This Is AuburnElectronic Theses and Dissertations

Integrating single-cell multi-omics and experimental therapeutics to identify and validate novel secondary therapies against relapsed/refractory cancers

Date

2023-02-27

Author

Chakravarti, Sayak

Type of Degree

PhD Dissertation

Department

Interdepartmental Pharmacy

Abstract

Drug resistance has remained the Achilles' heel in cancer chemotherapy which serves as the principal limiting factor in achieving favorable treatment outcomes in cancer patients. Drug resistance that exists even before drug exposure (intrinsic resistance) or resistance that develops with the course of treatment (acquired) is responsible for therapy failure and clinical progression (relapse or recurrence) in 90% of the cases. Intra-patient and inter-patient tumoral heterogeneity also play a significant role in therapy resistance and failure as they govern the treatment response. Recent evidence indicates that the underlying sub-cellular molecular characteristics of the tumor govern the heterogeneity in drug response. The treatment-refractory subpopulations of tumor cells or cancer stem-like cells (CSCs) are believed to drive drug resistance and disease relapse in various cancers. Due to their quiescent nature, which allows them to escape conventional therapeutics, standard agents fail to improve long-term clinical outcomes significantly. Thus, the development of drug resistance and disease relapse in cancer is primarily attributed to the treatment-refractory subpopulations of tumor cells or cancer stem-like cells (CSCs) with potential self-renewal and differentiation capacities. Moreover, a significant limitation of cancer drug discovery is the low predictive value of the pre-clinical studies as they mostly ignore the cellular heterogeneity and complexity, which resulted in extensive inter-individual variation in response, drug resistance, and dose-limiting toxicities. So, deciphering key features within patients’ underlying tumor heterogeneity and personalized sensitivity to chemotherapy is essential to predict the efficacy of anti-cancer drugs and prevent delays in selecting more effective alternative strategies.