Amos, Christopher I.

Abstract Lung cancer is the leading cancer-related cause of death in the United States. We had previously performed multipoint linkage analysis on families with a strong family history of lung cancer and found significant linkage to the 6q25 region. In order to find the source of that linkage signal, we performed targeted sequencing 6q23-6q27 on 75 individuals from the 9 most highly linked families. We performed two types of parametric linkage analysis, using an autosomal dominant mode of inheritance with 10% penetrance for carriers and a 1% phenocopy rate using a disease allele frequency of 1%. The first type was a two-point analysis using an Elston-Stewart algorithm. This approach did not lead to any genome-wide significant results but demonstrated significant heterogeneity throughout the nine families.In an effort to recover the power from the original multipoint analysis, we performed a regional linkage analysis using SEQLinkage and MERLIN. SEQLinkage built regional haplotypes that corresponded to a gene or an intergenic region - the regions were based on a customized map of our design. The regional markers were multiallelic which allowed for greater information content and were similar to the microsatellites that were used in the original multipoint analysis. The regional markers were then analyzed in a two-point linkage analysis via MERLIN. This allowed us to identify two genome-wide significant signals at PACRG-AS1 at 6q26 (HLOD = 3.4) and SAMD5 at 6q24 (3.3). The PACRG-AS1 is novel, though it is associated with the known lung cancer gene PARK2. SAMD5 may be involved in lung cancer cell proliferation. The heterogeneity of the two signals was particularly interesting. The PACRG-AS1 was driven primarily by two families. The SAMD5 marker was not being strongly driven by any of the families and appears to be a small cumulative effect across the nine families. In addition, several families have large, but non-significant LOD scores at other loci across the region. This further reinforces the locus heterogeneity within the region, and it is likely that our two significant signals here are not the only variants affecting the phenotype. We further attempted to localize the signals by running SEQLinkage using a custom map where genes are broken into exons and introns. Though this resulted in no significant markers, the highest signal was located in the intronic region of SAMD5 (HLOD = 3) and several other suggestive signals were localized to intronic regions of good candidate genes like SASH1 and ARID1B. Examination of predicted effects of the candidate regulatory variants using eQTL databases is ongoing. This is a region full of promising candidates, and it is likely that the two significant signals found here are just part of many that could be affecting lung cancer risk. We plan to do further analysis within the individual families to elucidate any more genes affecting this signal. Citation Format: Anthony M. Musolf, Claire L. Simpson, Bilal A. Moiz, Candace Middlebrooks, Mariza de Andrade, Diptasri Mandal, Colette Gaba, Ping Yang, Yafang Li, Ming You, Elena Y. Kupert, Marshall W. Anderson, Ann G. Schwartz, Susan M. Pinney, Christopher I. Amos, Joan E. Bailey-Wilson. A study in locus heterogeneity: Targeted sequencing analysis of 6q reveals multiple significant loci as the source of a previous linkage peak in familial lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4290. doi:10.1158/1538-7445.AM2017-4290

Abstract Lung cancer (LC) is the leading cancer killer of Americans; an estimated 158,000 people will die in the U.S. from LC in 2016. While it is well-known that LC risk is affected by the environment, particularly tobacco smoking, there is a substantial genetic risk to LC also. We examined genotype data from Illumina HumanCore-12v1-0 array (297,000 SNPs) on 175 individuals in 25 extended families with a strong history of LC recruited by the Genetic Epidemiology of Lung Cancer Consortium. The purpose of this study is to determine which families are segregating a high-penetrance genetic risk haplotype so that the most informative families can be selected for DNA sequencing studies. Quality control was performed to remove SNPs and individuals with greater than 1% missingness as well as monomorphic SNPs. SNPs with Mendelian errors in more than one family were dropped. Identity-by-descent values were calculated to confirm correct familial relationships; 1 individual was dropped. After quality control we were left with approximately 245,000 SNPs for analysis. We performed three types of parametric linkage analyses using an autosomal dominant model with 40% penetrance in carriers and 1% penetrance in non-carriers. A disease allele frequency of 0.01 was used. Standard single variant two-point analysis between the disease and each marker was performed using TwoPointLods. Multipoint linkage analysis was performed using SimWalk2. We also performed regional-based linkage analyses using SEQLinkage and MERLIN. SEQLinkage builds a multiallelic regional marker (similar to a microsatellite) that corresponds to a gene or a portion of a gene. Two-point linkage analyses were then performed on the regional markers using MERLIN. We identified five loci that were genome-wide significant (HLOD score ≥ 3.3) from the regional-based linkage analyses on 18p11.23 (HLOD = 4.1), 2p22.2 (3.9), 14q13.1 (3.7), 16p13.1 (3.4), and 20q13.11 (3.4). It is particularly exciting that the scores centered on prospective cancer genes. Our highest score was centered on PTPRM, a protein tyrosine phosphatase on chromosome 18 that has been implicated as a LC oncogene. The signal on 20q13 also centered on another protein tyrosine phosphatase (PTPRT) that has been shown to be mutated in LC cells. The source of the signal on 16p13 was RNA binding protein RBFOX1, which has been shown to be deleted in LC cell lines and the source of the signal on 2p22 was LRP1B, a LDL receptor-related protein that is often inactivated in LC cells. The source of the final signal on 14q13 was NPAS3, a transcription factor that is a tumor suppressor in brain tumors. It should be noted that all previous evidence linking these genes to cancer was based on somatic mutations; this is the first time any of these genes has been shown to be significantly linked to germline disease risk in a family-based study. We plan to perform targeted sequencing on the linked regions to elucidate the exact causal variant. Citation Format: Joan E. Bailey-Wilson, Anthony M. Musolf, Claire L. Simpson, Mariza de Andrade, Diptasri Mandal, Colette G. Gaba, Ping Yang, Ming You, Elena Y. Kupert, Marshall W. Anderson, Ann G. Schwartz, Susan M. Pinney, Christopher I. Amos. Familial lung cancer is significantly linked to cancer-associated genes on five chromosomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4268. doi:10.1158/1538-7445.AM2017-4268

Abstract Introduction: DNA topoisomerase inhibitors (e.g. etoposide, irinotecan, teniposide, topotecan) are commonly used for treating small cell lung cancer (SCLC). Tyrosyl-DNA phosphodiesterase (TDP1) plays a role in repairing DNA damage caused by DNA topoisomerase inhibitors and it is believed to be responsible for resistance to this class of drugs. The purpose of this study was to determine whether common TDP1 polymorphisms are associated with overall survival among SCLC patients. Method: Two common TDP1 SNPs (rs942190 and rs2401863, r2(European)=0.63 and r2(East Asian)=0.26) were analyzed in 898 patients from 10 studies in the International Lung Cancer Consortium, conducted in the U.S., Canada, Japan, Italy, Spain and the United Kingdom. Genotype data were obtained from either the OncoArray Consortium or other existing genome-wide association studies. Cox proportional hazard regression models with robust standard error were used to evaluate genotype associations for overall survival at 36 months post-diagnosis, adjusting for age, sex, race and tumor stage. Results: Patients homozygous for the minor allele (GG) of TDP1 SNP rs942190 were associated with poorer survival compared to those carrying both major alleles (AA), with a hazard ratio (HR) of 1.36 (95% confidence interval (CI): 1.07-1.72, p-value=0.01), but there was no association with survival for patients with the heterozygous genotype (HR=1.02, 95% CI: 0.83-1.27, p-value=0.83). For rs2401863, patients carrying both minor alleles (CC) tended to have better survival than patients carrying AA alleles, but this was within the limits of chance given no significant association (HR=0.79, 95% CI: 0.61-1.02, p-value=0.07). In addition, multi-tissue expression quantitative trait loci (eQTL) analyses from the Genotype-Tissue Expression Project indicated a potential effect of rs942190 on lung tissue, with higher TDP1 gene expression for the GG than AG or AA genotypes. Conclusion: We found that among SCLC patients the TDP1 rs942190 GG genotype was associated with poor overall survival. Further study could determine if this genotype can serve as a predictive marker for the treatment efficacy of DNA topoisomerase inhibitors in SCLC. Citation Format: Pawadee Lohavanichbutr, Lori C. Sakoda, Christopher I. Amos, Susanne M. Arnold, David C. Christiani, Michael P. Davies, John K. Field, Eric B. Haura, Rayjean J. Hung, Takashi Kohno, Maria Teresa Landi, Geoffrey Liu, Yi Liu, Michael W. Marcus, Grainne M. O'Kane, Matthew B. Schabath, Kouya Shiraishi, Stacey A. Slone, Adonina Tardón, Ping Yang, Kazushi Yoshida, Ruyang Zhang, Xuchen Zong, Noel S. Weiss, Chu Chen. Common TDP1 polymorphisms in relation to survival among small cell lung cancer patients in a multicenter study from the International Lung Cancer Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2248. doi:10.1158/1538-7445.AM2017-2248

Abstract BACKGROUND: Incidence of glioma varies significantly by sex, and most glioma histologies occur with greater incidence in males. Previous analyses have examined the impact of estrogen exposure as a risk factor for these tumors, but have found results of varying significance and low effect size. There may be differences in effect of previously discovered risk alleles that contribute to sex differences. METHODS: Using data collected for three previous glioma GWAS in European-ancestry populations (MD Anderson Cancer Center, the San Francisco Adult Glioma Study, and the Glioma International Case Control Study) we assessed sex-specific effects for 14 previously identified and 13 newly identified glioma risk SNPs (27 total) overall and for glioblastoma (GBM) and non-GBM tumors separately. There were 3,892 male cases (59% GBM), 4,522 male controls, 2,500 female cases (52% GBM) and 4,940 female controls. Sex-specific odds ratios (ORM and ORF), 95% confidence intervals (95% CI) and p values (pM and pF) were generated using stratified logistic regression models. Data from each study were analyzed separately and combined using inverse variance weighted meta-analysis. Results were considered statistically significant at p<6.2x10-4. RESULTS: In GBM, rs11979158 (7p11.2, pM=1.01x10-10, ORM=1.43 [95% CI: 1.28-1.59]; pF=3.43x10-3, ORF=1.22 [95% CI: 1.07-1.39]) and rs2562152 (16p13.3, pM=5.59x10-4, ORM=1.23 [95% CI: 1.09-1.39]; pF=1.22x10-1, ORF=1.12 [95% CI: 0.97-1.29]) had significant effect in males only. In non-GBM gliomas, rs12076373 (1q44, pM=3.56x10-7, ORM=1.41, 95% CI: 1.23-1.61; pF=1.76x10-2, ORF=1.20, 95% CI: 1.03-1.38), rs11979158 (7p11.2, pM=1.77x10-5, ORM=1.32 [95% CI: 1.16-1.49]; pF=2.73x10-1, ORF=1.08 [95% CI: 0.94-1.24]), and rs3751667 (16p13.3, pM=9.44x10-7, ORM=1.30 [95% CI: 1.17-1.44]; pF=5.18x10-2, ORF=1.13 [95% CI: 1.00-1.28]) had significant effect in males only. Effect size for rs55705857 (8q24.21) varied significantly by sex, with ORM=2.63 (95% CI: 2.24-3.09, pM=4.42x10-32), as compared to ORF=3.95 (95% CI: 3.28-4.76, pF=1.82x10-47). A sensitivity analysis was performed due to allele frequency heterogeneity by study and results did not change. CONCLUSIONS: Sex differences and other demographic differences in cancer susceptibility can provide important clues to etiology, and these differences can be leveraged for discovery in genetic association studies. Significant differences in effect size may suggest variation in genetic effect of risk alleles or in unidentified risk factors that vary in prevalence or effect by sex. There may also be differences in the distribution of molecular subtypes within each histology by sex. Further investigation using an agnostic approach may further elucidate the relationship between effect of risk alleles and sex. Citation Format: Quinn T. Ostrom, Ben Kinnersley, Margaret Wrensch, Jeanette E. Eckel-Passow, Georgina Armstrong, Terri Rice, Yanwen Chen, John Wiencke, Lucie McCoy, Helen Hansen, Christopher Amos, Jonine L. Bernstein, Elizabeth B. Claus, Dora Il'yasova, Christoffer Johansen, Daniel Lachance, Rose Lai, Ching C. Lau, Ryan T. Merrell, Sara H. Olson, Siegal Sadetzki, Joellen Schildkraut, Sanjay Shete, Richard S. Houlston, Robert B. Jenkins, Beatrice Melin, Melissa Bondy, Jill S. Barnholtz-Sloan. Estimating sex-specific effects of genetic loci associated with glioma risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1315. doi:10.1158/1538-7445.AM2017-1315

We discuss methods for detecting genetic linkage for quantitative data. The usual LOD score method uses a pseudolikelihood formulation and has optimal power provided all parameters are correctly specified, but can lead to erroneous estimates of the location for the locus influencing a trait under misspecification of parameters describing the variance of the trait. Alternative methods, in which attention focuses upon modelling covariation among relatives as a function of genetic marker, similarity lead to unbiased estimates of the location and major gene heritability of the trait influencing locus. The Haseman-Elston approach uses a regression method to perform linkage analysis and its properties have been widely studied. This method is generally less powerful than variance components procedures, but the maximum likelihood-based variance components procedures require normality of the trait to ensure robustness of the genetic linkage tests (i.e. a correct false positive rate). When samples are non-randomly selected an ascertainment correction is generally required in order to obtain unbiased parameter estimates when applying variance components methods. For quantitative traits, ascertainment corrections usually condition either on the proband exceeding a threshold, or on the trait value of the proband. We summarize simulations that show that both approaches lead to similar efficiencies for estimating genetic effects. Finally, we discuss methods for analysing diseases that include time-to-onset information. A variety of methods are available for the linkage analysis of quantitative traits. Here, we have reviewed the most commonly used methods.

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 < 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.

Lung cancer is the deadliest cancer in the United States, killing roughly one of four cancer patients in 2016. While it is well-established that lung cancer is caused primarily by environmental effects (particularly tobacco smoking), there is evidence for genetic susceptibility. Lung cancer has been shown to aggregate in families, and segregation analyses have hypothesized a major susceptibility locus for the disease. Genetic association studies have provided strong evidence for common risk variants of small-to-moderate effect. Rare and highly penetrant alleles have been identified by linkage studies, including on 6q23–25. Though not common, some germline mutations have also been identified via sequencing studies. Ongoing genomics studies aim to identify additional high penetrance germline susceptibility alleles for this deadly disease.

Objective To identify risk alleles relevant to the causal and biologic mechanisms of antineutrophil cytoplasmic antibody (ANCA)–associated vasculitis (AAV). Methods A genome‐wide association study and subsequent replication study were conducted in a total cohort of 1,986 cases of AAV (patients with granulomatosis with polyangiitis [Wegener's] [GPA] or microscopic polyangiitis [MPA]) and 4,723 healthy controls. Meta‐analysis of these data sets and functional annotation of identified risk loci were performed, and candidate disease variants with unknown functional effects were investigated for their impact on gene expression and/or protein function. Results Among the genome‐wide significant associations identified, the largest effect on risk of AAV came from the single‐nucleotide polymorphism variants rs141530233 and rs1042169 at the HLA–DPB1 locus (odds ratio [OR] 2.99 and OR 2.82, respectively) which, together with a third variant, rs386699872, constitute a triallelic risk haplotype associated with reduced expression of the HLA–DPB1 gene and HLA–DP protein in B cells and monocytes and with increased frequency of complementary proteinase 3 (PR3)–reactive T cells relative to that in carriers of the protective haplotype. Significant associations were also observed at the SERPINA1 and PTPN22 loci, the peak signals arising from functionally relevant missense variants, and at PRTN3 , in which the top‐scoring variant correlated with increased PRTN3 expression in neutrophils. Effects of individual loci on AAV risk differed between patients with GPA and those with MPA or between patients with PR3‐ANCAs and those with myeloperoxidase‐ANCAs, but the collective population attributable fraction for these variants was substantive, at 77%. Conclusion This study reveals the association of susceptibility to GPA and MPA with functional gene variants that explain much of the genetic etiology of AAV, could influence and possibly be predictors of the clinical presentation, and appear to alter immune cell proteins and responses likely to be key factors in the pathogenesis of AAV.

Abstract Background: Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits. Methods: The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers, and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background. Results: The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis. Conclusions: Results from these analyses will enable researchers to identify new susceptibility loci, perform fine-mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental, and lifestyle-related exposures. Impact: Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. Cancer Epidemiol Biomarkers Prev; 26(1); 126–35. ©2016 AACR.

Telomeres cap chromosome ends, protecting them from degradation, double‐strand breaks, and end‐to‐end fusions. Telomeres are maintained by telomerase, a reverse transcriptase encoded by TERT, and an RNA template encoded by TERC. Loci in the TERT and adjoining CLPTM1L region are associated with risk of multiple cancers. We therefore investigated associations between variants in 22 telomere structure and maintenance gene regions and colorectal, breast, prostate, ovarian, and lung cancer risk. We performed subset‐based meta‐analyses of 204,993 directly‐measured and imputed SNPs among 61,851 cancer cases and 74,457 controls of European descent. Independent associations for SNP minor alleles were identified using sequential conditional analysis (with gene‐level p value cutoffs ≤3.08 × 10−5). Of the thirteen independent SNPs observed to be associated with cancer risk, novel findings were observed for seven loci. Across the DCLRE1B region, rs974494 and rs12144215 were inversely associated with prostate and lung cancers, and colorectal, breast, and prostate cancers, respectively. Across the TERC region, rs75316749 was positively associated with colorectal, breast, ovarian, and lung cancers. Across the DCLRE1B region, rs974404 and rs12144215 were inversely associated with prostate and lung cancers, and colorectal, breast, and prostate cancers, respectively. Near POT1, rs116895242 was inversely associated with colorectal, ovarian, and lung cancers, and RTEL1 rs34978822 was inversely associated with prostate and lung cancers. The complex association patterns in telomere‐related genes across cancer types may provide insight into mechanisms through which telomere dysfunction in different tissues influences cancer risk.