pre-treatment, Day 0	WGS	Lung	100	7851997400	5193256600	2.06
			Cancer
CGPLLU14	Post-treatment, Day 0.33	WGS	Lung	100	7193040800	4869701600	1.93
			Cancer
CGPLLU14	Post-treatment, Day 7	WGS	Lung	100	7102050000	4741432600	1.88
			Cancer
CGPLLU144	Preoperative	WGS	Lung	100	4934013600	3415936400	1.36
	treatment naïve		Cancer
CGPLLU147	Preoperative	WGS	Lung	100	24409561000	2118672800	5.61
	treatment naïve		Cancer
CGPLLU161	Preoperative	WGS	Lung	100	8998813400	6016145000	2.39
	treatment naïve		Cancer
CGPLLU162	Preoperative	WGS	Lung	100	9709792400	6407866400	2.54
	treatment naïve		Cancer
CGPLLU163	Preoperative	WGS	Lung	100	9150620200	6063569800	2.41
	treatment naïve		Cancer
CGPLLU165	Preoperative	WGS	Lung	100	28374436400	2651138600	7.01
	treatment naïve		Cancer
CGPLLU168	Preoperative	WGS	Lung	100	5692739400	3695191000	1.47
	treatment naïve		Cancer
CGPLLU169	Preoperative	WGS	Lung	100	9093975600	5805320800	2.30
	treatment naïve		Cancer
CGPLLU175	Preoperative	WGS	Lung	100	33794816800	3418750400	9.04
	treatment naïve		Cancer
CGPLLU176	Preoperative	WGS	Lung	100	8778553800	5794950200	2.30
	treatment naïve		Cancer
CGPLLU177	Preoperative	WGS	Lung	100	3734614800	2578696200	1.02
	treatment naïve		Cancer
CGPLLU180	Preoperative	WGS	Lung	100	28305936600	2756034200	7.29
	treatment naïve		Cancer
CGPLLU198	Preoperative	WGS	Lung	100	23244959200	2218577200	5.86
	treatment naïve		Cancer
CGPLLU202	Preoperative	WGS	Lung	100	21110128200	1831279400	4.84
	treatment naïve		Cancer
CGPLLU203	Preoperative	WGS	Lung	100	4304235600	2896429000	1.15
	treatment naïve		Cancer
CGPLLU205	Preoperative	WGS	Lung	100	10502467000	7386984800	2.93
	treatment naïve		Cancer
CGPLLU206	Preoperative	WGS	Lung	100	21888248200	2026666000	5.36
	treatment naïve		Cancer
CGPLLU207	Preoperative	WGS	Lung	100	10806230600	7363049000	2.92
	treatment naïve		Cancer
CGPLLU208	Preoperative	WGS	Lung	100	7795426800	5199545800	2.06
	treatment naïve		Cancer
CGPLLU209	Preoperative	WGS	Lung	100	26174542000	2621961800	6.93
	treatment naïve		Cancer
CGPLLU244	Pre-treatment, Day −7	WGS	Lung	100	9967531400	6704365800	2.66
			Cancer
CGPLLU244	Pre-treatment, Day −1	WGS	Lung	100	9547119200	5785172600	2.30
			Cancer
CGPLLU244	Post-treatment Day 6	WGS	Lung	100	9535898600	6452174000	2.56
			Cancer
CGPLLU244	Post-treatment, Day 62	WGS	Lung	100	8783628600	5914149000	2.35
			Cancer
CGPLLU245	Pre-treatment, Day −32	WGS	Lung	100	10025823200	6313303800	2.51
			Cancer
CGPLLU245	Pre-treatment, Day 0	WGS	Lung	100	9462480400	6612867800	2.62
			Cancer
CGPLLU245	Post-treatment, Day 7	WGS	Lung	100	9143825000	6431013200	2.55
			Cancer
CGPLLU245	Post-treatment, Day 21	WGS	Lung	100	9072713800	6368533000	2.53
			Cancer
CGPLLU246	Pre-treatment, Day −21	WGS	Lung	100	9579787000	6458003400	2.56
			Cancer
CGPLLU246	Pre-treatment, Day 0	WGS	Lung	100	9512703600	6440535600	2.56
			Cancer
CGPLLU246	Post-treatment, Day 9	WGS	Lung	100	9512646000	6300939200	2.50
			Cancer
CGPLLU246	Post-treatment, Day 42	WGS	Lung	100	11136103000	7358747400	2.92
			Cancer
CGPLLU264	Pre-treatment, Day −1	WGS	Lung	100	9196005000	6239803600	2.48
			Cancer
CGPLLU264	Post-treatment, Day 6	WGS	Lung	100	8247416600	5600454200	2.22
			Cancer
CGPLLU264	Post-treatment, Day 27	WGS	Lung	100	8681022200	5856109000	2.32
			Cancer
CGPLLU264	Post-treatment, Day 69	WGS	Lung	100	8931976400	5974246000	2.37
			Cancer
CGPLLU265	Pre-treatment, Day 0	WGS	Lung	100	9460534000	6111185200	2.43
			Cancer
CGPLLU265	Post-treatment, Day 3	WGS	Lung	100	8051601200	4984166600	1.98
			Cancer
CGPLLU265	Post-treatment, Day 7	WGS	Lung	100	8082224600	5110092600	2.03
			Cancer
CGPLLU265	Post-treatment, Day 84	WGS	Lung	100	8368637400	5369526400	2.13
			Cancer
CGPLLU266	Pre-treatment, Day 0	WGS	Lung	100	8583766400	5846473600	2.32
			Cancer
CGPLL266	Post-treatment, Day 16	WGS	Lung	100	8795793600	5984531400	2.37
			Cancer
CGPLLU266	Post-treatment, Day 83	WGS	Lung	100	9157947600	6227735000	2.47
			Cancer
CGPLLU266	Post-treatment, Day 328	WGS	Lung	100	7299455400	5049379000	2.00
			Cancer
CGPLLU267	Pre-treatment, Day −1	WGS	Lung	100	10658657800	6892067000	2.73
			Cancer
CGPLLU267	Post-treatment, Day 34	WGS	Lung	100	8492833400	5101097800	2.02
			Cancer
CGPLLU267	Post-treatment, Day 90	WGS	Lung	100	12030314800	7757930400	3.08
			Cancer
CGPLLU269	Pre-treatment, Day 0	WGS	Lung	100	9170168000	5830454400	2.31
			Cancer
CGPLLU269	Post-treatment, Day 9	WGS	Lung	100	8905640400	5298461400	2.10
			Cancer
CGPLLU269	Post-treatment, Day 28	WGS	Lung	100	8455306600	5387927400	2.14
			Cancer
CGPLLU271	Post-treatment, Day 259	WGS	Lung	100	8112060400	5404979000	2.14
			Cancer
CGPLLU271	Pre-treatment, Day 0	WGS	Lung	100	13150818200	8570453400	3.40
			Cancer
CGPLLU271	Post-treatment, Day 6	WGS	Lung	100	9008880600	5854051400	2.32
			Cancer
CGPLLU271	Post-treatment, Day 20	WGS	Lung	100	8670913000	5461577000	2.17
			Cancer
CGPLLU271	Post-treatment, Day 104	WGS	Lung	100	8887441400	5609039000	2.23
			Cancer
CGPLLU43	Pre-treatment, Day −1	WGS	Lung	100	8407811200	5203486400	2.06
			Cancer
CGPLLU43	Post-treatment, Day 6	WGS	Lung	100	9264335200	5626714400	2.23
			Cancer
CGPLLU43	Post-treatment, Day 27	WGS	Lung	100	8902283000	5485656200	2.18
			Cancer
CGPLLU43	Post-treatment, Day 83	WGS	Lung	100	9201509200	5075084200	2.33
			Cancer
CGPLLU86	Pre-treatment, Day 0	WGS	Lung	100	9152729200	6248173200	2.48
			Cancer
CGPLLU86	Post-treatment, Day 0.5	WGS	Lung	100	6703253000	4663026800	1.85
			Cancer
CGPLLU86	Post-treatment, Day 7	WGS	Lung	100	6590121400	4559562400	1.81
			Cancer
CGPLLU86	Post-treatment, Day 17	WGS	Lung	100	8653551800	5900136000	2.34
			Cancer
CGPLLU88	Pre-treatment, Day 0	WGS	Lung	100	8096528000	5505475400	2.18
			Cancer
CGPLLU88	Post-treatment, Day 7	WGS	Lung	100	8283192200	5784217600	2.30
			Cancer
CGPLLU88	Post-treatment, Day 297	WGS	Lung	100	9297110800	6407258000	2.54
			Cancer
CGPLLU89	Pre-treatment, Day 0	WGS	Lung	100	7842145200	5356095400	2.13
			Cancer
CGPLLU89	Post-treatment, Day 7	WGS	Lung	100	7234220200	4930375200	1.96
			Cancer
CGPLLU89	Post-treatment, Day 22	WGS	Lung	100	6242889800	4057361000	1.61
			Cancer
CGPLOV11	Preoperative	WGS	Ovarian	100	8985130400	5871959600	2.33
	treatment naïve		Cancer
CGPLOV12	Preoperative	WGS	Ovarian	100	9705820000	6430505400	2.55
	treatment naïve		Cancer
CGPLOV13	Preoperative	WGS	Ovarian	100	10307949490	7029712000	2.79
	treatment naïve		Cancer
CGPLOV15	Preoperative	WGS	Ovarian	100	8472829400	5562142400	2.21
	treatment naïve		Cancer
CGPLOV16	Preoperative	WGS	Ovarian	100	10977781000	7538581600	2.99
	treatment naïve		Cancer
CGPLOV19	Preoperative	WGS	Ovarian	100	8800876200	5855304000	2.32
	treatment naïve		Cancer
CGPLOV20	Preoperative	WGS	Ovarian	100	8714443600	5695165800	2.26
	treatment naïve		Cancer
CGPLOV21	Preoperative	WGS	Ovarian	100	10180394800	7120260400	2.83
	treatment naïve		Cancer
CGPLOV22	Preoperative	WGS	Ovarian	100	10107760000	6821916800	2.71
	treatment naïve		Cancer
CGPLOV23	Preoperative	WGS	Ovarian	100	10643399800	7206330800	2.86
	treatment naïve		Cancer
CGPLOV24	Preoperative	WGS	Ovarian	100	6780929000	4623300400	1.83
	treatment naïve		Cancer
CGPLOV25	Preoperative	WGS	Ovarian	100	7817548600	5359975200	2.13
	treatment naïve		Cancer
CGPLOV26	Preoperative	WGS	Ovarian	100	11763101400	8178024400	3.25
	treatment naïve		Cancer
CGPLOV28	Preoperative	WGS	Ovarian	100	9522546400	6259423400	2.48
	treatment naïve		Cancer
CGPLOV31	Preoperative	WGS	Ovarian	100	9104831200	6109358400	2.42
	treatment naïve		Cancer
CGPLOV32	Preoperative	WGS	Ovarian	100	9222073600	6035150000	2.39
	treatment naïve		Cancer
CGPLOV37	Preoperative	WGS	Ovarian	100	8898328600	5971018200	2.37
	treatment naïve		Cancer
CGPLOV38	Preoperative	WGS	Ovarian	100	8756025200	5861536600	2.33
	treatment naïve		Cancer
CGPLOV40	Preoperative	WGS	Ovarian	100	9709391600	6654707200	2.64
	treatment naïve		Cancer
CGPLOV41	Preoperative	WGS	Ovarian	100	8923625000	5973070400	2.37
	treatment naïve		Cancer
CGPLOV42	Preoperative	WGS	Ovarian	100	10719380400	7353214200	2.92
	treatment naïve		Cancer
CGPLOV43	Preoperative	WGS	Ovarian	100	10272189000	6423288600	2.55
	treatment naïve		Cancer
CGPLOV44	Preoperative	WGS	Ovarian	100	9861862600	6769185800	2.69
	treatment naïve		Cancer
CGPLOV46	Preoperative	WGS	Ovarian	100	8788956400	5789863400	2.30
	treatment naïve		Cancer
CGPLOV47	Preoperative	WGS	Ovarian	100	9380561800	6480763600	2.57
	treatment naïve		Cancer
CGPLOV48	Preoperative	WGS	Ovarian	100	9258552600	6380106400	2.53
	treatment naïve		Cancer
CGPLOV49	Preoperative	WGS	Ovarian	100	8787025400	6134503600	2.43
	treatment naïve		Cancer
CGPLOV50	Preoperative	WGS	Ovarian	100	10144154400	6984721400	2.77
	treatment naïve		Cancer
CGPLPA112	Preoperative	WGS	Pancreatic	100	12740651400	9045622000	3.59
	treatment naïve		Cancer
CGPLPA113	Preoperative	WGS	Duodenal	100	8802479000	5909030800	2.34
	treatment naïve		Cancer
CGPLPA114	Preoperative	WGS	Bile Duct	100	8792313600	6019061000	2.39
	treatment naïve		Cancer
CGPLPA115	Preoperative	WGS	Bile Duct	100	8636551400	5958809000	2.36
	treatment naïve		Cancer
CGPLPA117	Preoperative	WGS	Bile Duct	100	9128885200	6288833200	2.50
	treatment naïve		Cancer
CGPLPA118	Preoperative	WGS	Bile Duct	100	7931485800	5407532800	2.15
	treatment naïve		Cancer
CGPLPA122	Preoperative	WGS	Bile Duct	100	10888985000	7530118800	2.99
	treatment naïve		Cancer
CGPLPA124	Preoperative	WGS	Bile Duct	100	8562012400	5860171000	2.33
	treatment naïve		Cancer
CGPLPA125	Preoperative	WGS	Bile Duct	100	9715576600	6390321000	2.54
	treatment naïve		Cancer
CGPLPA126	Preoperative	WGS	Bile Duct	100	8056768800	5651600800	2.24
	treatment naïve		Cancer
CGPLPA127	Preoperative	WGS	Bile Duct	100	8000301000	5382987600	2.14
	treatment naïve		Cancer
CGPLPA128	Preoperative	WGS	Bile Duct	100	6165751600	4256521400	1.69
	treatment naïve		Cancer
CGPLPA129	Preoperative	WGS	Bile Duct	100	7143147400	4917370400	1.95
	treatment naïve		Cancer
CGPLPA130	Preoperative	WGS	Bile Duct	100	5664035000	3603919400	1.43
	treatment naïve		Cancer
CGPLPA131	Preoperative	WGS	Bile Duct	100	8292982000	5844942000	2.32
	treatment naïve		Cancer
CGPLPA134	Preoperative	WGS	Bile Duct	100	7088917000	5048887600	2.00
	treatment naïve		Cancer
CGPLPA135	Preoperative	WGS	Bile Duct	100	8759665600	5800618200	2.30
	treatment naïve		Cancer
CGPLPA136	Preoperative	WGS	Bile Duct	100	7539715800	5248227600	2.08
	treatment naïve		Cancer
CGPLPA137	Preoperative	WGS	Bile Duct	100	8391815400	5901273800	2.34
	treatment naïve		Cancer
CGPLPA139	Preoperative	WGS	Bile Duct	100	8992280200	6328314400	2.51
	treatment naïve		Cancer
CGPLPA14	Preoperative	WGS	Pancreatic	100	8787706200	5731317600	2.27
	treatment naïve		Cancer
CGPLPA140	Preoperative	WGS	Bile Duct	100	16365641800	11216732000	4.45
	treatment naïve		Cancer
CGPLPA141	Preoperative	WGS	Bile Duct	100	15086298000	10114790200	4.01
	treatment naïve		Cancer
CGPLPA15	Preoperative	WGS	Pancreatic	100	8255566800	5531677600	2.20
	treatment naïve		Cancer
CGPLPA155	Preoperative	WGS	Bile Duct	100	9457155800	6621881800	2.63
	treatment naïve		Cancer
CGPLPA156	Preoperative	WGS	Pancreatic	100	9345385800	6728653000	2.67
	treatment naïve		Cancer
CGPLPA165	Preoperative	WGS	Bile Duct	100	8356604600	5829895800	2.31
	treatment naïve		Cancer
CGPLPA168	Preoperative	WGS	Bile Duct	100	10355661600	7048115500	2.80
	treatment naïve		Cancer
CGPLPA17	Preoperative	WGS	Pancreatic	100	8073547400	4687808000	1.86
	treatment naïve		Cancer
CGPLPA184	Preoperative	WGS	Bile Duct	100	9014218400	6230922200	2.47
	treatment naïve		Cancer
CGPLPA187	Preoperative	WGS	Bile Duct	100	8883536200	6140874400	2.44
	treatment naïve		Cancer
CGPLPA23	Preoperative	WGS	Pancreatic	100	9335452000	6246525400	2.48
	treatment naïve		Cancer
CGPLPA25	Preoperative	WGS	Pancreatic	100	10077515400	6103322200	2.42
	treatment naïve		Cancer
CGPLPA26	Preoperative	WGS	Pancreatic	100	8354272400	5725781000	2.27
	treatment naïve		Cancer
CGPLPA28	Preoperative	WGS	Pancreatic	100	8477461600	5688846800	2.26
	treatment naïve		Cancer
CGPLPA33	Preoperative	WGS	Pancreatic	100	7287615600	4596723800	1.82
	treatment naïve		Cancer
CGPLPA34	Preoperative	WGS	Pancreatic	100	6122902400	4094828000	1.62
	treatment naïve		Cancer
CGPLPA37	Preoperative	WGS	Pancreatic	100	12714888200	8527779200	3.38
	treatment naïve		Cancer
CGPLPA38	Preoperative	WGS	Pancreatic	100	8525500600	5501341400	2.18
	treatment naïve		Cancer
CGPLPA39	Preoperative	WGS	Pancreatic	100	10602663600	6812333000	2.70
	treatment naïve		Cancer
CGPLPA40	Preoperative	WGS	Pancreatic	100	9083670000	5394717800	2.14
	treatment naïve		Cancer
CGPLPA42	Preoperative	WGS	Pancreatic	100	5972126600	3890395200	1.54
	treatment naïve		Cancer
CGPLPA46	Preoperative	WGS	Pancreatic	100	4720090200	2626298800	1.04
	treatment naïve		Cancer
CGPLPA47	Preoperative	WGS	Pancreatic	100	7317385800	4543833000	1.80
	treatment naïve		Cancer
CGPLPA48	Preoperative	WGS	Pancreatic	100	7553856200	5022695600	1.99
	treatment naïve		Cancer
CGPLPA52	Preoperative	WGS	Pancreatic	100	5655875000	3551861600	1.41
	treatment naïve		Cancer
CGPLPA53	Preoperative	WGS	Pancreatic	100	9504749000	6323344800	2.51
	treatment naïve		Cancer
CGPLPA58	Preoperative	WGS	Pancreatic	100	8088090200	5118138200	2.03
	treatment naïve		Cancer
CGPLPA59	Preoperative	WGS	Pancreatic	100	14547364600	9617778600	3.82
	treatment naïve		Cancer
CGPLPA67	Preoperative	WGS	Pancreatic	100	8222177400	5351172600	2.12
	treatment naïve		Cancer
CGPLPA69	Preoperative	WGS	Pancreatic	100	7899181400	5006114800	1.99
	treatment naïve		Cancer
CGPLPA71	Preoperative	WGS	Pancreatic	100	7349620400	4955417400	1.97
	treatment naïve		Cancer
CGPLPA74	Preoperative	WGS	Pancreatic	100	6666371400	4571394200	1.81
	treatment naïve		Cancer
CGPLPA76	Preoperative	WGS	Pancreatic	100	9755658600	6412606800	2.54
	treatment naïve		Cancer
CGPLPA85	Preoperative	WGS	Pancreatic	100	10856223000	7309498600	2.90
	treatment naïve		Cancer
CGPLPA86	Preoperative	WGS	Pancreatic	100	8744365400	5514523200	2.19
	treatment naïve		Cancer
CGPLPA92	Preoperative	WGS	Pancreatic	100	8073791200	5390492800	2.14
	treatment naïve		Cancer
CGPLPA93	Preoperative	WGS	Pancreatic	100	10390273000	7186589400	2.85
	treatment naïve		Cancer
CGPLPA94	Preoperative	WGS	Pancreatic	100	11060347600	7641336400	3.03
	treatment naïve		Cancer
CGPLPA95	Preoperative	WGS	Pancreatic	100	12416627200	7206503800	2.86
	treatment naïve		Cancer
CGST102	Preoperative	WGS	Gastric	100	6637004600	4545072600	1.80
	treatment naïve		cancer
CGST11	Preoperative	WGS	Gastric	100	9718427800	6259679600	2.48
	treatment naïve		cancer
CGST110	Preoperative	WGS	Gastric	100	9319661600	6359317400	2.52
	treatment naïve		cancer
CGST114	Preoperative	WGS	Gastric	100	6865213000	4841171600	1.92
	treatment naïve		cancer
CGST13	Preoperative	WGS	Gastric	100	9284554800	6360843800	2.52
	treatment naïve		cancer
CGST131	Preoperative	WGS	Gastric	100	5924382000	3860677200	1.53
	treatment naïve		cancer
CGST141	Preoperative	WGS	Gastric	100	8486380800	5860491000	2.33
	treatment naïve		cancer
CGST16	Preoperative	WGS	Gastric	100	13820725800	9377828000	3.72
	treatment naïve		cancer
CGST18	Preoperative	WGS	Gastric	100	7781288000	5278862400	2.09
	treatment naïve		cancer
CGST21	Preoperative	WGS	Gastric	100	7171165400	4103970800	1.63
	treatment naïve		cancer
CGST26	Preoperative	WGS	Gastric	100	8983961800	6053405600	2.40
	treatment naïve		cancer
CGST28	Preoperative	WGS	Gastric	100	9683035400	6745116400	2.68
	treatment naïve		cancer
CGST30	Preoperative	WGS	Gastric	100	8584086600	5741416000	2.28
	treatment naïve		cancer
CGST32	Preoperative	WGS	Gastric	100	8568194600	5783369200	2.29
	treatment naïve		cancer
CGST33	Preoperative	WGS	Gastric	100	9351699600	6448718400	2.56
	treatment naïve		cancer
CGST38	Preoperative	WGS	Gastric	100	8409876400	5770989200	2.29
	treatment naïve		cancer
CGST39	Preoperative	WGS	Gastric	100	10573763000	7597016000	3.01
	treatment naïve		cancer
CGST41	Preoperative	WGS	Gastric	100	9434854200	6609415400	2.62
	treatment naïve		cancer
CGST45	Preoperative	WGS	Gastric	100	8203868600	5625223000	2.23
	treatment naïve		cancer
CGST47	Preoperative	WGS	Gastric	100	8938597600	6178990600	2.45
	treatment naïve		cancer
CGST48	Preoperative	WGS	Gastric	100	9106628800	6517085200	2.59
	treatment naïve		cancer
CGST53	Preoperative	WGS	Gastric	100	9005374200	5854996200	2.32
	treatment naïve		cancer
CGST58	Preoperative	WGS	Gastric	100	10020368600	6133458400	2.43
	treatment naïve		cancer
CGST67	Preoperative	WGS	Gastric	100	9198135600	5911071000	2.35
	treatment naïve		cancer
CGST77	Preoperative	WGS	Gastric	100	8228789400	5119116800	2.03
	treatment naïve		cancer
CGST80	Preoperative	WGS	Gastric	100	10596963400	7283152800	2.89
	treatment naïve		cancer
CGST81	Preoperative	WGS	Gastric	100	8494881200	5838064000	2.32
	treatment naïve		cancer

TABLE 5

APPENDIX E: High coverage whole genome cfDNA analyses of healthy individuals and lung cancer patients

						Correlation	Correlation of	Correction
						of Fragment	GC Corrected	of Fragment	Correlation
						Ratio Profile	Fragment Ratio	Ratio Profile	of Fragment
						to Median	Profile to	to Median	Ratio
					Median	Fragment	Median Fragment	Fragment	Profile to
					cfDNA	Ratio Profile	Ratio Profile	Ratio	Lymphocyte
	Patient	Analysis		Stage at	Fragment	of Healthy	of Healthy	Profile of	Nucleosome
Patient	Type	Type	Timepoint	Diagnosis	Size (bp)	Individuals	Individuals	Lymphocytes	Distances

CGPLH75	Healthy	WGS	Preoperative	NA	168	0.977	0.952	0.920	−0.886
			treatment naïve
CGPLH77	Healthy	WGS	Preoperative	NA	166	0.970	0.960	0.904	−0.912
			treatment naïve
CGPLH80	Healthy	WGS	Preoperative	NA	168	0.955	0.949	0.960	−0.917
			treatment naïve
CGPLH81	Healthy	WGS	Preoperative	NA	167	0.949	0.953	0.869	−0.883
			treatment naïve
CGPLH82	Healthy	WGS	Preoperative	NA	166	0.969	0.949	0.954	−0.917
			treatment naïve
CGPLH83	Healthy	WGS	Preoperative	NA		167	0.949	0.939	0.919	−0.904
			treatment naïve
CGPLH84	Healthy	WGS	Preoperative	NA	168	0.967	0.948	0.951	−0.913
			treatment naïve
CGPLH52	Healthy	WGS	Preoperative	NA		167	0.946	0.968	0.952	−0.924
			treatment naïve
CGPLH35	Healthy	WGS	Preoperative	NA	166	0.981	0.973	0.945	−0.921
			treatment naïve
CGPLH37	Healthy	WGS	Preoperative	NA	168	0.968	0.970	0.951	−0.922
			treatment naïve
CGPLH54	Healthy	WGS	Preoperative	NA	167	0.968	0.976	0.948	−0.925
			treatment naïve
CGPLH55	Healthy	WGS	Preoperative	NA	166	0.947	0.964	0.948	−0.917
			treatment naïve
CGPLH48	Healthy	WGS	Preoperative	NA	168	0.959	0.965	0.960	−0.923
			treatment naïve
CGPLH50	Healthy	WGS	Preoperative	NA	167	0.960	0.968	0.952	−0.921
			treatment naïve
CGPLH36	Healthy	WGS	Preoperative	NA	168	0.955	0.954	0.955	−0.919
			treatment naïve
CGPLH42	Healthy	WGS	Preoperative	NA	167	0.973	0.963	0.948	−0.918
			treatment naïve
CGPLH43	Healthy	WGS	Preoperative	NA	166	0.952	0.958	0.953	−0.928
			treatment naïve
CGPLH69	Healthy	WGS	Preoperative	NA	168	0.970	0.965	0.951	−0.925
			treatment naïve
CGPLH45	Healthy	WGS	Preoperative	NA	168	0.965	0.950	0.949	−0.911
			treatment naïve
CGPLH47	Healthy	WGS	Preoperative	NA	167	0.952	0.944	0.954	−0.921
			treatment naïve
CGPLH46	Healthy	WGS	Preoperative	NA	168	0.966	0.965	0.953	−0.923
			treatment naïve
CGPLH63	Healthy	WGS	Preoperative	NA	168	0.977	0.968	0.939	−0.920
			treatment naïve
CGPLH51	Healthy	WGS	Preoperative	NA	168	0.935	0.955	0.957	−0.914
			treatment naïve
CGPLH57	Healthy	WGS	Preoperative	NA	169	0.965	0.954	0.955	−0.917
			treatment naïve
CGPLH49	Healthy	WGS	Preoperative	NA	168	0.958	0.951	0.950	−0.924
			treatment naïve
CGPLH56	Healthy	WGS	Preoperative	NA	166	0.940	0.957	0.959	−0.911
			treatment naïve
CGPLH64	Healthy	WGS	Preoperative	NA	169	0.960	0.940	0.949	−0.918
			treatment naïve
CGPLH78	Healthy	WGS	Preoperative	NA	166	0.956	0.936	0.958	−0.911
			treatment naïve
CGPLH79	Healthy	WGS	Preoperative	NA	168	0.960	0.957	0.953	−0.917
			treatment naïve
CGPLH76	Healthy	WGS	Preoperative	NA		167	0.969	0.965	0.953	−0.917
			treatment naïve
CGPLLU175	Lung	WGS	Preoperative	I	165	0.316	0.284	0.244	−0.262
	Cancer		treatment naïve
CGPLLU180	Lung	WGS	Preoperative	I	166	0.907	0.846	0.826	−0.819
	Cancer		treatment naïve
CGPLLU198	Lung	WGS	Preoperative	I	166	0.972	0.946	0.928	−0.911
	Cancer		treatment naïve
CGPLLU202	Lung	WGS	Preoperative	I	163	0.821	0.605	0.905	−0.843
	Cancer		treatment naïve
CGPLLU165	Lung	WGS	Preoperative	II	163	0.924	0.961	0.815	−0.851
	Cancer		treatment naïve
CGPLLU209	Lung	WGS	Preoperative	II	163	0.578	0.526	0.513	−0.534
	Cancer		treatment naïve
CGPLLU147	Lung	WGS	Preoperative	III	166	0.953	0.919	0.939	−0.912
	Cancer		treatment naïve
CGPLLU206	Lung	WGS	Preoperative	III	158	0.488	0.343	0.460	−0.481
	Cancer		treatment naïve

TABLE 6

APPENDIX F: Monitoring response to therapy using whole genome analyses of cfDNA fragmentation profiles and targeted mutations analyses

						Correlation
						or Fragment	Correlation
						Ratio Profile	of Fragment
						to Median	Ratio
					Progression-	Fragment	Profile to		Maximum
					free	Ratio Profile	Lymphocyte		Mutant
	Patient				Survival	of Healthy	Nucleosome	Targeted	Allele
Patient	Type	Analysis Type	Timepoint	Stage	(months)	Individuals	Distances	Mutation	Fraction

CGPLLU14	Lung	Targeted Mutation	Pre-treatment,	IV	15.4	0.941	−0.841	EGFR 861L>Q	0.89%
	Cancer	Analysis and WGS	Day −38
CGPLLU14	Lung	Targeted Mutation	Pre-treatment,	IV	15.4	0.933	−0.833	EGFR 861L>Q	0.18%
	Cancer	Analysis and WGS	Day −16
CGPLLU14	Lung	Targeted Mutation	Pre-treatment,	IV	15.4	0.908	−0.814	EGFR 719G>S	0.49%
	Cancer	Analysis and WGS	Day 3
CGPLLU14	Lung	Targeted Mutation	Pre-treatment,	IV	15.4	0.883	−0.752	EGFR 861L>Q	1.39%
	Cancer	Analysis and WGS	Day 0
CGPLLU14	Lung	Targeted Mutation	Post-treatment,	IV	15.4	0.820	−0.692	EGFR 719G>S	1.05%
	Cancer	Analysis and WGS	Day 0.33
CGPLLU14	Lung	Targeted Mutation	Post-treatment,	IV	15.4	0.927	−0.887	EGFR 861L>Q	0.00%
	Cancer	Analysis and WGS	Day 7
CGPLLU88	Lung	Targeted Mutation	Pre-treatment,	IV	18.0	0.657	−0.584	EGFR	9.06%
	Cancer	Analysis and WGS	Day 0					745KELREA>T
CGPLLU88	Lung	Targeted Mutation	Post-treatment,	IV	18.0	0.939	−0.799	EGFR 790T>M	0.15%
	Cancer	Analysis and WGS	Day 7
CGPLLU88	Lung	Targeted Mutation	Post-treatment,	IV	18.0	0.946	−0.869	EGFR	0.93%
	Cancer	Analysis and WGS	Day 297					745KELREA>T
CGPLLU244	Lung	Targeted Mutation	Pre-treatment,	IV	1.2	0.850	−0.706	EGFR 858L>R	4.98%
	Cancer	Analysis and WGS	Day −7
CGPLLU244	Lung	Targeted Mutation	Pre-treatment,	IV	1.2	0.867	−0.764	EGFR 62L>R	3.41%
	Cancer	Analysis and WGS	Day −1
CGPLLU244	Lung	Targeted Mutation	Post-treatment,	IV	1.2	0.703	−0.639	EGFR 858L>R	5.57%
	Cancer	Analysis and WGS	Day 6
GGPLLU244	Lung	Targeted Mutation	Post-treatment,	IV	1.2	0.659	−0.660	EGFR 858L>R	11.80%
	Cancer	Analysis and WGS	Day 82
CGPLLU245	Lung	Targeted Mutation	Pre-treatment,	IV	1.7	0.871	−0.724	EGFR	10.60%
	Cancer	Analysis and WGS	Day −32					745KELREA>K
CGPLLU245	Lung	Targeted Mutation	Pre-treatment,	IV	1.7	0.736	−0.608	EGFR	14.10%
	Cancer	Analysis and WGS	Day 0					745KELREA>K
CGPLLU245	Lung	Targeted Mutation	Post-treatment,	IV	1.7	0.731	−0.559	EGFR	6.56%
	Cancer	Analysis and WGS	Day 7					745KELREA>K
CGPLLU245	Lung	Targeted Mutation	Post-treatment,	IV	1.7	0.613	−0.426	EGFR	10.69%
	Cancer	Analysis and WGS	Day 21					745KELREA>K
CGPLLU246	Lung	Targeted Mutation	Pre-treatment,	IV	1.3	0.897	−0.757	EGFR 790T>M	0.49%
	Cancer	Analysis and WGS	Day −21
CGPLLU246	Lung	Targeted Mutation	Pre-treatment,	IV	1.3	0.469	0.376	EGFR 858L>R	6.17%
	Cancer	Analysis and WGS	Day 0
CGPLLU246	Lung	Targeted Mutation	Post-treatment,	IV	1.3	0.874	−0.746	EGFR 858L>R	1.72%
	Cancer	Analysis and WGS	Day 9
CGPLLU246	Lung	Targeted Mutation	Post-treatment,	IV	1.3	0.775	−0.665	EGFR 858L>R	5.29%
	Cancer	Analysis and WGS	Day 42
CGPLLU86	Lung	Targeted Mutation	Pre-treatment,	IV	12.4	0.817	−0.630	EGFR	0.00%
	Cancer	Analysis and WGS	Day 0					746ELREATS>D
CGPLLU86	Lung	Targeted Mutation	Post-treatment,	IV	12.4	0.916	−0.811	EGER	0.19%
	Cancer	Analysis and WGS	Day 0.5					746ELREATS>D
CGPLLU86	Lung	Targeted Mutation	Post-treatment,	IV	12.4	0.859	−0.694	EGFR	0.00%
	Cancer	Analysis and WGS	Day 7					746ELREATS>D
CGPLLU86	Lung	Targeted Mutation	Post-treatment,	IV	12.4	0.932	−0.848	EGFR	0.00%
	Cancer	Analysis and WGS	Day 17					746ELREATS>D
CGPLLU89	Lung	Targeted Mutation	Pre-treatment,	IV	6.7	0.864	−0.729	EGFR	0.42%
	Cancer	Analysis and WGS	Day 0					747LREATS>—
CGPLLU89	Lung	Targeted Mutation	Post-treatment,	IV	6.7	0.908	−0.803	EGFR	0.20%
	Cancer	Analysis and WGS	Day 7					747LREATS>—
CGPLLU89	Lung	Targeted Mutation	Post-treatment,	IV	6.7	0.853	−0.881	EGFR	0.00%
	Cancer	Analysis and WGS	Day 22					747LREATS>—
CGLU316	Lung	Targeted Mutation	Pre-treatment,	IV	1.4	0.331	−0.351	EGFR L861Q	15.72%
	Cancer	Analysis and WGS	Day −53
CGLU316	Lung	Targeted Mutation	Pre-treatment,	IV	1.4	0.225	−0.253	EGFR L861Q	45.67%
	Cancer	Analysis and WGS	Day −4
CGLU316	Lung	Targeted Mutation	Post-treatment,	IV	1.4	0.336	−0.364	EGFR G719A	33.38%
	Cancer	Analysis and WGS	Day 18
CGLU316	Lung	Targeted Mutation	Post-treatment,	IV	1.4	0.340	−0.364	EGFR L861Q	66.01%
	Cancer	Analysis and WGS	Day 87
CGLU344	Lung	Targeted Mutation	Pre-treatment,	IV	Ongoing	0.935	−0.818	EGFR	0.00%
	Cancer	Analysis and WGS	Day −21					E746_A750del
CGLU344	Lung	Targeted Mutation	Pre-treatment,	IV	Ongoing	0.919	−0.774	EGFR	0.22%
	Cancer	Analysis and WGS	Day 0					E746_A750del
CGLU344	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.953	−0.860	EGFR	0.40%
	Cancer	Analysis and WGS	Day 0.1675					E746_A750del
CGLU344	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.944	−0.832	EGFR	0.00%
	Cancer	Analysis and WGS	Day 59					E746_A750del
CGLU369	Lung	Targeted Mutation	Pre-treatment,	IV	7.5	0.825	−0.826	EGFR L858R	20.61%
	Cancer	Analysis and WGS	Day −2
CGLU369	Lung	Targeted Mutation	Post-treatment,	IV	7.5	0.950	−0.903	EGFR L858R	0.22%
	Cancer	Analysis and WGS	Day 12
CGLU369	Lung	Targeted Mutation	Post-treatment,	IV	7.5	0.945	−0.889	EGFR L858R	0.16%
	Cancer	Analysis and WGS	Day 68
CGLU369	Lung	Targeted Mutation	Post-treatment,	IV	7.5	0.886	−0.883	EGFR L858R	0.10%
	Cancer	Analysis and WGS	Day 110
CGLU373	Lung	Targeted Mutation	Pre-treatment,	IV	Ongoing	0.922	−0.804	EGFR	0.82%
	Cancer	Analysis and WGS	Day −2					E746_A750del
CGLU373	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.959	−0.853	EGFR	0.00%
	Cancer	Analysis and WGS	Day 0.125					E746_A750del
CGLU373	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.967	−0.886	EGFR	0.15%
	Cancer	Analysis and WGS	Day 7					E746_A750del
CGLU373	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.951	−0.890	EGFR	0.00%
	Cancer	Analysis and WGS	Day 47					E746_A750del
CGPLLU13	Lung	Targeted Mutation	Pre-treatment,	IV	1.5	0.425	−0.400	EGFR	7.66%
	Cancer	Analysis and WGS	Day −2					E746_A750del
CGPLLU13	Lung	Targeted Mutation	Post-treatment,	IV	1.5	0.272	−0.257	EGFR	13.10%
	Cancer	Analysis and WGS	Day 5					E746_A750del
CGPLLU13	Lung	Targeted Mutation	Post-treatment,	IV	1.5	0.584	−0.536	EGFR	6.09%
	Cancer	Analysis and WGS	Day 28					E746_A750del
CGPLLU13	Lung	Targeted Mutation	Post-treatment,	IV	1.5	0.530	−0.513	EGFR	9.28%
	Cancer	Analysis and WGS	Day 91					E746_A750del
CGPLLU264	Lung	Targeted Mutation	Pre-treatment,	IV	Ongoing	0.946	−0.824	EGFR D761N	0.00%
	Cancer	Analysis and WGS	Day −1
CGPLLU264	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.927	−0.788	EGFR D761N	0.16%
	Cancer	Analysis and WGS	Day 6
CGPLLU264	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.962	−0.856	EGFR D761N	0.00%
	Cancer	Analysis and WGS	Day 27
CGPLLU264	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.960	−0.894	EGFR D76IN	0.00%
	Cancer	Analysis and WGS	Day 69
CGPLLU265	Lung	Targeted Mutation	Pre-treatment,	IV	Ongoing	0.953	−0.859	EGFR L858R	0.21%
	Cancer	Analysis and WGS	Day 0
CGPLLU265	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.949	−0.842	EGFR L858R	0.21%
	Cancer	Analysis and WGS	Day 3
CGPLLU265	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.955	−0.844	EGFR T790M	0.21%
	Cancer	Analysis and WGS	Day 7
CGPLLU265	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.946	−0.825	EGFR L858R	0.00%
	Cancer	Analysis and WGS	Day 84
CGPLLU266	Lung	Targeted Mutation	Pre-treatment,	IV	9.6	0.961	−0.904	NA	0.00%
	Cancer	Analysis and WGS	Day 0
CGPLLU266	Lung	Targeted Mutation	Post-treatment,	IV	9.6	0.959	−0.886	NA	0.00%
	Cancer	Analysis and WGS	Day 16
CGPLLU266	Lung	Targeted Mutation	Post-treatment,	IV	9.6	0.961	−0.880	NA	0.00%
	Cancer	Analysis and WGS	Day 83
CGPLLU266	Lung	Targeted Mutation	Post-treatment,	IV	9.6	0.958	−0.855	NA	0.00%
	Cancer	Analysis and WGS	Day 328
CGPLLU267	Lung	Targeted Mutation	Pre-treatment,	IV	3.9	0.919	−0.863	EGFR L858R	1.93%
	Cancer	Analysis and WGS	Lay −1
CGPLLU267	Lung	Targeted Mutation	Post-treatment,	IV	3.9	0.363	−0.889	EGFR L858R	0.14%
	Cancer	Analysis and WGS	Day 34
CGPLLU267	Lung	Targeted Mutation	Post-treatment,	IV	3.9	0.962	−0.876	EGFR L858R	0.38%
	Cancer	Analysis and WGS	Day 90
CGPLLU269	Lung	Targeted Mutation	Pre-treatment,	IV	Ongoing	0.951	−0.864	EGFR L858R	0.10%
	Cancer	Analysis and WGS	Day 0
CGPLLU269	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.941	−0.894	EGFR L858R	0.00%
	Cancer	Analysis and WGS	Day 9
CGPLLU269	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.957	−0.876	EGFR L858R	0.00%
	Cancer	Analysis and WGS	Day 28
CGPLLU271	Lung	Targeted Mutation	Pre-treatment,	IV	8.2	0.371	−0.284	EGFR	3.36%
	Cancer	Analysis and WGS	Day 0					E746_A750del
CGPLLU271	Lung	Targeted Mutation	Post-treatment,	IV	8.2	0.947	0.826	EGFR	0.17%
	Cancer	Analysis and WGS	Day 6					E746_A750del
CGPLLU271	Lung	Targeted Mutation	Post-treatment,	IV	8.2	0.952	−0.839	EGFR	0.00%
	Cancer	Analysis and WGS	Day 20					E746_A750del
CGPLLU271	Lung	Targeted Mutation	Post-treatment,	IV	8.2	0.944	−0.810	EGFR	0.00%
	Cancer	Analysis and WGS	Day 104					E746_A750del
CGPLLU271	Lung	Targeted Mutation	Post-treatment,	IV	8.2	0.950	−0.831	EGFR	0.44%
	Cancer	Analysis and WGS	Day 259					E746_A750del
CGPLLU43	Lung	Targeted Mutation	Pre-treatment,	IV	Ongoing	0.944	−0.903	NA	0.00%
	Cancer	Analysis and WGS	Day −1
CGPLLU43	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.956	−0.899	NA	0.00%
	Cancer	Analysis and WGS	Day 6
CGPLLU43	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.959	−0.901	NA	0.00%
	Cancer	Analysis and WGS	Day 27
CGPLLU43	Lung	Targeted Mutation	Post-treatment,	IV	Ongoing	0.965	−0.896	NA	0.00%
	Cancer	Analysis and WGS	Day 83

TABLE 7

APPENDIX G: Whole genome cfDNA analyses in healthy individuals and cancer patients

						Correlation
						of Fragment
						Ratio Profile
						to Median
					Median	Fragment
					cfDNA	Ratio Profile
	Patient			Stage at	Fragment	of Healthy
Patient	Type	Analysis Type	Timepoint	Diagnosis	Size (bp)	Individuals

CGCRC291	Colorectal	Targeted Mutation	Preoperative	V	163	0.1972
	Cancer	Analysis and WGS	treatment naïve
CGCRC292	Colorectal	Targeted Mutation	Preoperative	V	168	0.7804
	Cancer	Analysis and WGS	treatment naïve
CGCRC293	Colorectal	Targeted Mutation	Preoperative	V	166	0.9335
	Cancer	Analysis and WGS	treatment naïve
CGCRC294	Colorectal	Targeted Mutation	Preoperative	II	166	0.6531
	Cancer	Analysis and WGS	treatment naïve
CGCRC295	Colorectal	Targeted Mutation	Preoperative	II	166	0.8161
	Cancer	Analysis and WGS	treatment naïve
CGCRC299	Colorectal	Targeted Mutation	Preoperative	I	162	0.7325
	Cancer	Analysis and WGS	treatment naïve
CGCRC300	Colorectal	Targeted Mutation	Preoperative	I	167	0.9382
	Cancer	Analysis and WGS	treatment naïve
CGCRC301	Colorectal	Targeted Mutation	Preoperative	I	165	0.8252
	Cancer	Analysis and WGS	treatment naïve
CGCRC302	Colorectal	Targeted Mutation	Preoperative	II	163	0.7499
	Cancer	Analysis and WGS	treatment naïve
CGCRC304	Colorectal	Targeted Mutation	Preoperative	II	162	0.4642
	Cancer	Analysis and WGS	treatment naïve
CGCRC305	Colorectal	Targeted Mutation	Preoperative	II	165	0.8909
	Cancer	Analysis and WGS	treatment naïve
CGCRG306	Colorectal	Targeted Mutation	Preoperative	II	165	0.8523
	Cancer	Analysis and WGS	treatment naïve
CGCRC307	Colorectal	Targeted Mutation	Preoperative	II	165	0.9140
	Cancer	Analysis and WGS	treatment naïve
CGCRC306	Colorectal	Targeted Mutation	Preoperative	III	165	0.8734
	Cancer	Analysis and WGS	treatment naïve
CGCRC311	Colorectal	Targeted Mutation	Preoperative	I	166	0.8535
	Cancer	Analysis and WGS	treatment naïve
CGCRC315	Colorectal	Targeted Mutation	Preoperative	III	167	0.8083
	Cancer	Analysis and WGS	treatment naïve
CGCRC316	Colorectal	Targeted Mutation	Preoperative	III	161	0.1546
	Cancer	Analysis and WGS	treatment naïve
CGCRC317	Colorectal	Targeted Mutation	Preoperative	III	163	0.6242
	Cancer	Analysis and WGS	treatment naïve
CGCRC318	Colorectal	Targeted Mutation	Preoperative	I	166	0.8824
	Cancer	Analysis and WGS	treatment naïve
CGCRC319	Colorectal	Targeted Mutation	Preoperative	III	160	0.5979
	Cancer	Analysis and WGS	treatment naïve
CGCRC320	Colorectal	Targeted Mutation	Preoperative	I	167	0.7949
	Cancer	Analysis and WGS	treatment naïve
CGCRC321	Colorectal	Targeted Mutation	Preoperative	I	164	0.7604
	Cancer	Analysis and WGS	treatment naïve
CGCRC333	Colorectal	Targeted Mutation	Preoperative	V	163	0.4263
	Cancer	Analysis and WGS	treatment naïve
CGCRC335	Colorectal	Targeted Mutation	Preoperative	V	162	0.6466
	Cancer	Analysis and WGS	treatment naïve
CGCRC338	Colorectal	Targeted Mutation	Preoperative	V	162	0.7740
	Cancer	Analysis and WGS	treatment naïve
CGCRC341	Colorectal	Targeted Mutation	Preoperative	V	164	0.8995
	Cancer	Analysis and WGS	treatment naïve
CGCRC342	Colorectal	Targeted Mutation	Preoperative	V	158	0.2524
	Cancer	Analysis and WGS	treatment naïve
CGPLBR100	Breast	Targeted Mutation	Preoperative	III	166	0.9440
	Cancer	Analysis and WGS	treatment naïve
CGPLBR101	Breast	Targeted Mutation	Preoperative	II	169	0.8664
	Cancer	Analysis and WGS	treatment naïve
CGPLBR102	Breast	Targeted Mutation	Preoperative	II	169	0.9617
	Cancer	Analysis and WGS	treatment naïve
CGPLBR103	Breast	Targeted Mutation	Preoperative	II	168	0.9498
	Cancer	Analysis and WGS	treatment naïve
CGPLBR104	Breast	Targeted Mutation	Preoperative	II	167	0.8490
	Cancer	Analysis and WGS	treatment naïve
CGPLBR12	Breast	WGS	Preoperative	III	164	0.8350
	Cancer		treatment naïve
CGPLBR18	Breast	WGS	Preoperative	III	163	0.8411
	Cancer		treatment naïve
CGPLBR23	Breast	WGS	Preoperative	II	166	0.9714
	Cancer		treatment naïve
CGPLBR24	Breast	WGS	Preoperative	II	156	0.8402
	Cancer		treatment naïve
CGPLBR26	Breast	WGS	Preoperative	III	165	0.9584
	Cancer		treatment naïve
CGPLBR30	Breast	WGS	Preoperative	II	161	0.6951
	Cancer		treatment naïve
CGPLBR31	Breast	WGS	Preoperative	II	167	0.9719
	Cancer		treatment naïve
CGPLBR32	Breast	WGS	Preoperative	II	165	0.9590
	Cancer		treatment naïve
CGPLBR33	Breast	WGS	Preoperative	II	166	0.9706
	Cancer		treatment naïve
CGPLBR34	Breast	WGS	Preoperative	II	163	0.3735
	Cancer		treatment naïve
CGPLBR35	Breast	WGS	Preoperative	II	168	0.9655
	Cancer		treatment naïve
CGPLBP36	Breast	WGS	Preoperative	II	169	0.9394
	Cancer		treatment naïve
CGPLBR37	Breast	WGS	Preoperative	II	167	0.9591
	Cancer		treatment naïve
CGPLBR38	Breast	Targeted Mutation	Preoperative	I	165	0.9105
	Cancer	Analysis and WGS	treatment naïve
CGPLBR40	Breast	Targeted Mutation	Preoperative	III	167	0.9273
	Cancer	Analysis and WGS	treatment naïve
CGPLBR41	Breast	Targeted Mutation	Preoperative	III	168	0.9626
	Cancer	Analysis and WGS	treatment naïve
CGPLBR45	Breast	WGS	Preoperative	II	164	0.9615
	Cancer		treatment naïve
CGPLBR46	Breast	WGS	Preoperative	III	168	0.9322
	Cancer		treatment naïve
CGPLBR47	Breast	WGS	Preoperative	I	166	0.9461
	Cancer		treatment naïve
CGPLBR48	Breast	Targeted Mutation	Preoperative	II	169	0.7686
	Cancer	Analysis and WGS	treatment naïve
CGPLBR49	Breast	Targeted Mutation	Preoperative	II	171	0.8867
	Cancer	Analysis and WGS	treatment naïve
CGPLBR50	Breast	WGS	Preoperative	I	160	0.8593
	Cancer		treatment naïve
CGPLBR51	Breast	WGS	Preoperative	II	165	0.9353
	Cancer		treatment naïve
CGPLBR52	Breast	WGS	Preoperative	III	164	0.8688
	Cancer		treatment naïve
CGPLBR55	Breast	Targeted Mutation	Preoperative	III	165	0.9634
	Cancer	Analysis and WGS	treatment naïve
CGPLBR56	Breast	WGS	Preoperative	II	163	0.9459
	Cancer		treatment naïve
CGPLBR57	Breast	Targeted Mutation	Preoperative	III	166	0.9672
	Cancer	Analysis and WGS	treatment naïve
CGPLBR59	Breast	Targeted Mutation	Preoperative	I	168	0.9438
	Cancer	Analysis and WGS	treatment naïve
CGPLBR60	Breast	WGS	Preoperative	II	167	0.9479
	Cancer		treatment naïve
CGPLBR61	Breast	Targeted Mutation	Preoperative	II	165	0.9611
	Cancer	Analysis and WGS	treatment naïve
CGPLBR63	Breast	Targeted Mutation	Preoperative	II	168	0.9555
	Cancer	Analysis and WGS	treatment naïve
CGPLBR65	Breast	WGS	Preoperative	II	167	0.9506
	Cancer		treatment naïve
CGPLBR68	Breast	Targeted Mutation	Preoperative	III	163	0.9154
	Cancer	Analysis and WGS	treatment naïve
CGPLBR69	Breast	Targeted Mutation	Preoperative	II	165	0.9460
	Cancer	Analysis and WGS	treatment naïve
CGPLBR70	Breast	Targeted Mutation	Preoperative	II	168	0.9651
	Cancer	Analysis and WGS	treatment naïve
CGPLBR71	Breast	Targeted Mutation	Preoperative	II	165	0.9577
	Cancer	Analysis and WGS	treatment naïve
CGPLBR72	Breast	Targeted Mutation	Preoperative	II	167	0.9786
	Cancer	Analysis and WGS	treatment naïve
CGPLBR73	Breast	Targeted Mutation	Preoperative	II	167	0.9576
	Cancer	Analysis and WGS	treatment naïve
CGPLBR76	Breast	Targeted Mutation	Preoperative	II	170	0.9410
	Cancer	Analysis and WGS	treatment naïve
CGPLBR81	Breast	WGS	Preoperative	II	170	0.9043
	Cancer		treatment naïve
CGPLBR82	Breast	Targeted Mutation	Preoperative	I	166	0.9254
	Cancer	Analysis and WGS	treatment naïve
CGPLBR83	Breast	Targeted Mutation	Preoperative	II	169	0.9451
	Cancer	Analysis and WGS	treatment naïve
CGPLBR84	Breast	WGS	Preoperative	III	169	0.9315
	Cancer		treatment naïve
CGPLBR87	Breast	Targeted Mutation	Preoperative	II	166	0.9154
	Cancer	Analysis and WGS	treatment naïve
CGPLBR88	Breast	Targeted Mutation	Preoperative	II	169	0.9370
	Cancer	Analysis and WGS	treatment naïve
CGPLBR90	Breast	WGS	Preoperative	II	169	0.9002
	Cancer		treatment naïve
CGPLBR91	Breast	Targeted Mutation	Preoperative	III	164	0.7955
	Cancer	Analysis and WGS	treatment naïve
CGPLBR92	Breast	Targeted Mutation	Preoperative	II	162	0.6774
	Cancer	Analysis and WGS	treatment naïve
CGPLBR93	Breast	Targeted Mutation	Preoperative	II	164	0.8773
	Cancer	Analysis and WGS	treatment naïve
CGPLH189	Healthy	WGS	Preoperative	NA	168	0.9325
			treatment naïve
CGPLH190	Healthy	WGS	Preoperative	NA	167	0.9433
			treatment naïve
CGPLH192	Healthy	WGS	Preoperative	NA	167	0.9646
			treatment naïve
CGPLH193	Healthy	WGS	Preoperative	NA	167	0.5423
			treatment naïve
CGPLH194	Healthy	WGS	Preoperative	NA	168	0.9567
			treatment naïve
CGPLH196	Healthy	WGS	Preoperative	NA	167	0.9709
			treatment naïve
CGPLH197	Healthy	WGS	Preoperative	NA	166	0.9605
			treatment naïve
CGPLH198	Healthy	WGS	Preoperative	NA	167	0.9238
			treatment naïve
CGPLH199	Healthy	WGS	Preoperative	NA	165	0.9618
			treatment naïve
CGPLH200	Healthy	WGS	Preoperative	NA	167	0.9183
			treatment naïve
CGPLH201	Healthy	WGS	Preoperative	NA	168	0.9548
			treatment naïve
CGPLH202	Healthy	WGS	Preoperative	NA	168	0.9471
			treatment naïve
CGPLH203	Healthy	WGS	Preoperative	NA		167	0.9534
			treatment naïve
CGPLH205	Healthy	WGS	Preoperative	NA	168	0.9075
			treatment naïve
CGPLH208	Healthy	WGS	Preoperative	NA	168	0.9422
			treatment naïve
CGPLH209	Healthy	WGS	Preoperative	NA	169	0.9556
			treatment naïve
CGPLH210	Healthy	WGS	Preoperative	NA	169	0.9447
			treatment naïve
CGPLH211	Healthy	WGS	Preoperative	NA	169	0.5538
			treatment naïve
CGPLH300	Healthy	WGS	Preoperative	NA	168	0.9019
			treatment naïve
CGPLH307	Healthy	WGS	Preoperative	NA	168	0.9576
			treatment naïve
CGPLH308	Healthy	WGS	Preoperative	NA	168	0.9481
			treatment naïve
CGPLH309	Healthy	WGS	Preoperative	NA	168	0.9672
			treatment naïve
CGPLN310	Healthy	WGS	Preoperative	NA	165	0.9547
			treatment naïve
CGPLH311	Healthy	WGS	Preoperative	NA	167	0.9302
			treatment naïve
CGPLH314	Healthy	WGS	Preoperative	NA	167	0.9482
			treatment naïve
CGPLH315	Healthy	WGS	Preoperative	NA	167	0.8659
			treatment naïve
CGPLH316	Healthy	WGS	Preoperative	NA	165	0.9374
			treatment naïve
CGPLH317	Healthy	WGS	Preoperative	NA	169	0.9542
			treatment naïve
CGPLH319	Healthy	WGS	Preoperative	NA	167	0.9578
			treatment naïve
CGPLR320	Healthy	WGS	Preoperative	NA	164	0.8913
			treatment naïve
CGPLH322	Healthy	WGS	Preoperative	NA	167	0.8751
			treatment naïve
CGPLH324	Healthy	WGS	Preoperative	NA	169	0.9519
			treatment naïve
CGPLH325	Healthy	WGS	Preoperative	NA	167	0.9124
			treatment naïve
CGPLH326	Healthy	WGS	Preoperative	NA	166	0.9574
			treatment naïve
CGPLH327	Healthy	WGS	Preoperative	NA	168	0.9533
			treatment naïve
CGPLH328	Healthy	WGS	Preoperative	NA	166	0.9643
			treatment naïve
CGPLH329	Healthy	WGS	Preoperative	NA		167	0.9609
			treatment naïve
CGPLH330	Healthy	WGS	Preoperative	NA	167	0.9118
			treatment naïve
CGPLH331	Healthy	WGS	Preoperative	NA	166	0.9679
			treatment naïve
CGPLH333	Healthy	WGS	Preoperative	NA	169	0.9474
			treatment naïve
CGPLH335	Healthy	WGS	Preoperative	NA	167	0.8909
			treatment naïve
CGPLH336	Healthy	WGS	Preoperative	NA	169	0.9248
			treatment naïve
CGPLH337	Healthy	WGS	Preoperative	NA	167	0.9533
			treatment naïve
CGPLH338	Healthy	WGS	Preoperative	NA	165	0.9388
			treatment naïve
CGPLH339	Healthy	WGS	Preoperative	NA	167	0.9396
			treatment naïve
CGPLH340	Healthy	WGS	Preoperative	NA		167	0.9488
			treatment naïve
CGPLH341	Healthy	WGS	Preoperative	NA	166	0.9533
			treatment naïve
CGPLH342	Healthy	WGS	Preoperative	NA	166	0.7858
			treatment naïve
CGPLH343	Healthy	WGS	Preoperative	NA		167	0.9421
			treatment naïve
CGPLH344	Healthy	WGS	Preoperative	NA	169	0.9192
			treatment naïve
CGPLH345	Healthy	WGS	Preoperative	NA	169	0.9345
			treatment naïve
CGPLH346	Healthy	WGS	Preoperative	NA	169	0.9475
			treatment naïve
CGPLH350	Healthy	WGS	Preoperative	NA	171	0.9570
			treatment naïve
CGPLH351	Healthy	WGS	Preoperative	NA	166	0.8176
			treatment naïve
CGPLH352	Healthy	WGS	Preoperative	NA	168	0.9521
			treatment naïve
CGPLH353	Healthy	WGS	Preoperative	NA	167	0.9435
			treatment naïve
CGPLH354	Healthy	WGS	Preoperative	NA	168	0.9481
			treatment naïve
CGPLH355	Healthy	WGS	Preoperative	NA	167	0.9613
			treatment naïve
CGPLH356	Healthy	WGS	Preoperative	NA	165	0.9474
			treatment naïve
CGPLH357	Healthy	WGS	Preoperative	NA	167	0.9255
			treatment naïve
CGPLH358	Healthy	WGS	Preoperative	NA	167	0.7777
			treatment naïve
CGPLH360	Healthy	WGS	Preoperative	NA	166	0.8500
			treatment naïve
CGPLH361	Healthy	WGS	Preoperative	NA		167	0.9261
			treatment naïve
CGPLH362	Healthy	WGS	Preoperative	NA		167	0.9236
			treatment naïve
CGPLH363	Healthy	WGS	Preoperative	NA	167	0.9488
			treatment naïve
CGPLH364	Healthy	WGS	Preoperative	NA	168	0.9311
			treatment naïve
CGPLH365	Healthy	WGS	Preoperative	NA	165	0.9371
			treatment naïve
CGPLH366	Healthy	WGS	Preoperative	NA	167	0.9536
			treatment naïve
CGPLH367	Healthy	WGS	Preoperative	NA	166	0.8748
			treatment naïve
CGPLH368	Healthy	WGS	Preoperative	NA	169	0.9490
			treatment naïve
CGPLH369	Healthy	WGS	Preoperative	NA	167	0.9428
			treatment naïve
CGPLH370	Healthy	WGS	Preoperative	NA		167	0.9642
			treatment naïve
CGPLH371	Healthy	WGS	Preoperative	NA	168	0.9621
			treatment naïve
CGPLH380	Healthy	WGS	Preoperative	NA	170	0.9662
			treatment naïve
CGPLH381	Healthy	WGS	Preoperative	NA	169	0.9541
			treatment naïve
CGPLH382	Healthy	WGS	Preoperative	NA	167	0.9380
			treatment naïve
CGPLH383	Healthy	WGS	Preoperative	NA	168	0.9700
			treatment naïve
CGPLH384	Healthy	WGS	Preoperative	NA	169	0.8061
			treatment naïve
CGPLH385	Healthy	WGS	Preoperative	NA	167	0.8666
			treatment naïve
CGPLH386	Healthy	WGS	Preoperative	NA	167	0.6920
			treatment naïve
CGPLH387	Healthy	WGS	Preoperative	NA	169	0.9583
			treatment naïve
CGPLH388	Healthy	WGS	Preoperative	NA		167	0.9348
			treatment naïve
CGPLH389	Healthy	WGS	Preoperative	NA	168	0.9409
			treatment naïve
CGPLH390	Healthy	WGS	Preoperative	NA	167	0.9216
			treatment naïve
CGPLH391	Healthy	WGS	Preoperative	NA	166	0.9334
			treatment naïve
CGPLH392	Healthy	WGS	Preoperative	NA	167	0.9165
			treatment naïve
CGPLH393	Healthy	WGS	Preoperative	NA	169	0.9256
			treatment naïve
CGPLH394	Healthy	WGS	Preoperative	NA	167	0.9257
			treatment naïve
CGPLH395	Healthy	WGS	Preoperative	NA	166	0.8611
			treatment naïve
CGPLH396	Healthy	WGS	Preoperative	NA		167	0.7884
			treatment naïve
CGPLH398	Healthy	WGS	Preoperative	NA		167	0.9463
			treatment naïve
CGPLH399	Healthy	WGS	Preoperative	NA	169	0.8780
			treatment naïve
CGPLH400	Healthy	WGS	Preoperative	NA	168	0.6662
			treatment naïve
CGPLH401	Healthy	WGS	Preoperative	NA	167	0.9428
			treatment naïve
CGPLH402	Healthy	WGS	Preoperative	NA		167	0.9353
			treatment naïve
CGPLH403	Healthy	WGS	Preoperative	NA	168	0.9329
			treatment naïve
CGPLH404	Healthy	WGS	Preoperative	NA	169	0.9402
			treatment naïve
CGPLH405	Healthy	WGS	Preoperative	NA	166	0.9579
			treatment naïve
CGPLH406	Healthy	WGS	Preoperative	NA	167	0.8188
			treatment naïve
CGPLH407	Healthy	WGS	Preoperative	NA	169	0.9527
			treatment naïve
CGPLH408	Healthy	WGS	Preoperative	NA	167	0.9584
			treatment naïve
CGPLH409	Healthy	WGS	Preoperative	NA	198	0.9220
			treatment naïve
CGPLH410	Healthy	WGS	Preoperative	NA	168	0.9102
			treatment naïve
CGPLH411	Healthy	WGS	Preoperative	NA		167	0.9392
			treatment naïve
CGPLH412	Healthy	WGS	Preoperative	NA		167	0.9561
			treatment naïve
CGPLH413	Healthy	WGS	Preoperative	NA	167	0.9461
			treatment naïve
CGPLH414	Healthy	WGS	Preoperative	NA	168	0.9258
			treatment naïve
CGPLH415	Healthy	WGS	Preoperative	NA	169	0.9217
			treatment naïve
CGPLH416	Healthy	WGS	Preoperative	NA		167	0.9672
			treatment naïve
CGPLH417	Healthy	WGS	Preoperative	NA	168	0.9578
			treatment naïve
CGPLH418	Healthy	WGS	Preoperative	NA	169	0.9376
			treatment naïve
CGPLH419	Healthy	WGS	Preoperative	NA	167	0.9228
			treatment naïve
CGPLH420	Healthy	WGS	Preoperative	NA	169	0.9164
			treatment naïve
CGPLH422	Healthy	WGS	Preoperative	NA	166	0.9069
			treatment naïve
CGPLH423	Healthy	WGS	Preoperative	NA	169	0.9606
			treatment naïve
CGPLH424	Healthy	WGS	Preoperative	NA	167	0.9553
			treatment naïve
CGPLH425	Healthy	WGS	Preoperative	NA	168	0.9722
			treatment naïve
CGPLH426	Healthy	WGS	Preoperative	NA	168	0.9560
			treatment naïve
CGPLH427	Healthy	WGS	Preoperative	NA	167	0.9594
			treatment naïve
CGPLH428	Healthy	WGS	Preoperative	NA	167	0.9591
			treatment naïve
CGPLH429	Healthy	WGS	Preoperative	NA	168	0.9358
			treatment naïve
CGPLH430	Healthy	WGS	Preoperative	NA	167	0.9639
			treatment naïve
CGPLH431	Healthy	WGS	Preoperative	NA		167	0.9570
			treatment naïve
CGPLH432	Healthy	WGS	Preoperative	NA	168	0.9485
			treatment naïve
CGPLH434	Healthy	WGS	Preoperative	NA	168	0.9571
			treatment naïve
CGPLH435	Healthy	WGS	Preoperative	NA	170	0.9133
			treatment naïve
CGPLH436	Healthy	WGS	Preoperative	NA	168	0.9360
			treatment naïve
CGPLH437	Healthy	WGS	Preoperative	NA	170	0.9445
			treatment naïve
CGPLH438	Healthy	WGS	Preoperative	NA	170	0.9537
			treatment naïve
CGPLM439	Healthy	WGS	Preoperative	NA	171	0.9547
			treatment naïve
CGPLH440	Healthy	WGS	Preoperative	NA	169	0.9562
			treatment naïve
CGPLH441	Healthy	WGS	Preoperative	NA	167	0.9660
			treatment naïve
CGPLH442	Healthy	WGS	Preoperative	NA	167	0.9569
			treatment naïve
CGPLH443	Healthy	WGS	Preoperative	NA	170	0.9431
			treatment naïve
CGPLH444	Healthy	WGS	Preoperative	NA	171	0.9429
			treatment naïve
CGPLH445	Healthy	WGS	Preoperative	NA	171	0.9446
			treatment naïve
CGPLH446	Healthy	WGS	Preoperative	NA		167	0.9502
			treatment naïve
CGPLH447	Healthy	WGS	Preoperative	NA	169	0.9421
			treatment naïve
CGPLH448	Healthy	WGS	Preoperative	NA	167	0.9553
			treatment naïve
CGPLH449	Healthy	WGS	Preoperative	NA	167	0.9550
			treatment naïve
CGPLH450	Healthy	WGS	Preoperative	NA		167	0.9572
			treatment naïve
CGPLH451	Healthy	WGS	Preoperative	NA	169	0.9548
			treatment naïve
CGPLH452	Healthy	WGS	Preoperative	NA	167	0.9498
			treatment naïve
CGPLH453	Healthy	WGS	Preoperative	NA	166	0.9572
			treatment naïve
CGPLH455	Healthy	WGS	Preoperative	NA	166	0.9526
			treatment naïve
CGPLH450	Healthy	WGS	Preoperative	NA	166	0.9507
			treatment naïve
CGPLH457	Healthy	WGS	Preoperative	NA	167	0.9429
			treatment naïve
CGPLH458	Healthy	WGS	Preoperative	NA	167	0.9511
			treatment naïve
CGPLH459	Healthy	WGS	Preoperative	NA	168	0.9609
			treatment naïve
CGPLH460	Healthy	WGS	Preoperative	NA	168	0.9331
			treatment naïve
CGPLH463	Healthy	WGS	Preoperative	NA	167	0.9506
			treatment naïve
CGPLH464	Healthy	WGS	Preoperative	NA	170	0.9133
			treatment naïve
CGPLH465	Healthy	WGS	Preoperative	NA	167	0.9251
			treatment naïve
CGPLH466	Healthy	WGS	Preoperative	NA	167	0.9679
			treatment naïve
CGPLH467	Healthy	WGS	Preoperative	NA	168	0.9273
			treatment naïve
CGPLH468	Healthy	WGS	Preoperative	NA	167	0.8553
			treatment naïve
CGPLH469	Healthy	WGS	Preoperative	NA	169	0.8225
			treatment naïve
CGPLH470	Healthy	WGS	Preoperative	NA	168	0.9073
			treatment naïve
CGPLH471	Healthy	WGS	Preoperative	NA		167	0.9354
			treatment naïve
CGPLH472	Healthy	WGS	Preoperative	NA	166	0.8509
			treatment naïve
CGPLH473	Healthy	WGS	Preoperative	NA	167	0.9206
			treatment naïve
CGPLH474	Healthy	WGS	Preoperative	NA	168	0.8474
			treatment naïve
CGPLH475	Healthy	WGS	Preoperative	NA	167	0.9155
			treatment naïve
CGPLH476	Healthy	WGS	Preoperative	NA	169	0.8807
			treatment naïve
CGPLH477	Healthy	WGS	Preoperative	NA	169	0.9129
			treatment naïve
CGPLH478	Healthy	WGS	Preoperative	NA	167	0.9588
			treatment naïve
CGPLN479	Healthy	WGS	Preoperative	NA	167	0.9503
			treatment naïve
CGPLH480	Healthy	WGS	Preoperative	NA	169	0.9522
			treatment naïve
CGPLH481	Healthy	WGS	Preoperative	NA	168	0.9568
			treatment naïve
CGPLH482	Healthy	WGS	Preoperative	NA	168	0.9379
			treatment naïve
CGPLH483	Healthy	WGS	Preoperative	NA	168	0.9518
			treatment naïve
CGPLH484	Healthy	WGS	Preoperative	NA	166	0.9630
			treatment naïve
CGPLH485	Healthy	WGS	Preoperative	NA	166	0.9547
			treatment naïve
CGPLH486	Healthy	WGS	Preoperative	NA	169	0.9199
			treatment naïve
CGPLH487	Healthy	WGS	Preoperative	NA	169	0.9575
			treatment naïve
CGPLH488	Healthy	WGS	Preoperative	NA	167	0.9618
			treatment naïve
CGPLH490	Healthy	WGS	Preoperative	NA		167	0.8950
			treatment naïve
CGPLH491	Healthy	WGS	Preoperative	NA	168	0.9631
			treatment naïve
CGPLH492	Healthy	WGS	Preoperative	NA	170	0.9335
			treatment naïve
CGPLH493	Healthy	WGS	Preoperative	NA	168	0.8718
			treatment naïve
CGPLH494	Healthy	WGS	Preoperative	NA	169	0.9623
			treatment naïve
CGPLH495	Healthy	WGS	Preoperative	NA	166	0.8777
			treatment naïve
CGPLH496	Healthy	WGS	Preoperative	NA	166	0.8788
			treatment naïve
CGPLH497	Healthy	WGS	Preoperative	NA	167	0.9576
			treatment naïve
CGPLH498	Healthy	WGS	Preoperative	NA	167	0.9526
			treatment naïve
CGPLH499	Healthy	WGS	Preoperative	NA		167	0.9733
			treatment naïve
CGPLH500	Healthy	WGS	Preoperative	NA	168	0.9542
			treatment naïve
CGPLH501	Healthy	WGS	Preoperative	NA	169	0.9526
			treatment naïve
CGPLH502	Healthy	WGS	Preoperative	NA	167	0.9512
			treatment naïve
CGPLH503	Healthy	WGS	Preoperative	NA	169	0.8947
			treatment naïve
CGPLH504	Healthy	WGS	Preoperative	NA	167	0.9561
			treatment naïve
CGPLH505	Healthy	WGS	Preoperative	NA	166	0.9554
			treatment naïve
CGPLH506	Healthy	WGS	Preoperative	NA	167	0.9733
			treatment naïve
CGPLH507	Healthy	WGS	Preoperative	NA	168	0.9222
			treatment naïve
CGPLH508	Healthy	WGS	Preoperative	NA	167	0.9674
			treatment naïve
CGPLH509	Healthy	WGS	Preoperative	NA		167	0.9475
			treatment naïve
CGPLH510	Healthy	WGS	Preoperative	NA	167	0.9459
			treatment naïve
CGPLH511	Healthy	WGS	Preoperative	NA	166	0.9714
			treatment naïve
CGPLH512	Healthy	WGS	Preoperative	NA	168	0.9442
			treatment naïve
CGPLH513	Healthy	WGS	Preoperative	NA	166	0.9705
			treatment naïve
CGPLH514	Healthy	WGS	Preoperative	NA	167	0.9690
			treatment naïve
CGPLH515	Healthy	WGS	Preoperative	NA	167	0.9568
			treatment naïve
CGPLH516	Healthy	WGS	Preoperative	NA	166	0.9508
			treatment naïve
CGPLH517	Healthy	WGS	Preoperative	NA	168	0.9635
			treatment naïve
CGPLH518	Healthy	WGS	Preoperative	NA	168	0.9647
			treatment naïve
CGPLH519	Healthy	WGS	Preoperative	NA	166	0.9366
			treatment naïve
CGPLH520	Healthy	WGS	Preoperative	NA	166	0.3649
			treatment naïve
CGPLH625	Healthy	WGS	Preoperative	NA	166	0.8766
			treatment naïve
CGPLH626	Healthy	WGS	Preoperative	NA	170	0.9011
			treatment naïve
CGPLH639	Healthy	WGS	Preoperative	NA	165	0.9482
			treatment naïve
CGPLH640	Healthy	WGS	Preoperative	NA	166	0.9131
			treatment naïve
CGPLH642	Healthy	WGS	Preoperative	NA	167	0.9641
			treatment naïve
CGPLH643	Healthy	WGS	Preoperative	NA	169	0.9450
			treatment naïve
CGPLH644	Healthy	WGS	Preoperative	NA	170	0.9398
			treatment naïve
CGPLH646	Healthy	WGS	Preoperative	NA	172	0.9296
			treatment naïve
CGPLLU144	Lung	Targeted Mutation	Preoperative	II	164	0.8702
	Cancer	Analysis and WGS	treatment naïve
CGPLLU161	Lung	Targeted Mutation	Preoperative	II	165	0.9128
	Cancer	Analysis and WGS	treatment naïve
CGPLLU162	Lung	Targeted Mutation	Preoperative	II	165	0.7753
	Cancer	Analysis and WGS	treatment naïve
CGPLLU163	Lung	Targeted Mutation	Preoperative	II	166	0.4770
	Cancer	Analysis and WGS	treatment naïve
CGPLLU168	Lung	Targeted Mutation	Preoperative	I	163	0.9164
	Cancer	Analysis and WGS	treatment naïve
CGPLLU169	Lung	Targeted Mutation	Preoperative	I	163	0.9326
	Cancer	Analysis and WGS	treatment naïve
CGPLLU176	Lung	Targeted Mutation	Preoperative	I	168	0.9572
	Cancer	Analysis and WGS	treatment naïve
CGPLLU177	Lung	Targeted Mutation	Preoperative	II	166	0.8472
	Cancer	Analysis and WGS	treatment naïve
CGPLLU203	Lung	Targeted Mutation	Preoperative	II	164	0.9119
	Cancer	Analysis and WGS	treatment naïve
CGPLLU205	Lung	Targeted Mutation	Preoperative	II	163	0.9518
	Cancer	Analysis and WGS	treatment naïve
CGPLLU207	Lung	Targeted Mutation	Preoperative	II	166	0.9344
	Cancer	Analysis and WGS	treatment naïve
CGPLLU208	Lung	Targeted Mutation	Preoperative	II	164	0.9091
	Cancer	Analysis and WGS	treatment naïve
CGPLOV11	Ovarian	Targeted Mutation	Preoperative	V	166	0.8902
	Cancer	Analysis and WGS	treatment naïve
CGPLOV12	Ovarian	Targeted Mutation	Preoperative	I		167	0.8779
	Cancer	Analysis and WGS	treatment naïve
CGPLOV13	Ovarian	Targeted Mutation	Preoperative	V	166	0.7560
	Cancer	Analysis and WGS	treatment naïve
CGPLOV15	Ovarian	Targeted Mutation	Preoperative	III	155	0.8585
	Cancer	Analysis and WGS	treatment naïve
CGPLOV18	Ovarian	Targeted Mutation	Preoperative	III	165	0.9052
	Cancer	Analysis and WGS	treatment naïve
CGPLOV19	Ovarian	Targeted Mutation	Preoperative	II	165	0.7854
	Cancer	Analysis and WGS	treatment naïve
CGPLOV20	Ovarian	Targeted Mutation	Preoperative	II	165	0.8711
	Cancer	Analysis and WGS	treatment naïve
CGPLOV21	Ovarian	Targeted Mutation	Preoperative	V	167	0.8942
	Cancer	Analysis and WGS	treatment naïve
CGPLOV22	Ovarian	Targeted Mutation	Preoperative	III	164	0.8944
	Cancer	Analysis and WGS	treatment naïve
CGPLOV23	Ovarian	Targeted Mutation	Preoperative	I	169	0.8510
	Cancer	Analysis and WGS	treatment naïve
CGPLOV24	Ovarian	Targeted Mutation	Preoperative	I	166	0.9449
	Cancer	Analysis and WGS	treatment naïve
CGPLOV25	Ovarian	Targeted Mutation	Preoperative	I	166	0.9590
	Cancer	Analysis and WGS	treatment naïve
CGPLOV26	Ovarian	Targeted Mutation	Preoperative	I	161	0.8148
	Cancer	Analysis and WGS	treatment naïve
CGPLOV28	Ovarian	Targeted Mutation	Preoperative	I	167	0.9635
	Cancer	Analysis and WGS	treatment naïve
CGPLOV31	Ovarian	Targeted Mutation	Preoperative	III	167	0.9461
	Cancer	Analysis and WGS	treatment naïve
CGPLOV32	Ovarian	Targeted Mutation	Preoperative	I	168	0.9582
	Cancer	Analysis and WGS	treatment naïve
CGPLOV37	Ovarian	Targeted Mutation	Preoperative	I	170	0.9397
	Cancer	Analysis and WGS	treatment naïve
CGPLOV38	Ovarian	Targeted Mutation	Preoperative	I	166	0.5779
	Cancer	Analysis and WGS	treatment naïve
CGPLOV40	Ovarian	Targeted Mutation	Preoperative	V	170	0.6097
	Cancer	Analysis and WGS	treatment naïve
CGPLOV41	Ovarian	Targeted Mutation	Preoperative	V		167	0.9403
	Cancer	Analysis and WGS	treatment naïve
CGPLOV42	Ovarian	Targeted Mutation	Preoperative	I	166	0.9265
	Cancer	Analysis and WGS	treatment naïve
CGPLOV43	Ovarian	Targeted Mutation	Preoperative	I	167	0.9626
	Cancer	Analysis and WGS	treatment naïve
CGPLOV44	Ovarian	Targeted Mutation	Preoperative	I	164	0.9536
	Cancer	Analysis and WGS	treatment naïve
CGPLOV45	Ovarian	Targeted Mutation	Preoperative	I	166	0.9622
	Cancer	Analysis and WGS	treatment naïve
CGPLOV47	Ovarian	Targeted Mutation	Preoperative	I	165	0.9704
	Cancer	Analysis and WGS	treatment naïve
CGPLOV48	Ovarian	Targeted Mutation	Preoperative	I	167	0.9675
	Cancer	Analysis and WGS	treatment naïve
CGPLOV49	Ovarian	Targeted Mutation	Preoperative	III	164	0.8998
	Cancer	Analysis and WGS	treatment naïve
CGPLOV50	Ovarian	Targeted Mutation	Preoperative	III	165	0.9682
	Cancer	Analysis and WGS	treatment naïve
CGPLPA112	Pancreatic	WGS	Preoperative	II	164	0.8914
	Cancer		treatment naïve
CGPLPA113	Duodenal	WGS	Preoperative	I	170	0.8751
	Cancer		treatment naïve
CGPLPA114	Bile Duct	WGS	Preoperative	II	166	0.9098
	Cancer		treatment naïve
CGPLPA115	Bile Duct	WGS	Preoperative	V	165	0.8053
	Cancer		treatment naïve
CGPLPA117	Bile Duct	WGS	Preoperative	II	165	0.9395
	Cancer		treatment naïve
CGPLPA118	Bile Duct	Targeted Mutation	Preoperative	I	157	0.9406
	Cancer	Analysis and WGS	treatment naïve
CGPLPA122	Bile Duct	Targeted Mutation	Preoperative	II	164	0.8231
	Cancer	Analysis and WGS	treatment naïve
CGPLPA124	Bile Duct	Targeted Mutation	Preoperative	II	166	0.9108
	Cancer	Analysis and WGS	treatment naïve
CGPLPA125	Bile Duct	WGS	Preoperative	II	165	0.9675
	Cancer		treatment naïve
CGPLPA126	Bile Duct	Targeted Mutation	Preoperative	II	166	0.9155
	Cancer	Analysis and WGS	treatment naïve
CGPLPA127	Bile Duct	WGS	Preoperative	V		167	0.8916
	Cancer		treatment naïve
CGPLPA128	Bile Duct	Targeted Mutation	Preoperative	II	167	0.9262
	Cancer	Analysis and WGS	treatment naïve
CGPLPA129	Bile Duct	Targeted Mutation	Preoperative	II	166	0.9220
	Cancer	Analysis and WGS	treatment naïve
CGPLPA130	Bile Duct	Targeted Mutation	Preoperative	II	169	0.8586
	Cancer	Analysis and WGS	treatment naïve
CGPLPA131	Bile Duct	Targeted Mutation	Preoperative	II	165	0.7707
	Cancer	Analysis and WGS	treatment naïve
CGPLPA134	Bile Duct	Targeted Mutation	Preoperative	II	160	0.7502
	Cancer	Analysis and WGS	treatment naïve
CGPLPA135	Bile Duct	WGS	Preoperative	I	165	0.9495
	Cancer		treatment naïve
CGPLPA136	Bile Duct	Targeted Mutation	Preoperative	II	164	0.9289
	Cancer	Analysis and WGS	treatment naïve
CGPLPA137	Bile Duct	WGS	Preoperative	II	166	0.9568
	Cancer		treatment naïve
CGPLPA139	Bile Duct	WGS	Preoperative	V	166	0.9511
	Cancer		treatment naïve
CGPLPA14	Pancreatic	WGS	Preoperative	II	167	0.8718
	Cancer		treatment naïve
CGPLPA140	Bile Duct	Targeted Mutation	Preoperative	II	166	0.9215
	Cancer	Analysis and WGS	treatment naïve
CGPLPA141	Bile Duct	WGS	Preoperative	II	165	0.3172
	Cancer		treatment naïve
CGPLPA15	Pancreatic	WGS	Preoperative	II	167	0.9111
	Cancer		treatment naïve
CGPLPA155	Bile Duct	WGS	Preoperative	II	165	0.9496
	Cancer		treatment naïve
CGPLPA156	Pancreatic	WGS	Preoperative	II	167	0.9479
	Cancer		treatment naïve
CGPLPA165	Bile Duct	WGS	Preoperative	I	168	0.9596
	Cancer		treatment naïve
CGPLPA168	Bile Duct	WGS	Preoperative	II	162	0.7838
	Cancer		treatment naïve
CGPLPA17	Pancreatic	WGS	Preoperative	II	166	0.8624
	Center		treatment naïve
CGPLPA184	Bile Duct	WGS	Preoperative	II	165	0.9100
	Cancer		treatment naïve
CGPLPA187	Bile Duct	WGS	Preoperative	II	165	0.8577
	Cancer		treatment naïve
CGPLPA23	Pancreatic	WGS	Preoperative	II	165	0.7887
	Cancer		treatment naïve
CGPLPA25	Pancreatic	WGS	Preoperative	II	166	0.9549
	Cancer		treatment naïve
CGPLPA26	Pancreatic	WGS	Preoperative	II	166	0.9598
	Cancer		treatment naïve
CGPLPA28	Pancreatic	WGS	Preoperative	II	165	0.9069
	Cancer		treatment naïve
CGPLPA33	Pancreatic	WGS	Preoperative	II	166	0.8361
	Cancer		treatment naïve
CGPLPA34	Pancreatic	WGS	Preoperative	II	168	0.9846
	Cancer		treatment naïve
CGPLPA37	Pancreatic	WGS	Preoperative	II	165	0.8840
	Cancer		treatment naïve
CGPLPA38	Pancreatic	WGS	Preoperative	II	167	0.8746
	Cancer		treatment naïve
CGPLPA39	Pancreatic	WGS	Preoperative	II	167	0.8562
	Cancer		treatment naïve
CGPLPA40	Pancreatic	WGS	Preoperative	II	165	0.8563
	Cancer		treatment naïve
CGPLPA42	Pancreatic	WGS	Preoperative	II	167	0.9126
	Cancer		treatment naïve
CGPLPA46	Pancreatic	WGS	Preoperative	II	169	0.8274
	Cancer		treatment naïve
CGPLPA47	Pancreatic	WGS	Preoperative	II	166	0.8376
	Cancer		treatment naïve
CGPLPA48	Pancreatic	WGS	Preoperative	I	167	0.9391
	Cancer		treatment naïve
CGPLPA52	Pancreatic	WGS	Preoperative	II	167	0.9452
	Cancer		treatment naïve
CGPLPA53	Pancreatic	WGS	Preoperative	I	163	0.9175
	Cancer		treatment naïve
CGPLPA58	Pancreatic	WGS	Preoperative	II	165	0.9587
	Cancer		treatment naïve
CGPLPA59	Pancreatic	WGS	Preoperative	II	163	0.9230
	Cancer		treatment naïve
CGPLPA67	Pancreatic	WGS	Preoperative	II	165	0.9574
	Cancer		treatment naïve
CGPLPA69	Pancreatic	WGS	Preoperative	I	168	0.9172
	Cancer		treatment naïve
CGPLPA71	Pancreatic	WGS	Preoperative	II	167	0.9424
	Cancer		treatment naïve
CGPLPA74	Pancreatic	WGS	Preoperative	II	165	0.9688
	Cancer		treatment naïve
CGPLPA78	Pancreatic	WGS	Preoperative	II	163	0.9681
	Cancer		treatment naïve
CGPLPA85	Pancreatic	WGS	Preoperative	II	165	0.9137
	Cancer		treatment naïve
CGPLPA86	Pancreatic	WGS	Preoperative	II	165	0.8875
	Cancer		treatment naïve
CGPLPA92	Pancreatic	WGS	Preoperative	II	167	0.9389
	Cancer		treatment naïve
CGPLPA93	Pancreatic	WGS	Preoperative	II	166	0.8585
	Cancer		treatment naïve
CGPLPA94	Pancreatic	WGS	Preoperative	II	162	0.9365
	Cancer		treatment naïve
CGPLPA95	Pancreatic	WGS	Preoperative	II	163	0.8542
	Cancer		treatment naïve
CGST102	Gastric	Targeted Mutation	Preoperative	II	167	0.9496
	cancer	Analysis and WGS	treatment naïve
CGST11	Gastric	WGS	Preoperative	IV	169	0.9419
	cancer		treatment naïve
CGST110	Gastric	Targeted Mutation	Preoperative	II	167	0.9626
	cancer	Analysis and WGS	treatment naïve
CGST114	Gastric	Targeted Mutation	Preoperative	II	164	0.9535
	cancer	Analysis and WGS	treatment naïve
CGST13	Gastric	Targeted Mutation	Preoperative	II	166	0.9369
	cancer	Analysis and WGS	treatment naïve
CGST131	Gastric	WGS	Preoperative	II	171	0.9428
	cancer		treatment naïve
CGST141	Gastric	Targeted Mutation	Preoperative	II	168	0.9621
	cancer	Analysis and WGS	treatment naïve
CGST16	Gastric	Targeted Mutation	Preoperative	II	166	0.7804
	cancer	Analysis and WGS	treatment naïve
CGST18	Gastric	Targeted Mutation	Preoperative	II	169	0.9523
	cancer	Analysis and WGS	treatment naïve
CGST21	Gastric	WGS	Preoperative	II	165	−0.4778
	cancer		treatment naïve
CGST26	Gastric	WGS	Preoperative	IV	166	0.9554
	cancer		treatment naïve
CG3T28	Gastric	Targeted Mutation	Preoperative	X	169	0.9076
	cancer	Analysis and WGS	treatment naïve
CGST30	Gastric	Targeted Mutation	Preoperative	II	169	0.9246
	cancer	Analysis and WGS	treatment naïve
CGST32	Gastric	Targeted Mutation	Preoperative	II	169	0.9431
	cancer	Analysis and WGS	treatment naïve
CGST33	Gastric	Targeted Mutation	Preoperative	I	168	0.7999
	cancer	Analysis and WGS	treatment naïve
CGST38	Gastric	WGS	Preoperative	0	168	0.9368
	cancer		treatment naïve
CGST39	Gastric	Targeted Mutation	Preoperative	IV	164	0.8742
	cancer	Analysis and WGS	treatment naïve
CGST41	Gastric	Targeted Mutation	Preoperative	IV	168	0.8194
	cancer	Analysis and WGS	treatment naïve
CGST45	Gastric	Targeted Mutation	Preoperative	II	168	0.9576
	cancer	Analysis and WGS	treatment naïve
CGST47	Gastric	Targeted Mutation	Preoperative	I	168	0.9611
	cancer	Analysis and WGS	treatment naïve
CGST48	Gastric	Targeted Mutation	Preoperative	IV	167	0.7469
	cancer	Analysis and WGS	treatment naïve
CGST53	Gastric	WGS	Preoperative	0	173	0.0019
	cancer		treatment naïve
CGST58	Gastric	Targeted Mutation	Preoperative	II	169	0.9470
	cancer	Analysis and WGS	treatment naïve
CGST67	Gastric	WGS	Preoperative	I	170	0.9352
	cancer		treatment naïve
CGST77	Gastric	WGS	Preoperative	IV	170	0.00438
	cancer		treatment naïve
CGST80	Gastric	Targeted Mutation	Preoperative	II	168	0.9313
	cancer	Analysis and WGS	treatment naïve
CGST81	Gastric	Targeted Mutation	Preoperative	I	168	0.9480
	cancer	Analysis and WGS	treatment naïve

	Correlation of
	GC Corrected
	Fragment					Mutant
	Ratio Profile					Alelle
	to Median	Fraction				Fraction
	Fragment	of Reads		Detected	Detected	Detected
	Ratio Profile	Mapped to		using	using	using
	of Healthy	Mitochondrial	DELFI	DELFI (95%	DELFI (98%	Targeted
Patient	Individuals	Genome	Scene	specificity)	specificity)	sequencing*

CGCRC291	0.5268	0.0484%	0.9976	Y	Y	22.85%
CGCRC232	0.8835	0.0270%	0.7299	Y	N	1.41%
CGCRC293	0.9206	0.0748%	0.5534	N	N	0.35%
CGCRC294	0.8904	0.0188%	0.8757	Y	Y	0.17%
CGCRC295	0.8895	0.0369%	0.9951	Y	Y	ND
CGCRC299	0.9268	0.0392%	0.9648	Y	Y	ND
CGCRC300	0.0303	0.0235%	0.4447	N	N	ND
CGCRC301	0.9151	0.0310%	0.2190	N	N	0.21%
CGCRC302	0.9243	0.0112%	0.9897	Y	Y	0.12%
CGCRC304	0.9360	0.0393%	0.9358	Y	Y	0.27%
CGCRC305	0.9250	0.0120%	0.3988	Y	Y	0.19%
CGCRG306	0.8186	0.0781%	0.9486	Y	Y	8.02%
CGCRC307	0.9342	0.0181%	0.7042	Y	N	0.58%
CGCRC306	0.9324	0.0078%	0.9082	Y	Y	0.11%
CGCRC311	0.9156	0.0173%	0.1887	N	N	ND
CGCRC315	0.8846	0.0241%	0.6422	Y	N	0.27%
CGCRC316	0.5879	0.0315%	0.9971	Y	Y	5.52%
CGCRC317	0.8944	0.0184%	0.9855	Y	Y	0.36%
CGCRC318	0.9140	0.0156%	0.5615	N	N	ND
CGCRC319	0.8230	0.1259%	0.9925	Y	Y	3.11%
CGCRC320	0.9101	0.0383%	0.8019	Y	Y	0.84%
CGCRC321	0.9021	0.0829%	0.9759	Y	Y	0.20%
CGCRC333	0.4355	0.4284%	0.9974	Y	Y	43.03%
CGCRC335	0.6856	0.1154%	0.9887	Y	Y	81.61%
CGCRC338	0.7573	0.1436%	0.9976	Y	Y	36.00%
CGCRC341	0.9191	0.0197%	0.9670	Y	Y	ND
CGCRC342	0.1345	0.1732%	0.9987	Y	Y	30.72%
CGPLBR100	0.8945	0.1234%	0.8684	Y	Y	ND
CGPLBR101	0.9304	0.0709%	0.9385	Y	Y	ND
CGPLBR102	0.9345	0.4742%	0.9052	Y	Y	0.25%
CGPLBR103	0.9251	0.0775%	0.5994	N	N	ND
CGPLBR104	0.9192	0.0532%	0.9950	Y	Y	0.13%
CGPLBR12	0.7760	0.1407%	0.7598	Y	Y	—
CGPLBR18	0.9534	0.0267%	0.3886	N	N	—
CGPLBR23	0.9312	0.0144%	0.1235	N	N	—
CGPLBR24	0.8766	0.0210%	0.7480	Y	Y	—
CGPLBR26	0.9120	0.1456%	0.9630	Y	Y	—
CGPLBR30	0.6611	0.0952%	0.9956	Y	Y	—
CGPLBR31	0.9556	0.0427%	0.2227	N	N	—
CGPLBR32	0.9229	0.0306%	0.9815	Y	Y	—
CGPLBR33	0.9432	0.0817%	0.2853	N	N	—
CGPLBR34	0.9425	0.0115%	0.1637	N	N	—
CGPLBR35	0.9348	0.1371%	0.5057	N	N	—
CGPLBR36	0.8884	0.0813%	0.4017	N	N	—
CGPLBR37	0.9495	0.0516%	0.0314	N	N	—
CGPLBR38	0.0349	0.1352%	0.8983	Y	Y	0.53%
CGPLBR40	0.9244	0.0923%	0.9846	Y	Y	ND
CGPLBR41	0.9346	0.0544%	0.9416	Y	Y	0.32%
CGPLBR45	0.9286	0.0296%	0.3860	N	N
CGPLBR46	0.9005	0.0345%	0.7270	Y	N	—
CGPLBR47	0.2028	0.0591%	0.8247	Y	Y	—
CGPLBR48	0.8246	0.0504%	0.9973	Y	Y	0.18%
CGPLBR49	0.7887	0.0377%	0.9946	Y	Y	ND
CGPLBR50	0.8332	0.0137%	0.6820	Y	N	—
CGPLBR51	0.9160	0.0863%	0.6915	Y	N	—
CGPLBR52	0.9196	0.0165%	0.6390	Y	N	—
CGPLBR55	0.9341	0.0356%	0.9494	Y	Y	0.68%
CGPLBR56	0.9428	0.2025%	0.4700	N	N	—
CGPLBR57	0.9416	0.0902%	0.9090	Y	Y	ND
CGPLBR59	0.9130	0.0761%	0.5828	N	N	ND
CGPLBR60	0.8916	0.0626%	0.8779	Y	Y	—
CGPLBR61	0.9422	0.0601%	0.4417	N	N	0.44%
CGPLBR63	0.9132	0.0514%	0.8788	Y	Y	ND
CGPLBR65	0.8970	0.0264%	0.9048	Y	Y	—
CGPLBR68	0.9532	0.0164%	0.7863	Y	Y	ND
CGPLBR69	0.9474	0.0279%	0.0600	N	N	ND
CGPLBR70	0.9388	0.0171%	0.6447	Y	N	0.36%
CGPLBR71	0.9368	0.0271%	0.6706	Y	N	0.10%
CGPLBR72	0.9640	0.0263%	0.6129	N	N	ND
CGPLBR73	0.9421	0.0142%	0.0746	N	N	0.27%
CGPLBR76	0.9254	0.0775%	0.9334	Y	Y	3.12%
CGPLBR81	0.8193	0.0241%	0.9899	Y	Y	—
CGPLBR82	0.9288	0.1640%	0.9834	Y	Y	0.12%
CGPLBR83	0.9138	0.0419%	0.9810	Y	Y	0.28%
CGPLBR84	0.8359	0.0274%	0.9901	Y	Y	—
CGPLBR87	0.8797	0.0294%	0.9988	Y	Y	0.45%
CGPLBR88	0.8547	0.0181%	0.9988	Y	Y	0.38%
CGPLBR90	0.8330	0.0417%	0.9687	Y	Y
CGPLBR91	0.9408	0.0799%	0.8710	Y	Y	ND
CGPLBR92	0.8835	0.1042%	0.9866	Y	Y	0.20%
CGPLBP93	0.9072	0.0352%	0.7253	Y	N	ND
CGPLH189	0.8947	0.0591%	0.1748	N	N	—
CGPLH190	0.9369	0.1193%	0.5188	N	N	—
CGPLH192	0.9487	0.0276%	0.0178	N	N	—
CGPLH193	0.9442	0.0420%	0.5794	N	N	—
CGPLH194	0.9289	0.0407%	0.1616	N	N	—
CGPLH196	0.9512	0.0266%	0.0999	N	N	—
CGPLH197	0.9416	0.0334%	0.4699	N	N	—
CGPLH198	0.9457	0.0302%	0.6571	Y	N
CGPLH199	0.9439	0.0170%	0.5564	N	N	—
CGPLH200	0.9391	0.0362%	0.3833	N	N	—
CGPLH201	0.9180	0.0470%	0.8395	Y	Y	—
CGPLH202	0.9436	0.0501%	0.1088	N	N	—
CGPLH203	0.9575	0.0455%	0.2485	N	N	—
CGPLH205	0.9283	0.0409%	0.4401	N	N	—
CGPLH208	0.9409	0.0371%	0.2706	N	N	—
CGPLH209	0.9367	0.0427%	0.2213	N	N	—
CGPLH210	0.9181	0.0279%	0.3500	N	N	—
CGPLH211	0.9410	0.0317%	0.1752	N	N	—
CGPLH300	0.9200	0.0397%	0.0226	N	N
CGPLH307	0.9167	0.0388%	0.1789	N	N	—
CGPLH308	0.9352	0.0311%	0.0185	N	N	—
CGPLH309	0.9451	0.0226%	0.0441	N	N	—
CGPLN310	0.9527	0.0145%	0.7135	Y	N	—
CGPLH311	0.9348	0.0202%	0.2589	N	N	—
CGPLH314	0.9491	0.0212%	0.1632	N	N	—
CGPLH315	0.9427	0.0071%	0.3450	N	N	—
CGPLH316	0.9552	0.0191%	0.4697	N	N	—
CGPLH317	0.9352	0.0232%	0.1330	N	N	—
CGPLH319	0.9189	0.0263%	0.2232	N	N	—
CGPLR320	0.9166	0.0222%	0.1095	N	N	—
CGPLH322	0.9411	0.0248%	0.0749	N	N	—
CGPLH324	0.9133	0.0402%	0.0128	N	N	—
CGPLH325	0.9202	0.0711%	0.0102	N	N	—
CGPLH326	0.9408	0.0213%	0.0475	N	N	—
CGPLH327	0.9071	0.1275%	0.4891	N	N	—
CGPLH328	0.9332	0.0256%	0.0234	N	N	—
CGPLH329	0.9396	0.0269%	0.0139	N	N	—
CGPLH330	0.9403	0.0203%	0.2642	N	N	—
CGPLH331	0.9377	0.0314%	0.0304	N	N	—
CGPLH333	0.9132	0.0350%	0.1633	N	N	—
CGPLH335	0.9333	0.0285%	0.0096	N	N	—
CGPLH336	0.9159	0.0159%	0.3872	N	N	—
CGPLH337	0.9262	0.0367%	0.2976	N	N	—
CGPLH338	0.9303	0.0103%	0.0431	N	N	—
CGPLH339	0.9338	0.0280%	0.0379	N	N	—
CGPLH340	0.9321	0.0210%	0.0379	N	N	—
CGPLH341	0.9187	0.0448%	0.1775	N	N	—
CGPLH342	0.8986	0.0283%	0.0904	N	N	—
CGPLH343	0.9067	0.0632%	0.0160	N	N	—
CGPLH344	0.8998	0.0257%	0.0120	N	N	—
CGPLH345	0.9107	0.0445%	0.0031	N	N	—
CGPLH346	0.9074	0.0208%	0.0686	N	N	—
CGPLH350	0.9288	0.0284%	0.0071	N	N	—
CGPLH351	0.9294	0.0223%	0.0207	N	N	—
CGPLH352	0.9190	0.0613%	0.0512	N	N	—
CGPLH353	0.9130	0.0408%	0.0132	N	N	—
CGPLH354	0.9121	0.0318%	0.0082	N	N	—
CGPLH355	0.9308	0.0400%	0.6407	Y	N	—
CGPLH356	0.9312	0.0427%	0.2437	N	N	—
CGPLH357	0.9340	0.0217%	0.0070	N	N
CGPLH358	0.9372	0.0174%	0.1451	N	N	—
CGPLH360	0.8775	0.3395%	0.0048	N	N	—
CGPLH361	0.9283	0.0266%	0.1524	N	N	—
CGPLH362	0.9503	0.0309%	0.4832	N	N	—
CGPLH363	0.9187	0.0620%	0.0199	N	N	—
CGPLH364	0.9480	0.0282%	0.8719	Y	Y	—
CGPLH365	0.9051	0.1740%	0.9683	Y	Y	—
CGPLH366	0.9170	0.0344%	0.0952	N	N	—
CGPLH367	0.9181	0.0353%	0.1235	N	N	—
CGPLH368	0.9076	0.1073%	0.1252	N	N	—
CGPLH369	0.9541	0.0246%	0.2821	N	N	—
CGPLH370	0.9423	0.0410%	0.0989	N	N	—
CGPLH371	0.9414	0.0734%	0.2173	N	N	—
CGPLH380	0.9424	0.0523%	0.0128	N	N	—
CGPLH381	0.9501	0.0435%	0.0152	N	N	—
CGPLH382	0.9584	0.0340%	0.0326	N	N	—
CGPLH383	0.9407	0.0389%	0.0035	N	N	—
CGPLH384	0.9043	0.0207%	0.0258	N	N	—
CGPLH385	0.9245	0.0165%	0.0566	N	N	—
CGPLH386	0.8859	0.0502%	0.2677	N	N
CGPLH387	0.9223	0.0375%	0.0081	N	N	—
CGPLH388	0.9266	0.0527%	0.0499	N	N	—
CGPLH389	0.9035	0.0667%	0.6585	Y	N	—
CGPLH390	0.9182	0.0229%	0.0837	N	N	—
CGPLH391	0.9162	0.0223%	0.0716	N	N	—
CGPLH392	0.9014	0.0424%	0.1305	N	N	—
CGPLH393	0.9045	0.0407%	0.0037	N	N	—
CGPLH394	0.9292	0.6522%	0.1073	N	N	—
CGPLH395	0.9254	0.0424%	0.0171	N	N	—
CGPLH396	0.8928	0.0393%	0.0303	N	N	—
CGPLH398	0.9578	0.0242%	0.3195	N	N
CGPLH399	0.9195	0.0573%	0.0685	N	N	—
CGPLH400	0.9047	0.0300%	0.2103	N	N	—
CGPLH401	0.9339	0.0146%	0.0620	N	N	—
CGPLH402	0.8800	0.1516%	0.0395	N	N	—
CGPLH403	0.8829	0.0515%	0.0223	N	N	—
CGPLH404	0.8948	0.0528%	0.0027	N	N	—
CGPLH405	0.9204	0.0359%	0.0188	N	N	—
CGPLH406	0.8592	0.0667%	0.0206	N	N	—
CGPLH407	0.9099	0.0229%	0.0040	N	N	—
CGPLH408	0.9192	0.0415%	0.1257	N	N	—
CGPLH409	0.8950	0.0302%	0.0056	N	N	—
CGPLH410	0.9006	0.0453%	0.0019	N	N	—
CGPLH411	0.8857	0.0621%	0.0188	N	N	—
CGPLH412	0.9191	0.0140%	0.0417	N	N	—
CGPLH413	0.9145	0.0355%	0.0084	N	N	—
CGPLH414	0.9127	0.0290%	0.0284	N	N	—
CGPLH415	0.9025	0.0296%	0.0131	N	N	—
CGPLH416	0.9388	0.0198%	0.0645	N	N	—
CGPLH417	0.9192	0.0241%	0.0836	N	N	—
CGPLH418	0.9234	0.0306%	0.0052	N	N	—
CGPLH419	0.9295	0.0280%	0.0469	N	N	—
CGPLH420	0.9108	0.0187%	0.0420	N	N	—
CGPLH422	0.9006	0.0209%	0.0324	N	N	—
CGPLH423	0.9289	0.0832%	0.0139	N	N	—
CGPLH424	0.9265	0.1119%	0.0864	N	N	—
CGPLH425	0.9488	0.0722%	0.0156	N	N	—
CGPLH426	0.9080	0.0548%	0.1075	N	N	—
CGPLH427	0.9257	0.0182%	0.0470	N	N	—
CGPLH428	0.9272	0.0346%	0.0182	N	N	—
CGPLH429	0.8757	0.0593%	0.8143	Y	Y	—
CGPLH430	0.9307	0.0258%	0.0369	N	N	—
CGPLH431	0.9185	0.0234%	0.0174	N	N	—
CGPLH432	0.9082	0.0433%	0.0181	N	N	—
CGPLH434	0.9442	0.0297%	0.0050	N	N	—
CGPLH435	0.9097	0.0179%	0.0441	N	N	—
CGPLH436	0.9158	0.0290%	0.0958	N	N	—
CGPLH437	0.3245	0.0156%	0.0136	N	N	—
CGPLH438	0.9138	0.0169%	0.1041	N	N	—
CGPLM439	0.9028	0.0225%	0.0078	N	N	—
CGPLH440	0.9295	0.0330%	0.0887	N	N	—
CGPLH441	0.9430	0.0178%	0.0085	N	N
CGPLH442	0.9406	0.0169%	0.0582	N	N	—
CGPLH443	0.8801	0.0207%	0.0578	N	N	—
CGPLH444	0.9066	0.6464%	0.0097	N	N	—
CGPLH445	0.8750	0.0267%	0.1939	N	N	—
CGPLH446	0.9257	0.0281%	0.0340	N	N	—
CGPLH447	0.8968	0.0167%	0.0017	N	N	—
CGPLH448	0.8181	0.0401%	0.0389	N	N	—
CGPLH449	0.9254	0.0236%	0.0116	N	N	—
CGPLH450	0.9195	0.0331%	0.0597	N	N	—
CGPLH451	0.9167	0.0262%	0.0104	N	N	—
CGPLH452	0.8948	0.0480%	0.4722	N	N	—
CGPLH453	0.9339	0.0186%	0.3419	N	N	—
CGPLH455	0.9322	0.0455%	0.4536	N	N	—
CGPLH450	0.9098	0.0207%	0.0365	N	N	—
CGPLH457	0.9022	0.0298%	0.0354	N	N	—
CGPLH458	0.9275	0.0298%	0.1891	N	N	—
CGPLH459	0.9209	0.0281%	0.0371	N	N	—
CGPLH460	0.8863	0.0227%	0.1157	N	N	—
CGPLH463	0.9372	0.0130%	0.0865	N	N	—
CGPLH464	0.8511	0.0659%	0.2040	N	N
CGPLH465	0.9164	0.0325%	0.0121	N	N	—
CGPLH466	0.9408	0.0155%	0.1733	N	N	—
CGPLH467	0.9024	0.0229%	0.2303	N	N	—
CGPLH468	0.9345	0.0247%	0.5427	N	N	—
CGPLH469	0.8799	0.0201%	0.5351	N	N	—
CGPLH470	0.2228	0.0715%	0.0327	N	N	—
CGPLH471	0.9333	0.0153%	0.0406	N	N	—
CGPLH472	0.8915	0.0481%	0.6152	N	N	—
CGPLH473	0.9128	0.0443%	0.2995	N	N	—
CGPLH474	0.9245	0.0316%	0.5246	Y	N	—
CGPLH475	0.9233	0.0269%	0.0736	N	N
CGPLH476	0.9059	0.0236%	0.0143	N	N	—
CGPLH477	0.9376	0.0382%	0.1111	N	N	—
CGPLH478	0.9344	0.0256%	0.0828	N	N	—
CGPLN479	0.9207	0.0221%	0.0648	N	N	—
CGPLH480	0.9046	0.0672%	0.7473	Y	N	—
CGPLH481	0.9113	0.0311%	0.0282	N	N	—
CGPLH482	0.9336	0.0162%	0.0058	N	N	—
CGPLH483	0.9275	0.0251%	0.0495	N	N	—
CGPLH484	0.9366	0.0261%	0.0048	N	N	—
CGPLH485	0.9128	0.0291%	0.1084	N	N	—
CGPLH486	0.9042	0.0220%	0.0820	N	N	—
CGPLH487	0.9098	0.0594%	0.2154	N	N	—
CGPLH488	0.9298	0.0409%	0.0903	N	N	—
CGPLH490	0.8794	0.0432%	0.0424	N	N	—
CGPLH491	0.9332	0.0144%	0.0223	N	N	—
CGPLH492	0.8799	0.0322%	0.0311	N	N	—
CGPLH493	0.9330	0.0065%	0.0280	N	N	—
CGPLH494	0.9303	0.0232%	0.0824	N	N	—
CGPLH495	0.8908	0.0513%	0.0465	N	N	—
CGPLH496	0.9398	0.0208%	0.0572	N	N	—
CGPLH497	0.9330	0.0335%	0.0404	N	N	—
CGPLH498	0.9315	0.0403%	0.0752	N	N	—
CGPLH499	0.9442	0.0198%	0.0149	N	N	—
CGPLH500	0.9240	0.0433%	0.0754	N	N	—
CGPLH501	0.9308	0.0300%	0.0159	N	N	—
CGPLH502	0.9200	0.0351%	0.0841	N	N	—
CGPLH503	0.8939	0.0398%	0.0649	N	N	—
CGPLH504	0.9324	0.0440%	0.1231	N	N	—
CGPLH505	0.9243	0.0605%	0.1889	N	N	—
CGPLH506	0.9498	0.0284%	0.0180	N	N	—
CGPLH507	0.9192	0.0186%	0.0848	N	N	—
CGPLH508	0.9410	0.0150%	0.1077	N	N	—
CGPLH509	0.9323	0.0163%	0.0828	N	N	—
CGPLH510	0.9548	0.0128%	0.0378	N	N	—
CGPLH511	0.9493	0.0224%	0.1779	N	N	—
CGPLH512	0.9244	0.0094%	0.0076	N	N	—
CGPLH513	0.9595	0.0441%	0.5250	N	N	—
CGPLH514	0.9369	0.0114%	0.3131	N	N	—
CGPLH515	0.9283	0.0352%	0.4936	N	N	—
CGPLH516	0.9298	0.0175%	0.0916	N	N	—
CGPLH517	0.9494	0.0161%	0.0059	N	N
CGPLH518	0.9432	0.0274%	0.0130	N	N	—
CGPLH519	0.9351	0.0171%	0.0949	N	N	—
CGPLH520	0.9476	0.0241%	0.0944	N	N	—
CGPLH625	0.9231	0.0697%	0.4977	N	N	—
CGPLH626	0.9269	0.0231%	0.3100	N	N	—
CGPLH639	0.9410	0.0549%	0.0773	N	N	—
CGPLH640	0.9264	0.0232%	0.0327	N	N	—
CGPLH642	0.9376	0.0768%	0.0555	N	N	—
CGPLH643	0.9271	0.0579%	0.1325	N	N	—
CGPLH644	0.8948	0.0621%	0.3819	N	N	—
CGPLH646	0.8691	0.0462%	0.2423	N	N	—
CGPLLU144	0.8681	0.0423%	0.9892	Y	Y	5.10%
CGPLLU161	0.9187	0.0273%	0.9955	Y	Y	0.20%
CGPLLU162	0.8836	0.1410%	0.9986	Y	Y	0.22%
CGPLLU163	0.3033	0.0724%	0.9940	Y	Y	0.21%
CGPLLU168	0.8842	0.0712%	0.9861	Y	Y	0.07%
CGPLLU169	0.9189	0.0846%	0.9866	Y	Y	0.13%
CGPLLU176	0.9081	0.0626%	0.8769	Y	Y	ND
CGPLLU177	0.6790	0.0564%	0.9924	Y	Y	3.22%
CGPLLU203	0.8741	0.0568%	0.9178	Y	Y	0.11%
CGPLLU205	0.9476	0.0495%	0.9677	Y	Y	ND
CGPLLU207	0.9379	0.0421%	0.9908	Y	Y	0.32%
CGPLLU208	0.8342	0.0815%	0.9273	Y	Y	1.33%
CGPLOV11	0.8872	0.0463%	0.9343	Y	Y	0.87%
CGPLOV12	0.8973	0.2767%	0.9764	Y	Y	ND
CGPLOV13	0.9146	0.1017%	0.9690	Y	Y	0.35%
CGPLOV15	0.8552	0.0876%	0.9945	Y	Y	3.54%
CGPLOV18	0.9046	0.0400%	0.9983	Y	Y	1.12%
CGPLOV19	0.7578	0.1089%	0.9989	Y	Y	46.35%
CGPLOV20	0.9154	0.0581%	0.9749	Y	Y	0.21%
CGPLOV21	0.8889	0.0677%	0.9951	Y	Y	14.36%
CGPLOV22	0.9355	0.0251%	0.9775	Y	V	0.49%
CGPLOV23	0.8850	0.1520%	0.9916	Y	Y	1.39%
CGPLOV24	0.8995	0.0303%	0.9856	Y	Y	ND
CGPLOV25	0.9228	0.0141%	0.8544	Y	Y	ND
CGPLOV26	0.9351	0.0646%	0.9946	Y	Y	ND
CGPLOV28	0.9378	0.0647%	0.8160	Y	Y	ND
CGPLOV31	0.9293	0.1605%	0.9795	Y	Y	ND
CGPLOV32	0.9338	0.1351%	0.8609	Y	Y	ND
CGPLOV37	0.8831	0.0986%	0.9849	Y	Y	0.29%
CGPLOV38	0.6502	0.0490%	0.9990	Y	Y	4.89%
CGPLOV40	0.8127	0.6145%	0.9983	Y	Y	6.73%
CGPLOV41	0.8929	0.1110%	0.9484	Y	Y	0.60%
CGPLOV42	0.9086	0.0489%	0.9979	Y	Y	1.24%
CGPLOV43	0.9342	0.0432%	0.6042	N	N	ND
CGPLOV44	0.9173	0.1946%	0.9962	Y	Y	0.37%
CGPLOV45	0.9291	0.0801%	0.9128	Y	Y	ND
CGPLOV47	0.9461	0.0270%	0.3410	N	N	3.20%
CGPLOV48	0.9429	0.0422%	0.4874	N	N	10.70%
CGPLOV49	0.8083	0.1527%	0.9897	Y	Y	2.03%
CGPLOV50	0.9382	0.0807%	0.9955	Y	Y	ND
CGPLPA112	0.0429	0.0268%	0.0856	N	N	—
CGPLPA113	0.7674	1.0116%	0.9935	Y	Y	—
CGPLPA114	0.9246	0.0836%	0.7598	Y	Y	—
CGPLPA115	0.8310	0.0763%	0.9974	Y	Y	—
CGPLPA117	0.8767	0.1084%	0.9049	Y	Y	—
CGPLPA118	0.9001	0.1842%	0.9859	Y	Y	0.14%
CGPLPA122	0.8058	0.2047%	0.9983	Y	Y	37.22%
CGPLPA124	0.9238	0.1542%	0.8791	Y	Y	0.62%
CGPLPA125	0.9373	0.0273%	0.0228	N	N	—
CGPLPA126	0.9139	0.4349%	0.9908	Y	Y	ND
CGPLPA127	0.8117	0.4371%	0.9789	Y	Y	—
CGPLPA128	0.9003	0.1317%	0.9812	Y	Y	ND
CGPLPA129	0.9155	0.0612%	0.9839	Y	Y	ND
CGPLPA130	0.8499	0.1005%	0.9895	Y	Y	ND
CGPLPA131	0.9195	0.0780%	0.9885	Y	Y	3.21%
CGPLPA134	0.8847	0.0260%	0.9896	Y	Y	0.93%
CGPLPA135	0.9184	0.0558%	0.6594	Y	N	—
CGPLPA136	0.9050	0.0769%	0.9596	Y	Y	0.10%
CGPLPA137	0.9320	0.0499%	0.7282	Y	N	—
CGPLPA139	0.9374	0.0465%	0.0743	N	N	—
CGPLPA14	0.9069	0.0515%	0.9824	Y	Y
CGPLPA140	0.9548	0.0330%	0.9761	Y	Y	0.21%
CGPLPA141	0.9381	0.0920%	0.9988	Y	Y	—
CGPLPA15	0.8927	0.0160%	0.8737	Y	Y	—
CGPLPA155	0.9313	0.0260%	0.8013	Y	Y	—
CGPLPA156	0.9432	0.0290%	0.0159	N	N	—
CGPLPA165	0.9309	0.0555%	0.2158	N	N	—
CGPLPA168	0.7757	0.3123%	0.9878	Y	Y	—
CGPLPA17	0.6771	1.2600%	0.9956	Y	Y	—
CGPLPA184	0.9203	0.0897%	0.9926	Y	Y
CGPLPA187	0.8968	0.0658%	0.9675	Y	Y	—
CGPLPA23	0.6938	0.5785%	0.9984	Y	Y	—
CGPLPA25	0.9239	0.0380%	0.8103	Y	Y	—
CGPLPA26	0.9356	0.0247%	0.8231	Y	Y	—
CGPLPA28	0.8938	0.0546%	0.9036	Y	Y	—
CGPLPA33	0.8553	0.0894%	0.9367	Y	Y	—
CGPLPA34	0.8885	0.0439%	0.7977	Y	Y	—
CGPLPA37	0.9294	0.0410%	0.9924	Y	Y	—
CGPLPA38	0.8941	0.0372%	0.9851	Y	Y	—
CGPLPA39	0.7972	0.5058%	0.9951	Y	Y	—
CGPLPA40	0.8865	0.2268%	0.9920	Y	Y
CGPLPA42	0.8363	0.0283%	0.3544	N	N	—
CGPLPA46	0.7525	1.0982%	0.9952	Y	Y	—
CGPLPA47	0.8439	0.1596%	0.9346	Y	Y	—
CGPLPA48	0.9207	1.0232%	0.2251	N	N	—
CGPLPA52	0.8863	0.0154%	0.0963	N	N	—
CGPLPA53	0.8776	0.1824%	0.8946	Y	Y	—
CGPLPA58	0.9224	0.0803%	0.9056	Y	Y	—
CGPLPA59	0.9193	0.1479%	0.9759	Y	Y	—
CGPLPA67	0.9248	0.0329%	0.6716	Y	N	—
CGPLPA69	0.8592	0.0459%	0.1245	N	N	—
CGPLPA71	0.8888	0.0479%	0.0524	N	N	—
CGPLPA74	0.9372	0.0292%	0.0108	N	N	—
CGPLPA78	0.9441	0.0345%	0.0897	N	N	—
CGPLPA85	0.9337	0.0363%	0.0508	N	N	—
CGPLPA86	0.8042	0.7564%	0.9864	Y	Y	—
CGPLPA92	0.9003	0.1459%	0.7061	Y	N	—
CGPLPA93	0.8023	0.6250%	0.9978	Y	Y	—
CGPLPA94	0.9433	0.0160%	0.9025	Y	Y	—
CGPLPA95	0.8571	0.0815%	0.9941	Y	Y
CGST102	0.9057	0.0704%	0.8581	Y	Y	0.43%
CGST11	0.9161	0.0651%	0.1435	N	N	—
CGST110	0.9232	0.0817%	0.8900	Y	Y	ND
CGST114	0.9038	0.0317%	0.5593	N	N	ND
CGST13	0.9156	0.0321%	0.9754	Y	Y	ND
CGST131	0.8886	0.2752%	0.9409	Y	Y	—
CGST141	0.9206	0.0338%	0.2008	N	N	ND
CGST16	0.8355	0.1744%	0.9974	Y	Y	0.93%
CGST18	0.9111	0.0299%	0.3842	N	N	0.14%
CGST21	0.2687	0.2299%	0.9910	Y	Y	—
CGST26	0.9140	0.0399%	0.5009	N	N	—
CG3T28	0.7832	0.1295%	0.9955	Y	Y	1.62%
CGST30	0.9121	0.0338%	0.9183	Y	Y	0.42%
CGST32	0.8639	0.0247%	0.9612	Y	Y	2.99%
CGST33	0.7770	0.0799%	0.9805	Y	Y	2.32%
CGST38	0.8758	0.0540%	0.9416	Y	Y	—
CGST39	0.9401	0.0287%	0.8480	Y	Y	ND
CGST41	0.9284	0.0398%	0.9263	Y	Y	ND
CGST45	0.9036	0.0220%	0.9713	Y	Y	ND
CGST47	0.9096	0.0157%	0.9687	Y	Y	0.45%
CGST48	0.5445	0.0220%	0.9975	Y	Y	4.21%
CGST53	0.7888	0.1140%	0.9914	Y	Y
CGST58	0.9094	0.0596%	0.9705	Y	Y	ND
CGST67	0.8853	0.3245%	0.9002	Y	Y	—
CGST77	0.8295	0.1851%	0.9981	Y	Y	—
CGST80	0.8846	0.0490%	0.9513	Y	Y	1.04%
CGST81	0.8851	0.0138%	0.9748	Y	Y	0.20%

*ND indicates not detected. Please see reference 10 for additional information on targeted sequencing analyes. DELFI cancer detection at 95% and 98% specificity is based on scores greater than 0.6200 and 0.7500, respectively.

Claims

1. A method of determining a cell free DNA (cIDNA) fragmentation profile of a mammal, the method comprising:

processing cfDNA fragments obtained from a sample obtained from the mammal into sequencing libraries;

subjecting the sequencing libraries to low-coverage whole genome sequencing to obtain sequenced fragments;

mapping the sequenced fragments to a genome to obtain windows of mapped sequences; and

analyzing the windows of mapped sequences to determine cfDNA fragment lengths.

2. The method of claim 1, wherein the mapped sequences comprise tens to thousands of windows.

3. The method of claim 1, wherein the windows are non overlapping windows.

4. The method of claim 1, wherein the windows each comprise about 5 million base pairs.

5. The method of claim 1, wherein a cfDNA fragmentation profile is determined within each window.

6. The method of claim 1, wherein cfDNA fragmentation profile comprises a median fragment size.

7. The method of claim 1, wherein cfDNA fragmentation profile comprises a fragment size distribution.

8. The method of claim 1, wherein the cfDNA fragmentation profile comprises a ratio of small cfDNA fragments to large cfDNA fragments in said windows of mapped sequences.

9. The method of claim 1, wherein the cfDNA fragmentation profile comprises the sequence coverage of small cfDNA fragments in windows across the genome.

10. The method of claim 1, wherein the cfDNA fragmentation profile comprises the sequence coverage of large cfDNA fragments in windows across the genome.

11. The method of claim 1, wherein the cfDNA fragmentation profile comprises the sequence coverage of small and large cfDNA fragments in windows across the genome.

12. The method of claim 1, wherein the cfDNA fragmentation profile is over the whole genome.

13. The method of claim 1, wherein the cfDNA fragmentation profile is over a subgenomic interval.

14. A method of identifying a mammal as having cancer, the method comprising:

determining a cell free DNA (cfDNA) fragmentation profile in a sample obtained from the mammal;

comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile; and

identifying the mammal as having cancer when the cfDNA fragmentation profile obtained from the mammal is different from the reference cfDNA fragmentation profile.

15. The method of claim 14, wherein the reference cfDNA fragmentation profile is a cfDNA fragmentation profile of a healthy mammal.

16. The method of claim 15, wherein the reference cfDNA fragmentation profile is generated by determining a cfDNA fragmentation profile in a sample obtained from the healthy mammal.

17. The method of claim 14, wherein the reference DNA fragmentation pattern is a reference nucleosome cfDNA fragmentation profile.

18. The method of claim 14, wherein the cfDNA fragmentation profile comprises a median fragment size, and wherein a median fragment size of the cfDNA fragmentation profile is shorter than a median fragment size of the reference cfDNA fragmentation profile.

19. The method of claim 14, wherein the cfDNA fragmentation profile comprises a fragment size distribution, and wherein a fragment size distribution of the cfDNA fragmentation profile differs by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile.

20. The method of claim 14, wherein the cfDNA fragmentation profile comprises a ratio of small cfDNA fragments to large cfDNA fragments in said windows of mapped sequences, wherein a small cfDNA fragment is 100 base pairs (bp) to 150 bp in length, wherein a large cfDNA fragments is 151 bp to 220 bp in length, and wherein a correlation of fragment ratios in the cfDNA fragmentation profile is lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile.

21-67. (canceled)