Interpreting TP53 pathogenic variants in germline testing and cancer genomics: pathogenicity, origin, and clinical...

Next-generation sequencing has increased the sensitivity of pathogenic variant (PV) detection and enabled the quantitative assessment of variant allele fractions (VAFs) in clinical cancer genetics. However, these advances have exposed the limitations of equating molecular detection with a definitive germline diagnosis. The most clinically consequential example is TP53: in leukocyte-derived DNA, a detected TP53 PV may represent constitutional germline heterozygosity, postzygotic constitutional mosaicism, or blood-limited clonal hematopoiesis, including acquired clonal expansion to high VAF. Rarely, a detected signal may reflect circulating tumor cell-related tumor-in-normal contamination. Separately, an apparent variant signal may reflect a technical artifact rather than a true biological finding. Accurate origin attribution is essential because Li-Fraumeni syndrome (LFS) confers a high lifetime risk of multiple cancers, and confirmed constitutional TP53 PVs have major implications for lifelong surveillance, cancer treatment, reproductive counseling, and cascade testing. Misattribution may lead to either overdiagnosis with unnecessary interventions or under-recognition of clinically significant constitutional mosaicism. This interpretive challenge now extends beyond hereditary cancer clinics. In tumor-only cancer genomic profiling (CGP) or liquid biopsy, a TP53 PV does not by itself establish germline origin but may prompt germline follow-up when allele fraction, tumor context, and clinical features are compatible. This issue is particularly relevant in Japan, where germline TP53 testing for suspected LFS has not been routinely available under the national health insurance system and where CGP has become an important entry point to presumed germline or uncertain-origin findings. This review proposes an origin-aware, three-layer framework comprising variant pathogenicity assessment, origin attribution, and clinical actionability. The goal is structured management of uncertainty to support proportionate surveillance, cascade testing, and shared decision-making.