TP53 Variant Complexity in Context: Multidimensional Functional Profiling for Precision Oncology

Objetivo

The genomic landscape of cancer reveals profound complexity, with tumor cells typically accumulating thousands of mutations. While next-generation sequencing maps these alterations across malignancies, translating mutation catalogs into functional and clinically actionable knowledge remains a critical bottleneck in precision oncology. This challenge is exemplified by TP53, a master regulator of cellular stress responses and the most frequently mutated gene in human cancer. Despite its clinical prominence, most TP53 variants remain uncharacterized, with over 1,000 classified as variants of uncertain significance. This reflects the mechanistic and biological complexity of mutant TP53 dysfunction, spanning loss-of-function, gain-of-function, and dominant-negative effects—each shaped by genetic background, cellular identity, and microenvironmental signals. Existing assays fail to capture this multidimensional complexity, limiting actionable insights for clinical decision-making.
TP53in4D will transform TP53 variant interpretation by developing SWAP-IN, an innovative genomic replacement technology enabling saturation mutagenesis of the endogenous TP53 locus under native regulatory control. Applied across diverse cellular models and genetic backgrounds, and integrated with single-cell transcriptomics and time-resolved phenotypic assays, TP53in4D will (1) map variant effects along all functional axes; (2) define how genetic, cellular, and microenvironmental contexts modulate these effects; and (3) build a mechanistic classification framework linking variants to clinical outcome. The resulting resource will resolve long-standing ambiguities in TP53 interpretation, reveal context-specific therapeutic vulnerabilities, and deliver a validated clinical annotation system. Beyond TP53, SWAP-IN establishes a scalable blueprint for decoding cancer genome variation, bridging the gap between mutation detection and biological understanding in precision oncology.