Intern, Computational Oncology
About The Position
Revolution Medicines is a late-stage clinical oncology company developing novel targeted therapies for patients with RAS-addicted cancers. The company’s R&D pipeline comprises RAS(ON) inhibitors designed to suppress diverse oncogenic variants of RAS proteins. The company’s RAS(ON) inhibitors daraxonrasib (RMC-6236), a RAS(ON) multi-selective inhibitor; elironrasib (RMC-6291), a RAS(ON) G12C-selective inhibitor; zoldonrasib (RMC-9805), a RAS(ON) G12D-selective inhibitor; and RMC-5127, a RAS(ON) G12V-selective inhibitor, are currently in clinical development. As a new member of the Revolution Medicines team, you will join other outstanding professionals in a tireless commitment to patients with cancers harboring mutations in the RAS signaling pathway. The Opportunity: Revolution Medicines is seeking a motivated summer intern to evaluate computational strategies for resolving tumor and tumor microenvironment (TME) states from bulk, single-cell, and spatial transcriptomic data in oncology. This project will focus on benchmarking deconvolution methods and assessing single-cell analysis best practices to determine the most biologically accurate and reproducible approaches for studying treatment response and resistance to targeted therapies, including RAS(ON) inhibitors. Evaluate Deconvolution Methods: Review leading bulk and spatial deconvolution tools. Benchmark selected methods using curated datasets and single-cell references. Assess robustness in detecting immune shifts, resistant tumor states, and TME remodeling. Test Single-Cell Analysis Best Practices: Compare normalization, integration, and batch correction strategies. Evaluate clustering robustness and annotation reproducibility. Assess the impact of different processing steps on biological interpretation.
Requirements
- Pursuing a BS or MS in Computational Biology, Bioinformatics, Systems Biology, or related field.
- Proficiency in R and Python.
Nice To Haves
- Experience analyzing RNA-seq data (bulk and/or single-cell).
- Experience with Seurat, Scanpy, or scVI.
- Familiarity with tumor microenvironment biology.
Responsibilities
- Evaluate Deconvolution Methods: Review leading bulk and spatial deconvolution tools.
- Benchmark selected methods using curated datasets and single-cell references.
- Assess robustness in detecting immune shifts, resistant tumor states, and TME remodeling.
- Test Single-Cell Analysis Best Practices: Compare normalization, integration, and batch correction strategies.
- Evaluate clustering robustness and annotation reproducibility.
- Assess the impact of different processing steps on biological interpretation.
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What This Job Offers
Career Level
Intern
Number of Employees
251-500 employees
