Integrative Statistical Models For TNBC Biomarker Discovery

Funding: R01CA200987 PI: Chen X

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. 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 (reference) and implemented the algorithm in the software “TNBCtype” (reference). 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.

We propose to develop and validate a robust TNBC subtyping model, identify TNBC subtype specific chemotherapy response gene signatures, and to discover TNBC chemotherapy resistant biomarkers by using integrative genomic approaches. Our long term goal is to build 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.