mdhewei/bioinfor-claw/pan-cancer-analysis-for-gene

Pan-Cancer Gene Report

Overview

Use this skill to create a figure-backed pan-cancer report for one human gene. The script generates a multi-page PDF and CSV appendix using public TCGA/GDC Hub, cBioPortal, DepMap, and CPTAC/cBioPortal sources.

Core outputs:

• TCGA expression distributions across 33 cancer projects.
• TCGA expression-associated survival using a within-project median split.
• TCGA mutation and GISTIC copy-number alteration counts plus alteration-associated survival.
• DepMap Public 26Q1 cell-line expression, copy-number signal, expression-copy-number correlation, and mutation landscape.
• CPTAC protein abundance distributions from public cBioPortal CPTAC proteomics studies.
• A CSV appendix with summary statistics.

Quick Start

1. Resolve the gene's human Ensembl gene ID without version, for example PRNP -> ENSG00000171867 or NADK -> ENSG00000008130.
2. Run the bundled script:

python scripts/generate_pancancer_gene_report.py \
  --gene NADK \
  --ensembl ENSG00000008130 \
  --outdir ./output

1. The script fetches public data from TCGA/GDC Hub, DepMap, and cBioPortal (network access required).

Output files:

• <gene>_pancancer_clinical_relevance_report_with_depmap.pdf
• <gene>_pancancer_appendix.csv

Script

The reusable script is:

scripts/generate_pancancer_gene_report.py

Arguments:

• --gene: HGNC symbol, uppercase preferred.
• --ensembl: Ensembl gene ID without version.
• --outdir: Output directory (auto-created if it does not exist).
• --keep-data: Keep downloaded intermediate data files. By default, the script deletes all cached data after the report is built so only the PDF and CSV appendix are delivered. Pass --keep-data only when the user explicitly asks for the raw datasets.

The script is intentionally self-contained. Prefer running it rather than rewriting the analysis. Patch it only when the user asks for a changed report structure or additional data source.

Interpretation Rules

Expression survival:

• Use a separate median split within each TCGA project.
• Label samples High when expression is >= that cancer-specific median.
• Label samples Low when expression is below the median.
• The report table's High events and Low events columns are deaths/events, not group sizes.
• Treat median-split p-values as exploratory and not multiple-testing corrected.

Alteration survival:

• TCGA mutation and copy-number statuses are fetched from cBioPortal PanCancer Atlas profiles.
• Copy-number categories use cBioPortal/GISTIC discrete calls: 2 = high-level amplification, 1 = gain, 0 = neutral, -1 = shallow deletion, -2 = deep deletion.
• The script tests mutation, amplification, gain-or-amplification, and deletion separately.
• Skip or mark as insufficient when altered groups are too small for a meaningful KM/log-rank comparison.

DepMap:

• Use DepMap Public 26Q1 from the DepMap download API.
• Expression uses OmicsExpressionTPMLogp1HumanProteinCodingGenes.csv.
• Copy number uses PortalOmicsCNGeneLog2.csv.
• Mutations use OmicsSomaticMutations.csv.
• Interpret high copy-number signals as relative screens, not clinical amplification calls.

CPTAC:

• Use public cBioPortal CPTAC protein_quantification profiles where the gene is present.
• Do not merge CPTAC protein values into one harmonized scale without additional normalization; present study-level distributions.

Validation Checklist

After generating a report:

• Confirm the PDF exists and opens as a PDF.
• Confirm page ordering: expression plots, expression survival screen/KMs, expression-survival summary, TCGA alteration survival, DepMap, CPTAC, interpretation.
• Confirm the appendix has TCGA, TCGA_cBioPortal, DepMap_26Q1, and CPTAC_cBioPortal rows when data are available.
• Summarize counts for the user: TCGA projects, alteration rows, CPTAC studies/samples, and any nominal hits if relevant.

Useful QA snippet:

/Users/whe3/.cache/codex-runtimes/codex-primary-runtime/dependencies/python/bin/python3 - <<'PY'
from pypdf import PdfReader
r = PdfReader("nadk_pancancer_clinical_relevance_report_with_depmap.pdf")
print("pages", len(r.pages))
for i, p in enumerate(r.pages, 1):
    txt = (p.extract_text() or "")[:240].replace("\n", " | ")
    if "Expression-Survival Summary" in txt or "Mutation and Copy-Number" in txt or "CPTAC" in txt:
        print(i, txt)
PY