CN117098853A - Biomarkers for diagnosing breast cancer - Google Patents

Abstract

Embodiments of the invention include a system and method for using biomarkers in the diagnosis of diseases such as breast cancer. The subject may be screened for breast cancer based on changes in the expression of one or more mRNA in blood, plasma, or saliva. Embodiments include 26 specific mRNAs that are used as biomarkers to screen or distinguish healthy individuals from individuals with breast cancer. Levels of more than one such mRNA can be used to create biomarker fingerprints for early screening of breast cancer. Embodiments also include a kit for screening a healthy subject with a subject having breast cancer.

Description

Biomarkers for diagnosing breast cancer

Technical Field

The present invention relates to diagnosing disease using biomarkers, and more particularly, to a system and method for diagnosing breast cancer based on changes in the expression of one or more specific mRNAs.

Background

Breast cancer is a cancer that develops from breast tissue. Worldwide, breast cancer is the major type of female cancer, accounting for 25% of all cases. In 2018, it caused 200 thousands of new cases and 627,000 deaths. Risk factors for developing breast cancer include those that are female, obese, lack of physical exercise, alcoholism, climacteric hormone replacement therapy, ionizing radiation, early-onset, late-onset or non-living children, older, past history of breast cancer, and family history of breast cancer. About 5 to 10% of cases are the result of genetic predisposition from parents of humans, including BRCA1 and BRCA2, and the like.

The most common manifestation of breast cancer is a lump, which feels different from the rest of the breast tissue. Over 80% of cases are found when a person detects such a bump with a fingertip. However, the earliest stage breast cancer was detected by mammograms. The tumor found in the lymph nodes located in the armpit may also be indicative of breast cancer.

Breast cancer may begin in different parts of the breast. The breast consists of three main parts: leaflets, catheters, and connective tissue. The leaflets are the glands that produce milk. The conduit is the tube that delivers milk to the nipple. Connective tissue (composed of fibrous tissue and adipose tissue) encloses and holds everything together. Most breast cancers begin with ducts or leaflets. The most common manifestation of breast cancer is a lump, which feels different from the rest of the breast tissue. Breast cancer can spread out of the breast through blood and lymphatic vessels. When breast cancer spreads to other parts of the body, it is said to have metastasized.

Diagnosis of breast cancer can be confirmed by biopsy of suspicious tissue. Once a diagnosis is made, further tests can determine if the cancer has spread out of the breast and which treatments are most likely to be effective. The outcome of breast cancer varies depending on the type of cancer, the extent of the disease and the age of the person. Five year survival rates in the united kingdom and the united states are 80 to 90%. In developing countries, five-year survival rates are lower.

For those patients who have been diagnosed with cancer, a variety of treatments may be used, including surgery, radiation therapy, chemotherapy, hormonal therapy, and targeted therapy. The types of surgery vary from breast conservation surgery to mastectomy. Breast reconstruction may occur at the time of surgery or on a future day. For patients where cancer has spread to other parts of the body, treatment is primarily aimed at improving quality of life and comfort.

Early detection of breast cancer is critical to effective treatment. According to the american cancer society, five-year relative survival is 99% when breast cancer is detected early and in a localized stage. Conventional methods of early detection include monthly breast self-exams, periodic clinical breast exams, and mammograms. However, the method of early detection has limitations.

Currently, breast cancer is usually detected by mammograms. Some national authorities recommend screening for breast cancer. For average women, the american preventive services working group and american society of physicians recommend mammography once every two years for women 50 to 74 years old, the european council recommend mammography for women 50 to 69 years old, most projects use a frequency of 2 years, while the european commission recommends mammography for women 45 to 75 years old every 2 to 3 years, and screening is performed in canada with a frequency of 2 to 3 years for women 50 to 74 years old. After detection, diagnosis of breast cancer is confirmed by biopsy of tissue suspected of having breast cancer. Once a diagnosis is made, further tests are performed to determine if the cancer has spread out of the breast and which therapeutic treatments are most likely to be effective.

The use of mammography as a screening tool to detect early breast cancer in asymptomatic healthy females is controversial. Diagnosing errors, overtreatment, and adverse effects of radiation exposure. The balance of advantages and disadvantages of breast cancer screening is controversial. The coxyland review (Cochrane review) of 2013 found that it is not clear whether mammography screening would be more disadvantageous because the test results for most women are false positives.

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

For example, serum PSA is commonly used for prostate cancer screening in men over 50 years old, but its use is still controversial due to benign disease and elevated serum in prostate cancer. However, PSA represents one of the most useful serological markers currently available. PSA is only strongly expressed in prostate tissue in healthy men, establishing lower levels in serum by normal diffusion of various anatomical barriers. These anatomical barriers are destroyed when prostate cancer develops, allowing increased amounts of PSA to enter the circulation.

Other common serological biomarkers include carcinoembryonic antigen (CEA) and carbohydrate antigen 19.9 (CA 19.9) against gastrointestinal cancer, CEA, CYFRA 21-1 (cytokeratin 19 fragment), neuron-specific enolase (NSE), tissue Polypeptide Antigen (TPA), progastrin release peptide (pro-GRP), and SCC antigen against lung cancer, CA 125 against ovarian cancer, and prostate-specific antigen in prostate cancer (PSA, also known as KLK 3). However, these biomarkers have limitations and often lack the proper sensitivity and specificity to be suitable for early cancer detection. Furthermore, there are no biomarkers available for detecting breast cancer.

Due to the limitations of accurately detecting breast cancer, it is difficult to study disease progression and develop drugs/therapies. Furthermore, drug developers have difficulty recruiting appropriate patients for their clinical trials. Furthermore, patients are often reluctant to accept breast biopsies due to the invasiveness and risk. Thus, there is a need for accurate and affordable non-invasive breast cancer testing.

There is a need for improved diagnostic assays and methods for detecting cancer, particularly breast cancer. Conventional diagnostic assays typically rely on a single biomarker and are unreliable in detecting the presence of cancer or tumor progression. Thus, there is a need to identify alternative molecular markers that overcome these limitations.

Disclosure of Invention

The following summary is provided to facilitate an understanding of some of the innovative features unique to the embodiments disclosed and is not intended to be a full description. A full appreciation of the various aspects of the embodiments disclosed herein can be gained by taking the entire specification, claims, and abstract as a whole.

Embodiments include a system and method for detecting and diagnosing breast cancer and its progression.

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

Additional embodiments include mRNA biomarkers that distinguish breast cancer types. Embodiments include mRNA biomarkers that distinguish between different genetic forms of breast cancer. Breast cancer may be, for example, ductal carcinoma in situ, invasive lobular carcinoma in situ, tubular/ethmoid carcinoma, mucinous (glioblastoma), medullary carcinoma, papillary carcinoma, and metaplasia carcinoma.

Embodiments include mRNA biomarkers that distinguish breast cancer from healthy breast. Embodiments also include the use of nucleic acids, proteins, and/or peptides (e.g., as identified in table 1) to distinguish breast cancer from healthy breast.

Embodiments include mRNA biomarkers for monitoring progression of breast cancer in a patient. Embodiments include mRNA biomarkers that provide guidance for selection in one or more therapies and/or drugs for treating a breast cancer patient.

Embodiments include a system and method for determining a preferred treatment for a patient having breast cancer. Embodiments also include a system and method for determining the likelihood of a patient responding positively to a surgical procedure, such as a mastectomy.

Embodiments include a method of diagnosing breast cancer using one or more algorithms based on the levels of one or more mRNA biomarkers.

Embodiments include a method of diagnosing breast cancer using one or more algorithms based on the levels of one or more mRNA biomarkers.

Embodiments also include a method of diagnosing breast cancer using one or more algorithms based on the levels of one or more protein biomarkers.

Embodiments include methods of using one or more biomarkers to distinguish stage 0, stage 1, stage 2, stage 3, stage 4, and stage 5 of breast cancer.

The methods and assays disclosed herein relate to examining the amount of one or more biomarkers in a biological sample, wherein the determination of the amount of one or more such biomarkers is predictive or indicative of the presence of breast cancer. The disclosed methods and assays provide a convenient, effective, and potentially cost-effective means to obtain data and information useful for assessing appropriate or effective therapies for treating patients.

Methods for detecting any biomarker that needs to be assessed include protocols for checking for the presence and/or expression of a desired nucleic acid. For example, genetic markers mRNA or DNA from tissue or cell samples of a mammal can be conveniently determined using Northern, dot blot or Polymerase Chain Reaction (PCR) analysis, array hybridization, ribonuclease protection assays, or using DNA SNP chip microarrays (which are commercially available, including DNA microarray snapshots). For example, real-time PCR (RT-PCR) assays, such as quantitative PCR assays, are well known in the art.

More specifically, embodiments include methods of detecting breast cancer based on specific genes/mRNAs whose expression levels have been altered. The present inventors have identified 26 specific mRNAs that can be used as biomarkers to distinguish healthy individuals from individuals suffering from breast cancer. The use of biomarkers is non-invasive and may be more sensitive than conventional methods. Types of breast cancer include ductal carcinoma in situ, invasive lobular carcinoma in situ, tubular/squamous carcinoma, mucinous (colloid) carcinoma, medullary carcinoma, papillary carcinoma, and metaplasia carcinoma. Embodiments also include methods of prognosis, patient monitoring, and distinguishing between different breast cancer types. Based on the prognosis, an appropriate treatment plan can be designed.

Embodiments also include the use of one or more of the following genes (or mRNAs) as biomarkers: SLC8A1, RP11-452L6.1, HINT3, prosd 1P, PRDX6, GABRB2, EPS8, SH3RF1, gb_acc AA814006, hypothetical proteins LOC284058, ZNF160, SLC25a45, DST, MTAP, GB _acc AK024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, and PTGS2.

Embodiments include a method of detecting cancer or determining the prognosis of a subject with cancer (e.g., breast cancer), the method comprising the steps of: a) Measuring the expression level of at least one mRNA in a test sample of plasma from a subject; b) Comparing the level of expression of mRNA in the test sample to the level in the base sample; and c) detecting or determining the prognosis of the cancer based on the change in the expression of mRNA in the test sample. The method can distinguish between different stages of breast cancer (corresponding to stages 0 to 5). The method may further comprise the step of treating the cancer based on the detection/prognosis. Common treatments include surgery, radiation, chemotherapy, hormonal therapy, targeted drug therapy and/or immunotherapy.

Embodiments also include a method of detecting cancer or determining the prognosis of a subject with cancer (e.g., breast cancer), the method comprising the steps of: a) Measuring the expression level of at least one mRNA in a test sample of plasma from a subject; b) Comparing the level of expression of mRNA in the test sample to the level in the base sample; and c) detecting or determining the prognosis of the cancer based on the change in the expression of mRNA in the test sample. The method may include the step of treating the cancer based on the detection/prognosis. The method may be used with other body fluids, including saliva.

Embodiments also include a method of detecting cancer or determining the prognosis of a test subject with cancer (e.g., breast cancer), the method comprising the steps of: a) Measuring the expression levels of two or more mRNA in a plasma sample from a subject having cancer; b) Measuring the expression level of the same mRNA in a plasma sample from a healthy subject; c) Comparing the level of expression of mRNA in a plasma sample from a subject with cancer to the level in a plasma sample from a healthy subject; d) Identifying mRNA whose expression level has been altered in a plasma sample from a subject having cancer; e) Creating a biomarker fingerprint from mRNA having altered expression levels; and f) diagnosing or determining the prognosis of cancer in the test subject by comparing the mRNA levels from the plasma of the test subject with the mRNA levels in the biomarker fingerprint. The method may further comprise the step of treating the cancer based on the detection/prognosis.

Embodiments also include a method of diagnosing cancer or determining prognosis of a test subject with cancer (e.g., breast cancer), the method comprising the steps of: a) Measuring the expression levels of two or more mRNAs in saliva obtained from the blood of a subject suffering from cancer; b) Measuring the expression levels of two or more mRNA in saliva obtained from blood of a sample from a healthy subject; c) Comparing the expression levels of two or more mRNA in saliva obtained from a subject with cancer to the levels in a plasma sample from a healthy subject; d) Identifying mRNA whose expression level has been altered in saliva obtained from blood of a sample from a subject having cancer; e) Creating a biomarker fingerprint from mRNA having altered expression levels; and f) diagnosing or determining the prognosis of cancer in the test subject by comparing the mRNA levels from the plasma of the test subject with the mRNA levels in the biomarker fingerprint. The method may include the step of treating the cancer based on the detection/prognosis.

Embodiments also include a diagnostic kit for diagnosing breast cancer or detecting the presence of a tumor. The kit can detect the presence of breast cancer tumor cells in plasma or saliva from a patient. The kit may include a plurality of nucleic acid molecules, each nucleic acid molecule encoding an mRNA sequence. The nucleic acid molecules identify changes 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 may represent a nucleic acid expression fingerprint indicative of the presence of a tumor or breast cancer.

Embodiments also include a method for identifying one or more mammalian target cells exhibiting breast cancer, the method comprising the steps of: a) Collecting plasma from a test subject; b) Hybridizing at least one nucleic acid molecule biomarker encoding an mRNA sequence to a portion of plasma; c) Quantifying mRNA expression; d) Determining the expression level of a plurality of nucleic acid molecules, each nucleic acid molecule encoding an mRNA sequence; e) Determining the expression level of the plurality of nucleic acid molecules in one or more control cells; and f) identifying one or more nucleic acid molecules that are differentially expressed in the target cell and the control cell from the plurality of nucleic acid molecules by comparing the corresponding expression levels obtained in steps (d) and (e). The method may include the step of treating the cancer based on the detection/prognosis. Together, the differentially expressed nucleic acid molecules may represent a nucleic acid expression biomarker fingerprint that is indicative of the presence of breast cancer.

Definition of the definition

Reference in the specification to "one embodiment/aspect" or "an 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 disclosure. The use of the phrases "in one embodiment/aspect" or "in another embodiment/aspect" in various places throughout the specification is not necessarily all referring to the same embodiment/aspect, nor is it a separate or alternative embodiment/aspect mutually exclusive of other embodiments/aspects. Furthermore, various features are described which may be exhibited by some embodiments/aspects and not by others. Similarly, various requirements are described which may be requirements of some embodiments/aspects but not other embodiments/aspects. Embodiments and aspects may be used interchangeably in some cases.

The terms used in this specification generally have their ordinary meaning in the art, in the context of this disclosure, and in the specific context in which each term is used. Certain terms used to describe the disclosure are discussed below or elsewhere in the specification to provide additional guidance to the practitioner regarding the description of the disclosure. It should be understood that the same thing can be expressed in more than one way.

Accordingly, alternative languages and synonyms may be used for any one or more of the terms discussed herein. No particular meaning is given to the term whether or not it is elaborated or discussed herein. Synonyms for certain terms are provided. The recitation 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 terms discussed herein, is illustrative only and is not intended to further limit the scope and meaning of the disclosure or any exemplary terms. Also, the present disclosure is not limited to the various embodiments presented in this specification.

Without intending to further limit the scope of the disclosure, examples of instruments, devices, methods, and related results according to embodiments of the disclosure are given below. Note that headings or sub-headings may be used in the examples for the convenience of the reader and should not in any way limit the scope of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. In case of conflict, the present document, including definitions, will control.

As used herein in the specification and the appended claims, the term "about" or "generally" means a margin of +/-20%, if applicable, unless indicated otherwise. Moreover, as used herein in the specification and the appended claims, the term "substantially" means a margin of +/-10%, unless indicated otherwise. It is to be understood that not all uses of the above terms are quantitative, such that the recited ranges may apply.

The term "algorithm" refers to a specific set of instructions or an exact list of well-defined instructions for executing a program, typically through a well-defined series of sequential states, and eventually ending in an end state.

The term "biomarker" generally refers to a molecular marker based on DNA, RNA, protein, carbohydrate, or glycolipid, the expression or presence of which in a sample of a subject can be detected by standard methods (or methods disclosed herein) and is a prediction or prognosis of effective responsiveness or sensitivity of a mammalian subject having cancer. The biomarker may be present in the test sample but not in the control sample, in the test sample but in the control sample, or the amount of biomarker may vary between the test sample and the control sample. For example, the genetic biomarker (e.g., a particular mutation and/or SNP) being evaluated may be present in such a sample, but not in a control sample, or certain biomarkers may be seropositive in a sample, but seronegative in a control sample. Further, optionally, it may be determined that the expression of such biomarkers is higher than observed for control samples. The terms "marker" and "biomarker" are used interchangeably herein.

As used herein, "additional biomedical information" refers to one or more evaluations of an individual associated with a breast cancer risk in addition to the use of any of the biomarkers described herein. "additional biomedical information" includes any of the following: physical descriptors of individuals, physical descriptors of lung nodules observed by CT imaging, height and/or weight of individuals, sex of individuals, race of individuals, smoking history, occupational history, exposure to any of known carcinogens (e.g., exposure to asbestos, radon gas, chemicals, fire smoke, and air pollution, which may include emissions from fixed or mobile sources, such as industrial/factory or car/boat/airplane emissions), family history of exposure to second-hand smoke, breast cancer (or other cancers), presence of lung nodules, size of nodules, location of nodules, morphology of nodules (e.g., observed by CT imaging, hair glass-like shadows (GGO), solids, non-solids), edge characteristics of nodules (e.g., smooth, split, sharp and smooth, spike-like, infiltrations), and the like. Additional biomedical information may be obtained from the individual using conventional techniques known in the art, such as from the individual itself by using conventional patient questionnaires or health history questionnaires, or the like, or from a medical practitioner, or the like. Alternatively, additional biomedical information may be obtained from conventional imaging techniques, including CT imaging (e.g., low dose CT imaging) and X-rays. The biomarker level, the test of biomarker level in combination with the evaluation of any additional biomedical information, may, for example, increase the sensitivity/specificity and/or AUC of detecting breast cancer (or other breast cancer-related uses) as compared to the biomarker test alone or evaluating any particular additional item of biomedical information alone (e.g., CT imaging alone).

The term "area under the curve" or "AUC" refers to the area under the curve of the Receiver Operating Characteristic (ROC) curve, both of which are well known in the art. AUC measurements can be used to compare the accuracy of the classifier over the entire data range. Classifiers with greater AUC have greater ability to correctly classify unknowns between two groups of interest (e.g., breast cancer samples and normal or control samples). ROC curves can be used to map the performance of a particular feature (e.g., any of the biomarkers described herein and/or any additional biomedical information items) in distinguishing between two populations (e.g., cases with breast cancer and controls without breast cancer). Typically, feature data for the entire population (e.g., cases and controls) is ordered in ascending order based on the values of the individual features. Then, for each value of the 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 the 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 the feature and then dividing by the total number of controls. Although the definition refers to the case where the feature in the case is elevated compared to the control, the definition also applies to the case where the feature in the case is lowered compared to the control (in such cases, samples below the value of the feature will be counted). ROC curves may be generated for individual features and other individual outputs, for example, a combination of two or more features may be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide an individual sum value, and the individual sum value may be plotted in a ROC curve. Additionally, any combination of features may be plotted in the ROC curve, where the combination derives a single output value. A combination of these features may constitute a test. The ROC curve is a graph of the true positive rate (sensitivity) of the test versus the false positive rate (1-specificity) of the test.

As used herein, "detecting" or "determining" with respect to a biomarker value includes using the instrumentation required to observe and record the signal corresponding to the biomarker value and the material required to generate the signal. In various embodiments, biomarker values are detected using any suitable method, including: fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectrometry, raman spectrometry, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like. .

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

The term "fingerprint", "disease fingerprint" or "biomarker signature" refers to a plurality of biomarkers or biomarker patterns having an elevated or reduced level in a subject suffering from a disease. Fingerprints can be generated by comparing subjects with disease to healthy subjects and used for screening/diagnosis of disease.

The term "miRNA" or "microrna", "miRNA biomarker" or "microrna" refers to small endogenous RNA molecules that can be used as serum diagnostic biomarkers for diseases including cancer.

The term "messenger RNA" or "mRNA" refers to a single-stranded RNA molecule that corresponds to the genetic sequence of a gene and is read by the ribosome during the synthesis of a protein. As used herein, mRNA may also include "mirnas" and small interfering RNAs (sirnas).

An "up-regulated" mRNA generally refers to an increase in the expression level of the mRNA in response to a given treatment or condition. "downregulated" mRNA generally refers to a "decrease" in the expression level of mRNA in response to a given treatment or condition. In some cases, mRNA levels may remain unchanged for a given treatment or condition. mRNA from a patient sample may be "up-regulated" (i.e., mRNA levels may be increased). Alternatively, mRNA may be "down-regulated" (i.e., mRNA levels may be reduced).

An "up-regulated" mRNA generally refers to an increase in the expression level of the mRNA in response to a given treatment or condition. "downregulated" mRNA generally refers to a "decrease" in the expression level of mRNA in response to a given treatment or condition. In some cases, mRNA levels may remain unchanged for a given treatment or condition. mRNA from a patient sample may be "up-regulated," i.e., mRNA levels 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 reference mRNA level or reference level. Alternatively, mRNA may be "down-regulated," i.e., mRNA levels may be reduced by, 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 a comparative control mRNA level or reference level.

Similarly, the level of a polypeptide, protein, or peptide from a patient sample can be increased compared to a control or reference level. Such an 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 comparison control protein level or reference level. Alternatively, the level of protein biomarker may be reduced. Such a decrease may be present, 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 level of the control protein or the reference level.

The terms "polypeptide", "peptide" and "protein" are used interchangeably herein to refer to a polymer of amino acid residues. These terms apply to amino acid polymers in which one or more amino acid residues are artificial chemical mimics of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers and non-naturally occurring amino acid polymers. Methods for obtaining (e.g., producing, isolating, purifying, synthesizing, and recombinantly producing) polypeptides are well known to those of ordinary skill in the art. The level of the polypeptide, protein, or peptide from the patient sample may be increased compared to a control or reference level. Alternatively, the level of protein biomarker may be reduced.

The term "amino acid" refers to naturally occurring and synthetic amino acids, as well as amino acid analogs and amino acid mimics that function in a manner similar 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-carboxyglutamic acid, and O-phosphoserine. Amino acid analogs refer to compounds that have the same basic chemical structure as a naturally occurring amino acid, i.e., a carbon that is bound to a hydrogen, a carboxyl group, an amino group, and an R group, e.g., homoserine, norleucine, methionine sulfoxide, methionine methyl sulfonium. Such analogs have modified R groups (e.g., norleucine) or modified peptide backbones, but retain the same basic chemical structure as a naturally occurring amino acid. Amino acid mimetics refers to chemical compounds that have a structure that differs from the general chemical structure of an amino acid, but that function in a manner similar to a naturally occurring amino acid.

Amino acids may be represented herein by their commonly known three-letter symbols or by the one-letter symbols suggested by the IUPAC-IUB biochemical nomenclature committee. Likewise, nucleotides may also be referred to by their commonly accepted single-letter codes.

The term "drug" refers to an active drug that treats cancer, such as breast cancer, or a sign or symptom of cancer, or a side effect.

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

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

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. The samples also included synovial tissue, skin, hair follicles, and bone marrow. Methods for obtaining tissue biopsies and body fluids from mammals are well known in the art.

The term "subject" or "patient" refers to any individual animal in need of treatment, more preferably a mammal (including non-human animals such as dogs, cats, horses, rabbits, zoo animals, cattle, pigs, sheep, and non-human primates). Most preferably, the patient herein is a human.

The term "nucleic acid probe" or "oligonucleotide probe" refers to a nucleic acid capable of binding to a complementary sequence of a target nucleic acid, such as an mRNA biomarker provided herein, through one or more types of chemical bonds, typically by complementary base pairing, typically by hydrogen bonding. As used herein, a probe may include natural (e.g., A, G, C or T) or modified bases (7-deazaguanosine, inosine, etc.). In addition, the bases in the probe may be joined by bonds other than phosphodiester bonds, so long as it does not interfere with hybridization. It will be appreciated by those skilled in the art that depending on the stringency of the hybridization conditions, probes can bind target sequences that lack complete complementarity with the probe sequences. The probe is preferably directly labelled with an isotope such as a chromophore, a luminophore, a chromogen, or indirectly labelled with biotin, and the streptavidin complex may then be bound to biotin. By determining the presence or absence of the probe, the presence or absence of the target mRNA biomarker of interest can be detected.

The terms "probe ID", "probe set identifier" or "Affymetrix probe set ID" refer to an identifier that refers to a set of probe pairs selected to represent an expressed sequence on an array. (_at=all probes hit one known transcript; _a=all probes in the set hit alternate transcripts from the same gene; s=all probes in the set hit transcripts from different genes; x=some probes hit transcripts from different genes).

Other technical terms used herein have their ordinary meaning in the field of use as exemplified by the various technical dictionaries. The particular values and configurations discussed in these non-limiting examples can be varied and are cited merely to illustrate at least one embodiment and are not intended to limit the scope thereof.

Detailed Description

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 claimed. Additional features and advantages of the subject technology will be set forth in the description which follows, and in part will be apparent 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.

Conventional methods of diagnosing breast cancer are not necessarily reliable and involve invasive procedures such as tumor biopsy. Currently, breast cancer is typically detected by mammogram, which is confirmed by biopsy of tissue suspected of having breast cancer at the time of diagnosis of breast cancer.

Recent studies have demonstrated the presence of mRNA in saliva and circulating blood and its potential for use as a biomarker in the diagnosis of various diseases. In particular, they have been proposed as biomarkers for the early detection of cancers such as breast cancer.

The present inventors have recognized mRNA expression profiles in subjects with breast cancer. Specific mRNAs are expressed abnormally in malignant tissue compared to non-malignant breast tissue. Furthermore, mRNA expression may provide insight into cellular processes involved in malignant transformation and progression. Thus, the expression level of mRNA can also be used for breast cancer prognosis. In particular, the present technology provides diagnostic methods for predicting and/or indicating the effectiveness of a treatment.

The present application is based on the finding that breast cancer can be reliably identified with high sensitivity and specificity based on specific mRNA expression profiles. Expression of a biomarker typically includes up-and down-regulated mRNA levels. Analysis of mRNA expression biomarkers allows for the creation of a "fingerprint" by analyzing mRNA expression patterns in diseased and healthy subjects. Thereafter, the individual mRNA expression levels can be used to detect breast cancer at an early stage of the disease. Biomarkers can also be used to distinguish between different subtypes of breast cancer and monitor progression of breast cancer. Additional embodiments include mRNA biomarkers that distinguish between different stages of breast cancer (corresponding to stages 0 to 5). Additional embodiments include mRNA biomarkers that distinguish breast cancer types. Embodiments include mRNA biomarkers that distinguish between different genetic forms of breast cancer. In another embodiment, proteins transcribed from the identified mRNA are used as biomarkers.

Early diagnosis of breast cancer allows intervention and/or treatment to avoid further injury, including metastasis of breast cancer cells into other tissues and organs. For example, the biomarkers described herein can be measured and analyzed to determine whether a patient has a healthy breast without cancerous tissue or has breast cancer, including diagnosis at an early stage of breast cancer. Conventional methods of diagnosing breast cancer rely on palpating the breast to identify tumors or using mammograms to identify cancerous tumors. Because the early stages of breast cancer are often asymptomatic, these methods may not be effective in identifying patients with breast cancer at the best stage of treatment. Researchers and drug developers can use early, accurate and reliable breast cancer detection to recruit patients for clinical trials by developing drugs that support them, as well as supporting drugs after approval.

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 one of five stages: stage 0, stage 1, stage 2, stage 3 and stage 4. Efforts may be made to slow (or reverse) the progression of the disease, if possible. Conventional methods lack reliability and sensitivity to distinguish between these conditions and monitor progression.

Embodiments include a set of diagnostic markers or molecular fingerprints for rapid and reliable identification and/or treatment of cells exhibiting or having a propensity to develop different subtypes of breast cancer. Embodiments also include methods of diagnosing cancer based on specific mRNAs whose expression levels have been altered. Although individual mRNAs can be monitored, the invention includes 26 mRNAs of particular value as biomarkers to screen or distinguish healthy individuals from individuals affected by the disease. mRNA of particular interest (detailed in Table 1) included: SLC8A1, RP11-452L6.1, HINT3, prosd 1P, PRDX6, GABRB2, EPS8, SH3RF1, gb_acc AA814006, hypothetical proteins LOC284058, ZNF160, SLC25a45, DST, MTAP, GB _acc AK024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, and PTGS2.

Embodiments include a method of identifying an initial set of peripheral diagnostic biomarkers for detecting breast cancer and validating the biomarkers on an additional dataset comprising saliva and peripheral blood. The method can be implemented by the following steps:

a) Generating a binary classifier to distinguish between patients with confirmed positive breast cancer biopsies and patients with confirmed negative breast cancer biopsies;

b) Identifying a first minimal biomarker panel that maximizes diagnostic accuracy;

c) Resting the first minimum group and repeating the analysis with the remaining biomarkers to determine the next minimum group; and

d) Repeating the above steps until a significant decrease in diagnostic accuracy is observed.

Methods and materials can be used to evaluate cancer, such as breast cancer, in a subject (e.g., a human patient). For example, embodiments include materials and methods for using identifiable markers to aid a clinician in assessing breast cancer disease activity, assessing the likelihood of response and outcome of therapy, and predicting long-term disease outcome. Furthermore, a subject with breast cancer may be diagnosed based on the presence of certain diagnostic indicators in plasma or saliva from the subject. Thus, the present technology allows for diagnosing breast cancer based on one or more combinations of markers.

The severity of breast cancer can be described as falling into one of five stages. Stage 0 is a pre-cancerous or marker condition that is typically associated with a Ductal Carcinoma In Situ (DCIS) or a Lobular Carcinoma In Situ (LCIS). Stage 1 to 3 tumor and/or cancer cells are found in the breast or regional lymph nodes. When an individual is diagnosed with stage 4 breast cancer, this means that the breast cancer has become "metastatic" cancer, in which the cancer cells have spread to other tissues and/or organs of the patient. Patients diagnosed with stage 4 breast cancer have a more adverse prognosis than patients diagnosed at stages 0 to 3 because the cancer cells have spread beyond the breast where the cancer is located and the lymph nodes located near the breast.

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 responsiveness to a cancer treatment or therapy, wherein the kit is calibrated to measure marker levels in a sample from a patient.

According to some embodiments, the amount of biomarker may be determined by using an agent that specifically binds to, for example, a biomarker protein or fragment thereof (e.g., an antibody, antibody fragment, or antibody derivative). Expression levels may be determined using methods commonly used in the art, such as proteomics, flow cytometry, immunocytochemistry, immunohistochemistry, enzyme-linked immunosorbent assays, multichannel enzyme-linked immunosorbent assays, and variants thereof. The expression level of a biomarker in a biological sample may also be determined by detecting the expression level of a transcribed biomarker polynucleotide or fragment thereof encoded by a biomarker gene, which may be cDNA, mRNA or heterogeneous nuclear RNA (hnRNA). The detecting step may comprise amplifying the transcribed biomarker polynucleotide, and may use a quantitative reverse transcription polymerase chain reaction method. The level of expression of the biomarker can be assessed by detecting the presence of the transcribed biomarker polynucleotide or fragment thereof in the sample with a probe that recombines into a double stranded form with the transcribed biomarker polynucleotide or fragment thereof under stringent hybridization conditions.

Also provided herein are compositions and kits for practicing these methods. For example, in some embodiments, reagents (e.g., primers, probes) specific for one or more labels are provided separately or in groups (e.g., primer pair sets for amplifying multiple labels). Additional reagents for performing the detection assay (e.g., enzymes, buffers, positive and negative controls for performing a QuARTS, PCR, sequencing, bisulfite, or other assay) may also be provided. In some embodiments, kits are provided that contain sufficient or useful one or more reagents necessary to perform the method. Reaction mixtures containing the reagents are also provided. A primary mix reagent set containing a plurality of reagents that can be added to each other and/or to a test sample to complete a reaction mixture is also provided.

In some embodiments, the techniques described herein are associated with a programmable machine designed to perform a series of arithmetic or logical operations provided by the methods described herein. For example, some embodiments of the present technology are associated with (e.g., implemented in) computer software and/or computer hardware. In one aspect, the present technology relates to a computer comprising a form of memory, elements for performing arithmetic and logical operations, and a processing element (e.g., a microprocessor) for executing a series of instructions (e.g., the methods provided herein) to read, manipulate, and store data. Accordingly, certain embodiments employ processes that involve data stored in or transferred through one or more computer systems or other processing systems. Embodiments disclosed herein also relate to an apparatus for performing these operations. The apparatus may be specially constructed for the required purposes, or it may be a general-purpose computer (or a group of computers) selectively activated or reconfigured by a computer program and/or data structure stored in the computers. In some embodiments, a set of processors cooperatively (e.g., via network or cloud computing) and/or in parallel perform some or all of the analysis operations.

In some embodiments, the microprocessor is part of a system for determining the presence of one or more mRNA or miRNA (labeled herein as hsa-miR or has-miRs) associated with cancer; generating a standard curve; determining the specificity and/or sensitivity of the assay or label; calculating an ROC curve; sequence analysis; as described herein or as known in the art.

In some embodiments, the microprocessor is part of a system for determining the amount, e.g., concentration, of one or more mrnas associated with the cancer; generating a standard curve; determining the specificity and/or sensitivity of the assay or label; calculating an ROC curve; sequence analysis; as described herein or as 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 the skilled artisan to measure mRNA levels.

In some embodiments, the microprocessor or computer uses an algorithm to measure the amount of an mRNA or mrnas. The algorithm may include mathematical interactions between marker measurements or mathematical transformations of marker measurements. The mathematical interactions and/or mathematical transformations may be presented in a linear, non-linear, discontinuous or discrete fashion.

In some embodiments, the software or hardware component receives the results of the plurality of assays and determines a single value result based on the results of the plurality of assays for reporting to the user, the single value result being indicative of the risk of cancer. Related embodiments calculate risk factors based on mathematical combinations (e.g., weighted combinations, linear combinations) of results from multiple assays disclosed herein.

Some embodiments include a storage medium and a memory component. Memory components (e.g., volatile and/or nonvolatile memory) may be used to store instructions (e.g., embodiments of the processes provided herein) and/or data (e.g., artifacts such as methylation measurements, sequences, and statistical descriptions associated therewith). Some embodiments relate to systems that also 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).

Programmable machines associated with the present technology include conventional existing technologies and technologies under development or yet to be developed (e.g., quantum computers, chemical computers, DNA computers, optical computers, spintronic-based computers, etc.).

In some embodiments, the present technology includes wired (e.g., metal cable, fiber optic) 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), wide Area Network (WAN), ad hoc network, the internet, etc.). In some embodiments, the programmable machine exists as a peer on such a network, and in some embodiments, the programmable machine has a client/server relationship.

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, and the like.

In some embodiments, the techniques provided herein are associated with a plurality of programmable devices that cooperate to perform the methods described herein. For example, in some embodiments, multiple computers (e.g., through a network connection) may work in parallel to collect and process data, e.g., in an implementation of clustered 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) through a conventional network interface such as ethernet, fiber optic, or through wireless network technology.

For example, some embodiments provide a computer comprising a computer readable medium. The embodiment includes a Random Access Memory (RAM) coupled to the processor. The processor executes computer-executable program instructions stored in the memory. Such processors may include microprocessors, ASICs, state machines, or other processors, and may be any of a variety of computer processors, such as processors from intel corporation (Intel Corporation) of Santa Clara, calif, california and motorola corporation (Motorola Corporation) of shamburg, ill, il. Such processors include, or may be in communication with, a medium, such as a computer-readable medium, storing instructions that, when executed by a processor, cause the processor to perform the steps described herein.

Embodiments of computer readable media may 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 diskettes, CD-ROMs, DVDs, magnetic disks, memory chips, ROM, RAM, ASIC, a configured processor, all optical media, all magnetic tape or other magnetic media, or any other media from which a computer processor may read instructions. In addition, various other forms of computer-readable media may transmit or carry instructions to a computer, including routers, private or public networks, or other wired and wireless transmission devices or channels. The instructions may contain code from any suitable computer programming language including, for example, C, C ++, c#, visual Basic, java, python, perl, and JavaScript.

In some embodiments, the computer is connected to a network. The computer may also include a number of external or internal devices such as a mouse, CD-ROM, DVD, keyboard, display, or other input or output devices. Examples of computers are personal computers, digital assistants, personal digital assistants, cellular telephones, mobile telephones, smart phones, pagers, digital tablets, laptop computers, internet appliances, and other processor-based devices. In general, the computers associated with aspects of the technology provided herein may be any type of processor-based platform operating on any operating system capable of supporting one or more programs that incorporate the technology provided herein, such as Microsoft Windows, linux, UNIX, mac OS X, and the like. Some embodiments include a personal computer executing other applications (e.g., applications). These applications may be contained in memory and may include, for example, word processing applications, spreadsheet applications, email applications, instant messaging applications, presentation applications, internet browser applications, calendar/organizer applications, and any other application capable of being executed by a client device.

All such components, computers, and systems described herein as being associated with the present technology may be logical or virtual.

It is also contemplated that embodiments may be implemented as a computer signal embodied in a carrier wave, as well as a signal (e.g., an electrical signal and an optical signal) propagating through a transmission medium. Accordingly, various types of information discussed above can be formatted in a data structure or the like and transmitted as an electrical signal over a transmission medium or stored on a computer readable medium.

In some embodiments, the present disclosure provides a system for predicting breast cancer progression. The breast cancer may be one or more of ductal carcinoma in situ, invasive ductal carcinoma, inflammatory breast cancer, and metastatic breast cancer. In embodiments, breast cancer may be identified and predicted in an individual, the system comprising: a device configured to determine the expression level of a nucleic acid, protein, peptide or other molecule from a biological sample taken from an individual; and hardware logic designed or configured to perform operations comprising: (a) Receiving expression levels from a signature gene set from a biological sample taken from the individual, wherein the signature gene set comprises at least two genes selected from the sequences set forth in table 1.

Information related to diagnosis of a patient includes, but is not limited to, age, race, tumor location, related past history related to co-morbidities, other tumor history, family history of cancer, physical examination results, radiological results, biopsy date, biopsy results, type of surgery performed (post-pubic or perineal radical prostatectomy), neoadjuvant therapy (i.e., chemotherapy, hormone), adjuvant or salvage radiation therapy, hormonal therapy, local or distant disease recurrence, and survival results. In various embodiments, these clinical variables may be included in a predictive model.

Once a biomarker or set of biomarkers is selected, a method for diagnosing an individual who is likely to have breast cancer. In embodiments, a biomarker or set of biomarkers is selected, a method for diagnosing an individual who may have breast cancer, and may comprise one or more of the following steps: 1) Collecting or otherwise obtaining a biological sample; 2) Performing an analytical method to detect and measure one or more biomarkers in the panel in the biological sample; 3) Performing any data normalization or normalization required for the method for collecting biomarker values; 4) Calculating a biomarker score; 5) Combining the biomarker scores to obtain a total diagnostic score; and 6) reporting the diagnostic score of the individual. Such diagnostic methods can be performed using computer and software programs for analyzing data collected from nucleic acids, proteins, peptides, or other biomolecules. In this approach, the diagnostic score may be a single number determined by the sum of all markers calculated, which is compared to a preset threshold value, which is an indication of the presence or absence of a disease. Alternatively, the diagnostic score may be a series of bars, each bar representing a biomarker value, and the response pattern may be compared to a preset pattern to determine the presence or absence of a disease.

For both DNA and RNA, the nucleic acid may be isolated from saliva, plasma or blood samples. The DNA or RNA may be extracellular or extracted from cells in a plasma or blood sample. DNA or RNA can also be extracted from cell biopsies, including from tumors, including solid tumors in the breast.

For proteins or peptides or other biomolecules, they may be isolated from saliva, plasma or blood samples. The protein or peptide or other biological molecule may be extracellular or extracted from cells in saliva, plasma or blood samples. Proteins or peptides or other biomolecules may also be extracted from cell biopsies, including from tumors, including solid tumors in the breast.

It should also be noted that many of the structures, materials, and acts described herein may be recited as means for performing a function or steps for performing a function. Thus, it should be understood that such language is intended to cover all such structures, materials, or acts disclosed in this specification, including the equivalents thereof, which are incorporated herein by reference.

The breast cancer biomarker analysis system may provide functionality and operation to accomplish data analysis, such as data collection, processing, analysis, reporting, and/or diagnosis. For example, in one embodiment, a computer system may execute a computer program that may receive, store, search, analyze, and report information related to a biomarker. The computer program may contain a plurality of 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 the supplemental data to generate a breast cancer status and/or diagnosis. Diagnosing the status of breast cancer may comprise: generating or collecting any other information, including additional biomedical information about the individual's condition relative to the disease; identifying whether further testing is required; or otherwise assess the health status of an individual.

The breast cancer biomarker analysis system may provide functionality and operation to accomplish data analysis, such as data collection, processing, analysis, reporting, and/or diagnosis. For example, in one embodiment, a computer system may execute a computer program that may receive, store, search, analyze, and report information related to breast cancer biomarkers. The computer program may contain a plurality of 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 the supplemental data to generate a breast cancer status and/or diagnosis. Diagnosing a breast cancer state may comprise: generating or collecting any other information, including additional biomedical information about the individual's condition relative to the disease; identifying whether further testing is required; or otherwise assess the health status of an individual.

As used herein, a "computer program product" refers to an organized set of instructions in the form of natural or programming language statements that are contained on a physical medium of any nature (e.g., written, electronic, magnetic, optical, or otherwise) and that may be used 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 act according to the particular content of the statement. The computer program product includes, without limitation: source code and object code embedded in a computer readable medium and/or a program in a test or database. Furthermore, a computer program product that enables a computer system or data processing apparatus to act in a preselected manner may be provided in a variety of forms including, but not limited to, raw source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing, and any and all equivalents.

In one embodiment, a computer program product for indicating a likelihood of breast cancer is provided. The computer program product includes a computer readable medium embodying program code executable by a processor of a computing device or system, the program code comprising: code for retrieving data attributed to a biological sample from an individual, wherein the data comprises biomarker values that each correspond to one of at least N biomarkers in the biological sample selected from the group consisting of the biomarkers provided in table 1; and code for performing a classification method, the code indicating a breast cancer status of the individual as a function of the biomarker value.

In yet another embodiment, a computer program product for indicating a likelihood of breast cancer is provided. The computer program product includes a computer readable medium embodying program code executable by a processor of a computing device or system, the program code comprising: code that retrieves data attributed to a biological sample from an individual, wherein the data comprises biomarker values corresponding to biomarkers in the biological sample selected from the group consisting of the biomarkers provided in table 1; and code for performing a classification method, the code indicating a breast cancer status of the individual as a function of the biomarker value.

The kit (i.e., diagnostic kit) may include reagents for determining the amount of mRNA or gene mutation from a plasma sample of a subject based on assaying nucleic acids, proteins, peptides, or other biomolecules isolated from breast cancer, circulating cells, or residues of circulating cells present in plasma, including proteins, peptides, or other biomolecules. The nucleic acid 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 RNAs (ncrnas), transfer RNAs (trnas), messenger RNAs (mrnas), small interfering RNAs (sirnas), piwi RNAs (pirnas), micrornas (snornas), micrornas (snrnas), extracellular RNAs (exrnas), and ribosomal RNAs (rrnas).

The disclosed methods and assays provide a convenient, effective, and potentially cost-effective means to obtain data and information useful for assessing appropriate or effective therapies for treating patients. The kit may detect the biomarker using conventional methods, whether the protein, peptide, other biomolecule or RNA or DNA to be evaluated includes a protocol for checking for the presence and/or expression of a desired nucleic acid, e.g., SNP, in the sample. For example, genetic markers RNA, including mRNA or DNA in embodiments, from tissue or cell samples of a mammal may be conveniently determined using Northern, dot blot or Polymerase Chain Reaction (PCR) analysis, array hybridization, ribonuclease protection assays, or using DNA SNP chip microarrays (which are commercially available, including DNA microarray snapshots). For example, real-time PCR (RT-PCR) assays, such as quantitative PCR assays, are well known in the art.

Probes for PCR may be labeled with a detectable label, such as a radioisotope, a fluorescent compound, a bioluminescent compound, a chemiluminescent compound, a metal chelator, or an enzyme. Such probes and primers can be used to detect DNA, RNA, and in one embodiment the presence of mutations in mRNA in a sample, and as a means of detecting cells expressing mRNA. As will be appreciated by those skilled in the art, many different primers and probes can be prepared based on known sequences and effectively used to amplify, clone and/or determine the presence and/or level of mRNA.

Other DNA tests to determine the presence or absence of mutations include Fluorescence In Situ Hybridization (FISH). By using FISH, mutations can be detected in cells, tissues including breast tissue, including tumors, and further including breast tumors.

Other methods include protocols for detecting or detecting mutations in DNA or RNA. These other methods include protocols for examining or detecting mRNA in tissue or cell samples by microarray techniques. Using a nucleic acid microarray, test and control RNAs, in embodiments including mRNA samples from test and control tissue samples are reverse transcribed and spiked to generate cDNA probes. The probes are then hybridized to a nucleic acid array immobilized on a solid support. The array is configured such that the order and location of each member of the array is known. For example, a selected set of genes having expression potential in certain disease states may be arranged on a solid support. Hybridization of a tracer probe to a particular array member indicates that the gene is expressed in the sample from which the probe was derived. Differential gene expression analysis of diseased tissue can provide valuable information. Microarray technology uses nucleic acid hybridization and computational techniques to evaluate mRNA expression profiles of thousands of genes within a single experiment.

Biomarkers are particularly useful in cancer diagnosis because their expression patterns are different when comparing healthy subjects to subjects with breast cancer. Expression of a biomarker typically includes up-and down-regulated mRNA levels. In embodiments, the biomarkers set forth herein can determine whether a patient has breast cancer or does not have breast cancer. In embodiments, each is a form of breast cancer (e.g., ductal carcinoma in situ, invasive ductal carcinoma, inflammatory breast cancer, or metastatic breast cancer).

The following biomarkers may be detected in DNA, in embodiments comprising one or more mutations associated with a gene region, snp, or one or more mutations on one or more chromosomes. The following biomarkers may also be detected in RNA, including miRNA, tRNA, mRNA or other forms of RNA in embodiments.

mRNA as biomarker

Table 1 includes a list of mRNA biomarkers for detecting breast cancer.

TABLE 1 mRNA biomarkers for breast cancer

_at = all probes hit one known transcript;

a = all probes in the group hit alternate transcripts from the same gene;

s = all probes in the group hit transcripts from different genes;

_x = some probes hit transcripts from different genes.

In addition to RT-PCR or another PCR-based method, methods for determining biomarker levels include proteomic techniques, as well as personalized genetic profiles. Based on patient responses at the molecular level, personalized genetic profiles may be used to treat RA. A specialized microarray (e.g., an oligonucleotide microarray or a cDNA microarray) herein can include one or more biomarkers having an expression profile associated with sensitivity or resistance to one or more antibodies. While a single biomarker may provide evidence of breast cancer, reliability and accuracy are improved when multiple biomarkers are used in the methods described herein.

Breast cancer diagnosis method using mRNA as biomarker

Detection and quantification of expressed mRNA can be accomplished using standard techniques known to those skilled in the art. The expression or amount of a specific mRNA in the identified body fluid sample or tissue sample or breast biopsy sample or breast tissue sample is determined, for example, by an Immunohistochemical (IHC) method, by an Immunofluorescence (IF) method, by in situ RNA hybridization, by Reverse Transcriptase Polymerase Chain Reaction (RTPCR), in particular quantitative real-time RT-PCR (qRT-PCR), or by a combination of these methods. Other methods for determining the level (expression) of a particular mRNA include MALDI-MS (including surface enhanced laser desorption/ionization mass spectrometry (SELDI-MS), particularly Surface Enhanced Affinity Capture (SEAC), surface enhanced demand desorption (SEND) or surface enhanced photoperiod attachment and release (sear)), antibody tests (including immunoprecipitation, western blotting, enzyme-linked immunosorbent assays (ELISA), enzyme-linked immunosorbent assays (RIA), dissociation Enhanced Lanthanide Fluorescence Immunoassays (DELFIA), scintillation Proximity Assays (SPA) and quantitative nucleic acid tests, particularly PCR, LCR and RT-PCR of samples for detection and quantification of marker (KRT 23) mRNA.

Preferably, the amount of protein of the biomarker or the difference in 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 protein amount of the biomarker or expressed mRNA is more preferably at least 50% or may even be as high as 75% or 100%. More preferably, the difference in expression level or protein amount is at least 200%, i.e. 2-fold; at least 500%, i.e. 5-fold; or at least 1000%, i.e. 10 times. The expression level of the biomarker according to the present application in a breast cancer cell sample is lower or higher than in a healthy normal breast sample by at least 5%, 10% or 20%, more preferably by at least 50% or even 75% or 100%, i.e. by a factor of 2, preferably by a factor of at least 10. Whether biomarker levels increase in a given assay can be established by analyzing a large number of disease samples with a given assay. This may then form an appropriate level from which the "increased" status may be determined; for example, the% difference or fold change described above is increased. A change in the expression level of the biomarker may be indicative of breast cancer. In some cases, no detectable biomarker is expressed in the healthy sample. In such cases, any detection using a normal test system is already at an "increased" level in the sense of the present application.

One or more of the biomarkers can be used in a method of diagnosing cancer or determining prognosis of a test subject with breast cancer. In this way, 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 of diagnosing breast cancer or determining the prognosis of a test subject with breast cancer. In this way, 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 of diagnosing cancer or determining prognosis of a test subject having breast cancer.

In this way, no more than one biomarker or 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 biomarker combinations can be used in a method of diagnosing breast cancer or determining prognosis in a test subject with cancer. In this way, 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 in a method of diagnosing breast cancer or determining the prognosis of a test subject having breast cancer.

In a first step, the expression level of one or more nucleic acids, including DNA and/or RNA (e.g., mRNA), is measured in saliva, plasma, blood, or tissue samples from healthy subjects and samples from subjects with cancer. In embodiments, the expression level 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 a patient. In embodiments, the expression level 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.

In embodiments, expression levels of no more than one biomarker or 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 combinations of biomarkers can be used to generate a footprint or signature for subsequent diagnosis of a patient. In embodiments, the expression level 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 the patient.

Next, the expression level of the same nucleic acid, including DNA and/or RNA (e.g., mRNA), is measured in saliva, plasma, blood, or tissue samples from healthy subjects. This was used as a control. Thereafter, a sample from a healthy patient can be compared to an identified mRNA whose expression level has been altered in a plasma sample from a subject with cancer. Biomarker fingerprints or signatures can be created from mRNA with altered expression levels. This can be used to diagnose or determine the prognosis of cancer in a test subject by comparing mRNA levels from the plasma of the test subject. Conventional statistical analysis may be used to determine, for example, a confidence level.

Examples

The following non-limiting examples are provided for illustrative purposes only in order to facilitate a more complete understanding of the representative embodiments now contemplated. These examples are intended to be only a subset of all possible contexts in which the components of the formulation may be combined. Accordingly, these examples should not be construed as limiting any of the embodiments described in this specification, including those related to the type and amount of components of the formulation and/or the methods and uses thereof.

Predictive model

In accordance with some embodiments of the subject technology, combinations of small numbers of markers (four or less) have been determined to differentiate responses with high accuracy based on the dataset of studies related to breast cancer. Markers have been validated against the dataset as described below.

Example 1

Discovery of biomarkers

GSE27567 dataset-serum from peripheral blood

Published datasets are used to discover biomarkers that predict or indicate the presence of breast cancer. The study included 72 breast cancer biopsy positive patients and 68 breast cancer biopsy negative patients. A total of 54,613 mRNA biomarkers were measured from peripheral blood mononuclear cells. Patient data were evenly and randomly distributed across three subgroups: training, selection and sample out-of-test (maintaining the ratio of the two diagnostic categories).

The present study considers all 54,613 variables using applicant's mathematical evolution modeling platform (see, e.g., U.S. patent No. 9,845,505). The published studies cited are the "integral factor analysis and transgenic mouse model of lableche HG et al to reveal peripheral blood predictors of breast tumors (Integrating factor analysis and a transgenic mouse model to reveal a peripheral blood predictor of breast tumors)", BMC medical genomics (BMC Med genomics), month 2011, 7, 22; the ratio of the PMID to the doi to the 2178289 is 4:61.doi:10.1186/1755-8794-4-61.PMID; PMCID PMC3178481. The dataset and Affymetrix human genome U133 Plus 2.0 array can be found at the NCBI website (NCBI > GEO > entry display (Accession Display), platform GPL570 and platform GSE 27567).

Seventeen biomarkers were identified as: (1) SLC8A1, (2) RP11-452L6.1, (3) HINT3, (4) PRORSD1P, (5) PRDX6, (6) GABRB2, (7) EPS8, (8) SH3RF1, (9) GB_ACC AA814006, (10) hypothesis protein LOC284058, (11) ZNF160, (12) SLC25A45, (13) DST, (14) MTAP, (15) GB_Acc AK024901, (16) TFB1M and (17) SPRED1. To verify the marker panel, the applicant obtained four additional data sets of gene expression markers for breast cancer patients. These data sets are discussed in the following examples:

example 2

Verification of biomarkers

GSE20266 dataset-saliva

Published datasets were used to verify the effectiveness of biomarkers as predictions or indicators of the presence of breast cancer. The study included 10 breast cancer biopsy positive patients and 10 breast cancer biopsy negative patients. A total of 54,613 mRNA biomarkers were measured from peripheral blood mononuclear cells. Patient data were evenly and randomly distributed across three subgroups: training, selection and sample out-of-test (maintaining the ratio of the two diagnostic categories).

As described above, the present study considered 54,613 variables using applicant's mathematical evolution modeling platform. The published studies cited are LaBreche HG et al, "integral factor analysis and transgenic mouse model to reveal peripheral blood predictors of breast tumors" [ BMC medical genomics ] 2011, month 7, 22; the ratio of the PMID to the doi to the 2178289 is 4:61.doi:10.1186/1755-8794-4-61.PMID; PMCID PMC3178481. The dataset and Affymetrix human genome U133 Plus 2.0 array can be found at the NCBI website (NCBI > GEO > entry display; platform GPL570 and platform GSE 27567).

Example 3

Verification of biomarkers

GSE47860 dataset, canadian Ontario queue-blood

A second published dataset is used to verify the effectiveness of the biomarker as a predictor or indicator of the presence of breast cancer. The study included 21 breast cancer biopsy positive patients and 15 breast cancer biopsy negative patients. A total of 54,613 mRNA biomarkers were measured from peripheral blood mononuclear cells. Patients were evenly randomized in three subgroups: training, selection and sample out-of-test (maintaining the ratio of the two diagnostic categories).

As described above, the present study considered 38,079 mRNA biomarkers obtained from peripheral blood mononuclear cells. The published study cited is Piccolo SR et al, "comprehensive analysis reveals signaling pathways behind familial breast cancer susceptibility (Integrative analyses reveal signaling pathways underlying familial breast cancer susceptibility)", molecular systems biology (Mol System biol.) ", month 2016, 3, 10;12 (3) 860.doi:10.15252/msb.20151806.PMID: 26969729; PMCID PMC4812528. The dataset and Affymetrix human genome U133 Plus 2.0 array can be found at the NCBI website (NCBI > GEO > entry display; platform GSE47860 and platform GPL 12729).

Example 4

Verification of biomarkers

GSE47860 dataset, utah queue-blood

A third published dataset was used to verify the effectiveness of the biomarker as a predictor or indicator of the presence of breast cancer. The study included 61 breast cancer biopsy positive patients and 63 breast cancer biopsy negative patients. A total of 54,613 mRNA biomarkers were measured from peripheral blood mononuclear cells. Patients were evenly randomized in three subgroups: training, selection and sample out-of-test (maintaining the ratio of the two diagnostic categories).

As described above, the present study considered 38,079 mRNA biomarkers obtained from peripheral blood mononuclear cells. The published study cited is Piccolo SR et al, "comprehensive analysis reveals the signaling pathway behind susceptibility to familial breast cancer" [ molecular systems biology ] month 2016, 3, 10;12 (3) 860.doi:10.15252/msb.20151806.PMID: 26969729; PMCID PMC4812528. The dataset and Affymetrix human genome U133 Plus 2.0 array can be found at the NCBI website (NCBI > GEO > entry display; platform GSE47860 and platform GPL 17279).

Example 5

Verification of biomarkers

GSE47861 dataset, schematic queue-blood

A fourth published dataset was used to verify the effectiveness of the biomarker as a predictor or indicator of the presence of breast cancer. The study included 15 breast cancer biopsy positive patients and 22 breast cancer biopsy negative patients. A total of 54,613 mRNA biomarkers were measured from peripheral blood mononuclear cells. Patients were evenly randomized in three subgroups: training, selection and sample out-of-test (maintaining the ratio of the two diagnostic categories).

As described above, the present study considered 38,079 mRNA biomarkers obtained from peripheral blood mononuclear cells. The published study cited is Piccolo SR et al, "comprehensive analysis reveals the signaling pathway behind susceptibility to familial breast cancer" [ molecular systems biology ] month 2016, 3, 10;12 (3) 860.doi:10.15252/msb.20151806.PMID: 26969729; PMCID PMC4812528. The dataset and Affymetrix human genome U133 Plus 2.0 array can be found at the NCBI website (NCBI > GEO > entry display; platform GSE47860 and platform GPL 17279).

The results of the discovery and validation studies are summarized in table 2. The positive and negative predictive values (PPV and NPV, respectively) are the ratio of positive and negative results in statistical and diagnostic tests, which are true positive and true negative results, respectively.

TABLE 2 summary of discovery/validation studies

Example 6

Discovery of additional biomarkers

GSE27567 dataset-serum from peripheral blood

Applicants pursued additional markers by conducting a second discovery study. The same discovery and validation dataset was used as described above using the following approach:

1) "emergence (Emerge)" was configured to ignore the first 17 biomarkers and consider the remaining 54,596 gene expression probes;

2) Generating a binary classifier to distinguish between patients with confirmed positive breast cancer biopsies and patients with confirmed negative breast cancer biopsies;

3) Identifying a first minimal biomarker panel that maximizes diagnostic accuracy;

4) Resting the first minimum group and repeating the analysis with the remaining biomarkers to determine the next minimum group; and

5) Repeated until a significant drop in diagnostic accuracy is observed.

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

TABLE 3 summary of discovery/validation studies

Example 7

Early detection of breast cancer using biomarkers

Pre-symptomatic diagnosis of breast cancer patients would be of great value not only to better understand the pathophysiology of the disease, but also to provide early therapeutic and palliative effort. In this example, the patient has early stage breast cancer, which cannot be identified by manual manipulation of the hand to identify the mass in the breast. The patient was a 50 year old female, she hoped to evaluate her likelihood of breast cancer by a physician. Patients are critically obese with a history of alcohol abuse and hypertension. The patient also had a family history of breast cancer. Due to these risk factors, the physician schedules a mammogram.

As a result of the mammogram, no identifiable mass was detected, but there was some irregular cell growth. Patients were then screened using the biomarkers of table 1. The patient provides a sample (e.g., blood, plasma, urine, or saliva) for biomarker analysis. The results indicated that the patient had early stage breast cancer. Based on these results, the patient is transferred to an expert for further evaluation and possible therapeutic treatment. Physicians also recommend testing on a regular basis (i.e., quarterly, half a year, or annually) to ensure that breast cancer does not develop.

Example 8

Detection of breast cancer using biomarkers after identification of tumors in the breast

In this example, the patient looks to the doctor after identifying the presence of an abnormal mass in her left breast. Her doctor schedules her to take a mammogram, she takes a picture, and a tumor is found in her left breast.

In this example, it is necessary to confirm diagnosis of breast cancer. For this purpose, the physician arranges for a biopsy from a site in the breast of the patient suspected of being a tumor. After taking a biopsy sample, a biomarker test as described herein is performed using one or more of the seventeen biomarkers identified in table 1 to confirm the presence and determine the progression of breast cancer. The patient provides a sample for biomarker analysis. In particular, the test determines progression from benign to metastatic cancer. The test also identified the type of breast cancer and the genetic type of breast cancer.

The test indicates that the patient has breast cancer. As with the first example, the patient is referred to an expert for further evaluation, and the expert will then determine the course of treatment and the therapy and treatment regimen to be used. Physicians also recommend testing on a regular basis (i.e., quarterly, half a year, or annually) to ensure that breast cancer does not develop.

Example 9

Detection of breast cancer (asymptomatic) using biomarkers

In this example, the patient had no symptoms of breast cancer, but was evaluated due to known genetic risk factors. Manual manipulation of the breast using the doctor's hand and mammograms do not show breast cancer tumors. However, the patient complains of breast pain.

Physicians require testing of the biomarker tests described herein (table 1) to detect/determine the prognosis of breast cancer. The patient provides a sample for biomarker analysis. This test provides the physician with a level of biomarker that indicates the presence of early breast cancer. As with the first example, the patient is transferred to an expert for further evaluation and discussion of treatment options.

Depending on the progression of breast cancer, an expert may design a treatment plan. Medical treatment may be required to remove, shrink or slow down the growth of the tumor. Typically, treatment is based on the type of breast cancer and its stage. Other factors, including the overall health of the patient, the climacteric status, and personal preferences. Common treatments include surgery, radiation, chemotherapy, hormonal therapy, targeted drug therapy and immunotherapy. Medicaments (i.e., systemic therapies) can be administered to treat breast cancer. Common targeted drugs include trastuzumab (HERCEPTIN) ^TM ) Pertuzumab (PERJETA) ^TM ) Or Abeli (abelmoschlib) (VERZENIO) ^TM ). The most common form of treatment for breast cancer is surgery. This involves removal of the tumor and nearby edges. Surgical options may include lumpectomy, partial lumpectomy, radical lumpectomy, and reconstructive surgery.

In this example, the expert suggests hormone therapy (i.e., TAMOXIFEN ^TM An aromatase inhibitor) and a suspicious tissue biopsy. Physicians also recommend testing on a regular (i.e., quarterly) basis to ensure that breast cancer does not develop.

Example 10

Detection of breast cancer (asymptomatic, genetic risk factors) using biomarkers

In this example, the patient had no symptoms of breast cancer, but was evaluated due to known genetic risk factors. The manual manipulation of the breast using the doctor's hand and mammograms found a tumor, which the doctor suspects to be a breast cancer tumor. Physicians have also suspected that breast cancer has metastasized.

The physician collects a blood sample from the patient and arranges for use of one or more of the biomarkers identified in table 1 to determine whether breast cancer has metastasized (as determined by the presence of circulating breast cancer cells). The test provides the physician with a level of biomarker that diagnoses the patient's blood as having breast cancer cells, indicating that the cancer has metastasized. The test also identified the type of breast cancer and the genetic type of breast cancer. As with the first example, the patient is transferred to an expert for further evaluation and discussion of treatment options.

In this example, the specialist suggests a combination of surgery and targeted drug therapy. Physicians also recommend testing to monitor the progression of cancer on a regular (i.e., quarterly) basis.

Example 11

Detection of breast cancer (asymptomatic, genetic risk factors) using biomarkers

In this example, the patient had no symptoms of breast cancer, but was evaluated due to known genetic risk factors. Manual manipulation of the breast using the doctor's hand and mammograms do not show breast cancer tumors. However, the patient complains of breast pain.

The physician biopsies the tissue of the patient where the tumor is located. The doctor schedules the use of a subset of the biomarkers found in table 1 to determine the presence of breast cancer. To confirm the results, the physician arranges to conduct a second test with the remaining biomarkers not used in the first test. The patient provides a sample for biomarker analysis. The test provides the physician with a level of biomarker sufficient to diagnose that the patient has breast cancer and confirm the results by a second test. As in the first example, the patient is then transferred to an expert for further evaluation and discussion of treatment options.

Example 12

Diagnostic kit for rapid screening of breast cancer

The following working examples are based on the above configuration. Embodiments of the invention may be assembled into diagnostic kits for diagnosing breast cancer. The kit can identify one or more target cells having a breast cancer biomarker in plasma from a test subject.

The kit may include a collection of nucleic acid molecules such that each nucleic acid molecule encodes an mRNA sequence. The nucleic acid molecules can be used to identify changes in the expression level of one or more mRNAs in a plasma sample from a test subject. The expression level of mRNA can be used for comparison/analysis of the test sample with a fingerprint indicative of the presence of cancer.

In certain embodiments, the present disclosure provides kits for diagnosing breast cancer. These kits can include one biomarker disclosed herein 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. Those of skill in the art will appreciate that the number of biomarkers can vary without departing from the essence of the disclosure, and thus the disclosure also encompasses other combinations of biomarkers. The skilled person will know which 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 the patient suffering from breast cancer.

In specific embodiments, the kit comprises one biomarker disclosed herein 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. In certain embodiments, the kit is for diagnosing breast cancer. The kit may optionally also include instructions for use. The kit may also optionally include (e.g., comprise, consist essentially of, consist of, or consist of …, …) a tube, applicator, vial or other storage container having one or more of the biomarkers described above and/or a vial containing the biomarker. In embodiments, 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 the tube, applicator, vial, or storage container.

The kit, whatever the type, generally comprises one or more containers in which 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 is placed, and preferably aliquoted appropriately. The components of the kit may be packaged in an aqueous medium or in lyophilized form.

Example 13

Screening patients for breast cancer

A 56 year old female smoker introduced a family history of breast cancer to her doctor, but her breast did not have obvious bumps or physical manifestations. The doctor draws a blood sample and sends it to the laboratory for breast cancer testing. A blood sample was prepared and plasma was obtained. The plasma was then tested to identify the presence of biomarkers associated with breast cancer. The laboratory uses one or more of the following biomarkers in its test: SLC8A1, RP11-452L6.1, HINT3, prosd 1P, PRDX6, GABRB2, EPS8, SH3RF1, gb_acc AA814006, hypothetical proteins 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 one or more biomarkers for testing breast cancer indicate the presence of cancer. The laboratory sends the test results to the physician. On subsequent visits to the doctor, the patient is informed of the outcome. The patient is thoroughly examined (including mammograms) and a doctor takes a sample of the suspicious tissue for biopsy.

Finally, it should be understood that, although aspects of the present description have been highlighted by reference to specific embodiments, those skilled in the art will readily understand that these disclosed embodiments are merely illustrative of the principles of the subject matter disclosed herein. Therefore, it is to be understood that the disclosed subject matter is in no way limited to the specific methods, protocols, and/or reagents, etc. described herein. As such, various modifications or changes or alternative arrangements may be made to the disclosed subject matter in accordance with the teachings herein without departing from the spirit of the present specification. Finally, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present invention, which will be limited only by the appended claims. The invention is therefore not limited to the exact details shown and described.

Certain embodiments of the invention are described herein, including the best mode known to the inventors for carrying out the invention. Of course, variations on the described embodiments will become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for 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 otherwise clearly contradicted by context.

The grouping of alternative embodiments, elements or steps of the present invention should not be construed as limiting. Each group member may be cited and claimed individually or in any combination with other group members disclosed herein. It is envisioned that one or more members of a group may be included in or deleted from the group for convenience and/or patentability. When any such inclusion or deletion occurs, the specification is considered to contain the modified group so as to satisfy the written description of all Markush (Markush) groups used in the appended claims.

Unless otherwise indicated, all numbers expressing features, items, quantities, parameters, characteristics, terms, and so forth used in the specification and claims are to be understood as being modified in all instances by the term "about". As used herein, the term "about" means that a feature, item, quantity, parameter, characteristic, or term so defined encompasses a range of up and down by 10% of the value of the feature, item, quantity, parameter, characteristic, or term. Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification and attached 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 at least be construed 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. Any numerical range or value, however, inherently contains certain errors necessarily resulting from the standard deviation found in their respective testing measurements. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value of a numerical range is incorporated into the specification as if it were individually recited herein.

Claims (46)

1. A method of detecting breast cancer or determining the prognosis of a subject with breast cancer, comprising the steps of:

a) Measuring the expression level of at least one mRNA in a test sample from the subject,

b) Receiving said expression level of said at least one mRNA in said test sample by a computer, and

c) Comparing the expression level of the at least one mRNA in the test sample with the same level of the at least one mRNA in a base sample, and

d) Receiving a result of comparing the expression level of the at least one mRNA in the test sample measured in a) and the base sample measured in c), and

e) Detecting or determining the prognosis of breast cancer based on the change in expression of the at least mRNA in the test sample as compared to the base sample.

2. The method of claim 1, further comprising the step of treating the subject based on the detection or prognosis of breast cancer.

3. The method of claim 2, wherein the treatment is one or more of surgery, radiation, chemotherapy, hormonal therapy, targeted drug therapy, and immunotherapy.

4. The method of claim 1, further comprising the step of distinguishing stage 0, stage 1, stage 2, stage 3, stage 4 and stage 5 breast cancer.

5. The method of claim 1, wherein the test sample is a blood sample or a saliva sample.

6. The method of claim 1, wherein the breast cancer is one or more of a ductal carcinoma in situ, an invasive lobular carcinoma in situ, a tubular/screen cancer, a mucinous (glioblastoma), a medullary carcinoma, a papillary carcinoma, and a metaplasia carcinoma.

7. The method of claim 1, wherein the at least one mRNA is identified as one or more of: SLC8A1, RP11-452L6.1, HINT3, prosd 1P, PRDX6, GABRB2, EPS8, SH3RF1, gb_acc AA814006, hypothetical proteins LOC284058, ZNF160, SLC25a45, DST, MTAP, GB _acc AK024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, and PTGS2.

8. The method of claim 1, wherein the method is repeated to confirm the detection or prognosis of breast cancer.

9. The method of claim 1, wherein the at least one mRNA comprises 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 mRNA biomarkers.

10. A method of detecting breast cancer or determining prognosis of a test subject with breast cancer, comprising the steps of:

a) Measuring the expression levels of two or more mRNAs in a sample from a subject suffering from breast cancer,

b) Measuring the expression levels of the two or more mRNAs in a sample from a healthy subject,

c) Comparing the expression levels of the two or more mRNAs in the sample from the subject with breast cancer with the levels in the sample from the healthy subject using a computer,

d) Identifying mRNA whose expression level has been altered in a plasma sample from said subject having breast cancer,

e) Creating a biomarker fingerprint from the mRNA having altered expression levels, and

f) Diagnosing or determining the prognosis of breast cancer in a test subject by obtaining a score that sets forth the result of comparing the test subject's mRNA level with the mRNA level in the biomarker fingerprint.

11. The method of claim 10, further comprising the step of treating the subject based on detection or prognosis of breast cancer.

12. The method of claim 11, wherein the treatment is one or more of surgery, radiation, chemotherapy, hormonal therapy, targeted drug therapy, and immunotherapy.

13. The method of claim 10, further comprising the step of distinguishing stage 0, stage 1, stage 2, stage 3, stage 4 and stage 5 breast cancer.

14. The method of claim 10, wherein the sample from a subject with breast cancer and the sample from a healthy subject are blood samples or saliva samples.

15. The method of claim 10, wherein the breast cancer is one or more of a ductal carcinoma in situ, an invasive lobular carcinoma in situ, a tubular/screen cancer, a mucinous (glioblastoma), a medullary carcinoma, a papillary carcinoma, and a metaplasia carcinoma.

16. The method of claim 10, wherein the two or more mrnas are identified from genes in the group consisting of: SLC8A1, RP11-452L6.1, HINT3, prosd 1P, PRDX6, GABRB2, EPS8, SH3RF1, gb_acc AA814006, hypothetical proteins LOC284058, ZNF160, SLC25a45, DST, MTAP, GB _acc AK024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, and/or PTGS2.

17. The method of claim 10, wherein the two or more mrnas comprise 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 mRNA biomarkers.

18. The method of claim 10, wherein the method is repeated to create a second biomarker footprint to confirm the detection or prognosis of breast cancer.

19. A diagnostic kit for detecting breast cancer or determining the prognosis of a test subject with breast cancer,

wherein the kit comprises a plurality of nucleic acid molecules, each nucleic acid molecule encoding an mRNA sequence,

wherein the plurality of nucleic acid molecules identify a change in the expression level of one or more mRNAs in a sample from the test subject, and

wherein at least the plurality of nucleic acid molecules comprises a plurality of base sample nucleic acid molecules, and

wherein the expression level of one or more mRNAs represents a nucleic acid expression fingerprint that is indicative of the presence of breast cancer, an

Wherein the kit identifies one or more target cells that exhibit mRNA expression from the sample from the test subject to detect breast cancer or to determine a prognosis of a test subject with breast cancer.

20. The diagnostic kit of claim 19, wherein the breast cancer is one or more of a ductal carcinoma in situ, an invasive lobular carcinoma in situ, a tubular/screen cancer, a mucinous (colloid) cancer, a medullary carcinoma, a papillary carcinoma, and a metaplasia cancer.

21. The diagnostic kit of claim 19, wherein the one or more mrnas are identified as genes from the group consisting of: SLC8A1, RP11-452L6.1, HINT3, prosd 1P, PRDX6, GABRB2, EPS8, SH3RF1, gb_acc AA814006, hypothetical proteins LOC284058, ZNF160, SLC25a45, DST, MTAP, GB _acc AK024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, and PTGS2.

22. The diagnostic kit of claim 19, wherein the test sample is a blood sample or a saliva sample.

23. The diagnostic kit of claim 19, wherein the breast cancer is one or more of a ductal carcinoma in situ, an invasive lobular carcinoma in situ, a tubular/screen cancer, a mucinous (colloid) cancer, a medullary carcinoma, a papillary carcinoma, and a metaplasia cancer.

24. The diagnostic kit of claim 19, wherein the one or more mrnas comprise 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.

25. A method for identifying one or more target cells exhibiting breast cancer in a patient, the method comprising the steps of:

(a) Collecting a patient sample from the patient;

(b) Hybridizing one or more nucleic acid molecule biomarkers encoding mRNA sequences to a portion of the patient sample;

(c) Quantifying the expression level of one or more mRNAs using a computer;

(d) Comparing the expression level of the one or more mrnas to an expression level in a control sample;

(e) Compiling a change in the expression level of the one or more mrnas;

(f) Determining whether the patient has breast cancer and/or determining a prognosis of the breast cancer based on the change in the expression level of the one or more mrnas.

26. The method of claim 25, wherein the breast cancer is one or more of a ductal carcinoma in situ, an invasive lobular carcinoma in situ, a tubular/screen cancer, a mucinous (glioblastoma), a medullary carcinoma, a papillary carcinoma, and a metaplasia carcinoma.

27. The method of claim 25, wherein the plurality of nucleic acid molecules are identified from genes consisting of: SLC8A1, RP11-452L6.1, HINT3, prosd 1P, PRDX6, GABRB2, EPS8, SH3RF1, gb_acc AA814006, hypothetical proteins LOC284058, ZNF160, SLC25a45, DST, MTAP, GB _acc AK024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, and/or PTGS2.

28. The method of claim 25, wherein the one or more mrnas comprise 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.

29. The method of claim 25, further comprising the step of treating the subject based on detection or prognosis of breast cancer.

30. The method of claim 29, wherein the treatment is one or more of surgery, radiation, chemotherapy, hormonal therapy, targeted drug therapy, and immunotherapy.

31. The method of claim 25, further comprising the step of distinguishing stage 0, stage 1, stage 2, stage 3, stage 4 and stage 5 breast cancer.

32. The method of claim 25, wherein the patient sample is a blood sample or a saliva sample.

33. The method of claim 25, wherein the method is repeated to confirm the detection or prognosis of breast cancer.

34. A method of diagnosing breast cancer or determining the prognosis of a subject with breast cancer, comprising the steps of:

a) Measuring the expression level of at least one nucleic acid, protein or peptide in a test sample of plasma from the subject,

b) Receiving the result of the measurement a) by a computer, and

c) Comparing the expression level of the at least one nucleic acid, protein or peptide in the test sample with the measured level of the same at least one nucleic acid, protein or peptide in a base sample, and

d) Receiving a result of comparing the expression level of the at least one nucleic acid, protein or peptide in the test sample measured in a) and the base sample measured in c), and

e) Diagnosing or determining the prognosis of breast cancer based on a change in expression of the at least one nucleic acid, protein or peptide in the test sample as determined in a computer as compared to the base sample.

35. The method of claim 34, wherein the at least one nucleic acid, protein, or peptide is identified from a gene consisting of: SLC8A1, RP11-452L6.1, HINT3, prosd 1P, PRDX6, GABRB2, EPS8, SH3RF1, gb_acc AA814006, hypothetical proteins LOC284058, ZNF160, SLC25a45, DST, MTAP, GB _acc AK024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, and/or PTGS2.

36. The method of claim 34, wherein the breast cancer is one or more of a ductal carcinoma in situ, an invasive lobular carcinoma in situ, a tubular/screen cancer, a mucinous (glioblastoma), a medullary carcinoma, a papillary carcinoma, and a metaplasia carcinoma.

37. The method of claim 34, further comprising the step of treating the subject based on detection or prognosis of breast cancer.

38. The method of claim 37, wherein the treatment is one or more of surgery, radiation, chemotherapy, hormonal therapy, targeted drug therapy, and immunotherapy.

39. The method of claim 34, further comprising the step of distinguishing stage 0, stage 1, stage 2, stage 3, stage 4 and stage 5 breast cancer.

40. The method of claim 34, wherein the test sample is a blood sample or a saliva sample.

41. A method of evaluating the probability of a subject having breast cancer, diagnosing breast cancer, and/or monitoring breast cancer progression, comprising the steps of: (a) Measuring the amount of one or more mRNA biomarkers selected from genes consisting of: SLC8A1, RP11-452L6.1, HINT3, prosd 1P, PRDX6, GABRB2, EPS8, SH3RF1, gb_acc AA814006, hypothetical proteins LOC284058, ZNF160, SLC25a45, DST, MTAP, GB _acc AK024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, PTGS2; (b) Comparing the measured amount to a control and detecting an increase in the amount of the one or more mRNA biomarkers compared to the control; and (c) identifying the subject as having the breast cancer or having an increased probability of having the breast cancer when an increase in the one or more mRNA biomarkers compared to a control is detected.

42. The method of claim 41, wherein the breast cancer is one or more of a ductal carcinoma in situ, an invasive lobular carcinoma in situ, a tubular/screen carcinoma, a mucinous (glioblastoma), a medullary carcinoma, a papillary carcinoma, and a metaplasia carcinoma.

43. The method of claim 41, further comprising the step of treating the subject based on detection or prognosis of breast cancer.

44. A method of diagnosing breast cancer or determining the prognosis of a subject with breast cancer, comprising the steps of:

a) Measuring the expression level of at least one mRNA or miRNA in a test sample from said subject,

b) The expression level is received by a computer,

c) Comparing the expression level in the test sample to the same level of the at least one mRNA or miRNA in one or more base samples,

d) Receiving a result of comparing the expression level of the at least one mRNA or miRNA in the test sample measured in a) and the base sample measured in c), and

e) Determining whether a patient has breast cancer and/or determining the prognosis of the breast cancer based on an alteration in the expression of the at least one mRNA or miRNA in the test sample,

Wherein the at least one mRNA or miRNA is identified from a gene consisting of: SLC8A1, RP11-452L6.1, HINT3, prosd 1P, PRDX6, GABRB2, EPS8, SH3RF1, gb_accaa814006, hypothetical proteins LOC284058, ZNF160, SLC25a45, DST, MTAP, GB _accak024901, TFB1M, SPRED1, CAV1, MUC1, NAP1L3, MDH1B, TMEM125, PDE9A, TSHZ2, LAMB4, and PTGS2.

45. The method of claim 44, further comprising the step of treating the subject based on detection or prognosis of breast cancer.

46. The method of claim 44, wherein the breast cancer is one or more of a ductal carcinoma in situ, an invasive lobular carcinoma in situ, a tubular/screen carcinoma, a mucinous (glioblastoma), a medullary carcinoma, a papillary carcinoma, and a metaplasia carcinoma.