Malignant cell CNV evidence
Use CompBio single-cell CNV handoff artifacts to review malignant tumor populations, CNV-defined subclones, inferred copy-number events, and downstream oncology features in one study context.
Research question
Which malignant populations and CNV-defined subclones are present in a tumor single-cell study, what inferred events support those calls, and how should those labels inform communication, mutation, immune, survival, or response analysis?
Who this is for
- Tumor biology teams that need malignant labels before interpreting TME communication
- Immuno-oncology groups studying malignant subclone sender programs and immune/stromal receiver states
- Translational researchers connecting inferred CNV burden, mutation evidence, and clinical response features
Data requirements
| Data | Required | Purpose |
|---|---|---|
CompBio single_cell_cnv handoff or result JSON | Yes | Source of malignant calls, CNV events, subclones, and review caveats |
| Mutation landscape result JSON | Optional | Links inferred CNV-event genes to mutation/CNA evidence when available |
| Communication result JSON | Optional | Adds context for malignant subclone sender and immune/stromal receiver interpretation |
| Survival result JSON | Optional | Keeps survival/response feature export tied to completed outcome analyses |
Workflow
CompBio Single-Cell CNV → Oncology Analyze → Malignant Cell Detection → downstream communication / mutation / survival
Step 1 — Generate the CompBio handoff
Run Computational Biology Single-Cell CNV on the tumor single-cell dataset. Review reference QC, heatmap, malignant group evidence, subclones, and inferred events before using the result in Oncology.
Step 2 — Run Malignant Cell Detection in Oncology
Open the Oncology study Analyze page and choose Malignant cell detection. Select the CompBio CNV handoff/result dataset or paste the oncology_handoff.json path. Optionally provide mutation, communication, or survival result JSON paths for cross-workflow context.
Step 3 — Review tumor biology outputs
The result summarizes:
- malignant populations and top CNV evidence
- CNV-defined subclone features for TME communication
- inferred CNV events and state-call context
- mutation overlap when mutation results are supplied
- survival/response feature rows for downstream modeling
- caveats carried forward from the CompBio CNV run
Step 4 — Use downstream features
Copy the malignant-population, summary, or survival-feature artifact paths from the result panel. Use cnv_malignant_status, cnv_malignant_score, and cnv_subclone_id as metadata columns when stratifying communication, immune profile, spatial TME, survival, or response analyses.
Expected outputs
- malignant population summary JSON
- survival/response feature rows with malignant fraction, CNV burden, subclone fractions, and key inferred events
- mutation-overlap summary for inferred event genes
- communication handoff guidance using malignant status and subclone metadata
- research caveats that state the evidence is expression-derived and should be compared with DNA CNA or mutation data when available
Typical analyses
| Analysis | Question |
|---|---|
| Malignant population review | Which clusters or groups are likely malignant? |
| Subclone communication | Do CNV-defined malignant subclones act as distinct senders to immune or stromal receivers? |
| Mutation/CNV comparison | Do inferred CNV events overlap genes seen in mutation or CNA outputs? |
| Survival/response export | Are malignant fraction, CNV burden, or subclone fractions useful response features? |
| Multi-run interpretation | Do CNV, immune, mutation, and survival evidence point to the same biology? |
Important caveats
This workflow does not run copy-number inference itself. It consumes the CompBio single_cell_cnv result so Oncology can interpret the output as tumor biology without duplicating the CNV engine. The calls remain expression-derived research evidence and should not be treated as clinical copy-number calls.