WO2017161201A1

WO2017161201A1 - Cancer detection assay and related compositions, methods and systems

Info

Publication number: WO2017161201A1
Application number: PCT/US2017/022833
Authority: WO
Inventors: William M. STRAUSS; Xiaohui Ni
Original assignee: Cynvenio Biosystems Inc.
Priority date: 2016-03-16
Filing date: 2017-03-16
Publication date: 2017-09-21

Abstract

Methods for the early detection, enumeration and analysis of circulating tumor cells (CTCs) are disclosed. These methods are useful for cancer screening, development of treatment regimens, and for monitoring for treatment responses, cancer recurrence or the like. Devices that facilitate the early detection, enumeration and analysis of such circulating tumor cells are also provided.

Description

CANCER DETECTION ASSAY AND RELATED COMPOSITIONS, METHODS AND

SYSTEMS

CROSS REFERENCE TO RELATED APPLICATIONS

[001] The present application claims priority to US provisional S/N 62/309,407 entitled "Cancer Early Detection Assay" filed on March 16, 2016, with attorney docket P1862-USP the content of which is incorporated by reference in its entirety.

FIELD

[002] This present disclosure relates to fields of oncology and diagnostic testing, and more particularly to cancer assay and related compositions, methods and systems including detection methods and systems for early cancer screening and for predicting and monitoring chemotherapy treatment responses, cancer recurrence or the like.

BACKGROUND

[003] Cancer continues to be a major cause of mortality in the world. In addition, lung cancer is the leading cause of cancer mortality in the US, which is estimated to account for more than 27% of all cancer deaths in 2016. For example, Small cell lung cancer (SCLC) is an exceptionally aggressive subtype with early metastasis, arising mostly in heavy smokers and representing 15% of all lung cancers. SCLC is usually diagnosed late, at unresectable stages, and consequently biopsy material is not available for genomic analyses.

[004] Despite development of various screening methods development of effective approaches and single molecularly targeted drug with clinical significance in SCLC remains challenging.

SUMMARY

[005] The present disclosure provides a cancer detection assay as well as methods and systems for diagnosing cancer in a patient, which, in several embodiments, allow early detection of cancer based on detection of copy number changes in the genome of an individual, preferably within set non-overlapping genetic regions. [006] According to a first aspect, an assay and related system are described, for screening a biological sample for presence or absence of abnormal cell such as circulating tumor cells. The assay comprises: enriching a number of possible abnormal cell and in particular circulating tumor cells (CTC) from the biological sample to provide enriched cells from the biological sample; analyzing copy number variation in a genome of the individual enriched cells; and determining if an individual enriched cell is an abnormal cell and in particular a CTC based on a detected copy number variation in the genome of the individual enriched cells, thus detecting presence or absence of circulating tumor cells in the biological sample.

The system comprises a reagent and/or a device to enrich a number of possible abnormal cell and in particular circulating tumor cells (CTC) from the biological sample and a device for detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell for simultaneous combined or sequential use in the method for screening a biological sample herein described.

[007] According to a second aspect a method and system are described to analyze copy number variation in a genome of an individual cell from a biological sample. The method comprises providing a reference genome with set non-overlapping genetic regions, each genetic region of the set non-overlapping genetic regions being preferably and independently from 100 kb to 500 kb in size. The method further comprises detecting the set non-overlapping genetic regions of the reference genome in the genome of the individual cell. The method also comprises detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell.

The system comprises a reagent and/or a device for detecting in the genome of the individual cell, set non-overlapping genetic regions of a reference genome, and a device for detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell for simultaneous combined or sequential use in the method to analyze copy number variation herein described.

In some embodiments, the method and system to analyze copy number variation in a genome of an individual cell from a biological sample can be used to screen a biological sample for presence or absence of abnormal cell such as circulating tumor cells.

[008] According to a third aspect a method and system to detect presence or absence of a reference cell, such as an abnormal such as a CTC cell, having a genome and a distinguishing copy number variation pattern within set non-overlapping genetic regions of the genome, each genetic region of the set non-overlapping genetic regions being preferably from 100 kb to 500 kb in size. The method comprises detecting the set non-overlapping genetic regions of the reference cell genome in a genome of an individual cell of the biological sample. The method further comprises detecting a copy number variation pattern within the detected set non-overlapping genetic regions of the genome of the individual cell, wherein if the detected copy number variation pattern matches the distinguishing copy number variation pattern of the reference cell, the reference cell is present in the biological sample and if the detected copy number variation pattern does not match the distinguishing copy number variation pattern of the reference cell, the reference cell is absent in the biological sample.

The system comprises a reagent and/or a device for detecting in a genome of an individual cell of the biological sample, the set non-overlapping genetic regions of the genome of the reference cell; a device for detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell; and a look-up table showing the distinguishing copy number variation pattern within the set non-overlapping genetic regions of the reference cell genome, for simultaneous combined or sequential use in the method to detect presence or absence in a biological sample of a reference cell herein described.

In some embodiments, the reference cell can comprise one or more abnormal cell such as one or more CTC cells each reference cell associated with a specific type of cancers and/or a specific stage of cancers. In some embodiments, the reference cell can comprise in alternative or in addition, an abnormal cell, and in particular a CTC cell from the individual.

[009] According to a fourth aspect, a method and system to characterize an individual cell of a biological sample are described. The method comprises providing a reference genome from a reference cell, the reference genome having set non-overlapping genetic regions, each genetic region of the set non-overlapping genetic regions being preferably from 100 kb to 500 kb in size. The method further comprises detecting the set non-overlapping genetic regions of the reference genome in the genome of the individual cell. The method also comprises detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell to provide a copy number variation pattern of the genome of the individual cell. The method additionally comprises comparing the detected copy number variation pattern of the genome of the individual cell with a distinguishing copy number variation pattern within the set non- overlapping genetic regions of the genome of the reference cell; and, marking the individual cell of the biological sample as the reference cell, when the detected copy number variation pattern matches a distinguishing copy number variation patterns of the reference genome.

The system comprises a reagent and/or a device for detecting in a genome of the individual cell, set non-overlapping genetic regions of a genome of a reference cell; a device for detecting copy number variation in the detected set non-overlapping genetic regions in the genome of the individual cell; and a look-up table showing a distinguishing copy number variation pattern within the set non-overlapping genetic regions of the genome of the reference cell, for simultaneous combined or sequential use in the method to characterize an individual cell of a biological sample herein described.

[0010] According to a fifth aspect a method and system, for diagnosing cancer in a patient are described. The method comprises screening a biological sample of the patient for presence or absence of abnormal cell such as circulating tumor cells with methods and/or systems herein described, and/or detecting in the biological sample for presence or absence of abnormal cell such as circulating tumor cells with methods and/or systems herein described. The method further comprises diagnosing the patient with cancer when presence of the abnormal cell such as circulating tumor cells is detected.

The system comprises reagents and/or devices to enrich a number of possible abnormal cell and in particular circulating tumor cells (CTC) from the biological sample; a reagent and/or a device for detecting in a genome of an individual cell of the biological sample, set non-overlapping genetic regions of the genome of the abnormal cell; a device for detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell; and a look-up table showing the distinguishing copy number variation pattern within the set non- overlapping genetic regions of the abnormal cell genome, for simultaneous combined or sequential use in the method for diagnosing cancer in a patient herein described.

[0011] According to a sixth aspect a method and system, for diagnosing and treating cancer in a patient are described. The method comprises screening a biological sample of the patient for presence or absence of abnormal cell such as circulating tumor cells with methods and/or systems herein described, and/or detecting in the biological sample for presence or absence of abnormal cell such as circulating tumor cells with methods and/or systems herein described. The method further comprises diagnosing the patient with cancer when presence of the abnormal cell such as circulating tumor cells is detected. The method also comprises administering an anticancer agent to the patient diagnosed with cancer.

The system comprises reagents and/or devices to enrich a number of possible abnormal cell and in particular circulating tumor cells (CTCs) from the biological sample; a reagent and/or a device for detecting in a genome of an individual cell of the biological sample, set non-overlapping genetic regions of the genome of the abnormal cell; a device for detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell; a lookup table showing the distinguishing copy number variation pattern within the set non- overlapping genetic regions of the abnormal cell genome, and an anti-cancer agent for simultaneous combined or sequential use in the method for diagnosing and treating cancer in a patient herein described.

[0012] The assays, methods and systems herein described allow in several embodiments detection of abnormal cells, and in particular cancer cell such as CTCs through detection of copy number variation in individual cell of a biological sample, possibly following enrichment of the abnormal cell. [0013] The assays, methods and systems herein described allow in several embodiments to use copy number variation in individual cells, as a marker of a cancerous state of the cell, thus allowing diagnosis of cancer at early stages as well as identification of abnormal and cancerous cell without need of detecting specific markers.

[0014] The assays, methods and systems herein described, in preferred embodiments wherein detection of copy number variation is performed on set non-overlapping genetic regions, allow detection of copy number changes with a high resolution and increased accuracy with respect to detection of copy number variation performed with approaches where copy number variation is performed on the genome in its entirety.

[0015] The assays, methods and systems herein described, in preferred embodiments wherein detection of copy number variation is performed on set non-overlapping genetic regions, allow identification of patterns of copy number variations which characterize and can be used as markers to identify and detect a reference cell in a biological cell of a patient.

[0016] Cancer detection assays and related compositions, methods and systems herein described can be used in connection with various applications wherein detection of abnormal and/or cancerous cell, and/or cancer diagnosis is desired. For example, the cancer detection assays herein described and related compositions methods and systems can be used in several fields including basic biology research, applied biology, molecular biology, medical research, medical diagnostics, therapeutics, and in additional fields identifiable by a skilled person upon reading of the present disclosure.

[0017] The details of one or more embodiments of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Fig 1 shows a set of data from capture of circulating tumor cells (CTCs) from late stage lung cancer patients.

[0019] Fig. 2 shows in one example CNVs from individual CTCs collected at different treatment time-points of a SCLC patient.

[0020] Fig. 3 shows an exemplary single-cell copy number analyses system.

[0021] Fig. 4 shows in one embodiment the copy number variations (CNVs) in CTC isolated from patients confirmed to be either normal or cancerous and analyzed by FISH. The fluorescence micrographs of FISH labeled cells (normal or abnormal) are shown in a gray scale version wherein the positive labels are shown with (+) or (·).

[0022] Fig. 5 shows in one embodiment the copy number variations (CNVs) in CTC isolated from patients confirmed to be either normal or cancerous and analyzed using single cell sequencing. A heatmap of detected CNVs across all of the chromosome of the genome of an individual cell within non-overlapping genetic region for 7 patients (PI to P7) and to controls (CI and C2) is shown in a grayscale version where the copy number of detected sections of the genome is shown with different shades of gray as reported in the bar on the right side of the Figure.

[0023] Fig. 6 shows an exemplary approach of single cell whole genome sequencing and multiplexed library preparation.

DETAILED DESCRIPTION

[0024] The present disclosure provides a cancer detection assay and methods and systems for diagnosing cancer in a patient.

[0025] The term "cancer" refers to a disease in which at least some of the cells of an individual begin to divide and typically show one or more of the following: cell growth and division absent the proper signals; continuous growth and division even given contrary signals; avoidance of programmed cell death; limitless number of cell divisions; promoting blood vessel construction; invasion of tissue and formation of metastases. One or more cells showing at least one of the above features are also indicated as abnormal cells or tumor cells and a related mass is also indicated as tumoral mass or tumor. The progression from normal cells to tumor cells that can form a detectable mass to outright cancer involves multiple steps known as malignant progression of the cancer. [0026] Cancers can be classified by the body part where the cancer originates. In addition or in the alternative, cancers can be classified by the type of cell that the tumor cell originate from. Cancer types classified accordingly comprise: (1) Carcinoma: Cancers derived from epithelial cells. This group comprises many of the most common cancers, particularly in older adults. Nearly all cancers developing in the breast, prostate, lung, pancreas, and colon are carcinomas. (2) Sarcoma: Cancers arising from connective tissue (i.e. bone, cartilage, fat, nerve), each of which develop from cells originating in mesenchymal cells outside the bone marrow. (3) Lymphoma and leukemia: These two classes of cancer arise from cells that make blood. (4) Germ cell tumor: Cancers derived from pluripotent cells, most often presenting in the testicle or the ovary (seminoma and dysgerminoma, respectively). (5) Blastoma: Cancers derived from immature "precursor" cells or embryonic tissue. Blastomas are more common in children than in older adults. Types of cancers comprise the following: acute lymphoblastic leukemia; acute myeloid leukemia; adrenocortical carcinoma; AIDS-related cancers; AIDS-related lymphoma; anal cancer; appendix cancer; astrocytoma, childhood cerebellar or cerebral; basal-cell carcinoma; bile duct cancer, extrahepatic (cholangiocarcinoma); bladder cancer; bone tumor, osteosarcoma/malignant fibrous histiocytoma; brainstem glioma; brain cancer; brain tumor, cerebellar astrocytoma; brain tumor, cerebral astrocytoma/malignant glioma; brain tumor, ependymoma; brain tumor, medulloblastoma; brain tumor, supratentorial primitive neuroectodermal tumors; brain tumor, visual pathway and hypothalamic glioma; breast cancer; bronchial adenomas/carcinoids; Burkitt's lymphoma; carcinoid tumor, childhood; carcinoid tumor, gastrointestinal; carcinoma of unknown primary; central nervous system lymphoma, primary; cerebellar astrocytoma, childhood; cerebral astrocytoma/malignant glioma, childhood; cervical cancer; childhood cancers; chondrosarcoma; chronic lymphocytic leukemia; chronic myelogenous leukemia; chronic myeloproliferative disorders; colon cancer; cutaneous T-cell lymphoma; desmoplastic small round cell tumor; endometrial cancer; ependymoma; epithelioid hemangioendothelioma (EHE); esophageal cancer; Ewing's sarcoma in the Ewing family of tumors; extracranial germ cell tumor, childhood; extragonadal germ cell tumor; extrahepatic bile duct cancer; eye cancer, intraocular melanoma; eye cancer, retinoblastoma; gallbladder cancer; gastric (stomach) cancer; gastrointestinal carcinoid tumor; gastrointestinal stromal tumor (gist); germ cell tumor: extracranial, extragonadal, or ovarian; gestational trophoblastic tumor; glioma of the brain stem; glioma, childhood cerebral astrocytoma; glioma, childhood visual pathway and hypothalamic; gastric carcinoid; hairy cell leukemia; head and neck cancer; heart cancer; hepatocellular (liver) cancer; hodgkin lymphoma; hypopharyngeal cancer; hypothalamic and visual pathway glioma, childhood; intraocular melanoma; islet cell carcinoma (endocrine pancreas); Kaposi sarcoma; kidney cancer (renal cell cancer); laryngeal cancer; leukaemias; leukaemia, acute lymphoblastic (also called acute lymphocytic leukaemia); leukaemia, acute myeloid (also called acute myelogenous leukemia); leukaemia, chronic lymphocytic (also called chronic lymphocytic leukemia); leukemia, chronic myelogenous (also called chronic myeloid leukemia); leukemia, hairy cell; lip and oral cavity cancer; liposarcoma; liver cancer (primary); lung cancer, non-small cell; lung cancer, small cell; lymphomas; lymphoma, AIDS-related; lymphoma, burkitt; lymphoma, cutaneous t-cell; lymphoma, Hodgkin; lymphomas, non-hodgkin (an old classification of all lymphomas except hodgkin's); lymphoma, primary central nervous system; macroglobulinemia, Waldenstrom; male breast cancer; malignant fibrous histiocytoma of bone/osteosarcoma; medulloblastoma, childhood; melanoma; melanoma, intraocular (eye); merkel cell cancer; mesothelioma, adult malignant; mesothelioma, childhood; metastatic squamous neck cancer with occult primary; mouth cancer; multiple endocrine neoplasia syndrome, childhood; multiple myeloma/plasma cell neoplasm; mycosis fungoides; myelodysplastic syndromes; myelodysplastic/myeloproliferative diseases; myelogenous leukemia, chronic; myeloid leukemia, adult acute; myeloid leukemia, childhood acute; myeloma, multiple (cancer of the bone-marrow); myeloproliferative disorders, chronic; myxoma; nasal cavity and paranasal sinus cancer; nasopharyngeal carcinoma; neuroblastoma; non-Hodgkin lymphoma; non-small cell lung cancer; oligodendroglioma; oral cancer; oropharyngeal cancer; osteosarcoma/malignant fibrous histiocytoma of bone; ovarian cancer; ovarian epithelial cancer (surface epithelial-stromal tumor); ovarian germ cell tumor; ovarian low malignant potential tumor; pancreatic cancer; pancreatic cancer, islet cell; paranasal sinus and nasal cavity cancer; parathyroid cancer; penile cancer; pharyngeal cancer; pheochromocytoma; pineal astrocytoma; pineal germinoma; pineoblastoma and supratentorial primitive neuroectodermal tumors, childhood; pituitary adenoma; plasma cell neoplasia/multiple myeloma; pleuropulmonary blastoma; primary central nervous system lymphoma; prostate cancer; rectal cancer; renal cell carcinoma (kidney cancer); renal pelvis and ureter, transitional cell cancer; retinoblastoma; rhabdomyosarcoma, childhood; salivary gland cancer; sarcoma, ewing family of tumors; sarcoma, Kaposi; sarcoma, soft tissue; sarcoma, uterine; Sezary syndrome; skin cancer (non- melanoma); skin cancer (melanoma); skin carcinoma, merkel cell; small cell lung cancer; small intestine cancer; soft tissue sarcoma; squamous cell carcinoma - see skin cancer (non- melanoma); squamous neck cancer with occult primary, metastatic; stomach cancer; supratentorial primitive neuroectodermal tumor, childhood; T-cell lymphoma, cutaneous; testicular cancer; throat cancer; thymoma, childhood; thymoma and thymic carcinoma; thyroid cancer; thyroid cancer, childhood; transitional cell cancer of the renal pelvis and ureter; trophoblastic tumor, gestational; adult unknown primary site, carcinoma; unknown primary site cancer of childhood; ureter and renal pelvis, transitional cell cancer; urethral cancer; uterine cancer, endometrial; uterine sarcoma; vaginal cancer; visual pathway and hypothalamic glioma, childhood; vulvar cancer; Waldenstrom macroglobulinemia; Wilms tumor (kidney cancer), childhood and others known to those skilled in the art. The most common types of cancer in males are lung cancer, prostate cancer, colorectal cancer and stomach cancer. In females, the most common types are breast cancer, colorectal cancer, lung cancer and cervical cancer. If skin cancer other than melanoma were included in total new cancers each year it would account for around 40% of cases. In children, acute lymphoblastic leukaemia and brain tumors are most common except in Africa where non-Hodgkin lymphoma occurs more often. Additional more detailed information about cancer and different types of cancer can be found in related publications and websites such as www.cancer.gov and other sources identifiable to a person of ordinary skill in the art.

[0027] Cancer detection assay and methods and systems herein described allow, in several embodiments, early detection of cancer in a patient.

[0028] The term "early detection" used herein refers to detection of a cancer, including a cancer recurrence, at its early stage. A stage of a cancer indicates a phase of progression of the cancer determined based on parameters such as the size of a tumor, whether one or more tumor cells have invaded adjacent organs, how many regional (e.g. nearby) lymph nodes one or more tumor cells have spread to (if any), and whether one or more tumor cells has been detected in more distant locations or metastasized. Standard practice to a stage a cancer based on the above detected parameter is to assign a number from 0 to IV to a cancer, based on the extent to which a cancer has developed by spreading. [0029] In particular, a Stage 0 cancer refers to an "in-situ" localized cancer in which the cancer cells are still in the place where they start and have not spread. Stage I cancer is usually a small cancer or tumor that has not grown deeply into nearby tissues. It also has not spread to the lymph nodes or other parts of the body. Stage II and III indicate large cancers or tumors that have grown more deeply into nearby tissue. They may have also spread to lymph nodes but not to other parts of the body. Stage IV means that the cancer has spread to other organs or parts of the body. It can also be called as advanced or metastatic cancer.

[0030] Stage 0 to Stage I cancers can be also identified as early-stage cancers which typically indicates a cancer that is early in its growth, and may have not spread to other parts of the body.

[0031] A stage of a cancer can also be determined with alternative methods such as the TNM Classification of Malignant Tumours (TNM) which is an alternative cancer staging notation system that describes the stage of a cancer which originates from a solid tumor with alphanumeric codes. 'T' describes the size of the original (primary) tumor and whether it has invaded nearby tissue, 'N' describes nearby (regional) lymph nodes that are involved, and 'M' describes distant metastasis (spread of cancer from one part of the body to another). [Denoix PF. Enquete permanent dans les centres anticancereaux. Bull Inst Nat Hyg 1946; 1 :70-5]. TNMS classification has gained wide international acceptance for many solid tumor cancers, but is not applicable to diffused cancers such as leukaemia and is of limited use for other cancers such as diffuse lymphoma and ovarian cancer [Tobias Jeffrey S., Hochhauser, Daniel, Cancer and its Management, p. 43, 2013 (6th edn)].

[0032] Parameters of the TNM staging system comprise: T: size or direct extent of the primary tumor as follows. Tx: tumour cannot be evaluated; Tis: carcinoma in situ; TO: no signs of tumour; Tl , T2, T3, T4: size and/or extension of the primary tumour; N: degree of spread to regional lymph nodes, as follows: Nx: lymph nodes cannot be evaluated; NO: tumour cells absent from regional lymph nodes; Nl : regional lymph node metastasis present; at some sites, tumour spread to closest or small number of regional lymph nodes; N2: tumour spread to an extent between Nl and N3 (N2 is not used at all sites); N3 : tumour spread to more distant or numerous regional lymph nodes (N3 is not used at all sites); M: presence of distant metastasis, as follows: M0: no distant metastasis; Ml : metastasis to distant organs (beyond regional lymph nodes) [ref: "Cancer Staging", National Cancer Institute website]. Detailed description of the parameters used in the TNM staging systems is shown in Table 1. For example, for occult carcinoma, an early stage IA can be represented by Tla NO MO and an early stage IB can be represented by Tib NO MO.

Table 1: Parameters used in TNM staging system

[0033] Late stage cancers and early stage cancer are typically characterized by different number and/or frequency of genetic alterations in the tumor cells of the individual. Comparative analysis of genetic alternations between early and late stage cancers have revealed that gene alterations accumulate in a stepwise way during cancer progression and that various genes are differentially expressed in association with the metastatic potential of cancer cells. Molecular analyses of cancer cells in various stages of progression have revealed that alterations in tumor suppressor genes and oncogenes accumulate during tumor progression and correlate with the clinical aggressiveness of cancer. For example, the number of genetic alterations in late stage tumors is usually more than those in early stage tumors in various types of cancers. In some cases, frequencies of alterations in some sets of genes are higher in late stage tumors than in early stage tumors, while the frequencies of alterations in other sets of genes are high in both early and late stage tumors. Thus, genetic models for tumor progression can be constructed in association with accumulation of genetic alterations in cancer cells. Molecular markers can be identified for the evaluation of prognosis in cancer patients.

[0034] Early stage cancers in the sense of the disclosure also comprise cancers that are recurrent in an individual. In particular, "recurrent cancer" refers to a disease wherein at least some of the cells of the individual become tumor cells following a treatment and a period of time when the tumor cells are not detected in the individual. The term "recurrence of cancer" as used herein refers to detection of cancer following treatment and after a period of time when the cancer could not be detected. In a recurrent cancer, tumor cells can form in the same location where tumor cells were originally detected, and/or in other locations in the body of the individual. Cancer recurrence can be classified based on the location where the tumor cells are detected (1) "Local recurrence" means that the tumor cells are detected in the same location where the tumor cells where originally detected. (2) "Regional recurrence" means that tumor cells are detected in lymph nodes near the place tumor cells were originally detected. (3) "Distant recurrence" means tumor cells are detected in another part of the body, some distance from where tumor cells were originally detected started (often the lungs, liver, bone, or brain).

[0035] The present disclosure provides methods and systems for the early detection of cancer based on detection of copy number changes or copy number variation in the genome of an individual, preferably within set non-overlapping genetic regions.

[0036] As used herein, the term "copy number variations" or "CNVs" refers to a type of structural variation in a genome generated through amplification, gain, loss and deletion of one or more sections of a genome that result in abnormal copy number of one or more sections of the genome.

[0037] A person skilled in the art would understand that a normal human somatic cell usually has two copies of its autosomal genetic material, with one copy from each parent, and that somatic cells from females normally also have two copies of genetic material comprised on the X-chromosomes and somatic cells from males normally have one copy of genetic material comprised on each of the X and Y chromosomes. In other words, the normative condition is defined as diploid for somatic autosomal chromosomes and in females for the X chromosome, for males the X chromosome is haploid and the Y chromosome is also present in haploid copy number. Alternated chromosome regions are regions that vary from this normative condition either by LOSS (less than the aforementioned copy number) or by GAIN (more than the aforementioned copy number).

[0038] When a CNV event takes place in a genome, a section of the genome that is normally diploid becomes aneuploid. As a result, its copy number deviates from the expected two copies observed in diploid genomes. Thus, a copy number state for each section of the genome that is copy-number variated is represented by a number greater or less than 2, which is considered as an abnormal copy number. Number 0 corresponds to a state in which both copies of a genomic region are deleted. Number 1 corresponds to a state in which one copy of a genomic region is deleted. Number 2 represents a normal state in which neither amplification nor deletion of a genomic region takes place. Numbers larger than 2 represent states in which a genetic amplification occurs in a genomic region.

[0039] In several embodiments of methods and systems herein described, copy number variations (CNVs) are not only detected in a tumor, but are also used as markers for identification and/or detection of abnormal cell. Copy number variations (CNVs) are associated with many malignant tumors. Genomic regions with recurrent CNVs in tumor genomes are believed to have high probability of containing cancer genes. Recent progress in single-cell genome sequencing has allowed quantitative characterization of both single-nucleotide variations (SNVs) and CNVs in individual tumor cells. In contrast to SNVs, which show substantial cell-to- cell heterogeneity, single nuclei from individual abnormal cell such as circulating tumor cells (CTCs) from a lung cancer patient or invasive ductal carcinoma of the breast have been found to exhibit consistent genomic alterations in their CNV patterns. These observations justify the use of CNV as the early genomic alterations in cancer and for use as a marker for early detection. Copy number variations across the entire genome, better identifies true CTCs in early stage SCLC and avoid false positives found with immune staining

[0040] In an assay and related system herein described, detection of copy number variation is performed in individual cells from a biological sample of an individual.

[0041] The term "sample" as used herein indicates a limited quantity of something that is indicative of a larger quantity of that something, including but not limited to fluids from a biological environment, specimen, cultures, tissues, commercial recombinant proteins, synthetic compounds or portions thereof. In particular biological sample can comprise one or more cells of any biological lineage, as being representative of the total population of similar cells in the sampled individual. Exemplary biological samples comprise the following: cheek tissue, whole blood, dried blood spots, organ tissue, plasma, urine, feces, skin, hair, or tumor cells, among others identifiable by a skilled person. Biological samples can be obtained using sterile techniques or non-sterile techniques, as appropriate for the sample type, as identifiable by persons skilled in the art. Depending on the type of biological sample and the intended analysis, biological samples can used freshly for sample preparation and analysis, can be fixed using fixative reagents identifiable to those skilled in the art, and/or can be stored until sample preparation and analysis, for example at room temperature, 4°C, -20°C, or -80°C, as appropriate, identifiable by those skilled in the art. When biological specimens are stored, ideally they remain equivalent to freshly-collected specimens for the purposes of analysis.

[0042] In some embodiments, the biological samples used herein are cell-containing samples obtained from a subject, and can be any sample that contains nucleated cells and encompasses any material in which CTCs can be detected. For example, the biological sample can be peripheral blood, blood, lymph nodes, bone marrow, cerebral spinal fluid, tissue, pleural fluid, stool or urine that contains cells.

[0043] In some embodiments, the biological samples are collected from a patient or individual. The term "patient" as used herein preferably refers to a human, but also encompasses other mammals. It is noted that, as used herein, the terms "organism", "individual", "subject", or "patient" are used as synonyms and interchangeably.

[0044] In at least one embodiment, a blood sample herein described is peripheral blood. As will be appreciated by those skilled in the art, a blood sample can include any fraction or component of blood, without limitation, T-cells, monocytes, neutrophils, erythrocytes, platelets and micro- vesicles such as exosomes and exosome-like vesicles. In the context of this disclosure, blood cells included in a blood sample encompass any nucleated cells and are not limited to components of whole blood. As such, blood cells include, for example, both white blood cells (WBCs) as well as rare cells, including CTCs.

[0045] Biological samples in the sense of the disclosure can be obtained by any means, including, e.g., by lysis and removal of the red blood cells in a 7.5 mL blood sample, deposition of the remaining nucleated cells on specialized microscope slides, each of which accommodates the equivalent of roughly 0.5 mL of whole blood. A blood sample can be extracted from any source known to include blood cells or components thereof, such as venous, arterial, peripheral, tissue, cord, and the like. The samples can be processed using well known and routine clinical methods (e.g., procedures for drawing and processing whole blood).

[0046] In some embodiments, a blood sample can be drawn into anti-coagulant blood collection tubes (BCT), which may contain EDTA (Ethylenediaminetetraacetic acid) or Streck Cell-Free DNA™. In other embodiments, a blood sample can be drawn into CellSave® tubes (Veridex). A blood sample may further be stored for up to 12 hours, 24 hours, 36 hours, 48 hours, or 60 hours before further processing.

[0047] In some embodiments, the biological sample can be obtained from a subject who has been diagnosed with cancer based on tissue or liquid biopsy and/or surgery or clinical grounds. In some embodiments, the biological sample is obtained from a subject showing a clinical manifestation of cancer, after initial surgery or radiation, or despite chemotherapy. In other embodiments, the biological sample is obtained from a healthy subject or a subject deemed to be at high risk for cancer and/or metastasis of existing cancer based on art known clinically established criteria including, for example, age, race, family and history.

[0048] In assays, methods and systems herein described, detecting copy number variation in single cells from a biological sample of an individual can be performed to detect presence or absence of an abnormal cell in the biological sample

[0049] In some embodiments herein described, the possible abnormal cells are circulating tumor cells (CTCs). As used herein, the term "circulating tumor cells" or "CTC" indicates tumor cells a that have shed into the vasculature or lymphatics from a primary tumor and are carried around the body in the circulation, and encompasses any rare cell that is present in a biological sample such as a blood sample or lymphatic tissue sample and that is related to cancer. CTCs, which can be present as single cells or in clusters of CTCs, are often circulating epithelial cells (CECs) shed from solid tumors found in very low concentrations in the circulation of patients. CTCs include "traditional CTCs," which are cytokeratin positive (CK+), CD45 negative (CD-), contain a DAPI nucleus, and are morphologically distinct from surrounding white blood cells. CTCs can be identified by the morphological characteristics of CTCs which comprise one or more of the group consisting of nucleus size, nucleus shape, presence of holes in nucleus, cell size, cell shape and nuclear to cytoplasmic ratio, nuclear detail, nuclear contour, prevalence of nucleoli, quality of cytoplasm, and quantity of cytoplasm. The term CTC also encompasses "non-traditional CTCs" which differ from a traditional CTC in at least one characteristic. Non-traditional CTCs include the five CTC subpopulations, including CTC clusters, CK negative (CK-). CTCs that are positive at least one additional biomarker that allows classification as a CTC, small CTCs, nucleoli CTCs and CK speckled CTCs. As used herein, the term "CTC cluster" means two or more CTCs with touching cell membranes.

[0050] In some particular embodiments, circulating tumor cells ("CTCs") are cells of epithelial origin that are present in the circulation of patients with different solid malignancies. In those embodiments, CTCs can be derived from clones of the primary tumor and maybe malignant. [Fehm et al., Clin. Cancer Res. 8: 2073-84, 2002.] Evidence has accumulated in the literature showing that CTCs can be considered an independent diagnostic for cancer progression of carcinomas [Beitsch & Clifford, Am. J. Surg. 180(6): 446-49, 2000 (breast); Feezor et εΛ., Αηη. Oncol. Surg. 9(10): 944-53, 2002 (colorectal); Ghossein et al., Diagn. Mol. Pathol. 8(4): 165-75, 1999 (melanoma, prostate, thyroid); Glaves, Br. J. Cancer 48: 665-73, 1983 (lung); Matsunami et al., Ann. Surg. Oncol. 10(2): 171-5, 2003 (gastric); Racila et al., 1998; Pantel et al., 1999].

[0051] CTCs in blood can be detected and analyzed using classical descriptive approaches using a variety of techniques [Racila E, et al. (1998) Proc Natl Acad Sci USA 95: 4589-4594]. As validated tools to identify epithelial cells in circulation have emerged, in cancer patients these circulating epithelial cells (CEC) cells were called CTC [Ring AE, et al. (2005). British Journal of Cancer 92: 906-912; Witzig TE, et al. (2002). Clinical Cancer Research 8: 1085-1091]. It was shown that these circulating epithelial cells (CEC) were in fact CTC and were prognostically related to metastatic disease in breast, prostate, and colorectal cancer. CECs in normal healthy individuals do not exhibit cancer-relevant DNA mutations [Strauss et al. Analysis of tumor template from compartments in a blood sample provides complementary access to peripheral tumor biomarkers. Oncotarget (2016).]. The CellSearch® (Veridex, NJ) platform defines CTC as a population of nucleated epithelial cells that can be selected using EpCAM ferrofluid, lack the lymphocyte marker CD45 and express the intracellular epithelial marker cytokeratin (CK). This definition was used to demonstrate recovery of elevated CEC from patients with cancer but not healthy volunteers [Allard WJ, et al. (2004). Clin Cancer Res 10: 6897-6904. doi: 10.1 158/1078-0432.CCR-04-0378.

[0052] Detection and enumeration of circulating tumor cells is important for patient care for a number of reasons. CTCs are detectable before the primary tumor, thus allowing early stage diagnosis. They decrease in response to therapy, so the ability to enumerate CTCs allows one to monitor the effectiveness of a give therapeutic regimen. CTCs can also be used as a tool to monitor for recurrence in patients with no measurable disease in the adjuvant setting. For example, CTC were found to be present in 36% of breast cancer patients 8-22 years after mastectomy, apparently from micrometastases (deposits of single tumor cells or very small clusters of neoplastic cells). [Meng et al., Clin. Can. Res. 1024): 8152-62, 2004.]

[0053] In addition, CTCs can be used to predict progression-free survival (PFS) and overall survival (OS), as the presence/number of circulating tumor cells in patients with metastatic carcinoma has been shown to be correlated with both PFS and OS. See e.g., Cristofanilli et al., J. Clin. Oncol. 23(1): 1420-1430, 2005; Cristofanilli et al., N. Engl J. Med. 351(8): 781-791 , 2004.

[0054] In assays, methods and systems herein described, detection, detection and/or identification of CTCs or other abnormal cells in an individual can be performed by analyzing a copy number variation in a genome of individual cells from a biological sample wherein detection of copy number variation is a marker for the CTC or another abnormal cell.

[0055] In several embodiments of assays methods and systems herein described, analyzing the copy number variation can be performed on individual cells of the biological sample following enriching the CTC or other abnormal cell of the biological sample. In particular, in some embodiments, the sample is treated to increase the abundance of CTCs in the biological sample and further differentiate the CTC from other cells of the sample, such as white blood cells of a blood sample.

[0056] The term "cell enrichment" as used herein indicates a process to isolate from a mixture of cells a relatively pure population of one particular kind of cell (e.g. CTCs) and remove other kinds of cells (e.g., non-CTCs). Suitable methods to separate cells comprise sorting based on surface markers with techniques such as Magnetically activated Cell Sorting and/or additional techniques identifiable by a skilled person.

[0057] In particular, in some embodiments, the methods and systems herein described comprise enriching the possible number of circulating tumor cells (CTCs). The enriching can be performed by various methods. Such methods include isolation based on one or more CTC characteristics selected of (i) number of CTCs; (ii) location of markers; (iii) status of nucleus; (iv) degree of cytokeratin 8 expression; (v) degree of cytokeratin 18 expression; (vi) degree of cytokeratin 19 expression; (vii) degree of EpCAM expression; (viii) degree of vimentin expression; (ix) degree of PD-L1 expression; (x) degree of uroplakin expression; (xi) degree of HER2 expression; (xii) degree of Trop2 expression; (xiii) degree of NCAM expression; (xiv) degree of CgA expression; (xv) degree of TTF-1 expression; (xvi) cytokeratin morphology; and (xvii) intensity of marker staining.

[0058] Cells from a biological sample that have one or more of the above characteristics can then be isolated using methods such as capture with integrated on-chip fluorescent microscopic analytic capability, size/deformability exclusion methodology, non-targeted cell depletion based negative selective, positive selection with antibodies, density gradient centrifuge (FICOLL), microfluidic chip and flow cytometry, or a combination thereof.

[0059] A person skilled in the art will appreciate that a number of methods can be used to detect and analyze CTC characteristics in cells from a biological sample in during the enriching, including microscopy based approaches, including fluorescence scanning microscopy (see, e.g., Marrinucci D. et al, 2012, Phys. Biol. 9 016003), mass spectrometry approaches, such as MS/MS, LC-MS/MS, multiple reaction monitoring (MRM) or super-resolution microscopy (SRM) and product-ion monitoring (PIM) and also including antibody based methods such as immunofluorescence, immunohistochemistry, immunoassays such as Western blots, enzyme- linked immunosorbant assay (ELISA), immunoprecipitation, radioimmunoassay, dot blotting, and fluorescence-activated cell sorting (FACS). Immunoassay techniques and protocols are generally known to those skilled in the art [Price and Newman, Principles and Practice of Immunoassay, 2nd Edition, Grove's Dictionaries, 1997; and Gosling, Immunoassays: A Practical Approach, Oxford University Press, 2000.] A variety of immunoassay techniques, including competitive and non-competitive immunoassays, can be used [Self et al, Curr. Opin. Biotechnol, 7:60-65 (1996), see also John R. Crowther, The ELISA Guidebook, 1 st ed., Humana Press 2000, ISBN 0896037282 and, An Introduction to Radioimmunoassay and Related Techniques, by Chard T, ed., Elsevier Science 1995, ISBN 0444821 198].

[0060] In some embodiments, the detection and analysis of CTC's characteristics in cells from a biological sample can be performed by fluorescent scanning microscopy. In certain embodiments, the microscopic method provides high-resolution images of CTCs and their surrounding WBCs (see, e.g., Marrinucci D. et al., 2012, Phys. Biol. 9 016003). In some embodiments, a slide coated with a monolayer of nucleated cells from a sample, such as a non- enriched blood sample, is scanned by a fluorescent scanning microscope and the fluorescence intensities from immunofluorescent markers and nuclear stains are recorded to allow for the determination of the prevalence of each immunofluorescent marker and the assessment of the morphology of the nucleated cells. In some embodiments, microscopic data collection and analysis is conducted in an automated manner.

[0061] In some embodiments, the prevalence of immunofluorescent markers in nucleated cells is determined by selecting the exposure times during the fluorescence scanning process such that all immunofluorescent markers achieve a pre-set level of fluorescence on the WBCs in the field of view. Under these conditions, CTC-specific immunofluorescent markers, even though absent on WBCs, are visible in the WBCs as background signals with fixed heights. Moreover, WBC- specific immunofluorescent markers that are absent on CTCs are visible in the CTCs as background signals with fixed heights. A cell is considered positive for an immunofluorescent marker (i.e., the marker is considered present) if its fluorescent signal for the respective marker is significantly higher than the fixed background signal (e.g., 2-fold, 3 -fold, 5-fold, or 10-fold higher than the background; e.g., 2σ or 3σ over background). For example, a nucleated cell is considered CD 45 positive (CD 45 ) if its fluorescent signal for CD 45 is significantly higher than the background signal. A cell is considered negative for an immunofluorescent marker (i.e., the marker is considered absent) if the cell's fluorescence signal for the respective marker is not significantly above the background signal (e.g., <1.5-fold or <2.0-fold higher than the background signal; e.g., <1.5σ or <2.0σ over background).

[0062] In some embodiments, enriching the number of circulating tumor cells can be accomplished with an automated CTC capture platform, for example LiquidBiopsy® (Cynvenio Biosystems). The LiquidBiopsy® platform is an automated cell isolation platform that provides reliable access to rare populations of cancer cells in whole blood. This platform uses a multiplayer sheath flow with density-adjusted buffers to prevent nonspecific binding of non- target cells to chamber surfaces. In some embodiments, the platform can be combined with antibody-based capture cocktails described below to further increase the capture efficiency in patients with cancers such as breast cancer. In some embodiments, cocktail-based positive selection for target cancer and leukocyte-depletion-based negative strategies can be employed with the LiquidBiopsy® platform to improve the CTC capture efficiency for early detection. Detailed information about the LiquidBiopsy® platform can be found in related publications such as Strauss WM et. al., Oncotarget, 2016, 7(18): 26724-38 and Winer- Jones JP et.al., PLoS One, 2014; 9(1): e86717 and patents such as US Patent No. 8,263,387, the disclosure of which is incorporated herein by reference in its entirety.

[0063] In some embodiments of assays, methods and systems herein described, enriching the number of CTCs from the biological sample comprises incubating the biological sample with one or more target binding moieties that bind to a target found on cancer cells and isolating the cells that bind to the one or more target binding moieties. The one or more target binding moieties that bind to the target found on cancer cells are used to enrich CTCs. Such cancer target binding moiety can bind to targets or biomarkers, such as synaptophysin (Syn), neural cell adhesion (NCAM), chromogranin-A (CgA), thyroid transcription factor (TTF-1) or EpCAM. Cells with detected binding of the target binding moiety can then be isolated to enrich the CTCs according to some embodiments herein described, [0064] In at least one embodiment, the one or more target binding moiety is an antibody. A person of skill in the art will further appreciate that the prevalence of protein biomarkers may be detected using any class of marker-specific binding reagents known in the art, including, e.g., antibodies, aptamers, fusion proteins, such as fusion proteins including protein receptor or protein ligand components, or biomarker-specific small molecule binders. In some embodiments, the prevalence of AR, CK or CD45 is determined by an antibody. The antibodies of this disclosure bind specifically to a protein biomarker. The antibody can be prepared using any suitable methods known in the art. See, e.g., Coligan, Current Protocols in Immunology (1991); Harlow & Lane, Antibodies: A Laboratory Manual (1988); Goding, Monoclonal Antibodies: Principles and Practice (2d ed. 1986). The antibody can be any immunoglobulin or derivative thereof, whether natural or wholly or partially synthetically produced. All derivatives thereof which maintain specific binding ability are also included in the term. The antibody has a binding domain that is homologous or largely homologous to an immunoglobulin binding domain and can be derived from natural sources, or partly or wholly synthetically produced. The antibody can be a monoclonal or polyclonal antibody. In some embodiments, an antibody is a single chain antibody. Those of ordinary skill in the art will appreciate that antibody can be provided in any of a variety of forms including, for example, humanized, partially humanized, chimeric, chimeric humanized, and additional forms identifiable by a skilled person.

[0065] The antibody can be an antibody fragment including Fab, Fab', F(ab')2, scFv, Fv, dsFv diabody, and Fd fragments. The antibody can be produced by any means. For example, the antibody can be enzymatically or chemically produced by fragmentation of an intact antibody and/or it can be recombinantly produced from a gene encoding the partial antibody sequence. The antibody can comprise a single chain antibody fragment.

[0066] Alternatively or additionally, the antibody can comprise multiple chains which are linked together, for example, by disulfide linkages, and any functional fragments obtained from such molecules, wherein such fragments retain specific-binding properties of the parent antibody molecule. Because of their smaller size as functional components of the whole molecule, antibody fragments can offer advantages over intact antibodies for use in certain immunochemical techniques and experimental applications. [0067] In at least one embodiment, a cocktail of more than one cancer cell binding moieties is used to enrich CTCs.

[0068] In at least one embodiment, a binding moiety that binds to EpCAM is used to enrich CTCs.

[0069] In at least one embodiment, enrichment of CTCs can be performed with the CellSearch Epithelial Cell Kit.

[0070] In at least one embodiment, a cocktail of cancer target binding moieties that binds to EpCAM/Her2/Trop2 can be used to enrich CTCs in breast cancer detection.

[0071] In at least one embodiment, a negative selection approach is used in the enrichment of CTCs. In those embodiments, the mixture of cells is treated to remove cells showing characteristics that are not present in CTCs. Such negative selection approach can include for example, leukocyte depletion.

[0072] In some embodiments, the assays methods and systems herein described comprise analyzing copy number variations (CNVs) in a genome of individual cells from a biological sample, optionally following an enriching of abnormal cell from the sample. In particular, in some of these embodiments analyzing the CNV is performed to detect a section of the genome having a copy number greater or less than 2. In some of those embodiments, analyzing the CNV is performed to detect a single cell copy number variation pattern or profile.

[0073] The term "copy number variation pattern" or "copy number variation profile" indicates a set of sections of a genome of a single cell, each section having a copy number greater or less than 2. A copy number variation profile can be generated from a genome- wide copy number variation analysis of chromosomal rearrangement in a single cell and contains a copy number state for each of the sections of the set of sections herein described.

[0074] In some embodiments, analyzing the copy number variations can be performed by amplifying the genome of individual cells; assessing the coverage of genomic regions; and informing or detecting the copy number variations across the genome. [0075] In at least one embodiment, the step of assessing the coverage of genomic regions is performed with techniques selected from the group consisting of: whole-genome sequencing, comparative genomic hybridization (CGH), single-nucleotide polymorphic allele (SNP) array, and chromosome painting.

[0076] . The term "sequencing" or "nucleic acid sequencing" is a method for determining the order of nucleotides present in a given DNA or RNA molecule or other polynucleotide molecule, the order of the four bases adenine, guanine, cytosine and thymine in a strand of DNA or the four bases adenine, guanine, cytosine and uracil in RNA.

[0077] Approaches to DNA or RNA sequencing include dideoxy sequencing, also known as Sanger sequencing, cyclic array sequencing, sequencing by hybridization, microelectrosphoresis, mass spectrometry and nanopore sequencing. Detailed information about these sequencing techniques can be found in related literatures and will be understood by a person of ordinary skill in the art.

[0078] The term "whole-genome sequencing" refers to the process of determining the complete DNA sequence of an organism's chromosomal DNA as well as DNA contained in other organelles such as mitochondria or chloroplast in plants. Genomic information obtained from whole-genome sequencing can aid in identifying inherited disorders, characterizing the mutations that may drive cancer progression and tracking disease outbreaks and progression.

[0079] In some embodiments, the whole-genome sequencing can be performed using next- generation sequencing (NGS) approached, also known as high-throughput sequencing. NGS is a term used to describe a number of different modern nucleic acid sequencing technologies including Illumia™ sequencing, Roche 454™ sequencing, Ion torrent: Protein/PGM™ sequencing and SOLiD™ sequencing. Next-generation sequencing (NGS) generally refers to non-Sanger-based high- throughput DNA sequencing technologies. The NGS technologies can be based on immobilization of the nucleotide samples onto a solid support, cyclic sequencing reactions using automated fluidics devices and detection of molecular events by imaging. Cyclic array platforms achieve low costs by simultaneously decoding a two-dimensional array bearing millions or billions of distinct sequencing features, each containing one species of DNA physically immobilized on an array. In each cycle, an enzymatic process is applied to interrogate the identity of a single base position for all features in parallel. The enzymatic process is coupled to either the production of light or the incorporation of a fluorescent group. At the end of each cycle, data are acquired by imaging of the array. Subsequent cycles are typically performed interrogating different base position within the sequence. Detailed information about various next-generation sequencing approaches can be found in related literation and documents and will be understood by a person skilled in the art.

[0080] In some embodiments, the whole-genome sequencing is single-cell sequencing, that is, each captured cell is individually sequenced in an automated platform using the NGS sequencing methods herein described. In particular, single-cell sequencing approaches perform nucleic acid sequencing on an individual cell isolated from primary samples using NGS technologies herein described. The single-cell sequencing provides higher-resolution views of the genomic content of samples by reducing the complexity of the genomic signal through the physical separation of cells or chromosomes. The single-cell sequencing can be performed with single cell sequencing apparatus such as those from Fludigm, WaferGen Biosystems and others identifiable to a skilled person.

[0081] The sequencing of single cells can also be accomplished by the sequencing of pooled cells which have been individually barcoded. The sequencing of pooled cells can be combined with sample multiplexing for targeting specific genomic regions. A multiplex detection method can combine the use of color, multiplicity of signal intensity, and/or mathematical strategies to circumvent degeneracy and ensure an infinite number of unique codes that can be unambiguously decoded in any combination of occurrences. For example, individual "barcode" sequence can be added to an individual cell to distinguish and sort the cell during data analysis. Detailed information about multiplex sequencing assay and single-cell sequencing can be found in related publications and online resources identifiable to a person skilled in the art.

[0082] The sequencing approaches herein described can produce sequence files typically in text- based format such as a plain sequence format, a FASTQ format, a FASTA format, an EMBL format, and other formats identifiable to a skilled person in the art. These sequence files can contain one or more nucleotide sequences, its identification number, and corresponding quality scores as well as some annotation information in some data formats, which can be used later for sequence analysis.

[0083] In some embodiments of assays, methods and systems comprising analyzing the copy number variations following amplifying the genome of individual cells and assessing the coverage of genomic regions on the amplified genome, the method further comprises informing or detecting the copy number variations across the genome and determining if a cell is an abnormal cell based on the copy number variation analysis.

[0084] In at least one embodiment, the step of informing or detecting the copy number variations across the genome is performed with stringent analyses of copy number status or a visual check of the genome coverage to detect a section of the genome having a copy number greater or less than 2, and/or to detect a single cell copy number variation pattern or profile.

[0085] In at least one embodiment, the step of determining if a cell is an abnormal cell based on the copy number variation analysis is performed based on criteria including the percentage of chromosome regions alternated from diploid regions and/or the presence of certain alternated chromosome regions.

[0086] In some embodiments, the copy number variations are analyzed and informed using computational CNV detection methods based on the NGS data obtained from the NGS sequencing approaches herein described. Many copy number variation analysis methods can be applied to the sequencing data files outputted from the NGS approaches. As a person skilled in the art would understand, there are several categories of CNV detection methods including paired-end mapping, split read, read depth and de novo assembly of a genome, combination of the above or other approaches identifiable to a person skilled in the art.

[0087] Exemplary CNV detection programs include SegSeq, ReadDepth, BICseq, Patchwalk, OncoSNP-SEQ, HMMCOPY, CONSERTING and others identifiable to a person of ordinary skill in the art. A CNV detection program generally takes one or more data types as inputs. These data types include, for example, read counts, read depth, B Allele frequency (BAF), soft-clipped reads, obtained from processing sequence data. The program then combines all the reads from same continuous region into a segment with determined boundaries, also referred to as "segmentation", in order to distinguish the data variation caused by genuine CNV from that by random effects. Exemplary algorithm used in the segmentation process includes Hidden Markov Model (HMM), Circular Binary Segmentation (CBS), regression tree, minimizing Bayesin Information Criterion (BIC), HAPSEG, SegSeq, Lasso, probalistic methods and others identifiable to a person skilled in the art. Next, the program will merge adjacent data points with same copy number into one segment and divide or classify regions with different copy numbers into different segments. The copy number state (gain or loss) of each segment can be determined from data interpretation. Detailed information about using computational methods for detecting and informing copy number variations in combination with next generation sequencing can be found in related publication such as Liu B. et. al., Oncotarget, 2013; 4: 1868-1881.

[0088] In some embodiments, the methods and systems herein described further comprise determining if a cell is an abnormal cell based on the copy number variation analysis.

[0089] In at least one embodiment, the step of determining if a cell is an abnormal cell based on the copy number variation analysis is performed based on criteria including the presence of additional chromosome regions alternated from diploid regions visualized by extra microscopic spots and/or the presence of certain alternated chromosome regions.

[0090] In at least one embodiment, the step of determining if a cell is an abnormal cell based on the copy number variation analysis is performed by comparing a detected copy number variation of an individual cell and a distinguishing copy number variation characterizing a normal cell from a normal healthy individual and/or a distinguishing copy number variation unique to a set cancer type, cancer stage type and/or tumor cell of the patient. The distinguishing copy number variation can be provided in a lookup table reporting features of the distinguishing copy number variation that are representative of the distinguishing copy number variation and are in a form allowing comparison with detectable features of the detected copy number variation as will be understood by a skilled person. In some embodiments, the distinguishing copy number variation comprise a distinguishing copy number variation profile in the sense of the disclosure and the look up table reports features characterizing the distinguishing copy number variation profile in a form allowing comparison with detectable features of the detected copy number variation profile, as will be understood by a skilled person. [0091] In at least one embodiment, the step of determining if a cell is an abnormal cell based on the copy number variation analysis is performed based on criteria including the percentage of chromosome regions alternated from diploid regions and/or the presence of certain alternated chromosome regions. In some cases, the percentage of chromosome regions alternated from diploid regions is about 0.05-0.1% of the genome

[0092] In some embodiments, the methods and systems herein described includes a method for detecting the presence of one or more cancer cells in a biological sample, the method comprising: enriching the number of possible circulating tumor cells from the biological sample; performing whole-genome amplification on individual cells; analyzing the copy number variation in the individual cells; and determining if a cell is a circulating tumor cell based on the copy number variation analysis.

[0093] In some embodiments, the performing whole-genome amplification on individual cells is omitted. Instead, specific regions of the genome are isolated and used as molecular probes.

[0094] In these embodiments, the method for detecting the presence of one or more cancer cells in a biological sample comprises: enriching the number of possible circulating tumor cells from the biological sample; isolating particular regions of the genome to use as probes on individual cells; analyzing the copy number variation in the individual cells; and determining if a cell is a circulating tumor cell based on the copy number variation analysis

[0095] In preferred embodiments of assays, methods and systems herein described, analyzing copy number variation in a genome of an individual cell from a biological sample can be performed by detecting of copy number variation on a set non-overlapping genetic regions on the genome of the individual cell to increase resolution of the analysis as will be understood by a skilled person.

[0096] In particular, performing detection of copy number variation on the set non-overlapping genetic regions, allows a more accurate detection of sections of the genome having a copy number greater or less than 2, and/or a more accurate detection of copy number variation profiles, compared to a CNV detection performed on the entire genome of the individual cell or on other portions of such genome. The set non-overlapping genetic regions can be provided from a reference genome such as the genome of a tumor cell of a set type of cancer, a set stage of cancer and/or of a patient.

[0097] In assays, methods and systems herein described, the set non-overlapping genetic regions can have various length as will be understood by a skilled person upon reading of the present disclosure. Preferably each genetic region of the set non-overlapping genetic regions is independently from 100 kb to 500 kb in size, more preferably from 100 kB to 300 kb in size. In some embodiments each genetic region of the set non-overlapping genetic regions has a same size. In some embodiments, at least some, preferably all genetic regions of the set non- overlapping genetic regions are contiguous on a reference genome. In some embodiments the set non-overlapping genetic regions cover a reference genome in its entirety.

[0098] In some embodiments, the reference genome is the genome of the patient. In some embodiments, the reference genome is the genome of a tumor cell and in particular can be the genome of a tumor cell with a set cancer type and/or cancer stage as will be understood by a skilled person.

[0099] In some embodiments, analyzing copy number variation can be performed by providing the reference genome with the set non-overlapping genetic regions and detecting the set non- overlapping genetic regions of the reference genome in the genome of the individual cell, typically following amplification of the genome of the individual cell of the sample.

[00100] In some embodiments, detecting the set non-overlapping genetic regions in the genome of the individual cell can be performed by providing a set of polynucleotides configured to hybridize with genetic regions of the set non-overlapping genetic regions or with portions thereof.

[00101] In some of those embodiments, the set polynucleotides comprise labeled polynucleotide. The terms "label" and "labeled molecule" as used herein as a component of a molecule refers to a compound or moiety capable of detection, including but not limited to radioactive isotopes, fluorophores, chemiluminescent dyes, chromophores, enzymes, enzymes substrates, enzyme cofactors, enzyme inhibitors, dyes, metal ions, nanoparticles, metal sols, ligands (such as biotin, avidin, streptavidin or haptens) and the like. The term "fluorophore" refers to a substance or a portion thereof which is capable of exhibiting fluorescence in a detectable image. As a consequence, the wording "labeling signal" as used herein indicates the signal emitted from the label that allows detection of the label, including but not limited to radioactivity, fluorescence, chemiluminescence, production of a compound in outcome of an enzymatic reaction and the like. Techniques allowing detection of genetic regions with labeled polynucleotides (e.g. FISH and chromosome painting, comparative genomic hybridization, and array comparative genomic hybridization) are identifiable by a skilled person.

[00102] The term "FISH", or "fluorescence in situ hybridization", as used herein refers to a cytogenetic technique that is used to detect and localize the presence or absence of specific DNA sequences on chromosomes. FISH uses fluorescent polynucleotide probes that bind to only specific regions of the chromosome with which the probes show a high degree of sequence complementarity. Fluorescence microscopy can be used to find out where the fluorescent probe bound to the chromosomes. FISH can be used for finding specific features in DNA for use in genetic counseling, medicine, and species identification. FISH can also be used to detect and localize specific mRNAs within tissue samples. It can be used in some instances to define genetic alterations such as copy number variations within cells or tissues.

[00103] For example, an individual enriched cell sample can be added to a solution of suitable FISH hybridization reagents and incubated for a suitable period of time to allow hybridization of the labeled FISH probes to nucleic acids in the individual enriched cell. Once the hybridization is terminated, the labeled sample can be filtered and excess reagents removed.

[00104] FISH requires nucleic acid probes, including deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or nucleic acid analogs, labeled directly with fluorophores, or capable of indirect association with fluorophores. The nucleic acid probes provide the FISH assay with its specificity through complementary pairing of the probe nucleotides with nucleotides of the target nucleic acid. The appended fluorophores provide the ability to visually detect the homologous regions within the cellular structure using a fluorescence microscope. Photographic or electronic cameras can also be used to provide permanent images of the fluorescence staining patterns, and the latter can be used to provide quantitative measurements of the probe fluorescence as will be understood to a person skilled in the art. [00105] The probe used in FISH need to have the right size, that is, large enough to hybridize specifically with its target but not so large as to impede the hybridization process. The probe can be tagged directly with fluorophores, with targets for antibodies or with biotin, among other techniques known to those skilled in the art. Tagging can be done in various ways, such as nick translation, or PCR using tagged nucleotides, among others known to those skilled in the art. Probes can be prepared from genomic libraries, such as those in BAC, PAC or YAC libraries. Genomic DNA library collections that are available include but not limited to BAC or PAC genomic DNA clone resources such as those from the California Institute of Technology and Roswell Park Cancer Institute, among others know to those skilled in the art. Probes can be prepared from BAC and PAC libraries from commercial vendors, such as ThermoFisher Scientific, Clontech, and Invitrogen, among others. The probes can be selected from random BAC libraries that have been mapped and aligned to a consensus human genome map, after DNA sequencing of the ends of the BAC DNA. An interphase or metaphase chromosome preparation of sample cells is produced. This can be performed using standard techniques known in the art, such as described in [ref: Fluorescence In Situ Hybridization (FISH) - Application Guide, (2009) T. Liehr (ed.) Springer-Verlag Berlin Heidelberg 2009.]. The chromosome preparation can be performed using cells isolated from a biological sample, or using CTCs enriched from a biological sample. The cells can be fixed, such as using Carnoy fixative (methanol/acetic acid), and then can be spread onto slides. The chromosomes are firmly attached to a substrate, usually glass. Repetitive DNA sequences can be blocked by adding short fragments of DNA to the sample. The probe is then applied to the chromosome DNA and incubated for approximately 12 hours while hybridizing. Several wash steps remove all unhybridized or partially hybridized probes. The results are then visualized and quantified using a microscope that is capable of exciting the dye and recording images. If the fluorescent signal is weak, amplification of the signal may be necessary in order to exceed the detection threshold of the microscope. Fluorescent signal strength depends on many factors such as probe labeling efficiency, the type of probe, and the type of dye. Fluorescently tagged antibodies or streptavidin can be bound to the dye molecule. These secondary components are selected so that they have a strong signal.

[00106] A mixture of probes can be used for FISH. The mixture of probe sequences determines the type of feature the probe can detect. For example, probes that hybridize along an entire chromosome are used to count the number of a certain chromosome, show translocations, or identify extra-chromosomal fragments of chromatin. This is often called "whole-chromosome painting." It is possible to create a mixture of smaller probes that are specific to a particular region (locus) of DNA; these mixtures are used to detect deletion mutations. When combined with a specific color, a locus-specific probe mixture is used to detect very specific translocations. Special locus-specific probe mixtures are often used to count chromosomes, by binding to the centromeric regions of chromosomes, which are distinctive enough to identify each chromosome (with the exception of Chromosome 13, 14, 21 , 22.) A variety of other techniques use mixtures of differently colored probes. A range of colors in mixtures of fluorescent dyes can be detected, so each human chromosome can be identified by a characteristic color using whole-chromosome probe mixtures and a variety of ratios of colors. Although there are more chromosomes than easily distinguishable fluorescent dye colors, ratios of probe mixtures can be used to create secondary colors. Similar to comparative genomic hybridization, the probe mixture for the secondary colors is created by mixing the correct ratio of two sets of differently colored probes for the same chromosome. This technique is sometimes called M-FISH. The same physics that make a variety of colors possible for M-FISH can be used for the detection of translocations. That is, colors that are adjacent appear to overlap; a secondary color is observed. Some assays are designed so that the secondary color will be present or absent in cases of interest. An example is the detection of BCR/ABL translocations, where the secondary color indicates disease. This variation is often called double-fusion FISH or D-FISH. In the opposite situation— where the absence of the secondary color is pathological— is illustrated by an assay used to investigate translocations where only one of the breakpoints is known or constant. Locus-specific probes are made for one side of the breakpoint and the other intact chromosome. In normal cells, the secondary color is observed, but only the primary colors are observed when the translocation occurs. This technique is sometimes called "break-apart FISH". In an alternative technique to interphase or metaphase preparations, fiber FISH, interphase chromosomes are attached to a slide in such a way that they are stretched out in a straight line, rather than being tightly coiled, as in conventional FISH, or adopting a chromosome territory conformation, as in interphase FISH. This is accomplished by applying mechanical shear along the length of the slide, either to cells that have been fixed to the slide and then lysed, or to a solution of purified DNA. A technique known as chromosome combing is increasingly used for this purpose. The extended conformation of the chromosomes allows dramatically higher resolution - even down to a few kilobases.

[00107] Other FISH-based techniques comprise those such as Q-FISH, which combines FISH with PNAs and computer software to quantify fluorescence intensity, and Flow-FISH, which uses flow cytometry to perform FISH automatically using per-cell fluorescence measurements.

[00108] In some embodiments, non-overlapping contiguous FISH probes from a human genome library can be selected to survey tumor cells on slides in a FISH experiment. Probes that show CNV on a panel of tumor cells can then be used to screen cells from an individual known or suspected to have cancer. For example, probes that bind to regions of chromosome 3 or 10 that have been previously used to detect CNV in tumor cells can be used to perform FISH on cells sampled from an individual (see Fig. 4).

[00109] In some embodiments, to access copy number variation on a genome-wide level, a plurality of FISH probes are prepared from genomic library construction of a reference genome. The term "genomic library" as used herein refers to a collection of the total genomic DNA from a single organism such as a human genome. The DNA is cloned into a population of identical vectors, each containing a different insert of a DNA fragment from the reference genome. In order to construct a genomic library, an organism's DNA is extracted from cells and then digested with a restriction enzyme to cut the DNA into fragments of a specific size. The fragments are then inserted into the vector using DNA ligase. Next, the vector DNA can be taken up by a host organism with each cell containing one vector molecule and each vector molecule containing a piece of DNA fragment. Using a host cell to carry the vector allows for easy amplification and retrieval of specific clones from the library for analysis.

[00110] Several types of vectors available with various insert capacities are known to those skilled in the art. Vectors to use for generating genomic libraries can include YAC (Yeast Artificial Chromosomes) with about 250-2000 kb capacity, BAC (Bacterial Artificial Chromosomes) with about 100-500 kb capacity, PAC (PI -derived Artificial Chromosomes) with about 130-150 kb capacity, bacteriophage PI with about 70-100 kb capacity, cosmids with up to 45 kb capacity, phage lambda with up to 25 kb capacity, and plasmids with up to -15 kb capacity.

[00111] In some embodiments herein described, vectors that can maintain large inserts of at least lOOkb such as PACs, BACs or YACs are used for generating FISH probes homologous to unique sequence DNA of a reference genome. In some embodiments, the FISH probes used for CNV detection as described herein span at least lOOkb of contiguous sequence and up to 500kb.

[00112] In some embodiments herein described, the FISH probes can be prepared using Bacterial artificial chromosomes (BACs). BACs are circular DNA molecules, usually about 7kb in length and are capable of holding inserts up to ~300kb in size. BAC vectors contain a replicon derived from E. coli F factor, which ensures they are maintained at one copy per cell. Once an insert is ligated into a BAC, the BAC can be introduced into recombination deficient strains of E. coli by electroporation. BAC vectors can contain a gene for antibiotic resistance and also a positive selection marker.

[00113] In some embodiments, FISH probes can be prepared using yeast artificial chromosomes (YACs). YACs are linear DNA molecules containing the necessary features of an authentic yeast chromosome, including telomeres, a centromere, and an origin of replication. The recombinant YAC is introduced into yeast by transformation; selectable markers present in the YAC allow for the identification of successful transformants. YACs can hold inserts up to 2000kb, but most YAC libraries contain inserts 250-400kb in size.

[00114] In some embodiments, the FISH probes can be prepared using PI artificial chromosomes (PACs). PACs have features of both PI vectors and Bacterial Artificial Chromosomes (BACs). Similar to PI vector, PACs contain a plasmid and a lytic replicon as described above. Unlike PI vectors, PACs do not need to be packaged into bacteriophage particles for transduction. Instead they are introduced into E. coli as circular DNA molecules through electroporation just as BACs are.

[00115] In some embodiments, the set polynucleotide used for detecting the set non-overlapping genetic regions in the genome of the individual comprises primers configured to perform sequencing of genetic regions of the set non-overlapping genetic regions or portions thereof. In those embodiments, the detecting can be performed by sequencing segments of the genome of the individual cell with primers specific for genetic regions of the set non-overlapping genetic regions and mapping and/or assembling the sequenced segments to reconstruct the non- overlapping genetic regions on the genome of the individual cell. Techniques suitable to detect the set non-overlapping genetic regions by sequencing segments and mapping the sequenced segments (such as whole genome sequencing and multiplexed library preparation, PCR, SNP array, dideoxy sequencing, also known as Sanger sequencing, cyclic array sequencing, sequencing by hybridization, microelectrosphoresis, mass spectrometry, nanopore sequencing, next-generation sequencing, and DNA-Seq,) are identifiable by a skilled person.

[00116] In some of those embodiments, in addition to other sequencing techniques herein described and/or identifiable by a skilled person, read-depth based approaches are used to detect a single-cell copy number variation profile by examining the full spectrum of variants in the whole genome. In general, short reads generated from a sequencing procedure herein described are aligned to a reference genome using alignment/assembly tools identifiable by a person skilled in the art. "Reads" used herein are defined as a sequenced range of DNA or RNA. Then, genetic regions sometimes also called "bins", along the reference genome are defined. Each bin corresponds to a non-overlapping genetic region of the set non-overlapping genetic regions along a chromosome. In some embodiments, bins can be contiguous and non-overlapping with a size in a range from lOOkb to 500 kb, preferably from lOOkb to 300kb. Read depth is calculated according to the number of mapped reads in the predefined genomic windows. Next, normalization and correction of potential biases in read depths are carried out to correct biases mainly caused by GC contents and repeat genomic regions. With the normalized read depths, the copy number is then estimated for each bin along the chromosome to determine the gain or loss. Bins over-represented with reads are classified as "GAIN", i.e. a copy number greater than 2, and bins under-represented with reads are classified as "LOSS", i.e. a copy number less than 2. The genomic regions with a similar copy number can be merged to detect discordant copy number regions.

[00117] In some of those embodiments, the sequencing can be performed following amplifying with whole genome sequence amplification for robust amplification of an entire genome, starting with nanogram quantities of DNA and resulting in microgram quantities of amplified products. Several methods have been developed for high-fidelity whole genome amplification, such as Multiple Displacement Amplification (MDA), Degenerate Oligonucleotide PCR (DOP-PCR), Primer Extension Preamplification (PEP), and Multiple Annealing and Looping Based Amplification Cycles (MALBAC) among others known to a skilled person. Kits to perform WGA are commercially available from vendors such as Qiagen, NEB, and Sigma-Aldrich, among others.

[00118] In some embodiments, DOP-PCR can be used for whole-genome amplification of the individual enriched cells. DOP-PCR uses a partially degenerate primer which binds at many sites throughout the genome during several low-temperature annealing cycles. Then more specific priming at the fragments will be generated by increasing the annealing temperature. The starting template DNA of DOP-PCR can be as little as 15 pg or as much as 400 ng, and the quantity of DNA products is much greater than that of PEP [Wells et al., Nucleic Acids Res. 1999;27(4): 1214-1218; Peng et al., Eur J Obstet Gynecol Reprod Biol. 2007; 131(1): 13-20].

[00119] In some embodiments, multiple annealing looping-based amplification cycles (MALBAC) can be used for whole-genome amplification. MALBAC is a quasilinear whole genome amplification method. Unlike conventional DNA amplification methods that are nonlinear or exponential (in each cycle, DNA copied can serve as template for subsequent cycles), MALBAC utilizes special primers that allow amplicons to have complementary ends and therefore to loop, preventing DNA from being copied exponentially. This results in amplification of only the original genomic DNA and therefore reduces amplification bias. MALBAC is used to create overlapped 'shotgun' amplicons covering most of the genome [Zong, C; Lu, S.; Chapman, A.R.; Xie, S. (2012) "Genome-wide detection of single-nucleotide and copy-number variations of a single human cell." Science 338, 1622]. For NGS approaches herein described, MALBAC is followed by regular PCR which is used to further amplify amplicons.

[00120] In several embodiments, of assays, methods and systems herein described to analyze copy number variation in a genome of an individual cell, detecting the set non-overlapping genetic regions on the genome of the individual cell provides a detected set non-overlapping genetic regions of the genome of the individual cells of the biological sample which is then used to perform detection of copy number variation to complete the CNV analysis. [00121] In some embodiments, detecting copy number variation in a detected set non- overlapping genetic regions of the genome of the individual cells of the biological sample is performed by detecting in the detected set non-overlapping genetic regions, a section of the genome having a copy number greater or less than 2, and/or a copy number variation pattern or profile with techniques and related reagents, devices and/or softwares, identifiable by a skilled person upon reading of the present disclosure.

[00122] In some embodiments, a detected copy number variation can then be compared with a distinguishing copy number variation which characterizes a set reference cell and/or a related set reference genome to detect presence or absence of the reference cell in the biological sample. In particular, in those embodiments if the detected copy number variation pattern matches the distinguishing copy number variation pattern of the reference cell, the reference cell is present in the biological sample and if the detected copy number variation pattern does not match the distinguishing copy number variation pattern of the reference cell, the reference cell is absent in the biological sample.

[00123] In some embodiments, a detected copy number variation can then be compared with a distinguishing copy number variation which characterizes a set reference cell and/or a related set reference genome to characterize an individual cell of a biological sample. In those embodiments, the individual cell of the biological sample can be marked as the reference cell, when the detected copy number variation pattern matches a distinguishing copy number variation patterns of the reference genome.

[00124] Any method capable of determining a DNA copy number profile of a particular sample can be used herein provided the resolution is sufficient to identify the biomarkers of the invention. The skilled artisan is aware of and capable of using a number of different platforms for assessing whole genome copy number changes at a resolution sufficient to identify the copy number of the one or more biomarkers of the invention.

[00125] In some embodiments, a copy number variation profile contains one or more genomic section assigned with a copy number within the non-overlapping contiguous genetic regions each having a size in the range of lOOkb to 500kb, preferably in the range of lOOkb to 300kb, and each comprising at least one section of the genome. Exemplary single-cell copy number variation profiles are shown in Fig. 5. In particular, Fig. 5 shows the single-cell CNV patterns across the genome for CTC cells isolated from seven patients along with two normal leukocytes cells. The single-cell CNV patterns from the CTC cells show that non-overlapping genetic regions having abnormal copy numbers greater than 2 span a large portion of the chromosome regions.

[00126] In at least one embodiment, a barcoding strategy for multiplexed MALBAC-based single cell sequencing can be developed as described in Example 1 and shown in Fig. 6 to detect a copy number variation profile in an individual cell of a patient.

[00127] Thus, in some embodiments, detecting a copy number profile in the individual cell comprises multiplex single-cell sequencing the individual enriched cells and generating a single- cell copy number profile based on the sequencing. The multiple genetic variations are tested in the same cell sample, each assigned with a different barcode sequence, color and/or multiplicity of signal intensity as will be understood by the skilled person.

[00128] A comparison of a single-cell copy number variation profile between a an individual cell of the sample and a reference cell, such as a normal cell and a possible abnormal cell can be conducted to determine whether the individual cell of the sample is an abnormal cell such as a CTC cell (see Fig. 4 and 5). Comparison of single-cell copy number variation profiles can also be conducted between CTC cells collected at different treatment time-points of a cancer patient, such as before chemotherapy, after first-line chemotherapy, and after second-line chemotherapy (see Fig. 2), or between CTC cells collected from various cancer stages such as stages 0-IV, to identify genetic regions responsible for disease progression. Comparison of single-cell copy number variation profiles can also be conducted between a normal cell and a cancer cell to identify genetic biomarkers in association with that particular cancer type. Copy number variations also vary between different individuals. Therefore, comparison of single-cell copy number variation profiles between one individual from another can provide useful information with respect to genetic variation at particular loci on a chromosome, thus allowing for personalized targeted therapies.

[00129] For example, Fig. 2 shows CNV patterns at a whole-genome scale from a SCLC patient at different treatment time-points (before chemotherapy, after first-line chemotherapy, and after second-line chemotherapy) [Ni, X. et al. Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients. Proc Natl Acad Sci U S A 110, 21083- 21088 (2013)]. Every CTC collected at different therapeutic stages exhibited similar characteristic copy number variation patterns, thus indicating that the reproducible CNV patterns observed were not affected by drug treatment. These characteristic genome alterations can be used to distinguish SCLC from other type of cancers.

[00130] In at least one embodiment, assays, methods and systems herein described can be used in a method for detecting cancer in a patient comprising enriching a group of cells from a patient sample; analyzing the copy number variations in the individual enriched cells; and confirming the detection of cancer by identifying abnormal cells with CNVs.

[00131] In at least one embodiment, the patient sample is peripheral blood and the group of abnormal cells is circulating tumor cells (CTCs).

[00132] In another embodiment the single cell sequencing is performed by pools of cells individually barcoded, and de-multiplexed after sequencing.

[00133] In some embodiments, a method of performing a CTC enrichment assay is described. The method comprises: incubating a patient biological sample with an antibody, wherein the antibody comprises an antibody that bind to EpCAM; and identifying one or more cells that have a signal from an antibody that binds to EpCAM. The method can further comprise generating a single cell copy number variation profile of the identified one or more cells.

[00134] In some embodiments, a system for detecting cancer by testing a biological sample is described. The system comprises: a reagent comprising an cancer target binding moiety; a CTC isolation apparatus; a single cell sequencing apparatus; and a copy number variation profile analyzer apparatus; wherein the CTC isolation apparatus is configured so that the presence and distribution of cells that are bound to the cancer target binding moiety are identified; wherein the sequencing apparatus is configured so that the individual cells that bound to the cancer target binding moiety are sequenced for copy number variations; and wherein the copy number variation profile analyzer apparatus is configured so that a copy number variation profile is compared to known normal cell profiles or known cancerous or precancerous cell profiles.

[00135] In at least one embodiment, single cell sequencing apparatus includes those from Fludigm and WaferGen Biosystems.

[00136] In some embodiments, a method of providing information required for diagnosis or prognosis of cancer is described. The method comprises: obtaining a cell from a biological sample isolated from a human; generating a genomic copy number variation profile of a cell; and determining if the copy number variation profile is of cancer origin.

[00137] In some embodiments, a method of screening a subject for cancer is described. The method comprises detecting CTCs in a biological sample by copy number variation profiles.

[00138] In some embodiments, a method of diagnosing cancer in a subject is described. The method comprises screening a biological sample of the patient for presence or absence of abnormal cell such as circulating tumor cells with methods and/or systems herein described, and/or detecting in the biological sample for presence or absence of abnormal cell such as circulating tumor cells with methods and/or systems herein described; wherein when circulating tumor cells are detected in the biological sample by copy number variation and in particular by copy number variation profiles, the subject is diagnosed with cancer.

[00139] In some embodiments, the method is a method of diagnosing and treating a cancer and the method further comprises administering an anti-cancer agent or therapeutic (e.g. radiation and/or chemotherapic agent) to the subject when the subject is diagnosed with cancer. In some particular embodiments, the method further comprises a recommendation for a doctor to test with other diagnostic tools.

[00140] In some embodiments, a method for detecting recurrence of cancer in a subject is described. The method comprises screening a biological sample of the patient for presence or absence of abnormal cell such as circulating tumor cells with methods and/or systems herein described, and/or detecting in the biological sample for presence or absence of abnormal cell such as circulating tumor cells with methods and/or systems herein described from a subject previously treated for cancer, wherein when circulating tumor cells are detected in the biological sample by copy number variation analysis, recurrence of cancer is detected.

[00141] In some embodiments, detection of recurrence of a cancer can be performed in a patient even in the absence of clinical symptoms.

[00142] In at least one embodiment, the cancer is a solid tumor. In at least one embodiment, the cancer is Stage 0, Stage I, Stage II, Stage III, or Stage IV cancer. In at least one embodiment, the cancer is an epithelial cell cancer. In at least one embodiment, the cancer is breast, prostate, lung, pancreatic, or colorectal. In at least one embodiment, the cancer is lung cancer. In at least one embodiment, the cancer is small cell lung cancer.

[00143] In some embodiments herein described, the circulating tumor cell is of lung cancer origin. In some embodiments, the lung cancer includes subtypes such as non-small cell lung cancer (NSCLC) or small cell lung cancer (SCLC).

[00144] In at least one embodiment, the biological sample is obtained from a subject who has a history of smoking cigarettes. In at least one embodiment, the biological sample is obtained from a subject who does not have a history of smoking cigarettes. In at least one embodiment, the biological sample is obtained from a subject who has lung cancer and has not been treated for lung cancer. In at least one embodiment, the biological sample is obtained from a subject who has received a lung cancer treatment consisting of: radiation therapy, chemotherapy, surgery, or combinations thereof. In at least one embodiment, the biological sample is from a patient not currently diagnosed with cancer.

[00145] Currently, standard treatment of primary tumors consists of surgical excision followed by either radiation or IV administered chemotherapy. The typical chemotherapy regime consists of either DNA alkylating agents, DNA intercalating agents, CDK inhibitors, or microtubule poisons. The chemotherapy doses used are just below the maximal tolerated dose and therefore dose limiting toxicities typically include, nausea, vomiting, diarrhea, hair loss, neutropenia and the like. There are large numbers of antineoplastic agents available in commercial use, in clinical evaluation and in pre-clinical development, which would be selected for treatment of neoplasia by combination drug chemotherapy. Such antineoplastic agents fall into several major categories, namely, antibiotic-type agents, alkylating agents, antimetabolite agents, hormonal agents, immunological agents, interferon- type agents and a category of miscellaneous agents.

[00146] A first family of antineoplastic agents which may be used in combination with compounds of the present disclosure comprise antimetabolite-type/thymidilate synthase inhibitor antineoplastic agents. Suitable antimetabolite antineoplastic agents may be selected from but not limited to the group consisting of 5-FU, fibrinogen, acanthifolic acid, aminothiadiazole, brequinar sodium, carmofur, Ciba-Geigy CGP-30694, cyclopentyl cytosine, cytarabine phosphate stearate, cytarabine conjugates, Lilly DATHF, errel Dow DDFC, dezaguanine, dideoxycytidine, dideoxyguanosine, didox, Yoshitomi DMDC, doxifluridine, Wellcome EHNA, Merck & Co. EX-015, fazarabine, floxuridine, fludarabine phosphate, 5- fluorouracil, N-(2'- furanidyl)-5-fluorouracil, Daiichi Seiyaku FO- 152, isopropyl pyrrolizine, Lilly LY- 1 8801 1 , Lilly LY-26461 8, methobenzaprim, methotrexate, Wellcome MZPES, norspermidine, NCI NSC- 127716, NCI NSC-264880, NCI NSC-39661 , NCI NSC-612567, Warner-Lambert PALA, pentostatin, piritrexim, plicamycin, Asahi Chemical PL-AC, Takeda TAC-788, thioguanine, tiazofurin, Erbamont TIF, trimetrexate, tyrosine kinase inhibitors, Taiho UFT and uricytin.

[00147] A second family of antineoplastic agents which may be used in combination with compounds of the present invention consists of alkylating-type antineoplastic agents. Suitable alkylating-type antineoplastic agents may be selected from but not limited to the group consisting of Shionogi 254-S, aldo-phosphamide analogues, altretamine, anaxirone, Boehringer Mannheim BBR-2207, bestrabucil, budotitane, Wakunaga CA- 102, carboplatin, carmustine, Chinoin- 139, Chinoin- 1 53, chlorambucil, cisplatin, cyclophosphamide, American Cyanamid CL-286558, Sanofi CY-233, cyplatate, Degussa D- 19-384, Sumimoto DACHP(Myr)2, diphenylspiromustine, diplatinum cytostatic, Erba distamycin derivatives, Chugai DWA- 21 14R, ITI E09, elmustine, Erbamont FCE-24517, estramustine phosphate sodium, fotemustine, Unimed G-6-M, Chinoin GYKI- 1 7230, hepsul-fam, ifosfamide, iproplatin, lomustine, mafosfamide, mitolactol, Nippon Kayaku N - 121 , NCI NSC-264395, NCI NSC- 342215, oxaliplatin, Upjohn PCNU, prednimustine, Proter PTT- 1 19, ranimustine, semustine, SmithKline SK&F- I 01772, Yakult Honsha SN-22, spiromus-tine, Tanabe Seiyaku TA-077, tauromustine, temozolomide, teroxirone, tetraplatin and trimelamol.

[00148] A third family of antineoplastic agents which may be used in combination with compounds of the present invention consists of antibiotic-type antineoplastic agents. Suitable antibiotic-type antineoplastic agents may be selected from but not limited to the group consisting of Taiho 4181 -A, aclarubicin, actinomycin D, actinoplanone, Erbamont ADR-456, aeroplysinin derivative, Ajinomoto AN-201 -II, Ajinomoto AN-3, Nippon Soda anisomycins, anthracycline, azino-mycin-A, bisucaberin, Bristol-Myers BL-6859, Bristol-Myers BMY- 25067, Bristol-Myers BMY-2555 1 , Bristol-Myers BMY-26605, Bristol-Myers BMY-27557, Bristol-Myers BMY- 28438, bleomycin sulfate, bryostatin- 1 , Taiho C- 1027, calichemycin, chromoximycin, dactinomycin, daunorubicin, yowa Hakko DC- 102, Kyowa Hakko DC-79, Kyowa Hakko DC- 88A, Kyowa Hakko DC89-A, Kyowa Hakko DC92-B, ditrisarubicin B, Shionogi DOB-41 , doxorubicin, doxorubicin-fibrinogen, elsamicin-A, epirubicin, erbstatin, esorubicin, esperamicin- Al , esperamicin-Alb, Erbamont FCE-21954, Fujisawa FK-973, fostriecih, Fujisawa FR-900482, glidobactin, gregatin-A, grincamycin, herbimycin, idarubicin, illudins, kazusamycin, kesarirhodins, Kyowa Hakko KM-5539, Kirin Brewery KRN-8602, Kyowa Hakko KT-5432, Kyowa Hakko KT-5594, Kyowa Hakko KT-61 9, American Cyanamid LL-D491 94, Meiji Seika ME 2303, menogaril, mitomycin, mitoxantrone, SmithKline M-TAG, neoenactin, Nippon Kayaku NK-313, Nippon Kayaku NKT-01 , SRI International NSC-357704, oxalysine, oxaunomycin, peplomycin, pilatin, pirarubicin, porothramycin, pyrindanycin A, Tobishi RA-1 , rapamycin, rhizoxin, rodorubicin, sibanomicin, siwenmycin, Sumitomo SM-5887, Snow Brand SN-706, Snow Brand SN-07, sorangicin-A, sparsomycin, SS Pharmaceutical SS-21020, SS Pharmaceutical SS-7313B, SS Pharmaceutical SS-98 16B, steffimycin B, Taiho 4181 -2, talisomycin, Takeda TAN-868A, terpentecin, thrazine, tricrozarin A, Upjohn U-73975, Kyowa Hakko UCN- 10028A, Fujisawa WF-3405, Yoshitomi Y-25024 and zorubicin.

[00149] A fourth family of antineoplastic agents which may be used in combination with compounds of the present invention consists of a miscellaneous family of antineoplastic agents, including tubulin interacting agents, topoisomerase II inhibitors, topoisomerase I inhibitors and hormonal agents, selected from but not limited to the group consisting of a- carotene, cc- difluoromethyl-arginine, acitretin, Biotec AD-5, Kyorin AHC-52, alstonine, amonafide, amphethinile, amsacrine, Angiostat, ankinomycin, anti-neoplaston A 10, antineoplaston A2, antineoplaston A3, antineoplaston A5, antineoplaston AS2-1 , Henkel APD, aphidicolin glycinate, asparaginase, Avarol, baccharin, batracylin, benfluron, benzotript, Ipsen- Beaufour BIM-23015, bisantrene, Bristol-Myers BMY-40481 , Vestar boron- 10, bromofosfamide, Wellcome BW-502, Wellcome BW-773, caracemide, carmethizole hydrochloride, Ajinomoto CDAF, chlorsulfaquinoxalone, Chemes CHX-2053, Chemex CHX- 100, Warner-Lambert CI- 921 , Warner-Lambert CI-937, Warner-Lambert CI-941 , Warner- Lambert CI-958, clanfenur, claviridenone, ICN compound 1259, ICN compound 471 1 , Contracan, Yakult Honsha CPT- 1 1 , crisnatol, curaderm, cytochalasin B, cytarabine, cytocytin, Merz D-609, DAB I S maleate, dacarbazine, datelliptinium, didemnin-B, dihaematoporphyrin 10- ether, dihydrolenperone, dinaline, distamycin, Toyo Pharmar DM-341 , Toyo Pharmar DM-75, Daiichi Seiyaku DN-9693, docetaxel elliprabin, elliptinium acetate, Tsumura EPMTC, the epothilones, ergotamine, etoposide, etretinate, fenretinide, Fujisawa FR-57704, gallium nitrate, genkwadaphnin, Chugai GLA-43, Glaxo GR-631 78, grifolan NMF-5N, hexadecylphosphocholine, Green Cross HO-221 , homoharringtonine, hydroxyurea, BTG ICRF- 1 87, ilmofosine, isoglutamine, isotretinoin, Otsuka JI-36, Ramot K-477, Otsuak K- 76COONa, Kureha Chemical K-AM, MECT Corp KI-81 10, American Cyanamid L-623, leukoregulin, lonidamine, Lundbeck LU-23- 1 12, Lilly LY- 1 86641 , NCI (US) MAP, marycin, Merrel Dow MDL-27048, Medco MEDR-340, merbarone, merocyanlne derivatives, methylanilinoacridine, Molecular Genetics MGI- 136, minactivin, mitonafide, mitoquidone mopidamol, motretinide, Zenyaku Kogyo MST-16, N-(retinoyl)amino acids, Nisshin Flour Milling N-021 , N-acylated-dehydroalanines, nafazatrom, Taisho NCU- 190, nocodazole derivative, Normosang, NCI NSC- 145813, NCI NSC-361456, NCI NSC-604782, NCI NSC- 95580, ocreotide, Ono ONO- 1 12, oquizanocine, Akzo Org- 101 72, paclitaxel, pancratistatin, pazelliptine, Warner-Lambert PD- 1 1 1 707, Warner-Lambert PD- 1 15934, Warner-Lambert PD- 131 141 , Pierre Fabre PE- 1001 , 1CRT peptide D, piroxantrone, polyhaematoporphyrin, polypreic acid, Efamol porphyrin, probimane, procarbazine, proglumide, Invitron protease nexin I, Tobishi RA-700, razoxane, Sapporo Breweries RBS, restrictin-P, retelliptine, retinoicacid, Rhone-Poulenc RP-49532, Rhone-Poulenc RP-56976, SmithKIine SK&F- I 04864, Sumitomo SM- 108, Kuraray SMANCS, SeaPharm SP- 10094, spatol, spirocyclopropane derivatives, spirogermanium, Unimed, SS Pharmaceutical SS-554, strypoldinone, Stypoldione, Suntory SLTN 0237, Suntory SUN 2071 , superoxide dismutase, Toyama T-506, Toyama T-680, taxol, Teijin TEI-0303, teniposide, thaliblastine, Eastman Kodak TJB-29, tocotrienol, topotecan, Topostin, Teijin TT-82, Kyowa Hakko UCN-01 , Kyowa Hakko UCN- 1028, ukrain, Eastman Kodak USB-006, vinblastine sulfate, vincristine, vindesine, vinestramide, vinorelbine, vintriptol, vinzolidine, withanolides and Yamanouchi YM-534.

[00150] Alternatively, the present compounds can also be used in co-therapies with other antineoplastic agents, such as acemannan, aclarubicin, aldesleukin, alemtuzumab, alitretinoin, altretamine, amifostine, aminolevulinic acid, amrubicin, amsacrine, anagrelide, anastrozole, ANCER, ancestim, ARGLABIN, arsenic trioxide, BAM 002 (Novelos), bexarotene, bicalutamide, broxuridine, capecitabine, celmoleukin, cetrorelix, cladribine, clotrimazole, cytarabine ocfosfate, DA 3030 (Dong-A), daclizumab, denileukin diftitox, deslorelin, dexrazoxane, dilazep, docetaxel, docosanol, doxercalciferol, doxifluridine, doxorubicin, bromocriptine, carmustine, cytarabine, fluorouracil, HIT diclofenac, interferon alfa, daunorubicin, doxorubicin, tretinoin, edelfosine, edrecolomab, eflornithine, emitefur, epirubicin, epoetin beta, etoposide phosphate, exemestane, exisulind, fadrozole, filgrastim, 1 1 - finasteride, fludarabine phosphate, formestane, fotemustine, gallium nitrate, gemcitabine, gemtuzumab zogamicin, gimeracil/oteracil/tegafur combination, glycopine, goserelin, heptaplatin, human chorionic gonadotropin, human fetal alpha fetoprotein, ibandronic acid, idarubicin, imiquimod, interferon alfa, interferon alfa, natural, interferon alfa-2, interferon alfa-2a, interferon alfa-2b, interferon alfa- 1 , interferon alfa-n3, interferon alfacon- 1 , interferon alpha, natural, interferon beta, interferon beta- 1 a, interferon beta- 1 b, interferon gamma, natural interferon gamma- 1 a, interferon gamma- 1 b, interleukin- 1 beta, iobenguane, irinotecan, irsogladine, lanreotide, LC 9018 (Yakult), leflunomide, lenograstim, lentinan sulfate, letrozole, leukocyte alpha interferon, leuprorelin, levamisole + fluorouracil, liarozole, lobaplatin, lonidamine, lovastatin, masoprocol, melarsoprol, metoclopramide, mifepristone, miltefosine, mirimostim, mismatched double stranded RNA, mitoguazone, mitolactol, mitoxantrone, molgramostim, nafarelin, naloxone + pentazocine, nartograstim, nedaplatin, nilutamide, noscapine, novel erythropoiesis stimulating protein, NSC 63 1 570 octreotide, oprelvekin, osaterone, oxaliplatin, paclitaxel, pamidronic acid, pegaspargase, peginterferon alfa-2b, pentosan polysulfate sodium, pentostatin, picibanil, pirarubicin, rabbit antithymocyte polyclonal antibody, polyethylene glycol interferon alfa-2a, porfimer sodium, raloxifene, raltitrexed, rasburicase, rhenium Re 186 etidronate, II retinamide, rituximab, romurtide, samarium (153 Sm) lexidronam, sargramostim, sizofiran, sobuzoxane, sonermin, strontium-89 chloride, suramin, tasonermin, tazarotene, tegafur, temoporfin, temozolomide, teniposide, tetrachlorodecaoxide, thalidomide, thymalfasin, thyrotropin alfa, topotecan, toremifene, tositumomab-iodine 13 1 , trastuzumab, treosulfan, tretinoin, trilostane, trimetrexate, triptorelin, tumor necrosis factor alpha, natural, ubenimex, bladder cancer vaccine, aruyama vaccine, melanoma lysate vaccine, valrubicin, verteporfin, vinorelbine, VIRUL1ZIN, zinostatin stimalamer, or zoledronic acid; abarelix; AE 941 (Aeterna), ambamustine, antisense oligonucleotide, bcl-2 (Genta), APC 801 5 (Dendreon), cetuximab, decitabine, dexaminoglutethimide, diaziquone, EL 532 (Elan), EM 800 (Endorecherche), eniluracil, etanidazole, fenretinide, filgrastim SD01 (Amgen), fulvestrant, galocitabine, gastrin 17 immunogen, HLA-B7 gene therapy (Vical), granulocyte macrophage colony stimulating factor, histamine dihydrochloride, ibritumomab tiuxetan, ilomastat, IM 862 (Cytran), interleukin-2, iproxifene, LDI 200 (Milkhaus), leridistim, lintuzumab, CA 1 25 MAb (Biomira), cancer MAb (Japan Pharmaceutical Development), HER-2 and Fc MAb (Medarex), idiotypic 105AD7 MAb (CRC Technology), idiotypic CEA MAb (Trilex), LYM- 1 -iodine 131 MAb (Techniclone), polymorphic epithelial mucin-yttrium 90 MAb (Antisoma), marimastat, menogaril, mitumomab, motexafin gadolinium, MX 6 (Galderma), nelarabine, nolatrexed, P 30 protein, pegvisomant, pemetrexed, porfiromycin, prinomastat, RL 0903 (Shire), rubitecan, satraplatin, sodium phenylacetate, sparfosic acid, SRL 172 (SR Pharma), SU 5416 (SUGEN), TA 077 (Tanabe), tetrathiomolybdate, thaliblastine, thrombopoietin, tin ethyl etiopurpurin, tirapazamine, cancer vaccine (Biomira), melanoma vaccine (New York University), melanoma vaccine (Sloan Kettering Institute), melanoma oncolysate vaccine (New York Medical College), viral melanoma cell lysates vaccine (Royal Newcastle Hospital), or valspodar.

[00151] Taxanes are a group of drugs that includes paclitaxel (Taxol®) and docetaxel (Taxotere®).

[00152] A platinum-containing anti-cancer drug includes cisplatin (Platinol®. Bristol- Myers Squibb), carboplatin (Paraplatin®. Bristol-Myers Squibb), and oxaliplatin (Eloxatin®, Sanofi- Synthelabo).

[00153] Standard molecular biology techniques known in the art and not specifically described are generally followed as in Sambrook et al, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, New York (1989), and as in Ausubel et al, Current Protocols in Molecular Biology, John Wiley and Sons, Baltimore, Md. (1989) and as in Perbal, A Practical Guide to Molecular Cloning, John Wiley & Sons, New York (1988), and as in Watson et al., Recombinant DNA, Scientific American Books, New York and as in Birren et al (eds) Genome Analysis: A Laboratory Manual Series, Vols. 1-4 Cold Spring Harbor Laboratory Press, New York (1998). Polymerase chain reaction (PCR) can be carried out generally as in PCR Protocols: A Guide to Methods and Applications, Academic Press, San Diego, Calif. (1990).

[00154] In some embodiments, the assays, methods and systems for screening a biological sample of the patient for presence or absence of abnormal cell such as circulating tumor cells, and/or detecting in the biological sample for presence or absence of abnormal cell such as circulating tumor cells, encompass the use of a predictive model. In particular, in assays and related methods and systems wherein a detected copy number variation is compared with a distinguishing copy number variation of a reference genome and/or a reference cell, such comparison can be a direct comparison to the distinguishing copy number variation or an indirect comparison where the distinguishing copy number variation has been incorporated into the predictive model.

[00155] In some embodiments the predictive model comprises one or more of a linear discriminant analysis model, a support vector machine classification algorithm, a recursive feature elimination model, a prediction analysis of microarray model, a logistic regression model, a CART algorithm, a flex tree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, or a combination thereof. In particular embodiments, the analysis comprises logistic regression. In additional embodiments, the detection of CTC in a patient afflicted with cancer is expressed as a risk score.

[00156] An analytic classification process can use any one of a variety of statistical analytic methods to manipulate the quantitative data and provide for classification of the sample. Examples of useful methods include linear discriminant analysis, recursive feature elimination, a prediction analysis of microarray, a logistic regression, a CART algorithm, a FlexTree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, machine learning algorithms and other methods known to those skilled in the art. [00157] Classification can be made according to predictive modeling methods that set a threshold for determining the probability that a sample belongs to a given class. The probability preferably is at least 50%, or at least 60%, or at least 70%, or at least 80%, or at least 90%, or higher. Classifications also can be made by determining whether a comparison between an obtained dataset and a reference dataset yields a statistically significant difference. If so, then the sample from which the dataset was obtained is classified as not belonging to the reference dataset class. Conversely, if such a comparison is not statistically significantly different from the reference dataset, then the sample from which the dataset was obtained is classified as belonging to the reference dataset class.

[00158] The predictive ability of a model can be evaluated according to its ability to provide a quality metric, e.g. AUC (area under the ROC- receiver operating characteristic - curve) or, or accuracy of a particular value, or range of values. Area-under-the-curve measurements are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest. ROC analysis can be used to select the optimal threshold under a variety of clinical circumstances, balancing the inherent tradeoffs that exist between specificity and sensitivity. In some embodiments, a desired quality threshold is a predictive model that will classify a sample with an accuracy of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, or higher. As an alternative measure, a desired quality threshold can refer to a predictive model that will classify a sample with an AUROC of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

[00159] As is known in the art, the relative sensitivity and specificity of a predictive model can be adjusted to favor either the specificity metric or the sensitivity metric, where the two metrics have an inverse relationship. The limits in a model as described above can be adjusted to provide a selected sensitivity or specificity level, depending on the particular requirements of the test being performed. One or both of sensitivity and specificity can be at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

[00160] The raw data can be initially analyzed by measuring the values for each measurable feature or biomarker, usually in triplicate or in multiple triplicates. The data can be manipulated, for example, raw data can be transformed using standard curves, and the average of triplicate measurements used to calculate the average and standard deviation for each patient. These values can be transformed before being used in the models, e.g. log-transformed, Box-Cox transformed (Box and Cox, Royal Stat. Soc, Series B, 26:21 1-246(1964). The data are then input into a predictive model, which will classify the sample according to the state. The resulting information can be communicated to a patient or health care provider.

[00161] Reagents and/or devices to enrich a number of possible abnormal cell and in particular circulating tumor cells (CTC) from the biological sample and a device for detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell for simultaneous combined or sequential use in the method for screening a biological sample herein described

[00162] In embodiments herein described reagents and/or devices for enriching abnormal cell, reagents and/or devices for amplifying the genome of an individual cell, reagents and/or a device for detecting in the genome of the individual cell, set non-overlapping genetic regions of a reference genome, reagents and/or devices for detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell, and/or a look-up table showing the distinguishing copy number variation pattern within the set non-overlapping genetic regions of the reference cell genome, can form part of combinations or systems to perform the assays and methods herein described as will be understood by a skilled person. In some embodiments, the combination can take the form of a kit of parts wherein the components of the kits are selected to provide a combination suitable to perform at least one method of assay according to embodiments herein described.

[00163] Further details concerning the methods and systems as well as compositions, can be identified by the person skilled in the art will be apparent upon reading of the present disclosure.

EXAMPLES Example 1: Multiplex whole genome amplification

[00164] The NGS systems presently require micro- to nano-gram of DNA input. To obtain genomic information from single cell with ~6pg DNA, it is necessary to first uniformly amplify the single cell genome. Currently three major approaches have been developed to amplify the entire genome from a single cell. Among them, PCR-based methods, such as DOP-PCR, employ degenerate oligonucleotide-primed PCR to amplify the genome (22). DOP-PCR achieves the most uniform amplification of the genome for accurate CNV calling, but it has low genome coverage and high false-negative rate (FNR) / false-positive rate (FPR) for SNV calling.

[00165] The multiple displacement amplification (MDA) based isothermal approach gives the greatest single cell genome coverage and lowest FNR/FPR for SNV calling but the non-uniform amplification impedes CNV calling (Dean FB, Nelson JR, Giesler TL, Lasken RS. Rapid amplification of plasmid and phage DNA using Phi 29 DNA polymerase and multiply-primed rolling circle amplification. Genome Res. 2001 ;l l(6): 1095-9. PMID: 1 1381035; PMCID: 31 1 129). Multi-cycle isothermal amplification approach, such as MALBAC(15) and PicoPlex(24), can achieve moderate genome coverage and amplification uniformity.

[00166] In this example, both DOP-PCR- and MALBAC-based methods are utilized for the whole genome amplification of individual single cells captured in the LipidBiopsy platform. While WGA with DOP-PCR produces CNV with the greatest uniformity, WGA product from MALBAC-based approach can be used not only for CNV but also for further somatic mutation analyses.

[00167] To increase the sequencing throughput and reduce cost, DNA barcoding strategies are developed for each cell. For DOP-PCRbased WGA approach, DNA from individual cells will be barcoded as previously described (Baslan T, Kendall J, Ward B, Cox H, Leotta A, Rodgers L, Riggs M, D'ltalia S, Sun G, Yong M, Miskimen K, Gilmore H, Saborowski M, Dimitrova N, Krasnitz A, Harris L, Wigler M, Hicks J. Optimizing sparse sequencing of single cells for highly multiplex copy number profiling. Genome Res. 2015;25(5):714-24. PMID: 25858951 ; PMCID: 44171 19) with a few modifications. The barcodes will be added the end of the degenerate oligonucleotide for priming instead of the Illumina adapter ligation step. In doing so, the amplification product can be pooled before the Illumina adapter ligation reaction with reduced cost. [00168] The barcoding strategy developed for multiplexed MALBAC-based single cell sequencing is shown in Fig. 6. 6 pg of DNA from individual cells will first be amplified with first round (6 cycles) of MALBAC quasi-linear amplification followed by 21 cycles of exponential amplification. Tn5 transposition reaction with specifically designed transposon DNA sequence will conduct to achieve DNA fragmentation and adapter tagging. This single transposition step tagmentation (fragmentation and tagging) was originally developed by Epicenter (now acquired by Illumina) in its Nextera kit. The transposon DNA in this example will be designed to contain a barcode region and a priming site of partial sequencing adapter. After this tagmentation step, DNA products from each cell can be pooled together. Enrichment PCR will extend the partial adapter to standard full-length adapter. Pooled library preparation can be sequenced together with any other samples with suitable sequencing index.

Example 2: Capture of CTCs from peripheral blood of lung cancer patients

[00169] Twenty lung cancer patients were analyzed for the presence of CTCs in their blood. Using peripheral blood samples [2-7.5 ml] from late stage lung cancer patients and analyzing with the LiquidBiopsy® (Cynvenio Biosystems) platform and a capture cocktail of Anti- EpCAM. CTCs from SCLC patients (24-250 cells/ml) are more abundant in the circulation as compared to other subtypes of lung cancer (see Fig. 1).

Example 3: A single-cell copy number analysis system

[00170] Fig. 3 shows an exemplary illustration of a single-cell copy number analyses system. A tube of blood is collected. The tube of blood can be processed either manually or using an automated device to enrich the CTCs from the blood sample. The enrichment can be accomplished by positive selection of CTC or by negative depletion of red blood cells and white blood cells from the sample, thus leaving a population enriched for CTCs.

[00171] Binding moieties such as those that bind to EpCam can be used to detect CTCs. EpCam positive cells are isolated from the blood based on Cynvenio LiquidBiopy and dispersed to different wells. The CTCs can either be sequenced cell by cell (single-cell sequencing) or cellularly barcoded and sequences as a pool (multiplex sequencing).

[00172] Bioinformatics analysis tools can be used to align the sequence reads into bins in a size between lOOkb and 500kb across the genome. The read depth is at least 5-10 fold per across the genome. Regions that exhibit less than this coverage are termed loss and greater than this coverage are termed gain. Based on the bioinformatics analysis of the sequencing data, CNV patterns of CTC cells of particular cancer types as well as normal cells can be obtained for further comparison.

Example 4: CNV analysis and detection in CTC cells from patients and normal individuals using FISH

[00173] In this example, CNV analysis and detection was performed in CTC cells isolated from patients confirmed to be either normal or cancerous and analyzed by FISH (see Fig. 4).

[00174] DNA probes were selected from random BAC libraries that have been mapped and aligned to the human genome after sequencing the ends the BAC DNA.

[00175] Blood samples from normal individuals and cancer patients were collected, fixed and depleted by a cocktail of antibodies directed against red blood cells and white blood cells and then spread on slides. Fluorescent DNA probes derived from chromosomes 3 and 10 were then hybridized with the samples and the resulting fluorescent spot visualized microscopically.

[00176] Fig. 4 shows the FISH images of normal cells and abnormal cells. Locus 1 and 2 correspond to two loci from chromosomes 3 and 10, each having a sequence complementary to the corresponding DNA probe used in the FISH experiments. For both loci, two fluorescent spots were identified in the normal cells while three in the abnormal cells, indicating that both loci have been amplified in the abnormal cells.

Example 5: CNV analysis and detection in CTC cells from patients and normal individuals using genome sequencing

[00177] In this example, CNV analysis and detection was performed in CTC cells isolated from patients confirmed to be either normal or cancerous and analyzed using single-cell sequencing and CNV detection tools (see Fig. 5).

[00178] To examine whether CNVs exhibit heterogeneity from one individual to another as well as from cancer patients to normal healthy individuals, blood samples from seven lung cancer patients and two healthy individuals were collected and processed to capture EpCam positive cells. These cells were then individually picked manually under a microscope. Next, the cells were lysed and DNA was amplified to create individually barcoded cell DNA sequencing libraries. These libraries were subjected to DNA sequencing. The resulting reads were assembled by alignment to the reference human genome, assigned to sequential, contiguous, non- overlapping binds. Each bin was then evaluated for the number of reads mapped with each bin. Over-represented binds were classified as "GAIN", i.e. a copy number greater than 2, and under- represented bins were classified as "LOSS", i.e. a copy number less than 2.

[00179] Fig. 5 shows the CNV patterns across the genome for CTC cells isolated from the seven patients, along with two normal leukocytes cells. The CNV patterns in each CTC from cancer patients were distinctly different from each other as well as from that of the normal leukocytes, with a large portion of chromosomal regions having copy number gain equal to or larger than 3.

[00180] In summary, in several embodiments described herein are assays, methods, and systems are described for the early detection, enumeration and analysis of abnormal cells such as circulating tumor cells (CTCs) useful for cancer screening, development of treatment regimens, and/or for monitoring for treatment responses, cancer recurrence or the like. Devices that facilitate the early detection, enumeration and analysis of abnormal cells are also provided as will be understood by a skilled person.

[00181] According to a first set of embodiments, a method for detecting the presence of abnormal cells in a biological sample is described. The method comprises (a) enriching the number of possible abnormal cells from the biological sample; (b) analyzing the copy number variation in the individual enriched cells; and (c) determining if a cell is an abnormal cell based on the copy number variation analysis.

[00182] In some embodiments of the method of the first set of embodiments, the abnormal cells can be circulating tumor cells and the biological sample is peripheral blood. In some embodiments of the method of the first aspect, the abnormal cells the abnormal cells can be fetal cells and the biological sample is maternal blood. [00183] In some embodiments of the method of the first set of embodiments, the abnormal cells the steps of analyzing the copy number variations comprise or consist of: (a) amplifying the genome of individual cells or defining a set of molecular probes corresponding to one or more genetic regions; (b) assessing the coverage of genomic regions; and (c) informing the copy number variations across the genome. In some of those embodiments the steps of assessing the coverage of genomic regions is performed with techniques selected from the group consisting of: whole-genome sequencing, comparative genomic hybridization (CGH), single-nucleotide polymorphic allele (SNP) array, and chromosome painting. In addition or in the alternative in some of those embodiments the step of informing the copy number variations across the genome is performed with stringent analyses of copy number status or a visual check of the genome coverage.

[00184] In some of embodiments of the method according to the first set of embodiments the step of determining if a cell is an abnormal cell based on the copy number variation analysis is performed based on criteria including the percentage of chromosome regions alternated from diploid regions and/or the presence of certain alternated chromosome regions.

[00185] In some of embodiments of the method according to the first set of embodiments the method further includes the step of: identifying the tissue of origin of an abnormal cell based on the genomic profile of abnormal cells.

[00186] According to a second set of embodiments, a method for detecting the presence of one or more cancer cells in a biological sample is described, the method comprising: (a) enriching the number of possible circulating tumor cells from the biological sample; (b) performing whole- genome amplification on individual cells; (c) analyzing the copy number variation in the individual cells; and (d) determining if a cell is a circulating tumor cell based on the copy number variation analysis.

[00187] According to a third set of embodiments, a method for detecting cancer in a patient is described, comprising: (a) enriching a group of cells from a patient sample; (b) analyzing the copy number variations in the individual enriched cells; and (c) confirming the detection of cancer by identifying abnormal cells with copy number variations (CNVs). In some embodiments of the method according to the third aspect, the patient sample is peripheral blood and the group of abnormal cells are circulating tumor cells (CTCs).

[00188] According to a fourth set of embodiments, a method of performing a CTC enrichment assay is described comprising: (a) incubating a patient biological sample with an antibody, wherein the antibody comprises an antibody that binds to EpCAM; and (b) identifying one or more cells that have a signal from an antibody that binds to EpCAM.

[00189] According to a fifth set of embodiments, a first system for detecting cancer by testing a biological sample is described, the system comprising: (a) a reagent comprising an cancer target binding moiety; (b) a CTC isolation apparatus; (c) a single cell sequencing apparatus; and (d) a copy number variation profile analyzer apparatus; wherein the CTC isolation apparatus is configured so that the presence and distribution of cells that are bound to the cancer target binding moiety are identified; wherein the sequencing apparatus is configured so that the individual cells that bound to the cancer target binding moiety are sequenced for copy number variations; and wherein the copy number variation profile analyzer apparatus is configured so that a copy number variation profile is compared to known normal cell profiles or known cancerous or precancerous cell profiles.

[00190] According to a sixth set of embodiments, a method of providing information required for diagnosis or prognosis of cancer is described, comprising: (A) obtaining a cell from a biological sample isolated from a human; (B) generating a genomic copy number variation profile of a cell; and (C) determining if the copy number variation profile is of cancer origin.

[00191] According to a seventh set of embodiments, a method is described for screening a subject for cancer, comprising detecting CTCs in a biological sample by copy number variation profiles.

[00192] According to an eighth set of embodiments, a method of diagnosing cancer in a subject is described, comprising identifying CTCs in a biological sample by copy number variation profile; wherein when circulating tumor cells are detected in the biological sample by copy number variation profiles, the subject is diagnosed with cancer. [00193] In some embodiments of the fifteenth set of embodiments, the method further comprises administering an anti-cancer therapeutic to the subject when the subject is diagnosed with cancer.

[00194] In some embodiments of the fifteenth set of embodiments, the method further comprises a recommendation for testing with other diagnostic tools.

[00195] According to a ninth set of embodiments, a method of detecting CTCs in a biological sample is described, comprising: (a) contacting said sample with an EpCAM binding agent; (b) selecting the cells based on binding with the EpCAM binding agent; and (c) analyzing the selected cells for copy number variations.

[00196] According to a tenth set of embodiments, a method of detecting CTCs in a sample is described, comprising: (a) depleting non-cancerous cells; and (b) analyzing the copy number variations of individual left-over cells.

[00197] According to an eleventh set of embodiments, a method for detecting recurrence of cancer in a subject is described, comprising detecting circulating tumor cells in a biological sample by copy number variation analysis from a subject previously treated for cancer, wherein when circulating tumor cells are detected in the biological sample by copy number variation analysis, recurrence of cancer is detected.

[00198] According to a twelfth set of embodiments, a method for determining whether circulating tumor cells are present in a sample in an individual is described, comprising: (A) analyzing the sample to determine a copy number variation in a cell; and (B) determining whether the profile of the copy number variation is indicative of the presence of circulating tumor cells in the sample.

[00199] In some embodiments of the twelfth set of embodiments wherein potential circulating tumor cells are detected by one or more means selected from size/deformability exclusion methodology, non-targeted cell depletion based negative selective, positive selection with antibodies, density gradient centrifuge (FICOLL), microfluidic chip and flow cytometry, or a combination thereof.

[00200] In some embodiments of the twelfth set of embodiments, the biological sample is peripheral blood, blood, lymph nodes, bone marrow, cerebral spinal fluid, tissue, pleural fluid, stool or urine.

[00201] In some embodiments of the twelfth set of embodiments, the biological sample is peripheral blood.

[00202] In a thirteen set of embodiments, a method is described to analyze copy number variation in a genome of an individual cell from a biological sample, the method comprising: providing a reference genome with set non-overlapping genetic regions; detecting the set non- overlapping genetic regions of the reference genome in the genome of the individual cell; and detecting copy number variation (CNV) in the detected set non-overlapping genetic regions of the genome of the individual cell.

[00203] In some embodiments of the thirteen set of embodiments, the reference genome is a genome of an individual where the biological sample is obtained or a genome of another individual different from the individual where the biological sample is obtain.

[00204] In some embodiments of the thirteen set of embodiments, the reference genome is a genome of a healthy individual.

[00205] In some embodiments of the thirteen set of embodiments, the reference genome is a genome of a cancer patient.

[00206] In a first set of sub-embodiments of the thirteenth set of embodiments, the detecting the set non-overlapping genetic regions of the reference genome comprises providing a set of fluorescence in situ hybridization (FISH) probes, each probe comprising each genetic region of the set non-overlapping genetic regions. In some of those embodiments the set of FISH probes is provided from genomic library construction. In some of those embodiments the genomic library is selected from the group consisting of BACs, YACs and PACs.

[00207] In a second set of sub-embodiments of the thirteenth set of embodiments, the detecting the set non-overlapping genetic regions of the reference genome comprises whole-genome sequencing of the individual cell to generate sequencing reads and assembling the sequencing reads. In some of those embodiments the assembling the sequencing reads further comprises mapping the sequencing reads to the reference genome. In addition or in the alternative the whole-genome sequencing is performed using next-generation sequencing approaches.

[00208] In some of embodiments of the second set of sub-embodiments of the thirteenth set of embodiments, the detecting copy number variation in the detected set non-overlapping genetic regions comprises computationally assessing the CNV for each genetic region of the set non- overlapping genetic regions. In some of those embodiments, the computationally assessing the CNV for each genetic region of the set non-overlapping genetic regions further comprises calculating a read depth for each genetic region of the set non-overlapping genetic regions; and calculating a copy number for each genetic region of the set non-overlapping genetic regions based on the calculated read depth. In some of those embodiments the copy number is greater or less than 2.

[00209] In some embodiments of the thirteenth set of embodiments, the detecting copy number variation in the detected set non-overlapping genetic regions further comprises generating a single-cell copy number variation profile.

[00210] In a fourteenth set of embodiments, a system is described to analyze copy number variation in a genome of an individual cell from a biological sample, the systems comprising a reagent and/or a device for detecting in the genome of the individual cell, set non-overlapping genetic regions of a reference genome, and a device for detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell for simultaneous combined or sequential use in the method to analyze copy number variation in a method according the thirteenth set of embodiments.

[00211] In some embodiments of the fourteenth set of embodiments, the reagent for detecting set non-overlapping genetic regions of a reference genome comprises polynucleotide configured to hybridize with the set non-overlapping genetic regions or segments thereof.

[00212] In some embodiments of the thirteenth set of embodiments, the device for detecting set non-overlapping genetic regions of a reference genome comprises a sequencing platform.

[00213] In some embodiments of the thirteenth set of embodiments, the device for detecting copy number variation comprises means for performing computational assessing the CNV for each genetic region of the set non-overlapping genetic regions.

[00214] In some embodiments of the thirteenth set of embodiments, the device for detecting copy number variation comprises means for performing FISH.

[00215] In a fifteenth set of embodiments, a method is described to detect in a biological sample, presence or absence of a reference cell having a genome and a distinguishing copy number variation pattern within set non-overlapping genetic regions of the genome, the method comprising detecting the set non-overlapping genetic regions of the genome of the reference cell in a genome of an individual cell of the biological sample; and detecting a copy number variation pattern within the detected set non-overlapping genetic regions of the genome of the individual cell, wherein if the detected copy number variation pattern matches the distinguishing copy number variation pattern of the reference cell, the reference cell is present in the biological sample and if the detected copy number variation pattern does not match the distinguishing copy number variation pattern of the reference cell, the reference cell is absent in the biological sample.

[00216] In some embodiments of the fifteenth set of embodiments, the distinguishing copy number variation pattern within set non-overlapping genetic regions of the genome comprises a copy number for each of the set non-overlapping genetic regions of the genome.

[00217] In some embodiments of the fifteenth set of embodiments, the distinguishing copy number variation pattern is from a healthy individual.

[00218] In some embodiments of the fifteenth set of embodiments, the distinguishing copy number variation pattern is from a cancer patient.

[00219] In a sixteenth set of embodiments, a system is described to detect presence or absence in a biological sample of a reference cell having a genome and a distinguishing copy number variation pattern within set non-overlapping genetic regions of the genome, the system comprising: a reagent and/or a device for detecting in a genome of an individual cell of the biological sample, the set non-overlapping genetic regions of the genome of the reference cell; a device for detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell; and a look-up table showing the distinguishing copy number variation pattern within the set non-overlapping genetic regions of the reference cell genome, for simultaneous combined or sequential use in the method to detect presence or absence in a biological sample of a reference cell of the fifteenth set of embodiments.

[00220] In a seventeenth set of embodiments, a method is described to characterize an individual cell of a biological sample, the method comprising providing a reference genome from a reference cell, the reference genome having set non-overlapping genetic regions; detecting the set non-overlapping genetic regions of the reference genome in the genome of the individual cell; detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell to provide a copy number variation pattern of the genome of the individual cell; comparing the detected copy number variation pattern of the genome of the individual cell with a distinguishing copy number variation pattern within the set non- overlapping genetic regions of the genome of the reference cell; and marking the individual cell of the biological sample as the reference cell, when the detected copy number variation pattern matches a distinguishing copy number variation patterns of the reference genome.

[00221] In some embodiments of the seventeenth set of embodiments, the comparing is performed by a visual check or using a predictive model.

[00222] In an eighteenth set of embodiments a system is described to characterize an individual cell of a biological sample, the system comprising: a reagent and/or a device for detecting in a genome of the individual cell, set non-overlapping genetic regions of a genome of a reference cell; a device for detecting copy number variation in the detected set non-overlapping genetic regions in the genome of the individual cell; and a look-up table showing a distinguishing copy number variation pattern within the set non-overlapping genetic regions of the genome of the reference cell, for simultaneous combined or sequential use in the method to characterize an individual cell of a biological sample of the seventeenth set of embodiments.

[00223] In some embodiments of any one of the first to the eighteenth set of embodiments, the tumor cells are from a solid tumor. [00224] In some embodiments of any one of the first to the eighteenth set of embodiments, the tumor cell are from a cancer which is Stage I, Stage II, Stage III, or Stage IV cancer.

[00225] In some embodiments of any one of the first to the eighteenth set of embodiments, the tumor cell are from an epithelial cell cancer.

[00226] In some embodiments of any one of the first to the eighteenth set of embodiments, the tumor cell are from breast, prostate, lung, pancreatic, or colorectal cancer.

[00227] In some embodiments of any one of the first to the eighteenth set of embodiments, the tumor cell are from lung cancer.

[00228] In some embodiments of any one of the first to the eighteenth set of embodiments, the tumor cell are from small cell lung cancer.

[00229] In some embodiments of any one of the first to the eighteenth set of embodiments, possible CTCs are isolated based on one or more characteristics selected from (i) number of CTCs; (ii) location of markers; (iii) status of nucleus; (iv) degree of cytokeratin 8 expression; (v) degree of cytokeratin 18 expression; (vi) degree of cytokeratin 19 expression; (vii) degree of EpCAM expression; (viii) degree of vimentin expression; (ix) degree of PD-L1 expression; (x) cytokeratin morphology; (xi) degree of HER2 expression; (xii) degree of Trop2 expression; (xiii) degree of NCAM expression; (xiv) degree of CgA expression; (xv) degree of TTF-1 expression; (xvi) cytokeratin morphology; and (xvii) intensity of marker staining.

[00230] In some embodiments of any one of the first to the eighteenth set of embodiments, the circulating tumor cell is of lung cancer origin.

[00231] In some embodiments of any one of the first to the eighteenth set of embodiments, the biological sample is obtained from a subject who has a history of smoking cigarettes.

[00232] In some embodiments of any one of the first to the eighteenth set of embodiments, the biological sample is obtained from a subject who does not have a history of smoking cigarettes.

[00233] In some embodiments of any one of the first to the eighteenth set of embodiments, the biological sample is obtained from a subject who has lung cancer and has not been treated for lung cancer.

[00234] In some embodiments of any one of the first to the eighteenth set of embodiments, the biological sample is obtained from a subject who has received a lung cancer treatment consisting of: radiation therapy, chemotherapy, surgery, or combinations thereof.

[00235] In some embodiments of any one of the first to the eighteenth set of embodiments, the biological sample is not cell free.

[00236] In some embodiments of any one of the first to the eighteenth set of embodiments, the biological sample is from a patient not currently diagnosed with cancer.

[00237] In some embodiments of any one of the first to the eighteenth set of embodiments, an enrichment step comprises the addition of a cancer target binding moiety and the isolation of cells that bind to the cancer target binding moiety. In some of those embodiments, the cancer target binding moiety is an antibody. In addition or in the alternative the cancer target binding moiety binds to synaptophysin (Syn), neural cell adhesion (NCAM), chromogranin-A (CgA), thyroid transcription factor (TTF-1) or EpCAM.

[00238] In some embodiments, of any one of the thirteenth to the eighteenth set of embodiments, detecting the set non-overlapping genetic regions of the genome of the reference cell in a genome of an individual cell of the biological sample is preceded by amplifying the genome of the individual cell.

[00239] In some embodiments, of any one of the thirteenth to the eighteenth set of embodiments, each genetic region of the set non-overlapping genetic regions is independently from 100 kb to 500 kb in size.

[00240] In some embodiments, of any one of the thirteenth to the eighteenth set of embodiments each genetic region of the set non-overlapping genetic regions is independently from 100 kb to 300 kb in size.

[00241] The examples set forth above are provided to give those of ordinary skill in the art a complete disclosure and description of how to make and use the embodiments of the materials, compositions, systems and methods of the disclosure, and are not intended to limit the scope of what the inventors regard as their disclosure. Those skilled in the art will recognize how to adapt the features of the exemplified assays methods and systems herein described and related compositions according to various embodiments and scope of the claims.

[00242] All patents and publications mentioned in the specification are indicative of the levels of skill of those skilled in the art to which the disclosure pertains.

[00243] The entire disclosure of each document cited (including patents, patent applications, journal articles, abstracts, laboratory manuals, books, or other disclosures) in the Background, Summary, Detailed Description, and Examples is hereby incorporated herein by reference. All references cited in this disclosure are incorporated by reference to the same extent as if each reference had been incorporated by reference in its entirety individually. However, if any inconsistency arises between a cited reference and the present disclosure, the present disclosure takes precedence.

[00244] The terms and expressions which have been employed herein are used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the disclosure claimed. Thus, it should be understood that although the disclosure has been specifically disclosed by embodiments, exemplary embodiments and optional features, modification and variation of the concepts herein disclosed can be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this disclosure as defined by the appended claims.

[00245] 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. As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise. The term "plurality" includes two or more referents unless the content clearly dictates otherwise. 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 the disclosure pertains.

[00246] When a Markush group or other grouping is used herein, all individual members of the group and all combinations and possible subcombinations of the group are intended to be individually included in the disclosure. Every combination of components or materials described or exemplified herein can be used to practice the disclosure, unless otherwise stated. One of ordinary skill in the art will appreciate that methods, device elements, and materials other than those specifically exemplified may be employed in the practice of the disclosure without resort to undue experimentation. All art-known functional equivalents, of any such methods, device elements, and materials are intended to be included in this disclosure. Whenever a range is given in the specification, for example, a temperature range, a frequency range, a time range, or a composition range, all intermediate ranges and all subranges, as well as, all individual values included in the ranges given are intended to be included in the disclosure. Any one or more individual members of a range or group disclosed herein may be excluded from a claim of this disclosure. The disclosure illustratively described herein suitably may be practiced in the absence of any element or elements, limitation or limitations which is not specifically disclosed herein.

[00247] A number of embodiments of the disclosure have been described. The specific embodiments provided herein are examples of useful embodiments of the invention and it will be apparent to one skilled in the art that the disclosure can be carried out using a large number of variations of the devices, device components, methods steps set forth in the present description. As will be obvious to one of skill in the art, methods and devices useful for the present methods may include a large number of optional composition and processing elements and steps.

[00248] In particular, it will be understood that various modifications may be made without departing from the spirit and scope of the present disclosure. Accordingly, other embodiments are within the scope of the following claims

Claims

1. An assay for screening a biological sample for presence or absence of circulating tumor cells, the assay comprising : enriching a number of possible circulating tumor cells (CTC) from the biological sample to provide enriched cells from the biological sample; analyzing copy number variation in a genome of the individual enriched cells; and determining if an individual enriched cell is a CTC based on a detected copy number variation in the genome of the individual enriched cells, thus detecting presence or absence of circulating tumor cells in the biological sample.

2. The assay of claim 1, wherein the biological sample is peripheral blood.

3. The assay of claim 1, wherein the biological sample is maternal blood.

4. The assay of any one of claims 1 to 3, wherein the enriching a number of possible circulating tumor cells further comprises incubating the biological sample with an antibody, wherein the antibody comprises an antibody that binds to EpCAM; and identifying one or more cells that have a signal from the antibody that binds to EpCAM.

5. A method to analyze copy number variation in a genome of an individual cell from a biological sample, the method comprising: providing a reference genome with set non- overlapping genetic regions; detecting the set non-overlapping genetic regions of the reference genome in the genome of the individual cell; and detecting copy number variation (CNV) in the detected set non-overlapping genetic regions of the genome of the individual cell.

6. The method of claim 5, wherein each genetic region of the set non- overlapping genetic regions is independently from 100 kb to 500 kb in size.

7. The method of claim 5 or 6, wherein each genetic region of the set non- overlapping genetic regions is independently from 100 kb to 300 kb in size.

8. The method of any one of claims 5 to 7, wherein the reference genome is a genome of the individual or a genome of another individual.

9. The method of any one of claims 5 to 8, wherein the reference genome is a genome of a healthy individual or a genome of a cancer patient.

10. The method of any one of claims 5 to 9, wherein the detecting the set non-overlapping genetic regions of the reference genome comprises providing a set of fluorescence in situ hybridization (FISH) probes, each probe comprising each genetic region of the set non- overlapping genetic regions.

11. The method of claim 10, wherein the set of FISH probes is provided from genomic library construction.

12. The method of claim 1 1, wherein the genomic library is selected from the group consisting of BACs, YACs and PACs.

13. The method of any one of claims 5 to 9, wherein the detecting the set non-overlapping genetic regions of the reference genome comprises whole-genome sequencing of the individual cell to generate sequencing reads and assembling the generated sequencing reads.

14. The method of claim 13, wherein assembling the sequencing reads further comprises mapping the sequencing reads to the reference genome.

15. The method of claim 14, wherein the whole-genome sequencing is performed using next- generation sequencing approaches.

16. The method of any one of claims 13 to 15, wherein the detecting copy number variation in the detected set non-overlapping genetic regions comprises computationally assessing the CNV for each genetic region of the set non- overlapping genetic regions.

17. The method of claim 16, wherein the computationally assessing the CNV for each genetic region of the set non-overlapping genetic regions further comprises calculating a read depth for each genetic region of the set non- overlapping genetic regions; and calculating a copy number for each genetic region of the set non-overlapping genetic regions based on the calculated read depth.

18. The method of claim 17, wherein the copy number is greater or less than 2.

19. The method of any one of claims 10 to 12, wherein the detecting copy number variation in the detected set non- overlapping genetic regions comprises performing FISH using the set of fluorescence in situ hybridization (FISH) probes; and detecting a copy number for each genetic region of the set non-overlapping genetic regions using a fluorescence microscope.

20. The method of any one of claims 5 to 19, wherein the detecting copy number variation in the detected set non- overlapping genetic regions further comprises generating a copy number variation profile of the individual cell from the biological sample.

21. The method of any one of claims 5 to 20, wherein the biological sample is peripheral blood, blood, lymph nodes, bone marrow, cerebral spinal fluid, tissue, pleural fluid, stool or urine.

22. The method of any one of claims 5 to 20, wherein the biological sample is peripheral blood.

23. The method of any one of claims 5 to 22, wherein individual is a subject who has lung cancer and has not been treated for lung cancer.

24. The method of any one of claims 5 to 22, wherein the individual is a subject who has received a lung cancer treatment consisting of: radiation therapy, chemotherapy, surgery, or combinations thereof.

25. The method of any one of claims 5 to 22, wherein the individual is a patient not currently diagnosed with cancer.

26. A system to analyze copy number variation in a genome of an individual cell from a biological sample, the systems comprising a reagent and/or a device for detecting in the genome of the individual cell, set non- overlapping genetic regions of a reference genome, and a device for detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell for simultaneous combined or sequential use in the method to analyze copy number variation in of any one of claims 5 to 25.

27. The system of claim 26, wherein each genetic region of the set non-overlapping genetic regions is independently from 100 kb to 500 kb in size.

28. The system of claim 26 or 27, wherein the reagent for detecting set non-overlapping genetic regions of a reference genome comprises polynucleotide configured to hybridize with the set non-overlapping genetic regions or segments thereof.

29. The system of any one of claims 26 to 28, wherein the device for detecting set non- overlapping genetic regions of a reference genome comprises a sequencing platform.

30. The system of claim 29, wherein the device for detecting copy number variation comprises means for performing computational assessing the CNV for each genetic region of the set non- overlapping genetic regions.

31. The system of any one of claims 26 to 28, wherein the device for detecting copy number variation comprises means for performing FISH.

32. A method to detect in a biological sample, presence or absence of a reference cell having a genome and a distinguishing copy number variation pattern within set non-overlapping genetic regions of the genome, the method comprising detecting the set non- overlapping genetic regions of the genome of the reference cell in a genome of an individual cell of the biological sample; and detecting a copy number variation pattern within the detected set non-overlapping genetic regions of the genome of the individual cell, wherein if the detected copy number variation pattern matches the distinguishing copy number variation pattern of the reference cell, the reference cell is present in the biological sample and if the detected copy number variation pattern does not match the distinguishing copy number variation pattern of the reference cell, the reference cell is absent in the biological sample.

33. The method of claim 32, wherein each genetic region of the set non-overlapping genetic regions is independently from 100 kb to 500 kb in size.

34 The method of claims 32 or 33, wherein the distinguishing copy number variation pattern within set non-overlapping genetic regions of the genome comprises a copy number for each of the set non- overlapping genetic regions of the genome.

35. The method of any one of claims 32 to 34, wherein the reference cell is a cell from a healthy individual.

36 The method of any one of claims 32 to 34, wherein reference cell is a cell from a cancer patient.

37. The method of any one of claims 32 to 34, wherein the biological sample is obtained from a lung cancer patient.

38 A system to detect presence or absence in a biological sample of a reference cell having a genome and a distinguishing copy number variation pattern within set non-overlapping genetic regions of the genome, the system comprising a reagent and/or a device for detecting in a genome of an individual cell of the biological sample, the set non-overlapping genetic regions of the genome of the reference cell; a device for detecting copy number variation in the detected set non-overlapping genetic regions of the genome of the individual cell; and a look-up table showing the distinguishing copy number variation pattern within the set non-overlapping genetic regions of the reference cell genome, for simultaneous combined or sequential use in the method to detect presence or absence in a biological sample of a reference cell of any one of claims 32 to 37.

39. The system of claim 38, wherein each genetic region of the set non-overlapping genetic regions is independently from 100 kb to 500 kb in size.

40. A method to characterize an individual cell of a biological sample, the method comprising providing a reference genome from a reference cell, the reference genome having set non-overlapping genetic regions; detecting the set non- overlapping genetic regions of the reference genome in the genome of the individual cell; detecting copy number variation in the detected set non- overlapping genetic regions of the genome of the individual cell to provide a copy number variation pattern of the genome of the individual cell; comparing the detected copy number variation pattern of the genome of the individual cell with a distinguishing copy number variation pattern within the set non- overlapping genetic regions of the genome of the reference cell; and marking the individual cell of the biological sample as the reference cell, when the detected copy number variation pattern matches a distinguishing copy number variation patterns of the reference genome.

41. The method of claim 40, wherein each genetic region of the set non-overlapping genetic regions is independently from 100 kb to 500 kb in size.

42. The method of claim 40 or 41, wherein the comparing is performed by a visual check or using a predictive model.

43. A system to characterize an individual cell of a biological sample, the system comprising a reagent and/or a device for detecting in a genome of the individual cell, set non- overlapping genetic regions of a genome of a reference cell; a device for detecting copy number variation in the detected set non-overlapping genetic regions in the genome of the individual cell; and a look-up table showing a distinguishing copy number variation pattern within the set non-overlapping genetic regions of the genome of the reference cell, for simultaneous combined or sequential use in the method to characterize an individual cell of a biological sample of any one of claims 40to 42.

44. The system of claim 43, wherein each genetic region of the set non-overlapping genetic regions is independently from 100 kb to 500 kb in size.

45. A method for diagnosing cancer in a patient, the method comprising screening a biological sample of the patient for presence or absence of abnormal cell such as circulating tumor cells with the method of any one of claims 1 to 4, and/or detecting in the biological sample presence or absence of abnormal with the method of any one of claims 32 to 37; and diagnosing the patient with cancer when presence of the abnormal cell is detected.

46. The method of claim 45, wherein the cancer is a solid tumor.

47. The method of claim 45 or 46, wherein the cancer is Stage 0, Stage I, Stage II, Stage III, or Stage IV cancer.

48. The method of any one of claims 45 to 47, wherein the cancer is an epithelial cell cancer.

49. The method of any one of claims 45 to 47, wherein the cancer is breast, prostate, lung, pancreatic, or colorectal.

50. The method of any one of claims 45 to 47, wherein the cancer is lung cancer.

51. The method of any one of claims 45 to 47, wherein the cancer is small cell lung cancer.