Lin, Hui‐Yi

Abstract Introduction Men with African ancestry have the highest incidence and mortality rates of prostate cancer (PCa) worldwide. Methods This study aimed to identify differentially methylated genes between tumor vs. adjacent normal and aggressive vs. indolent PCa in 121 African American patients. Epigenome‐wide DNA methylation patterns in tumor DNA were assessed using the human Illumina Methylation EPIC V1 array. Results Around 5,139 differentially methylated CpG‐sites (q < 0.01, lΔβl > 0.2) were identified when comparing normal vs. tumor, with an overall trend of hypermethylation in prostate tumors. Multiple representative differentially methylated regions (DMRs), including immune‐related genes, such as CD40 , Galectin3 , OX40L , and STING , were detected in prostate tumors when compared to adjacent normal tissues. Based on an epigenetic clock model, we observed that tumors’ total number of stem cell divisions and the stem cell division rate were significantly higher than adjacent normal tissues. Regarding PCa aggressiveness, 2,061 differentially methylated CpG‐sites (q < 0.05, lΔβl > .05) were identified when the grade group (GG)1 was compared with GG4/5. Among these 2,061 CpG sites, 155 probes were consistently significant in more than one comparison. Among these genes, several immune system genes, such as COL18A1 , S100A2 , ITGA4 , HLA‐C , and ADCYAP1 , have previously been linked to tumor progression in PCa. Conclusion Several differentially methylated genes involved in immune‐oncologic pathways associated with disease risk or aggressiveness were identified. In addition, 261 African American‐specific differentially methylated genes related to the risk of PCa were identified. These results can shedlight on potential mechanisms contributing to PCa disparities in the African American Population.

Abstract Background: A polygenic hazard score (PHS), the weighted sum of 54 SNP genotypes, was previously validated for association with clinically significant prostate cancer and for improved prostate cancer screening accuracy. Here, we assess the potential impact of PHS-informed screening. Methods: United Kingdom population incidence data (Cancer Research United Kingdom) and data from the Cluster Randomized Trial of PSA Testing for Prostate Cancer were combined to estimate age-specific clinically significant prostate cancer incidence (Gleason score ≥7, stage T3–T4, PSA ≥10, or nodal/distant metastases). Using HRs estimated from the ProtecT prostate cancer trial, age-specific incidence rates were calculated for various PHS risk percentiles. Risk-equivalent age, when someone with a given PHS percentile has prostate cancer risk equivalent to an average 50-year-old man (50-year-standard risk), was derived from PHS and incidence data. Positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was calculated using PHS-adjusted age groups. Results: The expected age at diagnosis of clinically significant prostate cancer differs by 19 years between the 1st and 99th PHS percentiles: men with PHS in the 1st and 99th percentiles reach the 50-year-standard risk level at ages 60 and 41, respectively. PPV of PSA was higher for men with higher PHS-adjusted age. Conclusions: PHS provides individualized estimates of risk-equivalent age for clinically significant prostate cancer. Screening initiation could be adjusted by a man's PHS. Impact: Personalized genetic risk assessments could inform prostate cancer screening decisions.

AbstractMotivationThe use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP–SNP interactions.ResultsWe tested two candidate approaches: the ‘Five-Full’ and ‘AA9int’ method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP–SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP–SNP interactions in large-scale studies.Availability and implementationThe ‘AA9int’ and ‘parAA9int’ functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/.Supplementary informationSupplementary data are available at Bioinformatics online.

Abstract Background: Many cancers share specific genetic risk factors, including both rare high-penetrance mutations and common SNPs identified through genome-wide association studies (GWAS). However, little is known about the overall shared heritability across cancers. Quantifying the extent to which two distinct cancers share genetic origin will give insights to shared biological mechanisms underlying cancer and inform design for future genetic association studies. Methods: In this study, we estimated the pair-wise genetic correlation between six cancer types (breast, colorectal, lung, ovarian, pancreatic, and prostate) using cancer-specific GWAS summary statistics data based on 66,958 case and 70,665 control subjects of European ancestry. We also estimated genetic correlations between cancers and 14 noncancer diseases and traits. Results: After adjusting for 15 pair-wise genetic correlation tests between cancers, we found significant (P &lt; 0.003) genetic correlations between pancreatic and colorectal cancer (rg = 0.55, P = 0.003), lung and colorectal cancer (rg = 0.31, P = 0.001). We also found suggestive genetic correlations between lung and breast cancer (rg = 0.27, P = 0.009), and colorectal and breast cancer (rg = 0.22, P = 0.01). In contrast, we found no evidence that prostate cancer shared an appreciable proportion of heritability with other cancers. After adjusting for 84 tests studying genetic correlations between cancer types and other traits (Bonferroni-corrected P value: 0.0006), only the genetic correlation between lung cancer and smoking remained significant (rg = 0.41, P = 1.03 × 10−6). We also observed nominally significant genetic correlations between body mass index and all cancers except ovarian cancer. Conclusions: Our results highlight novel genetic correlations and lend support to previous observational studies that have observed links between cancers and risk factors. Impact: This study demonstrates modest genetic correlations between cancers; in particular, breast, colorectal, and lung cancer share some degree of genetic basis. Cancer Epidemiol Biomarkers Prev; 26(9); 1427–35. ©2017 AACR.

Abstract Motivation Testing SNP–SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP–SNP interactions are underdeveloped. Results We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR, EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP–SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. Availability and Implementation The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/. Supplementary information Supplementary data are available at Bioinformatics online.

Background The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study. Methods We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T>G and c.3113G>A, CHEK2 c.349A>G, c.538C>T, c.715G>A, c.1036C>T, c.1312G>T, and c.1343T>G and ATM c.7271T>G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant. Results For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10−5), PALB2 c.3113G>A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10−8) and ATM c.7271T>G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A>G OR 2.26 (95% CI 1.29 to 3.95), c.1036C>T OR 5.06 (95% CI 1.09 to 23.5) and c.538C>T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T>G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G>T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants. Conclusions This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.

Abstract Background: Epithelial Ovarian Cancer (EOC) is a lethal gynecologic malignancy. Mitochondria are cellular organelles that regulate energy metabolism and are the primary source of reactive oxygen species (ROS), which damage both the nuclear and mitochondria DNA (mtDNA), contributing to the pathogenesis of EOC. Previously, we identified statistically significant EOC risk associations with 54 single nucleotide polymorphisms (SNPs) from 11 mitochondria-related genes in a study of 1815 EOC cases and 1900 controls. In this replication study we expand our investigation of mitochondrial metabolism genetic variants and associations with EOC risk and overall survival (OS). Methods: In a discovery set of 3761 EOC cases and 2722 controls, we investigated risk associations in 4200 SNPs from 130 mitochondrial metabolism genes. We genotyped 431 of the most significant SNPs from 86 genes in 14,525 EOC cases and 23,447 controls (of European ancestry) from 43 studies in OCAC using an Illumina Infinium iSelect BeadChip. Risk analyses were conducted using unconditional logistic regression under a log-additive model and adjusted for study site and the first five principal components of population substructure. Both invasive cancers combined and the four main histological subtypes were analyzed. Survival analyses were conducted on the same SNPs using Cox hazards models to estimate hazard ratios (HRs) and 95% CIs considering overall survival in 10,084 cases from 28 OCAC studies. We adjusted for multiple tests using False Discovery Rate q&lt;20%. Results: The strongest evidence of an association for risk of invasive cancers combined and the serous subtype was SNP rs3096 in the pyridoxamine 5'-phosphate oxidase (PNPO) gene (OR=1.08, p=2.8x10-6). In clear cell carcinoma, the strongest association was a missense coding SNP rs28379632 (Ile421Met) in the MT-CYB gene (OR=4.0, p=0.003). For endometrioid cancer rs9330440 in the UNQ6494 gene was most significant (OR=0.89, p=0.001) while rs10267797 in the dihydrolipoamide dehydrogenase (DLD) gene was most significant in the mucinous subtype (OR=1.15, p=0.004). The strongest association with overall survival was mitochondrial transcription termination factor 1 (MTERF) rs2540571 (HR=0.92, p= 0.00011). Conclusion: These results suggest that mitochondrial metabolism gene variants appear to be associated with EOC risk and survival. Citation Format: Ganna Chornokur, Hui-Yi Lin, Alvaro Monteiro, Joellen Schildkraut, Ellen L. Goode, Paul DP Pharoah, Simon A. Gayther, Susan Ramus, Thomas A. Sellers, Catherine M. Phelan. Mitochondrial metabolism gene variants are associated with risk and survival in epithelial ovarian cancer (EOC). [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr A50.

Abstract Background: Epithelial Ovarian Cancer (EOC) is a lethal gynecologic malignancy and the fifth cause of cancer mortality in women in the US. Normal ovarian physiology is intricately connected to tightly regulated small GTP binding proteins of the Ras superfamily (Ras, Rac, Rho, Rab, Arf, and Ran) which regulate key cellular processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. These proteins function in a highly coordinated manner through signaling cascades and feedback loops within and among the small GTPase subfamilies. We hypothesized that single nucleotide polymorphisms (SNPs) in small GTPase genes are associated with epithelial ovarian cancer (EOC) risk and aberrant expression of these genes correlates with tumor characteristics including overall survival (OS). Methods: In a discovery set of 7931 EOC cases and 9206 controls we investigated 9,356 SNPs from 112 genes, 657 of which showed associations up to the significance level of p&lt;0.05. We genotyped 407 of the most significant SNPs from 112 genes in a combined dataset (discovery and replication) which consisted of 18,736 EOC cases and 23,448 controls (of European ancestry) from 43 studies in the Ovarian Cancer Association Consortium (OCAC), using an Illumina Infinium iSelect BeadChip as part of the Collaborative Oncological Gene-environment Study (COGS). Odds ratios and 95% confidence intervals were calculated using unconditional logistic regression under log-additive models. A False Discovery Rate (FDR) q&lt;0.2 was applied. Pearson's correlation tests were performed on the expression of genes with tumor characteristics and OS in 561 ovarian tumors: serous (n=464); endometrioid (n=52); clear cell carcinoma (n=28) and mucinous (n=17) subtypes. Results: The most significantly associated SNP associations with EOC were: AKAP6 rs1955513, (OR=0.9, p=3.3x10-4) in all invasive; ARGHEF10L rs10788679, (OR=1.05, p=2.6x10-4) in serous; RAB31 rs1166373, (OR=1.25, p=4.0x10-3) in endometrioid; KRAS rs4963872, (OR=1.35, P=4.5x10-4) in mucinous EOC and TNIK rs6780532, (OR=0.88, p=7.7x10-3) in clear cell carcinoma. Increased expression of AKAP6 and TNIK was marginally correlated with advanced EOC stage (p&lt;0.05) while high KRAS expression was correlated with OS (HR=1.18, p=0.038). Conclusions: Genetic variation in the small GTPase genes appears to be associated with ovarian cancer risk and aberrant expression of these genes correlates with tumor characteristics and OS. Citation Format: Hui-Yi Lin, Yin Xiong, Jonathan Tyrer, Douglas C. Marchion, Alvaro NA Monteiro, Andrew Berchuck, Joellen M. Schildkraut, Ellen L. Goode, Susan J. Ramus, Simon A. Gayther, Paul DP Pharoah, Steven A. Narod, Thomas A. Sellers, Catherine M. Phelan. Investigation of small GTPase genes in epithelial ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr B27.

Abstract Background: While numerous susceptibility loci for epithelial ovarian cancer (EOC) have been identified, few associations have been reported with overall survival. In the absence of common prognostic genetic markers, we hypothesize that rare coding variants may be associated with overall EOC survival and assessed their contribution in two exome-based genotyping projects of the Ovarian Cancer Association Consortium (OCAC). Methods: The primary patient set (Set 1) included 14 independent EOC studies (4,293 patients) and 227,892 variants, and a secondary patient set (Set 2) included six additional EOC studies (1,744 patients) and 114,620 variants. Because power to detect rare variants individually is reduced, gene-level tests were conducted. Sets were analyzed separately at individual variants and by gene, and then combined with meta-analyses (73,203 variants and 13,163 genes overlapped). Results: No individual variant reached genome-wide statistical significance. A SNP previously implicated to be associated with EOC risk and, to a lesser extent, survival, rs8170, showed the strongest evidence of association with survival and similar effect size estimates across sets (Pmeta = 1.1E−6, HRSet1 = 1.17, HRSet2 = 1.14). Rare variants in ATG2B, an autophagy gene important for apoptosis, were significantly associated with survival after multiple testing correction (Pmeta = 1.1E−6; Pcorrected = 0.01). Conclusions: Common variant rs8170 and rare variants in ATG2B may be associated with EOC overall survival, although further study is needed. Impact: This study represents the first exome-wide association study of EOC survival to include rare variant analyses, and suggests that complementary single variant and gene-level analyses in large studies are needed to identify rare variants that warrant follow-up study. Cancer Epidemiol Biomarkers Prev; 25(3); 446–54. ©2016 AACR.