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Chapter 6: Molecular Tumor Boards & Actionable Biomarker Databases

1 min read • Verified October 2026 • Peer-Reviewed Learning Resource
Tested Stacks: GATK 4.6.1 Samtools 1.21 Nextflow 24.10 Seurat v5.1 Python 3.12
Prerequisites: Basic Linux CLI & Sequencing Formats
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Chapter 6: Molecular Tumor Boards & Actionable Biomarker Databases

In a Molecular Tumor Board (MTB), medical oncologists, clinical geneticists, pathologists, and bioinformaticians evaluate comprehensive genomic profiling (CGP) reports to formulate individualized treatment strategies for refractory or advanced cancer patients.

The OncoKB Precision Oncology Knowledgebase

Maintained by Memorial Sloan Kettering Cancer Center, OncoKB assigns therapeutic levels of evidence:

  • Level 1: FDA-approved companion diagnostic for an FDA-approved drug in this specific cancer indication (e.g. Osimertinib for EGFR T790M NSCLC).
  • Level 2: Standard of care biomarker recommended by NCCN guidelines.
  • Level 3A: Compelling clinical evidence in this indication, or FDA-approved in another indication (Level 3B).
  • Level 4: Compelling biological/preclinical rationale (in vitro/in vivo drug sensitivity).

1. Programmatic Querying with the OncoKB Web API

import requests
import json

def query_oncokb(hugo_symbol: str, alteration: str, tumor_type: str, api_token: str):
    url = f"https://www.oncokb.org/api/v1/annotate/mutations/byGenomicChange"
    headers = {"Authorization": f"Bearer {api_token}"}
    params = {
        "hugoSymbol": hugo_symbol,
        "alteration": alteration,
        "cancerType": tumor_type
    }
    
    r = requests.get(url, headers=headers, params=params)
    if r.status_code == 200:
        data = r.json()
        print(f"Oncogenic Status: {data.get('oncogenic')}")
        for treatment in data.get('treatments', []):
            print(f"Level: {treatment['level']} -> Drugs: {', '.join([d['drugName'] for d in treatment['drugs']])}")

# Example: BRAF V600E in Cutaneous Melanoma
query_oncokb("BRAF", "V600E", "Melanoma", "YOUR_API_TOKEN")
⚠️

Common Pitfalls & Troubleshooting Checklist

  • Java Heap Exhaustion (GATK / Picard): If processes terminate with java.lang.OutOfMemoryError, set your environment variable explicitly: export _JAVA_OPTIONS="-Xmx16g -Djava.io.tmpdir=/tmp".
  • Index Timestamp Mismatch: If tools complain that a BAM index is missing or out of date, regenerate with samtools index -@ 4 sample.bam.
  • Conda Channel Conflicts: Avoid ABI incompatibilities between bioconda and conda-forge by setting strict priority: conda config --set channel_priority strict.
  • Chromosome Naming Contamination: Always verify whether reference FASTA headers use chr1 or 1; divergent naming causes silent alignment drops.
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