Systematic evaluation of cancer‐specific genetic risk score for 11 types of cancer in The Cancer Genome Atlas and Electronic Medical Records and Genomics cohorts

Abstract

Background Genetic risk score (GRS) is an odds ratio (OR)‐weighted and population‐standardized method for measuring cumulative effect of multiple risk‐associated single nucleotide polymorphisms (SNPs). We hypothesize that GRS is a valid tool for risk assessment of most common cancers. Methods Utilizing genotype and phenotype data from The Cancer Genome Atlas (TCGA) and Electronic Medical Records and Genomics (eMERGE), we tested 11 cancer‐specific GRSs (bladder, breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, prostate, renal, and thyroid cancer) for association with the respective cancer type. Cancer‐specific GRSs were calculated, for the first time in these cohorts, based on previously published risk‐associated SNPs using the Caucasian subjects in these two cohorts. Results Mean cancer‐specific GRS in the population controls of eMERGE approximated the expected value of 1.00 (between 0.98 and 1.02) for all 11 types of cancer. Mean cancer‐specific GRS was consistently higher in respective cancer patients than controls for all 11 types of cancer (P < 0.05). When subjects were categorized into low‐, average‐, and high‐risk groups based on cancer‐specific GRS (<0.5, 0.5‐1.5, and >1.5, respectively), significant dose‐response associations of higher cancer‐specific GRS with higher OR of respective type of cancer were found for nine types of cancer (P‐trend < 0.05). More than 64% subjects in the population controls of eMERGE can be classified as high risk for at least one type of these cancers. Conclusion Validity of GRS for predicting cancer risk is demonstrated for most types of cancer. If confirmed in larger studies, cancer‐specific GRS may have the potential for developing personalized cancer screening strategy.

Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction

“…Few pan-cancer PRS studies have been conducted in prospective cohorts and none have considered the breadth of modifiable risk factors that we evaluated. Shi et al 17 tested 11 cancer PRS in cases from The Cancer Genome Atlas and controls from the Electronic Medical Records and Genomics Network. This analysis was limited by fewer risk variants in each PRS, as well as potential for bias due to selection of cases and controls from different populations.…”

Evaluating polygenic risk scores in assessing risk of nine solid and hematologic cancers in European descendants

“…For example, Gu et al built a PRS for melanoma based on their metaanalysis, which reported an AUC of 0.64 (95% CI = 0.63‐0.66) and a 3.33‐fold increased risk when comparing the fifth to the first PRS quintile 27 . For glioma, significant dose‐response associations were observed for higher PRS with higher OR ( P value = 4.49 × 10 −31 ) 28 . For chronic lymphoid leukemia, PRS were strongly associated with disease risk (OR, 3.02; P value = 7.8 × 10 −30 ) and showed high discrimination ability (AUC = 0.78) 29 .…”

“…27 For glioma, significant dose-response associations were observed for higher PRS with higher OR (P value = 4.49 × 10 −31 ). 28 For chronic lymphoid leukemia, PRS were strongly associated with disease risk (OR, 3.02;…”

Cancer Diagnosis, Polygenic Risk, and Longevity-Associated Variants

“…The degree of overlap in the genetic determinants of cancers has been explored through the examination of the genetic correlations derived from individual GWAS results and data, as well as studies of the pleiotropic effects of individual genetic variants on multiple cancers. (7,18,20,49,50) In addition, many cancers have also been shown to be genetically correlated with non-cancer-related diseases. (24,51) These genetic correlations, possibly exacerbated by relevant environmental factors, are also quite likely to lead to cancer patients developing a second primary cancer (as opposed to recurrent cancers) or have comorbid illness.…”