Methods and resources to access mutation-dependent effects on cancer drug treatment

Area of Science:

• Oncology
• Genomics
• Pharmacology

Background:

• Genomic alterations significantly influence patient response to anticancer drugs.
• Identifying actionable mutations for targeted therapy is crucial but challenging due to incomplete cancer genome understanding.
• Next-generation sequencing (NGS) advances tumor molecular characterization, driving precision medicine in oncology.

Purpose of the Study:

• To review methods and resources for identifying mutation-dependent effects in cancer treatment.
• To discuss the integration of multiomics data for drug sensitivity prediction and biomarker discovery.
• To highlight remaining challenges and future directions in precision cancer therapy.

Main Methods:

• Analysis of clinical studies, functional genomics (e.g., CRISPR screening), and patient-derived models.
• Integration of multiomics data (genomics, transcriptomics, etc.) using computational models.
• Review of computational algorithms for drug sensitivity prediction and biomarker identification.

Main Results:

• Diverse data resources (clinical, cell line, CRISPR, patient-derived models) are available for identifying actionable mutations.
• Computational approaches leveraging multiomics data enable drug sensitivity prediction and in silico drug prioritization.
• Progress has been made in linking molecular features to drug response, aiding precision medicine.

Conclusions:

• Continued development of computational methods and data integration is essential for advancing precision cancer therapy.
• Addressing gaps in understanding cancer genomes will enhance the identification of actionable mutations.
• Future research should focus on robust validation and clinical translation of identified biomarkers and therapeutic strategies.