Association Studies Of Cancer Research Articles

Risk genetic polymorphism and haplotype associated with papillary thyroid cancer and their relation to associated diseases in Slovak population.

Risk for developing papillary thyroid carcinoma (PTC), the most common endocrine malignancy, is thought to be mediated by lifestyle, environmental exposures and genetic factors. Recent progress in the genome-wide association studies of thyroid cancer leads to the identification of several genetic variants conferring risk to this malignancy across different ethnicities. We set out to elucidate the impact of selected single nucleotide polymorphisms (SNPs) on papillary thyroid carcinoma risk and to evaluate the interactions of these genetic variants with associated diseases for the first time in the Slovak population. Six SNPs (rs966423, rs2439302, rs965513, rs116909374, rs1537424 and rs944289) were genotyped in 86 patients with PTC and 99 healthy control subjects. The association analysis and multivariable modelling of PTC risk by the genetic factors, supplemented with a rigorous statistical validation, were performed. One of the six SNPs rs966423 (DIRC3, OR=1.51, p=0.03) was significantly associated with PTC. Next two SNPs rs965513 (PTCSC2, OR=1.34) and rs116909374 (MBIP, OR=0.44) showed asuggestive association. Haplotype TTC (SNPs located on chromosome 14q13) showed asuggestive association with PTC (p=0.07, OR=1.55). In the PTC group, significant associations were observed between rs966423 (DIRC3) and ischemic heart diseases (p=0.009), rs965513 (PTCSC2) and diabetes mellitus (p=0.04) and haplotype 14q13 and musculoskeletal diseases. Next three associations rs966423 (DIRC3) and arterial hypertension; rs116909374 (MBIP) and other benign diseases; rs1537424 (MBIP) and disorder lipid metabolism, rs965513 (PTCSC2) and anti-Tg (thyroglobulin antibody) showed suggestive associations. These results indicate that germline variants not only predispose to PTC, but may also be related to other risk factors, including associated diseases. However, these associations were only moderate, and further multi-ethnic studies are required to evaluate the usefulness of these germline variants in the clinical stratification of PTC patients (Tab. 8, Ref. 37).

Allele-specific epigenetic activity in prostate cancer and normal prostate tissue implicates prostate cancer risk mechanisms

Genome-wide association studies (GWASs) of prostate cancer have identified >250 significant risk loci, but the causal variants and mechanisms for these loci remain largely unknown. Here, we sought to identify and characterize risk-harboring regulatory elements by integrating epigenomes from primary prostate tumor and normal tissues of 27 individuals across the H3K27ac, H3K4me3, and H3K4me2 histone marks and FOXA1 and HOXB13 transcription factors. We identified 7,371 peaks with significant allele specificity (allele-specific quantitative trait locus [asQTL] peaks). Showcasing their relevance to prostate cancer risk, H3K27ac T-asQTL peaks were the single annotation most enriched for prostate cancer GWAS heritability (40×), significantly higher than corresponding non-asQTL H3K27ac peaks (14×) or coding regions (14×). Surprisingly, fine-mapped GWAS risk variants were most significantly enriched for asQTL peaks observed in tumors, including asQTL peaks that were differentially imbalanced with respect to tumor-normal states. These data pinpointed putative causal regulatory elements at 20 GWAS loci, of which 11 were detected only in the tumor samples. More broadly, tumor-specific asQTLs were enriched for expression QTLs in benign tissues as well as accessible regions found in stem cells, supporting a hypothesis where some germline variants become reactivated during or after transformation and can be captured by epigenomic profiling of the tumor. Our study demonstrates the power of allele specificity in chromatin signals to uncover GWAS mechanisms, highlights the relevance of tumor-specific regulation in the context of cancer risk, and prioritizes multiple loci for experimental follow-up.

A pleiotropic ATM variant (rs1800057 C>G) is associated with risk of multiple cancers.

ATM (ataxia-telangiectasia mutated) is an important cell-cycle checkpoint kinase required for cellular response to DNA damage. Activated by DNA double strand breaks, ATM regulates the activities of many downstream proteins involved in various carcinogenic events. Therefore, ATM or its genetic variants may have a pleiotropic effect on cancer development. We conducted a pleiotropic analysis to evaluate associations between genetic variants of ATM and risk of multiple cancers. With genotyping data extracted from previously published genome-wide association studies of various cancers, we performed multivariate logistic regression analysis, followed by a meta-analysis for each cancer site, to identify cancer risk-associated single-nucleotide polymorphisms (SNPs). In the ASSET two-sided analysis, we found that two ATM SNPs were significantly associated with risk of multiple cancers. One tagging SNP (rs1800057 C>G) was associated with risk of multiple cancers (two-sided P = 5.27 × 10-7). Because ATM rs1800057 is a missense variant, we also explored the intermediate phenotypes through which this variant may confer risk of multiple cancers and identified a possible immune-mediated effect of this variant. Our findings indicate that genetic variants of ATM may have a pleiotropic effect on cancer risk and thus provide an important insight into common mechanisms of carcinogenesis.

Derandomizing Knockoffs

Model-X knockoffs is a general procedure that can leverage any feature importance measure to produce a variable selection algorithm, which discovers true effects while rigorously controlling the number or fraction of false positives. Model-X knockoffs is a randomized procedure which relies on the one-time construction of synthetic (random) variables. This article introduces a derandomization method by aggregating the selection results across multiple runs of the knockoffs algorithm. The derandomization step is designed to be flexible and can be adapted to any variable selection base procedure to yield stable decisions without compromising statistical power. When applied to the base procedure of Janson and Su, we prove that derandomized knockoffs controls both the per family error rate (PFER) and the k family-wise error rate (k-FWER). Furthermore, we carry out extensive numerical studies demonstrating tight Type I error control and markedly enhanced power when compared with alternative variable selection algorithms. Finally, we apply our approach to multistage genome-wide association studies of prostate cancer and report locations on the genome that are significantly associated with the disease. When cross-referenced with other studies, we find that the reported associations have been replicated. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

SNPs associated with colorectal cancer at 15q13.3 affect risk enhancers that modulate GREM1 gene expression.

Several genome wide association studies of colorectal cancer (CRC) have identified single nucleotide polymorphisms (SNPs) on chromosome 15q13.3 associated with CRC risk. To identify functional variant(s) underlying this association, we investigated SNPs in linkage disequilibrium with the risk‐associated SNP rs4779584 that overlapped regulatory regions/enhancer elements characterized in colon‐related tissues and cells. We identified several SNP‐containing regulatory regions that exhibited enhancer activity in vitro, including one SNP (rs1406389) that correlated with allele‐specific effects on enhancer activity. Deletion of either this enhancer or another enhancer that had previously been reported in this region correlated with decreased expression of GREM1 following CRISPR/Cas9 genome editing. That GREM1 is one target of these enhancers was further supported by an expression quantitative trait loci correlation between rs1406389 and GREM1 expression in the transverse but not sigmoid colon in the Genotype‐Tissue Expression dataset. Taken together, we conclude that the 15q13.3 region contains at least two functional variants that map to distinct enhancers and impact CRC risk through modulation of GREM1 expression.

Abstract PR003: Multi-tissue transcriptome-wide association study identifies genetic mechanisms underlying endometrial cancer susceptibility

Abstract To date, genome-wide association studies (GWAS) of endometrial cancer have identified 16 genetic risk regions; however, candidate susceptibility genes at these regions are yet to be confirmed. We performed a transcriptome-wide association study (TWAS) to identify candidate endometrial cancer susceptibility genes and provide evidence to support previously identified candidate genes. To maximise statistical power, we used a multi-tissue TWAS approach, integrating the largest endometrial cancer GWAS (12,906 cases and 108,979 controls of European ancestry) with cis-expression quantitative trait loci (eQTLs) from 48 tissues. Identified genes were prioritised by colocalisation analysis to reduce false positive findings. We found seven candidate endometrial cancer susceptibility genes, six of which had previously been identified as candidate susceptibility genes by parallel functional analyses. The remaining identified gene was a lncRNA located at a potentially novel endometrial cancer genetic risk region. Using phenome-wide association approaches, we found these genes associate with traits related to endometrial cancer risk factors, including cardiovascular phenotypes, diabetes, and hormone levels. This analysis additionally identified associations with traits related to bone health, haematopoiesis and liver function, providing avenues for future study. Comparing genetically predicted endometrial cancer gene expression with drug-induced gene expression profiles from the Connectivity Map database, we identified 14 drug repurposing candidates including tubulin inhibitors, a drug class already in use for treatment of advanced endometrial cancer. In summary, using TWAS we have identified candidate endometrial cancer susceptibility genes and provided evidence to prioritise existing candidates. Follow-up analyses have revealed further potential risk factors for endometrial cancer and compounds that could be repurposed for endometrial cancer treatment. Citation Format: Tracy A. O'Mara, Pik Fang Kho, Gabriel Cuellar Partida, Amanda B. Spurdle, Dylan M. Glubb. Multi-tissue transcriptome-wide association study identifies genetic mechanisms underlying endometrial cancer susceptibility [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr PR003.

The Contribution of Genetic Variants to the Risk of Papillary Thyroid Carcinoma in the Kazakh Population: Study of Common Single Nucleotide Polymorphisms and Their Clinicopathological Correlations.

ObjectiveRisk for developing papillary thyroid carcinoma (PTC), the most common endocrine malignancy, is thought to be mediated by lifestyle, environmental exposures and genetic factors. Recent progress in the genome-wide association studies of thyroid cancer leads to the identification of several genetic variants conferring risk to this malignancy across different ethnicities. We set out to elucidate the impact of selected single nucleotide polymorphisms (SNPs) on PTC risk and to evaluate clinicopathological correlations of these genetic variants in the Kazakh population for the first time.MethodsEight SNPs were genotyped in 485 patients with PTC and 1,008 healthy control Kazakh subjects. The association analysis and multivariable modeling of PTC risk by the genetic factors, supplemented with rigorous statistical validation, were performed.ResultFive of the eight SNPs: rs965513 (FOXE1/PTCSC2, P = 1.3E-16), rs1867277 (FOXE1 5’UTR, P = 7.5E-06), rs2439302 (NRG1 intron 1, P = 4.0E-05), rs944289 (PTCSC3/NKX2-1, P = 4.5E-06) and rs10136427 (BATF upstream, P = 9.8E-03) were significantly associated with PTC. rs966423 (DIRC3, P = 0.07) showed a suggestive association. rs7267944 (DHX35) was associated with PTC risk in males (P = 0.02), rs1867277 (FOXE1) conferred the higher risk in subjects older than 55 years (P = 7.0E-05), and rs6983267 (POU5F1B/CCAT2) was associated with pT3–T4 tumors (P = 0.01). The contribution of genetic component (unidirectional independent effects of rs965513, rs944289, rs2439302 and rs10136427 adjusted for age and sex) to PTC risk in the analyzed series was estimated to be 30–40%.ConclusionGenetic factors analyzed in the present work display significant association signals with PTC either on the whole group analysis or in particular clinicopathological groups and account for about one-third of the risk for PTC in the Kazakh population.

Cross-Cancer Genome-Wide Association Study of Endometrial Cancer and Epithelial Ovarian Cancer Identifies Genetic Risk Regions Associated with Risk of Both Cancers.

Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers. Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data. Genetic correlation analysis revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10-5). We found seven loci associated with risk for both cancers (P Bonferroni < 2.4 × 10-9). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified (P < 5 × 10-7). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation. Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis. Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.

Identifying Novel Susceptibility Genes for Colorectal Cancer Risk From a Transcriptome-Wide Association Study of 125,478 Subjects

Susceptibility genes and the underlying mechanisms for the majority of risk loci identified by genome-wide association studies (GWAS) for colorectal cancer (CRC) risk remain largely unknown. We conducted a transcriptome-wide association study (TWAS) to identify putative susceptibility genes. Gene-expression prediction models were built using transcriptome and genetic data from the 284 normal transverse colon tissues of European descendants from the Genotype-Tissue Expression (GTEx), and model performance was evaluated using data from The Cancer Genome Atlas (n= 355). We applied the gene-expression prediction models and GWAS data to evaluate associations of genetically predicted gene-expression with CRC risk in 58,131 CRC cases and 67,347 controls of European ancestry. Dual-luciferase reporter assays and knockdown experiments in CRC cells and tumor xenografts were conducted. We identified 25 genes associated with CRC risk at a Bonferroni-corrected threshold of P < 9.1 × 10-6, including genes in 4 novel loci, PYGL (14q22.1), RPL28 (19q13.42), CAPN12 (19q13.2), MYH7B (20q11.22), and MAP1L3CA (20q11.22). In 9 known GWAS-identified loci, we uncovered 9 genes that have not been reported previously, whereas 4 genes remained statistically significant after adjusting for the lead risk variant of the locus. Through colocalization analysis in GWAS loci, we additionally identified 12 putative susceptibility genes that were supported by TWAS analysis at P < .01. We showed that risk allele of the lead risk variant rs1741640 affected the promoter activity of CABLES2. Knockdown experiments confirmed that CABLES2 plays a vital role in colorectal carcinogenesis. Our study reveals new putative susceptibility genes and provides new insight into the biological mechanisms underlying CRC development.

Abstract 29: Integrating computational epigenetic and statistical approaches to investigate how genome-wide transcription factor (TF)-DNA bindings affect breast cancer risk

Abstract Background: Fine-mapping and functional genomic studies of risk loci identified by genome wide association studies (GWAS) of breast cancer suggest that risk-associated regulatory variants may disrupt DNA binding affinities of transcription factors (TFs), such as FOXA1 and ESR1, thus altering gene expression and affecting breast cancer risk. To date, however, no studies have directly investigated how genome-wide TF-DNA bindings in a tissue-specific context affect breast cancer risk. Methods: We recently developed an analytic framework using computational epigenetic approaches and generalized mixed models to identify breast cancer risk associated cis-regulatory elements that are occupied by TFs. Summary statistics derived from GWAS of imputed data for approximately 11 million genetic variants were obtained from the Breast Cancer Association Consortium (BCAC). A total of 113 ChIP-seq datasets for TFs and chromatin features were annotated from the Encyclopedia of DNA Elements (ENCODE) and Roadmap projects and the Cistrome database (http://cistrome.org/) in multiple breast cancer cell lines..Chi-square values reported in the BCAC dataset were used to measure breast cancer risk associated with a genetic variant. To investigate TF-DNA bindings for breast cancer risk, we constructed generalized mixed models to evaluate the associations between the Chi-square values and binding sites of single TF or co-occupied by multiple TFs, given linkage disequilibrium (LD) blocks of variants to handle their dependence. To define approximately independent LD blocks similar to other studies, we defined LD blocks using non-overlapping segments of 100k bps. In addition, we investigated the effect of putative cis-regulatory elements characterized by TF-DNA bindings and chromatin features on breast cancer risk. Results: We identified a total of 22 TFs where their TF-DNA bindings were significantly associated with breast cancer risk at P &amp;lt; 1 × 10−5, with the top five TFs being FOXA1, SIN3AK, ESR1, TCF7l2, and AR (P &amp;lt; 1 × 10−10). We further observed stronger associations of breast cancer risk with co-occupied TF-TF DNA bindings, with the top five TF pairs (P &amp;lt; 1 × 10−16) being FOXA1+E2F1, FOXA1+NR2F2, FOXA1+ESR1, FOXA1+SIN3, and FOXA1+TCF12. The interactions of TF-TF DNA bindings for these five TF pairs were highly significant (P &amp;lt; 1 × 10−5). In addition, our results showed a significant interaction (P &amp;lt; 1 × 10−5) between chromatin features and the TF-DNA bindings. As compared with quiescent/low chromatin features, more TF-DNA bindings in enhancers, strong or weak transcription, or heterochromatin were associated with lower breast cancer risk. Conclusions: Our findings suggest that putative regulatory variants, which alter the TF-DNA binding affinities, particularly those located in TF-TF co-occupied sites, and TF-DNA binding colocalized with chromatin features, play an important role in breast cancer risk. Our approaches can be generalized to investigate the effect of genome-wide TF-DNA bindings on the risk of any human cancers that have comprehensive ChIP-seq and large-scale GWAS data. Citation Format: Wanqing Wen, Zhishan Chen, Quan Long, Wei Zheng, Xingyi Guo. Integrating computational epigenetic and statistical approaches to investigate how genome-wide transcription factor (TF)-DNA bindings affect breast cancer risk [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 29.

Association between systemic sclerosis and risk of lung cancer: results from a pool of cohort studies and Mendelian randomization analysis.

Population-based cohort studies have indicated that systemic sclerosis (SSc) may be associated with an increased risk of lung cancer. However, there are few studies that comprehensively investigate their correlation and the causal effect remains unknown. A systematic search of PubMed, Web of Science, Cochrane Library and Embase from the inception dates to December 1, 2019 was carried out. Meta-analysis was performed to calculate odds ratio (OR) and corresponding 95% confidence interval (CI) using random-effects models. Subgroup analyses were performed regarding gender. Two-sample Mendelian randomization (MR) was carried out with summary data from published genome-wide association studies of SSc (Neale Lab, 3871 individuals; UK Biobank, 463,315 individuals) and lung cancer (International Lung Cancer Consortium, 27,209 individuals; UK Biobank, 508,977 individuals). Study-specific estimates were summarized using inverse variance-weighted, weighted median, and MR-Egger method. Through meta-analysis of 10 population-based cohort studies involving 12,218 patients, we observed a significantly increased risk of lung cancer among patients with SSc (OR 2.80, 95% CI 1.55-5.03). In accordance with subgroup analysis, male patients (OR 4.11, 95% CI 1.92-8.79) had a 1.5-fold higher lung cancer risk compared with female patients (OR 2.73, 95% CI 1.41-5.27). However, using a score of 11 SSc-related single nucleotide polymorphisms (p < 5*10-8) as instrumental variables, the MR study did not support a causality between SSc and lung cancer (OR 1.001, 95% CI 0.929-1.100, p = 0.800). Specifically, subgroup MR analyses indicated that SSc was not associated with increased risks of non-small-cell lung cancer (OR 1.000, 95% CI 0.999-1.000, p = 0.974), including lung adenocarcinoma (OR 0.996, 95% CI 0.906-1.094, p = 0.927), squamous cell lung carcinoma (OR 1.034, 95% CI 0.937-1.140, p = 0.507), nor small-cell lung cancer (OR 1.000, 95% CI 0.999-1.000, p = 0.837). This study indicated an increased risk of lung cancer among patients with SSc by meta-analysis, whereas the MR study did not support a causality between the two diseases. Further studies are warranted to investigate the factors underlying the attribution of SSc to lung cancer risk.

Abstract 28: Integrating genomic and transcriptomic data to identify genetic loci associated with breast cancer risk in women of African ancestry

Abstract Background: To date, most genome-wide association studies (GWAS) of breast cancer have been conducted only among women of Asian and European ancestry. It is difficult to generalize results from those studies to women of African ancestry (AA). We conducted a large genetic association study of breast cancer in women of AA by analyzing both genetic and transcriptomic data. Methods: This collaborative study included 11,073 cases and 11,095 controls of AA who were participants in more than 15 studies conducted in the U.S. and Africa. Genotyping data were harmonized and imputed using the 1000 Genomes Project database as the reference. Imputed genotypes were used for GWAS to identify novel genetic risk loci for breast cancer. To search for susceptibility genes, we conducted a transcriptome-wide association study (TWAS), in which gene expression prediction models were built using genetic and tumor tissue RNA sequencing data from ~400 AA patients and used to impute expression levels of genes across the transcriptome for association analyses in all cases and controls included in the GWAS mentioned above. Results: We identified five loci (5p15.33, 5q31.3, 10q26.13, 18q12.1, and 19p13.11) associated with breast cancer risk at P &amp;lt; 5 × 10−8, including a novel locus at 5q31.3 (allelic odds ratio, OR = 1.18, 95% CI = 1.11-1.25, P = 4.65 × 10−8, nearby gene, ARHGAP26). This locus was also identified in association with estrogen receptor (ER) positive breast cancer at P &amp;lt; 5 × 10−8. Analyses stratified by ER status replicated known loci associated specifically with ER-positive (10q26.13) or ER-negative (2q14.2, 2p11.2, 5p15.33) breast cancer at P &amp;lt; 5 × 10−8. Of the 165 lead risk SNPs reported from previous breast cancer GWAS, 35 SNPs were replicated with the same association direction at P &amp;lt; 0.05. We constructed a polygenic risk score using these 35 replicated SNPs and the lead risk SNP at the novel locus and estimated the AUC to be 0.575. Of the 7,592 genes tested in the TWAS, we identified one gene, AC091053.1, with an association at a Bonferroni-corrected threshold of 6.64 × 10−6 (0.05/7,592). AC091053.1 is a long non-coding RNA gene at locus 11p15.4, where no risk variants have been identified in any previous breast cancer GWAS. AC091053.1 is located in the region of protein coding gene DENND2B, which acts as a regulator of MAPK1/ERK2 kinase and reduces the tumorigenic phenotype in cells. The gene AC091053.1 was associated with ER-positive breast cancer with P = 4.11 × 10−5 and ER-negative breast cancer with P = 0.032. Conclusions: Our study, the largest genetic study conducted to date in AA, identified novel breast cancer risk loci at 5q31.3 and 11p15.4 (AC091053.1) among women of AA and replicated &amp;gt;30 associations reported in previous studies. Studies with a larger sample size are needed to further investigate genetic variants and genes associated with breast cancer risk in AA women. Citation Format: Guochong Jia, Jie Ping, Yaohua Yang, Maureen Sanderson, Qiuyin Cai, Xingyi Guo, William J. Blot, Bingshan Li, Elisa V. Bandera, Manjeet K. Bolla, Montserrat García-Closas, Douglas F. Easton, Mary K. Fadden, Jian Gu, Dezheng Huo, Esther M. John, Kathryn L. Lunetta, Olufunmilayo I. Olopade, Xiang Shu, Melissa A. Troester, Song Yao, Breast Cancer Association Consortium, Andrew F. Olshan, Christine B. Ambrosone, Christopher A. Haiman, Jirong Long, Julie R. Palmer, Wei Zheng. Integrating genomic and transcriptomic data to identify genetic loci associated with breast cancer risk in women of African ancestry [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 28.

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.

Leveraging Genome and Phenome-Wide Association Studies to Investigate Genetic Risk of Acute Lymphoblastic Leukemia.

Genome-wide association studies (GWAS) of childhood cancers remain limited, highlighting the need for novel analytic strategies. We describe a hybrid GWAS and phenome-wide association study (PheWAS) approach to uncover genotype-phenotype relationships and candidate risk loci, applying it to acute lymphoblastic leukemia (ALL). PheWAS was performed for 12 ALL SNPs identified by prior GWAS and two control SNP-sets using UK Biobank data. PheWAS-traits significantly associated with ALL SNPs compared with control SNPs were assessed for association with ALL risk (959 cases, 2,624 controls) using polygenic score and Mendelian randomization analyses. Trait-associated SNPs were tested for association with ALL risk in single-SNP analyses, with replication in an independent case-control dataset (1,618 cases, 9,409 controls). Platelet count was the trait most enriched for association with known ALL risk loci. A polygenic score for platelet count (223 SNPs) was not associated with ALL risk (P = 0.82) and Mendelian randomization did not suggest a causal relationship. However, twelve platelet count-associated SNPs were nominally associated with ALL risk in COG data and three were replicated in UK data (rs10058074, rs210142, rs2836441). In our hybrid GWAS-PheWAS approach, we identify pleiotropic genetic variation contributing to ALL risk and platelet count. Three SNPs known to influence platelet count were reproducibly associated with ALL risk, implicating genomic regions containing IRF1, proapoptotic protein BAK1, and ERG in platelet production and leukemogenesis. Incorporating PheWAS data into association studies can leverage genetic pleiotropy to identify cancer risk loci, highlighting the utility of our novel approach.

Family-based exome sequencing combined with linkage analyses identifies rare susceptibility variants of MUC4 for gastric cancer.

Genome-wide association studies of gastric cancer (GC) cases have revealed common gastric cancer susceptibility loci with low effect size. We investigated rare variants with high effect size via whole-exome sequencing (WES) of subjects with familial clustering of gastric cancer. WES of DNAs from the blood of 19 gastric cancer patients and 36 unaffected family members from 14 families with two or more gastric cancer patients were tested. Linkage analysis combined with association tests were performed using Pedigree Variant Annotation, Analysis, and Search Tool (pVAAST) software. Based on the logarithm of odds (LOD) and permutation-based composite likelihood ratio test (CLRT) from pVAAST, MUC4 was identified as a predisposing gene (LOD P-value = 1.9×10-5; permutation-based P-value of CLRT ≤ 9.9×10-9). In a larger cohort consisting of 597 GC patients and 9,759 healthy controls genotyped with SNP array, we discovered common variants in MUC4 regions (rs148735556, rs11717039, and rs547775645) significantly associated with GC supporting the association of MUC4 with gastric cancer. And the MUC4 variants were found in higher frequency in The Cancer Genome Atlas Study (TCGA) germline samples of patients with multiple cancer types. Immunohistochemistry indicated that MUC4 was downregulated in the noncancerous gastric mucosa of subjects with MUC4 germline missense variants, suggesting that loss of the protective function of MUC4 predisposes an individual to gastric cancer. Rare variants in MUC4 can be novel gastric cancer susceptibility loci in Koreans possessing the familial clustering of gastric cancer.

Abstract B065: A framework for transcriptome-wide association studies in breast cancer in diverse study populations

Abstract The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations. Traditional genome-wide association studies (GWAS) have interrogated survival associations in cohorts of predominantly European ancestries but are often underpowered due to subtype heterogeneity in breast cancer and a wide range of clinical covariates. Furthermore, these analyses tend to detect loci in non-coding regions, which require follow-up functional studies to interpret. Recent work in transcriptome-wide association studies (TWAS) has shown increased power in detecting functionally-relevant, trait-associated loci by leveraging information from expression quantitative trait loci (eQTLs) in external reference panels in relevant tissues. However, race-specific reference panels for TWAS may be needed to draw correct inference in large, ethnically-heterogeneous cohorts, and such panels for breast cancer are lacking. In this work, we provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled for women self-identifying as African American. Using Nanostring expression data from CBCS, we perform an eQTL analysis for 417 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. We use these models to impute expression in held-out samples from the CBCS and in The Cancer Genome Atlas, using a permutation method to assess predictive performance accounting for sampling variability. We find that these race-stratified expression models are not always applicable across race, depending on the imputation cohort. Furthermore, their predictive performance varies by breast cancer subtypes. Lastly, we conduct a small-scale TWAS for breast cancer mortality in CBCS (N = 3,828), controlling for covariates used in previous GWAS for breast cancer survival. At an false discovery rate-adjusted P-value less than 0.1, adjusting for self-reported race, we find hazardous associations near CAPN13 (2p23.1), VAV3 (1p13.3), and BLK (8p23.1) and a protective association near SERPINB5 (18q21.33) in TWAS that are underpowered in GWAS. This approach shows increased power for detection of survival-associated genomic loci, demonstrating the relative strength of TWAS over GWAS. A carefully implemented TWAS is an efficient alternative to GWAS for understanding the genetic architecture underpinning breast cancer outcomes in diverse human populations and across biologically distinct tumor types. Citation Format: Arjun Bhattacharya, Montserrat García-Closas, Andrew F. Olshan, Charles M. Perou, Melissa A. Troester, Michael I Love. A framework for transcriptome-wide association studies in breast cancer in diverse study populations [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr B065.

Abstract B054: Analysis of a cancer gene expression and next-generation sequencing dataset representing hispanic predominant South Florida population

Abstract People of different races and ethnicities have different likelihoods of being diagnosed with certain types of cancer and respond differently to therapeutic agents. However, genome-wide association studies of cancer have typically assumed fixed genetic effects across ethnicities, and rarely compared and contrasted findings across ethnic groups, especially the Hispanic population. Baptist Health South Florida enrolled more than 3,000 patients into the Total Cancer Care Protocol, a prospective longitudinal outcomes study designed to bank tissues and clinical information in order to allow scientists and clinicians to bring cancer treatment to a new level. Tissue samples were snap-frozen within 15 minutes of surgical removal, macrodissected to ≥85% tumor purity, and quantified for the percent of malignancy, cellularity, stroma, normalcy, and necrosis. Longitudinal clinical and pathologic data were extracted from patients' electronic medical records to annotate the biospecimens. We analyzed gene expression and sequencing data from 443 cancer patients enrolled in this study whose tumors were molecularly profiled. All the cases had gene expression data, and 32% (n=140) also had next-generation sequencing data. Most common primary cancers were breast cancer (44.2%, n=196), followed by large bowel (18.9%, n=84), lung (15.8%, n=70), and uterine (9.5%, n=42) cancer. The uniqueness of this data is the distinctive racial and ethnic distribution of the patients; the majority of them were Hispanic (51.7%, n=229) specifically from Cuban, Mexican, Puerto Rican, Central American, and South American populations, followed by whites (36.3%, n=161), blacks (6.1%, n=27), other racial/ethnic groups (5.0%, n=22) and unknown (1.0%, n=4). Microarrays, across all sites of origin, were normalized using IRON against the median sample. An RNA quality-related technical artifact was observed and corrected by subtracting the 1st principal component of a partial least squares (PLS) model trained against the RNA integrity number (RIN), a measure of RNA quality. All samples were then extracted from this master normalized quality-corrected dataset and gene expression analysis was performed. For sequencing data, reads were aligned to the human reference genome (hs37d5) using the Burrows-Wheeler Aligner (BWA). Duplicate reads were marked with Picard-Tools. Indel realignment and base quality score recalibration were performed with GATK. Variations observed among ethnic and racial groups for each primary cancer are described in this representative South Florida population. Citation Format: Zuanel Diaz, Arpit Mehta, Zasha Pou, Muni B. Rubens, Don Parris, Miguel Villalona-Calero. Analysis of a cancer gene expression and next-generation sequencing dataset representing hispanic predominant South Florida population [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr B054.

Polygenic risk score of non-melanoma skin cancer predicts post-transplant skin cancer across multiple organ types.

Polygenic risk scores (PRSs) calculated from genome-wide association studies (GWASs) of non-melanoma skin cancer (NMSC) in a general, non-transplant setting have recently been shown to predict risk of and time to post-renal transplant skin cancer. In this study, we set out to test these findings in a cohort of heart, lung, and liver transplant patients to see whether these scores could be applied across different organ transplant types. Using the PRS from Stapleton et al (2018), PRS was calculated for each sample across a European ancestry heart, lung, and liver transplant cohorts (n=523) and tested as predictor of time to NMSC post-transplant. The top PRS, squamous cell carcinoma (SCC) pT1x10-5 , (n SNPs=1953), SCC pT1x10-6 , and SCC pT1x10-6 (n SNPs=1061) were significantly predictive in the time to NMSC, SCC, and basal cell carcinoma (BCC) analysis across organ (P=.006, .02, and .02, respectively). We observed here a similar direction of effect and effect size [NMSC HR=1.31(1.08-1.59)] to that in the original discovery study with increased polygenic burden leading to a faster time to developing NMSC. In summary, we found that PRS of NMSC calculated from GWAS of NMSC in non-transplant populations independently replicated in this cohort of heart, lung, and liver transplant.

Validation of genome-wide association study-identified single nucleotide polymorphisms in a case-control study of pancreatic cancer from Taiwan

BackgroundDue to differences in genetic background, it is unclear whether the genetic loci identified by the previous genome-wide association studies (GWAS) of pancreatic cancer also play significant roles in the development of pancreatic cancer among the Taiwanese population.MethodsThis study aimed to validate the 25 pancreatic cancer GWAS-identified single nucleotide polymorphisms (SNPs) in a case-control study (278 cases and 658 controls) of pancreatic cancer conducted in Taiwan. Statistical analyses were conducted to determine the associations between the GWAS-identified SNPs and pancreatic cancer risk. Gene-environment interaction analysis was conducted to evaluate the interactions between SNPs and environmental factors on pancreatic cancer risk.ResultsAmong the 25 GWAS-identified SNPs, 7 (rs2816938 (~ 11 kb upstream of NR5A2), rs10094872 (~ 28 kb upstream of MYC), rs9581943 (200 bp upstream of PDX1) and 4 chromosome 13q22.1 SNPs: rs4885093, rs9573163, rs9543325, rs9573166) showed a statistically significant association with pancreatic cancer risk in the current study. Additional analyses showed two significant gene-environment interactions (between poor oral hygiene and NR5A2 rs2816938 and between obesity and PDX1 rs9581943) on the risk of pancreatic cancer.ConclusionsThe current study confirmed the associations between 7 of the 25 GWAS-identified SNPs and pancreatic risk among the Taiwanese population.Furthermore, pancreatic cancer was jointly influenced by lifestyle and medical factors, genetic polymorphisms, and gene-environment interaction. Additional GWAS is needed to determine the genetic polymorphisms that are more relevant to the pancreatic cancer cases occurring in Taiwan.

A framework for transcriptome-wide association studies in breast cancer in diverse study populations

BackgroundThe relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking.ResultsWe provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS.ConclusionsWe show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.