pan-cancer-multiomics-agent

Core Capabilities

1. Pan-Cancer Subtyping: ML-based clustering across 32+ cancer types to identify molecular subtypes transcending tissue of origin.

2. Driver Discovery: Integrate mutation, expression, and CNV data to identify oncogenic drivers using pan-cancer statistical power.

3. Multi-Omics Fusion: Deep learning integration of mRNA, miRNA, methylation, and protein data for comprehensive profiles.

4. Pathway Analysis: Identify dysregulated pathways with pan-cancer prevalence and therapeutic implications.

5. Survival Modeling: PRISM framework for multi-omics prognostic marker discovery and survival prediction.

6. Therapeutic Matching: Map patient profiles to pan-cancer drug sensitivity data and clinical trial evidence.

TCGA Pan-Cancer Atlas Integration

Data Type	Samples	Application
Somatic mutations	11,000+	Driver identification
Copy number	11,000+	Amplifications/deletions
mRNA expression	11,000+	Expression subtypes
miRNA expression	10,000+	Regulatory networks
DNA methylation	10,000+	Epigenetic subtypes
Protein (RPPA)	8,000+	Pathway activation

Workflow

1. Input: Patient multi-omics data (mutations, CNV, expression, methylation).

2. Normalization: Harmonize data to TCGA reference standards.

3. Classification: Assign to pan-cancer molecular subtypes.

4. Driver Analysis: Identify patient-specific drivers in pan-cancer context.

5. Pathway Scoring: Calculate pathway activation scores.

6. Therapeutic Matching: Identify actionable targets and trial matches.

7. Output: Pan-cancer classification, driver report, pathway profiles, treatment recommendations.