Hidden in the Noise: Low-Variant Allele Frequency Mutations and Their Impact on Precision Oncology

Intratumoral heterogeneity is a defining feature of cancer, yet standard sequencing and reporting practices often overlook somatic variants present at low variant allele frequencies (VAFs), commonly below 5%. Increasing evidence indicates that these rare alleles can represent clinically meaningful subclones involved in tumor evolution, therapeutic resistance, minimal residual disease, and metastatic dissemination. However, detecting and interpreting low-VAF variants is technically and analytically challenging because background error rates, library artifacts, genomic context, and caller assumptions increasingly overlap with true signal as allele fraction decreases. In this review, we integrate biological and clinical evidence supporting the relevance of low-VAFs and evaluate constraints across sequencing strategies, including whole genome and whole exome approaches and deep targeted panels. We discuss why detectability depends strongly on variant class and genome architecture, with SNVs generally more tractable than indels and structural variants. We then summarize practical approaches that improve sensitivity and specificity beyond increasing depth, including proper tissue handling, molecular enrichment, unique molecular identifiers, duplex-consensus methods, advanced error modeling, and orthogonal validation. Finally, we highlight emerging single-cell, spatial, and multiomic technologies that resolve rare variants in a cellular context. Collectively, these advances support incorporating low-VAF detection into precision oncology frameworks.