Cell-free DNA for cancer assessment and / or treatment

Abstract

The present invention relates to methods and materials for evaluating, monitoring, and / or treating mammals (such as humans) suffering from cancer. For example, methods and materials for identifying that a mammal has cancer (such as, local cancer) are provided. For example, methods and materials for evaluating, monitoring, and / or treating mammals suffering from cancer are provided.

Description

[0001] Related Applications

[0002] This application claims the benefit of priority of U.S. Patent Application No. 62 / 673,516, filed May 18, 2018, and U.S. Patent Application No. 62 / 795,900, filed Jan. 23, 2019, the disclosures of the prior applications are considered part of the disclosure of this application (and are incorporated herein by reference).

[0003] Government Authorization

[0004] This invention was made with government support under National Institutes of Health grant number CA121113. The United States government has certain rights in this invention. Technical Field

[0005] The present invention relates to methods and materials for evaluating and / or treating mammals (e.g., humans) suffering from cancer. For example, the present invention provides methods and materials for identifying mammals suffering from cancer (e.g., local cancer). For example, the present invention provides methods and materials for monitoring and / or treating mammals suffering from cancer. Background Art

[0006] The incidence and mortality of the majority of human cancers worldwide are the result of late diagnosis of the disease, and the treatment effect for these diseases is poor (Torre et al., 2015 CA Cancer J Clin 65:87; and World Health Organization, 2017 Guide to Cancer Early Diagnosis). Unfortunately, clinically validated biomarkers that can be used for the extensive diagnosis and treatment of patients have not been 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 of the Invention

[0007] Recent analyses of cell-free DNA have shown that such methods 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).

[0008] The present invention 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 certain cases, determining the cfDNA fragmentation profile in a mammal can be used to identify whether the mammal has cancer. For example, cfDNA fragments obtained from a mammal (e.g., 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 evaluated to determine the cfDNA fragmentation profile. The present invention also provides methods and materials for evaluating and / or treating a mammal (e.g., a human) having or suspected of having cancer. In certain cases, the present invention provides methods and materials for identifying that a mammal has cancer. For example, a sample obtained from a mammal (e.g., a blood sample) can be evaluated (at least in part based on) the cfDNA fragmentation profile to determine whether the mammal has cancer. In certain cases, the present invention 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 on or at least in part on the cfDNA fragmentation profile) to treat the mammal.

[0009] This specification describes a non-invasive method for the early detection and localization of cancer. CfDNA in the blood can provide a non-invasive diagnostic approach for cancer patients. As shown in this specification, "DNA Evaluation of Early Intercepted Fragments" (DELFI) was developed and used to evaluate the whole-genome fragment patterns of 236 individuals with breast, colorectal, lung, ovarian, pancreatic, gastric, or cholangiocarcinoma and 245 healthy individuals. These analyses showed that the cfDNA profiles of healthy individuals reflected the nucleosome fragmentation profiles of white blood cells, while the fragmentation profiles of cancer patients were altered. In seven cancer types, the detection sensitivity of DELFI was 57% to >99%, the specificity in the seven cancers was 98%, and in 75% of cases, the tissue of origin of the cancer could be identified as one of several defined sites. Evaluating cfDNA (e.g., using DELFI) can provide a screening method for the early detection of cancer, which can increase the chances of successfully treating cancer patients. Evaluating cfDNA (e.g., using DELFI) can also provide a method for monitoring cancer, which can increase the chances of success and improve the treatment outcomes of patients with cancer. Additionally, cfDNA fragmentation profiles can be obtained from limited amounts of cfDNA using inexpensive reagents and / or instruments.

[0010] Generally speaking, one aspect of this specification features a method for determining the cfDNA fragment map of a mammal. The method may comprise or consist essentially of: processing cfDNA fragments obtained from a sample obtained from a mammal into a sequencing library, performing whole-genome sequencing (e.g., low-coverage whole-genome sequencing) on the sequencing library to obtain sequencing fragments, mapping the sequencing fragments to the genome to obtain windows of mapped sequences, and analyzing the windows of mapped sequences to determine the cfDNA fragment lengths. The mapped sequences may include dozens to thousands of windows. The windows of the mapped sequences may be non-overlapping windows. Each window of the mapped sequences may contain approximately 5 million base pairs. The cfDNA fragment map may be determined within each window. The cfDNA fragment map may include the median fragment size. The cfDNA fragment map may include the fragment size distribution. The cfDNA fragment map may include the ratio of small cfDNA fragments to large cfDNA fragments in the mapped sequence window. The cfDNA fragment map may cover the entire genome. The cfDNA fragment map may span sub-genomic intervals (e.g., intervals in a part of a chromosome).

[0011] On the other hand, the present specification features a method for identifying a mammal suffering from cancer. The method may comprise or consist essentially of the following steps: determining a cell-free DNA (cfDNA) fragment map in a sample obtained from a mammal, comparing the cfDNA fragment map with a reference cfDNA fragment map, and identifying the mammal as suffering from cancer when the cfDNA fragment map obtained from the mammal is different from the reference cfDNA fragment map. The reference cfDNA fragment map may be a cfDNA fragment map of a healthy mammal. The reference cfDNA fragment map may be generated by determining the cfDNA fragment map in a sample obtained from a healthy mammal. The reference DNA fragmentation pattern may be a fragment map of reference nucleosomal cfDNA. The cfDNA fragment map may include a median fragment size, and the median fragment size of the cfDNA fragment map may be shorter than the median fragment size of the reference cfDNA fragment map. The cfDNA fragment map may include a fragment size distribution, and the fragment size distribution of the cfDNA fragment map may differ by at least 10 nucleotides compared to the fragment size distribution of the reference cfDNA fragment map. The cfDNA fragment map may contain position-related differences in the fragment pattern, including the ratio of small cfDNA fragments to large cfDNA fragments, where the length of the small cfDNA fragments may be 100 base pairs (bp) to 150 bp, the length of the large cfDNA fragments may be 151 bp to 220 bp, and the correlation of the fragment ratios in the cfDNA fragment map may be lower than the correlation of the fragment ratios in the reference cfDNA fragment map. The cfDNA fragment map may include the coverage sequences of small cfDNA fragments, large cfDNA fragments, or both small and large cfDNA fragments throughout the genome. The cancer may be colorectal cancer, lung cancer, breast cancer, cholangiocarcinoma, pancreatic cancer, gastric cancer, and ovarian cancer. The comparing step may include comparing the cfDNA fragment map with the reference cfDNA fragment map in windows across the entire genome. The comparing step may include comparing the cfDNA fragment map with the reference cfDNA fragment map within sub-genomic intervals (e.g., intervals in a part of a chromosome). The mammal may have been previously treated for cancer to treat the cancer. The cancer treatment may be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combination thereof. The method may further comprise administering a cancer treatment (e.g., surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combination thereof) to the mammal. After administering the cancer treatment, the mammal may be monitored for the presence of cancer.

[0012] On the other hand, the present invention features a method for treating a mammal suffering from cancer. The method may comprise or consist essentially of the steps of: identifying a mammal suffering from cancer, wherein the identification includes determining a cfDNA fragment profile in a sample obtained from the mammal, comparing the cfDNA fragment profile with a reference cfDNA fragment profile, and identifying the mammal as suffering from cancer when the cfDNA fragment profile obtained from the mammal is different from the reference cfDNA fragment profile; treating the mammal for cancer. The mammal may be a human. The cancer may be colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma or ovarian cancer. The cancer treatment may be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy or a combination thereof. The reference cfDNA fragment profile may be a cfDNA fragment profile of a healthy mammal. The reference cfDNA fragment profile may be generated by determining a cfDNA fragment profile in a sample obtained from a healthy mammal. The reference DNA fragmentation pattern may be a reference nucleosomal cfDNA fragment profile. The cfDNA fragment profile may include a median fragment size, wherein the median fragment size of the cfDNA fragment profile is shorter than the median fragment size of the reference cfDNA fragment profile. The cfDNA fragment profile may include a fragment size distribution, wherein the fragment size distribution of the cfDNA fragment profile differs from the fragment size distribution of the reference cfDNA fragment profile by at least 10 nucleotides. The cfDNA fragment profile may include a ratio of small cfDNA fragments to large cfDNA fragments in a mapping sequence window, wherein the length of the small cfDNA fragments is from 100 bp to 150 bp, wherein the length of the large cfDNA fragments is from 151 bp to 220 bp, and the correlation of the fragment ratio in the cfDNA fragment profile is lower than the correlation of the fragment ratio in the reference cfDNA fragment profile. The cfDNA fragment profile may include the sequence coverage of small cfDNA fragments in a whole genome window. The cfDNA fragment profile may include the sequence coverage of large cfDNA fragments in a whole genome window. The cfDNA fragment profile may include the sequence coverage of small and large cfDNA fragments in a whole genome window. The comparing step may include comparing the cfDNA fragment profile with the reference cfDNA fragment profile across the whole genome. The comparing step may include comparing the cfDNA fragment profile with the reference cfDNA fragment profile within a sub-genomic interval. The mammal may have previously received cancer treatment to treat the cancer. The cancer treatment may be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy or a combination thereof. The method may further include monitoring the mammal for the presence of cancer after administering the cancer treatment.

[0013] 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 belongs. Although methods and materials similar or equivalent to those described herein can be used to practice the present invention, the appropriate methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated herein 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.

[0014] 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 become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic diagram of an exemplary DELFI method. Blood is collected from a group of healthy individuals and cancer patients. Nucleosome-protected cfDNA is extracted from the plasma fraction, processed into a sequencing library, detected by whole-genome sequencing, mapped to the genome, and analyzed to determine the cfDNA fragment profile in different windows across the genome. Machine learning methods are used to classify individuals as healthy or having cancer and to identify the origin of tumor tissue using the whole-genome cfDNA fragment pattern.

[0016] Figure 2 is a simulation of non-invasive cancer detection based on the analysis of the number of alterations and the distribution of tumor-derived cfDNA fragments. Monte Carlo simulations are performed using different numbers of tumor-specific alterations to evaluate the probability of detecting cancer-occurring alterations on cfDNA in a specified proportion of tumor-derived molecules. The simulations are performed assuming an average of 2000 genomic equivalents of cfDNA and requiring 5 or more observations of any alteration. These analyses show that an increase in the number of tumor-specific alterations can improve the sensitivity of circulating tumor DNA detection.

[0017] Figure 3 is the distribution of tumor-derived cfDNA fragments. The cumulative density function of the cfDNA fragment lengths at 42 loci containing tumor-specific alterations from 30 patients with breast cancer, colorectal cancer, lung cancer, or ovarian cancer is shown with a 95% confidence interval (blue). At these loci, the length sizes of mutant cfDNA fragments are significantly different compared to wild-type cfDNA fragments (red).

[0018] Figure 4A and 4B represent the GC content and fragment length of tumor-derived cfDNA. Figure 4A Shows that the GC content of mutant and non-mutant fragments is similar.Figure 4B This indicates that the GC content is independent of the fragment length.

[0019] Figure 5 This is the fragment distribution of germline cfDNA. It shows the cumulative density function of the fragment lengths of 44 loci containing germline alterations (non-tumor origin) from 38 patients with breast cancer, colorectal cancer, lung cancer, or ovarian cancer, with a 95% confidence level. The lengths of the fragments with germline mutations (blue) are comparable to those of wild-type cfDNA fragments (red).

[0020] Figure 6 This is the fragment distribution of hematopoietic cfDNA. It shows the cumulative density function of the fragment lengths of 41 loci containing hematological alterations (non-tumor origin) from 28 patients with breast cancer, colorectal cancer, lung cancer, or ovarian cancer, with a 95% confidence level. After multiple testing corrections, there is no significant difference in the size distribution between mutant hematopoietic cfDNA fragments (blue) and wild-type cfDNA fragments (red) (α = 0.05).

[0021] Figures 7A to 7F This is the cfDNA fragment map in healthy individuals and cancer patients. Figure 7A This represents the genome-wide cfDNA fragment map (defined as the ratio of short fragments to long fragments) from approximately 9x whole-genome sequencing in 30 healthy individuals (top) and 8 lung cancer patients (bottom), shown in 5Mb bins (bottom figure). Figure 7B This is the fragment map and lymphocyte map of cfDNA from healthy individuals on chromosome 1 analyzed at 1Mb resolution (top), cfDNA from lung cancer patients (middle), and healthy lymphocytes (bottom). The map of healthy lymphocytes is measured with the same standard deviation as the cfDNA map of median healthy individuals. The cfDNA pattern of healthy individuals is very similar to that of healthy lymphocytes, while the cfDNA map of lung cancer patients is more different and distinct from the maps of healthy individuals and healthy lymphocytes. Figure 7C This is the smooth median distance between adjacent nucleosomes centered at 0, depicting the first eigenvector of the genomic contact matrix using cfDNA from 100kb bins of healthy individuals (top) and nuclease-digested healthy lymphocytes (middle), which is obtained from the Hi-C analysis of previously reported lymphoblasts (bottom). The nucleosome distances of cfDNA from healthy individuals are very similar to those of nuclease-digested lymphocytes and the nucleosome distances of lymphoblasts in Hi-C analysis. The fragment map of cfDNA from healthy individuals (n = 30) has a high correlation with the median fragment maps of lymphocytes (D), cfDNA of healthy individuals (E), and lymphocyte nucleosome (F) distances, while the correlation in lung cancer patients is lower.

[0022] Figure 8 is the density of cfDNA fragment lengths in healthy individuals and lung cancer patients. The cfDNA fragment lengths of healthy individuals (n = 30, gray) and lung cancer patients (n = 8, blue) are shown.

[0023] Figure 9A and 9B is the subsampling of whole-genome sequencing data for analyzing cfDNA fragment profiles. Figure 9A The high-coverage (9x) whole-genome sequencing data was subsampled to coverage levels of 2x, 1x, 0.5x, 0.2x, and 0.1x. For each subsampled coverage level, the fragment profiles within the mean-centered whole-genome range in 5 Mb bins were depicted for 30 healthy individuals and 8 lung cancer patients, with the median plots shown in blue. Figure 9B is the Pearson correlation between the subsampled profiles and the initial sampling profiles in healthy individuals and lung cancer patients at 9-fold coverage.

[0024] Figure 10 are the cfDNA fragment profiles and sequence alterations during treatment. Detection and monitoring of cancer in serial blood samples of non-small cell lung cancer (NSCLC) patients (n = 19) receiving targeted tyrosine kinase inhibitor (black arrow) treatment were performed using targeted sequencing (top) and whole-genome fragment profiles (bottom). For each case, the vertical axis in the bottom panel shows -1 times the correlation of each sample with the median of the cfDNA fragment profiles of healthy individuals. Error bars depict the confidence intervals of the mutant allele fractions from binomial tests and the confidence intervals of the whole-genome fragment profiles calculated using Fisher transformation. Although these methods analyze different aspects of cfDNA (whole-genome vs. specific alterations comparison), the targeted sequencing and fragment profiles of patients responding to treatment and those with stable or progressive disease are similar. Since fragment profiles reflect genomic and epigenomic alterations while mutant allele fragments only reflect individual mutations, only mutant allele fragments may not reflect the absolute level of correlation of the fragment profiles of healthy individuals.

[0025] Figures 11A to 11C are the cfDNA fragment profiles in healthy individuals and cancer patients. Figure 11A is the fragment profile (bottom) in the context of tumor copy number changes (top) from parallel analysis of tumor tissue in colorectal cancer patients. The distribution of segment means and integer copy numbers is shown in the upper right in the indicated colors. Altered fragment profiles are present in copy-neutral regions of the genome and are further affected in regions of copy number changes. Figure 11BGC-adjusted fragment profiles from 1-2x whole-genome sequencing of healthy individuals and cancer patients were depicted for each cancer type using a 5Mb window. The median profile of healthy individuals is shown in black, while the 98% confidence is shown in grey. For patients with cancer, individual profiles were coloured according to their correlation to the healthy individual median. Figure 11C Windows were coloured orange if the fragment ratio of more than 10% of cancer patient samples exceeded three standard deviations compared to the fragment ratio of median healthy individuals. These analyses highlight alterations associated with numerous positions across the cfDNA genome of cancer individuals.

[0026] Figure 12A and Figure 12B are cfDNA fragment length profiles in copy-neutral regions in a healthy individual and a colorectal cancer patient. Figure 12A are fragment profiles in 211 copy-neutral windows on chromosomes 1 to 6 in 25 randomly selected healthy individuals (grey). For a colorectal cancer patient (CGCRC291) with an estimated mutant allele ratio of 20%, their cancer fragment length profile was diluted to approximately 10% tumour contribution (blue). Figure 12A and Figure 12B , although the marginal densities of the fragment profiles of healthy samples and cancer patients show substantial overlap ( Figure 12A , right), the fragment profiles are different as seen in the visualised fragment profiles ( Figure 12A , left), and samples of colorectal cancer patients are separated from healthy individuals in principal component analysis ( Figure 12B ).

[0027] Figure 13A and 13B are genome-wide GC correction of cfDNA fragments. To estimate and control the effect of GC content on sequencing coverage, coverage in non-overlapping 100kb genomic windows on autosomes was calculated. For each window, the average GC of aligned fragments was calculated. Figure 13A, Loess-smoothed raw coverage (upper row) for aneuploidy detection (PA score < 2.35) was performed on two randomly selected healthy subjects (CGPLH189 and CGPLH380) and two cancer patients (CGPLLU161 and CGPLBR24). After subtracting the mean coverage predicted by the Loess model, the residuals were rescaled to the median autosomal coverage (lower row). Since the length of the fragments can also cause coverage bias, this GC correction procedure was performed separately for short fragments (≤150 bp) and long fragments (≥151 bp). Although the 100-kb bins on chromosome 19 (blue dots) were always less covered than predicted by the Loess model, we did not implement chromosome-specific correction because this approach would eliminate the effect of chromosome copy number on coverage. Figure 13B , Overall, there was a limited correlation between the corrected short- or long-fragment coverage and GC content in healthy subjects and cancer patients with PA score < 3.

[0028] Figure 14 is a schematic diagram of the machine learning model. Gradient tree boosting machine learning was used to examine whether cfDNA could be classified as characteristic of cancer patients or healthy individuals. The machine learning model included fragment size and coverage features in whole-genome windows, as well as chromosome arm and mitochondrial DNA copy numbers. A 10-fold cross-validation approach was used to randomly assign each sample to a fold, with 9 folds (90% of the data) used for training and 1 fold (10% of the data) used for testing. The prediction accuracy of a single cross-validation was the average of 10 possible combinations of the test set and the training set. Since this prediction accuracy could reflect the bias in the initial random grouping of patients, the entire process was repeated, including randomly grouping the patients 10 times. For all cases, feature selection and model estimation were performed on the training data and validated on the test data, and the test data was never used for feature selection. Finally, the DELFI score was obtained, which can be used to classify individuals as potentially healthy or having cancer.

[0029] Figure 15 is the distribution of AUC in repeated 10-fold cross-validation. The dashed lines represent the 25th, 50th, and 75th percentiles of 100 AUCs in a cohort of 215 healthy individuals and 208 cancer patients.

[0030] Figure 16A and 16B is the chromosomal arm copy number variation and mitochondrial genome representation in whole-genome analysis. Figure 16ADescribes the Z - values for each autosomal arm in healthy individuals (n = 215) and cancer patients (n = 208). The vertical axis represents the normal copy when it is zero, where positive and negative values represent increases and decreases in the arm, respectively. Z - values greater than 50 or less than - 50 are thresholded at that indicated value. Figure 16B Depicts the fraction of sequencing reads mapped to the mitochondrial genome for healthy individuals and cancer patients.

[0031] Figure 17A and 17B , cancer is detected using DELFI. Figure 17A , for a cohort of 215 healthy individuals and 208 cancer patients (DELFI, AUC = 0.94), cfDNA fragment profiles and other whole - genome features are used as receiver operator characteristics for detecting cancer in a machine - learning approach, with ≥95% specificity shaded in blue. Machine - learning analysis of chromosomal arm copy number (Chr copy number (ML)) and mitochondrial genome copy number (mtDNA) is shown in the indicated colors. Figure 17B , the AUC range for analyzing individual cancer types using the DELFI combined method is 0.86 to >0.99.

[0032] Figure 18 , DELFI detects cancer in stages. CfDNA fragment profiles and other whole - genome features are used as receiver operator characteristics for detecting cancer in a machine - learning approach, which depicts a cohort of 215 healthy individuals and 208 cancer patients at each stage, with ≥95% specificity shaded in blue.

[0033] Figure 19 , DELFI tissue - origin prediction. It describes the receiver operator characteristics of DELFI for predicting cholangiocarcinoma, breast cancer, colorectal cancer, gastric cancer, lung cancer, ovarian cancer, and pancreatic cancer. To increase the sample size in the cancer - type categories, cases detected with 90% specificity are also included, and the lung - cancer cohort is supplemented with cfDNA baseline data from 18 previously treated lung - cancer patients (see, e.g., Shen et al., 2018 Nature, 563:579–583).

[0034] Figure 20Detection of cancer using DELFI and mutation-based cfDNA methods. In a cohort of 126 patients with breast, cholangiocarcinoma, colorectal, gastric, lung, or ovarian cancer, DELFI (green) and targeted sequencing for mutation identification (blue) were performed separately. For DELFI testing, the number of individuals detected by each method and by the combined method had 98% specificity, >99% targeted sequencing specificity, and 98% combined specificity. ND indicates not detected. Detailed implementation

[0035] The present invention provides methods and materials for determining the 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 may be used interchangeably. In certain cases, determining the cfDNA fragmentation profile in a mammal can be used to identify that the mammal has cancer. For example, cfDNA fragments obtained from a mammal (e.g., 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 evaluated to determine the cfDNA fragmentation profile. As described in the present specification, the cfDNA fragmentation profile of a mammal with cancer is more heterogeneous (e.g., fragment length) than that of a healthy mammal (e.g., a mammal without cancer). Accordingly, the present specification also provides methods and materials for evaluating, monitoring, and / or treating a mammal (e.g., a human) having or suspected of having cancer. In certain cases, the present specification provides methods and materials for identifying that a mammal has cancer. For example, a sample (e.g., a blood sample) taken from a mammal can be evaluated at least in part based on the cfDNA fragment analysis of the mammal to determine the presence of cancer in the mammal and optionally to determine the origin tissue of the cancer. In certain cases, the present specification provides methods and materials for monitoring a mammal having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be evaluated at least in part based on the cfDNA fragmentation profile of the mammal to determine the presence of cancer in the mammal. In certain cases, the present specification provides methods and materials for identifying that a mammal has 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 evaluated to determine whether the mammal has cancer at least in part based on the cfDNA fragmentation profile of the mammal, and one or more cancer treatments can be administered to the mammal.

[0036] The cfDNA fragment profile can include one or more cfDNA fragment patterns. A cfDNA fragment pattern can include any suitable cfDNA fragment pattern. Examples of cfDNA fragment patterns include, but are not limited to, median fragment size, fragment size distribution, the ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In some cases, the cfDNA fragment pattern includes two or more (e.g., two, three, or four) of median fragment size, fragment size distribution, the ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In certain cases, the cfDNA fragment profile can be a genome-wide cfDNA profile (e.g., a genome-wide cfDNA profile across genomic windows). In certain cases, the cfDNA fragment profile can be a target region profile. The target region can be any suitable part of the genome (e.g., a chromosomal region). Examples of chromosomal regions that can be determined by the cfDNA fragment profile described in this specification can be, including but not limited to, a part of a chromosome (e.g., a part of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and / or 14q) and chromosomal arms (e.g., chromosomal arms of 8q, 13q, 11q, and / or 3p). In certain cases, the cfDNA fragment profile can include two or more target region profiles.

[0037] In certain cases, the cfDNA fragment profile can be used to identify changes (e.g., alterations) in the length of cfDNA fragments. The alteration can be a genome-wide alteration or an alteration in one or more target regions / locations. The target region can be any region containing one or more cancer-specific alterations. Examples of cancer-specific alterations and their chromosomal locations are shown in, including but not limited to, Table 3 (Appendix C) and Table 6 (Appendix F). In some cases, the cfDNA fragment 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, about 200 to about 500, about 300 to about 500, about 10 to about 400, about 10 to about 300, about 10 to about 200, about 10 to about 100, about 10 to about 50, about 20 to about 400, about 30 to about 300, about 40 to about 200, about 50 to about 100, about 20 to about 100, about 25 to about 75, about 50 to about 250, or about 100 to 200, etc. alterations).

[0038] In some cases, cfDNA fragment profiles can be used to detect tumor-derived DNA. For example, tumor-derived DNA is detected by comparing the cfDNA fragment profile of a mammal having or suspected of having cancer with a reference cfDNA fragment profile (e.g., the cfDNA fragment profile of a healthy mammal and / or the nucleosomal DNA fragment profile of healthy cells from a mammal having or suspected of having cancer). In some cases, the reference cfDNA fragment profile is a profile previously generated from a healthy mammal. For example, the methods provided in this specification can be used to determine the reference cfDNA fragment profile in a healthy mammal, and the reference cfDNA fragment profile can be stored (e.g., in a computer or other electronic storage medium) for future comparison with the test cfDNA fragment profile of a mammal having or suspected of having cancer. In some cases, the reference cfDNA fragment profile of a healthy mammal is determined across the entire genome (e.g., the stored cfDNA fragment profile). In some cases, the reference cfDNA fragment profile of a healthy mammal is determined within sub-genomic intervals (e.g., the stored cfDNA fragment profile).

[0039] In some cases, cfDNA fragment profiles can be used to identify a mammal (e.g., a human) having cancer (such as colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma, and / or ovarian cancer).

[0040] The cfDNA fragment distribution map can include cfDNA fragment size patterns. The cfDNA fragments can be of any suitable size. For example, the length of the cfDNA fragments can be from about 50 base pairs (bp) to about 400 bp. As described in this specification, a mammal having cancer can have a cfDNA fragment size pattern that includes a median cfDNA fragment size that is shorter than the median cfDNA fragment size in the cfDNA fragments of a healthy mammal. A healthy mammal (e.g., a mammal without cancer) can have cfDNA fragments with a median cfDNA fragment size of from about 166.6 bp to about 167.2 bp (e.g., about 166.9 bp). In some cases, the cfDNA fragment size in a mammal having cancer is on average about 1.28 bp to about 2.49 bp (e.g., about 1.88 bp) shorter than the cfDNA fragment size in a healthy mammal. For example, a mammal having cancer can have cfDNA fragments with a median cfDNA fragment size of from about 164.11 bp to about 165.92 bp (e.g., about 165.02 bp).

[0041] The cfDNA fragment profile can include the cfDNA fragment size distribution. As described herein, a mammalian subject having cancer can have a more variable cfDNA size distribution than the cfDNA fragment size distribution in a healthy mammalian subject. In some cases, the size distribution can be within a targeted region. The targeted region cfDNA fragment size distribution in a healthy mammalian subject (e.g., a mammalian subject without cancer) can be about 1 or less than about 1. In some cases, the targeted region cfDNA fragment size distribution in a mammalian subject having cancer can be longer than the targeted region cfDNA fragment size distribution in a healthy mammalian subject (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 base pairs or more, or any base pair between these numbers). In some cases, the targeted region cfDNA fragment size distribution in a mammalian subject having cancer can be shorter than the targeted region cfDNA fragment size distribution in a healthy mammalian subject (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 or fewer base pairs, or any base pair between these numbers). In some cases, the targeted region cfDNA fragment size distribution in a mammalian subject having cancer is shorter than about 47 base pairs and longer than about 30 base pairs compared to the targeted region cfDNA fragment size distribution in a healthy mammalian subject. In some cases, the average length difference in the targeted region cfDNA fragment size distribution in a mammalian subject having cancer is 10, 11, 12, 13, 14, 15, 15, 17, 18, 19, 20 base pairs or more. For example, a mammalian subject having cancer can have an average length difference in the size distribution of targeted region cfDNA fragments of about 13 base pairs. In some cases, the size distribution can be a genome-wide size distribution. The long and short cfDNA fragment distributions in the genome of a healthy mammalian subject (e.g., a mammalian subject without cancer) are very similar. In some cases, a mammalian subject having cancer can have one or more alterations (e.g., increases and decreases) in the cfDNA fragment size in the genome. The one or more alterations can be any suitable chromosomal region of the genome. For example, the alteration can be in a portion of a chromosome. Examples of portions of a chromosome that can include one or more alterations in the cfDNA fragment size include, but are not limited to, portions of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and 14q. For example, the alteration can span a chromosome arm (e.g., an entire chromosome arm).

[0042] The cfDNA fragment distribution map may include the ratio of small cfDNA fragments to large cfDNA fragments and the correlation of the fragment ratio with the reference fragment ratio. As used in the present invention, with respect to the ratio of small cfDNA fragments to large cfDNA fragments, the length of the small cfDNA fragments may be from about 100 bp to about 150 bp. As used in the present invention, with respect to the ratio of small cfDNA fragments to large cfDNA fragments, the length of the large cfDNA fragments may be from about 151 bp to 220 bp. As described herein, a mammal having cancer may have a lower correlation of the fragment ratio (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) (e.g., the correlation of the cfDNA fragment ratio with the reference DNA fragment ratio from one or more healthy mammals) than a healthy mammal. A healthy mammal (e.g., a mammal without cancer) may have a correlation of the fragment ratio of about 1 (e.g., about 0.96) (e.g., the correlation of the cfDNA fragment ratio with the reference DNA fragment ratio from one or more healthy mammals, for example). In some cases, the correlation of the fragment ratio in a mammal having cancer (e.g., the correlation of the cfDNA fragment ratio with the reference DNA fragment ratio from one or more healthy mammals, for example) is on average about 0.19 to about 0.30 (e.g., about 0.25) lower than the correlation of the fragment ratio in a healthy mammal.

[0043] The cfDNA fragment map may include the coverage of all fragments. The coverage of all fragments may include windows of coverage (e.g., non-overlapping windows). In some cases, the coverage of all fragments may include windows of small fragments (e.g., fragments having a length from about 100 bp to about 150 bp). In some cases, the coverage of all fragments may include windows of large fragments (e.g., fragments having a length from about 151 bp to about 220 bp).

[0044] In some cases, the cfDNA fragment map can be used to identify the tissue of origin of cancer (e.g., colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma or ovarian cancer). For example, the cfDNA fragment map can be used to identify local cancer. When the cfDNA fragment map includes a targeted region map, one or more of the alterations described in this specification (e.g., Table 3 (Appendix C) and / or Table 6 (Appendix F)) can be used to identify the tissue of origin of cancer. In some cases, one or more alterations in chromosomal regions can be used to identify the tissue of origin of cancer.

[0045] A cfDNA fragment profile can be obtained using any suitable method. In some cases, cfDNA from a mammal (e.g., a mammal with cancer or suspected of having cancer) can be processed into a sequencing library and then subjected to whole-genome sequencing (e.g., low-coverage whole-genome sequencing), mapped to the genome, and analyzed to determine cfDNA fragment lengths. The mapped sequences can be analyzed in non-overlapping windows that cover the genome. The windows can be of any suitable size. For example, the length of a window can range from thousands to millions of bases. As a non-limiting example, a window can be about 5 megabases (Mb) long. Any number of windows can be mapped. For example, dozens to thousands of windows can be mapped in the genome. For example, hundreds to thousands of windows can be mapped in the genome. The cfDNA fragment profile can be determined within each window. In some cases, a cfDNA fragment profile can be obtained as described in Example 1. In some cases, a cfDNA fragment profile can be obtained as Figure 1 shown.

[0046] In some cases, the methods and materials described herein can also include machine learning. For example, machine learning can be used to identify altered fragment profiles (e.g., using the coverage of cfDNA fragments, the fragment size of cfDNA fragments, the coverage of chromosomes, and mtDNA).

[0047] In some cases, the methods and materials described in this specification can be a single method for identifying a mammal (e.g., a human) with cancer (e.g., colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma, and / or ovarian cancer). For example, determining a cfDNA fragment profile can be a single method for identifying a mammal with cancer.

[0048] In some cases, the methods and materials described herein can be used in conjunction with one or more other methods for identifying a mammal (e.g., a human) with cancer (e.g., colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma, and / or ovarian cancer). Examples of methods for identifying a mammal with cancer include, but are not limited to, 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 fragment profile can be used in conjunction with identifying one or more cancer-specific mutations in the genome of a mammal to identify a mammal with cancer. For example, determining a cfDNA fragment profile can be used in conjunction with identifying one or more aneuploidies in the genome of a mammal to identify a mammal with cancer.

[0049] In certain aspects, the present specification also provides methods and materials for evaluating, monitoring, and / or treating a mammal (e.g., a human) having or suspected of having cancer. In certain cases, the present specification provides methods and materials for identifying that a mammal has cancer. For example, a sample obtained from a mammal (e.g., a blood sample) can be evaluated to determine whether the mammal has cancer, at least in part based on cfDNA fragments of the mammal. In certain cases, the present specification provides methods and materials for identifying the location of cancer (e.g., an anatomical site or tissue of origin) in a mammal. For example, a sample obtained from a mammal (e.g., a blood sample) can be evaluated, at least in part based on a cfDNA fragment profile of the mammal, to determine the tissue of origin of cancer in the mammal. In certain cases, the present invention provides methods and materials for identifying that a mammal has cancer and treating the mammal with one or more cancer treatments to treat the mammal. For example, a sample obtained from a mammal (e.g., a blood sample) can be evaluated to determine whether the mammal has cancer, at least in part based on a cfDNA fragment profile of the mammal, and the mammal can be treated with one or more cancer treatments. In certain cases, the present specification provides methods and materials for treating a mammal having cancer. For example, a mammal identified as having cancer can be treated with one or more cancer treatments (e.g., at least in part based on a cfDNA fragment profile of the mammal) to treat the mammal. In certain cases, during or after a cancer treatment (e.g., any cancer treatment described herein), a mammal can be monitored (or selected for increased monitoring) and / or further diagnostic tests can be performed. In some cases, monitoring can include evaluating a mammal having or suspected of having cancer by, for example, evaluating a sample obtained from the mammal (e.g., a blood sample) to determine cfDNA fragments of the mammal as described herein. Changes in the cfDNA fragment profile over time can be used to identify a response to treatment and / or to identify a mammal having cancer (e.g., a residual cancer).

[0050] Any suitable mammal can be evaluated, monitored, and / or treated as described in the present specification. The mammal can be a mammal having cancer. The mammal can be a mammal suspected of having cancer. Examples of mammals that can be evaluated, monitored, and / or treated as described in the present specification include, but are not limited to, humans, primates such as monkeys, dogs, cats, horses, cows, pigs, sheep, mice, and rats. For example, a human having cancer or suspected of having cancer can be evaluated to determine that they have a cfDNA fragment profile as described in the present specification, and optionally, can be treated with one or more cancer treatment methods described herein.

[0051] Any suitable sample from a mammal can be evaluated as described herein (e.g., to evaluate DNA fragmentation patterns). In some cases, the sample can contain DNA (genomic DNA). In some cases, the sample can contain cfDNA (e.g., circulating tumor DNA (ctDNA)). In some cases, the sample can be a fluid sample (e.g., liquid biopsy). Examples of samples that can contain DNA and / or polypeptides include, but are not limited to, blood (e.g., whole blood, serum, or plasma), amniotic fluid, tissue, urine, cerebrospinal fluid, saliva, sputum, bronchoalveolar lavage fluid, bile, lymphatic fluid, cyst fluid, feces, ascites, cervical smear, breast milk, and exhaled breath condensate. For example, a plasma sample can be evaluated to determine a cfDNA fragmentation profile as described herein.

[0052] The sample from a mammal to be evaluated as described herein (e.g., to evaluate DNA fragmentation patterns) can include any suitable amount of cfDNA. In some cases, the sample can contain a limited amount of DNA. For example, a cfDNA fragmentation profile can be obtained from a sample that contains less DNA than is typically required for other cfDNA analysis methods. See, 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.

[0053] In some cases, the 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 a detergent and / or surfactant), 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 a detergent and / or surfactant), DNA removal (e.g., using a DNase), and / or RNA removal (e.g., using an RNase).

[0054] The methods and materials described in this specification can be used to evaluate (e.g., determine cfDNA fragment profiles) mammals having (or suspected of having) any suitable type of cancer and / or to treat (e.g., by subjecting the mammal to one or more cancer treatments) such mammals. The cancer can be at any stage of cancer. In some cases, the cancer can be early-stage cancer. In some cases, the cancer can be asymptomatic cancer. In some cases, the cancer can be residual disease and / or recurrence (e.g., after surgical resection and / or after cancer treatment). The cancer can be any type of cancer. Examples of cancer types that can be evaluated, monitored, and / or treated as described in this specification include, but are not limited to, colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma, and ovarian cancer.

[0055] When treating a mammal having or suspected of having a cancer described herein, the mammal can be subjected to one or more cancer treatments. The cancer treatment can be any suitable cancer treatment. The one or more cancer treatments described in this specification can be administered to the mammal at any suitable frequency (e.g., once or multiple times over a period of days to weeks). Examples of cancer treatments include, but are not limited to, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy (e.g., T cells having chimeric antigen receptors and / or wild-type or modified T cell receptors), targeted therapy such as administration of kinase inhibitors (e.g., kinase inhibitors targeting specific genetic lesions, such as translocations or mutations), (e.g., kinase inhibitors, antibodies, bispecific antibodies), signal transduction inhibitors, bispecific antibodies or antibody fragments (e.g., BiTE), monoclonal antibodies, immune checkpoint inhibitors, surgery (e.g., surgical resection), or any combination of the foregoing. In some cases, the cancer treatment can reduce the severity of the cancer, alleviate the symptoms of the cancer, and / or reduce the number of cancer cells present in the mammal.

[0056] In some cases, cancer treatment may include immune checkpoint inhibitors. 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:S1525-7304(17)30043-8; Zou and Awad(2017) Ann Oncol 28(4):685-687; Sorscher(2017) N Engl J Med 376(10:996-7; Huie 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.

[0057] In some cases, cancer treatment can be adoptive T cell therapy (e.g., T cells with chimeric antigen receptors and / or 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; US 9,233,125; which are incorporated herein by reference in their entirety.

[0058] In some cases, cancer treatment can be a chemotherapeutic agent.Non-limiting examples of chemotherapeutic agents include: amsacrine, azacitidine, axathioprine, bevacizumab (or its antigen-binding fragment), bleomycin, busulfan, carboplatin, capecitabine, chlorambucil, cisplatin, cyclophosphamide, cytarabine, dacarbazine, daunorubicin, docetaxel, doxifluridine, doxorubicin, epirubicin, erlotinib hydrochlorides, etoposide, fludarabine, 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.Other examples of anticancer therapies are known in the art. See, for example, the treatment guidelines of the American Society of Clinical Oncology (ASCO), the European Society for Medical Oncology (ESMO), or the National Comprehensive Cancer Network (NCCN).

[0059] When monitoring a mammal having or suspected of having cancer as described herein (e.g., at least in part based on the cfDNA fragment profile of the mammal), the monitoring can be performed before, during, and / or after a cancer treatment process. The monitoring methods provided herein can be used to determine the efficacy of one or more cancer treatments and / or to select a mammal to enhance the monitoring. In certain cases, the monitoring can include identifying a cfDNA fragment profile as described herein. For example, a cfDNA fragment profile can be obtained before administering one or more cancer treatments to a mammal having or suspected of having cancer, the mammal can be administered one or more cancer treatments, and one or more cfDNA fragment profiles can be obtained during the cancer treatment process of the mammal. In some cases, the cfDNA fragment profile can change during a cancer treatment (e.g., any cancer treatment as described herein). For example, a cfDNA fragment profile indicating that a mammal has cancer can change to a cfDNA fragment profile indicating that the mammal does not have cancer. Such a change in the cfDNA fragment profile may indicate that the cancer treatment is working. Conversely, during a cancer treatment (e.g., any cancer treatment as described herein), the cfDNA fragment profile may remain static (e.g., the same or approximately the same). Such a static cfDNA fragment profile may indicate that the cancer treatment is ineffective. In certain 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, compared to a mammal not selected for increased monitoring, a mammal selected for increased monitoring can have diagnostic tests (e.g., any diagnostic test disclosed in this specification) performed at an increased frequency. For example, diagnostic tests can be performed on a mammal selected for increased monitoring twice a day, once a day, once every two weeks, once a week, once every two months, once a month, once a quarter, once every six months, once a year, or at any of these frequencies. In certain cases, compared to a mammal not selected for enhanced monitoring, one or more additional diagnostic tests can be performed on a mammal selected for enhanced monitoring. For example, two diagnostic tests can be performed on a mammal selected for enhanced monitoring, while a mammal not selected for enhanced monitoring is only given a single diagnostic test (or no diagnostic test). In certain cases, a mammal that has already been selected for enhanced monitoring can also be selected for further diagnostic tests. Once the presence of a tumor or cancer (e.g., cancer cells) has been identified (e.g., by any of the various methods disclosed in this specification), it may be beneficial to perform enhanced monitoring (e.g., evaluate the progression of the tumor or cancer in the mammal and / or evaluate the development of one or more cancer biomarkers (e.g., mutations)) on the mammal and perform further diagnostic tests (e.g., determine the tumor size and / or exact location (e.g., origin tissue) of the cancer).In some cases, one or more cancer treatments can be administered to a selected mammalian subject for enhanced surveillance after detection of a cancer biomarker and / or after the cfDNA fragment profile of the mammalian subject has not improved or deteriorated. Any cancer treatment disclosed in this specification or known in the art can be administered. For example, a mammalian subject that has been selected for increased surveillance can be further monitored, and if cancer cells persist throughout the enhanced surveillance period, cancer treatment can be administered. Additionally or alternatively, a mammalian subject that has been selected for increased surveillance can be administered cancer treatment and further monitored as the cancer treatment progresses. In some cases, after cancer treatment is administered to a mammalian subject that has been selected for enhanced surveillance, the enhanced surveillance will reveal one or more cancer biomarkers (e.g., mutations). In some cases, such one or more cancer biomarkers will provide a basis for administering a different cancer treatment (e.g., during cancer treatment, drug-resistant mutations may arise in cancer cells, and cancer cells with such drug-resistant mutations are resistant to the original cancer treatment).

[0060] When a mammal is identified as having cancer as described in this specification (e.g., at least in part based on the cfDNA fragment map of the mammal), the identification can be performed before and / or during cancer treatment. The method provided by the present invention for identifying a mammal having cancer can be used as a first diagnosis for identifying the mammal (e.g., having cancer before any treatment process) and / or selecting the mammal for further diagnostic tests. In some cases, once it is determined that a mammal has cancer, the mammal can be further examined and / or selected for further diagnostic examinations. In some cases, the methods provided herein can be used to select a mammal for further diagnostic tests at a time prior to the time when conventional techniques are able to diagnose a mammal having early-stage cancer. For example, when a mammal is not diagnosed as having cancer by conventional methods and / or when a mammal does not have cancer, the method for selecting a mammal for further diagnostic tests provided in this specification can be used. In some cases, compared with a mammal not selected for further diagnostic tests, a mammal selected for further diagnostic tests can be subjected to diagnostic tests (e.g., any diagnostic test disclosed in this specification) at an increased frequency. For example, a mammal selected for further diagnostic tests can be subjected to diagnostic tests twice a day, daily, every two weeks, weekly, every two months, monthly, quarterly, semi-annually, annually, biennially, or at any frequency therein. In some cases, compared with a mammal not yet selected for further diagnostic tests, one or more other diagnostic tests can be performed on a mammal selected for further diagnostic tests. For example, a mammal selected for further diagnostic tests can be subjected to two diagnostic tests, while a mammal not yet selected for further diagnostic tests is subjected to only a single diagnostic test (or no diagnostic test). In some cases, the diagnostic test method can determine the presence of the same type of cancer (e.g., having the same tissue or origin) as the cancer initially detected (e.g., at least in part based on the cfDNA fragment map of the mammal). Additionally or alternatively, the diagnostic test method can determine the presence of a different type of cancer from the cancer initially detected. In some cases, the diagnostic test method is a scan. In some cases, the scan is a computed tomography (CT), CT angiography (CTA), esophagography (barium meal), barium enema, magnetic resonance imaging (MRI), PET scan, ultrasound examination (e.g., endobronchial ultrasound, endoscopic ultrasound), X-ray, DEXA scan.In some cases, diagnostic test methods are physical examinations, such as anoscopy, bronchoscopy (e.g., autofluorescence bronchoscopy, white light bronchoscopy, navigational bronchoscopy), colonoscopy, digital breast tomosynthesis, endoscopic retrograde cholangiopancreatography (ERCP), endoscopy, duodenoscopy, nipple smear, pelvic examination, positron emission tomography and computed tomography (PET-CT) scan. In some cases, mammals that have been selected for further diagnostic testing can also be selected to improve the level of monitoring. Once the presence of a tumor or cancer (e.g., cancer cells) has been identified (e.g., by any of the various methods disclosed in this specification), enhanced monitoring of the mammal (e.g., assessing the progression of the tumor or cancer in the mammal and / or assessing the development of one or more cancer biomarkers (e.g., mutations)) may be beneficial and further diagnostic tests may be performed (e.g., determining the size and / or exact location of the tumor or cancer). In some cases, after detection of a cancer biomarker and / or after the cfDNA fragmentation profile of the mammal has not improved or deteriorated, cancer treatment is administered to the mammal that has been selected for further diagnostic testing. Any cancer treatment disclosed in this specification or known in the art may be administered. For example, a mammal that has been selected for further diagnostic testing may be subjected to further diagnostic tests, and if the presence of a tumor or cancer is confirmed, cancer treatment may be performed. Additionally or alternatively, a mammal that has been selected for further diagnostic testing may be subjected to cancer treatment and may be further monitored as the cancer treatment progresses. In some cases, after cancer treatment has been administered to a mammal that has been selected for further diagnostic testing, other tests will reveal one or more cancer biomarkers (e.g., mutations). In some cases, one or more cancer biomarkers (e.g., mutations) will form the basis for implementing a different cancer treatment (e.g., during cancer treatment, cancer cells may develop drug-resistant mutations, and cancer cells with drug-resistant mutations are resistant to the initial cancer treatment).

[0061] The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

[0062] [Examples]

[0063] Example 1: Cell-Free DNA Fragmentation in Cancer Patients

[0064] The analysis of cell-free DNA has mainly focused on targeted sequencing of specific genes. Such studies can detect a small number of tumor-specific alterations in cancer patients, 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 detecting such changes has been challenging, in part because it is difficult to distinguish a small number of abnormalities from normal chromosomal variations (Leary et al., 2010 Sci Transl Med 2:20ra14; and Leary et al., 2012 Sci Transl Med 4:162ra154). Other findings have suggested that the nucleosome patterns and chromatin structures may differ between cancerous and normal tissues, and cfDNA from cancer patients may result in abnormal cfDNA fragment sizes and positions (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 required for nucleosome footprint analysis of cfDNA is impractical for routine analysis.

[0065] The sensitivity of any cell-free DNA method depends on the number of potential alterations being examined and the technical and biological limitations of detecting such alterations. Since a typical blood sample contains approximately 2000 genomic equivalents of cfDNA per milliliter of plasma (Phallen et al., 2017 Sci Transl Med 9), the limit for detecting a single variant may theoretically be no more than one wild-type molecule among thousands of mutants. Methods that detect a large number of changes in the same number of genomic equivalents will be more sensitive for detecting cancer in circulation. Monte Carlo simulations have shown that increasing the number of potential abnormalities detected from just a few to dozens or hundreds can potentially improve the detection limit by several orders of magnitude, similar to the recent probabilistic analysis of multiple methylation changes in cfDNA ( Figure 2 ).

[0066] This study presents a new method called DELFI for detecting cancer and further identifying the tissue of origin using whole-genome sequencing ( Figure 1)。This method uses cfDNA fragment profiles and machine learning to distinguish patterns of healthy blood cell DNA and tumor-derived DNA and to identify primary tumor tissue. DELFI was used to retrospectively analyze cfDNA from 245 healthy individuals and 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or cholangiocarcinoma, with most patients presenting with local disease. Assuming this method has a sensitivity ≥0.80 for distinguishing cancer patients from healthy individuals at a specificity of 0.95, a study of at least 200 cancer patients would be able to estimate the true sensitivity with a margin of error of 0.06 at an expected specificity of 0.95 or greater than 0.95.

[0067] Materials and Methods

[0068] Patient and Sample Characteristics

[0069] Plasma samples from healthy individuals and plasma and tissue samples from patients with breast, lung, ovarian, colorectal, cholangiocarcinoma, or gastric cancer were obtained from ILSBio / Bioreclamation, Aarhus University, Herlev Hospital, University of Copenhagen, Hvidovre Hospital, University Medical Center Utrecht, Academic Medical Center Amsterdam, Netherlands Cancer Institute, and University of California, San Diego. All samples were obtained according to protocols approved by institutional review boards and informed consent was obtained for participating institutions to conduct the study. Plasma samples were obtained from healthy individuals during routine screening (including colonoscopy or cervical smear). They were considered healthy if they had no previous history of cancer and had negative screening results.

[0070] Plasma samples were obtained from individuals with breast, colorectal, gastric, lung, ovarian, pancreatic, and cholangiocarcinoma at the time of diagnosis, before tumor resection, or before treatment. cfDNA fragment profile changes at multiple time points were analyzed in 19 lung cancer patients receiving anti-EGFR or anti-ERBB2 treatment (see, e.g., Phallen et al., 2019 Cancer Research 15, 1204 - 1213). Clinical data for all patients in the study are listed in Table 1 (Appendix A). Gender was confirmed by the representation of the X and Y chromosomes in genomic analysis. Pathological staging was performed for gastric cancer patients after neoadjuvant treatment. Samples with unknown tumor stage were designated as stage X or unknown.

[0071] Nucleosomal DNA Purification

[0072] Purify viable cryopreserved lymphocytes by leukapheresis from white blood cells of male (C0618) and female (D0808-L) (Advanced Biotechnologies Inc., Eldersburg, MD) healthy individuals. Using the EZ Nucleosomal DNA Preparation Kit (Zymo Research, Irvine, CA), aliquots of 1 x 10 6 cells were used for nucleosomal DNA purification. The 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, the supernatant was discarded, and the pelleted nuclei were treated twice with 100 μl of Atlantis digestion buffer or 100 μl of micrococcal nuclease (MN) digestion buffer. Finally, the cellular nucleic DNA was fragmented with 0.5 U of Atlantis dsDNase at 42 °C for 20 minutes and with 1.5 U of MNase at 37 °C for 20 minutes. The reaction was terminated using 5X MN Stop Buffer, and the DNA was purified using a Zymo-Spin TM IIC column. The concentration and quality of the eluted cellular nucleic DNA were analyzed using a Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA).

[0073] Sample Preparation and cfDNA Sequencing

[0074] For three cancer patients participating in the monitoring analysis, whole blood was collected in EDTA tubes and processed immediately or within one day after storage at 4 °C, or collected in Streck tubes and processed within two days. Plasma and cellular components were separated by centrifugation at 800 g for 10 minutes at 4 °C. Plasma was centrifuged a second time at 18,000 g at room temperature to remove all residual cell debris and stored at -80 °C until DNA extraction. DNA was isolated from plasma using the Qiagen Circulating Nucleic Acid Kit (Qiagen GmbH) and eluted in LoBind tubes (Eppendorf AG). The concentration and quality of cfDNA were evaluated using a Bioanalyzer 2100 (Agilent Technologies).

[0075] As described elsewhere, NGS cfDNA libraries for whole-genome sequencing and targeted sequencing were prepared using 5 to 250 ng of cfDNA (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 major modifications to the manufacturer's guidelines: (i) the library purification step used an on-bead AMPure XP method to minimize sample loss during elution and tube transfer steps (see, e.g., Fisher et al., 2011 Genome Biol 12:R1); (ii) the volumes of NEBNext End Repair, A-tailing, and adapter ligase and buffer were appropriately adjusted to accommodate the on-bead AMPure XP purification strategy; (iii) eight unique Illumina dual-index adapters with 8 base pair (bp) barcodes were used in the ligation reaction, instead of the standard Illumina single- or dual-index adapters with 6 or 8 bp barcodes; (iv) the cfDNA library was amplified with Phusion Hot Start Polymerase.

[0076] The whole-genome libraries were sequenced directly. For the targeted libraries, capture was performed using Agilent SureSelect reagents and a custom hybridization probe set targeting 58 genes according to the manufacturer's guidelines (e.g., see Phallen et al., 2017 Sci Transl Med 9:eaan2415). The captured libraries were amplified with Phusion Hot Start Polymerase (NEB). The concentration and quality of the captured cfDNA libraries were evaluated on a Bioanalyzer 2100 using the DNA1000 kit (Agilent Technologies). The targeted libraries were sequenced using 100-bp paired-end sequencing on an Illumina HiSeq 2000 / 2500 (Illumina).

[0077] Analysis of targeted sequencing data of cfDNA

[0078] The targeted NGS data of cfDNA samples were analyzed as described in other literature (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 masking of multi-index and dual-index adapter sequences. Sequencing reads were aligned to the human reference genome (hg18 or hg19 version) using NovoAlign, and additional alignment of selected regions was performed using the Needleman-Wunsch method (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53). The positions of sequence alterations were not affected by different genome builds. Candidate mutations consisting of point mutations, small insertions, and deletions were identified within the targeted regions of interest using VariantDx (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53) (Personal Genome Diagnostics, Baltimore, MD).

[0079] To analyze the fragment lengths of cfDNA molecules, it was required that the Phred quality score of each read pair of cfDNA molecule sequencing reads ≥ 30. All duplicate ctDNA fragments, defined as having the same start, end, and index barcodes, were removed. For each mutation, only fragments containing one or two read pairs with the mutant (or wild-type) base at the given position were included. This analysis was completed using the R software packages Rsamtools and GenomicAlignments.

[0080] For each genomic locus where a somatic mutation was identified, the length of the fragment containing the mutant allele was compared to the length of the fragment containing the wild-type allele. If more than 100 mutant fragments were identified, the 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, N fragments containing the wild-type allele were sampled, where N represents the number of fragments with the mutation. For each bootstrap replicate of the wild-type fragments, their median lengths were calculated. The p-value was estimated as the proportion of bootstrap replicates where the median length of the wild-type fragments was equal to or greater than the observed median length of the mutant fragments.

[0081] Whole-genome sequencing data analysis of cfDNA

[0082] The whole-genome NGS data of cfDNA samples were initially processed using Illumina CASAVA (Consensus Assessment of Sequence and Variation) software (version 1.8.2), including demultiplexing and masking of multi-index adapter sequences. The sequence reads were aligned to the human reference genome (hg19 version) using ELAND.

[0083] Read pairs with a MAPQ score of less than 30 for either the read or the PCR duplicate were removed. The hg19 autosomes were tiled into 26,236 adjacent, non-overlapping 100-kb bins. Low mappable regions were removed according to the bins indicating the lowest 10% of coverage (see, e.g., Fortin et al., 2015 Genome Biol 16:180), and reads falling into the Duke blacklist regions (see, e.g., hgdownload.cse.ucsc.edu / goldenpath / hg19 / encodeDCC / wgEncodeMapability / ). Using this method, 361 Mb (13%) of the hg19 reference genome was excluded, including centromeric and telomeric regions. Short reads were defined as having lengths between 100 and 150 bp, and long reads were defined as having lengths between 151 and 220 bp.

[0084] To illustrate the coverage bias attributable to genomic GC content, locally weighted smoother loess with a span of 3 / 4 was applied to the scatter plot of mean fragment GC rather than the coverage calculated for each 100-kb bin. Loess regression was performed separately for short and long reads to account for possible differences in the effect of fragment length on GC-affected plasma coverage (see, e.g., Benjamini et al., 2012 Nucleic Acids Res 40:e72). The predicted values of short- and long-term coverage explained by GC in the Loess model were subtracted to obtain short- and long-term residuals that were not related to GC. The residuals were brought back to the original level by adding the genome-wide estimates of medium-, short-, and long-term coverage. This process was repeated for each sample to account for possible differences in the effect of GC on coverage between samples. To further reduce the feature space and noise, the GC-adjusted total coverage was calculated in 5-Mb bins.

[0085] To compare the fragment length variability from healthy subjects to cancer patients at the fragment level, the standard deviation of the long-read profile was calculated for each individual. The standard deviations of the two groups were compared by the Wilcoxon rank-sum test.

[0086] Analysis of chromosomal arm copy number changes

[0087] To develop arm-level statistics for copy number variations, a method for aneuploidy detection in plasma described in other literature was adopted (see, e.g., Leary et al., 2012 Sci Transl Med 4:162ra154). This method divides the genome into non-overlapping 50KB bins and obtains GC-corrected log2 sequencing fragment depths after correction with loess with a span of 3 / 4. This loess-based correction method is comparable to the above method but is evaluated on a log2 scale to improve the robustness to outliers in smaller bins and does not stratify by fragment length. To obtain arm-specific Z-values for copy number changes, the average GC-adjusted sequencing fragment depth of each group of arms (GR) was centered and calibrated for healthy samples by the mean and standard deviation of GR values obtained from 50 independent groups, respectively.

[0088] Mitochondrial alignment analysis of cfDNA

[0089] Whole-genome sequence reads initially mapped to the mitochondrial genome were extracted from the bam file and realigned to the hg19 reference genome in end-to-end mode using Bowtie2 as described in other literature (see, e.g., Langmead et al., 2012 Nat Methods 9:357-359). The resulting aligned reads were filtered so that both pairs were aligned to the mitochondrial genome with MAPQ >= 30. The number of reads mapped to the mitochondrial genome was counted and converted to a percentage of the total number of reads in the original bam file.

[0090] Predictive model for cancer classification

[0091] To distinguish healthy patients from cancer patients using fragment profiles, a random gradient boosting model (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) was used. The sum short fragment coverage for all 504 bins was GC-corrected, centered, and scaled such that the mean for each sample was 0 and the unit standard deviation was one. Other features included the Z-value for each of the 39 autosomal arms and the mitochondrial representation (log10-transformed proportion of sequencing fragments mapped to the mitochondria). To estimate the prediction error of the method, 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, which was performed only on the training data in each cross-validation run, removed bins that were highly correlated (correlation > 0.9) or had variance close to zero. Random gradient boosting machine learning was performed using the R software package gbm with parameters n.trees = 150, interaction.depth = 3, shrinkage = 0.1, and n.minobsinside = 10. To average the prediction error from random patient groupings into folds, the 10-fold cross-validation procedure was repeated 10 times. Confidence intervals for sensitivity fixed at 98% and specificity at 95% were obtained from 2000 bootstrap replicates.

[0092] Prediction model for tumor tissue origin classification

[0093] For samples (n = 174) correctly classified as cancer patients with 90% specificity, a separate random gradient boosting model was trained to classify the origin tissue. To address the small number of lung cancer samples available for prediction, 18 cfDNA baseline samples from advanced lung cancer patients were included from the surveillance analysis. The performance characteristics of the model were evaluated by 10-fold cross-validation repeated 10 times. The gbm model was trained using the same features as the cancer classification model. As described previously, during cross-validation, features that showed a correlation higher than 0.9 or had variance close to zero with each other were removed from each training dataset. The tissue class probabilities for 10 replicate samples per patient were averaged, and the class with the highest probability was taken as the predicted tissue.

[0094] Nucleosomal DNA analysis of human lymphocytes and cfDNA

[0095] As described for whole genome cfDNA analysis, fragment sizes were analyzed in 5 Mb bins from nuclease-treated lymphocytes. A genome-wide map of nucleosome positions was constructed from nuclease-treated lymphocyte lines. The method determined local biases within the circular fragment coverage, showing regions that are 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 60 bp sliding window centered on each base, the WPS was calculated as the number of fragments that completely spanned the window minus the number of fragments with only one end in the window. Since the median length of fragments generated by nucleosomes is 167 bp, a high WPS indicates a likely nucleosome position. The WPS scores were centered to zero using a running median and smoothed using a Kolmogorov-Zurbenko filter (e.g., see Zurbenko, The spectral analysis of time series. North-Holland series in statistics and probability; Elsevier, New York, NY, 1986). For a WPS span of positive values between 50 and 450 bp, nucleosome peaks were defined as the set of base pairs with a WPS higher than the window median. In the same manner as for lymphocyte DNA, the nucleosome positions were calculated for cfDNA from 30 healthy individuals with a sequence coverage of 9x. To ensure that nucleosomes in healthy cfDNA were representative, a consensus track of nucleosomes was defined that consisted only of nucleosomes identified in two or more individuals. The median distance between adjacent nucleosomes was calculated from the consensus track.

[0096] Monte Carlo simulation of detection sensitivity

[0097] Monte Carlo simulations were used to estimate the likelihood of detecting molecules with tumor-derived alterations. Briefly, 1 million molecules were generated from a multinomial distribution. For simulations with m alterations, wild-type molecules were simulated with probability p and m tumor alterations were simulated with probability (1 - p) / m. Next, g*m molecules were randomly sampled and replaced, where g represents 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 to estimate the likelihood that a sample was correctly classified as cancer by the mean of the cancer metric in silico. Setting g = 2000 and s = 5, the number of tumor alterations was varied from 1 to 256 as a power of 2, and the proportion of tumor-derived molecules was varied from 0.0001% to 1%.

[0098] Statistical analysis

[0099] All statistical analyses were performed using R version 3.4.3. Classification of healthy individuals versus cancer patients and origin tissues was done using the R packages caret (version 6.0 - 79) and gbm (version 2.1 - 4). Confidence intervals of the model outputs were obtained using the pROC (version 1.13) R package (e.g., see Robin et al., 2011 BMC bioinformatics 12:77). Assuming a high prevalence of undiagnosed cancer cases in the population (1 or 2 cases per 100 healthy individuals), a genomic assay with a specificity of 0.95 and a sensitivity of 0.8 would have useful operating characteristics (positive predictive value of 0.25 and negative predictive value close to 1). Power calculations showed that analyzing over 200 cancer patients and approximately equal number of healthy individual controls could estimate sensitivity with a margin of error of 0.06 at an expected specificity of 0.95 or higher.

[0100] Data and code availability

[0101] The sequence data used in this study have been deposited in the European Genome - phenome Archive under study numbers EGAS00001003611 and EGAS00001002577. The analysis code is available from github.com / Cancer - Genomics / delfi_scripts.

[0102] Results

[0103] DELFI allows the simultaneous analysis of numerous aberrations in cfDNA through whole - genome fragment pattern analysis. The method is based on low - coverage whole - genome sequencing and analysis of isolated cfDNA. The mapped sequences are analyzed in non - overlapping windows that cover the genome. Conceptually, the window sizes can range from thousands to millions of bases, resulting in hundreds to thousands of windows in the genome. 5Mb windows were used to evaluate cfDNA fragment patterns, and each window provided over 20,000 sequencing fragments even at a limited genome coverage of 1 - 2x. Within each window, the coverage and size distribution of cfDNA fragments were examined. This method was used to evaluate changes in the whole - genome fragment distribution in healthy and cancer populations (Table 1; Appendix A). The whole - genome pattern of an individual can be compared to a reference population to determine whether the pattern is likely to be from a healthy or cancerous individual. Since the whole - genome map reveals location differences related to specific tissues that may be overlooked in the overall fragment size distribution, these patterns may also indicate the tissue origin of cfDNA.

[0104] The fragment sizes of cfDNA have received attention because it has been found that the size variations of cfDNA molecules derived from cancer may be greater than those of cfDNA from non-cancer cells. cfDNA fragments in the targeted regions from patients with breast cancer, colorectal cancer, lung cancer or ovarian cancer (Table 1 (Appendix A), Table 2 (Appendix B) and Table 1) were captured and sequenced at high coverage (total coverage 43,706, different coverage 8,044), and Table 3 (Appendix C) was preliminarily examined. Analysis of 165 tumor-specific altered sites from 81 patients (ranging from 1 - 7 alterations per patient) showed that the mean absolute difference between the average lengths of mutant and wild-type cfDNA fragments ( Figure 3 , Table 3 (Appendix C)) was 6.5 bp (95% CI, 5.4 - 7.6 bp). Compared to the wild-type sequences in these regions, the median size range of mutant cfDNA fragments ranged from 30 bases smaller at position 41,266,124 on chromosome 3 to 47 bases larger at position 108,117,753 on chromosome 11 (Table 3; Appendix C). The GC content of mutant and unmutated fragments was similar (Figure 4a), and there was no correlation between GC content and fragment length (Figure 4b). Similar analysis of 44 germline alterations from 38 patients identified a median cfDNA size difference of less than 1 bp between different allelic fragment lengths ( Figure 5 , Table 3 (Appendix C)). Additionally, 41 alterations related to clonal hematopoiesis were identified by comparing the DNA sequences of plasma, buffy coat and tumor from the same individual previously. Different from the fragments derived from tumors, there was no significant difference between the fragments related to hematopoietic alterations and wild-type fragments ( Figure 6 , Table 3 (Appendix C)). Overall, in certain genomic regions, cancer-derived cfDNA fragments have greater variability in length compared to non-cancer cfDNA fragments (p < 0.001, variance ratio test). Assuming that these differences may be due to changes in higher-order chromatin structure in cancer as well as other genomic and epigenomic abnormalities, cfDNA fragments in a locus-specific manner can thus serve as unique biomarkers for cancer detection.

[0105] Since targeted sequencing can only analyze a limited number of loci, large-scale whole-genome analysis was performed to detect other abnormalities in cfDNA fragments. cfDNA was isolated from approximately 4 ml of plasma from 8 patients with stage I to III lung cancer and 30 healthy individuals (Tables 1 (Appendix A), 4 (Appendix D), and 5 (Appendix E)). The cfDNA was converted into a next-generation sequencing library using an efficient method and whole-genome sequencing was performed at approximately 9-fold coverage (Table 4; Appendix D). The total cfDNA fragment length was larger in healthy individuals, with an average fragment size of 167.3 bp, while the average fragment size in cancer patients was 163.8 (p < 0.01, Welch's t-test) (Table 5; Appendix E). To examine differences in fragment size and coverage across the genome, the sequenced fragments were mapped to their genomic origin and the fragment lengths were evaluated in 504 windows of 5 Mb size, covering approximately 2.6 Gb of the genome. For each window, the ratio of small cfDNA fragments (length 100 to 150 bp) to larger cfDNA fragments (151 to 220 bp) and the total coverage were determined and used to obtain a whole-genome fragmentation map for each sample.

[0106] Healthy individuals had very similar fragment distribution maps across the genome (Figures 7 and Figure 8 ). To examine the origin of the fragment patterns commonly observed in cfDNA, nuclei were isolated from buffy coat lymphocytes from two healthy individuals and treated with DNA nuclease to obtain nucleosomal DNA fragments. Analysis of the cfDNA patterns observed in healthy individuals showed a high correlation with the nucleosomal DNA fragment distribution maps (Figures 7b and 7d) and nucleosome distances (Figures 7c and 7f) in lymphocytes. As revealed using the Hi-C method, the median distance between nucleosomes in lymphocytes was associated with the open (A) and closed (B) compartments of lymphoblasts (see, for example, Lieberman-Aiden et al., 2009 Science 326:289-293; and Fortin et al., 2015 Genome Biol 16:180) used to examine the three-dimensional structure of the genome (Figure 7c). These analyses suggest that the fragment pattern of normal cfDNA is the result of the nucleosomal DNA pattern, which largely reflects the chromatin structure of normal blood cells.

[0107] Compared to healthy cfDNA, cancer patients had multiple genomic differences in fragment size in different regions (Figures 7a and 7b). Similar to what we observed from the targeted analysis, the difference in whole-genome fragment length was also greater in cancer patients compared to healthy individuals.

[0108] To determine whether cfDNA fragment length patterns can be used to distinguish cancer patients from healthy individuals, a genome-wide correlation analysis of the ratio of cfDNA fragments from long to short was performed for each sample (Figures 7a, 7b, and 7e) and compared to the median fragment length profile calculated from healthy individuals. Although the cfDNA fragment profiles in healthy individuals were highly consistent (median correlation = 0.99), the median correlation of the genome-wide fragment ratios in 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 the fragment profiles of cancer patients to those in healthy lymphocytes or nucleosome distances (Figures 7c, 7d, and 7f). To account for potential bias attributable to GC content, a locally weighted smoother was applied to each sample separately, and it was found that after this adjustment, the differences in fragment profiles between healthy individuals and cancer patients remained (median correlation between cancer patients and healthy populations = 0.83) (Table 5; Appendix E).

[0109] Secondary sampling analysis of whole-genome sequence data at 9x coverage of cfDNA from cancer patients was performed at genomic coverages of ∼2x, ∼1x, ∼0.5x, ∼0.2x, and ∼0.1x, where it was determined that the fragment profiles that are prone to change can be identified even at 0.5-fold genomic coverage (Figure 9). Based on these observations, whole-genome sequencing was performed at 1 - 2x coverage to assess whether fragment profiles might change in a manner similar to monitoring sequence changes during targeted therapy. cfDNA of 19 non-small cell lung cancer patients during anti-EGFR or anti-ERBB2 therapy was evaluated, including 5 cases of local radiological response, 8 cases of stable disease, 4 cases of progressive disease, and 2 cases of unmeasurable disease (Table 6; Appendix F). As Figure 10 shown, the degree of abnormality of the fragment profiles during treatment closely matched the levels of EGFR or ERBB2 mutant allele fragments determined using targeted sequencing (Spearman correlation between mutant alleles and fragment profiles = 0.74). Since the genome-wide approach and the mutation-based approach are orthogonal and different cfDNA alterations that might be suppressed in these patients due to prior treatment were examined, these correlations were highly significant. Notably, all cases where the tumor did not progress and the survival was six months or longer showed a decrease or very low levels of ctDNA after initial treatment as determined by the fragment profiles, while the ctDNA increased in cases with poor clinical outcomes. These results demonstrate the feasibility of using fragment analysis to detect tumor-derived cfDNA and suggest that such analysis may also be useful for quantitative monitoring of cancer patients during treatment.

[0110] In patients with parallel analysis of tumor tissue, the fragment profiles were examined in the context of known copy number alterations. These analyses showed that altered fragment profiles were present in copy-neutral genomic regions and that these fragments could be further affected in regions of copy number change (Figures 11a and 12a). The differential positional correlation of fragment patterns can be used to distinguish cancer-derived cfDNA from cfDNA of healthy individuals in these regions (Figure 12a, b), whereas overall cfDNA fragment size measurements overlook this difference (Figure 12a).

[0111] These analyses were extended to independent cohorts of cancer patients and healthy individuals. cfDNA from a total of 208 cancer patients was whole-genome sequenced at 1-2x coverage, including breast cancer (n = 54), colorectal cancer (n = 27), lung cancer (n = 12), ovarian cancer (n = 28), pancreatic cancer (n = 54 n = 34), gastric cancer (n = 27), or cholangiocarcinoma (n = 26) and 215 individuals without cancer (Tables 1 (Appendix A) and 4 (Appendix D)) were performed. None of the cancer patients had received treatment and most had resectable disease (n = 183). After GC adjustment of short and long cfDNA fragment coverage (Figure 13a), the coverage and size characteristics of fragments were examined across the genome window (Figure 11b, Tables 4 (Appendix D) and 7 (Appendix G)). The genome-wide correlation of coverage with GC content was limited and no differences in these correlations were observed between cancer patients and healthy individuals (Figure 13b). Healthy individuals had highly consistent fragment profiles, while cancer patients had higher variability with reduced correlation to the median healthy profile (Table 7; Appendix G). Analysis of the most frequently altered fragmentation windows in the genomes of cancer patients showed a median of 60 affected windows among the cancer types analyzed, highlighting the numerous position-related changes in cfDNA fragments in cancer individuals (Figure 11c).

[0112] To determine whether position-related fragment changes could be used to detect individuals with cancer, a gradient tree boosting machine learning model was implemented to examine whether cfDNA could be classified as having the characteristics of cancer patients or healthy individuals, and the performance characteristics of this method were evaluated by repeating the 10-fold cross-validation ten times ( Figure 14 and 15)。The machine learning model includes features of GC-adjusted short and long fragment coverage across the entire genomic window. A machine learning classifier was also developed based on features related to copy number changes in chromosomal arms rather than a single score value (Figures 16a and Table 8 (Appendix H)), and includes mitochondrial copy number changes (Figure 16b), which can help distinguish cancer and healthy individuals. Using this implementation of DELFI, a score value was obtained that can be used to classify patients as healthy or having cancer. Among 208 cancer patients, 152 were detected (sensitivity 73%, 95% CI 67% - 79%), while 4 out of 215 healthy individuals were misclassified (specificity 98%) (Table 9). At a 95% specificity threshold, 80% of cancer patients were detected (95% CI, 74% - 85%), including 79% of resectable (stages I–III) patients (145 out of 183) and 82% of metastatic (stage IV) patients (18 out of 22) (Table 9). The AUC for the receiver operating characteristic analysis for detecting cancer patients was 0.94 (95% CI 0.92–0.96), with an AUC range for cancer types from 0.86 for pancreatic cancer to ≥0.99 for lung and ovarian cancer (Figures 17a and 17b), and an AUC ≥0.92 for all stages ( Figure 18 )。Among cancer patients or healthy individuals, there was no difference in DELFI classifier score values by age (Table 1; Appendix A).

[0113] Table 9. Performance results of DELFI for cancer detection

[0114]

[0115] To evaluate the contribution of fragment size and coverage, chromosomal arm copy number, or mitochondrial mapping to the model's prediction accuracy, a repeated 10-fold cross-validation procedure was implemented to separately assess the performance characteristics of these features. The individual fragment coverage feature (AUC = 0.94) was observed to be nearly identical to the classifier combining all features (AUC = 0.94) (Figure 17a). In contrast, the analysis performance for chromosomal copy number changes was lower (AUC = 0.88), but still more predictive than copy number changes based on a single score value (AUC = 0.78) or mitochondrial mapping (AUC = 0.72) (Figure 17a). These results suggest that fragment coverage is the main contributor to our classifier. Since information about cancer patients can be obtained from the same genomic sequence data, including all features in the prediction model may be complementary for the detection of cancer patients.

[0116] Since the fragment maps revealed regional differences in fragments between tissues, a similar machine learning approach was used to examine whether cfDNA patterns could identify the tissue of origin of these tumors. The method was found to have an accuracy of 61% (95% CI 53%-67%), with 76% for breast cancer, 44% for cholangiocarcinoma, 71% for colorectal cancer, 67% for gastric cancer, 53% for lung cancer, 48% for ovarian cancer, and 50% for pancreatic cancer ( Figure 19 , Table 10). When considering assigning patients with cfDNA aberrations to one of two sites of origin, the accuracy increased to 75% (95% CI 69%-81%) (Table 10). For all tumor types, the classification of tissue of origin by DELFI was significantly higher than that determined by random assignment (p < 0.01, binomial test, Table 10).

[0117] Table 10. DELFI Tissue Origin Prediction

[0118]

[0119] *Patients detected were based on DELFI tests with 90% specificity. The lung cohort included other lung cancer patients with prior treatment.

[0120] Since cancer-specific sequence alterations can be used to identify patients with cancer, it was evaluated whether combining DELFI with this method could improve the sensitivity of cancer detection ( Figure 20 ). After analyzing the cfDNA of a subset of untreated cancer patients using DELFI and targeted sequencing, it was found that the fragment maps were altered in 82% (103 out of 126) of the patients, while the sequences were altered in 66% (83 out of 126) of the patients. DELFI detected more than 89% of cases with mutant allele fraction values >1%, while the DELFI detection score for cases with mutant allele fraction values <1% was 80%, including those cases that could not be detected using targeted sequencing (Table 7; Appendix G). When these methods were used together, the sensitivity of the combined test increased to 91% (115 out of 126 patients), with a specificity of 98% ( Figure 20 ).

[0121] Overall, the whole-genome cfDNA fragment profiles differ between cancer patients and healthy individuals. The fragment lengths and coverages vary in a location-dependent manner across the entire genome, potentially explaining previously reported apparently contradictory observations in cfDNA analyses at specific loci or of total fragment sizes. In patients with cancer, the heterogeneous fragment patterns in cfDNA appear to result from a mixture of nucleosomal DNA in blood and neoplastic cells. This study provides a method that can simultaneously analyze dozens to hundreds of tumor-specific aberrations in trace amounts of cfDNA, thus obviating the limitations of the need for more sensitive analyses of cfDNA. Compared with previous cfDNA analysis methods that focused on sequence or overall fragment size, DELFI analysis detects a higher proportion of cancer patients (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 shown in this example, combining DELFI with the analysis of other cfDNA alterations can further improve the sensitivity of detection. Since the fragment profile appears to be related to the nucleosomal DNA pattern, DELFI can be used to determine the major source of tumor-derived cfDNA. By including clinical characteristics, other biomarkers (including methylation alterations), and other diagnostic methods, the identification of the origin of circulating tumor DNA in more than half of the analyzed patients can be further improved (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 method only requires a small amount of whole-genome sequencing without the need for deep sequencing typical of methods that focus on specific alterations. The performance characteristics and limited sequencing volume required by DELFI suggest that our method can be widely applied to the screening and management of cancer patients.

[0122] The results of this study indicate that the whole-genome cfDNA fragmentation profiles differ between cancer patients and healthy individuals. Therefore, cfDNA fragmentation profiles may be of great significance for future research and non-invasive methods for detecting human cancers.

[0123] Other embodiments

[0124] It should be understood that although the present invention has been described in conjunction with the detailed description of the present invention, the foregoing description is intended to illustrate rather than limit the scope of the present invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the appended claims.

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] Appendix - D: Summary of Table 4. Whole - Genome cfDNA Analysis

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

Claims

1. Use of a cell-free DNA (cfDNA) fragment map in preparing a material for identifying a mammalian subject with cancer, comprising: processing cfDNA fragments obtained from a sample obtained from the mammalian subject into a sequencing library; subjecting the sequencing library to low-coverage whole-genome sequencing to obtain sequencing reads; mapping the sequencing reads to a genome to obtain windows of mapped sequences; analyzing the windows of mapped sequences to determine cfDNA fragment lengths; using the lengths to determine the cfDNA fragment map; comparing the cfDNA fragment map with a reference cfDNA fragment map; and when the cfDNA fragment map obtained from the mammalian subject is different from the reference cfDNA fragment map, identifying the mammalian subject as having cancer.

2. The use according to claim 1, wherein the reference cfDNA fragment map is a cfDNA fragment map of a healthy mammalian subject.

3. The use according to claim 2, wherein, the reference cfDNA fragment map is generated by determining a cfDNA fragment map in a sample obtained from the healthy mammalian subject.

4. The use according to claim 1, wherein the reference DNA fragment pattern is a reference nucleosome cfDNA fragment map.

5. The use according to any one of claims 1 to 4, wherein, the cfDNA fragment map includes a median fragment size, and wherein the median fragment size of the cfDNA fragment map is shorter than the median fragment size of the reference cfDNA fragment map.

6. The use according to any one of claims 1 to 4, wherein, the cfDNA fragment map includes a fragment size distribution, and wherein the fragment size distribution of the cfDNA fragment map and the fragment size distribution of the reference cfDNA fragment map differ by at least 10 nucleotides.

7. The use according to any one of claims 1 to 4, wherein, the cfDNA fragment map includes a ratio of small cfDNA fragments to large cfDNA fragments in the mapped sequence windows, where small cfDNA fragments are 100 base pairs (bp) to 150 bp in length, where large cfDNA fragments are 151 bp to 220 bp in length, and wherein the correlation of the fragment ratios in the cfDNA fragment map is lower than the correlation of the fragment ratios in the reference cfDNA fragment map.

8. The use according to any one of claims 1 to 4, wherein, the cfDNA fragment map includes sequence coverage of small cfDNA fragments in windows across the genome.

9. The use according to any one of claims 1 to 4, wherein, the cfDNA fragment map includes sequence coverage of large cfDNA fragments in windows across the genome.

10. The use according to any one of claims 1 to 4, wherein, the cfDNA fragment map includes sequence coverage of small and large cfDNA fragments in windows across the genome.

11. Use according to any one of claims 1 to 4, wherein the cancer is selected from the group consisting of: colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma, and ovarian cancer.

12. Use according to claim 1, wherein the comparing step comprises comparing the cfDNA fragment map with a reference cfDNA fragment map across the entire genome.

13. Use according to claim 1, wherein the comparing step comprises comparing the cfDNA fragment map with a reference cfDNA fragment map within sub-genomic intervals.

14. Use according to any one of claims 1 to 4, wherein, the mammal has previously been administered a cancer treatment to treat the cancer.

15. Use according to claim 13, wherein the cancer treatment is selected from the group consisting of: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, and combinations thereof.

16. Use according to any one of claims 1 to 4, further comprising administering to the mammal a cancer treatment selected from the group consisting of: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, and combinations thereof.

17. Use according to claim 16, wherein after administering the cancer treatment, the mammal is monitored for the presence of cancer.

18. Use according to any one of claims 1 to 4, further comprising identifying one or more cancer-specific sequence alterations in the sample.

19. Use according to any one of claims 1 to 4, further comprising identifying one or more chromosomal abnormalities in the sample.

20. Use according to claim 19, wherein the one or more chromosomal abnormalities comprise copy number variations in one or more chromosomal arms.

21. Use of a cell-free DNA (cfDNA) fragment map in the preparation of a material for identifying a cancer origin tissue in a mammal identified as having cancer, comprising: processing cfDNA fragments obtained from a sample obtained from the mammal into a sequencing library; subjecting the sequencing library to low-coverage whole-genome sequencing to obtain sequencing fragments; mapping the sequencing fragments to the genome to obtain windows of mapped sequences; analyzing the windows of mapped sequences to determine cfDNA fragment lengths; using the lengths to determine the cfDNA fragment map; comparing the cfDNA fragment map with a reference cfDNA fragment map; and identifying the origin tissue of the cancer in the mammal when the cfDNA fragment map obtained from the mammal matches a reference cfDNA fragment map from a mammal with the same origin tissue identified as having cancer.

22. The use according to claim 21, wherein the reference cfDNA fragment map comprises a reference cfDNA fragment map from a mammal identified as having one or more of colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma, and ovarian cancer.

23. The use according to claim 22, wherein the reference cfDNA fragment map is generated from a cfDNA fragment map in a sample from a mammal identified as having one or more of colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma, and ovarian cancer.

24. The use according to claim 21, wherein the reference DNA fragment pattern is a reference nucleosome cfDNA fragment map.

25. The use according to any one of claims 21 to 24, wherein, the cfDNA fragment map comprises a median fragment size, and wherein the median fragment size of the cfDNA fragment map is shorter than the median fragment size of the reference cfDNA fragment map.

26. The use according to any one of claims 21 to 24, wherein, the cfDNA fragment map comprises a fragment size distribution, and wherein a fragment size distribution of the cfDNA fragment map differs from a fragment size distribution of the reference cfDNA fragment map by at least 10 nucleotides.

27. The use according to any one of claims 21 to 24, wherein the cfDNA fragment map comprises a ratio of small cfDNA fragments to large cfDNA fragments in the mapping sequence window, wherein the small cfDNA fragments are 100 base pairs (bp) to 150 bp in length, wherein the large cfDNA fragments are 151 bp to 220 bp in length, and wherein the correlation of the fragment ratios in the cfDNA fragment map is lower than the correlation of the fragment ratios in the reference cfDNA fragment map.

28. The use according to any one of claims 21 to 24, wherein, the cfDNA fragment map comprises the sequence coverage of small cfDNA fragments in a window across the genome.

29. The use according to any one of claims 21 to 24, wherein, the cfDNA fragment map comprises the sequence coverage of large cfDNA fragments in a window across the genome.

30. The use according to any one of claims 21 to 24, wherein, the cfDNA fragment map comprises the sequence coverage of small and large cfDNA fragments in a window across the genome.

31. The use according to any one of claims 21 to 24, wherein the cancer is selected from the group consisting of: colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma, and ovarian cancer.

32. The use according to claim 21, wherein the comparing step comprises comparing the cfDNA fragment map with the reference cfDNA fragment map across the entire genome.

33. The use according to claim 21, wherein the comparing step comprises comparing the cfDNA fragment map with the reference cfDNA fragment map within a sub-genomic interval.

34. The use according to any one of claims 21 to 24, further comprising identifying one or more cancer-specific sequence alterations in the sample.

35. The use according to any one of claims 21 to 24, further comprising identifying one or more chromosomal abnormalities in the sample.

36. The use according to claim 35, wherein the one or more chromosomal abnormalities include copy number variations in one or more chromosomal arms.

37. Use of a cell-free DNA (cfDNA) fragment map in the preparation of a material for treating a mammal suffering from cancer, comprising: processing cfDNA fragments obtained from a sample obtained from the mammal into a sequencing library; subjecting the sequencing library to low-coverage whole-genome sequencing to obtain sequencing fragments; mapping the sequencing fragments to a genome to obtain windows of mapped sequences; analyzing the windows of the mapped sequences to determine cfDNA fragment lengths; using the lengths to determine the cfDNA fragment map; and comparing the cfDNA fragment map with a reference cfDNA fragment map, wherein an increased variability of the cfDNA fragment map obtained from the mammal as compared to the reference cfDNA fragment map indicates that the mammal has cancer; and treating the mammal for cancer.

38. The use according to claim 37, wherein, the mammal is a human.

39. The use according to claim 37, wherein the cancer is selected from the group consisting of: colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, cholangiocarcinoma, and ovarian cancer.

40. The use according to any one of claims 37 to 39, wherein the cancer treatment is selected from the group consisting of: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, and combinations thereof.

41. The use according to any one of claims 37 to 39, wherein the reference cfDNA fragment map is a cfDNA fragment map of a healthy mammal.

42. The use according to claim 41, wherein, the reference cfDNA fragment map is generated by determining the cfDNA fragment map in a sample obtained from the healthy mammal.

43. The use according to any one of claims 37 to 39, wherein the reference DNA fragment pattern is a reference nucleosome cfDNA fragment map.

44. The use according to any one of claims 37 to 39, wherein, the cfDNA fragment map includes a median fragment size, and wherein the median fragment size of the cfDNA fragment map is shorter than the median fragment size of the reference cfDNA fragment map.

45. The use according to any one of claims 37 to 39, wherein, the cfDNA fragment map includes a fragment size distribution, and wherein a fragment size distribution of the cfDNA fragment map differs from a fragment size distribution of the reference cfDNA fragment map by at least 10 nucleotides.

46. Use according to any one of claims 37 to 39, wherein the cfDNA fragment profile comprises a ratio of small cfDNA fragments to large cfDNA fragments in the mapping sequence window, wherein a small cfDNA fragment is 100 base pairs (bp) to 150 bp in length, wherein a large cfDNA fragment is 151 bp to 220 bp in length, and wherein the correlation of the fragment ratios in the cfDNA fragment profile is lower than the correlation of the fragment ratios in the reference cfDNA fragment profile.

47. Use according to any one of claims 37 to 39, wherein, the cfDNA fragment profile comprises the sequence coverage of small cfDNA fragments in a window across the entire genome.

48. Use according to any one of claims 37 to 39, wherein, the cfDNA fragment profile comprises the sequence coverage of large cfDNA fragments in a window across the entire genome.

49. Use according to any one of claims 37 to 39, wherein, the cfDNA fragment profile comprises the sequence coverage of small and large cfDNA fragments in a window across the entire genome.

50. Use according to any one of claims 37 to 39, wherein the comparing step comprises comparing the cfDNA fragment profile with a reference cfDNA fragment profile across the entire genome.

51. Use according to any one of claims 37 to 39, wherein the comparing step comprises comparing the cfDNA fragment profile with a reference cfDNA fragment profile within a sub-genomic interval.

52. Use according to any one of claims 37 to 39, wherein the mammal has previously been administered cancer treatment to treat the cancer.

53. Use according to claim 52, wherein the cancer treatment is selected from the group consisting of: surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, and combinations thereof.

54. Use according to any one of claims 37 to 39, wherein after administering the cancer treatment, the mammal is monitored for the presence of cancer.

55. Use according to any one of claims 1, 21, and 37, wherein, the mapping sequence comprises dozens to thousands of windows.

56. Use according to any one of claims 1, 21, and 37, wherein, the windows are non-overlapping windows.

57. Use according to any one of claims 1, 21, and 37, wherein, each window comprises 5 million base pairs.

58. Use according to any one of claims 1, 21, and 37, wherein the cfDNA fragment profile is determined within each window.

59. Use according to any one of claims 1, 21, and 37, wherein, the cfDNA fragment profile comprises a median fragment size.

60. Use according to any one of claims 1, 21, and 37, wherein, the cfDNA fragment profile comprises a fragment size distribution.

61. The use according to any one of claims 1, 21, and 37, wherein the cfDNA fragment map comprises the ratio of small cfDNA fragments to large cfDNA fragments in the mapping sequence window.

62. The use according to any one of claims 1, 21, and 37, wherein, the cfDNA fragment map comprises the sequence coverage of small cfDNA fragments in a window across the entire genome.

63. The use according to any one of claims 1, 21, and 37, wherein, the cfDNA fragment map comprises the sequence coverage of large cfDNA fragments in a window across the entire genome.

64. The use according to any one of claims 1, 21, and 37, wherein, the cfDNA fragment map comprises the sequence coverage of small and large cfDNA fragments in a window across the entire genome.

65. The use according to any one of claims 1, 21, and 37, wherein, the cfDNA fragment map is across the entire genome.

66. The use according to any one of claims 1, 21, and 37, wherein, the cfDNA fragment map spans sub-genomic intervals.

Images