An integrated deep mutational scanning approach provides clinical insights on PTEN genotype-phenotype relationships

Germline variation in PTEN results in variable clinical presentations, including benign and malignant neoplasia and neurodevelopmental disorders. Despite decades of research, it remains unclear how PTEN genotype is related to clinical outcomes. In this study, we combined two recent deep mutational scanning (DMS) datasets probing the effects of single-amino acid variation on enzyme activity and steady-state cellular abundance with the largest well-curated clinical cohort of PTEN-variant carriers. We sought to connect variant-specific molecular phenotypes to the clinical outcomes of individuals with PTEN variants. We found that DMS data partially explain quantitative clinical traits, including head circumference and Cleveland Clinic (CC) score, which is a semi-quantitative surrogate of disease burden. We built a logistic regression model using DMS and CADD scores to separate clinical PTEN variation from gnomAD control-only variation with high accuracy (AUC = 0.892). Using a survival-like analysis, we identified molecular phenotype groups with differential risk of early-onset as well as lifetime risk of cancer. Finally, we identified classes of DMS-defined variants with significantly different risk levels for classical hamartoma-related features (odds ratios range of 4.1-102.9). In stark contrast, the risk for developing autism or developmental delay does not significantly change across variant classes (odds ratios range of 5.4-12.4). Together, these findings highlight the potential impact of combining DMS datasets with rich clinical data, and provide new insights that may guide personalized clinical decisions for PTEN-variant carriers.