Xingyi Guo

Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk

AbstractThe relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.

Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes

Abstract Alternative polyadenylation (APA) modulates mRNA processing in the 3′-untranslated regions (3′ UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. In this study, we conducted large APA-wide association studies to investigate associations between APA levels and cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA sequencing data from 1,337 samples from the Genotype-Tissue Expression project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European ancestry populations: breast, ovarian, prostate, colorectal, lung, and pancreatic cancers. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3′-UTR APA quantitative trait loci and colocalization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3′-UTR variants demonstrated that the risk alleles of 3′-UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the posttranscriptional activities of their target genes compared with reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers. Significance: Systematic evaluation of associations of alternative polyadenylation with cancer risk reveals 58 putative susceptibility genes, highlighting the contribution of genetically regulated alternative polyadenylation of 3′UTRs to genetic susceptibility to cancer.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention

Understanding risk factors for endometrial cancer in young women

Abstract Background The American Cancer Society recommends physicians inform average-risk women about endometrial cancer risk on reaching menopause, but new diagnoses are rising fastest in women aged younger than 50 years. Educating these younger women about endometrial cancer risks requires knowledge of risk factors. However, endometrial cancer in young women is rare and challenging to study in single study populations. Methods We included 13 846 incident endometrial cancer patients (1639 aged younger than 50 years) and 30 569 matched control individuals from the Epidemiology of Endometrial Cancer Consortium. We used generalized linear models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for 6 risk factors and endometrial cancer risk. We created a risk score to evaluate the combined associations and population attributable fractions for these factors. Results In younger and older women, we observed positive associations with body mass index and diabetes and inverse associations with age at menarche, oral contraceptive use, and parity. Current smoking was associated with reduced risk only in women aged 50 years and older (Phet < .01). Body mass index was the strongest risk factor (OR≥35 vs<25 kg/m2 = 5.57, 95% CI = 4.33 to 7.16, for ages younger than 50 years; OR≥35 vs<25 kg/m2 = 4.68, 95% CI = 4.30 to 5.09, for ages 50 years and older; Phet = .14). Possessing at least 4 risk factors was associated with approximately ninefold increased risk in women aged younger than 50 years and approximately fourfold increased risk in women aged 50 years and older (Phet < .01). Together, 59.1% of endometrial cancer in women aged younger than 50 years and 55.6% in women aged 50 years and older were attributable to these factors. Conclusions Our data confirm younger and older women share common endometrial cancer risk factors. Early educational efforts centered on these factors may help mitigate the rising endometrial cancer burden in young women.

Distinct Genomic Landscapes in Early-Onset and Late-Onset Endometrial Cancer

PURPOSEThe spectrum of somatic mutations among women with endometrial cancer (EC) younger than 50 years (early-onset EC) remains unknown. We investigated distinct somatic mutation patterns among early-onset and late-onset (age ≥ 50 years) EC patients.METHODSThis cohort study included individuals age 18+ years diagnosed with pathologically confirmed EC in the American Association of Cancer Research (AACR) Genomics Evidence Neoplasia Information Exchange (GENIE, v9.1) consortium. We explored tumor mutational burden (TMB) and genomic patterns of EC by age at clinical sequencing using multivariable regression models adjusted for race, ethnicity, histology, sequencing assay, sample type, and TMB.RESULTSAmong 2,425 women with EC, 176 (7.3%) had early-onset EC and 1,923 (79.3%) had nonhypermutated (< 17.78 mutations/Mb) tumors. TMB significantly differed across age and histology groups. Among nonhypermutated ECs, early-onset patients had significantly lower odds of presenting with nonsilent FGFR2 and PIK3R1 somatic mutations compared with late-onset EC patients in adjusted models ( FGFR2: odds ratio [OR] = 0.18, 95% CI, 0.04 to 0.76; PIK3R1: OR = 0.54, 95% CI, 0.31 to 0.92). By contrast, early-onset EC patients had increased odds of presenting with nonsilent CTNNB1 and BRCA2 mutations compared with late-onset patients ( CTNNB1: OR = 3.32, 95% CI, 2.14 to 5.16; BRCA2: OR = 4.01, 95% CI, 1.55 to 10.38). Subsequent analyses stratified by race, ethnicity, and tumor histology identified distinct patterns of APC, KMT2D, KMT2C, and KRAS by race, ethnicity, and PTEN and APC patterns by histologic subtypes.CONCLUSIONEarly-onset EC harbors a unique genomic landscape compared with late-onset disease. A distinct molecular phenotype of early-onset EC provides novel insights into a unique etiology and may yield clinical implications for developing targeted treatment modalities for younger patients.