Jessica L. Mester

PURPOSE: Age-adjusted cancer incidence and age-related penetrance studies have helped guide cancer risk assessment and management. PTEN hamartoma tumor syndrome (PHTS) is a term encompassing subsets of several clinical syndromes with germline mutations in the PTEN tumor suppressor gene. We conducted the first prospective study to clarify corresponding cancer risks to shed biologic insights on human germline PTEN mutations, and to better inform current surveillance recommendations on the basis of expert opinion. EXPERIMENTAL DESIGN: A series of 3,399 individuals meeting relaxed International Cowden Consortium PHTS criteria were prospectively recruited; 368 individuals were found to have deleterious germline PTEN mutations. Age-adjusted standardized incidence ratio (SIR) calculations and genotype-phenotype analyses were carried out. RESULTS: Elevated SIRs were found for carcinomas of the breast [25.4, 95% confidence interval (CI), 19.8-32.0], thyroid (51.1, 38.1-67.1), endometrium (42.9, 28.1-62.8), colorectum (10.3, 5.6-17.4), kidney (30.6, 17.8-49.4), and melanoma (8.5, 4.1-15.6). Estimated lifetime risks were, respectively, 85.2% (95% CI, 71.4%-99.1%), 35.2% (19.7%-50.7%), 28.2% (17.1%-39.3%), 9.0% (3.8%-14.1%), 33.6% (10.4%-56.9%), and 6% (1.6%-9.4%). Promoter mutations were associated with breast cancer, whereas colorectal cancer was associated with nonsense mutations. CONCLUSION: Lifetime risks for a variety of cancers, now extending to colorectal cancer, kidney cancer, and melanoma, are increased in patients with PTEN mutations. The genotype-phenotype associations here may provide new insights on PTEN structure and function. We propose a comprehensive approach to surveillance of patients with PTEN mutations.

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.