Bladder Cancer Progression & Immune Microenvironment
An end-to-end genomic pipeline that surfaces biomarker signal across 233 bladder cancer samples.
Overview
Bladder cancer outcomes vary enormously between patients, yet most clinical signals stop at staging. I set out to build a reproducible pipeline that could turn raw RNA-seq into progression and immune-response signal a diagnostics team could act on.
The pipeline ingests expression data, normalizes and quality-controls it, then runs differential expression, pathway enrichment, and immune-cell deconvolution to map each tumor's microenvironment. Patients are then clustered into progression-risk groups with the contributing genes and pathways made explicit.
The result is not just an analysis but a repeatable system: drop in a new cohort and get stratified groups, candidate biomarkers, and the evidence behind them — the foundation a real stratification product would be built on.
At a glance
Oncology teams lacked a reliable way to stratify bladder cancer patients by progression risk and immune response.
An end-to-end genomic analysis pipeline — expression profiling, pathway enrichment, and immune deconvolution — across 233 patient samples.
Surfaced candidate biomarkers for patient stratification, the kind of signal a diagnostics product would build on.
Highlights
- Processed and QC'd RNA-seq across 233 patient samples end to end
- Immune deconvolution to profile each tumor's microenvironment
- Pathway enrichment that explains why patients cluster as they do
- Candidate biomarkers surfaced for downstream validation