A Noninvasive Blood-based Combinatorial Proteomic Biomarker Assay to Detect Breast Cancer in Women Under the Age of 50 Years

Accepted Manuscript A Non-Invasive Blood-Based Combinatorial Proteomic Biomarker Assay to Detect Breast Cancer in Women Under the Age of 50 Years Ana P. Lourenco, Kasey L. Benson, Meredith C. Henderson, Michael Silver, Elias Letsios, Quynh Tran, Kelly J. Gordon, Sherri Borman, Christa Corn, Rao Mulpuri, Wendy Smith, Josie Alpers, Carrie Costantini, Nitin Rohatgi, Rebecca Yang, Ali Haythem, Shah Biren, Michael Morris, Fred Kass, David E. Reese PII:

S1526-8209(16)30412-8

DOI:

10.1016/j.clbc.2017.05.004

Reference:

CLBC 616

To appear in:

Clinical Breast Cancer

Received Date: 20 October 2016 Revised Date:

14 April 2017

Accepted Date: 14 May 2017

Please cite this article as: Lourenco AP, Benson KL, Henderson MC, Silver M, Letsios E, Tran Q, Gordon KJ, Borman S, Corn C, Mulpuri R, Smith W, Alpers J, Costantini C, Rohatgi N, Yang R, Haythem A, Biren S, Morris M, Kass F, Reese DE, A Non-Invasive Blood-Based Combinatorial Proteomic Biomarker Assay to Detect Breast Cancer in Women Under the Age of 50 Years, Clinical Breast Cancer (2017), doi: 10.1016/j.clbc.2017.05.004. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Title Page Original Study

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Ana P Lourenco2,j Kasey L. Benson1, a Meredith C. Henderson1,b Michael Silver1,c Elias Letsios1,d Quynh Tran1,e Kelly J. Gordon1,f Sherri Borman1,g Christa Corn1,h Rao Mulpuri1,i Wendy Smith2,k Josie Alpers3,l Carrie Costantini4,m Nitin Rohatgi5,n Rebecca Yang6,o Ali Haythem7, p Shah Biren7,q Michael Morris8,r Fred Kass9, s David E. Reese1*

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A Non-Invasive Blood-Based Combinatorial Proteomic Biomarker Assay to Detect Breast Cancer in Women Under the Age of 50 Years.

*Corresponding author: David E Reese, Ph.D., Provista Diagnostics 55 Broad Street, 18th floor New York, NY 10004, (212) 202-3175, and [email protected]

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1. Provista Diagnostics, 55 Broad Street, 18th floor New York, NY 10004, (212) 202-3175

a. b. c. d. e. f. g. h. i.

[email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected]

2. Brown University and Rhode Island Hospital, 93 Eddy Street Providence, RI 02903

j. [email protected] k. [email protected] Page 1 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer 3. Avera Cancer Institute, 1000 E. 23rd Street Suite 320 Sioux Falls, SD 57105

l. [email protected] 4. Scripps Cancer Clinic, 4020 Fifth Ave, MER401 San Diego, CA 92103 m. [email protected] 5. Sutter Institute for Medical Research, 2801 Capitol Ave Suite 400 Sacramento, CA 95816

n. [email protected] o. [email protected]

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6. Lahey Clinic, Lahey Medical Center, One Essex Center Drive Peabody, MA 01960 7. Henry Ford Hospital & Health Network, 2799 W. Grand Blvd Detroit, MI 48202

p. [email protected] q. [email protected]

8. Banner Research, 901 E. Willetta St. Room 209 Phoenix, AZ 85259

r. [email protected]

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9. Sansum Clinic, 540 West Pueblo St Santa Barbara, CA 93015

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s. [email protected]

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer MICROABSTRACT To improve breast cancer diagnosis, two prospective clinical trials were conducted to test (n=351) and validate (n=210) Videssa® Breast. If used in conjunction with imaging,

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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

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decisions for women under age 50.

ABSTRACT

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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, two prospective clinical trials were conducted to develop a blood-based

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Combinatorial Proteomic Biomarker Assay (Videssa® Breast) to accurately detect BC and reduce false positives (FP) from suspicious imaging findings.

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Patients and Methods:

Provista-001 and Provista-002 (cohort one) enrolled BI-RADS 3 or 4 women aged under

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50 years. Serum was evaluated for 11 serum protein biomarkers and 33 tumorassociated 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).

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer 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

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sensitivity and specificity were 66.7% and 81.5%, respectively. The negative predictive value (NPV) was high in both sets (99.3% and 98.8%, respectively). Videssa® Breast performance in the combined training and validation set was 99.1% NPV, 87.5%

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sensitivity, 83.8% specificity, and 25.2% positive predictive value (PPV); BC prevalence=5.87%). Overall, imaging resulted in 341 participants receiving follow-up

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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< 0.00001).

Conclusions:

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Videssa® Breast can effectively detect BC when used in conjunction with imaging and can substantially reduce unnecessary medical procedures, as well as provide

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assurance to women that they likely do not have BC.

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer KEYWORDS: Tumor-associated Autoantibodies, Breast Cancer, Biomarker, Imaging,

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Proteomics, Serum Proteins

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer INTRODUCTION Breast cancer (BC) is predicted to be the second leading cause of cancer deaths in U.S. women; approximately 232,000 cases of invasive breast cancer and 60,000 cases of

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ductal carcinoma in situ (DCIS) are diagnosed and 40,000 deaths occur annually1. However, if diagnosed early in a localized state, five-year survival rates are >98%2.

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Imaging [including mammography, ultrasound (US), magnetic resonance imaging (MRI), and 3-D tomosynthesis] is the gold standard for BC detection. It has been suggested

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that imaging ambiguity could be mitigated by the combination of a proteomic assay3. When imaging results are questionable (e.g., Breast Imaging-Reporting and Data System [BI-RADS] categories 3 or 4), NCCN guidelines recommend that BI-RADS 3 patients are followed with re-imaging at six months, while BI-RADS 4 patients are

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recommended for biopsy4. Confounding factors (e.g., breast density, prior biopsy, and lesion size) may limit the effectiveness of imaging5-7. Women with high breast density have a greater incidence of BC compared to women with low density5, 8. Despite recent

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advances in imaging, the rates of false positives (FPs) and false negatives (FNs) represent a significant problem in the early diagnosis of BC9-12. The sensitivities and

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specificities of various imaging modalities and/or their various combinations widely range from 50 percent upwards13-17. A comprehensive study by Berg et al. evaluated the supplemental cancer detection yield of ultrasound or MRI, when used in addition to mammography, in a large cohort of women (n = 2809, 21 sites) at elevated risk for breast cancer15. In this study, sensitivity ranged from 52 percent upwards and specificity ranged from 65 percent upwards, depending on the imaging modality used. The

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer addition of screening ultrasound or MRI to mammography resulted in more cancers being detected, but there was also an increase in the number of false positives15.

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In recent years, the role of protein biomarkers in the detection of BC has undergone a major shift from investigational use to evaluation of prognostic value for a given BC subtype11. With the discovery of key protein biomarkers and protein signatures for BC,

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proteomic technologies are currently poised to serve as an ideal diagnostic adjunct to imaging3, 18. Previous research studies have shown that breast tumors are associated

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with systemic changes in both serum protein biomarkers (SPB) and tumor-associated autoantibodies (TAAb)19-27. A very limited number of protein biomarkers, such as the estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (Her2/Neu), cancer-associated antigens (CA27.29 and CA15-3), and

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carcinoembryonic antigen (CEA) are currently used for prognosis and treatment monitoring, but their utility in detecting early BC has not been confirmed28, 29. In addition, several studies have demonstrated the potential use of a new set of protein biomarkers,

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TAAb, in early BC detection30, 31. Given the complexity and heterogeneity of BC, the use of individual protein biomarkers has lacked sensitivity and specificity; a combinatorial

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biomarker approach may be warranted to ensure the greatest success in detecting BC3.

Therefore, to address the unmet needs of the current BC detection paradigm in patients under the age of 50 years assessed as BI-RADS 3 or 4, the aim of this study was to develop a combinatorial proteomic biomarker assay comprised of SPB and TAAb,

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer integrated with patient-specific clinical data, to produce a diagnostic score that could

PATIENTS AND METHODS Study Design and Participants Provista-001

(Clinicaltrials.gov,

NCT01839045)

and

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reliably detect BC following suspicious imaging findings.

Provista-002

(cohort

one;

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Clinicaltrials.gov, NCT02078570), which were sponsored by Provista Diagnostics, enrolled women assessed as either a BI-RADS 3 or 4 at the time of enrollment. All

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imaging modalities, such as mammography, 3-D tomosynthesis, US, and MRI (and any combination of these modalities) were permitted for the assessment of BI-RADS. Participants were enrolled across 13 domestic clinical sites (Table 1 Supplement), and the study was IRB approved. Informed consent was obtained from all study participants

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prior to enrollment and sample collection. Blood samples were collected post-imaging and pre-biopsy for all patients enrolled in this study to minimize any potential collectionassociated variation. Participants who were not diagnosed with BC were followed for six

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months for additional clinical outcomes, which were assessed via additional imaging

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and/or pathology results.

Videssa® Breast results were not shared with clinicians during the trials to ensure that clinical decision-making was unaffected. An overview of the study design is provided in Fig. 1, and the clinical management workflow is summarized in Fig. 2. The study was designed by an external subject-matter expert and the authors, while a third-party Contract Research Organization (CRO) collected and monitored data.

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer

Study Objective The aim of this study was to develop a blood-based diagnostic test to detect BC for use

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in conjunction with imaging to aid healthcare providers make informed decisions on treating young women (under 50 years of age) with difficult-to-assess imaging findings.

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Measurement of Serum Protein Biomarkers (SPB) and Tumor-Associated Autoantibodies (TAAb)

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Serum was evaluated for the concentrations of 11 SPB and for the relative presence/absence of 33 TAAb (listed in Table 2 Supplement). Following informed consent and prior to biopsy, five tubes of blood were collected in a Vacutainer clot tube. Blood was allowed to coagulate for 30 minutes at room temperature, then placed in a

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centrifuge and spun at 1,100 x g for 10–15 minutes. Immediately after centrifugation, a series of aliquots were transferred into 5-mL cryovials, depending on serum yield. Tubes were labeled with a specimen ID number and date, then frozen prior to shipping.

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Samples were batched and shipped by the site to Provista’s laboratory. Upon receipt by

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Provista, cryovials were accessioned and placed immediately into -80 °C for storage.

SPB concentrations were determined using modified electro-chemiluminescent (ECL)based ELISA kits, following manufacturer’s specifications (Meso Scale Discovery (MSD); Rockville, MD)32. Each SPB plate contained six vendor-provided standards (in duplicate) to generate a standard curve. TAAb were detected using an indirect ELISA, which includes binding purified recombinant proteins to standard-bind plates (MSD).

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Proteins were diluted in 1x PBS and coated onto blank plates at a final concentration of 20 ng/well. All recombinant proteins, certified as >80% pure (SDS-PAGE), were purchased from Origene (Rockville, MD) or Abnova (Taiwan). Origene proteins were

tagged and produced in wheat germ cells.

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myc/DDK peptide-tagged and produced in HEK293 cells. Abnova proteins were GST-

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All samples were processed in duplicate both for SPB and TAAb, and mean values were used for data analysis. Appropriate controls (samples with known values,

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standards, and blanks) were included on each plate to monitor the performance of both assays. SPB concentrations were calculated by processing sample and standard data with the MSD Workbench 4.0 software using a weighted, four-parameter, logistic-fit (FourPL) algorithm. TAAb ratio values were determined using the following calculation,

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using normalization parameters modified from Anderson et al.33: (Target MFI – True Target MFI) / Median Sample BKG MFI where Target MFI = mean fluorescence intensity (MFI) of sample plus target, and True

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Target MFI = mean fluorescence intensity of corresponding target protein without

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sample (protein background)

Statistical Analysis

Using the Provista-001 dataset only (Fig. 1), training models to predict the presence or absence of BC were developed using the individual biomarkers (i.e., SPB and TAAb). Due to expectedly weak univariate associations between individual biomarkers based on previous studies34,

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, additional training models with and without participant’s

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer specific clinical data were built iteratively by altering SPB and TAAb features. Multivariable models were built using forward and backward selection methods and varying the alpha for inclusion in (or exclusion from) a model to identify a subset of

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predictors that consistently presented. These multivariable models were optimized by adding and subtracting additional markers iteratively, until a final model was created that met minimum performance criteria. Area under Receiver Operating Characteristic

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(AUROC) was used to determine model performance in regards to sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV); Table 1

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provides definitions for these terms. Confidence intervals (CIs) were reported as twosided binomial 95% CIs.

A logistic regression model, which included participant age, was created to combine a

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panel of transitions that were modeled to calculate a diagnostic score (Ds) between 0 and 1 for each sample, as follows:







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 = 1⁄⌈ 1 +  − −  ∗  −   ∗  −   ∗  !⌉

where Ds is diagnostic score, alpha is intercept, Beta1 is coefficient for age, Betai is coefficient for SPB, and betaj is coefficient for TAAb. Samples with Ds equal to or greater than the reference value (cutoff) were considered clinically positive (BC), and samples that were less than the reference value were clinically negative (Benign).

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer This training model was tested using the validation set, Provista-002 cohort one (Fig. 1). This model was then applied to the combined training (Provista-001) and validation

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(Provista-002) sets to evaluate its performance in a larger dataset.

Participant characteristics were summarized with medians and inter-quartile ranges (IQR) for numerical data, or sums and percentages for categorical data. To determine

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balance between sets, Wilcoxon Rank-Sum tests were used for continuous variables and chi-square tests or Fisher’s exact tests were used for categorical data, where

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applicable. All analyses were conducted using SAS (version 9.3; SAS, Cary, NC).

RESULTS Study Population

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The Provista-001 study enrolled 351 women under the age of 50 years at eight sites (Table 1 Supplement) across the U.S., who were assessed as either BI-RADS category 3 or 4 at the time of enrollment. Blood samples were collected post-imaging

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and pre-biopsy for all patients enrolled in this study to minimize any potential collectionassociated variation (Fig. 2). Of the 351 participants enrolled, samples collected from

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12 participants had to be excluded from analysis (Fig. 1), resulting in 339 participants being analyzed for biomarker expression (Table 2). Of these 339, 313 participants were diagnosed with a benign breast condition, either by biopsy during the initial visit or by additional imaging performed at the six-month follow-up. Twenty-six (26) participants were diagnosed as having invasive breast cancer (BC) (18) or DCIS (8); thus, cancer incidence was 7.7% in Provista-001 (26/339, Table 2). Of these, 24 participants were

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer diagnosed at the primary visit and an additional two participants were diagnosed during the six-month follow-up visit.

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The Provista-002 study enrolled 210 women under the age of 50 years at ten sites (Table 1 Supplement), of which five overlapped with the Provista-001 study, across the U.S. who were assessed as either BI-RADS category 3 or 4 at the time of enrollment.

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Of the 210 participants enrolled, samples collected from 4 participants were excluded from analysis (Fig. 1), leaving 206 participants that could be analyzed for biomarker

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expression (Table 2). Of these 206, there were 200 participants diagnosed with a benign breast condition (BBC), either by biopsy during the initial visit or by additional imaging performed at the six-month follow-up visit. Six participants were diagnosed as having BC: BC (2) or DCIS (4). Thus, cancer incidence was 2.9% in the Provista-002

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cohort one dataset (6/206, Table 2). Of these, all participants were diagnosed at the primary visit, and no additional BC cases were diagnosed during six-month follow-up

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visit.

Serum samples were collected post-BI-RADS assessment but prior to biopsy (Fig. 2).

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Samples were evaluated for SPB and TAAb expression as described in the Methods section, and these biomarker expression data were used for Videssa® Breast model development.

Videssa® Breast Model Development

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Previously published results34 suggested several SPB (e.g., OPN, FASL, TNF-α, CEA, IL12, HGF, and VEGFD) and TAAb (e.g., FRS3, RAC3, HOXD1, GPR157, ZMYM6, EIF3E, CSNK1E, ZNF510, BMX, SF3A1, and SOX2) that demonstrated a modest ability

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to distinguish benign from BC patients. Thus, models were developed using these preselected biomarkers23, 34 (Table 2 Supplement). These pre-selected markers were univariately evaluated in benign and BC populations; representative box-plots using

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both age- and BI-RADS-matched samples are provided in Figure 1 Supplement. Since this preselected group of markers was developed utilizing retrospectively-collected

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specimens from one group of patients from a single clinical site24, 34, the inclusion of additional markers was predicted to improve the detection of BC. Additionally, as the prospective cohorts described in this study only included patients with BI-RADS category 3 and 4 diagnoses, these samples represent an intended-use population

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different than previous studies, which included additional BI-RADS groups 0, 1, 2, 3, 4, and 5 and was not limited to patients aged under 50. The collection of samples from a separate intended-use population further added to the rationale of altering markers to

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model development.

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To develop models that could detect the presence or absence of BC in women under the age of 50 years scored as BI-RADS category 3 or 4, a multi-step process was utilized whereby biomarker expression and clinical characteristics of the Provista-001 participants (n = 339) were analyzed using logistic regression modeling36,

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, as

described in the Methods.

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Several training models to detect BC were assessed using different protein biomarker combinations (SPB and TAAb) with and without demographics and clinical characteristics, such as age, race, family history, and smoking status (multiple other

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characteristics were tested but did not show significant differences). Models involving either SPB or TAAb alone did not provide statistically significant results (Fig. 2 Supplement). The SPB model alone (six markers) demonstrated high sensitivity

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(88.5%) and the TAAb model alone (ten markers) demonstrated high specificity (82.5%); these findings confirmed our previous results

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; therefore, we deduced that

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combining SPB and TAAb biomarkers would result in a model with higher specificity and higher sensitivity than either biomarker type alone. Starting with our retrospective model34, combinatorial training models were built using both forwards and backwards selection methods to identify a subset of markers that consistently entered models at

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various alphas. We then iteratively included or excluded SPB (11) and TAAb (33) markers to optimize sensitivity and specificity while being as parsimonious as possible. No other participant demographic data, except for age, improved the model

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performance.

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The training model consisted of 18 protein biomarkers (eight SPB and 10 TAAb) identified from the original set of biomarkers evaluated in this study (Table 2 Supplement). The performance of this model in the training dataset (Provista-001; n = 339) was 92.3% for sensitivity, 85.3% for specificity, 99.3% for NPV, and 34.3% for PPV (Fig. 3).

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Provista-002 (cohort one; n = 206) used an independent validation set. The training model was locked (i.e., biomarker composition, coefficient values, and cut-off point used to detect the presence or absence of cancer) before clinical outcome data for the

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validation set (Provista-002 cohort one) was received from the blinded data broker. The training model developed using the training set (n = 339) was applied to the validation set (n = 206) to detect BC. A summary of the performance data is provided in Figure 3.

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The performance of Videssa® Breast when prospectively applied to the validation set (Provista-002 cohort one; n = 206) demonstrated a 66.7% sensitivity, 81.5% specificity,

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98.8% NPV, and 9.8% PPV (Fig. 3). NPV and specificity are measures of the number of true negative cases within a population. The NPV (98.8%) for Videssa® Breast remained extremely high for the validation cohort, which was comparable to the NPV observed for the training set (99.3%, p=0.3023). The same is true for specificity, which

validation, p=0.24459).

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decreased only slightly in the validation cohort (85.3% in training and 81.3% in

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All benign cases were included in both the training and validation sets, regardless of whether the subject received a biopsy. Of the benign samples collected for this study,

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43% were presumed to be benign (i.e., no pathological confirmation by biopsy, Fig. 2), and this included both BI-RADS category 3 and 4 subjects, irrespective of NCCN guidelines, which recommend that BI-RADS 3 patients are followed with re-imaging at six months, while BI-RADS 4 patients are recommended for biopsy4. Following model development, performance was specifically evaluated in the confirmed benign (i.e., by biopsy; Fig. 2) and BC sub-groups (Table 3 Supplement). Sensitivity was unaffected

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer and specificity was increased in both training and validation sets, which could be due to the reduction in FPs (Fig. 1 versus Table 3 Supplement). NPV slightly decreased (p=0.82287) and PPV increased (p=0.00101) in this subset analysis. These results

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demonstrate consistent model performance within the intended-use population (where the subject may not undergo biopsy), as well as in the clinically-confirmed population

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(Fig. 2).

Due to low cancer prevalence, both the training (Provista-001) and validation (Provista-

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002) data sets were combined to assess overall Videssa® Breast performance (combined BC prevalence=5.87%). Videssa® Breast correctly diagnosed 28 of the 32 participants with BC (Table 4 Supplement), with a NPV of 99.1%, sensitivity of 87.5%, specificity of 83.8%, PPV of 25.2%, and an AUC of 0.8477 (Fig. 3). Because of the

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higher BC prevalence in the training set, it is possible that the sensitivity may suffer from optimism bias; however, specificity and NPV were not impacted. Of note, there were two cases that Videssa® Breast identified as being positive at enrollment (Sample

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number 6043 [Fig. 4, upper panel] and Sample number 5007 [Table 4 Supplement]), and these cases were not recommended for biopsy after imaging. Subsequent imaging

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at follow-up recommended these cases for biopsy, and biopsy subsequently confirmed that cancer was present. Thus, Videssa® Breast may provide additional diagnostic power to detect early cases of BC.

Detailed timelines for two subjects are shown in Figure 4. Subject 6043 was assessed as BI-RADS 3 on initial visit and no biopsy was performed. At the 6-month follow-up

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer visit, the subject was assessed as BI-RADS 4 and a subsequent biopsy revealed a high-grade DCIS. Videssa Breast® was run on serum drawn at the initial visit and at the 6-month follow-up visit. Both samples resulted in a positive Videssa® Breast test result,

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indicating that this test had correctly identified the subject as likely having BC at the initial visit, when standard assessments failed to detect the presence of BC. Another case, Subject 2004, was assessed as BI-RADS 4 at the initial visit; this subject’s

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Videssa® Breast test result was positive. The subject’s biopsy revealed a low-grade DCIS; however, an additional biopsy 14 days later revealed a grade-2 BC, indicating

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that both imaging and Videssa® Breast correctly identified the subject as likely having breast adenocarcinoma. Indeed, in this study, when imaging and Videssa® concord, 100% detection was observed at the earliest stage.

Procedure Rate

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Comparison of Videssa® Breast to Imaging-Based Assessment on Medical

The imaging modalities used to diagnose BC in participants enrolled in this clinical trial

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included diagnostic mammogram, US, diagnostic mammogram combined with US, tomosynthesis, and/or MRI. The FP rate (defined as the identification of a benign breast

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condition by biopsy) of imaging at enrollment was compared to the potential FP rate for Videssa® Breast (Table 3). Imaging contributed to 339 participants receiving procedures and detected 30 cancers at enrollment, resulting in 309 FPs (91% FP rate). If Videssa® Breast had been used in assessment at the time of enrollment, it would have recommended 111 participants receive procedures, of which 83 would have resulted in a FP (75% FP rate). These data suggest that Videssa® Breast, when used in

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer conjunction with imaging, can reduce unnecessary biopsies by up to 67% (p ≤ 0.00001) compared to imaging modalities alone.

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DISCUSSION

In women with questionable or equivocal imaging findings, it is often difficult to determine whether to proceed with biopsy, further image, or reassess at a later time. Of

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particular concern is the high FP rate associated with BI-RADS 3 or 4 patients, who are either followed with repeat imaging assessment at six months or recommended for

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biopsy, respectively. The economic impact of FPs is multiplicative due to the cascade of follow-up diagnostic procedures, such as additional imaging/biopsy, resulting in cumbersome and costly follow-ups. In addition, these follow up procedures may impact the quality of life for the patient (e.g., missing work and family time). Furthermore, scar

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tissue remaining from biopsy can pose additional complications for future imaging. Perhaps more importantly, the anxiety and negative impact of a positive diagnosis (false or not) on the quality of life for patients is significant and may impact further compliance.

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Thus, there is a clear need for a diagnostic test that reduces FPs and provides a tool for

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clinicians to confirm negative findings.

Based on previously published studies suggesting the clinical value of SPB and TAAb3, we conducted a prospective study to determine if the diagnosis of BC could be improved through the complementary use of a combinational proteomic biomarker assay with imaging. Videssa® Breast was successfully developed and validated by combining eight SPB and 10 TAAb with participants’ demographic and clinical data; the

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer NPV was 98.8%, sensitivity was 66.7%, specificity was 81.5%, and PPV was 9.8% in the validation set.

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We note a decrease in clinical sensitivity between the training and validation sets. While possibly due to over-fitting of the training model, we feel the bulk of the reduction in Videssa® Breast sensitivity in the validation cohort is likely due to the marked reduced

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BC prevalence in the Provista-002 cohort as compared to the training set (7.7% versus 2.9% for Provista-001 and Provista-002, respectively; Table 2). This reduction may be

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due to changes in imaging assessments (as a means of decreasing imaging-related FN rates) as this has now been observed in greater than 1,350 patients enrolled in Provista’s prospective clinical trials over a period of three years. Other large studies have also seen a decrease in BI-RADS category 3 use and have witnessed increased

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BI-RADS category 4 assessment, likely due to medicolegal considerations. Since sensitivity and PPV were impacted, whereas specificity and NPV were not, it is likely that the decrease in sensitivity and PPV were attributed to low BC prevalence and not

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over-fitting.

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Of the 32 cancers detected by imaging, two patients were not recommended for followup procedures at enrollment, whereas Videssa® Breast would have recommended these patients for follow-up. Administration of Videssa® Breast would have significantly reduced the number of participants receiving procedures prescribed by imaging by 67% (p < 0.0001). Thus, based on these data, Videssa® Breast can reduce the number of medical procedures for low- or intermediate-risk BC patients under the age of 50.

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Conversely, if patients demonstrate positive Videssa® Breast results or if additional imaging suggests the presence of BC, further monitoring or biopsy may be warranted.

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A significant limitation of this study is that the confirmation of BC through biopsy is subject to sampling bias. A patient can be diagnosed with BC only if the biopsy comes from a region of the breast that includes cells with abnormal lesions. If a biopsy is not

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performed or is performed in a location absent of neoplasm, BC could be incorrectly diagnosed as a benign breast condition. For example, one of the participants in this

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study, Subject 1021, was assessed as BI-RADS 4 at the initial visit and underwent a cyst aspiration. Videssa® Breast testing on both the initial serum sample and the 6month follow-up serum sample revealed high levels of p53 TAAb, which is highly indicative of cancer based on previous literature38. Upon further review of the patient’s

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medical history, it was noted that this participant had two second-degree relatives and one first-degree relative diagnosed with breast cancer. There is the possibility that this participant had early breast cancer at the initial visit. When patients have both a positive

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protein signature for Videssa® Breast and a family history of breast cancer, this may warrant additional monitoring—thus, Videssa® Breast appears to have utility in aiding

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physicians to more effectively manage these patients.

Another limitation of this study is that patients were followed for six months rather than a 12-month follow-up. A six-month follow-up period may not be sufficient to identify all cancers in BI-RADS 3 and 4 patients; an additional 12-month follow-up may have yielded an increased cancer incidence. Therefore, it is possible that a subset of the

Page 21 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Videssa® Breast FPs are pre-clinical BCs that have yet to be detected. This study also relied on multiple imaging modalities to detect BC. The differential diagnostic methodologies used for patients in this clinical trial may have impacted the performance

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of Videssa® Breast, as these methodologies widely vary in their sensitivity and specificity15.

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An additional study limitation is the BC prevalence in the Provista-002 cohort one set. Whereas a BC prevalence of 20% was expected based on literature39, the actual

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prevalence was 7.7% for Videssa® Breast in Provista-001 (training set) and 2.9% for Provista-002 cohort one (validation set). Despite a reduction in sensitivity for the validation set (likely due to the reduction in BC prevalence from 7.7% to 2.9% for the training and validations sets, respectively), Videssa® Breast continued to demonstrate

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specificity (81.5%) and NPV (99.1%) in this independent validation set. These data provide quantifiable metrics supporting that Videssa® Breast can reduce diagnostic uncertainty for providers, based on Videssa® Breast specificity, and provide assurance

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to patients, based on Videssa® Breast NPV, that they do not have BC. Thus, Videssa® Breast could ultimately reduce the number of unnecessary medical procedures (i.e.,

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follow-up imaging and biopsies) and alleviate the stress of not knowing whether an abnormal imaging finding is a BC. Additional studies are being conducted to determine how to best maximize sensitivity (an optimal model for biopsy rule-out) and potential medical cost-savings associated with Videssa® Breast in women over age 50.

Conclusion

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer In summary, this study describes the development of an innovative, non-invasive, actionable tool to detect BC in women under the age of 50. To our knowledge, this is the first prospective study of a proteomic panel (composed of SPB and TAAb) being

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used in the precise detection of BC in woman with questionable imaging findings. Videssa® Breast can be used concomitant with imaging to help guide the management of women under the age of 50 with challenging imaging findings. The test exhibited

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consistently high specificity and NPV in all test sets in a prospective manner, further supporting its clinical use in this intended-use population (BI-RADS 3 or 4). Further

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studies evaluating whether Videssa® Breast performs similarly in broader age ranges, high-risk populations, and additional BI-RADS-defined patients will be important in assessing the totality of its clinical utility and expanding the clinical use of this personalized, precise, proteomic clinical assay. Additional model development is

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biopsy rule-out test.

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currently being conducted to maximize sensitivity, thereby increasing clinical utility as a

CLINICAL PRACTICE POINTS

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This study describes the development of an innovative, non-invasive, actionable tool, Videssa® Breast, to detect breast cancer in women of low- or intermediate risk (BIRADS 3 or 4) and under the age of 50. To our knowledge, this is the first prospective study of a proteomic panel (composed of serum protein biomarkers and tumorassociated autoantibodies) being used in the precise detection of breast cancer in woman with questionable imaging findings. The test exhibited consistently high specificity and negative predictive value in all test sets in a prospective manner, further Page 23 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer supporting its clinical use in this intended-use population. In women with questionable or equivocal imaging findings, it is often difficult to determine whether to proceed with biopsy, further image, or reassess at a later time. Of particular concern is the high false-

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positive rate associated with BI-RADS 3 or 4 patients, who are either followed with repeat imaging assessment at six months or recommended for biopsy, respectively. If used prospectively in conjunction with imaging, Videssa® Breast could have reduced

SC

unnecessary biopsies by up to 67%, compared with standard imaging modalities (p < 0.0001). Therefore, this study supports the use of Videssa® Breast, concomitant with

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imaging, to help guide the management of women under the age of 50 with challenging imaging findings.

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ABBREVIATIONS Breast cancer

BI-RADS

Breast Imaging-Reporting and Data System

DCIS

Ductal carcinoma in situ

FN

False negative

FP

False positive

NPV PPV

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MRI

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BC

Magnetic resonance imaging Negative predictive value

Positive predictive value

SPB

Serum protein biomarker

TAAb

Tumor-associated autoantibody

Page 24 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer True negative

TP

True positive

US

Ultrasound

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EP

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TN

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer DECLARATIONS Ethics, consent and permissions and consent to publish The Provista-001 and Provista-002 studies both received IRB approval prior to initiation.

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The respective IRBs for each site are provided in Table 1 Supplement. All participants were provided with informed consent and agreed to study participation prior to sample

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collection.

Availability of data and materials

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The datasets supporting the conclusions of this article are included within this article (Table 4 Supplement).

Competing Interests

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All authors who are active employees of Provista Diagnostics own stock of the company. Investigators not associated with Provista Diagnostics Inc. have no

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disclosures.

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Authors’ Contributions

Study Conception and Design – David E. Reese, Michael Silver Collection and Assembly of Data – Elias Letsios, Kasey L. Benson, Meredith C. Henderson, Quynh Tran, Sherri Borman Model Development: Michael Silver Data analysis and interpretation: Michael Silver, Meredith C. Henderson, Rao Mulpuri, and David E. Reese Page 26 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Manuscript writing: All authors

ACKNOWLEDGEMENTS

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Final Approval of manuscript: All authors

The authors wish to thank the research staff members at the clinical trial sites for

their valuable contribution to this work.

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Funding Sources

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helping conduct the study. The authors would also like to thank all trial participants for

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This research was funded by Provista Diagnostics.

Page 27 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer REFERENCES

7.

8. 9. 10. 11. 12. 13.

14.

15.

16. 17.

18.

RI PT

SC

6.

M AN U

5.

TE D

4.

EP

3.

Cancer Facts & Figures 2015. Atlanta, GA: American Cancer Society; 2015. Breast Cancer Facts & Figures 2011-2012. American Cancer Society. Atlanta: American Cancer Society, Inc.2011. Hollingsworth A, Reese D. Potential use of biomarkers to augment clinical decisions for the early detection of breast cancer. Oncology and Hematology Review. 2014;10:103-109. Bevers TB, Anderson BO, Bonaccio E, et al. NCCN clinical practice guidelines in oncology: breast cancer screening and diagnosis. Journal of the National Comprehensive Cancer Network : JNCCN. 2009;7:1060-1096. Boyd NF, Guo H, Martin LJ, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356:227-236. Carney PA, Miglioretti DL, Yankaskas BC, et al. Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann Intern Med. 2003;138:168-175. van Breest Smallenburg V, Duijm LE, Voogd AC, Jansen FH, Louwman MW. Mammographic changes resulting from benign breast surgery impair breast cancer detection at screening mammography. Eur J Cancer. 2012;48:2097-2103. Wang AT, Vachon CM, Brandt KR, Ghosh K. Breast density and breast cancer risk: a practical review. Mayo Clinic proceedings. 2014;89:548-557. Pace LE, Keating NL. A systematic assessment of benefits and risks to guide breast cancer screening decisions. Jama. 2014;311:1327-1335. Bleyer A, Welch HG. Effect of three decades of screening mammography on breastcancer incidence. The New England journal of medicine. 2012;367:1998-2005. Cole K, Tabernero M, Anderson KS. Biologic characteristics of premalignant breast disease. Cancer biomarkers : section A of Disease markers. 2010;9:177-192. Campbell JB. Breast cancer-race, ethnicity, and survival: a literature review. Breast Cancer Res Treat. 2002;74:187-192. Kim SA, Chang JM, Cho N, Yi A, Moon WK. Characterization of breast lesions: comparison of digital breast tomosynthesis and ultrasonography. Korean journal of radiology. 2015;16:229-238. Knuttel FM, Menezes GL, van den Bosch MA, Gilhuijs KG, Peters NH. Current clinical indications for magnetic resonance imaging of the breast. Journal of surgical oncology. 2014;110:26-31. Berg WA, Zhang Z, Lehrer D, et al. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. Jama. 2012;307:1394-1404. Berg WA. Tailored supplemental screening for breast cancer: what now and what next? AJR. American journal of roentgenology. 2009;192:390-399. Lei J, Yang P, Zhang L, Wang Y, Yang K. Diagnostic accuracy of digital breast tomosynthesis versus digital mammography for benign and malignant lesions in breasts: a meta-analysis. European radiology. 2014;24:595-602. Saving Women's Lives: Strategies for improving breast cancer detection and diagnosis. Washington, DC: The National Academies Press; 2005.

AC C

1. 2.

Page 28 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer

24.

25.

26. 27.

28.

29.

30. 31.

32.

33.

RI PT

SC

23.

M AN U

22.

TE D

21.

EP

20.

Moazzezy N, Farahany TZ, Oloomi M, Bouzari S. Relationship between preoperative serum CA 15-3 and CEA levels and clinicopathological parameters in breast cancer. Asian Pacific journal of cancer prevention : APJCP. 2014;15:1685-1688. Dehqanzada ZA, Storrer CE, Hueman MT, et al. Assessing serum cytokine profiles in breast cancer patients receiving a HER2/neu vaccine using Luminex technology. Oncol Rep. 2007;17:687-694. Lyon DE, McCain NL, Walter J, Schubert C. Cytokine comparisons between women with breast cancer and women with a negative breast biopsy. Nurs Res. 2008;57:5158. Rykala J, Przybylowska K, Majsterek I, et al. Angiogenesis markers quantification in breast cancer and their correlation with clinicopathological prognostic variables. Pathol Oncol Res. 2011;17:809-817. Anderson KS, Sibani S, Wallstrom G, et al. Protein microarray signature of autoantibody biomarkers for the early detection of breast cancer. Journal of proteome research. 2011;10:85-96. Benoy IH, Salgado R, Van Dam P, et al. Increased serum interleukin-8 in patients with early and metastatic breast cancer correlates with early dissemination and survival. Clinical cancer research : an official journal of the American Association for Cancer Research. 2004;10:7157-7162. Kovacs E. Investigation of interleukin-6 (IL-6), soluble IL-6 receptor (sIL-6R) and soluble gp130 (sgp130) in sera of cancer patients. Biomed Pharmacother. 2001;55:391-396. Kovacs E. The serum levels of IL-12 and IL-16 in cancer patients. Relation to the tumour stage and previous therapy. Biomed Pharmacother. 2001;55:111-116. Heer K, Kumar H, Read JR, Fox JN, Monson JR, Kerin MJ. Serum vascular endothelial growth factor in breast cancer: its relation with cancer type and estrogen receptor status. Clin Cancer Res. 2001;7:3491-3494. Harris L, Fritsche H, Mennel R, et al. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2007;25:5287-5312. Fuzery AK, Levin J, Chan MM, Chan DW. Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Clinical proteomics. 2013;10:13. Lacombe J, Mange A, Solassol J. Use of autoantibodies to detect the onset of breast cancer. Journal of immunology research. 2014;2014:574981. Yahalom G, Weiss D, Novikov I, et al. An Antibody-based Blood Test Utilizing a Panel of Biomarkers as a New Method for Improved Breast Cancer Diagnosis. Biomarkers in cancer. 2013;5:71-80. Kruse N, Schulz-Schaeffer WJ, Schlossmacher MG, Mollenhauer B. Development of electrochemiluminescence-based singleplex and multiplex assays for the quantification of alpha-synuclein and other proteins in cerebrospinal fluid. Methods. 2012;56:514-518. Anderson KS, Cramer DW, Sibani S, et al. Autoantibody signature for the serologic detection of ovarian cancer. J Proteome Res. 2015;14:578-586.

AC C

19.

Page 29 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer

RI PT

39.

SC

38.

M AN U

37.

TE D

36.

EP

35.

Henderson MC, Hollingsworth AB, Gordon K, et al. Integration of Serum Protein Biomarker and Tumor Associated Autoantibody Expression Data Increases the Ability of a Blood-Based Proteomic Assay to Identify Breast Cancer. PLoS One. 2016;11:e0157692. Jesneck JL, Mukherjee S, Yurkovetsky Z, et al. Do serum biomarkers really measure breast cancer? BMC cancer. 2009;9:164. Friedman JH, Tibshirani R. Additive logistic regression: a statistical view of boosting. The Annals of Statistics. 2000;28:337-407. Dettling M, Buhlmann P. Boosting for tumor classification with gene expression data. Bioinformatics. 2003;19:1061-1069. Soussi T. p53 Antibodies in the sera of patients with various types of cancer: a review. Cancer research. 2000;60:1777-1788. Sickles E, D’Orsi CJ, Bassett LW, et al. ACR BI-RADS® Mammography. In: ACR BIRADS® Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology; 2013.

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34.

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ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer FIGURE LEGENDS Figure 1. Provista-001 and Provista-002 Cohort One (Clinicaltrials.gov Identifiers NCT01839045 and NCT02078570). These were prospective clinical trials that enrolled

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BI-RADS 3 and BI-RADS 4 patients.

Figure 2 Clinical Management Flowchart. Serum samples were collected from

SC

participants post-BI-RADS 3 or 4 assessment prior to biopsy in Provista-001 and Provista-002 (cohort one). BI-RADS 3 and 4 patients are differentially managed

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according to SOC, as summarized in this flowchart.

Figure 3. Clinical Performance of Videssa® Breast in Detecting Breast Cancer. (A) Performance data; (B) ROC Curves: Provista-001, Provista-002 cohort one, and

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combined sets.

Figure 4. Timeline progression of two study subjects. Pertinent dates are shown to

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indicate when imaging was performed, when serum was drawn, and when biopsies were performed. Imaging results are shown as BI-RADS assessments and Videssa

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Breast® outcomes are provided.

Page 31 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer TABLES Table 1. Glossary of Diagnostic Terms Used to Assess Videssa® Breast Performance.

False Positive (FP) Rate (1-specificity) Positive Predictive Value (PPV)

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EP

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Negative Predictive Value (NPV)

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False Negative (FN) Rate (1-sensitivity)

SC

Specificity (True Negatives, TN)

The proportion of subjects with the disease who had a positive test. Sensitivity = (True Positives) ÷ (True Positives + False Negatives) The proportion of subjects without the disease who had a negative test. Specificity = (True Negatives) ÷ (True Negatives + False Positives) The proportion of subjects with disease but who had a negative test result. False Negative Rate = (1 - Sensitivity) The proportion of subjects without disease who had a positive test result. False Positive Rate = (1 - Specificity) The proportion of subjects with a positive test result who actually have the disease. PPV = (True Positives) ÷ (True Positives + False Positives) The proportion of subjects with a negative test result who do not have disease. NPV = (True Negatives) ÷ (True Negatives + False Negatives)

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Sensitivity (True Positives, TP)

Page 32 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Table 2. Characteristics of Participants Enrolled in Provista-001 and Provista-002 Studies.

n= Age (Median) Range (Min-Max)

43 (26-49)

Ethnicity Hispanic or Latino Not Hispanic or Latino**

BI-RADS Category

Biopsies Performed

5

2%

162 23 7 5

10%

9

4%

37 302

11% 89%

22 184

11% 89%

139 200

41% 59%

69 137

136 (5) (131)

313

200

Pathology Confirmed Benign

(152)

(121)

Presumed Benign*** Lobular carcinoma in situ‡ (LCIS) Atypical Hyperplasia (AH)

(145) (2) (14)

(76) (2) (1)

EP

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Benign Breast Condition

Breast Cancer (% Incidence)

26

(7.7%)

0.01235b

3%

35

205 (15) (190)

BI-RADS 3 BI-RADS 4

79% 11% 3%

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79% 5% 4%

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3 4

266 18 15

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Caucasian Black/African American Asian American Indian / Alaska Native/Hawaiian / Pacific Islander Other*

0.1248a

44 (26-49)

Race

p-value

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Clinical Study Provista-001 Provista-002 Training Set Validation Set 339 206

33% 67%

0.94362b

0.07679b

0.20199b

0.01865b

(2.9%)

6

Invasive carcinoma (BC)

(18)

(2)

Ductal carcinoma in situ (DCIS)

(8)

(4)

0.81838b

†No detailed information on BI-RADS statuses reported; *Multicultural or not reported; **Includes participants that did not report ethnicity; ***Presumed all non-cancer participants to be benign; ‡LCIS participants were categorized as non-cancer (benign); aStatistical significance assessed by Wilcoxon rank b sum test; Statistical significance assessed by Fisher’s exact test

Page 33 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Table 3. Effect of Videssa® Breast on Rate of Medical Interventions when Used as an Adjunct to Imaging.

339

30

Combined Imaging*

(0) (30)

(19) (320)

Videssa® Breast

111

28 (37) (74)

BI-RADS 3 BI-RADS 4

% Reduction in FP‡

p-valuea

67%

<0.0001

309 (19) (290) 83

(1) (27)

(36) (47)

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BI-RADS 3 BI-RADS 4

False Positives† (FP)

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True Positives (TP)

SC

Total Participants Receiving Procedure(s)**

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*Standard-of care-imaging included diagnostic mammogram, US, diagnostic mammogram and US, tomosynthesis, and/or MRI; **Includes biopsies, cyst aspirations, reduction mammoplasties, lumpectomies, and mastectomies based on enrollment imaging; †Patients who were biopsied not diagnosed with BC on primary visit; ‡Percent reduction is the reduced number of biopsies that would ® a have been recommended by Videssa Breast compared to standard imaging; Statistical significance was assessed using Fisher’s exact test.

Page 34 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer SUPPLEMENTAL FIGURE LEGENDS AND TABLES

Figure 1 Supplement. Univariate Analysis of Pre-selected Biomarkers. Expression

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of individual SPB and TAAb (age- and BI-RADs-matched) were evaluated in the benign and BC populations. *denotes significant differences in expression between groups.

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Figure 2 Supplement. Performance of SPB and TAAb Independent Models.

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Training models consisting of only SPB and only TAAb were evaluated.

Table 1 Supplement. Enrollment Sites.

Provista-001 Provista-001

Rhode Island Hospital

State

IRB

Sioux Falls

South Dakota

Avera Cancer Institute

Providence

Rhode Island

Rhode Island Hospital

Scripps Cancer Clinic

San Diego

California

Scripps Cancer Center

Henry Ford Hospital

Detroit

Michigan

Henry Ford Health System

Sutter Institute for Medical Research

Sacramento

California

Chesapeake IRB

Banner Research

Phoenix

Arizona

Chesapeake IRB

Peabody

Massachusetts

Lahey Hospital Medical Center

California

Chesapeake IRB

Oklahoma

Mercy Health

Lahey Clinic

Sansum Clinic

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Provista-001

Avera Cancer Institute

City

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Provista-001/ Provista-002 Provista-001/ Provista-002 Provista-001/ Provista-002 Provista-001/ Provista-002 Provista-001/ Provista-002

Institution

EP

Clinical Trial

Santa Barbara Oklahoma City

Provista-002

Mercy Oncology Center

Provista-002

St. Joseph’s Hospital

Phoenix

Arizona

Dignity Health St Joseph’s

Provista-002

Sinai Grace Detroit Medical Center

Detroit

Michigan

Western IRB

Provista-002

Mayo Clinic Scottsdale

Scottsdale

Arizona

Mayo IRB

Provista-002

Mayo Clinic Rochester

Rochester

Minnesota

Mayo IRB

Page 35 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Table 2 Supplement.

Serum protein biomarkers (SPB) and tumor-associated

autoantibodies (TAAb) evaluated during this study. Highlighted biomarkers indicate

Protein Class

Protein

Uniprot ID

Full Name from Uniprot

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those included in Videssa® Breast. Uniprot Link

IL-6 IL-8

P05231 P10145

Interleukin-6 Interleukin-8

http://www.uniprot.org/uniprot/P05231 http://www.uniprot.org/uniprot/P10145

SPB SPB

TNF-α IFN-γ

P01375 P15260

http://www.uniprot.org/uniprot/P01375 http://www.uniprot.org/uniprot/P15260

SPB

CEA

P06731

SPB

ErbB2

P04626

SPB SPB

OPN HGF

P10451 P08581

SPB

FasL

P48023

SPB

VEGF-C

P49767

SPB

VEGF-D

O43915

TAAb

ALG10

TAAb

ATF3

Q5BKT41 P18847

TAAb

ATP6AP1

Q15904

TAAb

BAT4 (GPANK1)

O95872

TAAb

BDNF

P23560

Tumor necrosis factor Interferon gamma receptor 1 Carcinoembryonic antigen-related cell adhesion molecule 5 Receptor tyrosine-protein kinase erbB-2 Osteopontin Hepatocyte growth factor receptor Tumor necrosis factor ligand superfamily member 6 Vascular endothelial growth factor C Vascular endothelial growth factor D alpha-1,2glucosyltransferase Cyclic AMP-dependent transcription factor ATF-3 V-type proton ATPase subunit S1 G patch domain and ankyrin repeat-containing protein 1 Brain-derived neurotrophic factor Cytoplasmic tyrosineprotein kinase BMX Normal mucosa of esophagus-specific gene 1 protein Casein kinase I isoform epsilon Cancer/testis antigen 1

BMX

P51813

C15orf48 (NMES1)

Q9C002

CSNK1E

P49674

TAAb

CTAG1A

P78358

TAAb TAAb

CTAG2 CTBP1

O75638 Q13363

TAAb

TAAb

http://www.uniprot.org/uniprot/P06731

http://www.uniprot.org/uniprot/P04626

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EP

AC C

TAAb

SC

SPB SPB

Cancer/testis antigen 2 C-terminal-binding protein 1

http://www.uniprot.org/uniprot/P10451 http://www.uniprot.org/uniprot/P08581 http://www.uniprot.org/uniprot/P48023

http://www.uniprot.org/uniprot/P49767 http://www.uniprot.org/uniprot/O43915 http://www.uniprot.org/uniprot/Q5BKT4 http://www.uniprot.org/uniprot/P18847 http://www.uniprot.org/uniprot/Q15904 http://www.uniprot.org/uniprot/O95872

http://www.uniprot.org/uniprot/P23560 http://www.uniprot.org/uniprot/P51813 http://www.uniprot.org/uniprot/Q9C002

http://www.uniprot.org/uniprot/P49674 http://www.uniprot.org/uniprot/P78358 http://www.uniprot.org/uniprot/O75638 http://www.uniprot.org/uniprot/Q13363

Page 36 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer

EIF3E

P60228

TAAb

FRS3

O43559

TAAb

GPR157

Q5UAW9

TAAb

HOXD1

Q9GZZ0

TAAb

IGFBP2

P18065

TAAb TAAb

MUC1 MYOZ2

P15941 Q9NPC6

TAAb

p53

P04637

TAAb

PDCD6IP

Q8WUM4

TAAb

RAB5A

P20339

TAAb

RAC3

P60763

TAAb TAAb TAAb

SELL SERPINH1 SF3A1

P14151 P50454 Q15459

TAAb

SLC33A1

O00400

TAAb

SOX2

P48431

TAAb

TFCP2

Q12800

TAAb

TRIM32

TAAb

UBAP1

TAAb

Cellular tumor antigen p53 Programmed cell death 6-interacting protein Ras-related protein Rab5A Ras-related C3 botulinum toxin substrate 3 L-selectin Serpin H1 Splicing factor 3A subunit 1 Acetyl-coenzyme A transporter 1 Transcription factor SOX2 Alpha-globin transcription factor CP2 E3 ubiquitin-protein ligase TRIM32 Ubiquitin-associated protein 1 Zinc finger MYM-type protein 6 Zinc finger protein 510

EP Q9NZ09

AC C

TAAb

Q13049

ZMYM6

O95789

ZNF510

Q9Y2H8

http://www.uniprot.org/uniprot/P11182

http://www.uniprot.org/uniprot/P60228

RI PT

TAAb

Lipoamide acyltransferase component of branchedchain alpha-keto acid dehydrogenase complex, mitochondrial Eukaryotic translation initiation factor 3 subunit E Fibroblast growth factor receptor substrate 3 Probable G-protein coupled receptor 157 Homeobox protein HoxD1 Insulin-like growth factor binding protein 2 Mucin-1 Myozenin-2

http://www.uniprot.org/uniprot/O43559 http://www.uniprot.org/uniprot/Q5UAW9 http://www.uniprot.org/uniprot/Q9GZZ0 http://www.uniprot.org/uniprot/P18065

SC

P11182

http://www.uniprot.org/uniprot/P15941 http://www.uniprot.org/uniprot/Q9NPC6

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DBT

TE D

TAAb

http://www.uniprot.org/uniprot/P04637 http://www.uniprot.org/uniprot/Q8WUM4 http://www.uniprot.org/uniprot/P20339 http://www.uniprot.org/uniprot/P60763 http://www.uniprot.org/uniprot/P14151 http://www.uniprot.org/uniprot/P50454 http://www.uniprot.org/uniprot/Q15459 http://www.uniprot.org/uniprot/O00400 http://www.uniprot.org/uniprot/P48431 http://www.uniprot.org/uniprot/Q12800 http://www.uniprot.org/uniprot/Q13049 http://www.uniprot.org/uniprot/Q9NZ09 http://www.uniprot.org/uniprot/O95789 http://www.uniprot.org/uniprot/Q9Y2H8

Page 37 of 38

ACCEPTED MANUSCRIPT Blood-based Videssa® Breast Test to Detect Breast Cancer Clinical performance of Videssa® Breast in subjects where

Table 3 Supplement.

benign diagnosis was confirmed by biopsy. n= Cancer n= Benign Sensitivity Specificity

NPV

PPV

AUC

26

168

92.31%

87.50%

98.66% 53.33% 0.909

Validation

6

124

66.67%

82.26%

98.08% 15.38% 0.669

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Training

AC C

EP

TE D

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SC

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BBC BC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC BBC

EP

PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1 PDX-002-Cohort 1

AC C

206-016 206-018 206-019 206-020 206-021 206-026 206-027 206-028 206-030 206-033 206-034 206-036 206-037 206-038 206-039 206-042 206-044 206-046 206-048 206-052 206-054 206-059 206-064 206-065 206-070 206-074 206-075 206-076 206-079 206-081 206-082 208-001 208-002 208-003 208-008 208-009 208-010 209-003

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TP 28 87.5% 83.8% 99.1% 25.2%

FP 83

TN 430

FN 4

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Total Sens Spec NPV PPV

AC C

Total BC Total BBC

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