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Accepted Manuscript
A Non-Invasive Blood-Based Combinatorial Proteomic Biomarker Assay to DetectBreast Cancer in Women Under the Age of 50 Years
Ana P. Lourenco, Kasey L. Benson, Meredith C. Henderson, Michael Silver, EliasLetsios, Quynh Tran, Kelly J. Gordon, Sherri Borman, Christa Corn, Rao Mulpuri,Wendy Smith, Josie Alpers, Carrie Costantini, Nitin Rohatgi, Rebecca Yang, AliHaythem, 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 CombinatorialProteomic Biomarker Assay to Detect Breast Cancer in Women Under the Age of 50 Years, ClinicalBreast Cancer (2017), doi: 10.1016/j.clbc.2017.05.004.
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Title Page Original Study A Non-Invasive Blood-Based Combinatorial Proteomic Biomarker Assay to Detect Breast Cancer in Women Under the Age of 50 Years. 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* *Corresponding author: David E Reese, Ph.D., Provista Diagnostics 55 Broad Street, 18th floor New York, NY 10004, (212) 202-3175, and reesed@provistadx.com 1. Provista Diagnostics, 55 Broad Street, 18th floor New York, NY 10004, (212) 202-3175
a. bensonk@provistadx.com b. hendersonm@provistadx.com c. silverm@provistadx.com d. letsiose@provistadx.com e. tranq@provistadx.com f. gordonk@provistadx.com g. bormans@provistadx.com h. cornc@provistadx.com i. mulpurir@provistadx.com
2. Brown University and Rhode Island Hospital, 93 Eddy Street Providence, RI 02903 j. alourenco@lifespan.org k. WSmith@lifespan.org
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3. Avera Cancer Institute, 1000 E. 23rd Street Suite 320 Sioux Falls, SD 57105 l. Josie.alpers@avera.org
4. Scripps Cancer Clinic, 4020 Fifth Ave, MER401 San Diego, CA 92103 m. Costantini.carrie@scrippshealth.org
5. Sutter Institute for Medical Research, 2801 Capitol Ave Suite 400 Sacramento, CA 95816 n. rohatgN@sutterhealth.org
6. Lahey Clinic, Lahey Medical Center, One Essex Center Drive Peabody, MA 01960 o. Rebecca.c.yang@lahey.org
7. Henry Ford Hospital & Health Network, 2799 W. Grand Blvd Detroit, MI 48202 p. HALI1@hfhs.org q. birens@rad.hfh.edu
8. Banner Research, 901 E. Willetta St. Room 209 Phoenix, AZ 85259 r. Michael.morris@bannerhealth.com
9. Sansum Clinic, 540 West Pueblo St Santa Barbara, CA 93015 s. fkass@ccsb.org
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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,
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.
ABSTRACT
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
Combinatorial Proteomic Biomarker Assay (Videssa® Breast) to accurately detect BC
and reduce false positives (FP) from suspicious imaging findings.
Patients and Methods:
Provista-001 and Provista-002 (cohort one) enrolled BI-RADS 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).
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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 (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%
sensitivity, 83.8% specificity, and 25.2% positive predictive value (PPV); 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< 0.00001).
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.
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KEYWORDS: Tumor-associated Autoantibodies, Breast Cancer, Biomarker, Imaging,
Proteomics, Serum Proteins
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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
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.
Imaging [including mammography, ultrasound (US), magnetic resonance imaging (MRI),
and 3-D tomosynthesis] is the gold standard for BC detection. It has been suggested
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
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
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
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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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.
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 sub-
type11. With the discovery of key protein biomarkers and protein signatures for BC,
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
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
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,
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
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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integrated with patient-specific clinical data, to produce a diagnostic score that could
reliably detect BC following suspicious imaging findings.
PATIENTS AND METHODS
Study Design and Participants
Provista-001 (Clinicaltrials.gov, NCT01839045) and Provista-002 (cohort one;
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
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
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 collection-
associated variation. Participants who were not diagnosed with BC were followed for six
months for additional clinical outcomes, which were assessed via additional imaging
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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Study Objective
The aim of this study was to develop a blood-based diagnostic test to detect BC for use
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.
Measurement of Serum Protein Biomarkers (SPB) and T umor-Associated
Autoantibodies (TAAb)
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
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.
Samples were batched and shipped by the site to Provista’s laboratory. Upon receipt by
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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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
myc/DDK peptide-tagged and produced in HEK293 cells. Abnova proteins were GST-
tagged and produced in wheat germ cells.
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,
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,
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
Target MFI = mean fluorescence intensity of corresponding target protein without
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, 35, additional training models with and without participant’s
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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
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
(AUROC) was used to determine model performance in regards to sensitivity,
specificity, positive predictive value (PPV), and negative predictive value (NPV); Table 1
provides definitions for these terms. Confidence intervals (CIs) were reported as two-
sided binomial 95% CIs.
A logistic regression model, which included participant age, was created to combine a
panel of transitions that were modeled to calculate a diagnostic score (Ds) between 0
and 1 for each sample, as follows:
�� = 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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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
(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
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
applicable. All analyses were conducted using SAS (version 9.3; SAS, Cary, NC).
RESULTS
Study Population
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
and pre-biopsy for all patients enrolled in this study to minimize any potential collection-
associated variation (Fig. 2 ). Of the 351 participants enrolled, samples collected from
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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diagnosed at the primary visit and an additional two participants were diagnosed during
the six-month follow-up visit.
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.
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
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
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
visit.
Serum samples were collected post-BI-RADS assessment but prior to biopsy (Fig. 2 ).
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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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
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
both age- and BI-RADS-matched samples are provided in Figure 1 Supplement . Since
this preselected group of markers was developed utilizing retrospectively-collected
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
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
model development.
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, 37, as
described in the Methods.
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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
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
(88.5%) and the TAAb model alone (ten markers) demonstrated high specificity
(82.5%); these findings confirmed our previous results 34; therefore, we deduced that
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
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
performance.
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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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
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 .
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,
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
decreased only slightly in the validation cohort (85.3% in training and 81.3% in
validation, p=0.24459).
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,
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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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
demonstrate consistent model performance within the intended-use population (where
the subject may not undergo biopsy), as well as in the clinically-confirmed population
(Fig. 2).
Due to low cancer prevalence, both the training (Provista-001) and validation (Provista-
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
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
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
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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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,
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
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
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.
Comparison of Videssa ® Breast to Imaging-Based Assessment on Medical
Procedure Rate
The imaging modalities used to diagnose BC in participants enrolled in this clinical trial
included diagnostic mammogram, US, diagnostic mammogram combined with US,
tomosynthesis, and/or MRI. The FP rate (defined as the identification of a benign breast
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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conjunction with imaging, can reduce unnecessary biopsies by up to 67% (p ≤ 0.00001)
compared to imaging modalities alone.
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
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
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
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.
Thus, there is a clear need for a diagnostic test that reduces FPs and provides a tool for
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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NPV was 98.8%, sensitivity was 66.7%, specificity was 81.5%, and PPV was 9.8% in
the validation set.
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
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
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
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
over-fitting.
Of the 32 cancers detected by imaging, two patients were not recommended for follow-
up 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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Conversely, if patients demonstrate positive Videssa® Breast results or if additional
imaging suggests the presence of BC, further monitoring or biopsy may be warranted.
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
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
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 6-
month 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
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
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
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
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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
of Videssa® Breast, as these methodologies widely vary in their sensitivity and
specificity15.
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
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
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
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.,
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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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
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
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
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
currently being conducted to maximize sensitivity, thereby increasing clinical utility as a
biopsy rule-out test.
CLINICAL PRACTICE POINTS
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 (BI-
RADS 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 tumor-
associated 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
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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-
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
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
imaging, to help guide the management of women under the age of 50 with challenging
imaging findings.
ABBREVIATIONS
BC Breast cancer
BI-RADS Breast Imaging-Reporting and Data System
DCIS Ductal carcinoma in situ
FN False negative
FP False positive
MRI Magnetic resonance imaging
NPV Negative predictive value
PPV Positive predictive value
SPB Serum protein biomarker
TAAb Tumor-associated autoantibody
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TN True negative
TP True positive
US Ultrasound
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DECLARATIONS
Ethics, consent and permissions and consent to publ ish
The Provista-001 and Provista-002 studies both received IRB approval prior to initiation.
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
collection.
Availability of data and materials
The datasets supporting the conclusions of this article are included within this article
(Table 4 Supplement ).
Competing Interests
All authors who are active employees of Provista Diagnostics own stock of the
company. Investigators not associated with Provista Diagnostics Inc. have no
disclosures.
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
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Manuscript writing: All authors
Final Approval of manuscript: All authors
ACKNOWLEDGEMENTS
The authors wish to thank the research staff members at the clinical trial sites for
helping conduct the study. The authors would also like to thank all trial participants for
their valuable contribution to this work.
Funding Sources
This research was funded by Provista Diagnostics.
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FIGURE LEGENDS
Figure 1. Provista-001 and Provista-002 Cohort One (Clinicaltrials.gov Identifiers
NCT01839045 and NCT02078570). These were prospective clinical trials that enrolled
BI-RADS 3 and BI-RADS 4 patients.
Figure 2 Clinical Management Flowchart. Serum samples were collected from
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
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
combined sets.
Figure 4. Timeline progression of two study subjec ts. Pertinent dates are shown to
indicate when imaging was performed, when serum was drawn, and when biopsies
were performed. Imaging results are shown as BI-RADS assessments and Videssa
Breast® outcomes are provided.
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TABLES
Table 1. Glossary of Diagnostic Terms Used to Asse ss Videssa ® Breast Performance.
Sensitivity (True Positives, TP)
The proportion of subjects with the disease who had a positive test. Sensitivity = (True Positives) ÷ (True Positives + False Negatives)
Specificity (True Negatives, TN)
The proportion of subjects without the disease who had a negative test. Specificity = (True Negatives) ÷ (True Negatives + False Positives)
False Negative (FN) Rate
(1-sensitivity)
The proportion of subjects with disease but who had a negative test result. False Negative Rate = (1 - Sensitivity)
False Positive (FP) Rate
(1-specificity)
The proportion of subjects without disease who had a positive test result. False Positive Rate = (1 - Specificity)
Positive Predictive Value (PPV)
The proportion of subjects with a positive test res ult who actually have the disease. PPV = (True Positives) ÷ (True Positives + False Positives)
Negative Predictive Value (NPV)
The proportion of subjects with a negative test res ult who do not have disease. NPV = (True Negatives) ÷ (True Negatives + False Negatives)
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Table 2. Characteristics of Participants Enrolled in Provist a-001 and Provista-002 Studies.
Clinical Study
Provista -001 Training Set
Provista -002 Validation Set p-value
n= 339 206 Age (Median ) 43 44 0.1248a
Range (Min-Max) (26-49) (26-49)
Race Caucasian 266 79% 162 79%
0.01235b
Black/African American 18 5% 23 11% Asian 15 4% 7 3%
American Indian / Alaska Native/Hawaiian / Pacific Islander 5 2% 5 3%
Other* 35 10% 9 4%
Ethnicity Hispanic or Latino 37 11% 22 11%
0.94362b
Not Hispanic or Latino** 302 89% 184 89% BI-RADS Category 3 139 41% 69 33% 0.07679b
4 200 59% 137 67%
Biopsies Performed 205 136 0.20199b
BI-RADS 3 (15) (5) BI-RADS 4 (190) (131)
Benign Breast Condition 313 200
0.01865b Pathology Confirmed Benign (152) (121)
Presumed Benign*** (145) (76) Lobular carcinoma in situ‡ (LCIS) (2) (2)
Atypical Hyperplasia (AH) (14) (1)
Breast Cancer (% Incidence) 26 (7.7%) 6 (2.9%)
0.81838b
Invasive carcinoma (BC) (18) (2)
Ductal carcinoma in situ (DCIS) (8) (4) †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 sum test; bStatistical significance assessed by Fisher’s exact test
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Table 3. Effect of Videssa ® Breast on Rate of Medical Interventions when Used as
an Adjunct to Imaging.
Total Participants
Receiving Procedure(s)**
True Positives
(TP)
False Positives †
(FP)
% Reduction
in FP‡ p-valuea
Combined Imaging* 339 30 309
67% <0.0001
BI-RADS 3 (19) (0) (19) BI-RADS 4 (320) (30) (290)
Videssa® Breast 111 28 83
BI-RADS 3 (37) (1) (36) BI-RADS 4 (74) (27) (47)
*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 have been recommended by Videssa® Breast compared to standard imaging; aStatistical significance was assessed using Fisher’s exact test.
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SUPPLEMENTAL FIGURE LEGENDS AND TABLES
Figure 1 Supplement. Univariate Analysis of Pre-sel ected Biomarkers. Expression
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.
Figure 2 Supplement. Performance of SPB and TAAb In dependent Models.
Training models consisting of only SPB and only TAAb were evaluated.
Table 1 Supplement. Enrollment Sites. Clinical Trial Institution City State IRB
Provista-001/ Provista-002
Avera Cancer Institute Sioux Falls South Dakota Avera Cancer Institute
Provista-001/ Provista-002
Rhode Island Hospital Providence Rhode Island Rhode Island Hospital
Provista-001/ Provista-002 Scripps Cancer Clinic San Diego California Scripps Cancer Center
Provista-001/ Provista-002
Henry Ford Hospital Detroit Michigan Henry Ford Health System
Provista-001/ Provista-002
Sutter Institute for Medical Research
Sacramento California Chesapeake IRB
Provista-001 Banner Research Phoenix Arizona Chesapeake IRB
Provista-001 Lahey Clinic Peabody Massachusetts Lahey Hospital Medical Center
Provista-001 Sansum Clinic Santa Barbara California Chesapeake IRB
Provista-002 Mercy Oncology Center Oklahoma City
Oklahoma Mercy Health
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
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Table 2 Supplement. Serum protein biomarkers (SPB) and tumor-associated
autoantibodies (TAAb) evaluated during this study. Highlighted biomarkers indicate
those included in Videssa® Breast.
Protein Class
Protein Uniprot ID
Full Name from Uniprot Uniprot Link
SPB IL-6 P05231 Interleukin-6 http://www.uniprot.org/uniprot/P05231
SPB IL-8 P10145 Interleukin-8 http://www.uniprot.org/uniprot/P10145
SPB TNF-α P01375 Tumor necrosis factor http://www.uniprot.org/uniprot/P01375
SPB IFN-γ P15260 Interferon gamma receptor 1
http://www.uniprot.org/uniprot/P15260
SPB CEA P06731 Carcinoembryonic antigen-related cell adhesion molecule 5
http://www.uniprot.org/uniprot/P06731
SPB ErbB2 P04626 Receptor tyrosine-protein kinase erbB-2
http://www.uniprot.org/uniprot/P04626
SPB OPN P10451 Osteopontin http://www.uniprot.org/uniprot/P10451
SPB HGF P08581 Hepatocyte growth factor receptor
http://www.uniprot.org/uniprot/P08581
SPB FasL P48023 Tumor necrosis factor ligand superfamily member 6
http://www.uniprot.org/uniprot/P48023
SPB VEGF-C P49767 Vascular endothelial growth factor C
http://www.uniprot.org/uniprot/P49767
SPB VEGF-D O43915 Vascular endothelial growth factor D
http://www.uniprot.org/uniprot/O43915
TAAb ALG10 Q5BKT4-1
alpha-1,2-glucosyltransferase
http://www.uniprot.org/uniprot/Q5BKT4
TAAb ATF3 P18847 Cyclic AMP-dependent transcription factor ATF-3
http://www.uniprot.org/uniprot/P18847
TAAb ATP6AP1 Q15904 V-type proton ATPase subunit S1
http://www.uniprot.org/uniprot/Q15904
TAAb BAT4 (GPANK1)
O95872 G patch domain and ankyrin repeat-containing protein 1
http://www.uniprot.org/uniprot/O95872
TAAb BDNF P23560 Brain-derived neurotrophic factor
http://www.uniprot.org/uniprot/P23560
TAAb BMX P51813 Cytoplasmic tyrosine-protein kinase BMX
http://www.uniprot.org/uniprot/P51813
TAAb C15orf48 (NMES1)
Q9C002 Normal mucosa of esophagus-specific gene 1 protein
http://www.uniprot.org/uniprot/Q9C002
TAAb CSNK1E P49674 Casein kinase I isoform epsilon
http://www.uniprot.org/uniprot/P49674
TAAb CTAG1A P78358 Cancer/testis antigen 1 http://www.uniprot.org/uniprot/P78358
TAAb CTAG2 O75638 Cancer/testis antigen 2 http://www.uniprot.org/uniprot/O75638
TAAb CTBP1 Q13363 C-terminal-binding protein 1
http://www.uniprot.org/uniprot/Q13363
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TAAb DBT P11182 Lipoamide acyltransferase component of branched-chain alpha-keto acid dehydrogenase complex, mitochondrial
http://www.uniprot.org/uniprot/P11182
TAAb EIF3E P60228 Eukaryotic translation initiation factor 3 subunit E
http://www.uniprot.org/uniprot/P60228
TAAb FRS3 O43559 Fibroblast growth factor receptor substrate 3
http://www.uniprot.org/uniprot/O43559
TAAb GPR157 Q5UAW9 Probable G-protein coupled receptor 157
http://www.uniprot.org/uniprot/Q5UAW9
TAAb HOXD1 Q9GZZ0 Homeobox protein Hox-D1
http://www.uniprot.org/uniprot/Q9GZZ0
TAAb IGFBP2 P18065 Insulin-like growth factor binding protein 2
http://www.uniprot.org/uniprot/P18065
TAAb MUC1 P15941 Mucin-1 http://www.uniprot.org/uniprot/P15941
TAAb MYOZ2 Q9NPC6 Myozenin-2 http://www.uniprot.org/uniprot/Q9NPC6
TAAb p53 P04637 Cellular tumor antigen p53
http://www.uniprot.org/uniprot/P04637
TAAb PDCD6IP Q8WUM4 Programmed cell death 6-interacting protein
http://www.uniprot.org/uniprot/Q8WUM4
TAAb RAB5A P20339 Ras-related protein Rab-5A
http://www.uniprot.org/uniprot/P20339
TAAb RAC3 P60763 Ras-related C3 botulinum toxin substrate 3
http://www.uniprot.org/uniprot/P60763
TAAb SELL P14151 L-selectin http://www.uniprot.org/uniprot/P14151
TAAb SERPINH1 P50454 Serpin H1 http://www.uniprot.org/uniprot/P50454
TAAb SF3A1 Q15459 Splicing factor 3A subunit 1
http://www.uniprot.org/uniprot/Q15459
TAAb SLC33A1 O00400 Acetyl-coenzyme A transporter 1
http://www.uniprot.org/uniprot/O00400
TAAb SOX2 P48431 Transcription factor SOX-2
http://www.uniprot.org/uniprot/P48431
TAAb TFCP2 Q12800 Alpha-globin transcription factor CP2
http://www.uniprot.org/uniprot/Q12800
TAAb TRIM32 Q13049 E3 ubiquitin-protein ligase TRIM32
http://www.uniprot.org/uniprot/Q13049
TAAb UBAP1 Q9NZ09 Ubiquitin-associated protein 1
http://www.uniprot.org/uniprot/Q9NZ09
TAAb ZMYM6 O95789 Zinc finger MYM-type protein 6
http://www.uniprot.org/uniprot/O95789
TAAb ZNF510 Q9Y2H8 Zinc finger protein 510 http://www.uniprot.org/uniprot/Q9Y2H8
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Table 3 Supplement. Clinical performance of Videssa® Breast in subjects where
benign diagnosis was confirmed by biopsy.
n= Cancer n= Benign Sensitivity Specific ity NPV PPV AUC
Training 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
Table 4 Supplement. Comprehensive Clinical Performa nce Line Data.
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1001 PDX-001 BBC 3 0 0 1 0
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1069 PDX-001 BBC 4 0 1 0 0
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TABLE 4 Supplement
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1093 PDX-001 BBC 4 0 0 1 0
1094 PDX-001 BBC 4 0 0 1 0
2001 PDX-001 BBC 4 0 0 1 0
2002 PDX-001 BBC 3 0 1 0 0
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2006 PDX-001 BBC 4 0 0 1 0
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204-020 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-021 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-022 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-024 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-025 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-029 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-031 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-034 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-035 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-037 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-039 PDX-002-Cohort 1 BBC 4 0 0 1 0
204-045 PDX-002-Cohort 1 BBC 3 0 1 0 0
204-048 PDX-002-Cohort 1 BBC 3 0 1 0 0
204-049 PDX-002-Cohort 1 BBC 3 0 1 0 0
204-056 PDX-002-Cohort 1 BBC 4 0 1 0 0
204-057 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-059 PDX-002-Cohort 1 BBC 3 0 0 1 0
204-062 PDX-002-Cohort 1 BBC 3 0 1 0 0
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205-003 PDX-002-Cohort 1 BBC 3 0 0 1 0
205-004 PDX-002-Cohort 1 BBC 4 0 0 1 0
205-006 PDX-002-Cohort 1 BBC 3 0 0 1 0
205-008 PDX-002-Cohort 1 BBC 3 0 0 1 0
205-009 PDX-002-Cohort 1 BBC 3 0 0 1 0
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205-014 PDX-002-Cohort 1 BBC 3 0 1 0 0
205-015 PDX-002-Cohort 1 BBC 4 0 0 1 0
205-016 PDX-002-Cohort 1 BBC 3 0 0 1 0
205-018 PDX-002-Cohort 1 BBC 3 0 0 1 0
205-019 PDX-002-Cohort 1 BBC 3 0 0 1 0
205-023 PDX-002-Cohort 1 BBC 3 0 0 1 0
206-001 PDX-002-Cohort 1 BBC 4 0 1 0 0
206-002 PDX-002-Cohort 1 BBC 4 0 1 0 0
206-006 PDX-002-Cohort 1 BBC 4 0 1 0 0
206-007 PDX-002-Cohort 1 BBC 4 0 0 1 0
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206-020 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-021 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-026 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-027 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-028 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-030 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-033 PDX-002-Cohort 1 BBC 4 0 1 0 0
206-034 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-036 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-037 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-038 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-039 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-042 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-044 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-046 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-048 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-052 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-054 PDX-002-Cohort 1 BC 4 1 0 0 0
206-059 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-064 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-065 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-070 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-074 PDX-002-Cohort 1 BBC 4 0 1 0 0
206-075 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-076 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-079 PDX-002-Cohort 1 BBC 3 0 0 1 0
206-081 PDX-002-Cohort 1 BBC 4 0 0 1 0
206-082 PDX-002-Cohort 1 BBC 4 0 0 1 0
208-001 PDX-002-Cohort 1 BBC 4 0 0 1 0
208-002 PDX-002-Cohort 1 BBC 4 0 0 1 0
208-003 PDX-002-Cohort 1 BBC 4 0 0 1 0
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208-009 PDX-002-Cohort 1 BBC 3 0 0 1 0
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209-003 PDX-002-Cohort 1 BBC 4 0 0 1 0
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ACCEPTED MANUSCRIPTTotal BC 32 TP FP TN FN
Total BBC 513 Total 28 83 430 4
Sens 87.5%
Spec 83.8%
NPV 99.1%
PPV 25.2%
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