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biomarker-pathway-analysis

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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.

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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:

GoalQuery 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

ScenarioRecommended approach
Single gene of interestQuery Reactome + UniProt for function context
Gene panel (5-20 genes)Pathway enrichment: find shared pathways
Drug target validationOpen Targets tractability + existing drugs
Mechanism explanationFull pathway walk: gene -> pathway -> disease
Novel biomarker discoveryCombine 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