DrugSniper

DrugSniper analyzes genome-wide loss-of-function screens (RNA interference (RNAi) and CRISPR-Cas9 libraries) to identify drug targets, reposition drugs, and associate predictive biomarkers for precision oncology.

Key Features:

• Loss-of-function data sources: Utilizes genome-wide RNAi and CRISPR-Cas9 libraries to link genetic perturbations to drug response.
• Large-scale modeling: Models sensitivity of 6,237 inhibitors across 30 tumor types to predict biomarkers of drug sensitivity.
• SCLC discovery: Identified genes extensively studied in SCLC, including Aurora kinases and epigenetic agents, and highlighted PLK1 vulnerability in CREBBP-mutant SCLC cells.
• In vitro validation: Validated predictions using four CREBBP-mutant and four wild-type SCLC cell lines treated with PLK1 inhibitors Volasertib and BI2536, showing efficacy dependent on CREBBP mutational status.
• In-silico validation: Demonstrated predictive performance in silico by recapitulating sensitivity of Tyrosine Kinase Inhibitors (TKIs) to FLT3 mutant cells and Vemurafenib to BRAF mutant cells.

Scientific Applications:

• Personalized therapy identification: Links specific genetic mutations to drug vulnerabilities to inform individualized treatment hypotheses.
• SCLC research: Pinpoints mutation-associated vulnerabilities in small cell lung cancer, exemplified by CREBBP-associated PLK1 sensitivity.
• Drug development and repositioning: Generates candidate targets and drug–biomarker associations for preclinical drug development and repositioning projects.
• Biomarker association across tumor types: Associates predictive biomarkers with targeted therapies across diverse tumor types to support precision oncology efforts.

Methodology:

Uses genome-wide loss-of-function screens (RNAi and CRISPR-Cas9 libraries) as input and models drug sensitivity of 6,237 inhibitors across 30 tumor types to predict biomarkers, with additional in-silico validation against known clinically used treatments (TKIs for FLT3 and Vemurafenib for BRAF).