Figure S4: ROC overlay demonstrating relationship between FSH values and menopause status. Separate curves were generated for pre (n=333) vs. post (n=317) and vs. post/peri (n=390). Subjects who had undergone hysterectomy were excluded in these analyses.

Figure S5: Individual ROC curves for Videssa Breast model training and validation. Curves shown correspond to those in Fig. 2.

Figure S1: Breakdown of imaging modalities used to enroll subjects in Provista-002. Some subjects underwent multiple imaging procedures prior to enrollment, these are noted in side boxes for screening mammogram and diagnostic mammogram.

Figure S3: Breakdown of subjects excluded in the Provista-002 trial. Reasons for exclusion shown in legend.

Figure S2: Clinical management workflow with inclusion/exclusion criteria for the PDX-002 clinical trial. *Details on malignancy risk from D'Orsi et al.[3]

Table S1. Enrollment Criteria Table S2. Enrollment Sites Table S3. Serum protein biomarkers (SPB) and tumor-associated autoantibodies (TAAb) evaluated in the current study. Table S4: Clinical conditions and criteria for inclusion. Table S5: Comparison of biomarker measurements between women divided by age and by FSH. Table S6: Clinical performance of Videssa Breast in all samples categorized by breast density (dense: categories a and b, non-dense: categories c and d) and by health exclusion. Table S7: Clinical performance of Videssa Breast in subjects ages 25-49.

AbstractPurpose: With improvements in breast cancer imaging, there has been a corresponding increase in false-positives and avoidable biopsies. There is a need to better differentiate when a breast biopsy is warranted and determine appropriate follow-up. This study describes the design and clinical performance of a combinatorial proteomic biomarker assay (CPBA), Videssa Breast, in women over age 50 years. Experimental Design: A BI-RADS 3, 4, or 5 assessment was required for clinical trial enrollment. Serum was collected prior to breast biopsy and subjects were followed for 6–12 months and clinically relevant outcomes were recorded. Samples were split into training (70%) and validation (30%) cohorts with an approximate 1:4 case:control ratio in both arms. Results: A CPBA that combines biomarker data with patient clinical data was developed using a training cohort (469 women, cancer incidence: 18.5%), resulting in 94% sensitivity and 97% negative predictive value (NPV). Independent validation of the final algorithm in 194 subjects (breast cancer incidence: 19.6%) demonstrated a sensitivity of 95% and a NPV of 97%. When combined with previously published data for women under age 50, Videssa Breast achieves a comprehensive 93% sensitivity and 98% NPV in a population of women ages 25–75. Had Videssa Breast results been incorporated into the clinical workflow, approximately 45% of biopsies might have been avoided. Conclusions: Videssa Breast combines serum biomarkers with clinical patient characteristics to provide clinicians with additional information for patients with indeterminate breast imaging results, potentially reducing false-positive breast biopsies.

Ovarian cancer is often fatal and incidence in the general population is low, underscoring the necessity (and the challenges) for advancements in screening and early detection. The goal of this study was to design a serum-based biomarker panel and corresponding multivariate algorithm that can be used to accurately detect ovarian cancer. A combinatorial protein biomarker assay (CPBA) that uses CA125, HE4, and 3 tumor-associated autoantibodies resulted in an area under the curve of 0.98. The CPBA Ov algorithm was trained using subjects who were suspected to have gynecological cancer and were scheduled for surgery. As a surgical rule-out test, the clinical performance achieves 100% sensitivity and 83.7% specificity. Although sample size (n = 60) is a limiting factor, the CPBA Ov algorithm performed better than either CA-125 alone or the Risk of Ovarian Malignancy Algorithm.

Abstract This abstract was not presented at the symposium.

Abstract This abstract was not presented at the symposium.

Abstract Breast density is associated with reduced imaging sensitivity and specificity for breast cancer (BC). Women with dense breasts are at a four- to six-fold increased risk of developing BC. A biochemical approach that is not affected by density would provide an additional tool to health-care professionals who are managing women with dense breasts and suspicious imaging findings. Videssa® Breast, a combinatorial proteomic biomarker assay, comprised of Serum Protein Biomarkers (SPBs) and Tumor –Associated Autoantibodies (TAAbs) integrated with clinical characteristic data to produce one diagnostic score that reliably detects BC was recently developed as an adjunctive tool to imaging. The goal of this study was to determine whether the diagnostic performance of Videssa® Breast was impacted by breast density. Provista-001 enrolled 351 participants under the age of 50 years with no prior history of breast biopsy, and Provista-002 cohort one enrolled 210 participants under the age of 50 years with no history of breast biopsy within six months; all participants were assessed as BI-RADS 3 or 4. Breast density status was retrospectively obtained for participants; the four American College of Radiology breast density categories (a, b, c, and d) used for clinical reporting were applied. Serum was collected and tested with Videssa® Breast. Women were categorized into Dense, which included categories c and d, and Non-dense, which included categories a and b, groups. To understand the performance of Videssa® Breast in women with dense breasts, the clinical sensitivity, specificity, NPV and PPV were evaluated in the dense and non-dense groups from the comprehensive Provista-001 and Provista-002 set (n=545). Of these 545, breast density information was available for 454; 62.6% (n=284) were categorized as having dense breasts and 37.4% (n=170) were categorized as having non-dense breasts. The sensitivity of Videssa® Breast in the non-dense and dense groups was 92.3% and 88.9%, respectively, and the specificity in the non-dense and dense groups was 86.6% and 81.2%, respectively. No significant differences were observed in the sensitivity (p=1.0) or specificity (p=0.1783) of Videssa® Breast in detecting BC in participants with non-dense breasts compared to those with dense breasts. The NPV in both groups exceeded 99%; the PPV was similar across groups. In summary, this study demonstrates that Videssa® Breast has comparable performance in women with dense and non-dense breasts. Videssa® Breast demonstrates high sensitivity and specificity for detecting BC (Grades I through III), irrespective of density status. Videssa® breast provides an additional tool for health-care providers when women with dense breasts present with challenging imaging findings. In addition, Videssa® breast provides assurance to a woman with dense breasts that she does not have BC, potentially reducing further anxiety in this higher risk patient population. Citation Format: Silver M, Tran Q, Gordon K, Benson KL, Henderson MC, Letsios E, Mulpuri R, Reese DE. A blood-based proteomic Videssa® breast assay performs comparably in women with dense and non-dense breasts [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-03-03.

To improve breast cancer diagnosis, 2 prospective clinical trials were conducted to test (n = 351) and validate (n = 210) Videssa Breast. If used in conjunction with imaging, Videssa Breast could have reduced unnecessary biopsies by up to 67%. These results support the joint use of breast imaging and Videssa Breast to better inform clinical decisions for women under age 50. Background: Despite significant advances in breast imaging, the ability to detect breast cancer (BC) remains a challenge. To address the unmet needs of the current BC detection paradigm, 2 prospective clinical trials were conducted to develop a blood-based combinatorial proteomic biomarker assay (Videssa Breast) to accurately detect BC and reduce false positives (FPs) from suspicious imaging findings. Patients and Methods: Provista-001 and Provista-002 (cohort one) enrolled Breast Imaging Reporting and Data System 3 or 4 women aged under 50 years. Serum was evaluated for 11 serum protein biomarkers and 33 tumor-associated autoantibodies. Individual biomarker expression, demographics, and clinical characteristics data from Provista-001 were combined to develop a logistic regression model to detect BC. The performance was tested using Provista-002 cohort one (validation set). Results: The training model had a sensitivity and specificity of 92.3% and 85.3% (BC prevalence, 7.7%), respectively. In the validation set (BC prevalence, 2.9%), the sensitivity and specificity were 66.7% and 81.5%, respectively. The negative predictive value was high in both sets (99.3% and 98.8%, respectively). Videssa Breast performance in the combined training and validation set was 99.1% negative predictive value, 87.5% sensitivity, 83.8% specificity, and 25.2% positive predictive value (BC prevalence, 5.87%). Overall, imaging resulted in 341 participants receiving follow-up procedures to detect 30 cancers (90.6% FP rate). Videssa Breast would have recommended 111 participants for follow-up, a 67% reduction in FPs (P <.00001). Conclusions: Videssa Breast can effectively detect BC when used in conjunction with imaging and can substantially reduce unnecessary medical procedures, as well as provide assurance to women that they likely do not have BC. (C) 2017 ProvistaDx. Published by Elsevier Inc.

Breast density is associated with reduced imaging resolution in the detection of breast cancer. A biochemical approach that is not affected by density would provide an important tool to healthcare professionals who are managing women with dense breasts and suspicious imaging findings. Videssa® Breast is a combinatorial proteomic biomarker assay (CPBA), comprised of Serum Protein Biomarkers (SPB) and Tumor Associated Autoantibodies (TAAb) integrated with patient-specific clinical data to produce a diagnostic score that reliably detects breast cancer (BC) as an adjunctive tool to imaging. The performance of Videssa® Breast was evaluated in the dense (a and b) and non-dense (c and d) groups in a population of n = 545 women under age 50. The sensitivity and specificity in the dense breast group were calculated to be 88.9% and 81.2%, respectively, and 92.3% and 86.6%, respectively, for the non-dense group. No significant differences were observed in the sensitivity (p = 1.0) or specificity (p = 0.18) between these groups. The NPV was 99.3% and 99.1% in non-dense and dense groups, respectively. Unlike imaging, Videssa® Breast does not appear to be impacted by breast density; it can effectively detect breast cancer in women with dense and non-dense breasts alike. Thus, Videssa® Breast provides a powerful tool for healthcare providers when women with dense breasts present with challenging imaging findings. In addition, Videssa® Breast provides assurance to women with dense breasts that they do not have breast cancer, reducing further anxiety in this higher risk patient population.

Abstract Current methods of breast cancer detection are often confounded by imaging limitations, such as lesion size, benign breast tissue, and dense breasts. These limitations result in unnecessary biopsies due to false positive findings based on imaging. Despite the increased ability to detect early breast cancer, the over-use of biopsy remains an issue. There is a critical need for new approaches to breast cancer detection that improve diagnostic accuracy when clinical assessment is challenging. Provista Diagnostics has developed Videssa® Breast - a blood-based proteomic test that measures serum protein biomarkers (SPBs) and tumor-associated autoantibodies (TAAbs). Patient biochemical data is combined with clinical data to generate a diagnostic score that correlates with either the absence or presence of breast cancer (Grades I through III). The ability of Videssa® breast to detect cancer (Invasive Breast Cancer and Ductal Carcinoma in situ) was evaluated using prospective, multi-center clinical trials. The Provista-001 study enrolled 351 women ages 25-49 and included a follow-up visit at 6 months with an additional blood draw. Eligible patients included women assessed as ACR BIRADS® 3 or 4 on imaging with no history of breast cancer or prior breast biopsy. Serum samples from the initial visit and 6 month follow-up visit of Provista-001 were analyzed using Videssa® Breast to determine if diagnostic results for benign subjects were similar over the course of the study. Samples were analyzed for SPBs and TAAbs in order to determine whether analyte levels and diagnostic scores change over a 6-month period in patients diagnosed with a benign breast condition. Linear regression data for analytes shows overall high fidelity between the initial visit and follow-up. In addition, samples that were TAAb-positive for a given target at the initial visit tended to remain positive at follow-up. Sample background, deriving from unidentified immunological factors, can confound the analytical output when measuring TAAbs in serum. Interestingly, sample background was highly reproducible between both visits, suggesting that these values are related to inherent patient-specific factors. Overall, these data demonstrate high analytical reproducibility for in expression of independent Videssa® Breast biomarkers in patients diagnosed with a benign breast condition over the course of six months. Data for 236 women were compared between visits and demonstrated greater than 80% concordance in diagnostic status. The ability of Videssa® breast to provide consistent diagnostic results over 6 months further supports use of the test as an adjunct to imaging for the early detection of breast cancer and provides physicians with an additional tool that can be used to inform the decision to biopsy or increase vigilance through active monitoring. Citation Format: Benson KL, Henderson MC, Silver M, Gordon K, Borman S, Letsios E, Tran Q, Mulpuri R, Reese DE. A liquid biopsy test for breast cancer detection provides consistent diagnostic results in patients over six months [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-01-07.

Despite significant advances in breast imaging, the ability to accurately detect Breast Cancer (BC) remains a challenge. With the discovery of key biomarkers and protein signatures for BC, proteomic technologies are currently poised to serve as an ideal diagnostic adjunct to imaging. Research studies have shown that breast tumors are associated with systemic changes in levels of both serum protein biomarkers (SPB) and tumor associated autoantibodies (TAAb). However, the independent contribution of SPB and TAAb expression data for identifying BC relative to a combinatorial SPB and TAAb approach has not been fully investigated. This study evaluates these contributions using a retrospective cohort of pre-biopsy serum samples with known clinical outcomes collected from a single site, thus minimizing potential site-to-site variation and enabling direct assessment of SPB and TAAb contributions to identify BC. All serum samples (n = 210) were collected prior to biopsy. These specimens were obtained from 18 participants with no evidence of breast disease (ND), 92 participants diagnosed with Benign Breast Disease (BBD) and 100 participants diagnosed with BC, including DCIS. All BBD and BC diagnoses were based on pathology results from biopsy. Statistical models were developed to differentiate BC from non-BC (i.e., BBD and ND) using expression data from SPB alone, TAAb alone, and a combination of SPB and TAAb. When SPB data was independently used for modeling, clinical sensitivity and specificity for detection of BC were 74.7% and 77.0%, respectively. When TAAb data was independently used, clinical sensitivity and specificity for detection of BC were 72.2% and 70.8%, respectively. When modeling integrated data from both SPB and TAAb, the clinical sensitivity and specificity for detection of BC improved to 81.0% and 78.8%, respectively. These data demonstrate the benefit of the integration of SPB and TAAb data and strongly support the further development of combinatorial proteomic approaches for detecting BC.

31 Background: An approach to detection that relies on biochemical markers of breast cancer would significantly contribute to more accurate detection in women with suspicious lesions. The combination of imaging, which identifies anatomical anomalies consistent with cancer with proteomic approaches promises to provide a powerful detection paradigm. A proteomic detection approach would provide a powerful tool for the detection of breast cancer in women with dense breast, a diagnosis that is difficult utilizing imaging alone. While protein signatures for the presence of breast cancer have remained elusive, we have developed a novel approach that combines serum protein biomarkers with tumor-associated autoantibodies. We utilized prospectively collected serum samples to develop novel algorithms for use in conjunction with imaging. We tested whether the assay was able to distinguish benign from invasive breast cancers in a prospective, randomized setting. Methods: Provista-002 enrolled 509 patients from multiple sites across the US and followed for 6 months after the first blood draw under IRB approval. Patients were consented after assessment of a BIRADS 3 or 4 and considered eligible if they were between 25 and 75 years of age, no history of cancer, no prior breast biopsy within the last six months, and were assessed as BIRADS 3 or 4 within 28 days. Upon enrollment, patients were randomized to either training or validation groups. Clinical truth was considered equal to or greater than 80% sensitivity and/or specificity. Serum protein biomarkers and tumor-associated autoantibodies identified in prior proteomic screens were measured prior to biopsy in a blinded and randomized fashion. Individual biomarker concentrations, together with specific patient data were evaluated using various logistic regression models developed from prior studies. Results: Provista-002 demonstrated a clear difference between women under the age of 50 from over the age of 50 in both markers required for early detection and the algorithm (models) used to distinguish benign from invasive breast cancer/DCIS. This is the first study that demonstrates clearly that modeling of proteomic patterns differs significantly in the BIRADS 3/4 setting and in the detection of early breast cancer lesions. As demonstrated in Provista – 001, we did not observe a statistical difference between early detection in women with dense breast and those with mostly fatty breast. The ability of the Videssa assay to distinguish between invasive breast cancer/DCIS from benign breast conditions was demonstrated as 85.7% sensitivity and 82.4% specificity for women under the age of 50 (although, unfortunately all lesions were pathologically confirmed to be CIS) and in women over the age of 50, the sensitivity was 86.4% and specificity was 83%. Conclusions: As above, both age groups of women needed distinct marker sets and linear regressions to distinguish benign (non-clinically significant) lesions from those that needed further evaluation (DCIS and IBC). Clinical trial information: NCT02078570.