A deep-dive guide to somatic variant detection, AMP-guided classification, and the evolving landscape of precision oncology diagnostics.
Precision medicine aims to transform healthcare by utilizing an individual's genetic makeup and clinical presentation to aid in diagnosis, treatment, and prevention. In the cancer space, data derived from next-generation sequencing (NGS) is used to diagnose and prognose diseases, select targeted therapies, and evaluate a patient's suitability for clinical trials.
This growth creates a significant challenge: labor-intensive diagnostic processes that require deep expertise are meeting rapidly increasing demand. Clinical laboratories can expect to multiply their workload within a few years while facing a shortage of clinical genetics experts with NGS-specific training. Automation of variant interpretation and reporting is the only viable path forward.
Cancer can be triggered by both germline and somatic variants. Germline variants occur in germ cells (egg or sperm) and are hereditary, such as mutations in BRCA1 and BRCA2. Somatic variants occur in any other cell type after birth, driven by environmental effects or DNA replication errors.
Both types can be activating (conferring new or increased cell activity that promotes tumor development) or inactivating (causing loss of function that inhibits tumor suppression). For example, mutated BRCA1 switches off a tumor-suppressor gene, and the homozygous loss of TP53 is frequently observed in colon, breast, and lung cancers.
The key standards for somatic variant interpretation were published by Li et al. (2017) as a joint consensus of the Association for Molecular Pathology (AMP), American Society of Clinical Oncology (ASCO), and College of American Pathologists (CAP). Unlike germline interpretation (which focuses on pathogenicity), somatic variant assessment evaluates drug sensitivity, drug resistance, and potential toxicity.
Modern oncology testing goes beyond individual variants. Three key biomarkers have become standard in tumor profiling for identifying patients eligible for immunotherapy and targeted treatments.
The number of somatic mutations per megabase. High TMB correlates with increased neoantigens and more robust T-cell activation. Pembrolizumab received FDA approval for advanced solid tumors with TMB >10 mut/Mb.
Results from failure of mismatch repair proteins to fix DNA replication errors, leading to frameshift mutations. Associated with colon, gastric, endometrial, and ovarian cancers. MSI-high tumors respond to checkpoint immunotherapy.
A deficiency in the HRR DNA repair pathway associated with breast, ovarian, prostate, and pancreatic cancers. HRD makes tumors more sensitive to platinum-based therapies and PARP inhibitors.
Somatic variant analysis requires distinguishing true oncogenic drivers from germline background and sequencing artifacts. A structured environment for high-sensitivity detection and evidence-based classification is essential.
Calculate allelic ratios and VAFs to identify low-frequency mutations. Filter against population databases (gnomAD, ExAC) to exclude rare germline variants and apply lab-specific blacklists to remove recurring artifacts.
Prioritize driver mutations using scoring algorithms that integrate COSMIC recurrence, in-silico functional predictions (SIFT/PolyPhen/CADD), and splice-site disruption analysis.
Transition from analysis to interpretation using AMP/ASCO/CAP guidelines. Automate the assignment of Tiers I–IV based on evidence levels A–D, connecting variants to professional guidelines and therapeutic insights.
Generate structured reports with curated cancer interpretations, FDA-approved therapy matches, and active clinical trial recommendations based on the patient's genomic profile.
FDA-approved therapy (Vemurafenib) indicated for patients with BRAF V600E mutation in melanoma.
Clinical cancer analysis relies on integrating multiple categories of databases. Interpretation is only as good as the underlying data.
gnomAD, dbSNP, ExAC, 1000 Genomes — used to exclude common germline variants from somatic analysis.
COSMIC, CIViC, Cancer Hotspots, TCGA — cataloging somatic mutations with tumor histology and clinical associations.
DrugBank, NCI Thesaurus, ClinicalTrials.gov — linking biomarkers to FDA-approved therapies and active clinical trials.
Professionally curated knowledgebase with report-ready interpretations for thousands of somatic biomarkers.
While the field has advanced rapidly, several key challenges remain for clinical laboratories performing somatic variant analysis.
Somatic mutations may occur in only 1–5% of sequence reads due to tumor heterogeneity and biopsy composition. Sensitivity down to 10% of tumor cells is desirable.
Many tests sequence only tumor tissue without matched normal, limiting the ability to distinguish somatic mutations from rare germline variants at novel sites.
Surveys show substantial variation in what is reported across labs — only 35–37% report CNVs and fusions, and labs use different classification schemes.
New cancer-gene associations, treatments, and clinical trials are published daily. Tier classifications are snapshots in time — Tier II variants may become Tier I as evidence grows.
Golden Helix provides the software and curated knowledge to put these concepts into practice. Explore our precision oncology solutions and clinical interpretation tools.
Clinical application of somatic analysis in cancer centers. AMP-guided interpretation and targeted therapy matching.
Automated ACMG and AMP scoring built into VarSeq. Professional-grade interpretation with curated evidence.
Somatic CNV detection, translocation and fusion analysis for comprehensive tumor profiling.
Expert insights into AMP guidelines, tumor profiling, and the evolving landscape of cancer genomics.
Join leading cancer centers worldwide that trust Golden Helix for high-sensitivity somatic variant detection and guideline-driven interpretation.