Multi-Omics Analysis of NCI-60 Cell Line Data Reveals Novel Metabolic Processes Linked with Resistance to Alkylating Anti-Cancer Agents

This study aimed to elucidate the molecular determinants influencing the response of cancer cells to alkylating agents, a major class of chemotherapeutic drugs used in cancer treatment. The study utilized data from the National Cancer Institute (NCI)-60 cell line screening program and employed a comprehensive multi-omics approach integrating transcriptomic, proteomic, metabolomic, and SNP data. Through integrated pathway analysis, the study identified key metabolic pathways, such as cysteine and methionine metabolism, starch and sucrose metabolism, pyrimidine metabolism, and purine metabolism, that differentiate drug-sensitive and drug-resistant cancer cells. The analysis also revealed potential druggable targets within these pathways. Furthermore, copy number variant (CNV) analysis, derived from SNP data, between sensitive and resistant cells identified notable differences in genes associated with metabolic changes (WWOX, CNTN5, DDAH1, PGR), protein trafficking (ARL17B, VAT1L), and miRNAs (MIR1302-2, MIR3163, MIR1244-3, MIR1302-9). The findings of this study provide a holistic view of the molecular landscape and dysregulated pathways underlying the response of cancer cells to alkylating agents. The insights gained from this research can contribute to the development of more effective therapeutic strategies and personalized treatment approaches, ultimately improving patient outcomes in cancer treatment.

Automated summary

This study used a multi-omics approach to explore the molecular determinants affecting the response of cancer cells to alkylating agents. Through integrated pathway analysis, key metabolic pathways were identified, and potential druggable targets were revealed. Copy number variant analysis also identified notable differences in genes associated with metabolic changes, protein trafficking, and miRNAs between drug-sensitive and drug-resistant cells. These findings provide a comprehensive view of the molecular landscape underlying the response to alkylating agents and can contribute to the development of personalized treatment strategies.