project-idea

GBM Subtype Classifier + Targeted Therapy Recommender

HVEM is selectively expressed in mesenchymal GBM, and its inhibition hits both direct tumor growth and immune evasion via BTLA — but only in the mesenchymal subtype, making subtype identification clinically critical.

Build a lightweight GBM molecular subtype classifier that takes RNA-seq or NanoString expression data as input and classifies tumors as proneural, classical, or mesenchymal — then maps the subtype to an evidence-based targeted therapy recommendation panel. For mesenchymal GBM, the tool would surface HVEM-targeting strategies (currently experimental) alongside standard-of-care options.

The classifier would be built on established GBM subtype gene signatures (TCGA-validated) and could run on standard expression input formats. The therapy recommendation layer would pull from ClinicalTrials.gov API to surface active trials matching the patient's subtype.

The real value here is connecting subtype classification — currently done by neuropathologists and genomics labs as separate steps — into a single actionable output that includes both the diagnosis and what to do next. This is particularly impactful for mesenchymal GBM patients, who currently have no approved targeted therapies and for whom trial enrollment is the best option.

Who Is This For?

Neuro-oncology researchers, translational scientists, and clinical trial coordinators at cancer centers treating GBM.

Skills & Tools Needed

RNA-seq data analysis and gene signature scoring (GSVA, ssGSEA)
GBM subtype classification algorithms (from TCGA literature)
ClinicalTrials.gov API integration
Python or R backend with REST API
Basic clinical report generation

Feasibility

medium — GBM subtype signatures are published and validated; the main work is engineering the end-to-end pipeline from expression input to clinical output, which requires both bioinformatics and software skills.

Inspired by: Inhibition of HVEM suppresses growth and invasion of mesenchymal glioblastoma

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