AI-powered pan-cancer analysis integrating genomic, transcriptomic, proteomic, and epigenomic data for cancer subtyping, driver identification, and cross-cancer pattern discovery.
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.
Pan-Cancer Subtyping: ML-based clustering across 32+ cancer types to identify molecular subtypes transcending tissue of origin.
: Integrate mutation, expression, and CNV data to identify oncogenic drivers using pan-cancer statistical power.
Multi-Omics Fusion: Deep learning integration of mRNA, miRNA, methylation, and protein data for comprehensive profiles.
Pathway Analysis: Identify dysregulated pathways with pan-cancer prevalence and therapeutic implications.
Survival Modeling: PRISM framework for multi-omics prognostic marker discovery and survival prediction.
Therapeutic Matching: Map patient profiles to pan-cancer drug sensitivity data and clinical trial evidence.
| 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 |
Input: Patient multi-omics data (mutations, CNV, expression, methylation).
Normalization: Harmonize data to TCGA reference standards.
Classification: Assign to pan-cancer molecular subtypes.
Driver Analysis: Identify patient-specific drivers in pan-cancer context.
Pathway Scoring: Calculate pathway activation scores.
Therapeutic Matching: Identify actionable targets and trial matches.
Output: Pan-cancer classification, driver report, pathway profiles, treatment recommendations.
User: "Classify this breast cancer patient's tumor in the pan-cancer context and identify shared drivers."
Agent Action:
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/
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 |
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
The agent integrates with MLOmics, providing:
AI Group - Biomedical AI Platform