Mutation landscape

Mutation landscape analysis summarizes somatic variants across a patient cohort — TMB, variant classification, mutational signatures, oncoprint visualization, copy number alterations, and driver pathway enrichment.

Algorithms are ported from maftools and cBioPortal analysis logic.

Prerequisites

MAF-style CSV with standard columns (Hugo_Symbol, Tumor_Sample_Barcode, Variant_Classification, etc.)
Optional copy number CSV with sample_id, gene, chromosome, and alteration columns

Launching the pipeline

Open the study Mutations page or dispatch via API:

curl -X POST http://localhost:8001/oncology/jobs/mutation-landscape \
  -H "Content-Type: application/json" \
  -d '{
    "study_id": "your-study-id",
    "parameters": {
      "mutation_path": "/path/to/cohort.maf",
      "copy_number_path": "/path/to/copy_number.csv",
      "panel_size_mb": 38.0,
      "top_genes": 20
    }
  }'

Key parameters

Parameter	Default	Purpose
`mutation_path`	—	Path to MAF-style CSV
`copy_number_path`	—	Copy number alteration table
`panel_size_mb`	38.0	Sequencing panel size for TMB per megabase
`top_genes`	20	Genes included in oncoprint matrix

Outputs

Output	Description
`sample_summary`	Per-sample variant counts and TMB per megabase
`gene_summary`	Gene-level mutation frequency across cohort
`oncoprint`	Sample × gene alteration matrix for visualization
`somatic_interactions`	Co-occurrence and mutual exclusivity pairs
`mutational_signatures`	COSMIC SBS/DBS/ID signature decomposition
`copy_number`	Arm-level and focal amplification/deletion summary
`pathway_enrichment`	Driver gene and oncogenic pathway enrichment

The frontend Mutations page renders oncoprint matrices, TMB summaries, signature pie charts, and variant classification waterfalls.

Typical use cases

Rank driver alterations by frequency across a trial cohort
Compare TMB between responders and non-responders
Identify dominant mutational processes (APOBEC, HR deficiency, mismatch repair)
Stratify Survival analysis by TMB or specific driver mutations

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