Mutation landscape

Mutation landscape analysis summarizes somatic variants across a patient cohort — TMB, variant classification, SBS-6/SBS-96 mutational signatures, oncoprint visualization, copy number alterations, driver pathway enrichment, and clinical-group mutation enrichment.

The workflow follows maftools- and cBioPortal-style analysis conventions using Python-native implementation code.

Prerequisites

• MAF-style CSV with standard columns (Hugo_Symbol, Tumor_Sample_Barcode, Variant_Classification, etc.)
• Optional trinucleotide context column (TriNucleotide_Context or context) for SBS-96 signature matrices
• Optional copy number CSV with sample_id, gene, chromosome, and alteration columns, or GISTIC-like peak columns such as Cytoband, Wide Peak Limits, q values, and Genes in Wide Peak
• Optional clinical metadata CSV with sample_id and a group column such as response_status, treatment_arm, tumor_type, or group_label

Launching the pipeline

Open the study Mutations page, select your MAF (and optional copy number / clinical) datasets, set the panel size and clinical group column, and click Run mutation landscape.

Key parameters

Parameter	Default	Purpose
mutation_path	—	Path to MAF-style CSV
copy_number_path	—	Copy number alteration or GISTIC-like peak table
clinical_path	—	Optional sample metadata for clinical enrichment
clinical_group_column	auto	Clinical group column for differential mutation analysis
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	Cohort and per-sample SBS-6 counts, optional SBS-96 matrix, COSMIC-like SBS assignment, cosine similarity, and clinical-group signature summaries
copy_number	Gene, chromosome, region/peak, and focal/arm CNA summaries; known driver CNA annotations; clinical-group CNA comparisons when sample-level CNA data are supplied
pathway_enrichment	Driver gene and oncogenic pathway enrichment
clinical	Clinical metadata columns and group summaries
differential_mutation	Two-group Fisher-test gene mutation enrichment with adjusted p-values
clinical_pathway_enrichment	Driver pathway enrichment by clinical group

The frontend Mutations page renders mutation summaries, top genes, driver pathways, dominant signature, SBS-96 context coverage, per-sample signature exposures, top CNA events, known driver CNA annotations, differential mutation hits, CNA clinical comparisons, and clinical pathway enrichment. Detailed oncoprint, signature, and CNA matrices remain available through the run artifact payload.

Typical use cases

• Rank driver alterations by frequency across a trial cohort
• Compare TMB between responders and non-responders
• Identify genes and driver pathways enriched in responders, non-responders, treatment arms, or tumor subtypes
• Identify dominant mutational processes (APOBEC, HR deficiency, mismatch repair)
• Stratify Survival analysis by TMB or specific driver mutations

Next steps

• Survival analysis
• Sample data — MAF test fixtures
• Pipelines reference