# Integrative statistical models for TNBC biomarker discovery

> **NIH NIH R01** · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · 2020 · $354,481

## Abstract

PROJECT SUMMARY/ABSTRACT
The “triple negative breast cancer” (TNBC), refers to a heterogeneous collection of the tumors that lack
expression of the estrogen receptor (ER), progesterone receptor (PR), and HER2 amplification. Unlike, ER-
positive and HER2-amplified breast cancers; the lack of high frequency oncogenic driver mutations in TNBC
has limited treatment options for women with the disease. However, TNBCs have higher rates of clinical
response to pre-surgical (neo-adjuvant) chemotherapy, despite the lack of targeted therapy. Despite better
responses to chemotherapy, TNBC patients still have a higher rate of distant recurrence and a poorer
prognosis than women with other breast cancer subtypes.
 TNBC patients who experience a pathologic complete response (pCR) to neoadjuvant chemotherapy have
significant improvements in both disease-free and overall survival compared with patients with residual
invasive disease. In contrast, those patients with residual disease have a much poorer prognosis and are 6
times more likely to have recurrence and 12 times more likely to die. While 30% of patients with TNBC benefit
from neoadjuvant chemotherapy, currently there is no effective way to identify those TNBC patients that would
benefit most.
 TNBC's heterogeneous response to chemotherapy suggests that different TNBC subtypes may exist and
are associated drug responses. We recently developed a novel gene expression signature with 2188 genes
based on a new algorithm to classify TNBCs into six subtypes and implemented the algorithm in the software
“TNBCtype”. Our study showed that each TNBC subtype displays a unique biology. Furthermore, we identified
representative TNBC cell line models for these subtypes that display differential sensitivity to targeted and
chemotherapy.
 Therefore, to translate our pre-clinical results, there is a critical need to develop new strategies to develop a
refined, reproducible, robust and clinically useful subtyping tool to identify TNBC patients most likely to benefit
from neoadjuvant chemotherapy, and discover the new biomarkers for targeted treatments in patients that are
resistant to chemotherapy. We propose the following specific aims to address these challenges: (1) develop
and validate a robust TNBC subtyping model; (2) identify TNBC subtype specific chemotherapy response gene
signatures; (3) discover TNBC chemotherapy resistant biomarkers by integrative genomic approach.

## Key facts

- **NIH application ID:** 9944462
- **Project number:** 5R01CA200987-05
- **Recipient organization:** UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
- **Principal Investigator:** Xi Steven Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $354,481
- **Award type:** 5
- **Project period:** 2016-07-12 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9944462

## Citation

> US National Institutes of Health, RePORTER application 9944462, Integrative statistical models for TNBC biomarker discovery (5R01CA200987-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9944462. Licensed CC0.

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