A zero-parameter framework for accurate TP53 missense variant functional classification.

Zero-parameter framework combining biophysical constraints to classify *TP53* missense variants without calibration on annotated variant data

This PLOS Computational Biology study presents a zero-parameter framework for functional classification of TP53 missense variants, based on structural and evolutionary biophysical constraints without calibration on annotated variant data. TP53 harbors over 2,000 distinct missense variants, many still classified as VUS. The model achieves high accuracy on independent validation datasets including experimental functional data (MAVE).

Synthesis written by Geno'X. For the full original abstract, please refer to the source publication.

TP53 VUS classification is a major clinical challenge in oncogenetics — each VUS can block surveillance or prophylaxis decisions for an entire family. A zero-parameter framework usable without local training data is particularly valuable for laboratories lacking sufficient internal cohorts to calibrate Bayesian models.

Clinical impact: 2/3 · Evidence strength: 3/3 · Novelty: 2/2 · Sample size: 1/1 · Publication status: 0/1 → Total: 8/10