Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)



First Advisor

Dr. Ritu Aneja

Second Advisor

Dr. Emad Rakha

Third Advisor

Dr. Zhi- Ren Liu


Breast cancer (BC) is a heterogeneous disease consisting of distinct subtypes that vary in prognosis. Routine diagnosis is limited to the assessment of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), there are just a few others that have been clinically validated to guide chemotherapy and medicolegal decisions for patients with BC. Although androgen receptor (AR) has recently emerged as a promising predictive and prognostic biomarker for BC, especially triple negative breast cancers (TNBCs), there is still an unmet need to risk-stratify risk BC patients and predict response to therapy. Thus, we hypothesis that a biomarker-guided deeper stratification of patients will improve prognostication; aid in tailored therapy and decision making in medicolegal cases.

My research has primarily focused on evaluating biomarkers that can determine in vivo tumor growth rate, predict response to neoadjuvant in BC patients, and risk-stratify TNBC patients using a combination of in silico analysis, in vitro assays and RNA- sequencing. Our clinically relevant growth rate model derived from Ki67, histological tumor size and mitotic index, stratified tumors into fast-growing versus slow-growing tumor subgroups, wherein patients with fast-growing tumors experienced poorer BC-specific survival. Evaluation of different biomarkers to predict pCR in BC patients revealed that HER2+ and TNBC subtypes had higher pCR rates compared with the luminal subtype. ER and PR negativity, HER2 positivity, Nottingham grade 3, increased TLI and SLI, high mitotic count and Ki67 score correlated significantly with pCR. Evaluating AR status shows population-specific patterns of association with patients’ overall survival after controlling for age, grade, population, and chemotherapy. My study validates the striking association of AR loss with worse clinical outcome. The collective data offers compelling evidence to support misregulation of oncogenic Wnt/β-catenin in AR negative scenario.

Collectively, my work has revealed a prognostic model that can predict the in vivo breast tumor growth rate and offers several useful application; identified immunohistochemical and clinicopathological biomarkers that are independent predictors of neoadjuvant chemotherapy; stratify risk in TNBC patients based on AR status; and uncovered molecular pathways that can optimize targeted therapy to combat TNBCs that lack AR.

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