Diagnostic assessment of patients with signs and symptoms prompting cancer using sets of free DNA fragments

Abstract

The present disclosure provides methods and uses thereof for improving diagnostic applications using a whole genome pattern of fragmented free DNA (cfDNA) from plasma obtained by low coverage whole genome sequencing and analyzed in conjunction with certain clinical and demographic characteristics of an individual patient. In particular, the present invention provides novel and effective methods for confirming the presence or absence of cancer in an individual patient suspected of having cancer.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Serial No. 63 / 341,323, filed on May 12, 2022. The disclosure of the prior application is considered part of the disclosure of the present application and is incorporated by reference in its entirety into the disclosure of the present application. Technical Field

[0003] The present invention relates generally to the diagnosis of cancer, and more particularly to the analysis of genome-wide patterns of fragmented cell-free DNA (cfDNA) and clinical and demographic characteristics of individual patients. Background Art

[0004] Individuals may present with symptoms or have abnormal imaging results that are suggestive of certain types of cancer but are not definitive. For example, individuals, especially those with a history of smoking, may present with symptoms or have abnormal chest imaging results that are suggestive of lung cancer but are not definitive. For these individuals, the diagnostic workup may be complicated, delaying resolution of the diagnosis. Delays in definitive diagnosis may lead to patient anxiety and delayed treatment, which may adversely affect clinical outcomes, especially for those who may benefit from surgery.

[0005] The reasons for delayed diagnosis are numerous and often related to the nonspecific nature of lung cancer symptoms. Patients with advanced lung cancer may present with severe symptoms that quickly lead to a definitive diagnosis, but patients with early-stage lung cancer may have general symptoms (e.g., pain, cough, fatigue, shortness of breath, and weight loss) that are not alarming enough to warrant seeking medical care or that may be easily mistaken for a chronic or benign condition. As a result, patients may undergo unnecessary treatment before being evaluated for lung cancer, prolonging the time to diagnostic resolution. While these reasons may affect any particular patient, the often complex diagnostic pathway may be particularly harmful to groups that already face suboptimal lung cancer outcomes based on race / ethnicity, socioeconomic status, and / or gender.

[0006] Recognizing the need for improved diagnostic pathways for lung cancer, medical societies around the world have developed guidelines for the diagnostic workup of patients with nonspecific respiratory symptoms or suspected lung cancer (Table 3). Although the guidelines agree to some extent on which signs and symptoms should trigger follow-up procedures (e.g., chest pain, cough, fatigue, clubbing, hemoptysis, shortness of breath, and weight loss), they differ in the specific triggering signs / symptoms, the types of patients who present with signs / symptoms, and the follow-up that should be performed. These variations in the guidelines may reflect the limited value of these symptoms, alone or in combination, as predictors of lung cancer. Summary of the invention

[0007] The present invention is based on the groundbreaking discovery that characterizing genome-wide patterns of fragmentation of cell-free DNA (cfDNA) in plasma using low-coverage whole-genome sequencing can improve cancer diagnosis when analyzed in conjunction with certain clinical and demographic characteristics of individual patients.

[0008] In one embodiment, the present invention provides a method for: processing cfDNA fragments from a sample obtained from a subject and generating a sequencing library; subjecting the sequencing library to whole genome sequencing to obtain sequenced fragments, wherein the genome coverage is about 9× to 0.1×; mapping the sequenced fragments to the genome to obtain genomic intervals of the mapped sequences; analyzing the genomic intervals of the mapped sequences to determine the cfDNA fragment lengths and amounts, thereby establishing a composite cfDNA fragmentation profile using the cfDNA fragment lengths and amounts; analyzing one or more demographic or clinical characteristics associated with the type of cancer to be identified from the subject; and detecting the composite cfDNA fragmentation profile based on variable lengths and amounts relative to a reference cfDNA fragmentation profile from a healthy subject, wherein increased variability in the cfDNA fragmentation profile and the presence of one or more demographic or clinical characteristics indicate that the subject suffers from the cancer type.

[0009] In some aspects, the genomic intervals of the mapped sequences are non-overlapping. In certain aspects, the genomic intervals each include thousands to millions of base pairs.

[0010] In further aspects, the cfDNA fragmentation profile is determined within each genomic interval. In some such aspects, the cfDNA fragmentation profile includes a median fragment size. In further aspects, the cfDNA fragmentation profile includes a fragment size distribution.

[0011] In other aspects, the cancer type to be identified is selected from the group consisting of: head and neck cancer, lung cancer, breast cancer, esophageal cancer, stomach cancer, bile duct cancer, liver cancer, pancreatic cancer, colorectal cancer, kidney cancer, bladder cancer, ovarian cancer and endometrial cancer. In some such aspects, the cancer type to be identified is lung cancer.

[0012] In further aspects, the one or more clinical characteristics are selected from the group consisting of pain, involuntary weight loss, fever, fatigue, skin changes, difficulty breathing, cough, hoarseness, difficulty swallowing, abnormal bleeding, anemia, changes in bowel or urinary habits, or swelling or mass anywhere in the subject's body.

[0013] In further aspects, the one or more demographic characteristics are selected from the group consisting of: age, gender, and smoking status.

[0014] In a further aspect, a subject identified as having a type of cancer is administered a therapeutic agent suitable for treating that type of cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It shows that for any given symptom reported, individuals with lung cancer have higher median DELFI scores than individuals without cancer.

[0016] Figure 2 It shows that for the number of symptoms reported, individuals with lung cancer have higher median DELFI scores than individuals without cancer.

[0017] Figure 3 The disclosed method using the DELFI score was demonstrated to predict lung cancer better than symptoms alone.

[0018] Figure 4 Exemplary embodiments of the disclosed workflow are presented.

[0019] Figure 5 A comparison of cfDNA fragmentation profiles in patients without lung cancer, patients without lung cancer but with benign nodules, and patients with lung cancer is shown, demonstrating that DELFI can distinguish lung cancer from non-cancer even in a symptomatic cohort, indicating the clinical utility of flow diagnostic workup for this patient population.

[0020] Figure 6 is an example computer 800 that can be used to implement the methods described herein. DETAILED DESCRIPTION

[0021] The present invention is based on the groundbreaking discovery that characterizing genome-wide patterns of fragmentation of cell-free DNA (cfDNA) in plasma using low-coverage whole-genome sequencing improves cancer diagnosis when analyzed in conjunction with certain clinical and demographic characteristics of individual patients.

[0022] Described herein are methods for: processing cfDNA fragments from a sample obtained from a subject and generating a sequencing library; subjecting the sequencing library to whole genome sequencing to obtain sequenced fragments, wherein the genome coverage is approximately 9× to 0.1×; mapping the sequenced fragments to the genome to obtain genomic intervals of the mapped sequences; analyzing the genomic intervals of the mapped sequences to determine cfDNA fragment lengths and amounts, thereby establishing a composite cfDNA fragmentation profile using the cfDNA fragment lengths and amounts; analyzing one or more demographic or clinical characteristics from the subject associated with the type of cancer to be identified; and detecting the composite cfDNA fragmentation profile based on variable lengths and amounts relative to a reference cfDNA fragmentation profile from a healthy subject, wherein increased variability in the cfDNA fragmentation profile and the presence of one or more demographic or clinical characteristics indicates that the subject suffers from the cancer type.

[0023] In some aspects, the genomic intervals of the mapped sequences are non-overlapping. In certain aspects, the genomic intervals each include thousands to millions of base pairs.

[0024] In further aspects, the cfDNA fragmentation profile is determined within each genomic interval. In some such aspects, the cfDNA fragmentation profile includes a median fragment size. In further aspects, the cfDNA fragmentation profile includes a fragment size distribution.

[0025] In other aspects, the cancer type to be identified is selected from the group consisting of: head and neck cancer, lung cancer, breast cancer, esophageal cancer, stomach cancer, bile duct cancer, liver cancer, pancreatic cancer, colorectal cancer, kidney cancer, bladder cancer, ovarian cancer and endometrial cancer. In some such aspects, the cancer type to be identified is lung cancer.

[0026] In further aspects, the one or more clinical characteristics are selected from the group consisting of pain, involuntary weight loss, fever, fatigue, skin changes, difficulty breathing, cough, hoarseness, difficulty swallowing, abnormal bleeding, anemia, changes in bowel or urinary habits, or swelling or mass anywhere in the subject's body.

[0027] In further aspects, the one or more demographic characteristics are selected from the group consisting of: age, gender, and smoking status.

[0028] In a further aspect, a subject identified as having a type of cancer is administered a therapeutic agent suitable for treating that type of cancer.

[0029] Before describing the compositions and methods of the present invention, it should be understood that the present invention is not limited to the specific methods and systems described, as such methods and systems may vary. It should also be understood that the terminology used herein is only for the purpose of describing specific embodiments, and is not intended to be limiting, as the scope of the present invention is limited only in the appended claims.

[0030] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "the method" includes one or more methods and / or steps of the type described herein that will become apparent to one skilled in the art upon reading this disclosure and the like.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those of ordinary skill in the art to which the invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, preferred methods and materials are now described.

[0032] Example Hardware Implementation

[0033] Figure 6 An example computer 800 that can be used to implement the methods described herein is shown. For example, the computer 800 may include a machine learning system that trains a machine learning model to generate a cfDNA fragmentation profile, a cancer diagnosis, or any combination thereof as described above, or a portion or combination thereof in some embodiments. The computer 800 may be any electronic device that runs a software application obtained from a compiled instruction, including but not limited to a personal computer, a server, a smart phone, a media player, an electronic tablet computer, a game console, an email device, etc. In some embodiments, the 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 via a bus 810, and in some embodiments, these components may be distributed between multiple physical locations and coupled via a network.

[0034] Display device 806 can be any known display technology, including but not limited to display devices using liquid crystal display (LCD) or light emitting diode (LED) technology. Processor 802 can use any known processor technology, including but not limited to graphics processors and multi-core processors. Input device 804 can be any known input device technology, including but not limited to keyboard (including virtual keyboard), mouse, trackball, camera and touch-sensitive pad or display. Bus 810 can be any known internal or external bus technology, including but not limited to ISA, EISA, PCI, PCI Express, USB, serial ATA or FireWire. Computer-readable medium 812 can be any non-temporary medium that participates in providing instructions to processor 804 for execution, including but not limited to non-volatile storage media (e.g., optical disks, disks, flash drives, etc.) or volatile media (e.g., SDRAM, ROM, etc.).

[0035] Computer-readable media 812 may include a computer program for implementing an operating system (e.g., Mac The operating system may be a multi-user, multi-processing, multi-tasking, multi-threaded, real-time operating system, etc. The operating system may perform basic tasks including, but not limited to: recognizing input from input device 804; sending output to display device 806; keeping track of files and directories on computer-readable medium 812; controlling peripheral devices (e.g., disk drives, printers, etc.) that may be controlled directly or through I / O controllers; and managing traffic on bus 810. Network communication instructions 816 may establish and maintain network connections (e.g., software for implementing communication protocols such as TCP / IP, HTTP, Ethernet, telephony, etc.).

[0036] The machine learning instructions 818 may include instructions that enable the computer 800 to act as a machine learning system and / or train a machine learning model to generate DMS values ​​as described herein. The application 820 may be an application that uses or implements the processes and / or other processes described herein. The processes may also be implemented in the operating system 814. For example, the application 820 and / or the operating system may create a task in the application described herein.

[0037] The described features may be implemented in 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. A computer program is a collection of instructions that can be used, directly or indirectly, in a computer to perform some activity or bring about some result. A 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, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0038] For example, processors suitable for executing instruction programs may include general-purpose microprocessors and special-purpose microprocessors and the sole processor or one of multiple processors or cores of any kind of computer. Typically, the processor can receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer may include a processor for executing instructions and one or more memories for storing instructions and data. Typically, a computer may also include one or more mass storage devices for storing data files, or may be operatively coupled to communicate with the mass storage devices; such devices include 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 all forms of non-volatile memory, including, for example, semiconductor memory devices (such as EPROM, EEPROM) and flash memory devices; disks, such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory may be supplemented by or incorporated into an ASIC (Application Specific Integrated Circuit).

[0039] To provide interaction with a user, the 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) through which the user can provide input to the computer.

[0040] Features may be implemented on a computer system including back-end components such as a data server, or including middleware components such as an application server or an Internet server, or including front-end components such as a user computer with a graphical user interface or an Internet browser, or any combination thereof. The components of the system may be connected by any form of digital data communication (such as a communication network) or medium for digital data communication. Examples of communication networks include, for example, telephone networks, LANs, WANs, and computers and networks forming the Internet.

[0041] A computer system may include clients and servers. Clients and servers may generally be remote from each other and may typically interact via a network. The relationship of client and server may arise by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0042] 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 that are 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.

[0043] An API can be implemented as one or more calls in program code that send or receive one or more parameters through a parameter list or other structure based on a calling convention defined in an API specification document. A parameter can be a constant, a key, a data structure, an object, a class of objects, a variable, a data type, a pointer, an array, a list, or another call. API calls and parameters can be implemented in any programming language. A programming language can define the vocabulary and calling conventions that a programmer will use to access functions that support an API.

[0044] In some embodiments, the API call can report to the application the capabilities of the device running the application, such as input capabilities, output capabilities, processing capabilities, power capabilities, communication capabilities, etc.

[0045] Although various embodiments have been described above, it should be understood that the embodiments are presented by way of example rather than limitation. It will be apparent to those skilled in the relevant art that various changes in form and detail may be made without departing from the spirit and scope. In fact, after reading the above description, it will be apparent to those skilled in the relevant art how to implement alternative embodiments. For example, other steps may be provided, or steps may be eliminated from the described process, and other components may be added to or removed from the described system. Therefore, other embodiments are within the scope of the following claims.

[0046] In addition, it should be understood that any drawings highlighting features and advantages are presented for illustrative purposes only. The disclosed methods and systems are each sufficiently flexible and configurable such that the methods and systems may be utilized in ways other than those shown.

[0047] Although the term "at least one" may be frequently used in the specification, claims, and drawings, the terms "a," "an," "the," "said," etc. also mean "at least one" or "the at least one" in the specification, claims, and drawings.

[0048] Finally, it is Applicant's intent that only claims that include the express language "means for" or "step for" will be interpreted under 35 U.S.C. § 112(f). Claims that do not expressly include the phrase "means for" or "step for" will not be interpreted under 35 U.S.C. § 112(f).

[0049] The methods and systems currently described can be used to detect cancer in a subject and optionally treat a cancer subject. Any suitable subject, such as a mammal, can be assessed and / or treated as described herein. Examples of some mammals that can be assessed and / or treated as described herein include, but are not limited to, humans, primates (such as monkeys), dogs, cats, horses, cattle, pigs, sheep, mice, and rats. For example, a person suffering from or suspected of having cancer can be assessed using the methods described herein, and optionally can be treated with one or more cancer treatments as described herein. The methods disclosed herein can include administering a therapeutic agent suitable for treating the cancer type to a subject identified as suffering from the cancer type.

[0050] The methods and systems described herein can be used to assess and / or treat subjects suffering from or suspected of having any appropriate type of cancer (e.g., by administering one or more cancer treatments to the subject). Cancer can be cancer at any stage. In some aspects, cancer can be early stage cancer. In some aspects, cancer can be asymptomatic cancer. In some aspects, cancer can be residual disease and / or recurrence (e.g., after surgical resection and / or after cancer therapy). Cancer can be any type of cancer. Examples of cancer types that can be assessed 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, gastric cancer, esophageal cancer, gastrointestinal stromal tumor (GIST), uterine cancer, and ovarian cancer. Other cancer types include, but are not limited to, myeloma, multiple myeloma, B cell lymphoma, follicular lymphoma, lymphocytic leukemia, leukemia, and myeloid leukemia. In some aspects, cancer is a solid tumor. In some aspects, cancer is a sarcoma, carcinoma, or lymphoma. In some aspects, the cancer is lung cancer, colorectal cancer, prostate cancer, breast cancer, pancreatic cancer, bile duct cancer, liver cancer, CNS cancer, gastric cancer, esophageal cancer, gastrointestinal stromal tumor (GIST), uterine cancer or ovarian cancer. In some aspects, the cancer is a blood cancer. In some aspects, the cancer is myeloma, multiple myeloma, B cell lymphoma, follicular lymphoma, lymphocytic leukemia, leukemia or myeloid leukemia.

[0051] When treating a subject suffering from or suspected of having cancer as described herein, one or more cancer treatments can be administered to the subject.Cancer treatment can be any appropriate cancer treatment.One or more cancer treatments described herein can be administered to the subject at any appropriate frequency (e.g., once or multiple times in a period of several days to several weeks).Examples of cancer treatments include, but are not limited to, surgical intervention, adjuvant chemotherapy, neoadjuvant chemotherapy, radiotherapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy (e.g., chimeric antigen receptors and / or T cells with wild-type or modified T cell receptors), targeted therapy, such as administration of kinase inhibitors (e.g., targeting kinase inhibitors of specific genetic damage such as translocation or mutation) (e.g., kinase inhibitors, antibodies, bispecific antibodies), signal transduction inhibitors, bispecific antibodies or antibody fragments (e.g., BiTEs), monoclonal antibodies, immune checkpoint inhibitors, surgery (e.g., surgical resection) or any combination thereof.In some aspects, cancer treatment can reduce the severity of cancer, alleviate the symptoms of cancer and / or reduce the number of cancer cells present in the subject.

[0052] In some aspects, the cancer treatment can be a chemotherapeutic agent. Non-limiting examples of chemotherapeutic agents include amsacrine, azacitidine, axathioprine, bevacizumab (or an antigen-binding fragment thereof), bleomycin, busulfan, carboplatin, capecitabine, chlorambucil, cisplatin, cyclophosphamide, cytarabine, dacarbazine, daunorubicin, docetaxel, doxifluridine, doxorubicin, epirubicin, erlotinib hydrochloride, dapoxetine ... hydrochloride), etoposide, fiudarabine, floxuridine, fludarabine, fluorouracil, gemcitabine, hydroxyurea, idarubicin, ifosfamide, irinotecan, lomustine, mechlorethamine, melphalan, mercaptopurine, methotrexate, mitomycin, mitoxantrone, oxantrone, oxaliplatin, paclitaxel, pemetrexed, procarbazine, all-trans retinoic acid, streptozocin, tafluposide, temozolomide, teniposide, tioguanine, topotecan, uramustine, valrubicin, vinblastine, vincristine, vindesine, vinorelbine, and combinations thereof.Additional examples of anti-cancer therapies are known in the art; see, for example, therapy guidelines from the American Society of Clinical Oncology (ASCO), the European Society for Medical Oncology (ESMO), or the National Comprehensive Cancer Network (NCCN).

[0053] In one embodiment, the methods and systems described herein can also be used to monitor subjects suffering from or suspected of suffering from cancer as described herein. In some aspects, monitoring can be performed before, during and / or after the cancer treatment process. The monitoring methods provided herein can be used to determine the efficacy of one or more cancer treatments and / or select subjects for enhanced monitoring.

[0054] In some aspects, 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 aspects, a diagnostic test (e.g., any of the diagnostic tests disclosed herein) can be administered to a subject selected for enhanced monitoring at an increased frequency compared to a subject not selected for enhanced monitoring. For example, a diagnostic test can be administered to a subject selected for enhanced monitoring twice a day, once a day, twice a week, once a week, twice a month, once a month, once a quarter, once every six months, once a year, or any frequency therein.

[0055] In various aspects, DNA is present in a biological sample taken from a subject and is used in the method of the present invention. Biological sample can actually be any type of biological sample including DNA. Biological sample is typically a fluid, such as whole blood or a part thereof with circulating cfDNA. In an embodiment, sample includes DNA from a tumor or liquid biopsy, and the liquid biopsy is such as but not limited to amniotic fluid, aqueous humor, vitreous humor, blood, whole blood, graded blood, plasma, serum, breast milk, cerebrospinal fluid (CSF), cerumen (ear wax), chyle, corns, endolymph, perilymph, feces, breathing, gastric acid, gastric juice, lymph, mucus (including nasal drainage and mucus), pericardial fluid, peritoneal fluid, pleural fluid, pus, nasal mucus, saliva, exhaled breath condensate, sebum, semen, sputum, sweat, synovial fluid, tears, vomitus, prostatic fluid, nipple aspiration fluid, tears, sweat, cheek swab, cell lysate, gastrointestinal fluid, biopsy tissue and urine or other biological fluids. In one aspect, the sample includes DNA from circulating tumor cells.

[0056] 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 puncture or phlebotomy. Suitably, the blood sample is about 0.1 to 20 ml, or alternatively about 1 to 15 ml, wherein the volume of blood is about 10 ml. Smaller amounts can also be used, as well as circulating free DNA in the blood. Microsampling and sampling by needle biopsy, catheters, excretion or production of body fluids containing DNA are also potential sources of biological samples.

[0057] The methods and systems of the present disclosure utilize nucleic acid sequence information and thus may include any method or sequencing device for performing nucleic acid sequencing, including nucleic acid amplification, polymerase chain reaction (PCR), nanopore sequencing, 454 sequencing, insertion tag sequencing. In some aspects, the methods or systems of the present disclosure utilize systems such as those provided by Illumina, Inc (including but not limited to HiSeq TM X10, HiSeq TM 1000, HiSeq TM 2000, HiSeq TM 2500, Genome Analyzers TM 、MiSeq TM , NextSeq, NovaSeq 6000 Systems, Applied Biosystems Life Technologies (SOLiD TM System, Ion PGM TM Sequencer, ion Proton TM Sequencer) or Genapsys or BGI MGI, as well as other systems. Nucleic acid analysis can also be performed by systems provided by the following companies: Oxford Nanopore Technologies (GridiON TM 、MiniON TM ) or Pacific Biosciences TM RS II or Sequel I or II).

[0058] The present invention includes systems for performing the steps of the disclosed methods and is described in part in terms of functional components and various processing steps. Such functional components and processing steps can be implemented by any number of components, operations, and techniques configured to perform specified functions and achieve various results. For example, the present invention can employ various biological samples, biomarkers, elements, materials, computers, data sources, storage systems and media, information collection techniques and processes, data processing standards, statistical analysis, regression analysis, etc. that can perform various functions.

[0059] Therefore, the present invention further provides a system for detecting, analyzing and / or evaluating cancer. In various aspects, 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 executing the method of the present invention.

[0060] In some aspects, the computer system further comprises one or more additional modules. For example, the system may include one or more of an extraction unit and / or a separation unit operable to select a suitable gene component analysis (e.g., cfDNA fragments of a specific size).

[0061] In some aspects, the computer system further comprises a visual display device. The visual display device may be operable to display the curve fit line, the reference curve fit line, and / or a comparison of the two.

[0062] The method for detecting and analyzing according to various aspects of the present invention can be implemented in any suitable manner (e.g., using a computer program operating on a computer system). As discussed herein, the exemplary system according to various aspects of the present invention can be implemented in conjunction with a computer system, which is a conventional computer system such as a processor and a random access memory, such as an application server, a network server, a personal computer or a workstation that can be accessed remotely. The computer system also suitably includes other memory devices or information storage systems, such as a mass storage system and a user interface, such as a conventional monitor, a keyboard and a tracking device. However, the computer system can include any suitable computer system and associated equipment, and can be configured in any suitable manner. In one embodiment, the computer system includes an independent system. In another embodiment, the computer system is a part of a computer network including a server and a database.

[0063] Can be implemented in a single device or in multiple devices, implement the software that needs to be used to receive, process and analyze information.Described software can be accessed through a network so that the storage and processing of information occur remotely relative to the user.System according to various aspects of the present invention and its various elements provide functions and operations that are convenient to detect and / or analyze, such as data collection, processing, analysis, reporting and / or diagnosis.For example, in this aspect, a computer system executes a computer program, and described computer program can receive, store, search, analyze and report the information relevant to human genome or its region.Computer program can comprise multiple modules that perform various functions or operations, such as processing module for processing raw data and generating supplementary data and analysis module for analyzing raw data and supplementary data to generate the quantitative assessment of disease state model and / or diagnostic information.

[0064] The programs executed by the system may include any suitable processes to facilitate analysis and / or cancer diagnosis. In one embodiment, the system is configured to model a disease state and / or determine a patient's disease state. Determining or identifying a disease state may include generating any useful information about a patient's condition relative to a disease, such as performing a diagnosis, providing information that aids in a diagnosis, assessing the stage or progression of a disease, identifying a condition that may indicate susceptibility to a disease, identifying whether further testing would be recommended, predicting and / or evaluating the efficacy of one or more treatment procedures, or otherwise assessing a patient's disease state, likelihood of disease, or other health aspects.

[0065] The following example is provided to further illustrate the advantages and features of the present invention, but is not intended to limit the scope of the present invention. Although this example is typical in the examples that can be used, other procedures, methods or techniques known to those skilled in the art may also be used alternatively.

[0066] Example 1

[0067] Cancer detection using the DELFI circulating cfDNA-based approach to diagnose and evaluate patients with lung cancer symptoms

[0068] Exemplary results are presented demonstrating that these methods can differentiate between lung cancer and non-lung cancer in a high-risk group presenting with recognized signs and symptoms suggestive of lung cancer.

[0069] Participants were adults referred to the Department of Respiratory Medicine at Bispebjerg Hospital in Copenhagen, Denmark (LUCAS cohort) with positive imaging findings on chest radiography 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 symptoms at presentation was extracted from the reason for referral documented in the medical record. Individuals with a cancer diagnosis or known active disease, who were receiving treatment at the time of recruitment, or whose cancer was determined to be of non-pulmonary origin were excluded. The study spanned the period from September 2012 to March 2013, and participants were followed up until death or April 2020, whichever occurred first. All participants gave written informed consent. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Danish Regional Ethics Committee and the Danish Data Protection Agency.

[0070] Study Design

[0071] Blood samples were collected from each participant at the initial visit, before diagnosis was made. Samples were processed within 2 hours of collection as previously described and stored at -80°C prior to cfDNA analysis by the DELFI method. [Mathios Nature Communications 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 from individuals with and without lung cancer. Libraries were sequenced on an Illumina HiSeq 2500 platform (Illumina, San Diego, CA) at 1-2× coverage / genome using 100-bp paired-end runs. A machine learning prediction model was used to detect cancer based on the composition of large-scale epigenetic signals in sequenced cfDNA fragments across 2.4GB of the autosomal genome in patients with lung cancer and non-cancer controls ( Figure 4 ).

[0072] Statistical analysis

[0073] Demographic and clinical characteristics were described according to cancer status. 2 Categorical variables were compared using the Student's t-test, and continuous variables were compared using the Student's t-test. Median DELFI scores were reported according to lung cancer outcome, number of symptoms reported, and individual symptoms reported, and the distribution of DELFI scores was visualized according to the same subgroups. The Kruskal-Wallis χ 2 Test (Kruskall Wallis χ 2 The distribution of DELFI scores was compared using the 2-way DELFI test. Receiver operating curves were constructed from logistic regression models to assess the association between individual-reported symptoms (with and without DELFI scores) and lung cancer outcomes. The area under the curve and 95% confidence interval were estimated for each receiver operating curve.

[0074] result

[0075] The LUCAS cohort consisted of 365 participants, of whom 346 were included in these analyses: 114 with lung cancer and 232 without cancer ( Figure 4 ). The remaining 19 patients were excluded from the current analysis because they had lung metastases from primary tumors of non-lung tissue. The demographic and clinical characteristics of the LUCAS cohort are shown in Table 4. Both the lung cancer and non-cancer cohorts had signs and symptoms commonly associated with lung cancer before diagnosis, and the number of signs and symptoms was balanced between the two groups.

[0076] 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 individuals without cancer, 67 were confirmed to have benign lung nodules after abnormal chest imaging results. The median DELFI scores were similar in non-cancer individuals with and without benign nodules (0.208 vs. 0.198, p = .299).

[0077] For each symptom, the median DELFI score for individuals with lung cancer who had that symptom was significantly higher than that for individuals without cancer who had that symptom ( Figure 4 ; Table 1).

[0078] Table 1. Median DELFI scores according to cancer status and specific symptoms recorded.

[0079]

[0080] Depending on the symptoms, the median DELFI scores ranged from 0.926 to 1.000 in the group with cancer and from 0.146 to 0.253 in the group without cancer. When the cohort was stratified according to the number of symptoms, the median DELFI scores of patients with lung cancer were significantly higher than those of patients without cancer in each stratum of the number of symptoms ( Figure 2 ; Table 2). Depending on the number of symptoms, the median DELFI scores ranged from 0.604 to 1.000 in the group with cancer and from 0.140 to 0.212 in the group without cancer.

[0081] Table 2. Median DELFI scores according to cancer status and number of symptoms recorded.

[0082]

[0083] In the logistic regression model for combined symptoms predicting the presence of lung cancer, the area under the curve was 0.62 (95% confidence interval, 0.55 to 0.68; Figure 3 Adding the DELFI score to the model with combined symptoms improved the area under the curve to 0.89 (95% confidence interval, 0.85 to 0.93; Figure 3 ).

[0084] discuss

[0085] Here, we demonstrate the potential value of the DELFI method in detecting lung cancer in a group presented for evaluation of signs or symptoms suggestive of lung cancer. By analyzing the fragmentation pattern of cfDNA present in plasma, the DELFI platform distinguishes lung cancer from non-cancer in relatively high-risk individuals (i.e., individuals with positive imaging results and various suspicious symptoms). Our findings provide proof-of-concept evidence that the use of the DELFI method in the diagnostic workup of individuals with signs and symptoms can shorten the time to diagnostic resolution. Even in a group with high-risk features, the DELFI score can further classify those individuals with relatively high and low likelihood of lung cancer. Those individuals with a higher likelihood of lung cancer can be guided to undergo LDCT screening and subsequent biopsy more urgently. Additional studies of the performance of the DELFI method (e.g., specificity, sensitivity, positive and negative predictive values) are needed to define the clinical utility of DELFI-based tests for such decisions.

[0086] The DELFI method correctly classified a benign lung nodule as noncancerous. Lung nodules are a common incidental finding on chest imaging. Although lung nodules are often benign, additional testing is still required, such as imaging scans or biopsies, all of which carry the risk of harming the patient. Classifying nodules as benign via the DELFI score could reduce the number of unnecessary procedures that many people would otherwise undergo.

[0087] Our study shows the feasibility of applying the DELFI method to archived blood samples systematically collected and processed from individuals cared for in routine practice.

[0088] In conclusion, genome-wide interrogation of the fragmentation pattern of cfDNA by the DELFI method represents a novel, non-invasive, feasible, and potentially useful tool for aiding the diagnosis of lung cancer in individuals with suggestive signs and symptoms. Use of the DELFI method in these ambiguous scenarios may allow for more informed and timely referrals for additional imaging studies or biopsies by identifying individuals who are more likely (and less likely) to have lung cancer. This approach may reduce the number of unnecessary procedures and shorten the time to diagnostic resolution by a considerable amount. Further study of the DELFI method is needed.

[0089] Table 3. Summary of diagnostic workup guidelines for individuals presenting with symptoms suggestive of lung cancer

[0090]

[0091] CCO = Cancer Care Ontario; NICE = National Institute for Health and Care Excellence; NZGG = New Zealand Guidelines Group

[0092] Table 4. Participant demographics and clinical characteristics

[0093]

[0094]

[0095] a Individuals with more than one symptom recorded were counted more than once (once for each symptom recorded); DELFI = DNA Assessment of Fragments for Early Interception; NA = not applicable; SD = standard deviation.

[0096] Although the invention has been described with reference to presently preferred embodiments, it should be understood that various modifications can be made without departing from the spirit of the invention.

Claims

1. A method comprising: processing cfDNA fragments from a sample obtained from a subject and generating a sequencing library; subjecting the sequencing library to whole genome sequencing to obtain sequenced fragments, wherein the genome coverage is about 9× to 0.1×; mapping the sequenced fragments to a genome to obtain a genomic interval of mapped sequences; analyzing the genomic intervals of the mapped sequences to determine cfDNA fragment lengths and amounts, thereby creating a composite cfDNA fragmentation profile using the cfDNA fragment lengths and amounts; analyzing one or more demographic or clinical characteristics from the subject associated with the type of cancer to be identified; and detecting a composite cfDNA fragmentation profile based on variable length and amount relative to a reference cfDNA fragmentation profile from a healthy subject, wherein the presence of increased variability in the cfDNA fragmentation profile and one or more demographic or clinical characteristics indicates that the subject has the cancer type.

2. The method of claim 1, wherein the genomic intervals are non-overlapping.

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

4. The method of claim 1, wherein a cfDNA fragmentation profile is determined within each genomic interval.

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

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

7. The method of 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.

8. The method of claim 7, wherein the cancer type is lung cancer.

9. The method of claim 7, wherein one or more clinical characteristics are selected from the group consisting of pain, involuntary weight loss, fever, fatigue, skin changes, difficulty breathing, cough, hoarseness, difficulty swallowing, abnormal bleeding, anemia, changes in bowel or urinary habits, or swelling or mass anywhere in the subject's body.

10. The method of claim 7, wherein the one or more demographic characteristics are selected from the group consisting of: age, gender, and smoking status.

11. The method of claim 1, further comprising administering to the subject identified as having the cancer type a therapeutic agent suitable for treating the cancer type.

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