Systematic characterization of germline variants from the DiscovEHR study endometrial carcinoma population

Abstract

Background: Endometrial cancer (EMCA) is the fifth most common cancer among women in the world. Identification of potentially pathogenic germline variants from individuals with EMCA will help characterize genetic features that underlie the disease and potentially predispose individuals to its pathogenesis.

Methods: The Geisinger Health System's (GHS) DiscovEHR cohort includes exome sequencing on over 50,000 consenting patients, 297 of whom have evidence of an EMCA diagnosis in their electronic health record. Here, rare variants were annotated as potentially pathogenic.

Results: Eight genes were identified as having increased burden in the EMCA cohort relative to the non-cancer control cohort. None of the eight genes had an increased burden in the other hormone related cancer cohort from GHS, suggesting they can help characterize the underlying genetic variation that gives rise to EMCA. Comparing GHS to the cancer genome atlas (TCGA) EMCA germline data illustrated 34 genes with potentially pathogenic variation and eight unique potentially pathogenic variants that were present in both studies. Thus, similar germline variation among genes can be observed in unique EMCA cohorts and could help prioritize genes to investigate for future work.

Conclusion: In summary, this systematic characterization of potentially pathogenic germline variants describes the genetic underpinnings of EMCA through the use of data from a single hospital system.

Keywords: DiscovEHR; Endometrial Cancer; Germline variants; TCGA; Uterine Cancer; Whole exome sequencing.

Fig. 1

Waterfall plot of all genes with pathogenic variants. Waterfall plot of all EMCA samples that contained rare variants that passed the filter from Additional file 1: Figure S1. The main heatmap contains columns which represent an individual participant (N = 86), and rows that represent genes, while the color that fills in the cell represents the type of variant present for a specific participant in a specific gene. The heatmap below illustrates that histology, cancer stage and patient survival status, each column represents a different participant. “Undiff” refers to undifferentiated histology. The graph to the left shows the percentage of participants who have a rare variant in a gene, relative to all participants with variants, while the bar plot above the main graph represents the variant burden for each participant

Fig. 2

Distribution of types of rare variants across cohorts. The total number of unique variants are represented by their VEP annotation. Additionally, each type of variant is grouped by the variant category (a). The percentage of each variant category represented in each cohort (b). The overlap between genes with variants represented in each cohort (c). The number of unique loci (variants) that overlap between each cohort (d)

Fig. 3

Non-synonymous and pLoF variants among EMCA to non-cancer control cohort. a For each gene with at least two variants in both EMCA and NCC, the ratio of non-synonymous variants across the EMCA cohort was divided by those in the NCC after adjusting for differences in cohort size. Orange, blue and red lines are used to delineate 2, 1 and 0.5 fold EMCA burden relative to the NCC cohort. b The total number of rare non-synonymous variants from each cohort for each gene. c and d the same as a and b, respectively, except pLoF variants were used

Fig. 4

Overlap between DiscovEHR and TCGA germline variants from EMCA and uterine cancer samples. Rare potentially pathogenic variants were identified from the germline uterine cancer cohort in TCGA. The overlap between TCGA variants and those from this study (DiscovEHR) is illustrated in the Venn diagram (a). The overlap of genes from TCGA and the genes with variants from this EMCA cohort from this study (b)