/biomarker-pathway-analysis

name: biomarker-pathway-analysis
description: Use when a researcher needs to analyze biological pathways for biomarker discovery, map disease mechanisms to druggable targets using Reactome/KEGG, identify pathway enrichment from gene sets, or understand mechanism-of-action for candidate biomarkers.

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:

| Goal | Query approach | |------|---------------| | Find pathways for a gene | "EGFR signaling pathways in Reactome" | | Find disease pathways | "pathways involved in non-small cell lung cancer" | | Get pathway interactions | "protein interaction network for CDK4" via STRING | | Validate drug targets | "CDK4 drug target tractability" via Open Targets | | Cross-reference function | "CDK4 molecular function and biological process" via UniProt |

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

| Scenario | Recommended approach | |----------|---------------------| | Single gene of interest | Query Reactome + UniProt for function context | | Gene panel (5-20 genes) | Pathway enrichment: find shared pathways | | Drug target validation | Open Targets tractability + existing drugs | | Mechanism explanation | Full pathway walk: gene -> pathway -> disease | | Novel biomarker discovery | Combine pathway + expression + survival data |

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