Peter Kraft

Associations of self-identified race and ethnicity and genetic ancestry with mortality among cancer survivors

Abstract Self-identified race and ethnicity (SIRE) and genetic ancestry are potentially associated with disparities in health outcomes; however, independent effects of SIRE and genetic ancestry on mortality in cancer survivors including when adjusting for multiple risk factors are understudied. Among 23 445 cancer survivors in the Prostate, Lung, Colorectal, and Ovarian Screening Trial, SIRE was associated with mortality among prostate, colorectal, lung, ovarian, and breast cancer survivors; genetic ancestry was associated with mortality among prostate, colorectal, and breast cancer survivors. Associations were strong when adjusting for age at cancer diagnosis, sex, and tumor characteristics but attenuated when adjusting for individual-level factors and population-level socioeconomic status. For example, mortality risk was higher among Black vs White prostate cancer survivors and African genetic ancestry vs European genetic ancestry, but associations were attenuated after multilevel adjustment. Results suggest that SIRE and genetic ancestry do not solely reflect biologic variation; rather, social factors may drive mortality differences by SIRE and genetic ancestry.

Risk prediction models for endometrial cancer: development and validation in an international consortium

Abstract Background Endometrial cancer risk stratification may help target interventions, screening, or prophylactic hysterectomy to mitigate the rising burden of this cancer. However, existing prediction models have been developed in select cohorts and have not considered genetic factors. Methods We developed endometrial cancer risk prediction models using data on postmenopausal White women aged 45-85 years from 19 case-control studies in the Epidemiology of Endometrial Cancer Consortium (E2C2). Relative risk estimates for predictors were combined with age-specific endometrial cancer incidence rates and estimates for the underlying risk factor distribution. We externally validated the models in 3 cohorts: Nurses’ Health Study (NHS), NHS II, and the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Results Area under the receiver operating characteristic curves for the epidemiologic model ranged from 0.64 (95% confidence interval [CI] = 0.62 to 0.67) to 0.69 (95% CI = 0.66 to 0.72). Improvements in discrimination from the addition of genetic factors were modest (no change in area under the receiver operating characteristic curves in NHS; PLCO = 0.64 to 0.66). The epidemiologic model was well calibrated in NHS II (overall expected-to-observed ratio [E/O] = 1.09, 95% CI = 0.98 to 1.22) and PLCO (overall E/O = 1.04, 95% CI = 0.95 to 1.13) but poorly calibrated in NHS (overall E/O = 0.55, 95% CI = 0.51 to 0.59). Conclusions Using data from the largest, most heterogeneous study population to date (to our knowledge), prediction models based on epidemiologic factors alone successfully identified women at high risk of endometrial cancer. Genetic factors offered limited improvements in discrimination. Further work is needed to refine this tool for clinical or public health practice and expand these models to multiethnic populations.

Germline Cancer Gene Expression Quantitative Trait Loci Are Associated with Local and Global Tumor Mutations

Abstract Somatic mutations drive cancer development and are relevant to patient responses to treatment. Emerging evidence shows that variations in the somatic genome can be influenced by the germline genetic background. However, the mechanisms underlying these germline–somatic associations remain largely obscure. We hypothesized that germline variants can influence somatic mutations in a nearby cancer gene (“local impact”) or a set of recurrently mutated cancer genes across the genome (“global impact”) through their regulatory effect on gene expression. To test this hypothesis, tumor targeted sequencing data from 12,413 patients across 11 cancer types in the Dana-Farber Profile cohort were integrated with germline cancer gene expression quantitative trait loci (eQTL) from the Genotype-Tissue Expression Project. Variants that upregulate ATM expression were associated with a decreased risk of somatic ATM mutations across 8 cancer types. GLI2, WRN, and CBFB eQTL were associated with global tumor mutational burden of cancer genes in ovarian cancer, glioma, and esophagogastric carcinoma, respectively. An EPHA5 eQTL was associated with mutations in cancer genes specific to colorectal cancer, and eQTL related to expression of APC, WRN, GLI1, FANCA, and TP53 were associated with mutations in genes specific to endometrial cancer. These findings provide evidence that germline–somatic associations are mediated through expression of specific cancer genes, opening new avenues for research on the underlying biological processes. Significance: Analysis of associations between the germline genetic background and somatic mutations in patients with cancer suggests that germline variants can influence local and global tumor mutations by altering expression of cancer-related genes. See related commentary by Kar, p. 1165.