IMPORTANCE CDH1 pathogenic variants have been estimated to confer a 40% to 70% and 56% to 83% lifetime risk for gastric cancer in men and women, respectively. These are likely to be overestimates owing to ascertainment of families with multiple cases of gastric cancer. To our knowledge, there are no penetrance estimates for CDH1 without this ascertainment bias. OBJECTIVE To estimate CDH1 penetrance in a patient cohort not exclusively ascertained based on strict hereditary diffuse gastric cancer (HDGC) criteria. DESIGN, SETTING, AND PARTICIPANTS Retrospective review of 75 families found to have pathogenic variants in CDH1 through clinical ascertainment and multigene panel testing at a large commercial diagnostic laboratory from August 5, 2013, to June 30, 2018. CDH1 pathogenic variants were identified in 238 individuals from 75 families. Pedigrees from those families included cancer status for 1679 relatives. Penetrance estimates are based on 41 families for which completed pedigrees were available. MAIN OUTCOMES AND MEASURES Gastric cancer standardized incidence ratio estimates relative to Surveillance, Epidemiology, and End Results (SEER) Program incidence for pathogenic CDH1 variants from families ascertained without regard to HDGC criteria. RESULTS Among the 238 individuals with a CDH1 pathogenic variant, mean (SD) age was 49.3 (18.1) years and 63.4% were female. Ethnicity was reported for 67 of 75 (89%) families; of these 67 families, 51 (76%) reported European ancestry, whereas Asian, African, Latino, and 2 or more ancestries were reported for 4 families (6%) each. Standardized incidence ratios for gastric and breast cancer were significantly elevated above SEER incidence. Extrapolated cumulative incidence of gastric cancer at age 80 years was 42% (95% CI, 30%-56%) for men and 33% (95% CI, 21%-43%) for women with pathogenic variants in CDH1, whereas cumulative incidence of female breast cancer was estimated at 55% (95% CI, 39%-68%). International Gastric Cancer Linkage Consortium criteria were met in 25 of the 75 (33%) families; however, dispensing with the requirement of confirmation of HDGC histologic subtype, 43 (57%) would meet criteria. CONCLUSIONS AND RELEVANCE The cumulative incidence of gastric cancer for individuals with pathogenic variants in CDH1 is significantly lower than previously described. Because prophylactic gastrectomy can have bearing upon both physical and psychological health, further discussion is warranted to assess whether this surgical recommendation is appropriate for all individuals with pathogenic variants in CDH1.

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The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/

Present guidelines for classification of constitutional variants do not incorporate inferences from mutations seen in tumors, even when these are associated with a specific molecular phenotype. When somatic mutations and constitutional mutations lead to the same molecular phenotype, as for the mismatch repair genes, information from somatic mutations may enable interpretation of previously unclassified variants. To test this idea, we first estimated likelihoods that somatic variants in MLH1, MSH2, MSH6, and PMS2 drive microsatellite instability and characteristic IHC staining patterns by calculating likelihoods of high versus low normalized variant read fractions of 153 mutations known to be pathogenic versus those of 760 intronic passenger mutations from 174 paired tumor-normal samples. Mutations that explained the tumor mismatch repair phenotype had likelihood ratio for high variant read fraction of 1.56 (95% CI 1.42-1.71) at sites with no loss of heterozygosity and of 26.5 (95% CI 13.2-53.0) at sites with loss of heterozygosity. Next, we applied these ratios to 165 missense, synonymous, and splice variants observed in tumors, combining in a Bayesian analysis the likelihood ratio corresponding with the adjusted variant read fraction with pretest probabilities derived from published analyses and public databases. We suggest classifications for 86 of 165 variants: 7 benign, 31 likely benign, 22 likely pathogenic, and 26 pathogenic. These results illustrate that for mismatch repair genes, characterization of tumor mutations permits tumor mutation data to inform constitutional variant classification. We suggest modifications to incorporate molecular phenotype in future variant classification guidelines.

All data used are fully available online from their respective sites. Source code and software is available from http://code.google.com/p/poisson-multigraph/.