Biomarker Pathway Analysis

Biomarker Pathway Analysis

When to use this skill

• Researcher asks which pathways a gene/biomarker belongs to
• Identify druggable targets within a disease pathway
• Map metagene clusters to biological mechanisms
• Understand mechanism-of-action for candidate biomarkers
• Perform pathway enrichment analysis on a gene set

MCP Server: biomni-research

Pathway analysis uses the biomni-research MCP server. Tools are discovered automatically — ask your question naturally and Claude will find the right tool.

Workflow: Pathway-Based Biomarker Discovery

Step 1: Identify the gene set of interest

Sources for gene sets:

• Output from biomarker-database-analysis (top genes by p-value)
• Known cancer driver genes (e.g., EGFR, KRAS, TP53, BRCA1/2)
• Metagene clusters from expression analysis
• Differentially expressed genes from cohort comparison

Step 2: Query pathway databases

Use the biomni-research server with natural language queries:

Step 3: Map pathway hierarchy

Reactome organizes pathways hierarchically. Navigate from broad to specific:

Top-level: Signal Transduction
  -> RAS signaling
    -> KRAS activation
      -> Downstream effectors (RAF, MEK, ERK)

Decision tree for pathway depth:

• Broad overview needed -> Query top-level pathways only
• Mechanism-of-action -> Drill into sub-pathways with specific reactions
• Drug target identification -> Find terminal nodes with known inhibitors

Step 4: Identify druggable targets in pathway

For each pathway hit, assess druggability:

1. Query Open Targets for tractability assessment:

   • Small molecule tractable
   • Antibody tractable
   • Other modalities (PROTAC, gene therapy)

2. Check existing drugs:

   • Approved drugs targeting this pathway node
   • Clinical trial compounds (Phase I-III)
   • Tool compounds for validation

3. Prioritize by:

   • Distance from disease-associated node (closer = better)
   • Number of approved drugs (validated target)
   • Safety profile of existing modulators

Step 5: Build pathway-to-biomarker rationale

Connect pathway findings back to biomarker candidates:

Gene (biomarker candidate)
  -> Pathway membership (Reactome)
    -> Disease relevance (pathway implicated in condition)
      -> Mechanistic explanation (how gene contributes to disease)
        -> Clinical utility (can measure this to stratify patients)

Pathway Analysis Patterns

EGFR pathway in NSCLC:

• Query: EGFR, KRAS, ALK, ROS1, BRAF, MET, HER2, RET
• Pathways: RTK signaling, RAS-MAPK, PI3K-AKT-mTOR
• Biomarker implication: Mutation status predicts TKI response

Metagene cluster interpretation:

• Cluster of co-expressed genes -> query each for pathway membership
• Identify shared pathways -> that pathway drives the co-expression
• Example: GDF15, POSTN, VCAN cluster -> TGF-beta / extracellular matrix remodeling

Survival-associated pathway enrichment:

1. Take top 10 genes by Cox regression p-value
2. Query Reactome for each gene
3. Count pathway overlaps (enrichment)
4. Pathways with 3+ genes = significantly enriched

Decision Framework: When to Use Pathway Analysis

Conventions

• Always report pathway evidence level (curated vs. inferred)
• Include Reactome stable IDs (R-HSA-xxxxx) for reproducibility
• For STRING interactions, use confidence threshold >= 0.7 (high confidence)
• When multiple pathways match, rank by: disease relevance > gene count > evidence level
• Cross-reference pathway findings with literature (PubMed) for validation