Abstract:
Methods are provided for predicting and diagnosing the presence of breast cancer in women with dense breasts, as well as for assessing the therapeutic efficacy of a cancer treatment and determining whether a subject potentially is developing cancer.

Description:
CROSS REFERENCE TO RELATED APPLICATION(S) 
       [0001]    This application claims the benefit under 35 U.S.C. §119(e) of U.S. Application Ser. No. 62/233,010, filed Sep. 25, 2015, the entire content of which is incorporated herein by reference. 
     
    
     BACKGROUND 
       [0002]    Field of Invention 
         [0003]    The present invention relates generally to methods for cancer detection, and more particularly to methods for predicting and diagnosing breast cancer in women having dense breasts. 
         [0004]    Background Information 
         [0005]    There is intense effort in the search for biomarkers that can detect early disease and/or monitor for disease progression and recurrence. With the advent of molecularly-targeted therapeutics, biomarkers that are associated with biological subtypes of cancer may be useful for predicting responses to therapeutic interventions. 
         [0006]    Protein-based approaches to distinguish cancer-bearing patient sera from healthy control sera have been challenged by the difficulty in identifying small quantities of protein fragments within complex protein mixtures, protein instability, and natural variations in protein content within patient populations. Autoantibodies (AAb) to tumor antigens have advantages as potential cancer biomarkers as they are stable, highly specific, easily purified from serum, and are readily detected with well-validated secondary reagents. Although they have high specificities to distinguish cancer from control sera, most tumor-associated autoantibodies (TAAbs) demonstrate poor sensitivities. Testing multiple antigens in parallel may serve to increase the predictive value of tumor-specific antibodies for use as immunodiagnostics. 
         [0007]    There are several platforms that may be utilized to screen for immune responses using tumor antigens. For example, protein microarrays offer a platform to present tumor antigens to screen for immune responses. Protein microarrays are capable of presenting and assessing hundreds of tumor antigens simultaneously. The responses are rapidly identified because the address of each protein is known in advance and there are no representation issues; all proteins, even rare ones, are represented equally (usually in duplicate). The proteins are arrayed on a single microscope slide requiring only a few microliters of serum per assay. Known tumor antigens as well as predicted tumor antigens can be included to generate a comprehensive protein tumor antigen array. 
         [0008]    A major need in the precise diagnosis of cancer is the use of complementary technologies to existing standard of care such as imaging and patient exam. This is particularly important in patients having dense breast tissue where imaging is less sensitive in detection of tumors. It is therefore advantageous to contemplate an improvement of existing standard of care (reduction of false positives and false negatives) utilizing a combination of proteomic and imaging approaches in the detection of breast cancer. 
       SUMMARY OF THE INVENTION 
       [0009]    The present invention generally relates to cancer biomarkers and particularly to biomarkers associated with breast cancer. It provides methods to predict, evaluate, diagnose, and monitor cancer, particularly breast cancer, by measuring certain biomarkers. A set of biomarkers including serum protein biomarkers (SPBs) and TAAbs provides a detectable molecular signature of breast cancer in a subject which are particularly useful for detection of breast cancer in wormen with dense breast tissue in combination with conventional imaging techniques. 
         [0010]    Accordingly, in one embodiment, the invention provides a method for detecting, diagnosing, or prognosing breast cancer in a subject having dense breasts. The method includes measuring a level of at least one autoantibody in the sample and at least one protein biomarker, both as compared with a healthy subject&#39;s sample; wherein a level of antibody and biomarker greater than that found in the healthy sample, is indicative of a subject having or at risk of having breast cancer; and performing image analysis of breast tissue of the subject to determine presence of a tumor. The method is performed in subject&#39;s having dense breast tissue. 
         [0011]    In another embodiment, the invention provides a method for determining susceptibility of a subject to a therapeutic regime to treat breast cancer, or monitoring progression of breast cancer in a subject having dense breast tissue. The method includes measuring a level of at least one protein biomarker and at least one autoantibody in a sample from the subject, wherein the at least one protein biomarker is selected from VEGF, FasL, TNF-A, IL- 8 , IL- 12 , HGF and CEA; performing image analysis of breast tissue of the subject to determine presence of a tumor; and assessing effectiveness of the therapeutic regime or progression of the cancer. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0012]      FIG. 1  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (IFNG) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0013]      FIG. 2  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (IL 6 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0014]      FIG. 3  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (IL 8 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0015]      FIG. 4  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (TNFA) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0016]      FIG. 5  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (VEGFD) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0017]      FIG. 6  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (VEGFC) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0018]      FIG. 7  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (HGF) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0019]      FIG. 8  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (FASL) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0020]      FIG. 9  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (ERBB 2 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0021]      FIG. 10  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (CEA) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0022]      FIG. 11  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (OPN) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0023]      FIG. 12  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (BDNF) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0024]      FIG. 13  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (FRS 3 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0025]      FIG. 14  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (GPR 157 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0026]      FIG. 15  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (HOXD 1 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1herein. 
           [0027]      FIG. 16  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (p 53 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0028]      FIG. 17  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (PDCD 6 IP) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0029]      FIG. 18  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (SELL) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0030]      FIG. 19  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (SERPINH 1 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0031]      FIG. 20  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (SF 3 A 1 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0032]      FIG. 21  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (TFCP 2 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0033]      FIG. 22  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (TRIM 32 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0034]      FIG. 23  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (UBAP 1 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0035]      FIG. 24  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (ATF 3 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0036]      FIG. 25  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (ATP 6 AP 1 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0037]      FIG. 26  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (CTBP 1 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0038]      FIG. 27  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (DBT) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0039]      FIG. 28  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (EIF 3 E) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0040]      FIG. 29  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (MYOZ 2 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0041]      FIG. 30  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (RAB 5 A) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0042]      FIG. 31  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (RAC 3 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0043]      FIG. 32  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (SLC 33 A 1 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0044]      FIG. 33  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (SOX 2 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0045]      FIG. 34  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (ZMYM 6 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0046]      FIG. 35  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (ZNF 510 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0047]      FIG. 36  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (BAT 4 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0048]      FIG. 37  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (BMX) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0049]      FIG. 38  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (C 15 of 48 ) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
           [0050]      FIG. 39  is a graphical representation presenting experimental data. The Figure presents a box plot depicting SPB concentration for a selected biomarker (CSNK 1 E) in patients with dense breasts and patients with non-dense breasts in the study described in Example 1 herein. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0051]    The present invention relates to biomarkers associated with breast cancer. It provides methods to predict, evaluate, diagnose, and monitor cancer, particularly breast cancer, by measuring certain biomarkers. A set of biomarkers including serum protein biomarkers and TAAbs provides a detectable molecular signature of breast cancer in a subject. Further, the present application evidences the proof of concept that AAbs and SPB combined provide greater sensitivity and specificity to differentiate benign and breast cancer than either biomarker alone. 
         [0052]    One of the biggest challenges in breast cancer detection is the density of breast tissue. Woman with high breast density are  4 - 5 times more likely to be diagnosed with breast cancer compared to women with normal breast density, which is largely due to the known limitations of current radiological screening methods. As a result, decreased sensitivity and specificity of imaging leads to an increase in false positive results that unnecessarily burden the patient and healthcare system. Therefore, there is a critical need for a technology that is independent of breast density and lesion size to reliably detect clinically significant disease, such as IBC/DCIS. 
         [0053]    Previously, data from two prospective, randomized, multi-center clinical trials (Provista-001 Trial, n=351 and the first cohort of Provista-002 Trial, n=501) demonstrated the ability of Videssa™ Breast to reduce false positive results, compared to imaging alone, and inform the decision to biopsy or proceed with  6 month follow-up imaging. Here, a retrospective analysis is reported of 848 prospectively-collected patient samples to determine the benefit of Videssa™ Breast (a Combinatorial Protein Biomarker Assay) for the detection of breast cancer in women with dense breast. Additionally, this analysis supports use of age stratifications to improve the utility of our combinatorial protein biomarker assay. Recently, data analysis was completed from the first cohort of the Provista-002 Trial (n=501). This data confirmed the results of a robust and highly sensitive biomarker assay for the detection of breast cancer in women with dense breasts. 
         [0054]    Before the present compositions and methods are further described, it is to be understood that this invention is not limited to particular compositions, methods, and experimental conditions described, as such compositions, methods, and conditions may vary. It is also to be understood that the terminology used herein is for purposes of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only in the appended claims. 
         [0055]    As used in this specification and the appended claims, the singular folins “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Thus, for example, references to “the method” includes one or more methods, and/or steps of the type described herein which will become apparent to those persons skilled in the art upon reading this disclosure and so forth. 
         [0056]    The term “about,” as used herein, is intended to qualify the numerical values which it modifies, denoting such a value as variable within a margin of error. When no particular margin of error, such as a standard deviation to a mean value given in a chart or table of data, is recited, the term “about” should be understood to mean that range which would encompass the recited value and the range which would be included by rounding up or down to that figure as well, taking into account significant figures. 
         [0057]    Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the invention, the preferred methods and materials are now described. 
         [0058]    Accordingly, in one embodiment, the invention provides a method for determining whether a subject having dense breasts has, or is at risk of having, breast cancer. The method includes obtaining a biological sample from the subject; measuring a level of at least one autoantibody in the sample and at least one protein biomarker, both as compared with a healthy subject&#39;s sample; wherein a level of antibody and biomarker greater than that found in the healthy sample, is indicative of a subject having or at risk of having breast cancer; and performing image analysis of breast tissue of the subject to determine presence of a tumor. The method is performed in subject&#39;s having dense breast tissue, wherein breast tissue is characterized as non-dense or dense as discussed in Example 1. 
         [0059]    The presently disclosed subject matter provides a panel of biomarkers including proteins, specifically serum proteins, in combination with TAAbs, that are useful for the detection, desirably early detection, of breast cancer in patients with dense breast tissue. The panel of biomarkers provided herein addresses certain limitations of early detection of tumors by other methods of screening alone. 
         [0060]    Several proteins were assessed. In various embodiments, the panel includes one or more of the following proteins as well as fragments thereof: FasL, TNFA, IL 8 , CEA, ERBB 2 , HGF, IFNG, IL 6 , OPN, VEGFC, VEGFD, ATF 3 , ATP 6 AP 1 , BDNF, CTBP 1 , DBT, EIF 3 E, FRS 3 , HOXD 1 , p 53 , PDCD 6 IP, RAC 3 , SELL, SF 3 A 1 , SOX 2 , TFCP 2 , TRIMP 2 , UBAP 1 , ZMYM 6 , IGF 2 PB 2 , MUC 1 , BAT 4 , BMX, C 15 orf 48 , CSNK 1 E, GPR 157  , MYOZ 2 , RAB 5 A, SERPINH 1 , SLC 33 A 1  and ZNF 510 . 
         [0061]    In combination with protein detection, the presently disclosed methodology utilizes detection of TAAbs, such as one or more TAAbs, each TAAB being specific for RAC 3 , IGF 2 BP 2 , MUC 1 , ErbB 2 , ATP 6 AP 1 , PDCD 6 IP, DBT, CSNK 1 E, FRS 3 , HOXD 1 , SF 3 A 1 , CTBP 1 , C 15 orf 48 , MYOZ 2 , EIF 3 E, BAT 4 , ATF 3 , BMX, RAB 5 A, UBAP 1 , SOX 2 , GPR 157 , BDNF, ZMYM 6 , SLC 33 A 1 , TRIM 32 , ALG 10 , TFCP 2 , SERPINH 1 , SELL, ZNF 510  or p 53 . Additionally, multiple TAAbs may be utilized, wherein each of the multiple TAABs is specific for only one of RAC 3 , IGF 2 BP 2 , MUC 1 , ErbB 2 , ATP 6 AP 1 , PDCD 6 IP, DBT, CSNK 1 E, FRS 3 , HOXD 1 , SF 3 A 1 , CTBP 1 , C 15 orf 48 , MYOZ 2 , EIF 3 E, BAT 4 , ATF 3 , BMX, RAB 5 A, UBAP 1 , SOX 2 , GPR 157 , BDNF, ZMYM 6 , SLC 33 A 1 , TRIM 32 , ALG 10 , TFCP 2 , SERPINH 1 , SELL, ZNF 510  or p 53 . In embodiments, multiple p 53 TAAbs may be utilized, for example, up to 12 or more p 53 TAAbs may be utilized. 
         [0062]    In various embodiments detection of TAAbs may be performed using any isoform or variant of RAC 3 , IGF 2 BP 2 , MUC 1 , ErbB 2 , ATP 6 AP 1 , PDCD 6 IP, DBT, CSNK 1 E, FRS 3 , HOXD 1 , SF 3 A 1 , CTBP 1 , C 15 orf 48 , MYOZ 2 , EIF 3 E, BAT 4 , ATF 3 , BMX, RAB 5 A, UBAP 1 , SOX 2 , GPR 157 , BDNF, ZMYM 6 , SLC 33 A 1 , TRIM 32 , ALG 10 , TFCP 2 , SERPINH 1 , SELL, ZNF 510  or p 53 , including wild-type, mutant, as well as protein fragments thereof. 
         [0063]    In combination, the presently disclosed biomarkers provide significant clinical utility for the early detection of breast cancer. Accordingly, in some embodiments methods are provided for assigning a subject to a group having a higher or lower probability of breast cancer. In one embodiment, the method includes determining the level of each of a panel of biomarkers in a sample from the patient, wherein the panel comprises at least one of FasL, TNFA, IL 8 , and CEA, and at least one TAAbs, such as at least one p 53  TAAB, and assigning the patient to the group having a higher or lower probability of breast cancer based on the determined amount of each biomarker in the panel. 
         [0064]    In some embodiments, a method is provided for assigning a subject to a high-risk group for breast cancer. 
         [0065]    In some embodiments, a method is provided for managing treatment of a subject with potential breast cancer. 
         [0066]    In various embodiments, the method of the present invention provides a sensitivity/specificity greater than use of SPBs or TAAbs alone. For example, the method of the present invention provides a sensitivity/specificity of detection greater than about 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98 or 99% utilizing SPBs in combination with AAbs. 
         [0067]    The level of each of the presently disclosed panel of biomarkers can be determined in a variety of animal tissues. In some embodiments, the biomarkers can be detected in samples from a subject, which include bodily fluids such as, but not limited to, serum, blood, blood plasma, urine, sputum, seminal fluid, cerebrospinal fluid, ascites, feces, lymph or nipple aspirate, breast tissue and the like. 
         [0068]    In some embodiments, the presently disclosed methods can comprise statistically analyzing the amounts of each biomarker. The statistical analysis can comprise applying a predetermined algorithm to the amounts of the biomarkers. The results of the algorithm can be employed to assign a subject to a group having a higher or lower likelihood of breast cancer. 
         [0069]    A “biomarker” in the context of the present invention is a molecular indicator of a specific biological property; a biochemical feature or facet that can be used to measure the progress of disease or the effects of treatment. “Biomarker” encompasses, without limitation, serum proteins and TAAbs, including their polymorphisms, mutations, variants, modifications, subunits, fragments, complexes, unique epitopes, and degradation products. 
         [0070]    The telin “polypeptide” is used in its broadest sense to refer to a polymer of subunit amino acids, amino acid analogs, or peptidomimetics, including proteins and peptoids. The polypeptides may be naturally occurring full length proteins or fragments thereof, processed fauns of naturally occurring polypeptides (such as by enzymatic digestion), chemically synthesized polypeptides, or recombinantly expressed polypeptides. The polypeptides may comprise D- and/or L-amino acids, as well as any other synthetic amino acid subunit, and may contain any other type of suitable modification, including but not limited to peptidomimetic bonds and reduced peptide bonds. 
         [0071]    In one embodiment, the disclosed methodology utilizes detection of one or more RAC 3  TAAb, one or more IGF 2 BP 2  TAAb, one or more MUC 1  TAAb, one or more ErbB 2  TAAb, ATP 6 AP 1  TAAb, one or more PDCD 6 IP TAAb, one or more DBT TAAb, one or more CSNK 1 E TAAb, one or more FRS 3  TAAb, one or more HOXD 1  TAAb, one or more SF 3 A 1  TAAb, one or more CTBP 1  TAAb, one or more C 15 orf 48  TAAb, one or more MYOZ 2  TAAb, one or more EIF 3 E TAAb, one or more BAT 4  TAAb, one or more ATF 3  TAAb, one or more BMX TAAb, one or more RAB 5 A TAAb, one or more UBAP 1  TAAb, one or more SOX 2  TAAb, one or more GPR 157  TAAb, one or more BDNF TAAb, one or more ZMYM 6  TAAb, one or more SLC 33 A 1  TAAb, one or more TRIM 32  TAAb, one or more ALG 10  TAAb, one or more TFCP 2  TAAb, one or more SERPINH 1  TAAb, one or more SELL TAAb, one or more ZNF 510  TAAb, or one or more p 53  TAAb. As such, the method may utilize detection of various antibodies that bind different “antigenic fragments” of RAC 3 , IGF 2 BP 2 , MUC 1 , ErbB 2 , ATP 6 AP 1 , PDCD 6 IP, DBT, CSNK 1 E, FRS 3 , HOXD 1 , SF 3 A 1 , CTBP 1 , Cl 5 orf 48 , MYOZ 2 , EIF 3 E, BAT 4 , ATF 3 , BMX, RABSA, UBAP 1 , SOX 2 , GPR 157 , BDNF, ZMYM 6 , SLC 33 A 1 , TRIM 32 , ALG 10 , TFCP 2 , SERPINH 1 , SELL, ZNF 510  or p 53 , or a variant or mutant of RAC 3 , IGF 2 BP 2 , MUC 1 , ErbB 2 , ATP 6 AP 1 , PDCD 6 IP, DBT, CSNK 1 E, FRS 3 , HOXD 1 , SF 3 A 1 , CTBP 1 , Cl 5 orf 48 , MYOZ 2 , EIF 3 E, BAT 4 , ATF 3 , BMX, RAB 5 A, UBAP 1 , SOX 2 , GPR 157 , BDNF, ZMYM 6 , SLC 33 A 1 , TRIM 32 , ALG 10 , TFCP 2 , SERPINH 1 , SELL, ZNF 510  or p 53 . As used herein, an “antigenic fragment” is any portion of at least  4 amino acids of a polypeptide that can give rise to an immune response. In various embodiments, an antigenic fragment is at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 151, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 75, 100, 150, 200, 250, 300, or the full amino acid sequence of a given polypeptide. 
         [0072]    A variety of algorithms can be employed in the presently disclosed methods. The algorithms employed are not limited to those described herein, but rather include algorithms as would be apparent to those of ordinary skill in the art upon a review of the instant disclosure. 
         [0073]    The level of each of a panel of biomarkers can be determined in the presently disclosed method. In some embodiments, the panel of biomarkers can comprise one or more serum proteins and at least one or more TAAbs. However, the presently disclosed subject matter is not limited to the panel of biomarkers described above. Any marker that correlates with breast cancer or the progression of breast cancer can be included in the biomarker panel provided herein, and is within the scope of the presently disclosed subject matter. Any suitable method can be utilized to identify additional breast cancer biomarkers suitable for use in the presently disclosed methods. For example, biomarkers that are known or identified as being up or down-regulated in breast cancer using methods known to those of ordinary skill in the art can be employed. Additional biomarkers can include one or more of polypeptides, small molecule metabolites, lipids and nucleotide sequences. Markers for inclusion on a panel can be selected by screening for their predictive value using any suitable method, including but not limited to, those described. 
         [0074]    As is apparent from the foregoing embodiments, the presently disclosed method is useful for screening patients for breast cancer, for the early detection of breast cancer, and for managing the treatment of patients with potential breast cancer or with known breast cancer. For example, in some embodiments, the panel of biomarkers can be useful for screening patients prior to imaging or other known methods for detecting breast tumors, to define patients at high risk or higher risk for breast cancer. Further, the presently disclosed method may be utilized in combination with other screening methods, such as imaging or histological analysis. 
         [0075]    In one embodiment, the presence of any amount of biomarker in a sample from a subject at risk of breast cancer can indicate a likelihood of breast cancer in the subject. In another embodiment, if biomarkers are present in a sample from a subject at risk of breast cancer, at levels which are higher than that of a control sample (a sample from a subject who does not have breast cancer) than the subject at risk of breast cancer has a likelihood of breast cancer. Subjects with a likelihood of breast cancer can then be tested for the actual presence of breast cancer using standard diagnostic techniques known to the skilled artisan, including biopsy, histological analysis or imaging, such as MRI. In various embodiments, the method results in an accurate diagnosis in at least 70% of cases; more preferably of at least  75 %, 80%, 85%, 90%, or more of the cases. 
         [0076]    Any suitable method can be employed for determining the level of each of the panel of biomarkers, as would be apparent to one skilled in the art upon a review of the present disclosure. For example, a method for detecting TAAbs may include use of biomolecules immobilized on a solid support or substrate. In one embodiment, Nucleic Acid Protein Programmable Array (NAPPA) technology can be used. NAPPA arrays are generated by printing full-length cDNAs encoding the target proteins at each feature of the array. The proteins are then transcribed and translated by a cell-free system and immobilized in situ using epitope tags fused to the proteins. Other suitable immobilization methods include, but are not limited to luciferase immunoprecipitation systems (LIPS), Luminex™ beads, mass spectrophotometer, standard immune dipstick assays, standard plate-based ELISA assays, microbead-based ELISA assays. 
         [0077]    As used herein, an array may be any arrangement or disposition of the polypeptides. In one embodiment, the polypeptides are at specific and identifiable locations on the array. Those of skill in the art will recognize that many such permutations of the polypeptides on the array are possible. In another non-limiting embodiment, each distinct location on the array comprises a distinct polypeptide. 
         [0078]    Any suitable support or surface may be used. Examples of such supports include, but are not limited to, microarrays, beads, columns, optical fibers, wipes, nitrocellulose, nylon, glass, quartz, diazotized membranes (paper or nylon), silicones, polyformaldehyde, cellulose, cellulose acetate, paper, ceramics, metals, metalloids, semiconductive materials, coated beads, magnetic particles; plastics such as polyethylene, polypropylene, and polystyrene; and gel-forming materials, such as proteins (e.g., gelatins), lipopolysaccharides, silicates, agarose, polyacrylamides, methylrnethracrylate polymers; sol gels; porous polymer hydrogels; nanostructured surfaces; nanotubes (such as carbon nanotubes), and nanoparticles (such as gold nanoparticles or quantum dots). 
         [0079]    In one embodiment, the support is a solid support. Any suitable “solid support” may be used to which the polypeptides can be attached including but not limited to dextrans, hydrogels, silicon, quartz, other piezoelectric materials such as langasite, nitrocellulose, nylon, glass, diazotized membranes (paper or nylon), polyfounaldehyde, cellulose, cellulose acetate, paper, ceramics, metals, metalloids, semiconductive materials, coated beads, magnetic particles; plastics such as polyethylene, polypropylene, and polystyrene; and gel-founing materials, such as proteins (e.g., gelatins), lipopolysaccharides, silicates, agarose and polyacrylamides. 
         [0080]    A variety of detection techniques are also suitable for detection of serum proteins. For example, methods for detecting proteins can include gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transfoun InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS). It is further understood that mass spectrometry techniques include, but are not limited to, the use of magnetic-sector and double focusing instruments, transmission quadrapole instrurnents, quadrupole ion-trap instruments, time-of-flight instruments (TOF), Fourier transform ion cyclotron resonance instruments (FT-MS), and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). 
         [0081]    In some embodiments, protein biomarkers can be detected using technologies well known to those of skill in the art such as gel electrophoresis, immunohistochemistry, and antibody binding. Methods for generating antibodies against a polypeptide of interest are well known to those of ordinary skill in the art. An antibody against a protein biomarker of the presently disclosed subject matter can be any monoclonal or polyclonal antibody, so long as it suitably recognizes the protein biomarker. In some embodiments, antibodies are produced using the protein biomarker as the immunogen according to any conventional antibody or antiserum preparation process. The presently disclosed subject matter provides for the use of both monoclonal and polyclonal antibodies. In addition, a protein used herein as the immunogen is not limited to any particular type of immunogen. For example, fragments of the protein biomarkers of the presently disclosed subject matter can be used as immunogens. The fragments can be obtained by any method including, but not limited to, expressing a fragment of the gene encoding the protein, enzymatic processing of the protein, chemical synthesis, and the like. 
         [0082]    Antibodies of the presently disclosed subject matter can be useful for detecting the protein biomarkers. For example, antibody binding is detected by techniques known in the art (e.g., radioimmunoassay, ELISA (enzyme-linked immunosorbant assay), “sandwich” immunoassays, immunoradiometric assays, gel diffusion precipitation reactions, immunodiffusion assays, in situ immunoassays (e.g., using colloidal gold, enzyme or radioisotope labels, for example), Western blots, precipitation reactions, agglutination assays (e.g., gel agglutination assays, hemagglutination assays, and the like), complement fixation assays, immunofluorescence assays, protein A assays, and immunoelectrophoresis assays, and the like. Upon review of the present disclosure, those skilled in the art will be familiar with numerous specific immunoassay fonnats and variations thereof that can be useful for carrying out the methods of the presently disclosed subject matter. 
         [0083]    In any embodiment of the invention, detection techniques may utilize a detectable tag, such as a detectable moiety. A tag may be linked to a polypeptide through covalent bonding, including, but not limited to, disulfide bonding, hydrogen bonding, electrostatic bonding, recombinant fusion and conformational bonding. Alternatively, a tag may be linked to a polypeptide by means of one or more linking compounds. Techniques for conjugating tags to polypeptides are well known to the skilled artisan. Detectable tags can be used diagnostically to, for example, assess the presence of antibodies, or antibodies to a protein in a sample; and thereby detect the presence of breast cancer, or monitor the development or progression of breast cancer as part of a clinical testing procedure. Any suitable detection tag can be used, including but not limited to enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials, radioactive materials, positron emitting metals, and nonradioactive paramagnetic metal ions. The tag used will depend on the specific detection/analysis/diagnosis techniques and/or methods used such as immunohistochemical staining of (tissue) samples, flow cytometric detection, scanning laser cytometric detection, fluorescent immunoassays, enzyme-linked immunosorbent assays (ELISAs), radioimmunoassays (RIAs), bioassays (e.g., neutralization assays), Western blotting applications, and the like. For immunohistochemical staining of tissue samples preferred tags are enzymes that catalyze production and local deposition of a detectable product. Enzymes typically conjugated to polypeptides to permit their immunohistochemical visualization are well known and include, but are not limited to, acetylcholinesterase, alkaline phosphatase, beta-galactosidase, glucose oxidase, horseradish peroxidase, and urease. Typical substrates for production and deposition of visually detectable products are also well known to the skilled person in the art. The polypeptides can be labeled using colloidal gold or they can be labeled with radioisotopes. 
         [0084]    Gene expression levels may be determined in a disclosed method using any technique known in the art. Exemplary techniques include, for example, methods based on hybridization analysis of polynucleotides (e.g., genomic nucleic acid sequences and/or transcripts (e.g., mRNA)), methods based on sequencing of polynucleotides, methods based on detecting proteins (e.g., immunohistochemistry and proteomics-based methods). 
         [0085]    The assays described herein can be adapted to be performed by lay users without a laboratory. The users may be health care professionals in point-of-care facilities or lay consumers in field conditions. The devices may have multiple embodiments including single-use devices, simple reusable devices and computerized biomonitors. The single-use devices, similar to over-the-counter lateral flow assays for pregnancy, enable subjective multi- biomarker assays to be performed. Simple reusable devices also enable objective biomarker assays that provide a refined or enhanced indication of solid state cancer mass, and may also enable remote data processing. 
         [0086]    Gene expression levels also can be determined by quantification of a microRNA or gene transcript (e.g., mRNA). Commonly used methods known in the art for the quantification of rnRNA expression in a sample include, without limitation, northern blotting and in situ hybridization; RNAse protection assays; and PCR-based methods, such as reverse transcription polymerase chain reaction (RT-PCR) and real time quantitative PCR (also referred to as qRT-PCR). Alternatively, antibodies may be employed that can recognize specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes, or DNA-protein duplexes. Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression (SAGE), and gene expression analysis by massively parallel signature sequencing (MPSS). 
         [0087]    Some method embodiments involving the determination of mRNA levels utilize RNA (e.g., total RNA) isolated from a target sample, such a breast cancer tissue sample. General methods for RNA (e.g., total RNA) isolation are well known in the art and are disclosed in standard textbooks of molecular biology. 
         [0088]    Differential gene expression also can be determined using microarray techniques. In these methods, specific binding partners, such as probes (including cDNAs or oligonucleotides) specific for RNAs of interest or antibodies specific for proteins of interest are plated, or arrayed, on a microchip substrate. The microarray is contacted with a sample containing one or more targets (e.g., microRNA, mRNA or protein) for one or more of the specific binding partners on the microarray. The arrayed specific binding partners form specific detectable interactions (e.g., hybridized or specifically bind to) their cognate targets in the sample of interest. 
         [0089]    In some examples, differential gene expression is determined using in situ hybridization techniques, such as fluorescence in situ hybridization (FISH) or chromogen in situ hybridization (CISH). In these methods, specific binding partners, such as probes labeled with a fluorophore or chromogen specific for a target cDNA, microRNA or mRNA (e.g., a biomarker cDNA or mRNA molecule or microRNA molecule) is contacted with a sample, such as a breast cancer sample mounted on a substrate (e.g., glass slide). The specific binding partners form specific detectable interactions (e.g., hybridized to) their cognate targets in the sample. For example, hybridization between the probes and the target nucleic acid can be detected, for example by detecting a label associated with the probe. In some examples, microscopy, such as fluorescence microscopy, is used. 
         [0090]    In various embodiments, the method of the invention includes image analysis of the subject&#39;s breast tissue, which may be performed before, after or simultaneously with biomarker detection. As used herein, image analysis is intended to include any number of conventional screening methods, including for example, mammography, visual inspection, magnetic resonance imaging and clinical breast exam (CBE). 
         [0091]    Another aspect of the present invention is that the assay can be provided in a kit which allows for more convenient laboratory-based biomarker analysis, as well as image analysis or guidelines thereon. The kits may include a plurality of components including reagents, supplies, written instructions, and/or software. The kits may have a plurality of embodiments including laboratory kits and mail-in kits. The kits can include secondary reagents. Secondary reagents may be antibodies, enzymes, labels, or chemicals and may enable a complete biomarker panel assay. 
         [0092]    Exemplary kits can include at least one means for detection of one or more of the disclosed panel constituents (such as, at least two, at least three, at least four, or at least five detection means). In some examples, such kits can further include at least one means for detection of one or more (e.g., one to three) housekeeping genes or proteins. Detection means can include, without limitation, a nucleic acid probe specific for a genomic sequence including a disclosed gene, a nucleic acid probe specific for a transcript (e.g., mRNA) encoded by a disclosed gene, a pair of primers for specific amplification of a disclose gene (e.g., genomic sequence or cDNA sequence of such gene), an antibody or antibody fragment specific for a protein encoded by a disclosed gene. 
         [0093]    In some kit embodiments, the primary detection means (e.g., nucleic acid probe, nucleic acid primer, or antibody) can be directly labeled, e.g., with a fluorophore, chromophore, or enzyme capable of producing a detectable product (such as alkaline phosphates, horseradish peroxidase and others commonly known in the art). Other kit embodiments will include secondary detection means; such as secondary antibodies (e.g., goat anti-rabbit antibodies, rabbit anti-mouse antibodies, anti-hapten antibodies) or non-antibody hapten-binding molecules (e.g., avidin or streptavidin). In some such instances, the secondary detection means will be directly labeled with a detectable moiety. In other instances, the secondary (or higher order) antibody will be conjugated to a hapten (such as biotin, DNP, and/or FITC), which is detectable by a detectably labeled cognate hapten binding molecule (e.g., streptavidin (SA) horseradish peroxidase, SA alkaline phosphatase, and/or SA QDot™). Some kit embodiments may include colorimetric reagents (e.g., DAB, and/or AEC) in suitable containers to be used in concert with primary or secondary (or higher order) detection means (e.g., antibodies) that are labeled with enzymes for the development of such colorimetric reagents. 
         [0094]    In some embodiments, a kit includes positive or negative control samples, such as a cell line or tissue known to express or not express a particular biomarker. 
         [0095]    In some embodiments, a kit includes instructional materials disclosing, for example, means of use of a probe or antibody that specifically binds a disclosed gene or its expression product (e.g., microRNA, mRNA or protein), or means of use for a particular primer or probe. The instructional materials may be written, in an electronic form (e.g., computer diskette or compact disk) or may be visual (e.g., video files). The kits may also include additional components to facilitate the particular application for which the kit is designed. Thus, for example, the kit can include buffers and other reagents routinely used for the practice of a particular disclosed method. Such kits and appropriate contents are well known to those of skill in the art. 
         [0096]    Certain kit embodiments can include a carrier means, such as a box, a bag, a satchel, plastic carton (such as molded plastic or other clear packaging), wrapper (such as, a sealed or sealable plastic, paper, or metallic wrapper), or other container. In some examples, kit components will be enclosed in a single packaging unit, such as a box or other container, which packaging unit may have compartments into which one or more components of the kit can be placed. In other examples, a kit includes a one or more containers, for instance vials, tubes, and the like that can retain, for example, one or more biological samples to be tested. 
         [0097]    Other kit embodiments include, for instance, syringes, cotton swabs, or latex gloves, which may be useful for handling, collecting and/or processing a biological sample. Kits may also optionally contain implements useful for moving a biological sample from one location to another, including, for example, droppers, syringes, and the like. Still other kit embodiments may include disposal means for discarding used or no longer needed items (such as subject samples). Such disposal means can include, without limitation, containers that are capable of containing leakage from discarded materials, such as plastic, metal or other impeimeable bags, boxes or containers. 
         [0098]    The kits can further include software. Software may include a training video that may provide additional support including demonstration of biomarker assays, examples of results, or educational materials for performing biomarker assays according to the invention. 
         [0099]    Although the invention has been described with reference to the above examples, it will be understood that modifications and variations are encompassed within the spirit and scope of the invention. Illustrative examples of the invention are attached herein as Exhibit A which is herein incorporated by reference in its entirety. 
       EXAMPLE 1 
       [0100]    Detection of Breast Cancer in Women with Dense Breasts 
         [0101]    Age-Related Variations: A Retrospective Analysis of 848 Prospectively-Collected Patient Samples to Determine the Benefit of Combinatorial Protein Biomarker Assay for the Detection of Breast Cancer in Women with Dense Breast. 
         [0102]    Abstract 
         [0103]    The precise diagnosis of breast cancer is often confounded by the presence of benign breast tissue and high breast density in women. However, developing new technologies to detect biochemical (protein) markers associated with breast cancer may improve the accuracy of detection. The combination of imaging, which identifies anatomical anomalies and proteomic approaches promises to provide a powerful detection paradigm. In addition, a proteomic-based approach would provide a robust tool to detect breast cancer in women with dense breast, the main reason for missing a breast cancer during annual screening. While protein signatures for the presence of breast cancer have remained elusive, here, we describe the optimization of Combinatorial Proteomic Biomarker Assay (CPBA, Videssa™ Breast) a that combines Serum Protein Biomarkers (SPBs), Tumor-Associated Autoantibodies (TAAbs) with patient clinical data. Two prospective, randomized, multi-center clinical trials were conducted to assess the clinical validity of Videssa™ Breast to distinguish benign breast disease from invasive breast cancer (IBC)/ductal carcinoma in situ (DCIS). 
         [0104]    Introduction 
         [0105]    One of the biggest challenges in breast cancer detection is the density of breast tissue. Woman with high breast density are 4-5× more likely to be diagnosed with breast cancer compared to women with normal breast density, which is largely due to the known limitations of current radiological screening methods. As a result, decreased sensitivity and specificity of imaging leads to an increase in false positive results that unnecessarily burden the patient and healthcare system. Therefore, there is a critical need for a technology that is independent of breast density and lesion size to reliably detect clinically significant disease, such as IBC/DCIS. 
         [0106]    Previously, data from two prospective, randomized, multi-center clinical trials (Provista-001 Trial, n=351 and the first cohort of Provista-002 Trial, n=501) demonstrated the ability of Videssa™ Breast to reduce false positive results, compared to imaging alone, and inform the decision to biopsy or proceed with  6 month follow-up imaging. Here, a retrospective analysis of 848 prospectively-collected patient samples to deteimine the benefit of Videssa™ Breast (a Combinatorial Protein Biomarker Assay) for the detection of breast cancer in women with dense breast tissue is report. Additionally, this analysis supports use of age stratifications to improve the utility of the combinatorial protein biomarker assay. Recently, data analysis was completed from the first cohort of the Provista-002 Trial (n=501). This data confirmed the results of a robust and highly sensitive biomarker assay for the detection of breast cancer in women with dense breasts. 
         [0107]    The Provista-001 and 002 Trials enrolled 852 patients from 15 sites across the US and patients were followed for 6 months to confirm cancer status. Four subjects were excluded for the study prior to blood draw. Eligible patients included women between the ages of 25 and 75, who were identified as ACR BIRADS® 3 or 4 on diagnostic imaging, and had no history of cancer or prior breast biopsy within the last six months (Table 1). A total of 645 subjects were determined to be benign, while 44 subjects were diagnosed with invasive breast cancer (IBC) and 33 subjects as ductal carcinoma in situ (DCIS), confirmed by pathology following biopsy or surgery. 
         [0108]    Results 
         [0000]    
       
         
               
             
               
               
               
             
               
               
               
               
             
               
               
               
               
             
               
               
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Demographics, Clinical Characteristics  
               
               
                 and Cancer Status of Enrolled Subjects 
               
             
          
           
               
                   
                 Density 
                   
               
             
          
           
               
                   
                 Non-Dense 
                 Dense 
                 P-value 
               
               
                   
               
             
          
           
               
                 Age, mean (SD) 
                 51.4 (10.6) 
                 47.2 (9.6) 
                 &lt;0.0001 
               
             
          
           
               
                 Menopausal 
                   
                   
                   
                   
                   
               
               
                 Status 
                   
                   
                   
                   
                   
               
               
                 Pre 
                 132 
                 42% 
                 243 
                 60% 
                 &lt;0.0001 
               
               
                 Peri 
                 35 
                 11% 
                 47 
                 12% 
                   
               
               
                 Post 
                 111 
                 35% 
                 74 
                 18% 
                   
               
               
                 Hysterectomy 
                 38 
                 12% 
                 40 
                 10% 
                   
               
               
                 Diagnosis 
                   
                   
                   
                   
                   
               
               
                 DCIS 
                 16 
                 5.1%  
                 17 
                 4.2%  
                 0.7235 
               
               
                 Breast Cancer 
                 21 
                 6.7%  
                 23 
                 5.7%  
                   
               
               
                 Benign 
                 279 
                 88.3%   
                 366 
                 90.2%   
                   
               
               
                 Ethnicity 
                   
                   
                   
                   
                   
               
               
                 Non-Hipsanic 
                 296 
                 93.7%   
                 367 
                 90.4%   
                 0.1181 
               
               
                 Hispanic 
                 20 
                 6.3%  
                 39 
                 9.6%  
                   
               
               
                 Race 
                   
                   
                   
                   
                   
               
               
                 African American 
                 33 
                 10.4%   
                 25 
                 6.2%  
                 0.09706 
               
               
                 Asian 
                 7 
                 2.2%  
                 13 
                 3.2%  
                   
               
               
                 Hispanic 
                 14 
                 4.4%  
                 20 
                 4.9%  
                   
               
               
                 Other 
                 7 
                 2.2%  
                 10 
                 2.5%  
                   
               
               
                 White 
                 255 
                 80.7%   
                 338 
                 83.3%   
               
               
                   
               
             
          
         
       
     
         [0109]    A previously defined set of SPBs and TAAbs (as in Hollingsworth and Reese, Oncology and Hematology Review, 10 (2), Fall. 2014 ; and Anderson et al.,  J Proteome Res  Jan 2011 10(1) 85-96, both incorporated herein by reference) were measured prior to biopsy. Individual concentrations of SPBs and TAAbs were combined with patient clinical data to develop a predictive Logit Boost model. The performance of the model is shown in Table 3. The model has an overall sensitivity of 87.8%, specificity of 83.7%, PPV (positive predictive value) of 46.8%, NPV (negative predictive value) of 97.7%, and AUC of 0.9018. 
         [0110]    In order to evaluate if Videssa™ Breast perfaunance would be affected by the presence of dense breast tissue, we collected dense breast information, categorized either as A, B (fatty to scattered areas of fibrograndular—classified herein as dense) or C, D (heterogeneous to extremely dense—classified herein as dense). 
         [0111]    Four categories of breast composition (a, b, c and d) were defined by the visually estimated content of fibroglandular-density tissue within the breasts (as defined by ACR BI-RADS® ATLAS—MAMMOGRAPHY—available on the World Wide Web at URL—acr.org/˜/media/ACR/Documents/PDF/QualitySafety/Resources/BIRADS/01%20Mamrnogra phy/02%20%20BIRADS%20Mammography%20Reporting.pdf; Table 2) 
         [0000]    
       
         
               
             
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Breast Tissue Breast Composition Categories 
               
             
          
           
               
                 Category 
                 Description 
               
               
                   
               
               
                 Non-Dense 
                   
               
               
                 A 
                 The breasts are almost entirely fatty 
               
               
                 B 
                 There are scattered areas of fibroglandular density 
               
               
                 Dense 
                   
               
               
                 C 
                 The breasts are heterogeneously dense 
               
               
                 D 
                 The breasts are extremely dense 
               
               
                   
               
             
          
         
       
     
         [0112]    A total of 256 patients were categorized as non-dense and 372 as dense breasts. Dense breast information was not collected for 220 patients. However, no statistical differences were observed in the performance of the Videssa™ Breast as assessed by sensitivity and specificity between non-dense (A, B) and dense breasts (C, D) (Table 3). Sensitivities ranged from 83.8% to 92.5% while the specificities ranged from 79.9% to 84.8%. These results strongly suggest that the Videssa™ Breast be used in combination with standard of care imaging for women with dense breasts. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 Performance of Videssa ™ Breast in  
               
               
                 Women with Various Breast Densities 
               
             
          
           
               
                   
                 Sens 
                 Spec 
                 PPV 
                 NPV 
               
               
                   
               
               
                 Overall 
                 87.8% 
                 83.7% 
                 46.8% 
                 97.7% 
               
               
                 Non-Dense 
                 83.8% 
                 79.9% 
                 44.9% 
                 96.2% 
               
               
                 Dense 
                 92.5% 
                 84.8% 
                 53.6% 
                 98.3% 
               
               
                   
               
             
          
         
       
     
         [0113]    Conclusions 
         [0114]    Analysis of 848 patients demonstrates the ability of a combinatorial protein biomarker assay, (combined SPBs and TAAbs and patient data), to differentiate benign breast disease from invasive breast cancer (IBC)/DCIS with high sensitivity and NPV, in women ages 25-75 years. 
         [0115]    Clear differences were evident between women under the age of 50 and women over the age of 50 for detection algorithms as previously observed. 
         [0116]    Breast density did not affect the perfaunance of Videssa™ Breast suggesting an important role of proteomic panels in breast cancer detection in women with dense breast. 
         [0117]    When used in combination with standard of care imaging, these results support the use of Videssa™ Breast for the early detection of breast cancer in women with dense breasts. 
         [0118]    Although the invention has been described with reference to the above example, it will be understood that modifications and variations are encompassed within the spirit and scope of the invention. Accordingly, the invention is limited only by the following claims.