학술논문

Linking Tumor Mutations to Drug Responses via a Quantitative Chemical–Genetic Interaction Map
Document Type
article
Source
Cancer Discovery. 5(2)
Subject
Biological Sciences
Biomedical and Clinical Sciences
Genetics
Oncology and Carcinogenesis
Cancer
Health Disparities
Human Genome
Women's Health
Cancer Genomics
Biotechnology
Precision Medicine
Breast Cancer
Aetiology
Development of treatments and therapeutic interventions
5.1 Pharmaceuticals
2.1 Biological and endogenous factors
Generic health relevance
Good Health and Well Being
Animals
Breast Neoplasms
Cell Line
Tumor
Drug Resistance
Neoplasm
Drug Screening Assays
Antitumor
Female
Genomics
High-Throughput Screening Assays
Humans
Mice
Mice
Inbred BALB C
Mice
Nude
Mutation
Random Allocation
Signal Transduction
Xenograft Model Antitumor Assays
Biochemistry and cell biology
Oncology and carcinogenesis
Language
Abstract
UnlabelledThere is an urgent need in oncology to link molecular aberrations in tumors with therapeutics that can be administered in a personalized fashion. One approach identifies synthetic-lethal genetic interactions or dependencies that cancer cells acquire in the presence of specific mutations. Using engineered isogenic cells, we generated a systematic and quantitative chemical-genetic interaction map that charts the influence of 51 aberrant cancer genes on 90 drug responses. The dataset strongly predicts drug responses found in cancer cell line collections, indicating that isogenic cells can model complex cellular contexts. Applying this dataset to triple-negative breast cancer, we report clinically actionable interactions with the MYC oncogene, including resistance to AKT-PI3K pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic lethal manner, providing new drug and biomarker pairs for clinical investigation. This scalable approach enables the prediction of drug responses from patient data and can accelerate the development of new genotype-directed therapies.SignificanceDetermining how the plethora of genomic abnormalities that exist within a given tumor cell affects drug responses remains a major challenge in oncology. Here, we develop a new mapping approach to connect cancer genotypes to drug responses using engineered isogenic cell lines and demonstrate how the resulting dataset can guide clinical interrogation.