Establishing the Resistant Cancer Cell Lines Database

Drug resistance makes cancer a major cause of mortality globally by significantly reducing the effectiveness of chemotherapy and targeted treatments for certain cancer types. Although many treatments exist, acquired drug resistance remains an obstacle in clinical oncology. Therefore, to tackle these issues and support precision oncology efforts, this research study focused on detailed profiling of drug-adapted cancer cell lines and exome-level analysis. This study includes two connected projects. Project 1 is based on the development and implementation of the Resistant Cancer Cell Line (RCCL) database, containing a subset of 376 drug-adapted and parental cancer cell lines from 11 different cancer types and 17 drug classes, from an overall 3,000 drug-adapted cancer cell lines. This database allows the user to visualise responses of different cancer cell lines to cytotoxic drugs by observing differences in IC50 and IC90 values, visualising dose-response visualisations, analysing short tandem repeats (STR) present in the cell lines, identifying acquired and not retained variants associated with specific cell lines, and using different search options. Thus, the RCCL database addresses a significant gap by bringing together high-quality data on drug-adapted cell lines. Additionally, it promotes research on cross-resistance and serves as a useful tool for designing combined therapies and repurposing drugs. Whereas Project 2 focused on the analysis of whole-exome sequence (WES) of colorectal cancer cell lines and their drug-adapted cell lines treated with cytotoxic drugs, including oxaliplatin, cisplatin, 5-fluorouracil, irinotecan, paclitaxel, and docetaxel. The WES analysis identified several acquired variants and lost variants in the drug-adapted exome sequences, including frameshift, missense, nonsense, and regulatory region variants. The pathways enrichment analysis revealed important processes such as DNA repair, regulation of apoptosis, and cell cycle pathways, highlighting the evolutionary adaptations that cancer cells exhibit under the pressure of drug treatment. Additionally, the integration of the results of WES analysis with the RCCL provides a framework for understanding acquired drug resistance mechanisms by mapping the variants identified in the genome sequences with the drug IC50 values. Therefore, this approach helps identify the markers associated with resistance and design combination therapies to combat resistance. This research study presents a significant contribution to the field of cancer research and personalised medicine by providing a user-friendly resource to study mechanisms of acquired drug resistance, which is beneficial for clinicians and researchers to improve treatment strategies. This research also helps in overcoming gaps between laboratory findings and clinical applications, thus supporting the development of targeted and personalised therapies to overcome acquired drug resistance and help improve patient survival.