Pan Cancer Multiomics Agent

---
name: pan-cancer-multiomics-agent
description: '---name: pan-cancer-multiomics-agent'
---

---name: pan-cancer-multiomics-agent
description: AI-powered pan-cancer analysis integrating genomic, transcriptomic, proteomic, and epigenomic data for cancer subtyping, driver identification, and cross-cancer pattern discovery.
license: MIT
metadata:
  author: AI Group
  version: "1.0.0"
  created: "2026-01-19"
compatibility:
  - system: Python 3.10+
allowed-tools:
  - run_shell_command
  - read_file
  - write_file

keywords:
  - pan-cancer-multiomics-agent
  - automation
  - biomedical
measurable_outcome: execute task with >95% success rate.
---"

# Pan-Cancer Multi-Omics Agent

The **Pan-Cancer Multi-Omics Agent** integrates multi-omics data across cancer types to identify shared oncogenic drivers, discover novel subtypes, and enable cross-cancer therapeutic insights. It leverages TCGA, CPTAC, and other pan-cancer resources with deep learning for comprehensive cancer characterization.

## When to Use This Skill

* When analyzing patient tumors in context of pan-cancer molecular profiles.
* To identify shared drivers and vulnerabilities across cancer types.
* For discovering novel molecular subtypes that span histological boundaries.
* When prioritizing therapeutic targets with pan-cancer evidence.
* To benchmark single-cancer findings against pan-cancer patterns.

## 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.

## Example Usage

**User**: "Classify this breast cancer patient's tumor in the pan-cancer context and identify shared drivers."

**Agent Action**:
```bash
python3 Skills/Oncology/Pan_Cancer_MultiOmics_Agent/pancancer_analyzer.py \
    --mutations patient_mutations.maf \
    --expression patient_rnaseq.tsv \
    --methylation patient_methylation.tsv \
    --cnv patient_cnv_segments.tsv \
    --reference tcga_pancancer \
    --subtype_method nmf_consensus \
    --output pancancer_report/
```

## Pan-Cancer Molecular Subtypes

Cross-cancer molecular taxonomy identifies patterns beyond histology:

| Subtype | Characteristics | Example Cancers |
|---------|-----------------|-----------------|
| C1-Wound healing | High proliferation, MYC amp | Breast, ovarian, bladder |
| C2-IFN-gamma dominant | Immune active, high TCR/BCR | Melanoma, lung, cervical |
| C3-Inflammatory | NF-kB, cytokine signatures | Head/neck, stomach |
| C4-Lymphocyte depleted | Low immune, PTEN loss | Glioma, uveal melanoma |
| C5-Immunologically quiet | Low expression overall | Kidney chromophobe, thyroid |
| C6-TGF-beta dominant | High TGF-B, fibrosis | Pancreas, rectum, glioma |

## Deep Learning Architecture

**Multi-Omics Integration Model**:
```
Input Layers:
  - Genomic encoder (mutations, CNV)
  - Transcriptomic encoder (mRNA, miRNA)
  - Epigenomic encoder (methylation)
  - Proteomic encoder (RPPA)

Fusion Layer:
  - Cross-attention mechanism
  - Multi-modal variational autoencoder

Output Heads:
  - Subtype classifier
  - Survival predictor
  - Drug response predictor
```

## MLOmics Database Access

The agent integrates with MLOmics, providing:
- 8,314 patient samples across 32 cancer types
- Pre-computed features for ML benchmarking
- Standardized train/test splits for reproducibility
- Drug sensitivity data for 300+ compounds

## Prerequisites

* Python 3.10+
* PyTorch with multi-modal architectures
* Access to TCGA, CPTAC, or local data
* 16GB+ RAM for pan-cancer analysis

## Related Skills

* Tumor_Clonal_Evolution - For intratumoral heterogeneity
* Multi_Omics_Integration - For single-patient integration
* Drug_Repurposing - For therapeutic matching

## Clinical Applications

1. **Cancer of Unknown Primary (CUP)**: Identify tissue of origin
2. **Cross-indication trials**: Find basket trial eligibility
3. **Driver prioritization**: Pan-cancer functional evidence
4. **Prognosis**: Multi-omics survival models

## Author

AI Group - Biomedical AI Platform