mslp

mslp predicts potential synthetic lethality partners (SLPs) of tumour mutations by integrating gene expression, mutation profiling, and cell line genetic screens data to identify cancer context-specific therapeutic targets.

Key Features:

• cBioPortal Integration: Built-in support for importing data from cBioPortal.
• Primary SLP Prediction - WT Correlation: Primary SLPs correlating with mutations in wild-type are predicted using GENIE3, a random forest based method.
• Primary SLP Prediction - Loss of Function Compensation: Primary SLPs compensating for loss of function of mutations are predicted using Rank Products.
• Consensus SLP Identification: Genetic screens are employed to identify consensus SLPs that lead to reduced cell viability when perturbed.

Scientific Applications:

• Cancer Target Identification: Identifies novel targets for personalized cancer treatments through mutation-specific synthetic lethality prediction.
• Drug Development: Aids in drug development and precision medicine by identifying cancer context-specific synthetic lethal interactions.
• Cell Viability Impact Assessment: Identifies consensus SLPs with demonstrated impact on cell viability compared to common genetic screen hits.

Methodology:

The pipeline integrates genomic and transcriptomic datasets from patient tumours and cancer cell lines. Primary SLPs are predicted using GENIE3 for wild-type correlation and Rank Products for loss of function compensation. Genetic screens identify consensus SLPs with consistent impact on cell viability.