Use of the cell-free DNA fragmentome in the diagnostic evaluation of patients with signs and symptoms suggestive of cancer

Abstract

The present disclosure provides a method of use thereof for improved diagnostic use, using genome-wide patterns of fragmented cell-free DNA (cfDNA) from plasma (derived by low-coverage whole-genome sequencing) analyzed in combination with specific clinical and demographic characteristics of individual patients. In particular, the present invention provides a novel and effective method for confirming the presence or absence of cancer in individual patients already suspected of having cancer.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims the benefit of priority under 35 U.S.C.§119(e) of U.S. Provisional Patent Application No. 63 / 341,323, filed on May 12, 2022. The disclosure of the prior application is considered a part of the disclosure of this application and is hereby incorporated by reference in its entirety.

[0002] Field of the Invention The present invention generally relates to the diagnosis of cancer, and more specifically, to the analysis of genome - wide patterns of fragmented cell - free DNA (cfDNA) in combination with the clinical and demographic characteristics of individual patients.

Background Art

[0003] Background An individual may have symptoms or abnormal imaging results that suggest but are not conclusive for a particular type of cancer. For example, an individual, especially one with a smoking history, may have symptoms or abnormal chest imaging results that suggest but are not conclusive for lung cancer. For these individuals, the diagnostic work - up becomes complex and the diagnosis may be delayed. A delayed definitive diagnosis can lead to patient anxiety and treatment delays, which can have an adverse impact on the clinical outcome, especially for patients who could benefit from surgery.

[0004] The reasons for the delay in diagnosis are diverse and are often related to the non-specific nature of the symptoms of lung cancer. Patients with advanced lung cancer may present with severe symptoms that lead to a definitive diagnosis, while patients with early-stage lung cancer may not have symptoms severe enough to prompt them to seek medical attention or may have common symptoms (such as chest tightness and pain, cough, fatigue, shortness of breath, and weight loss) that are easily confused with chronic diseases or benign conditions. As a result, patients may receive unnecessary treatment before being evaluated for lung cancer, which can lengthen the time to a diagnosis. While these reasons can affect any given patient, the complex pathway to diagnosis can be particularly harmful to groups that are already at a suboptimal stage of lung cancer based on race / ethnicity, socioeconomic status, and / or gender.

[0005] Recognizing the need to improve the diagnostic pathway for lung cancer, medical societies around the world have developed guidelines for the diagnostic workup of patients with non-specific respiratory symptoms or suspected lung cancer (Table 3). The guidelines are somewhat consistent in terms of which signs and symptoms should trigger a follow-up procedure (e.g., chest pain, cough, fatigue, clubbing, hemoptysis, shortness of breath, and weight loss), but they differ in terms of the specific triggering signs / symptoms, the type of patient in whom the signs / symptoms occur, and what follow-up should be done. These variations in the guidelines are thought to reflect the limited value of these symptoms as predictors of lung cancer, either alone or in combination. SUMMARY OF THE INVENTION

[0006] The present invention is based on the unique discovery that cancer diagnosis can be improved by characterizing the genome-wide pattern of fragmentation of cell-free DNA (cfDNA) in plasma using low-coverage whole-genome sequencing when analyzed in combination with the specific clinical and demographic characteristics of an individual patient.

[0007] In one embodiment, the present invention is A step of processing cfDNA fragments from a sample obtained from a subject and generating a sequencing library; A step of subjecting the sequencing library to whole genome sequencing to obtain sequenced fragments, wherein the genomic coverage is about 9X to 0.1X; A step of mapping the sequenced fragments to the genome to obtain genomic intervals of the mapped sequences; A step of analyzing genomic intervals of the mapped sequences to determine the length and amount of the cfDNA fragments in order to establish a composite cfDNA fragmentation profile using the length and amount of the cfDNA fragments; A step of analyzing one or more demographic or clinical characteristics from the subject associated with the type of cancer identified; and A step of detecting a composite cfDNA fragmentation profile based on length and amount that is variable compared to a reference cfDNA fragmentation profile from a healthy subject To provide a method for which an increase in the variability of the cfDNA fragmentation profile and the presence of the one or more demographic or clinical characteristics indicate that the subject has cancer of the type.

[0008] In some embodiments, the genomic intervals of the mapped sequences do not overlap. In certain embodiments, each of the genomic intervals contains thousands to millions of base pairs.

[0009] In a further embodiment, the cfDNA fragmentation profile is determined within each genomic interval. In some such embodiments, the cfDNA fragmentation profile includes a median fragment size. In a further embodiment, the cfDNA fragmentation profile includes a fragment size distribution.

[0010] In additional embodiments, the type of cancer identified is selected from the group consisting of head and neck cancer, lung cancer, breast cancer, esophageal cancer, gastric cancer, bile duct cancer, liver cancer, pancreatic cancer, colorectal cancer, kidney cancer, bladder cancer, ovarian cancer, and endometrial cancer. In certain such embodiments, the type of cancer identified is lung cancer.

[0011] In more embodiments, the one or more clinical features are selected from the group consisting of pain, unintended weight loss, fever, fatigue, skin changes, shortness of breath, cough, hoarseness, difficulty swallowing, abnormal bleeding, anemia, bowel or urinary tract changes, or a lump or bump anywhere on the subject's body.

[0012] In more embodiments, the one or more demographic features are selected from the group consisting of age, gender, and smoking status.

[0013] In a further embodiment, a subject identified as having the type of cancer is administered a therapeutic agent suitable for the treatment of the type of cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0014]

Figure 1

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Figure 6

DETAILED DESCRIPTION

[0015] Detailed Description The present invention is based on the innovative discovery that cancer diagnosis can be improved by characterizing genome-wide patterns of fragmentation of cell-free DNA (cfDNA) in plasma using low-coverage whole-genome sequencing when analyzed in combination with specific clinical and demographic characteristics of individual patients.

[0016] Described herein is processing cfDNA fragments from a sample obtained from a subject to generate a sequencing library; subjecting the sequencing library to whole-genome sequencing to obtain sequenced fragments, wherein the genomic coverage is from about 9X to 0.1X; mapping the sequenced fragments to a genome to obtain genomic intervals of the mapped sequences; analyzing the genomic intervals of the mapped sequences to determine the length and amount of the cfDNA fragments in order to establish a composite cfDNA fragmentation profile using the length and amount of the cfDNA fragments; analyzing one or more demographic or clinical characteristics from the subject that are associated with the type of cancer identified; and detecting a composite cfDNA fragmentation profile based on length and amount that is variable compared to a reference cfDNA fragmentation profile from a healthy subject A method for, an increase in the variability of the cfDNA fragmentation profile and the presence of the one or more demographic or clinical characteristics indicating that the subject has the type of cancer.

[0017] In some embodiments, the genomic intervals of the mapped array do not overlap. In certain embodiments, each of the genomic intervals comprises thousands to millions of base pairs.

[0018] In further embodiments, the cfDNA fragmentation profile is determined within each genomic interval. In some such embodiments, the cfDNA fragmentation profile includes the median fragment size. In further embodiments, the cfDNA fragmentation profile includes the fragment size distribution.

[0019] In additional embodiments, the type of cancer identified is selected from the group consisting of head and neck cancer, lung cancer, breast cancer, esophageal cancer, gastric cancer, bile duct cancer, liver cancer, pancreatic cancer, colorectal cancer, kidney cancer, bladder cancer, ovarian cancer, and endometrial cancer. In certain such embodiments, the type of cancer identified is lung cancer.

[0020] In more embodiments, the one or more clinical features are selected from the group consisting of pain, unintentional weight loss, fever, fatigue, skin changes, shortness of breath, cough, hoarseness, difficulty swallowing, abnormal bleeding, anemia, bowel or urinary tract changes, or a lump or swelling anywhere on the subject's body.

[0021] In more embodiments, the one or more demographic features are selected from the group consisting of age, gender, and smoking status.

[0022] In further embodiments, a subject identified as having the type of cancer is administered a therapeutic agent suitable for the treatment of the type of cancer.

[0023] Before describing the compositions and methods of the present invention, it is to be understood that the present invention is not limited to the specific methods and systems described, and such methods and systems may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting, and the scope of the present invention is limited only by the appended claims.

[0024] As used in this specification and the appended claims, the singular forms "a", "an", and "the" include plural references unless the context clearly dictates otherwise. Thus, for example, reference to "the method" includes one or more methods and / or steps of the kind described herein and will be apparent to those skilled in the art upon reading this disclosure.

[0025] Unless defined otherwise, 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. Any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the invention, but the preferred methods and materials are described herein.

[0026] Exemplary Hardware Implementations FIG. 6 shows an exemplary computer 800 that may be used to implement the methods described herein. For example, computer 800 may include a machine learning system that trains a machine learning model to generate the cfDNA fragmentation profiles, cancer diagnoses, or any combination thereof, or in some embodiments, a portion or combination thereof, described above. Computer 800 may be any electronic device that executes a software application derived from compiled instructions, including, but not limited to, a personal computer, server, smartphone, media player, electronic tablet, gaming console, email device, etc. In some implementations, computer 800 may include one or more processors 802, one or more input devices 804, one or more display devices 806, one or more network interfaces 808, and one or more computer-readable media 812. Each of these components may be coupled by a bus 810, and in some embodiments, these components may be distributed across multiple physical locations and coupled by a network.

[0027] The display device 806 may include any known display technology, including but not limited to a liquid crystal display (LCD) or a light emitting diode (LED) technology. The processor(s) 802 may use any known processor technology, including but not limited to a graphics processor and a multi-core processor. The input device 804 may be any known input device technology, including but not limited to a keyboard (including a virtual keyboard), a mouse, a trackball, a camera, and a touch sensing pad or display. The bus 810 may be any known internal or external bus technology, including but not limited to ISA, EISA, PCI, PCI Express, USB, Serial ATA, or FireWire. The computer-readable medium 812 may be any non-transitory medium involved in providing instructions to the processor(s) 804 for execution, including but not limited to a non-volatile storage medium (e.g., optical disk, magnetic disk, flash drive, etc.) or a volatile medium (e.g., SDRAM, ROM, etc.).

[0028] The computer-readable medium 812 may include various instructions 814 for implementing an operating system (e.g., Mac OS®, Windows®, Linux). The operating system may be multi-user, multi-processing, multi-tasking, multi-threaded, and real-time, etc. The operating system may perform basic tasks including but not limited to recognizing input from the input device 804, sending output to the display device 806, tracking files and directories on the computer-readable medium 812, controlling peripheral devices (e.g., disk drive, printer, etc.) that can be controlled directly or via an I / O controller, and managing traffic on the bus 810. The network communication instructions 816 may establish and maintain a network connection (e.g., software for implementing communication protocols such as TCP / IP, HTTP, Ethernet, telephone, etc.).

[0029] The machine learning instruction 818 may include an instruction that enables the computer 800 to function as a machine learning system and / or to train a machine learning model to generate DMS values as described herein. The application(s) 820 may be an application that uses or implements the processes described herein and / or other processes. The process may also be implemented in the operating system 814. For example, the application 820 and / or the operating system may create tasks within the application as described herein.

[0030] One or more computer programs executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to send data and instructions to, a data storage system, at least one input device, and at least one output device may implement the described features. A computer program is a series of instructions that can be used directly or indirectly within a computer to perform a particular activity or to cause a particular result. The computer program may be written in any form of programming language including compiled or interpreted languages (e.g., Objective-C, Java) and may be deployed in any form, such as as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0031] Suitable processors for the execution of a program of instructions may include, for example, both general and special purpose microprocessors of any kind of computer, and one of only a single processor or multiple processors or cores. Generally, a processor may receive instructions and data from read-only memory, random access memory, or both. Essential elements of a computer may include a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer may also include or be operably connected to communicate with one or more mass storage devices for storing data files. Such devices include magnetic disks such as internal hard disks and removable disks, magneto-optical disks, and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data may include any form of non-volatile memory including semiconductor memory devices comprising, for example, EPROM, EEPROM, and flash memory devices, magnetic disks such as internal hard disks and removable disks, magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and memory may be supplemented or incorporated by an ASIC (application specific integrated circuit).

[0032] To provide interaction with a user, the above features may be implemented on a computer having a display device such as an LED or LCD monitor for displaying information to the user, and a keyboard and pointing device such as a mouse or trackball by which the user can provide input to the computer.

[0033] In a computer system including backend components such as a data server, or including middleware components such as an application server or an Internet server, or including frontend components such as a graphical user interface or an Internet browser or a combination thereof, the above features may be implemented. The components of the system may be connected by any form or medium of digital data communication, such as a communication network. Examples of communication networks include telephone networks, LANs, WANs, and the computers and networks forming the Internet.

[0034] The computer system may include a client and a server. The client and the server may generally be remote from each other and typically may interact via a network. The relationship between the client and the server may be created by computer programs executed on respective computers and having a client-server relationship with each other.

[0035] One or more features or steps of the disclosed embodiments may be implemented using an application programming interface (API). The API may define one or more parameters passed between a calling application and other software code (e.g., an operating system, a library routine, a function) that provides a service, provides data, or performs an operation or calculation.

[0036] An API may be implemented as one or more calls within program code that send and receive one or more parameters via a parameter list or other structure based on the calling rules defined in the API specification document. The parameters may be constants, keys, data structures, objects, object classes, variables, data types, pointers, arrays, lists, or another call. The API calls and parameters may be implemented in any programming language. The programming language may define the vocabulary and calling rules used by a programmer to access functions that support the API.

[0037] In some implementations, the API calls may report to the application the capabilities of the device on which the application is executed, such as input capabilities, output capabilities, processing capabilities, power capabilities, communication capabilities, etc.

[0038] Although various embodiments have been described above, it should be understood that they are presented by way of example and not limitation. It will be apparent to those skilled in the art(s) that various changes in form and detail can be made without departing from the spirit and scope. Indeed, after reading the above specification, it will be apparent to those skilled in the relevant art(s) how to implement alternative embodiments. For example, other steps may be provided from or steps may be omitted from the described flow, and other components may be added to or removed from the described system. Accordingly, other implementations are within the scope of the following claims.

[0039] Furthermore, it should be understood that any diagrams highlighting functions and advantages are presented for illustrative purposes only. The disclosed methodologies and systems are each sufficiently flexible and configurable to be utilized in ways other than those shown.

[0040] The term "at least one" is often used in the specification, claims, and drawings, and terms such as "a", "an", "the", "said", etc. also mean "at least one" or "the at least one" in the specification, claims, and drawings.

[0041] Finally, under 35 U.S.C. § 112(f), it is applicant's intention that only claims containing the phrase "means for" or "step for" be construed. Claims not expressly containing the phrase "means for" or "step for" should not be construed under 35 U.S.C. § 112(f).

[0042] The methods and systems described herein are useful for detecting cancer in a subject and optionally treating the cancer subject. Any suitable subject, such as a mammal, can be evaluated and / or treated as described herein. Examples of some mammals that can be evaluated and / or treated as described herein include, but are not limited to, humans and primates such as monkeys, dogs, cats, horses, cows, pigs, sheep, mice, and rats. For example, a human having or suspected of having cancer can be evaluated using the methods described herein and optionally treated with one or more cancer treatments as described herein. The methods disclosed herein may also include administering to a subject identified as having a certain type of cancer a therapeutic agent suitable for treating that type of cancer.

[0043] A subject having any type of cancer that meets the criteria, or suspected of having cancer, can be evaluated and / or treated using the methods and systems described herein (e.g., by performing one or more cancer treatments on the subject). The cancer can be cancer at any stage. In some embodiments, the cancer can be cancer at an early stage. In some embodiments, the cancer can be asymptomatic cancer. In some embodiments, 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 types of cancer that can be evaluated and / or treated as described herein include, but are not limited to, lung cancer, colorectal cancer, prostate cancer, breast cancer, pancreatic cancer, bile duct cancer, liver cancer, CNS cancer, stomach cancer, esophageal cancer, gastrointestinal stromal tumor (GIST), uterine cancer, and ovarian cancer. Additional types of cancer include, but are not limited to, myeloma, multiple myeloma, B-cell lymphoma, follicular lymphoma, lymphocytic leukemia, leukemia, and myeloid leukemia. In some embodiments, the cancer is a solid tumor. In some embodiments, the cancer is a sarcoma, carcinoma, or lymphoma. In some embodiments, the cancer is lung cancer, colorectal cancer, prostate cancer, breast cancer, pancreatic cancer, bile duct cancer, liver cancer, CNS cancer, stomach cancer, esophageal cancer, gastrointestinal stromal tumor (GIST), uterine or ovarian cancer. In some embodiments, the cancer is a blood cancer. In some embodiments, the cancer is myeloma, multiple myeloma, B-cell lymphoma, follicular lymphoma, lymphocytic leukemia, leukemia, or myeloid leukemia.

[0044] When treating a subject having cancer or suspected of having cancer as described herein, one or more cancer treatments can be administered to the subject. The cancer treatment can be any suitable cancer treatment. The one or more cancer treatments described herein can be administered to the subject at any suitable frequency (e.g., once or multiple times over a period ranging from several days to several weeks). Examples of cancer treatments include, but are not limited to, surgical intervention, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive immune T cell therapy (e.g., T cells having chimeric antigen receptors and / or wild-type or modified T cells), 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, targeted therapies such as administration of immune checkpoint inhibitors, surgery (e.g., surgical resection), or any combination of the foregoing. In some embodiments, the cancer treatment can reduce the severity of the cancer, alleviate the symptoms of the cancer, and / or decrease the number of cancer cells present within the subject.

[0045] In some embodiments, the cancer treatment may be a chemotherapeutic agent. Non-limiting examples of chemotherapeutic agents include amsacrine, azacitidine, azathioprine, bevacizumab (or antigen-binding fragments thereof), bleomycin, busulfan, carboplatin, capecitabine, chlorambucil, cisplatin, cyclophosphamide, cytarabine, dacarbazine, daunorubicin, docetaxel, doxifluridine, doxorubicin, epirubicin, erlotinib hydrochloride, etoposide, fludarabine, floxuridine, fludarabine, fluorouracil, gemcitabine, hydroxyurea, idarubicin, ifosfamide, irinotecan, lomustine, mechlorethamine, melphalan, mercaptopurine, methotrexate, mitomycin, mitoxantrone, paclitaxel, pemetrexed, procarbazine, all-trans retinoic acid, streptozocin, tafurposide, temozolomide, teniposide, thioguanine, topotecan, uramustine, valrubicin, vinblastine, vincristine, vindesine, vinorelbine, and combinations thereof. Further examples of anti-cancer therapies are known in the art and can be found, for example, by referring to 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).

[0046] In one embodiment, the methods and systems described herein may also be used to monitor a subject having or suspected of having cancer as described herein. In some embodiments, the monitoring can be performed before, during, and / or after a course of cancer treatment. The monitoring methods provided herein can be used to determine the effectiveness of one or more cancer treatments and / or to select a subject for enhanced monitoring.

[0047] In some embodiments, monitoring can include conventional techniques capable of monitoring one or more cancer treatments (e.g., the effectiveness of one or more cancer treatments). In some embodiments, diagnostic tests (e.g., any of the diagnostic tests disclosed herein) can be performed on subjects selected for enhanced monitoring at a higher frequency compared to subjects not selected for enhanced monitoring. For example, diagnostic tests can be performed on subjects selected for enhanced monitoring twice daily, daily, bi-weekly, weekly, bi-monthly, monthly, quarterly, semi-annually, annually, or at any frequency therebetween.

[0048] In various embodiments, DNA is obtained from a subject and present in a biological sample used in the methodology of the present invention. The biological sample can be virtually any type of biological sample that contains DNA. The biological sample is typically a fluid such as whole blood or a portion thereof having circulating cfDNA. In embodiments, the sample is a tumor, or amniotic fluid, aqueous humor, vitreous humor, blood, whole blood, fractionated blood, plasma, serum, breast milk, cerebrospinal fluid (CSF), cerumen (ear wax), chyle, chyme, endolymph, perilymph, feces, exhaled breath, gastric acid, gastric juice, lymph, mucus (including nasal mucus and sputum), pericardial fluid, peritoneal fluid, pleural effusion, pus, eye mucus, saliva, exhaled breath condensate, sebum, semen, sputum, sweat, synovial fluid, tears, vomit, prostatic fluid, nipple aspirate fluid, sweat, buccal swab, cell lysate, gastrointestinal fluid, biopsy tissue, and urine or other body fluids, etc., but is not limited thereto, and includes DNA from a liquid biopsy. In one embodiment, the sample includes DNA from circulating tumor cells.

[0049] As disclosed above, the biological sample can be a blood sample. The blood sample can be obtained using methods known in the art such as finger prick or venipuncture. Suitably, the blood sample is about 0.1 - 20 ml, or about 1 - 15 ml, and the blood volume is about 10 ml. When using circulating free DNA in the blood, a smaller amount can be used. Microsampling and needle biopsy, sampling by catheter, excretion or production of body fluids containing DNA can also be potential sources of biological samples.

[0050] The methods and systems of the present disclosure utilize nucleic acid sequence information and can thus include any method or sequencing device for performing nucleic acid sequencing, including nucleic acid amplification, polymerase chain reaction (PCR), nanopore sequencing, 454 sequencing, tag-insertion sequencing. In some aspects, the methodology or system of the present disclosure utilizes systems such as those provided by Illumina, Inc. (including but not limited to HiSeq™ X10, HiSeq™ 1000, HiSeq™ 2000, HiSeq™ 2500, Genome Analyzers™, MiSeq™, NextSeq, NovaSeq 6000 systems), Applied Biosystems Life Technologies (SOLiD™ System, Ion PGM™ Sequencer, ion Proton™ Sequencer), or systems provided by Genapsys or BGI MGI and other systems. Nucleic acid analysis can also be performed by systems provided by Oxford Nanopore Technologies (GridiON™, MiniON™) or Pacific Biosciences (Pacbio™ RS II or Sequel I or II).

[0051] The present invention includes a system for performing the steps of the disclosed methods and is described in part with respect to functional components and various processing steps. Such functional components and processing steps may be implemented by any number of components, operations, and techniques configured to perform the specified functions and achieve various results. For example, the present invention may use various biological samples, biomarkers, elements, materials, computers, data sources, storage systems and media, information collection techniques and processes, data processing criteria, statistical analysis, and regression analysis that may exhibit various functions.

[0052] Accordingly, the present invention further provides a system for detecting, analyzing, and / or evaluating cancer. In various embodiments, the system includes (a) a sequencer configured to generate a low-coverage whole-genome sequencing data set of a sample, and (b) a computer system and / or processor having the function of performing the method of the present invention.

[0053] In some embodiments, the computer system further includes one or more additional modules. For example, the system may include one or more of an extraction and / or isolation unit operable to perform appropriate genetic component analysis, such as selecting cfDNA fragments of a particular size.

[0054] In some embodiments, the computer system further includes a visual display device. The visual display device may be operable to display a comparison of a curve fit line, a reference curve fit line, and / or both.

[0055] The methods of detection and analysis according to various embodiments of the present invention may be implemented in any suitable manner, for example, using a computer program operating on a computer system. As discussed herein, exemplary systems according to various embodiments of the present invention may be implemented in combination with a conventional computer system including a processor and random access memory, such as a remotely accessible application server, a network server, a personal computer, or a workstation. The computer system may also appropriately include additional memory devices or information storage systems, such as a mass storage system and a user interface, for example, a conventional monitor, keyboard, and tracking device. However, the computer system may include any suitable computer system and related equipment and may be configured in any suitable manner. In one embodiment, the computer system includes a stand-alone system. In another embodiment, the computer system is part of a network of computers including a server and a database.

[0056] The software necessary for information reception, processing, and analysis may be implemented in a single device or in multiple devices. The software may also be accessible via a network so that information storage and processing are performed remotely with respect to the user. Systems and various elements thereof according to various aspects of the present invention provide functions and operations that facilitate detection and / or analysis such as data collection, processing, analysis, reporting, and / or diagnosis. For example, in this aspect, a computer system may execute a computer program capable of receiving, storing, retrieving, analyzing, and reporting information regarding a human genome or a region thereof. The computer program may include a plurality of modules that perform various functions or operations, such as a processing module that processes raw data and generates supplementary data, and an analysis module that analyzes the raw data and the supplementary data to generate a quantitative evaluation of a disease state model and / or diagnostic information.

[0057] The procedures performed by the system may include any suitable process for facilitating analysis and / or cancer diagnosis. In one embodiment, the system is configured to establish a disease state model and / or determine the disease state in a patient. Determining or identifying the state of a disease may include performing a diagnosis, providing information useful for diagnosis, evaluating the stage or progression of the disease, indicating sensitivity to the disease, identifying whether further tests are recommended, predicting and / or evaluating the effectiveness of one or more treatment programs, or otherwise evaluating the patient's disease state, the likelihood of disease, or other aspects of health, including generating any useful information regarding the patient's medical condition with respect to the disease.

[0058] The following examples are provided to further illustrate the advantages and features of the present invention, but are not intended to limit the scope of the present invention. This example is typical of those that may be used, but alternatively, other procedures, methods, or techniques known to those skilled in the art may be used.

Example

[0059] Example 1 Use of a DELFI - circulating cfDNA - based approach for cancer detection for the diagnostic evaluation of patients with symptoms of lung cancer Exemplary results are presented, demonstrating that these methods can distinguish between lung cancer and non - cancer in a high - risk cohort presenting well - established signs and symptoms suggestive of lung cancer.

[0060] Participants were adults (LUCAS cohort) referred to the Department of Respiratory Medicine at Bispebjerg Hospital, Copenhagen, Denmark, in whom positive images were found on chest X - ray or chest CT scan. Mathios, D., et al., Detection and characterization of lung cancer using cell - free DNA fragmentomes. Nat Commun. 2021 Aug 20;12(1):5060. Information on presenting symptoms (s) was extracted from the reason for referral as documented in the medical records. Individuals diagnosed with cancer or with known active disease, individuals receiving treatment at the time of enrollment, or individuals determined to have metastases of non - lung origin were excluded. The study was conducted over the period from September 2012 to March 2013, and follow - up of participants continued until either death or April 2020, whichever came first. All participants provided written informed consent. The study was conducted in accordance with the Helsinki Declaration and approved by the Danish Regional Ethics Committee and the Danish Data Protection Agency.

[0061] Study design Blood samples were collected from each participant at the first clinic visit before diagnosis. Samples were processed within 2 hours after collection and stored at -80°C prior to cfDNA analysis using the DELFI approach as described above [Mathios Nat Commun 2021]. Briefly, cfDNA was isolated from 2 - 4 mL of plasma and stored at -80°C. Next-generation sequencing genomic libraries were prepared in batches from cfDNA of individuals with lung cancer and individuals without lung cancer. Libraries were sequenced at 1 - 2X coverage per genome using 100 bp paired-end reads on an Illumina HiSeq2500 platform (Illumina, Inc., San Diego, CA). A machine learning prediction model was used to detect cancer based on the composition of large-scale epigenetic signals of sequenced cfDNA fragments spanning 2.4 GB of the autosomal genome in lung cancer patients and non-cancer control patients (Figure 4).

[0062] Statistical analysis Demographic and clinical characteristics were explained by cancer status. The χ 2 test was used for comparison of categorical variables, and the Student's t-test was used for comparison of continuous variables. The median DELFI score was reported by lung cancer outcome, number of reported symptoms, and reported individual symptoms, and the distribution of DELFI scores was visualized by the same subgroups. The Kruskall Wallis χ 2 test was used to compare the distribution of DELFI scores. Receiver operating curves were constructed from logistic regression models that evaluated the association between individual reported symptoms (regardless of the presence or absence of the DELFI score) and lung cancer outcome. The area under the curve and 95% confidence intervals were estimated for each receiver operating curve.

[0063] Results The LUCAS cohort included 356 participants, of whom 346 were included in these analyses, 114 had lung cancer, and 232 had no cancer (Figure 4). The remaining 19 patients had lung metastases from primary tumors originating in tissues other than the lung and were excluded from the current analysis. The demographic and clinical characteristics of the LUCAS cohort are shown in Table 4. Both the lung cancer cohort and the non-cancer cohort had always had signs and symptoms associated with lung cancer before diagnosis, and the two groups had a balanced distribution of the number of signs and symptoms.

[0064] The median DELFI score was significantly higher in the group with lung cancer than in the group without cancer (0.871 vs. 0.203, p <.001). Of the 232 without cancer, 67 were confirmed to have benign lung nodules based on abnormal chest imaging results. The median DELFI score was similar in non-cancer patients with benign nodules and non-cancer patients without benign nodules (0.208 vs. 0.198, p =.299).

[0065] For each symptom, the median DELFI score was significantly higher in individuals with lung cancer with that symptom than in individuals without cancer with that symptom (Figure 4, Table 1).

[0066]

Table 1

[0067] The median DELFI score ranged from 0.926 to 1.000 in the group with cancer and from 0.146 to 0.253 in the group without cancer according to symptoms. When the cohort was stratified by the number of symptoms, the median DELFI score was significantly higher in lung cancer patients than in patients without lung cancer across each stratum of the number of symptoms (Figure 2, Table 2). The median DELFI score ranged from 0.604 to 1.000 in the group with cancer and from 0.140 to 0.212 in the group without cancer according to the number of symptoms.

[0068]

Table 2

[0069] In the logistic regression model of composite symptoms for predicting the presence of lung cancer, the area under the curve was 0.62 (95% confidence interval, 0.55 - 0.68, Figure 3). Adding the DELFI score to the model with composite symptoms improved the area under the curve to 0.89 (95% confidence interval, 0.85 - 0.93, Figure 3).

[0070] Discussion Here, the inventors demonstrate the potential value of the DELFI approach for detecting lung cancer in a cohort being evaluated with signs or symptoms suggestive of lung cancer. The DELFI platform distinguished between lung cancer and non-cancer by analyzing the fragmentation pattern of cfDNA present in plasma in relatively high-risk individuals, i.e., individuals with positive imaging results and various suspicious symptoms. The inventors' findings provide proof-of-concept evidence that the use of the DELFI approach in the diagnostic workup of symptomatic individuals can shorten the time to diagnosis. Among groups with high-risk characteristics, the DELFI score was able to further classify individuals with relatively high and low likelihoods of lung cancer. Those with a high likelihood of lung cancer could be instructed to undergo LDCT screening and subsequent biopsy more urgently. Additional investigation of the performance of the DELFI approach (e.g., specificity, sensitivity, positive predictive value, and negative predictive value) is required to define the clinical utility of DELFI-based tests for such decision-making.

[0071] The DELFI approach accurately classified benign lung nodules as non-cancer. Lung nodules are a common incidental finding on chest imaging. Lung nodules are often benign, but additional workup such as imaging scans or biopsies is required, all of which carry a risk of harm to the patient. Classification of nodules as benign by the DELFI score has the potential to reduce the number of unnecessary procedures many people undergo.

[0072] The inventors' research demonstrated the feasibility of applying the DELFI approach to archived blood specimens systematically collected and processed from individuals receiving care in routine medical practice.

[0073] In summary, genome-wide analysis of cfDNA fragmentation patterns by the DELFI approach is a novel, non-invasive, and potentially useful tool that can assist in the diagnosis of lung cancer in individuals with suggestive signs and symptoms. By identifying individuals with a high (or low) likelihood of having lung cancer, the use of the DELFI approach in these ambiguous scenarios enables more informed and timely referrals for additional imaging or biopsies. This approach has the potential to reduce the number of unnecessary procedures and shorten the time to diagnostic resolution for many people. Further investigation of the DELFI approach is warranted.

[0074]

Table 3

[0075]

Table 4

[0076] Although the present invention has been described with reference to presently preferred embodiments, it should be understood that various changes can be made without departing from the spirit of the invention.

Claims

1. as follows: processing cfDNA fragments from a sample obtained from a subject to generate a sequencing library; subjecting the sequencing library to whole genome sequencing to obtain sequenced fragments, wherein the genomic coverage is from about 9X to 0.1X; mapping the sequenced fragments to a genome to obtain genomic intervals of the mapped sequences; analyzing the genomic intervals of the mapped sequences to determine the length and amount of the cfDNA fragments in order to establish a composite cfDNA fragmentation profile using the length and amount of the cfDNA fragments; analyzing one or more demographic or clinical characteristics from the subject associated with the type of cancer identified; detecting a composite cfDNA fragmentation profile based on length and amount that is variable compared to a reference cfDNA fragmentation profile from a healthy subject; comprising an increase in the variability of the cfDNA fragmentation profile and the presence of the one or more demographic or clinical characteristics indicating that the subject has cancer of the type; method.

2. The method according to claim 1, wherein the genomic intervals do not overlap.

3. The method according to claim 1, wherein each of the genomic intervals contains thousands to millions of base pairs.

4. The method according to claim 1, wherein the cfDNA fragmentation profile is determined within each genomic interval.

5. The method according to claim 1, wherein a cfDNA fragmentation profile includes a median fragment size.

6. The method according to claim 1, wherein the cfDNA fragmentation profile comprises a fragment size distribution. **Claim 7** The method according to claim 1, wherein the cancer is selected from the group consisting of head and neck cancer, lung cancer, breast cancer, esophageal cancer, gastric cancer, bile duct cancer, liver cancer, pancreatic cancer, colorectal cancer, kidney cancer, bladder cancer, ovarian cancer, and endometrial cancer. **Claim 8** The method according to claim 7, wherein the type of cancer is lung cancer. **Claim 9** The method according to claim 7, wherein the one or more clinical features are selected from the group consisting of pain, unintentional weight loss, fever, fatigue, skin changes, dyspnea, cough, hoarseness, dysphagia, abnormal bleeding, anemia, bowel or urinary tract changes, or a lump or mass anywhere in the subject's body. **Claim 10** The method according to claim 7, wherein the one or more demographic features are selected from the group consisting of age, gender, and smoking status. **Claim 11** The method according to claim 1, further comprising administering to the subject identified as having the type of cancer a therapeutic agent suitable for the treatment of the type of cancer.

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