# Novel Multiplex Biomarker Discovery Methods To Predict Breast Cancer Risk After A Benign Biopsy

> **NIH NIH R21** · MAYO CLINIC  JACKSONVILLE · 2020 · $204,990

## Abstract

Project Abstract
More than one million women in the U.S. are diagnosed with biopsy proven benign breast disease (BBD)
annually. BBD is associated with increases in BC risk, ranging from 1.5-2.0 times for least severe to fourfold for
most severe categories. However, individual risk varies considerably within BBD categories. Previously, we
developed the BBD-BC model, which provides individual risk estimates based on self-reported factors, detailed
characteristics of BBD extent and severity, and assessment of involution (shrinkage and disappearance) of
surrounding histologic structures termed terminal duct lobular units (TDLUs) from which most BC precursors
arise. Increased levels of involution of TDLUs are associated with lower BC risk and limited data suggest that
biomarker analysis of TDLUs in BBD biopsies may inform BC risk overall and potentially by BC subtype;
however, this research has been limited by technical challenges. We hypothesize that by applying a novel
quantitative multiplex protein measurement technology, NanoString Digital Spatial Profiling (DSP) to
large core tissue microarrays (LC-TMAs) of TDLUs from BBD biopsies we can identify a prognostic
biomarker panel to predict BC risk. DSP offers potential advantages over conventional
immunohistochemistry, including improved quantitation and multiplexed analysis of 30 or more
markers per tissue section, thus reducing tissue utilization. As proof-of-principle, we aim to technically
validate candidate progression markers in the Mayo BBD cohort, which includes >14,000 women of whom
>1,200 developed BC in follow-up over an average of ~15 years. We will construct LC-TMAs of 3 TDLUs for
each of 50 BBD biopsies that preceded BC matched to comparable LC-TMAS derived from 50 BBD biopsies
that did not progress. We will apply DSP to regions of epithelium and lymphocytes to assess multiple candidate
progression markers in single tissue sections. We will perform conventional immunohistochemistry for the
same markers, as one marker per section. We will include BC cell lines in LC-TMAs in which markers were
measured by immunoblotting. We will compare agreement within and between methods (DSP vs.
immunohistochemistry) and to immunoblotting and identify candidate markers that predict BC risk. Then, we
will prepare similar TMAs of TDLUs surrounding BC and BC itself and evaluate the top markers that predicted
BBD progression to assess whether these markers might be associated with tumor subtype. Combining DSP
with LC-TMAs will enable analysis of multiple markers in multiple samples, efficiently and without exhausting
tissues containing TDLUs, which are microscopic. This transformative proposal aims to develop and validate a
method for quantitating protein in TDLUs using a cost-effective approach combining DSP and LC-TMAs, which
will enable the identification and validation of candidate prognostic biomarkers for BBD. Improved BC risk
prediction for women with BBD would facilitate precision management.

## Key facts

- **NIH application ID:** 10021604
- **Project number:** 5R21CA234827-02
- **Recipient organization:** MAYO CLINIC  JACKSONVILLE
- **Principal Investigator:** MARK E SHERMAN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $204,990
- **Award type:** 5
- **Project period:** 2019-09-20 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10021604

## Citation

> US National Institutes of Health, RePORTER application 10021604, Novel Multiplex Biomarker Discovery Methods To Predict Breast Cancer Risk After A Benign Biopsy (5R21CA234827-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10021604. Licensed CC0.

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