project-idea

ProteostasisVuln: A Cancer Proteostasis Vulnerability Screener

Cancer cells with low HSF1 expression become catastrophically sensitive to mTORC1 reactivation, creating a potential synthetic lethality. This selective vulnerability does not exist in normal cells, suggesting it could be mapped across many cancer types.

Build an open bioinformatics tool that cross-references public tumor datasets (TCGA, CCLE, DepMap) to identify cancer types and cell lines with co-occurring low HSF1 expression and high mTORC1 pathway activity — the combination this paper identifies as most vulnerable to proteomic catastrophe. The tool would produce a ranked "vulnerability index" across cancer types, helping researchers prioritize which tumors to test with HSF1 inhibitor + mTORC1 activator combinations.

A second component could aggregate existing literature on HSF1 inhibitors currently in development, mapping them to the vulnerability index to highlight the most actionable drug-target pairs. This would serve as a living database updated as new compounds and expression datasets become available.

The tool could be packaged as a web interface for non-computational researchers, with downloadable data tables and visualizations showing HSF1/mTORC1 co-expression heatmaps across the TCGA cancer atlas.

Who Is This For?

Cancer biologists, computational oncologists, and drug discovery teams interested in proteostasis-targeted therapies.

Skills & Tools Needed

Python or R for bioinformatics analysis
TCGA/CCLE/DepMap data access (via APIs or direct download)
Gene expression analysis and pathway scoring (GSEA/ssGSEA)
Data visualization (matplotlib, ggplot2, or D3.js)
Basic web development for a public-facing interface (optional)

Feasibility

medium — All required data is publicly available and the computational analysis is tractable, but biological validation of the vulnerability predictions would require wet lab resources beyond the tool itself.

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