Mutation to outcomes
Connect somatic mutation landscapes to clinical endpoints — TMB, driver alterations, mutational signatures, and oncoprint summaries stratified by survival or response.
Research question
Which mutational features — TMB, specific driver genes, copy number alterations, or mutational signatures — associate with overall survival, progression-free survival, or treatment response in my cohort?
Who this is for
- Translational oncology teams at cancer centers and precision medicine programs
- Computational oncology groups processing MAF cohorts with clinical follow-up
- Pharma biomarker teams linking sequencing endpoints to trial outcomes
Data requirements
| Data | Required | Purpose |
|---|---|---|
| MAF-style mutation table | Yes | Variant classification, TMB, oncoprint, signatures |
| Clinical endpoint table | Yes (for survival) | Kaplan-Meier, log-rank, Cox regression |
| Copy number alteration table | No | Amplification/deletion landscape |
| Molecular feature matrix | No | Multivariate Cox with TMB + other features |
Align Tumor_Sample_Barcode in the MAF with patient_id in clinical records via sample metadata.
Workflow
Register samples and clinical endpoints
→ Mutation landscape (TMB, oncoprint, signatures)
→ Survival analysis stratified by TMB or driver status
→ AI interpretation
Step 1 — Mutation landscape
Launch Mutation landscape from the Mutations page with your cohort MAF, optional copy number file, and optional clinical metadata for mutation enrichment.
Review per-sample TMB, gene frequency table, oncoprint matrix, co-occurrence/exclusivity pairs, dominant mutational signature, per-sample SBS exposure, differential mutations, and clinical pathway enrichment on the Mutations page.
Step 2 — Survival stratification
Export TMB or driver mutation status as features, then run Survival analysis from the Survival page, setting your stratification field (e.g. treatment arm) and Cox covariates (e.g. TMB).
Review Kaplan-Meier curves, log-rank p-values, and Cox hazard ratios on the Survival page.
Run Cohort comparison after mutation, immune, communication, CNA, signature, and survival jobs to rank integrated responder or treatment-arm evidence in one table.
Step 3 — Interpretation
Include both mutation landscape and survival run IDs in an interpret job for a multi-modal narrative linking alterations to outcomes.
Expected outputs
- Per-sample TMB and variant classification breakdown
- Oncoprint matrix with clinical annotation tracks
- Mutational signature exposure with SBS-6 counts, optional SBS-96 context matrix, cosine similarity, and clinical-group summaries
- Copy-number alteration summaries by gene, region/peak, and focal/arm scope with known driver CNA annotations
- Driver gene and pathway enrichment across subgroups
- Kaplan-Meier curves stratified by TMB quartile, driver status, or treatment arm
- Cox regression with hazard ratios for molecular covariates
Typical analyses
| Analysis | Stratification variable | Question |
|---|---|---|
| TMB and IO response | TMB high vs. low | Does mutation burden predict checkpoint inhibitor benefit? |
| Driver co-occurrence | TP53 + KRAS vs. either alone | Are alteration patterns mutually exclusive or co-occurring? |
| Signature and survival | APOBEC-dominant vs. other | Does mutational process associate with outcome? |
| Treatment arm comparison | anti-PD-1 vs. chemotherapy | Do molecular features differ in prognostic value by regimen? |
Related guides
- Mutation landscape
- Survival analysis
- Sample data — MAF and clinical test fixtures