Biomarkers for the diagnosis of breast cancer

Abstract

Embodiments of the invention include systems and methods for using biomarkers to diagnose disease, e.g., breast cancer. Patients can be screened for breast cancer based on changes in expression of one or more mRNAs in blood, plasma, or saliva. Embodiments include 26 specific mRNAs for use as biomarkers to screen or distinguish healthy individuals from those with breast cancer. The levels of two or more of the mRNAs can be used to create a biomarker fingerprint for early screening of breast cancer. Embodiments also include kits for screening healthy subjects from those with breast cancer.

Description

[Technical field]

[0001] The present invention relates to the diagnosis of disease using biomarkers, and more particularly to systems and methods for diagnosing breast cancer based on altered expression of one or more specific mRNAs. [Background technology]

[0002] Breast cancer is cancer that develops from breast tissue. Globally, breast cancer is the leading type of cancer in women, accounting for 25% of all cases. In 2018, 2 million new cases and 627,000 deaths occurred. Risk factors for developing breast cancer include being female, obesity, lack of physical activity, alcoholism, hormone replacement therapy during menopause, ionizing radiation, early age at first menstruation, having children later in life or not having children at all, older age, personal history of breast cancer, and family history of breast cancer. Approximately 5-10% of cases are the result of genetic predisposition inherited from a person's parents, including BRCA1 and BRCA2, among others.

[0003] Breast cancer most commonly appears as a lump that feels different from the rest of the breast tissue. More than 80% of cases are discovered when a person detects such a lump with their fingertip. However, most early stage breast cancers are detected by mammograms. A lump found in a lymph node under the arm may also indicate breast cancer.

[0004] Breast cancer can start in different parts of the breast. The breast is made up of three main parts: lobules, ducts, and connective tissue. Lobules are the glands that produce milk. Ducts are the tubes that carry milk to the nipple. Connective tissue (made up of fibrous and fatty tissue) surrounds everything and holds it together. Most breast cancers start in the ducts or lobules. Breast cancer most commonly appears as a lump that feels different from other parts of the breast tissue. Breast cancer can spread outside the breast through the blood and lymphatic vessels. When breast cancer spreads to other parts of the body, it is said to have metastasized.

[0005] A diagnosis of breast cancer can be confirmed by taking a biopsy of suspicious tissue. Once a diagnosis is made, further tests can determine whether the cancer has spread beyond the breast and what treatment is likely to be most effective. Breast cancer outcomes vary depending on the type of cancer, the extent of the disease, and the person's age. The five-year survival rate in the UK and USA is 80-90%. In developing countries, the five-year survival rate is lower.

[0006] In patients diagnosed with cancer, several treatments are available, including surgery, radiation, chemotherapy, hormone therapy, and targeted therapy. The type of surgery varies from breast-conserving surgery to mastectomy. Breast reconstruction may be performed at the time of surgery or at a future time. In patients whose cancer has spread to other parts of the body, treatment is primarily aimed at improving quality of life and comfort.

[0007] Early detection of breast cancer is essential for effective treatment. According to the American Cancer Society, when breast cancer is detected early and at a localized stage, the 5-year relative survival rate is 99%. Traditional methods of early detection include monthly breast self-examination, regular clinical breast examinations, and mammograms. However, methods of early detection have limitations.

[0008] Currently, breast cancer is commonly detected by mammograms. Many national agencies recommend breast cancer screening. For the average woman, the United States Preventive Services Task Force and the American College of Physicians recommend mammograms every two years for women ages 50-74, the Council of Europe recommends mammograms every two years for those ages 50-69 in most programs, the European Commission recommends mammograms every two to three years for those ages 45-75, and Canada recommends screening every two to three years for those ages 50-74. After detection, a diagnosis of breast cancer is confirmed by taking a biopsy of the suspected breast tissue. Once a diagnosis is made, further testing is done to determine whether the cancer has spread beyond the breast and which therapeutic regimen is likely to be most effective.

[0009] The use of mammography as a screening tool for the detection of early breast cancer in healthy women without symptoms is controversial. There are many reasons for this: diagnostic errors, overtreatment, and the harmful effects of radiation exposure. The balance between the benefits and harms of breast cancer screening is controversial. A 2013 Cochrane review found that it was unclear whether mammography screening is harmful because a large proportion of women had false-positive test results.

[0010] Biomarkers are non-invasive and cost-effective tools to aid in the clinical management of cancer patients, especially in the areas of disease detection, prognosis, monitoring and therapeutic stratification. For a serological biomarker to be useful for early detection, its presence in serum must be relatively low in healthy individuals and those with benign disease. The biomarker must be produced by the tumor or its microenvironment, enter the blood circulation and cause an increase in serum levels. Mechanisms that promote entry into the blood circulation include secretion or release, angiogenesis, invasion, and destruction of tissue architecture. The biomarker should preferably be tissue specific so that changes in serum levels can be directly attributed to disease (e.g., cancer) of that tissue.

[0011] For example, serum PSA is commonly used to screen men over 50 for prostate cancer, but its use is controversial because it is elevated in serum in benign disease as well as in prostate cancer. Nevertheless, PSA represents one of the most useful serological markers currently available. PSA is strongly expressed only in the prostate tissue of healthy men, and low levels in serum are established by normal diffusion through various anatomical barriers. These anatomical barriers are disrupted during the development of prostate cancer, allowing increased amounts of PSA to enter the circulation.

[0012] Other common serological biomarkers include carcinoembryonic antigen (CEA) and carbohydrate antigen 19.9 (CA19.9) for gastrointestinal cancer, CEA, CYFRA21-1 (cytokeratin 19 fragment), neuron-specific enolase (NSE), tissue polypeptide antigen (TPA), progastrin-releasing peptide (pro-GRP), and SCC antigen for lung cancer, CA125 for ovarian cancer, and prostate-specific antigen (PSA, also known as KLK3) for prostate cancer. However, these biomarkers have limitations and generally lack adequate sensitivity and specificity for early cancer detection. Furthermore, there are no biomarkers available for detecting breast cancer.

[0013] The limitations in accurately detecting breast cancer make it difficult to study disease progression and develop drugs / therapies. Furthermore, it is difficult for drug developers to adequately recruit patients for clinical trials. Furthermore, patients are generally reluctant to undergo breast biopsy due to its invasiveness and risks. Thus, there is a need for an accurate, affordable, non-invasive breast cancer test.

[0014] There is a need for improved diagnostic assays and methods for detecting cancer, particularly breast cancer. Conventional diagnostic assays often rely on a single biomarker and are unreliable in detecting the presence of cancer or tumor progression. Thus, there is a need for the identification of alternative molecular markers that overcome these limitations. Summary of the Invention

[0015] The following summary is provided to aid in understanding some of the innovative features unique to the disclosed embodiments and is not intended to be a complete description. Various aspects of the embodiments disclosed herein can be fully understood when considered in their entirety in the specification, claims, and abstract.

[0016] Embodiments include systems and methods for the detection and diagnosis of breast cancer and its progression.

[0017] Embodiments include a set of peripheral diagnostic biomarkers for detecting breast cancer.Embodiments include mRNA biomarkers for detecting breast cancer.Embodiments also include mRNA biomarkers for detecting breast cancer at an early stage in the progression of breast cancer.

[0018] Additional embodiments include mRNA biomarkers for distinguishing types of breast cancer. Embodiments include mRNA biomarkers for distinguishing different genetic forms of breast cancer. Breast cancer can be, for example, ductal carcinoma in situ, invasive lobular carcinoma in situ, tubular / cribriform carcinoma, mucinous (colloid) carcinoma, medullary carcinoma, papillary carcinoma, and metaplastic carcinoma.

[0019] Embodiments include mRNA biomarkers for distinguishing between breast cancer and healthy breast. Embodiments also include the use of nucleic acids, proteins and / or peptides (e.g., as identified in Table 1) for distinguishing between breast cancer and healthy breast.

[0020] Embodiments include mRNA biomarkers for monitoring the progression of breast cancer in a patient.Embodiments include mRNA biomarkers for providing guidance in selecting among one or more therapies and / or drugs for treating a patient with breast cancer.

[0021] Embodiments include systems and methods for determining a preferred treatment for a patient suffering from breast cancer. Embodiments also include systems and methods for determining the likelihood that a patient will respond favorably to a surgical treatment, such as a mastectomy.

[0022] Embodiments include methods of diagnosing breast cancer based on the levels of one or more mRNA biomarkers using one or more algorithms.

[0023] Embodiments include methods of diagnosing breast cancer based on the levels of one or more mRNA biomarkers using one or more algorithms.

[0024] Embodiments also include methods of diagnosing breast cancer based on the levels of one or more protein biomarkers using one or more algorithms.

[0025] Embodiments include methods of distinguishing between stage 0, stage 1, stage 2, stage 3, stage 4, and stage 5 breast cancer using one or more biomarkers.

[0026] The methods and assays disclosed herein are directed to the examination of the amount of one or more biomarkers in a biological sample, the determination of which amount of such biomarkers predicts or indicates the presence of breast cancer. The disclosed methods and assays provide a convenient, efficient, and potentially cost-effective means for obtaining data and information useful for evaluating appropriate or effective therapies for treating a patient.

[0027] Methods for detecting any biomarker that is desired to be evaluated include protocols that examine the presence and / or expression of the desired nucleic acid. Tissue or cell samples from a mammal can be conveniently assayed for genetic marker mRNA or DNA using, for example, Northern, dot blot, or polymerase chain reaction (PCR) analysis, array hybridization, RNase protection assay, or commercially available DNA SNP chip microarrays, including DNA microarray snapshots. Real-time PCR (RT-PCR) assays, such as quantitative PCR assays, are well known in the art.

[0028] More specifically, embodiments include methods of detecting breast cancer based on specific genes / mRNAs with altered expression levels. Applicants have identified 26 specific mRNAs that can be used as biomarkers to distinguish healthy individuals from those with breast cancer. The use of biomarkers is non-invasive and potentially more sensitive than traditional methods. Types of breast cancer include ductal carcinoma in situ, invasive lobular carcinoma in situ, tubular / cribriform carcinoma, mucinous (colloid), medullary carcinoma, papillary carcinoma, and metaplastic carcinoma. Embodiments also include methods of prognosis, patient monitoring, and differentiating between different types of breast cancer. Based on the prognosis, appropriate treatment plans can be devised.

[0029] Embodiments also include the use of one or more of the following genes (or mRNAs) as biomarkers: SLC8A1, RP11-452L6.1, HINT3, PRORSD1P, PRDX6, GABRB2, EPS8, SH3RF1, GB_ACC AA814006, hypothetical protein LOC284058, ZNF160, SLC25A45, DST, MTAP, GB_Acc AK024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, and PTGS2.

[0030] An embodiment is a method for detecting cancer or determining the prognosis of a subject suffering from cancer (such as breast cancer), comprising the steps of: a) measuring the expression level of at least one mRNA in a test sample from the subject's plasma; b) comparing the expression level of the mRNA in the test sample with a level in a reference sample; and c) detecting the cancer or determining the prognosis of the cancer based on the change in expression of the mRNA in the test sample. The method can distinguish different stages of breast cancer (corresponding to stages 0-5). The method can also include treating the cancer based on the detection / prognosis. Common treatments include surgery, radiation, chemotherapy, hormone therapy, targeted drug therapy and / or immunotherapy.

[0031] Embodiments also include a method of detecting cancer (such as breast cancer) or determining the prognosis of a subject suffering from cancer, comprising the steps of: a) measuring the expression level of at least one mRNA in a test sample from the subject's plasma; b) comparing the expression level of the mRNA in the test sample with a level in a reference sample; and c) detecting the cancer or determining the prognosis of the cancer based on changes in the expression of the mRNA in the test sample. The method may include treating the cancer based on the detection / prognosis. The method can be used with other body fluids, including saliva.

[0032] Embodiments also include a method of detecting cancer or determining a prognosis of a test subject with cancer (such as breast cancer), comprising: a) measuring expression levels of two or more mRNAs in a plasma sample from a subject with cancer, b) measuring expression levels of the same mRNAs in a plasma sample from a healthy subject, c) comparing the expression levels of the mRNAs in the plasma sample from the subject with cancer to the levels in the plasma sample from the healthy subject, d) identifying mRNAs with altered expression levels in the plasma sample from the subject with cancer, e) creating a biomarker fingerprint from the mRNAs with altered expression levels, and f) diagnosing or determining a prognosis of cancer in the test subject by comparing the levels of the mRNAs in the plasma of the test subject to the levels in the biomarker fingerprint. The method may also include treating the cancer based on the detection / prognosis.

[0033] Embodiments also include a method of diagnosing cancer or determining a prognosis of a test subject with cancer, comprising the steps of: a) measuring expression levels of two or more mRNAs in saliva obtained from blood from a subject with cancer, b) measuring expression levels of two or more mRNAs in saliva obtained from blood from a healthy subject, c) comparing the expression levels of the two or more mRNAs in saliva obtained from the subject with cancer to levels in a plasma sample obtained from the healthy subject, d) identifying mRNAs with altered expression levels in saliva obtained from blood from the subject with cancer, e) creating a biomarker fingerprint from the altered expression levels of the mRNAs, and f) diagnosing or determining a prognosis of cancer in the test subject by comparing the levels of mRNAs from the plasma of the test subject to the levels of mRNAs in the biomarker fingerprint. The method may include treating the cancer based on the detection / prognosis.

[0034] Embodiments also include diagnostic kits for diagnosing breast cancer or detecting the presence of a tumor. The kits can detect the presence of breast cancer tumor cells in a patient's plasma or saliva. The kits can include a plurality of nucleic acid molecules, each encoding an mRNA sequence. The nucleic acid molecules identify variations in the expression level of one or more mRNAs in a plasma or saliva sample from a test subject. The expression level of one or more mRNAs can represent a nucleic acid expression fingerprint indicative of the presence of a tumor or breast cancer.

[0035] The embodiment also includes a method for identifying one or more mammalian target cells indicative of breast cancer, comprising: a) collecting plasma from a test subject; b) hybridizing at least one nucleic acid molecule biomarker encoding an mRNA sequence to a portion of the plasma; c) quantifying mRNA expression; d) determining the expression levels of a plurality of nucleic acid molecules encoding the mRNA sequences; e) determining the expression levels of the plurality of nucleic acid molecules in one or more control cells; and f) identifying one or more nucleic acid molecules from the plurality of nucleic acid molecules that are differentially expressed in the target cells and the control cells by comparing the respective expression levels obtained in steps (d) and (e). The method may include treating the cancer based on the detection / prognosis. The differentially expressed nucleic acid molecules together can represent a nucleic acid expression biomarker fingerprint indicative of the presence of breast cancer.

[0036] definition Reference herein to "one embodiment / aspect" or "embodiment / aspect" means that a particular feature, structure, or characteristic described in connection with the embodiment / aspect is included in at least one embodiment / aspect of the present disclosure. The use of the phrase "in one embodiment / aspect" or "in another embodiment / aspect" in various places herein does not necessarily all refer to the same embodiment / aspect, nor are separate or alternative embodiments / aspects mutually exclusive with other embodiments / aspects. Furthermore, various features are described that may be exhibited by some embodiments / aspects and not by other embodiments / aspects. Similarly, various requirements are described that may be requirements for some embodiments / aspects but not other embodiments / aspects. Embodiments and aspects may be used interchangeably in certain instances.

[0037] The terms used in this specification generally have their ordinary meaning in the art, within the context of this disclosure, and in the specific context in which each term is used. Certain terms used to describe this disclosure are explained below or elsewhere in this specification to provide additional guidance to practitioners regarding the description of this disclosure. It will be understood that the same thing can be said in multiple ways.

[0038] Thus, alternative language and synonyms may be used for any one or more of the terms discussed herein. Also, no special significance is placed on whether a term is recited or discussed herein. Synonyms for certain terms are provided. The listing of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification, including examples of any term described herein, is illustrative only and is not intended to further limit the scope and meaning of the disclosure or any exemplary term. Similarly, the disclosure is not limited to the various embodiments shown herein.

[0039] Without intending to further limit the scope of the present disclosure, examples of instruments, devices, methods, and their related results according to the embodiments of the present disclosure are given below. Please note that the subject or sub-title may be used in the examples for the convenience of the reader, and this does not limit the scope of the present disclosure in any way. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure pertains. In case of conflict, the present document, including definitions, will take precedence.

[0040] As used herein, where applicable, in the specification and the appended claims, the term "about" or "generally" means a margin of + / - 20% unless otherwise indicated. Also, as used herein, where applicable, in the specification and the appended claims, the term "substantially" means a margin of + / - 10% unless otherwise indicated. It is to be understood that not all uses of the above terms are quantifiable so that the referenced ranges can be applied.

[0041] The term "algorithm" refers to a specific set of instructions or an explicit listing of well-defined instructions for carrying out a procedure, which usually progresses through a well-defined series of sequential states and eventually terminates in an end state.

[0042] The term "biomarker" generally refers to a DNA, RNA, protein, carbohydrate, or glycolipid-based molecular marker, the expression or presence of which in a subject's sample can be detected by standard methods (or the methods disclosed herein) and predicts or prognoses an effective response or susceptibility of a mammalian subject suffering from cancer. A biomarker may be present in a test sample but not in a control sample, may be absent in a test sample but present in a control sample, or the amount of the biomarker may differ between the test sample and the control sample. For example, a genetic biomarker (e.g., a particular mutation and / or SNP) evaluated may be present in such a sample but not in a control sample, or a particular biomarker is seropositive in a sample but seronegative in a control sample. Also, optionally, the expression of such a biomarker may be determined to be higher than the expression observed for the control sample. The terms "marker" and "biomarker" are used interchangeably herein.

[0043] As used herein, "additional biomedical information" refers to one or more assessments of an individual other than using any of the biomarkers described herein that are associated with breast cancer risk. "Additional biomedical information" includes physical descriptors of the individual, physical descriptors of the lung nodule observed by CT imaging, height and / or weight of the individual, sex of the individual, ethnicity of the individual, smoking history, occupational history, exposure to known carcinogens (e.g., exposure to any of asbestos, radon gas, chemicals, smoke from fire, and air pollution, which may include emissions from stationary or mobile sources such as industrial / factory or automobile / marine / aircraft emissions), exposure to passive smoking, family history of breast cancer (or other cancers), presence of lung nodules, size of the nodule, location of the nodule, morphology of the nodule (e.g., as observed by CT imaging, ground glass opacity (GGO), solid, non-solid), edge characteristics of the nodule (e.g., smooth, lobulated, sharp smooth, spiculated, invasive), and the like. The additional biomedical information can be obtained from the individual using routine techniques known in the art, such as from the individual themselves using routine patient questionnaires, health history questionnaires, etc., or from a medical practitioner, etc. Alternatively, the additional biomedical information can be obtained from routine imaging techniques including CT imaging (e.g., low-dose CT imaging) and X-rays. Testing biomarker levels in combination with evaluation of any additional biomedical information may improve the sensitivity, specificity, and / or AUC for detecting breast cancer (or other breast cancer-related applications), for example, compared to biomarker testing alone, or compared to evaluating any particular item of additional biomedical information alone (e.g., CT imaging alone).

[0044] The term "area under the curve" or "AUC" refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. The AUC measure is useful for comparing the accuracy of classifications across the complete data range. Classifications with a larger AUC have a greater ability to correctly classify unknowns between two groups of interest (e.g., breast cancer samples and normal or control samples). ROC curves are useful for depicting the performance of a particular feature (e.g., any of the biomarkers described herein and / or any item of additional biomedical information) in discriminating between two populations (e.g., cases with breast cancer and controls without breast cancer). Typically, feature data across populations (e.g., cases and controls) are sorted in ascending order based on the value of a single feature. Then, for each value of that feature, the true positive rate and false positive rate of the data are calculated. The true positive rate is determined by counting the number of cases above the value of that feature and then dividing by the total number of cases. The false positive rate is determined by counting the number of controls above the value of that feature and then dividing by the total number of controls. This definition refers to scenarios where a feature is elevated in cases compared to controls, but it also applies to scenarios where a feature is low in cases compared to controls (in such scenarios, samples below the value of the feature are counted). ROC curves can be generated for single features as well as other single outputs, for example, combinations of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide a single total value, which can be plotted on a ROC curve. In addition, any combination of multiple features whose combination derives a single output value can be plotted on a ROC curve. These combinations of features can include tests. A ROC curve plots the true positive rate (sensitivity) of a test against the false positive rate (1-specificity) of the test.

[0045] As used herein, "detecting" or "determining" with respect to a biomarker value includes the use of both the equipment necessary to observe and record a signal corresponding to the biomarker value, and the materials necessary to generate that signal. In various embodiments, the biomarker value is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

[0046] The term "prognosis" refers to a prediction or likely outcome of a disease. As used herein, it refers to the likely outcome of breast cancer, including whether the disease will respond to treatment or palliative efforts and / or the likelihood that the disease will progress.

[0047] The term "fingerprint", "disease fingerprint" or "biomarker signature" refers to a plurality of biomarkers or a pattern of biomarkers that are elevated or decreased in subjects suffering from a disease. Fingerprints can be generated by comparing subjects suffering from a disease with healthy subjects and can be used to screen / diagnose the disease.

[0048] The terms "miRNA" or "microRNA", "miRNA biomarker", or "MicroRNA" refer to small endogenous RNA molecules that can be used as serum diagnostic biomarkers for diseases, including cancer.

[0049] The term "messenger RNA" or "mRNA" refers to a single-stranded molecule of RNA that corresponds to the genetic sequence of a gene and is read by ribosomes in the process of synthesizing a protein. As used herein, mRNA can also include "miRNA" and small interfering RNA (siRNA).

[0050] "Unregulated" mRNA generally refers to an increase in the expression level of an mRNA in response to a given treatment or condition. "Downregulated" mRNA generally refers to a "decrease" in the level of expression of an mRNA in response to a given treatment or condition. In some situations, the mRNA level may remain unchanged in response to a given treatment or condition. The mRNA from a patient sample may be "unregulated" (i.e., the level of the mRNA may be increased). Alternatively, the mRNA may be "downregulated" (i.e., the level of the mRNA may be decreased).

[0051] "Unregulated" mRNA generally refers to an increase in the expression level of an mRNA in response to a given treatment or condition. "Downregulated" mRNA generally refers to a "decrease" in the expression level of an mRNA in response to a given treatment or condition. In some circumstances, the mRNA level may remain unchanged in response to a given treatment or condition. The mRNA from a patient sample may be "unregulated", i.e., the level of the mRNA may be increased, for example, by about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 90%, about 100%, about 200%, about 300%, about 500%, about 1,000%, about 5,000% or more of the comparative control mRNA level or reference level. Alternatively, the mRNA can be "down-regulated," i.e., the mRNA level can be reduced to, for example, about 99%, about 95%, about 90%, about 80%, about 70%, about 60%, about 50%, about 40%, about 30%, about 20%, about 10%, about 5%, about 2%, about 1% or less of the comparative control mRNA level or reference level.

[0052] Similarly, the level of a polypeptide, protein, or peptide from a patient sample may be increased compared to a control or reference level. The increase may be about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 90%, about 100%, about 200%, about 300%, about 500%, about 1,000%, about 5,000% or more of the comparative control protein level or reference level. Alternatively, the level of a protein biomarker may be decreased. The decrease may be, for example, at a level of about 99%, about 95%, about 90%, about 80%, about 70%, about 60%, about 50%, about 40%, about 30%, about 20%, about 10%, about 5%, about 2%, about 1% or less of the comparative control protein level or reference level.

[0053] The terms "polypeptide", "peptide" and "protein" are used interchangeably herein to refer to a polymer of amino acid residues. The term applies to amino acid polymers in which one or more amino acid residues are artificial chemical mimics of the corresponding natural amino acids, and to natural and non-natural amino acid polymers. Methods for obtaining (e.g., producing, isolating, purifying, synthesizing, and recombinantly producing) polypeptides are well known to those skilled in the art. The level of a polypeptide, protein, or peptide from a patient sample may be increased compared to a control or reference level. Alternatively, the level of a protein biomarker may be decreased.

[0054] The term "amino acid" refers to naturally occurring and synthetic amino acids, as well as amino acid analogs and amino acid mimetics that function similarly to naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as those amino acids that are later modified, such as hydroxyproline, gamma-carboxyglutamate, and O-phosphoserine. Amino acid analogs refer to compounds that have the same basic chemical structure as naturally occurring amino acids, i.e., carbons that are attached to hydrogen, carboxyl groups, amino groups, and R groups, such as homoserine, norleucine, methionine sulfoxide, and methionine methylsulfonium. Such analogs have modified R groups (e.g., norleucine) or modified peptide backbones, but retain the same basic chemical structure as naturally occurring amino acids. Amino acid mimetics refer to chemical compounds that have a structure that differs from the general chemical structure of an amino acid, but function similarly to a naturally occurring amino acid.

[0055] Amino acids may be referred to herein by either their commonly known three letter symbols or by the one-letter symbols recommended by the IUPAC-IUB Biochemical Nomenclature Commission. Nucleotides may likewise be referred to by their commonly accepted single-letter codes.

[0056] The term "medicinal product" refers to an active drug for treating cancer, such as breast cancer, or the signs or symptoms or side effects of cancer.

[0057] The term "plasma" or "plasma" refers to the liquid portion of blood that carries cells and proteins throughout the body. Plasma can be separated from blood by spinning tubes containing fresh blood containing an anticoagulant in a centrifuge until the blood cells fall to the bottom of the tube.

[0058] The term "PCR" or "polymerase chain reaction" refers to a common method used to make many copies of a particular DNA fragment. Variations of this technique can be used to determine the presence and amount of one or more mRNAs in a sample. For example, hydrolysis probe-based stem-loop quantitative reverse transcription PCR (RT-qPCR) assays can be performed to confirm and / or quantify the concentration of selected mRNAs in serum samples from patients and controls.

[0059] The term "sample" refers to a biological sample obtained from an individual, body fluid, body tissue, cell line, tissue culture, or other source. Body fluids are, for example, lymph, serum, fresh whole blood, peripheral blood mononuclear cells, frozen whole blood, plasma (including fresh or frozen), urine, saliva, semen, synovial fluid, and spinal fluid. Samples also include synovial tissue, skin, hair follicles, and bone marrow. Methods for obtaining tissue biopsies and body fluids from mammals are well known in the art.

[0060] The term "subject" or "patient" refers to any single animal, more preferably a mammal (including, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human animals such as non-human primates), for which treatment is desired. Most preferably, the patient herein is a human.

[0061] The term "nucleic acid probe" or "oligonucleotide probe" refers to a nucleic acid capable of binding to a target nucleic acid of complementary sequence, such as the mRNA biomarkers set forth herein, through one or more types of chemical bonds, usually through complementary base pairing, usually through the formation of hydrogen bonds. As used herein, a probe may contain natural (e.g., A, G, C, or T) or modified bases (7-deazaguanosine, inosine, etc.). In addition, bases in a probe may be linked by bonds other than phosphodiester bonds, so long as they do not prevent hybridization. Those skilled in the art will understand that a probe may bind to a target sequence that lacks complete complementarity with the probe sequence, depending on the stringency of the hybridization conditions. The probe is preferably directly labeled with an isotope, e.g., a chromophore, lumiphore, chromogen, or indirectly labeled with biotin to which a streptavidin complex may subsequently bind. The presence or absence of a target mRNA biomarker of interest can be detected by assaying for the presence or absence of the probe.

[0062] The term "probe id", "probe set identifier", or "Affymetrix probe set ID" refers to an identifier that refers to a set of probe pairs selected to represent expressed sequences on an array. (_at = all probes hit one known transcript, _a = all probes in the set hit alternative transcripts from the same gene, _s = all probes in the set hit transcripts from different genes, _x = some probes hit transcripts from different genes).

[0063] Other technical terms used herein have their ordinary meaning in the art in which they are used, as exemplified by various technical dictionaries. The specific values ​​and configurations described in these non-limiting examples may be varied and are cited merely to illustrate at least one embodiment and are not intended to limit the scope thereof. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0064] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the subject technology as set forth in the claims. Additional features and advantages of the subject technology will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the subject technology. The advantages of the subject technology will be realized and attained by the structure particularly pointed out in the written description and claims hereof.

[0065] Traditional methods of diagnosing breast cancer are not always reliable and involve invasive procedures such as tumor biopsies. Currently, breast cancer is typically detected by mammograms and confirmed at the time of diagnosis by undergoing a biopsy of suspected breast tissue.

[0066] Recent studies have demonstrated the presence of mRNA in saliva and circulating blood and its potential use as a biomarker in the diagnosis of various diseases, specifically, it has been proposed for use as a biomarker for the early detection of cancers such as breast cancer.

[0067] Applicant recognizes mRNA expression profiling in subjects suffering from breast cancer. Certain mRNAs are aberrantly expressed in malignant tissues compared to non-malignant breast tissues. Furthermore, mRNA expression can provide insight into the cellular processes involved in malignant transformation and progression. Thus, mRNA expression levels can also be used for breast cancer prognosis. In particular, this technology provides a diagnostic method for predicting and / or prognosticating the efficacy of treatment.

[0068] The present invention is based on the discovery that breast cancer can be reliably identified based on specific mRNA expression profiles with high sensitivity and specificity. The expression of a biomarker usually includes both upregulated and downregulated levels of mRNA. Analysis of mRNA expression biomarkers allows for the creation of a "fingerprint" by analyzing mRNA expression patterns in diseased and healthy subjects. The individual mRNA expression levels can then be used for the detection of breast cancer at an early stage of the disease. Biomarkers can also be used to distinguish different subtypes of breast cancer from each other and to monitor the progression of breast cancer. Additional embodiments include mRNA biomarkers for distinguishing different stages of breast cancer (corresponding to stages 0-5). Further embodiments include mRNA biomarkers for identifying types of breast cancer. An embodiment includes mRNA biomarkers for identifying different genetic forms of breast cancer. In another embodiment, proteins transcribed from the identified mRNA are used as biomarkers.

[0069] Early diagnosis of breast cancer allows for intervention and / or treatment and avoids further damage, including metastasis of breast cancer cells to other tissues and organs. For example, the biomarkers described herein can be measured and analyzed to determine whether a patient has a healthy breast free of cancerous tissue or has breast cancer, including early diagnosis of breast cancer. Traditional methods of diagnosing breast cancer rely on touching the breast to identify a lump or using a mammogram to identify a cancerous lump. Because early stages of breast cancer are often asymptomatic, these methods may not be effective in identifying patients with breast cancer at a stage where treatment may have the greatest effect. Early, accurate and reliable detection of breast cancer can be utilized by researchers and drug developers to recruit patients for clinical trials, support their drugs through development, and support drugs post-approval.

[0070] The disclosed biomarkers can also be used to monitor the progression of breast cancer. For example, the biomarkers described herein can be measured and analyzed to identify the stage of breast cancer. Breast cancer can be in one of five stages: stage 0, stage 1, stage 2, stage 3, stage 4. Efforts can be made to slow (or reverse) the progression of the disease, if possible. Conventional methods lack the reliability and sensitivity to identify these conditions and monitor progression.

[0071] Embodiments include a set of diagnostic markers or molecular fingerprints for rapid and reliable identification and / or treatment of cells that exhibit or have a predisposition to developing different subtypes of breast cancer.Embodiments further include methods of diagnosing cancer based on specific mRNAs with altered levels of expression.While individual mRNAs can be monitored, the present invention includes 26 mRNAs that have particular value as biomarkers for screening or distinguishing healthy from diseased individuals. mRNAs of particular interest (detailed in Table 1) include SLC8A1, RP11-452L6.1, HINT3, PRORSD1P, PRDX6, GABRB2, EPS8, SH3RF1, GB_ACC AA814006, hypothetical protein LOC284058, ZNF160, SLC25A45, DST, MTAP, GB_Acc AK024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, and PTGS2.

[0072] Embodiments include a method for identifying an initial set of peripheral diagnostic biomarkers for detecting breast cancer and validating these biomarkers on additional datasets, including saliva and peripheral blood. This method can be implemented by the following steps: a) creating a binary classification to distinguish between patients with confirmed positive breast cancer biopsy and patients with confirmed negative breast cancer biopsy; b) identifying a first minimal set of biomarkers that maximizes diagnostic accuracy; c) setting aside the first minimal set and repeating the analysis with the remaining biomarkers to determine a next minimal set; d) Repeating the above steps until a significant decrease in diagnostic accuracy is observed.

[0073] The methods and materials can be used to assess subjects (e.g., human patients) for cancer, such as breast cancer. For example, embodiments include materials and methods to assist clinicians in assessing breast cancer disease activity, evaluating treatment response likelihood and outcome, and predicting long-term disease outcomes using identifiable markers. Furthermore, subjects suffering from breast cancer can be diagnosed based on the presence of certain diagnostic indicators in plasma or saliva from the subject. Thus, this technology allows for the diagnosis of breast cancer based on one or more combinations of markers.

[0074] The severity of breast cancer can be described as being within one of five stages. Stage 0 is a pre-cancerous or marker state and is generally associated with either ductal carcinoma in situ (DCIS) or lobular carcinoma in situ (LCIS). Stages 1-3 Tumors and / or cancer cells are found within the breast or local lymph nodes. When an individual is diagnosed with stage 4 breast cancer, this means that the breast cancer has become a "metastatic" cancer and the cancer cells have spread to other tissues and / or organs of the patient. Patients diagnosed with stage 4 breast cancer do not have a better prognosis than patients diagnosed with stages 0-3 because the cancer cells have spread beyond the breast where the cancer is located and the lymph nodes located proximal to the breast.

[0075] Multiple mRNA biomarkers can be used from a single serum or saliva sample taken from a subject. According to some embodiments, multiple biomarkers are assessed and measured from different samples taken from a patient. According to some embodiments, the subject technology is used in a kit for predicting, diagnosing, or monitoring response to a cancer treatment or therapy, and the kit is calibrated to measure marker levels in a sample from a patient.

[0076] According to some embodiments, the amount of biomarker can be determined, for example, by using a reagent (e.g., an antibody, an antibody fragment, or an antibody derivative) that specifically binds to the biomarker protein or a fragment thereof. The expression level can be determined using methods common in the art, such as proteomics, flow cytometry, immunocytochemistry, immunohistochemistry, enzyme-linked immunosorbent assay, multichannel enzyme-linked immunosorbent assay, and variations thereof. The expression level of a biomarker in a biological sample can also be determined by detecting the expression level of a transcribed biomarker polynucleotide or a fragment thereof encoded by a biomarker gene, which can be a cDNA, mRNA, or heterogeneous nuclear RNA (hnRNA). The detection step can include amplifying the transcribed biomarker polynucleotide, and a method of quantitative reverse transcriptase polymerase chain reaction can be used. The expression level of a biomarker can be assessed by detecting the presence of a transcribed biomarker polynucleotide or a fragment thereof in a sample by a probe that anneals to the transcribed biomarker polynucleotide or a fragment thereof under stringent hybridization conditions.

[0077] Also provided herein are compositions and kits for carrying out the methods. For example, in some embodiments, reagents (e.g., primers, probes) specific to one or more markers are provided, either alone or in sets (e.g., a set of primer pairs for amplifying multiple markers). Additional reagents for carrying out detection assays (e.g., enzymes, buffers, positive and negative controls for carrying out QuARTS, PCR, sequencing, bisulfite, or other assays) may also be provided. In some embodiments, kits are provided that include one or more reagents necessary, sufficient, or useful for carrying out the methods. Also provided are reaction mixtures that contain the reagents. Further provided are master mix reagent sets that contain multiple reagents that can be added to each other and / or to test samples to complete a reaction mixture.

[0078] In some embodiments, the techniques described herein are associated with a programmable machine designed to perform a sequence of arithmetic or logical operations as provided by the methods described herein. For example, some embodiments of the techniques are associated with (e.g., implemented in) computer software and / or computer hardware. In one aspect, the techniques relate to a computer that includes a form of memory, elements for performing arithmetic and logical operations, and processing elements (e.g., microprocessors) for executing a sequence of instructions (e.g., the methods provided herein) for reading, manipulating, and storing data. Thus, certain embodiments employ processes that involve data stored in or transferred through one or more computer systems or other processing systems. The embodiments disclosed herein also relate to an apparatus for performing these operations. This apparatus may be specially configured for the required purpose, or it may be a general-purpose computer (or group of computers) selectively activated or reconfigured by computer programs and / or data structures stored in the computer. In some embodiments, a group of processors cooperatively (e.g., via a network or cloud computing) and / or in parallel perform some or all of the analytical operations listed.

[0079] In some embodiments, the microprocessor is part of a system that determines the presence of one or more mRNAs or miRNAs (labeled herein as hsa-miRs or has-miRs) associated with cancer, generates standard curves, determines the specificity and / or sensitivity of an assay or marker, calculates ROC curves, and performs sequence analysis, all of which are described herein or known in the art.

[0080] In some embodiments, the microprocessor is part of a system that determines the amount, such as the concentration, of one or more mRNAs associated with cancer, generates standard curves, determines the specificity and / or sensitivity of an assay or marker, calculates ROC curves, and performs sequence analysis, all of which are described herein or known in the art. The amount of one or more mRNAs can be determined by abundance measured per mole or millimole. The amount of mRNA can be determined by fluorescence, other measurements using optical signals, or other measurements known to those skilled in the art for measuring the level of mRNA.

[0081] In some embodiments, the microprocessor or computer uses an algorithm to measure the amount of an mRNA or mRNAs. The algorithm may include a mathematical interaction between the marker measurements or a mathematical transformation of the marker measurements. The mathematical interaction and / or the mathematical transformation may be represented in a linear, non-linear, discontinuous, or discrete manner.

[0082] In some embodiments, the software or hardware element receives the results of the multiple assays and determines to report to the user a single value result indicative of cancer risk based on the results of the multiple assays. Related embodiments calculate a risk factor based on a mathematical combination (e.g., weighted combination, linear combination) of the results from the multiple assays as disclosed herein.

[0083] Some embodiments include storage media and memory elements (e.g., volatile and / or non-volatile memory) used to store instructions (e.g., process embodiments provided herein) and / or data (e.g., processing objects such as methylation measurements, sequences, and associated statistical descriptions). Some embodiments also relate to systems that include one or more of a CPU, a graphics card, and a user interface (e.g., including an output device such as a display and an input device such as a keyboard).

[0084] Programmable machines relating to technology include conventional existing technologies as well as technologies under development or yet to be developed (eg, quantum computers, chemical computers, DNA computers, optical computers, spintronics-based computers, etc.).

[0085] In some embodiments, the techniques involve wired (e.g., metallic cable, fiber optics) or wireless transmission media for transmitting data. For example, some embodiments relate to data transmission over a network (e.g., a local area network (LAN), a wide area network (WAN), an ad-hoc network, the Internet, etc.). In some embodiments, the programmable machines reside on such a network as peers, and in some embodiments, the programmable machines have a client / server relationship.

[0086] In some embodiments, the data is stored on a computer readable storage medium, such as a hard disk, flash memory, memory stick, optical media, floppy disk, or the like.

[0087] In some embodiments, the techniques provided herein are associated with multiple programmable devices operating in concert to perform the methods described herein. For example, in some embodiments, multiple computers (e.g., connected by a network) may operate in parallel to collect and process data, for example in the implementation of cluster computing or grid computing or some other distributed computer architecture that relies on a complete computer (with on-board CPU, storage, power supply, network interface, etc.) connected to a network (private, public, or Internet) by a conventional network interface such as Ethernet, fiber optic, or wireless network technology.

[0088] For example, some embodiments provide a computer including a computer-readable medium. The embodiments include a random access memory (RAM) coupled to a processor. The processor executes computer-executable program instructions stored in the memory. Such a processor may include a microprocessor, an ASIC, a state machine, or other processor, and may be any of a number of computer processors, such as, for example, processors from Intel Corporation of Santa Clara, Calif. and Motorola Corporation of Schaumburg, Ill. Such a processor may include or be in communication with a medium, e.g., a computer-readable medium, that stores instructions that, when executed by the processor, cause the processor to perform the steps described herein.

[0089] Examples of computer-readable media include electronic, optical, magnetic, or other storage or transmission devices capable of providing computer-readable instructions to a processor. Other examples of suitable media include, but are not limited to, floppy disks, CD-ROMs, DVDs, magnetic disks, memory chips, ROMs, RAMs, ASICs, configured processors, any optical media, any magnetic tape or other magnetic media, or any other media from which a computer processor can read instructions. Various other forms of computer-readable media may also transmit or carry the instructions to a computer, including routers, private or public networks, or other transmission devices or channels, both wired and wireless. The instructions may include code from any suitable computer programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, and JavaScript.

[0090] The computer is connected to a network in some embodiments. The computer may also include several external or internal devices such as a mouse, CD-ROM, DVD, keyboard, display, or other input / output devices. Examples of computers are personal computers, digital assistants, personal digital assistants, cell phones, mobile phones, smartphones, pagers, digital tablets, laptop computers, Internet appliances, and other processor-based devices. In general, a computer relevant to aspects of the technology provided herein may be any type of processor-based platform capable of supporting one or more programs including the technology provided herein and running on any operating system such as Microsoft Windows, Linux, UNIX, Mac OS X, etc. Some embodiments include a personal computer running other application programs (e.g., applications). Applications may be included in memory and may include, for example, word processing applications, spreadsheet applications, email applications, instant messenger applications, presentation applications, Internet browser applications, calendar / organizer applications, and any other applications capable of being executed by a client device.

[0091] As related to the technology, all elements, computers, and systems described herein may be logical or virtual.

[0092] It is also envisioned that embodiments may be achieved as computer signals embodied in carrier waves and signals propagated over a transmission medium (e.g., electrical and optical). Thus, various types of information described above may be formatted in structures, such as data structures, and transmitted as electrical signals over a transmission medium or stored on a computer-readable medium.

[0093] In some embodiments, the present disclosure provides a system for predicting the progression of breast cancer. Breast cancer may be one or more of ductal carcinoma in situ, invasive ductal carcinoma, inflammatory breast cancer, and metastatic breast cancer. In one embodiment, the system can identify breast cancer and predict breast cancer in an individual, and includes an apparatus configured to determine an expression level of a nucleic acid, protein, peptide, or other molecule from a biological sample taken from the individual, and hardware logic designed or configured to perform the operation of: (a) receiving an expression level of a collection of signature genes from the biological sample taken from the individual, the collection of signature genes including at least two genes selected from the sequences shown in Table 1.

[0094] Information relevant to a patient's diagnosis includes, but is not limited to, age, ethnicity, tumor location, relevant past medical history related to comorbidities, other oncological history, family history of cancer, physical exam findings, radiological findings, biopsy date, biopsy results, type of surgery performed (retropubic or perineal prostatectomy), neoadjuvant therapy (i.e., chemotherapy, hormones), adjuvant or salvage radiation therapy, hormone therapy, local versus distant disease recurrence, and survival outcomes. These clinical variables may be included in the predictive model in various embodiments.

[0095] Once a biomarker or panel of biomarkers is selected, a method is selected for diagnosing an individual who may have breast cancer. In one embodiment, a method for diagnosing an individual who may have breast cancer, where a biomarker or panel of biomarkers is selected, may include one or more of the following steps: 1) collecting or otherwise obtaining a biological sample; 2) performing an analytical method to detect and measure a biomarker or biomarkers in the panel in the biological sample; 3) performing any data normalization or standardization required by the method used to collect the biomarker values; 4) calculating a biomarker score; 5) combining the biomarker scores to obtain a total diagnostic score; and 6) reporting the individual's diagnostic score. This diagnostic method can be performed using a computer and software program to analyze data collected from nucleic acids, proteins, peptides, or other biomolecules. In this approach, the diagnostic score can be a single number determined from the sum of all marker calculations compared to a pre-set threshold that is indicative of the presence or absence of disease. Or, the diagnostic score can be a series of bars, each representing a biomarker value, and the pattern of response can be compared to a pre-set pattern for determining the presence or absence of disease.

[0096] For both DNA and RNA, nucleic acids can be isolated from saliva, plasma or blood samples. DNA or RNA can be extracted from cells that are extracellular or in the plasma or blood sample. DNA or RNA can also be extracted from cell biopsies containing tumors, including solid tumors in the breast.

[0097] For proteins or peptides or other biomolecules, such can be isolated from saliva, plasma or blood samples. Proteins or peptides or other biomolecules can be extracellular or extracted from cells in the saliva, plasma or blood sample. Proteins or peptides or other biomolecules can also be extracted from cell biopsies containing tumors, including solid tumors in the breast.

[0098] It should also be noted that many of the structures, materials, and acts recited herein may be recited as a means for performing a function or a step for performing a function, and such language should therefore be understood to be entitled to cover all such structures, materials, or acts disclosed within this specification, including the material incorporated by reference, and equivalents thereof.

[0099] A breast cancer biomarker analysis system can provide functions and operations to complete data analysis, such as data collection, processing, analysis, reporting, and / or diagnosis. For example, in one embodiment, a computer system can execute a computer program that can receive, store, retrieve, analyze, and report information related to biomarkers. The computer program can include multiple modules that perform various functions or operations, such as a processing module for processing raw data and generating supplemental data, and an analysis module for analyzing the raw data and supplemental data to generate a breast cancer status and / or diagnosis. Diagnosing a breast cancer status can include generating or collecting any other information, including additional biomedical information regarding the individual's status relative to the disease, identifying whether further testing is desirable, or otherwise assessing the health status of the individual.

[0100] A breast cancer biomarker analysis system can provide functions and operations to complete data analysis, such as data collection, processing, analysis, reporting, and / or diagnosis. For example, in one embodiment, a computer system can execute a computer program that can receive, store, retrieve, analyze, and report information related to breast cancer biomarkers. The computer program can include multiple modules that perform various functions or operations, such as a processing module for processing raw data and generating supplemental data, and an analysis module for analyzing the raw data and supplemental data to generate a breast cancer status and / or diagnosis. Diagnosing a breast cancer status can include generating or collecting any other information, including additional biomedical information regarding the individual's status relative to the disease, identifying whether further testing is desirable, or otherwise assessing the individual's health status.

[0101] As used herein, a "computer program product" refers to an organized set of instructions in the form of natural language or programming language statements that can be contained in a physical medium of any nature (e.g., electronically, magnetically, optically or otherwise written) and used in conjunction with a computer or other automated data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to operate according to the specific content of the statements. Computer program products include, but are not limited to, programs and / or test or data libraries in source and object code embedded in a computer readable medium. Furthermore, computer program products that enable a computer system or data processing device to operate in a preselected manner can be provided in several forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed versions of any and all equivalents of the foregoing.

[0102] In one embodiment, a computer program product for indicating likelihood of breast cancer is provided, the computer program product including a computer readable medium embodying program code executable by a processor of a computing device or system, the program code including code for obtaining data attributable to a biological sample from an individual, the data including biomarker values ​​each corresponding to one of at least N biomarkers in the biological sample selected from the group of biomarkers shown in Table 1, and code for executing a classification method to indicate breast cancer status of the individual as a function of the biomarker values.

[0103] In yet another embodiment, a computer program product for indicating likelihood of breast cancer is provided, the computer program product including a computer readable medium embodying program code executable by a processor of a computing device or system, the program code including code for obtaining data originating from a biological sample from an individual, the data including biomarker values ​​corresponding to biomarkers in the biological sample selected from the group of biomarkers provided in Table 1, and code for executing a classification method to indicate breast cancer status of the individual as a function of the biomarker values.

[0104] The kit (i.e., diagnostic kit) may include reagents for determining the amount of mRNA or mutations in genes from a subject's plasma sample based on assaying nucleic acids, proteins, peptides, or other biomolecules isolated from breast cancer, circulating cells, or remnants of circulating cells present in plasma, including proteins, peptides, or other biomolecules. The nucleic acids may be deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and / or artificial nucleic acids, including artificial nucleic acid analogs. Along with mRNA, other RNAs include non-coding RNA (ncRNA), transfer RNA (tRNA), messenger RNA (mRNA), small interfering RNA (siRNA), piwiRNA (piRNA), small nuclear RNA (snoRNA), small nuclear (snRNA), extracellular RNA (exRNA), and ribosomal RNA (rRNA).

[0105] The disclosed methods and assays provide a convenient, efficient, and potentially cost-effective means for obtaining data and information useful for evaluating appropriate or effective therapies for treating a patient. The kits can use conventional methods for detecting biomarkers, whether they are proteins, peptides, other biomolecules, or RNA or DNA being evaluated, and include protocols for examining the presence and / or expression of desired nucleic acids, e.g., SNPs, in a sample. Tissue or cell samples from a mammal can be conveniently assayed for genetic marker RNA, including mRNA or DNA in one embodiment, using Northern, dot blot, or polymerase chain reaction (PCR) analysis, array hybridization, RNase protection assay, or DNA SNP chip microarrays, or using commercially available DNA SNP chip microarrays, including, for example, DNA microarray snapshots. Real-time PCR (RT-PCR) assays, such as, for example, quantitative PCR assays, are well known in the art.

[0106] The probes used in PCR can be labeled with a detectable marker, such as, for example, a radioisotope, a fluorescent compound, a bioluminescent compound, a chemiluminescent compound, a metal chelator, or an enzyme. Such probes and primers can be used as a means to detect the presence of mutations in DNA, RNA, and in one embodiment, mRNA in a sample, as well as to detect cells expressing mRNA. As will be appreciated by those skilled in the art, a large number of different primers and probes can be prepared based on known sequences and effectively used to amplify, clone, and / or determine the presence and / or levels of mRNA.

[0107] Other DNA tests to determine whether mutations are present include fluorescent in situ hybridization (FISH), which can be used to detect mutations in cells, tissues, tissues containing breast tissue, tissues containing tumors, and even tissues containing breast tumors.

[0108] Other methods include protocols to test or detect mutations in DNA or RNA. These other methods include protocols to test or detect mRNA in tissue or cell samples by microarray technology. Using nucleic acid microarrays, including one embodiment, test and control RNA, mRNA samples from test and control tissue samples are reverse transcribed and labeled to generate cDNA probes. The probes are then hybridized to an array of nucleic acids immobilized on a solid support. The array is constructed such that the sequence and location of each member of the array is known. For example, a selection of genes that have the potential to be expressed in a particular disease state can be arrayed on a solid support. Hybridization of a labeled probe with a particular array member indicates that the sample from which the probe is derived expresses that gene. Gene expression mutation analysis of disease tissue can provide valuable information. Microarray technology utilizes nucleic acid hybridization and computing techniques to evaluate the mRNA expression profile of thousands of genes in a single experiment.

[0109] Biomarkers are particularly useful for cancer diagnosis because their expression patterns differ when comparing healthy subjects to subjects with breast cancer. Expression of a biomarker usually includes both upregulated and downregulated levels of mRNA. In one embodiment, the biomarkers described herein can determine whether a patient has or does not have breast cancer. In one embodiment, each is a form of breast cancer (e.g., ductal carcinoma in situ, invasive ductal carcinoma, inflammatory breast cancer, or metastatic breast cancer).

[0110] The following biomarkers, in one embodiment, can be detected in DNA that contains one or more mutations associated with a region of a gene, a SNP, or one or more mutations on one or more chromosomes. The following biomarkers, in one embodiment, can also be detected in RNA, including miRNA, tRNA, mRNA, or other forms of RNA. mRNA as a biomarker

[0111] Table 1 contains a list of mRNA biomarkers for detecting breast cancer. [Table 1]

[0112] Besides RT-PCR or other PCR-based methods, methods for determining the level of biomarkers include proteomics techniques and personalized genetic profiles.Personalized genetic profiles can be used to treat RA based on patient response at the molecular level.The specialized microarrays (e.g., oligonucleotide microarrays or cDNA microarrays) herein can include one or more biomarkers with expression profiles that correlate with either sensitivity or resistance to one or more antibodies.Although a single biomarker can provide evidence of breast cancer, reliability and accuracy are improved when multiple biomarkers are used in the methods described herein.

[0113] Methods for diagnosing breast cancer using mRNA as a biomarker Detection and quantification of expressed mRNA can use standard techniques known to those skilled in the art. For example, the expression or amount of a particular mRNA in a body fluid or tissue sample, or in a breast biopsy or identified breast tissue sample, can be determined by immunohistochemistry (IHC), immunofluorescence (IF), RNA in situ hybridization, reverse transcriptase polymerase chain reaction (RTPCR), in particular quantitative real-time RT-PCR (qRT-PCR), or a combination of these methods. Other methods for determining the level (expression) of a particular mRNA include MALDI-MS (surface-enhanced laser desorption / ionization mass spectrometry (SELDI-MS), in particular surface-enhanced affinity capture (SEAC), surface-enhanced needle desorption (SEND) or surface-enhanced photolabel attachment and release (SEPAR), antibody tests (immunoprecipitation, Western blotting, enzyme-linked immunosorbent assay (ELISA), enzyme-linked immunosorbent assay (RIA), dissociation-enhanced lanthanide fluoroimmunoassay (DELFIA), scintillation proximity assay (SPA), and quantitative nucleic acid tests, in particular PCR, LCR and RT-PCR of samples of mRNA for detection and quantification of marker (KRT23) mRNA.

[0114] Preferably, the difference in biomarker protein amount or expressed mRNA is at least 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 110%, 120%, 130%, 140%, 150%, 160%, 165%, 170%, 180%, 190%, 200%, 210%, 220%, 230%, 240%, 250%, 260%, 270%, 280%, 290%, 300%, 325%, 350%, 375%, or 400%. Preferably, the difference in the amount of the protein or expressed mRNA of the biomarker may be at least 50%, or 75% or 100% higher, more preferably. More preferably, this difference in expression level or amount of the protein is at least 200%, i.e. 2-fold, at least 500%, i.e. 5-fold, or at least 1000%, i.e. 10-fold. The expression level of the biomarker according to the invention shows lower or higher expression in breast cancer cell samples than in healthy normal breast samples, and may be at least 5%, 10%, or 20%, more preferably at least 50%, or 75% or 100%, i.e. 2-fold higher, preferably at least 10-fold higher in breast cancer cell samples. Whether the biomarker level is increased in a given detection method can be established by the analysis of multiple disease samples by the given detection method. This can then form a suitable level at which the "increased" status can be determined, for example by the above % difference or fold change. A change in the level of expression of the biomarker may be indicative of breast cancer. In some cases, no expression of the biomarker is detected in healthy samples. In such cases, any detection by a conventional test system is already an "increased" level within the meaning of this application.

[0115] One or more of the biomarkers can be used in a method for diagnosing cancer or determining the prognosis of a test subject with breast cancer. Thus, one biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers can be used in a method for diagnosing breast cancer or determining the prognosis of a test subject with breast cancer. Thus, at least one biomarker or a combination of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers can be used in a method for diagnosing cancer or determining the prognosis of a test subject with breast cancer.

[0116] In this manner, a single biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers can be used in a method to diagnose breast cancer or determine the prognosis of a test subject with cancer. In this manner, a single biomarker or a combination of about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers can be used in a method to diagnose breast cancer or determine the prognosis of a test subject with breast cancer.

[0117] In a first step, expression levels of one or more nucleic acids, including DNA and / or RNA (e.g., mRNA), are measured in saliva, plasma, blood or tissue samples from healthy subject samples and from subjects suffering from cancer. In one embodiment, the expression levels of one biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers can be used to generate a footprint or signature for subsequent diagnosis of the patient. In one embodiment, the expression levels of at least one biomarker, or a combination of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers, can be used to generate a footprint or signature for subsequent diagnosis of the patient.

[0118] In one embodiment, the expression levels of no more than one biomarker or a combination of no more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers can be used to generate a footprint or signature for subsequent diagnosis of a patient. In one embodiment, the expression levels of about one biomarker or a combination of about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers can be used to generate a footprint or signature for subsequent diagnosis of a patient.

[0119] The expression level of the same nucleic acid, including DNA and / or RNA (e.g., mRNA), is then measured in saliva, plasma, blood, or tissue samples from healthy subjects, which are used as controls. The samples from healthy patients can then be compared to identify mRNAs with altered expression levels in plasma samples from subjects with cancer. Biomarker fingerprints or signatures can be generated from the mRNAs with altered expression levels. This can be used to diagnose or prognose cancer in test subjects by comparing the levels of mRNA from the plasma of the test subjects. Conventional statistical analysis can be used, for example, to determine confidence levels. EXAMPLES

[0120] The following non-limiting examples are provided for illustrative purposes only, to facilitate a more complete understanding of the representative embodiments currently contemplated.These examples are intended to be merely a subset of all possible contexts in which the components of the formulation can be combined.Therefore, these examples should not be construed as limiting any of the embodiments described herein, including those related to the type and amount of the components of the formulation and / or its method and use.

[0121] Predictive Model According to some embodiments of the subject technology, combinations of a small number of markers (four or less) have been determined to discriminate response with high accuracy based on a dataset of studies related to breast cancer. The markers have been validated against the dataset, as described below.

[0122] Example 1 Biomarker Discovery GSE27567 dataset - Serum from peripheral blood A published dataset was used to discover biomarkers that predict or indicate the presence of breast cancer. The study included 72 patients with positive breast cancer biopsies and 68 patients with negative breast cancer biopsies. In total, 54,613 mRNA biomarkers were measured from peripheral blood mononuclear cells. Patient data were randomized equally across three subgroups: training group, selection group, and out-of-sample test group (maintaining the proportion of the two diagnostic classes).

[0123] The study used applicant's mathematical evolutionary modeling platform to examine all 54,613 variables (see, e.g., U.S. Patent No. 9,845,505). The published study is cited as LaBreche HG, et.al, "Integrating factor analysis and a transgenic mouse model to reveal a peripheral blood predictor of breast tumors," BMC Med Genomics. 2011 Jul 22; 4:61. doi:10.1186 / 1755-8794-4-61 PMID: 21781289; PMCID: PMC3178481. The dataset and the Affymetrix Human Genome U133 Plus 2.0 array can be found on the ncbi website (NCBI > GEO > Additional Information; Platform GPL570 and Platform GSE27567).

[0124] Seventeen biomarkers were identified as follows: (1) SLC8A1, (2) RP11-452L6.1, (3) HINT3, (4) PRORSD1P, (5) PRDX6, (6) GABRB2, (7) EPS8, (8) SH3RF1, (9) GB_ACC AA814006, (10) hypothetical protein LOC284058, (11) ZNF160, (12) SLC25A45, (13) DST, (14) MTAP, (15) GB_AccAK024901, (16) TFB1M, and (17) SPRED1. To validate the set of markers, applicants obtained four additional datasets of gene expression markers in breast cancer patients. These datasets are discussed in the following examples.

[0125] Example 2 Biomarker validation GSE20266 Dataset - Saliva A publicly available dataset was used to validate biomarkers that predict or indicate the presence of breast cancer. The study included 10 patients with positive breast cancer biopsies and 10 patients with negative breast cancer biopsies. In total, 54,613 mRNA biomarkers were measured from peripheral blood mononuclear cells. Patient data were randomized equally across three subgroups: training group, selection group, and out-of-sample test group (maintaining the proportion of the two diagnostic classes).

[0126] As mentioned above, this study examined 54,613 variables using the applicant's mathematical evolutionary modeling platform. The published work is cited as LaBreche HG,et.al,“Integrating factor analysis and a transgenic mouse model to reveal a peripheral blood predictor of breast tumors,”BMC Med Genomics.2011Jul22;4:61.doi:10.1186 / 1755-8794-4-61.PMID:21781289;PMCID:PMC3178481. The dataset and the Affymetrix Human Genome U133 Plus 2.0 array can be found on the ncbi website (NCBI>GEO>Additional Attribution;PlatformGPL570 and>PlatformGSE27567).

[0127] Example 3 Biomarker validation GSE47860 Dataset, Ontario Canada Cohort - Hematology A second published dataset was used to validate the efficacy of biomarkers that predict or indicate the presence of breast cancer. The study included 21 patients with positive breast cancer biopsies and 15 patients with negative breast cancer biopsies. In total, 54,613 mRNA biomarkers were measured in peripheral blood mononuclear cells. Patients were randomized equally across three subgroups: a training group, a selection group, and an out-of-sample test group (maintaining the proportion of the two diagnostic classes).

[0128] As mentioned above, the study considered 38,079 mRNA biomarkers obtained from peripheral blood mononuclear cells. The published study is cited as Piccolo SR,et.al,“Integrative analyses reveal signaling pathways underlying familial breast cancer susceptibility,”Mol Syst Biol.2016Mar10;12(3):860. doi:10.15252 / msb.20156506.PMID:26969729;PMCID:PMC4812528. The dataset and the Affymetrix Human Genome U133 Plus2.0 array can be found on the ncbi website (NCBI>GEO>Additional Information; Platform GSE47860 and Platform GPL12729).

[0129] Example 4 Biomarker validation GSE47860 dataset, Utah cohort [1]-[2] Blood A third published dataset was used to validate the efficacy of biomarkers that predict or indicate the presence of breast cancer. The study included 61 patients with positive breast cancer biopsies and 63 patients with negative breast cancer biopsies. In total, 54,613 mRNA biomarkers were measured in peripheral blood mononuclear cells. Patients were randomized equally across three subgroups: a training group, a selection group, and an out-of-sample test group (maintaining the proportion of the two diagnostic classes).

[0130] As mentioned above, the study considered 38,079 mRNA biomarkers obtained from peripheral blood mononuclear cells. The published study is cited as Piccolo SR,et.al,“Integrative analyses reveal signaling pathways underlying familial breast cancer susceptibility,”Mol Syst Biol.2016Mar10;12(3):860.doi:10.15252 / msb.20156506.PMID:26969729;PMCID:PMC4812528. The dataset and the Affymetrix Human Genome U133 Plus2.0 array can be found on the ncbi website (NCBI>GEO>Additional Information; Platform GSE47860 and Platform GPL17279).

[0131] Example 5 Biomarker validation GSE47861 Dataset, Ontario Cohort-Blood A fourth published dataset was used to validate the efficacy of biomarkers that predict or indicate the presence of breast cancer. The study included 15 patients with positive breast cancer biopsies and 22 patients with negative breast cancer biopsies. In total, 54,613 mRNA biomarkers were measured in peripheral blood mononuclear cells. Patients were randomized equally across three subgroups: a training group, a selection group, and an out-of-sample test group (maintaining the proportion of the two diagnostic classes).

[0132] As mentioned above, the study considered 38,079 mRNA biomarkers obtained from peripheral blood mononuclear cells. The published study is cited as Piccolo SR,et.al,“Integrative analyses reveal signaling pathways underlying familial breast cancer susceptibility,”Mol Syst Biol.2016Mar10;12(3):860.doi:10.15252 / msb.20156506.PMID:26969729;PMCID:PMC4812528. The dataset and the Affymetrix Human Genome U133 Plus2.0 array can be found on the ncbi website (NCBI>GEO>Additional Information; Platform GSE47860 and Platform GPL17279).

[0133] The results of the discovery and validation studies are summarized in Table 2. Positive and negative predictive values ​​(PPV and NPV, respectively) are the proportion of positive and negative results in a statistical and diagnostic test that are true positive and true negative results, respectively. [Table 2]

[0134] Example 6 Additional biomarker discovery GSE27567 dataset - Serum from peripheral blood Applicants pursued additional markers by conducting a second discovery study using the same discovery and validation datasets as above, using the following approach: 1) construct expression to ignore the original 17 biomarkers and consider the remaining 54,596 gene expression probes; 2) create a binary classification to distinguish between patients with confirmed positive breast cancer biopsies and patients with confirmed negative breast cancer biopsies; 3) identify the first minimal set of biomarkers that maximizes diagnostic accuracy; 4) set aside the first minimal set and repeat the analysis with the remaining biomarkers to determine the next minimal set; 5) Repeat until a significant decrease in diagnostic accuracy is observed.

[0135] Two sets of biomarkers (i.e., group 1 and group 2) were obtained and validated as described above. Group 1 biomarkers were identified as follows: (18) CAV1, (19) MUC1, (20) NAP1L3, (21) MDH1B, (22) TMEM125, (23) PDE9A. Group 2 biomarkers were identified as follows: (24) TSHZ2, (25) LAMB4, and (26) PTGS2. The results of the discovery and validation studies are summarized in Table 3. [Table 3]

[0136] Example 7 Early detection of breast cancer using biomarkers Presymptomatic diagnosis of breast cancer patients would be of great value not only for a better understanding of the pathophysiology of the disease, but also for providing early treatment and palliative efforts. In this example, the patient has early stage breast cancer that cannot be identified by manual manipulation using hands to identify lumps within the breast. The patient is a 50-year-old woman who wishes to be evaluated by a physician for possible breast cancer. The patient is borderline obese and has a history of alcohol abuse and hypertension. The patient also has a family history of breast cancer. Due to these risk factors, the physician orders a mammogram.

[0137] The results or mammogram appear to show no identifiable lumps due to some irregular cell proliferation. The patient is then screened using the biomarkers in Table 1. The patient provides a sample (e.g., blood, plasma, urine or saliva) for biomarker analysis. The results indicate that the patient has an early stage of breast cancer. Based on these results, the patient is referred to a specialist for further evaluation and possible treatment therapy. The doctor also recommends regular (i.e., quarterly, semi-annual, or annual) testing to ensure that the breast cancer does not progress.

[0138] Example 8 Detecting breast cancer after identification of a lump in the breast using biomarkers In this example, the patient goes to her doctor after identifying an abnormal lump in her left breast. Her doctor directs her to have a mammogram, which she does, and a lump is found in her left breast.

[0139] In this example, confirmation of the diagnosis of breast cancer is desired. For this purpose, a physician prescribes a biopsy to be taken from a suspicious tumor site in the patient's breast. After taking the biopsy sample, a biomarker test described herein is performed using one or more of the 17 biomarkers identified in Table 1 to confirm the presence of breast cancer and to determine the progression of breast cancer. The patient provides a sample for biomarker analysis. Specifically, the test determines the progression from benign cancer to metastatic cancer. The test also identifies the type of breast cancer and the genetic type of breast cancer.

[0140] The test indicates that the patient has breast cancer. As in the first example, the patient is referred to a specialist for further evaluation, who then determines the course and treatment of care to be used, as well as the treatment protocol. The doctor also recommends regular (i.e., quarterly, semi-annual, or annual) testing to ensure that the breast cancer does not progress.

[0141] Example 9 Breast Cancer Detection Using Biomarkers (Asymptomatic) In this example, the patient has no symptoms of breast cancer but is evaluated due to known genetic risk factors. Manual manipulation of the breast using the physician's hands and a mammogram reveals no breast cancer tumors. However, the patient complains of breast pain.

[0142] A physician requests testing of the biomarker tests described herein (Table 1) to detect / determine breast cancer prognosis. The patient provides a sample for biomarker analysis. The test provides the physician with biomarker levels that indicate the presence of early stage breast cancer. As in the first example, the patient is referred to a specialist for further evaluation and treatment options are discussed.

[0143] Based on the progression of breast cancer, a specialist can devise a treatment plan. Medical procedures may be required to remove, shrink, or slow the growth of the tumor. Treatment is usually based on the type of breast cancer and its stage, as well as other factors such as the patient's overall health, menopausal status, and personal preferences. Common treatments include surgery, radiation, chemotherapy, hormone therapy, targeted drug therapy, and immunotherapy. Drugs (i.e., systemic therapy) can be administered to treat breast cancer. Common targeted drugs include trastuzumab (HERCEPTIN™), pertuzumab (PERJETA™), or abemaciclib (VERZENIO™). The most common form of treatment for breast cancer is surgery. This involves removing the tumor and its surrounding margins. Surgical options may include lumpectomy, partial mastectomy, radical mastectomy, and reconstruction.

[0144] In this example, the specialist suggests a combination of hormone therapy (i.e., TAMOXIFEN™, an aromatase inhibitor) and a biopsy of suspicious tissue. The doctor also recommends regular (i.e., quarterly) check-ups to ensure that the breast cancer does not progress.

[0145] Example 10 Breast cancer detection using biomarkers (asymptomatic, genetic risk factors) In this example, the patient has no symptoms of breast cancer but is evaluated due to known genetic risk factors. Manual manipulation of the breast using the physician's hands and a mammogram reveals a lump that the physician suspects to be a breast cancer tumor. The physician also suspects that the breast cancer has metastasized.

[0146] A physician collects a blood sample from the patient and prescribes the use of one or more of the biomarkers identified in Table 1 to determine if the breast cancer has metastasized (as determined by the presence of circulating breast cancer cells). This test provides the physician with biomarker levels to diagnose patients with breast cancer cells in the blood that suggest the cancer has metastasized. The test also identifies the type of breast cancer and the genetic type of breast cancer. As in the first example, the patient is referred to a specialist for further evaluation and to discuss treatment options.

[0147] In this example, the specialist suggests a combination of surgery and targeted drug therapy. The doctor also recommends regular (i.e., quarterly) check-ups to monitor the progression of the cancer.

[0148] Example 11 Breast cancer detection using biomarkers (asymptomatic, genetic risk factors) In this example, the patient has no symptoms of breast cancer but is evaluated due to known genetic risk factors. Manual manipulation of the breast using the physician's hands and a mammogram reveals no breast cancer tumors. However, the patient complains of breast pain.

[0149] The physician has a biopsy of the patient's tissue where the tumor is located. The physician orders a subset of the biomarkers found in Table 1 to be used to determine the presence of breast cancer. To confirm the results, the physician prescribes a second test using the remaining biomarkers not used in the first test. The patient provides a sample for biomarker analysis. This test indicates to the physician sufficient biomarker levels to diagnose the patient with breast cancer and confirm the results with the second test. As in the first example, the patient is then referred to a specialist for further evaluation and to discuss treatment options.

[0150] Example 12 Diagnostic kits for rapid screening of breast cancer The following working example builds on the above constructs. An embodiment of the present invention can be compiled into a diagnostic kit for diagnosing breast cancer. The kit can identify one or more target cells bearing biomarkers for breast cancer in plasma from a test subject.

[0151] The kit may include a collection of nucleic acid molecules, each nucleic acid molecule encoding an mRNA sequence. The nucleic acid molecules can be used to identify variations in the expression level of one or more mRNAs in plasma samples from test subjects. The expression level of the mRNA can be used to compare / analyze test samples with fingerprints indicative of the presence of cancer.

[0152] In certain embodiments, the present disclosure provides a kit for diagnosing breast cancer. The kit may include one biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers disclosed herein. Those skilled in the art will understand that the number of biomarkers can be changed without departing from the nature of the present disclosure, and therefore other combinations of biomarkers are also encompassed by the present disclosure. Those skilled in the art will know which one biomarker or combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers to use based on the symptoms of patients suffering from breast cancer.

[0153] In certain embodiments, the kit comprises one biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers disclosed herein. In certain embodiments, the kit is for diagnosing breast cancer. The kit may optionally further comprise instructions for use. The kit may further optionally comprise (e.g., comprise, consist essentially of, consist of) a tube, applicator, vial, or other storage container for storing the biomarkers described above and / or a vial containing one or more of the biomarkers. In one embodiment, each biomarker is in its own tube, applicator, vial or storage container, or 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or 26 biomarkers are in a tube, applicator, vial or storage container.

[0154] Regardless of type, the kit will generally include one or more containers in which is disposed, preferably appropriately aliquoted, one biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers. The components of the kit may be packaged either in aqueous media or in lyophilized form.

[0155] Example 13 Screening Breast Cancer Patients A 56-year-old female smoker presents to her physician with a family history of breast cancer but no noticeable lumps or physical signs in her breasts. The physician takes a sample of blood and sends it to a laboratory to be tested for breast cancer. The blood sample is prepared and plasma is obtained. The plasma is then tested to identify the presence of biomarkers associated with breast cancer. The laboratory uses one or more of the following biomarkers in the test: SLC8A1, RP11-452L6.1, HINT3, PRORSD1P, PRDX6, GABRB2, EPS8, SH3RF1, GB_ACC AA814006, hypothetical protein LOC284058, ZNF160, SLC25A45, DST, MTAP, GB_Acc AK024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, and / or PTGS2. After testing, the laboratory determines that the one or more biomarkers used to test for breast cancer indicate the presence of cancer. The laboratory sends the test results to the physician. The patient is then notified of the results during a visit to the physician. The patient is thoroughly examined (including a mammogram) and the doctor takes a sample of suspicious tissue for biopsy.

[0156] Finally, although aspects of the present specification are emphasized by reference to specific embodiments, those skilled in the art will readily appreciate that these disclosed embodiments are merely illustrative of the principles of the subject matter disclosed herein. Thus, it should be understood that the disclosed subject matter is in no way limited to the specific methodology, protocols, and / or reagents, etc., described herein. Thus, various improvements or modifications or alternative configurations of the disclosed subject matter may be made in accordance with the teachings of the present specification without departing from the spirit of the specification. Finally, the terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the scope of the present invention, which is defined solely by the claims. Thus, the present invention is not limited to the exact one shown and described.

[0157] Certain embodiments of the invention are described herein, including the best mode known to the inventors for carrying out the invention. Of course, variations of these described embodiments will become apparent to those of skill in the art upon reading the foregoing description. The inventors expect such variations to be adopted by those of skill in the art as appropriate, and the inventors intend the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described embodiments in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or clearly contradicted by context.

[0158] Grouping of alternative embodiments, elements, or steps of the invention should not be construed as limiting. Each group member may be referred to and claimed individually or in any combination with other group members disclosed herein. It is anticipated that one or more members of a group may be included in or deleted from a group for reasons of convenience and / or patentability. When any such inclusion or deletion occurs, the specification is considered to contain the group as modified, and thus fulfills the written description of all Markush groups used in the appended claims.

[0159] Unless otherwise indicated, all numbers expressing features, items, quantities, parameters, properties, terms, etc. used in the specification and claims should be understood to be modified in all instances by the term "about". As used herein, the term "about" means that the feature, item, quantity, parameter, property, or term so modified encompasses a range of plus or minus 10 percent above or below the value of the stated feature, item, quantity, parameter, property, or term. Thus, unless indicated to the contrary, the numerical parameters set forth in this specification and the appended claims are approximations that may vary. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of the claims, each numerical indication should be construed at least in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and values ​​setting forth the broad scope of the invention are approximations, the numerical ranges and values ​​set forth in the specific examples are reported as precisely as possible. However, any numerical range or value inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements. Recitation of numerical ranges of values ​​herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, and unless otherwise stated herein, each separate value of the numerical range is incorporated herein as if it were individually set forth herein.

Claims

1. A method for assisting in the diagnosis of breast cancer or determining a prognosis of breast cancer in a subject, comprising: (a) measuring the expression level of one or more biomarkers associated with breast cancer in a sample from said subject; (b) receiving, by a memory element comprising a computer executable program, said expression level of one or more biomarkers associated with breast cancer in the subject sample; (c) comparing the expression level of one or more biomarkers associated with breast cancer in the subject sample with a level of one or more biomarkers associated with breast cancer in a control sample; (d) receiving a result of the comparison in (c); (e) comparing the result received in (d) with a threshold; and (f) (i) if the comparison in (e) exceeds the threshold, then the result of the comparison in (e) indicates that the subject has breast cancer; or (ii) if the comparison in (A)(e) is equal to or less than the threshold, then the results of the comparison in (e) indicate that the subject has a good prognosis and is likely to respond to breast cancer treatment; or (B) if the comparison in (e) exceeds said threshold by at least about 10%, then the result of the comparison in (e) indicates that said subject has a worse prognosis as compared to (f)(ii)(A). A method comprising:

2. The method of claim 1, wherein the one or more biomarkers include a nucleic acid biomarker or a peptide biomarker.

3. The method of claim 2, wherein the nucleic acid biomarker is selected from the group consisting of RP11-452L6.1 mRNA, PRORSD1P mRNA, PRDX6 mRNA, SLC25A45 mRNA, GB_Acc AK024901 mRNA, NAP1L3 mRNA, TMEM125 mRNA, and TSHZ2 mRNA.

4. The method of claim 2, wherein the peptide marker is selected from the group consisting of RP11-452L6.1 peptide, PRORSD1P peptide, PRDX6 peptide, SLC25A45 peptide, GB_Acc AK024901 peptide, NAP1L3 peptide, TMEM125 peptide, and TSHZ2 peptide.

5. A method according to any one of claims 1 to 4, wherein the comparison in (f)(ii)(A) ​​is (i) less than or equal to about 1%, about 2%, or about 5% below the threshold value, (ii) approximately equal to the threshold value, or (iii) at least about 1%, at least about 2%, at least about 5%, at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 90%, or at least about 99% lower than the threshold value.

6. The method of claim 1, wherein the threshold in (e) is about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 90%, about 100%, about 200%, about 300%, about 500%, about 1000%, or about 5000% higher than the expression level of one or more biomarkers associated with breast cancer in the control sample.

7. The method of claim 1, wherein the threshold in (e) is about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, or about 99% lower than the expression level of one or more biomarkers associated with breast cancer in the control sample.

8. The method of claim 2, wherein the expression level of the nucleic acid biomarker is measured by one or more assays selected from the group consisting of quantitative reverse transcription polymerase chain reaction (RT-qPCR), oligonucleotide probes, fluorescent in situ hybridization (FISH) assays, microarrays, and ligase chain reaction (LCR).

9. The method of claim 2, wherein the expression level of the peptide biomarker is measured by one or more assays selected from the group consisting of immunohistochemistry (IHC) assay, immunofluorescence (IF) assay, mass spectrometry (MS), immunoprecipitation, Western blotting, enzyme-linked immunosorbent assay (ELISA), dissociation enhanced lanthanide fluoroimmunoassay (DELFIA), and scintillation proximity assay (SPA).

10. The method of claim 1, wherein the breast cancer comprises stage 0, stage 1, stage 2, stage 3, stage 4, or stage 5 breast cancer.

11. The method of claim 10, wherein the breast cancer is one or more of the following types: ductal carcinoma in situ, invasive lobular carcinoma in situ, tubular / cribriform carcinoma, mucinous (colloid) carcinoma, medullary carcinoma, papillary carcinoma, and metaplastic.

12. The method described in claim 1, wherein the one or more biomarkers consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 biomarkers.

13. The method of claim 1, wherein the subject sample comprises a saliva sample, a blood sample, a plasma sample, a tissue sample, or a urine sample.

14. A method for assisting in the diagnosis of breast cancer or determining a prognosis of breast cancer in a subject, comprising: (a) measuring the expression level of a panel of biomarkers associated with breast cancer in a sample from the subject, the panel of biomarkers associated with breast cancer comprising: (i) RP11-452L6.1 mRNA, PRORSD1P mRNA, PRDX6 mRNA, SLC25A45 mRNA, GB_Acc AK024901 mRNA, NAP1L3 mRNA, TMEM125 mRNA, and TSHZ2 mRNA, or (ii) RP11-452L6.1 peptide, PRORSD1P peptide, PRDX6 peptide, SLC25A45 peptide, GB_Acc AK024901 peptide, NAP1L3 peptide, TMEM125 peptide, and TSHZ2 peptide and (b) receiving, by a memory element comprising a computer executable program, the expression levels of the panel of biomarkers associated with breast cancer in a subject sample; (c) comparing the expression level of the breast cancer associated biomarker panel in the subject sample to the expression level of the breast cancer associated biomarker panel in a control sample; (d) receiving a result of the comparison in (c); (e) comparing the result received in (d) with a threshold; and (f) (i) if the comparison in (e) exceeds the threshold, then the result of the comparison in (e) indicates that the subject has breast cancer; or (ii) if the comparison in (A)(e) is equal to or less than the threshold, then the results of the comparison in (e) indicate that the subject has a good prognosis and is likely to respond to breast cancer treatment; or (B) if the comparison in (e) exceeds said threshold by at least about 10%, then the result of the comparison in (e) indicates that said subject has a worse prognosis as compared to (f)(ii)(A). A method comprising:

15. The method of claim 14, wherein the breast cancer comprises stage 0, stage 1, stage 2, stage 3, stage 4, or stage 5 breast cancer.

16. The method of claim 15, wherein the breast cancer is one or more of ductal carcinoma in situ, invasive lobular carcinoma in situ, tubular / cribriform carcinoma, mucinous (colloid) carcinoma, medullary carcinoma, papillary carcinoma, and metaplastic carcinoma.

17. The method of claim 14, wherein the threshold in (e) is about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 90%, about 100%, about 200%, about 300%, about 500%, about 1000%, or about 5000% higher than the expression level of the biomarker panel associated with breast cancer in the control sample.

18. The method of claim 14, wherein the threshold in (e) is about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, about 95%, or about 99% lower than the expression level of the biomarker panel associated with breast cancer in the control sample.

19. The method of claim 14, wherein the subject sample comprises a saliva sample, a blood sample, a plasma sample, a tissue sample, or a urine sample.

20. A kit comprising one or more reagents for measuring the expression level of a panel of biomarkers associated with breast cancer in a sample from a subject, comprising: the breast cancer associated biomarker panel comprising: (i) RP11-452L6.1 mRNA, PRORSD1P mRNA, PRDX6 mRNA, SLC25A45 mRNA, GB_Acc AK024901 mRNA, NAP1L3 mRNA, TMEM125 mRNA, and TSHZ2 mRNA, or (ii) RP11-452L6.1 peptide, PRORSD1P peptide, PRDX6 peptide, SLC25A45 peptide, GB_Acc AK024901 peptide, NAP1L3 peptide, TMEM125 peptide, and TSHZ2 peptide Including, kit.