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PURPOSE: Glioma is the most common type of primary malignant brain tumor, and incidence is 25% lower in U.S. Hispanics than White non-Hispanics. Though often pooled in epidemiological analyses, the U.S. Hispanic population is very diverse, and registry-based analyses may mask incidence and survival differences associated with geographic/ancestral origins. METHODS: County-level glioma incidence data in U.S. Hispanics from 2000-2017 were retrieved from the Central Brain Tumor Registry of the United States, and survival data from the CDC’s National Program of Cancer Registries was obtained for 2001-2017. American Community Survey data were used to determine county-level proportion of the Hispanic population of Mexican/Central-American origin and of Caribbean origin (Puerto Rican, Cuban, Dominican). State-level estimates of European and African admixture in Hispanics were obtained from published 23andMe data. Incidence rate ratios (IRRs) quantified the glioma incidence differences across groups. County-level incidence was modeled using linear regression to control for potential confounding factors (e.g., county size, median socioeconomic status), and Cox proportional hazards models were used to assess associations between individual-level survival, county-level origin percentages, and state-level admixture. RESULTS: Compared to Hispanics living in predominantly Caribbean-origin counties, Hispanics from predominantly Mexican/Central-American-origin counties had lower age-adjusted risks of glioma (IRR=0.83; P CONCLUSIONS: Glioma incidence and outcomes differ in association with the geographic origins of the Hispanic community, with residents of predominantly Mexican/Central-American-origin counties at significantly reduced risk and those of Caribbean-origin counties at comparatively greater risk. Although typically classified as a single ethnic group in registry data, appreciating the cultural, socioeconomic, and genetic diversity of U.S. Hispanics will help advance cancer disparities research. Citation Format: Kyle M. Walsh, Corey Neff, Melissa L. Bondy, Carol Kruchko, Jason T. Huse, Christopher I. Amos, Jill S. Barnholtz-Sloan, Quinn T. Ostrom. Influence of geographic/ancestral origin on glioma incidence and outcomes in US Hispanics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 26.

Introduction: Lung cancer is the leading cause of cancer-related deaths. 10% of never-smokers develop lung cancer. Currently, no genetic-based lung cancer screening tool exists. As a substitute, phenotypic traits can serve as surrogate markers for lung cancer risk. Our study focuses on identifying novel phenotypes associated with lung cancer. Genome-wide association studies (GWAS) are useful in elucidating complex inheritance patterns and genetic architecture. Cross-trait linkage disequilibrium score regression (LDSC) specifically allows for use of GWAS summary statistics to identify genetic correlations between phenotypes of interest. This method allows for accurate calculations of genetic correlation, as it neutralizes effects from population stratification or cryptic relatedness. Methods: We used LDSC to (1) confirm prior phenotypic trait associations with lung cancer and to (2) identify novel associations. We measured pairwise genetic correlation (rg) and SNP heritability (h2) (the proportion of phenotypic variance observed in a population that can be explained genetically) between multiple phenotypes and lung cancer using summary statistics from the UK Biobank and OncoArray lung consortium. In addition, we conducted analysis after removal of genome regions related to smoking effects that enables us to correct the potential confounding effect in lung cancer. Results: Significant negative genetic correlations were found to exist between lung cancer and environmental factors. Overall alcohol use was significantly correlated with lung cancers. The effect observed was split, with a positive correlation for beer and cider intake (rg = 0.2957, p = 3.936 × 10-8) and a negative correlation with wine intake (rg = -0.3281, p = 2.251 × 10-14) for overall lung. Significant correlations existed between lung cancer and health metrics. A positive correlation was found between lung cancer and increased BMI (rg = 0.1986, p = 3.57 × 10-9). This finding was consistent across other BMI related metrics and within histological subtypes of lung cancer, including for lung adenocarcinoma (rg = 0.1059, p = 3.688 × 10-3) and lung small cell carcinoma (rg = 0.2393, p = 6.463 × 10-7). In comparison, physical conditioning metrics such as cycling to work had a negative correlation with lung cancer (rg = -0.2714, p = 5.690 × 10-5). Further, negative correlations were observed between being breastfed as a baby and lung cancer (rg = -0.320, p = 1.554 × 10-6). Each of these associations maintained its significance even after the removal of smoking-related SNPs. Conclusions: This analysis demonstrates a genetic basis for the shared genetic architecture between environmental factors and lung oncogenesis. We identify several novel associations, including a correlation between breastfeeding and lung cancer. Further studies are necessary in order to confirm these associations and investigate driving genetic mechanisms. Citation Format: Rowland West Pettit, Jinyoung Byun, Younghun Han, Jacob Edelson, Quinn Ostrom, Kyle Walsh, Melissa Bondy, James McKay, Christopher Amos, INTEGRAL Consortium. Genetic correlation between lung cancer and environmental exposures [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 2121.

Introduction: Prior epidemiological studies in glioma have identified 25 germline risk variants, as well as risk association with exposure to ionizing radiation and protective association with history of allergies and aspirin use. In this analysis we applied LDscore regression methods, which leverages single SNP associations and known patterns of linkage disequilibrium (LD) to estimate the genetic correlation between phenotypes without bias for population structure, to confirm prior associations, and to identify novel phenotype associations for traits not previously assessed in any glioma study. We also used LDscore regression to partition heritability for glioma subtypes by immune cell type. This novel method may improve genetic prediction for glioma. Methods: We used the summary statistics for all glioma, glioblastoma (GBM), and non-GBM from a prior meta-analysis (Melin, et al.). We obtained the summary statistics for autoimmune, and atopic traits from the GWAS catalog and UK biobank. We generated pairwise genetic correlations (rg) between these phenotypic traits using LDscore regression as implemented in LDSC, and associations were considered significant at p Results: Heritability partitioned by immune cell type for glioma was significantly enriched in myeloid cells, natural killer cells, and t cells. We identified significant negative correlations between glioma and primary biliary cirrhosis (rg=-0.24, p=0.0190), and between celiac disease and glioblastoma only (rg=-0.31, p=0.0128), and we identified a female-specific association for rheumatoid arthritis (rg=-0.68, p=0.0225). Pleiotropic effects were identified for variants in telomere-associated regions (TERC, TERT, RTEL1) for glioma and multiple autoimmune traits. Conclusions: This analysis identified significant enrichment for heritability for glioma in immune cell types as well as novel associations between auto-immune traits. It did not validate a genetic basis for previously identified protective effects from allergic rhinitis, suggesting that it may be more strongly influenced by the environment. Further studies are needed to confirm these associations and identify the mechanism through which increased immune activity may lower risk of glioma. Citation Format: Quinn T. Ostrom, Jacob Edelson, Jinyoung Byun, Younghun Han, GLIOGENE Consortium, Kyle Walsh, Christopher Amos, Melissa Bondy. Genetic correlation analysis identifies glioma heritability enrichment in immune cell types and novel protective associations with auto-immune conditions [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 2327.

BACKGROUND: Approximately 5% of gliomas occur in individuals with a family history of glioma, and first-degree relatives of brain tumor cases have a two-fold increase in risk of brain tumor. Recent somatic characterization has shown that tumors from familial cases are indistinguishable from sporadic cases, suggesting that familial cases may arise through similar mechanisms of gliomagenesis, and therefore may be associated with common variants as well as rare mutations. In this analysis, we assessed whether previously identified common risk variants are associated with familial glioma. METHODS: Data were obtained from the Glioma International Case Control (GICC) and Gliogene studies for 448 cases with reported family history, 4,405 cases without reported family history, and 3,288 controls. We assessed 25 risk loci previously identified by glioma GWAS, and odds ratios (OR) and 95% confidence intervals (95%CI) were calculated using an additive genetic logistic regression model adjusted for age, sex, and the first two principal components for familial cases versus unaffected controls, and non-familial cases versus controls. Results were considered significant at p RESULTS: Significant associations were detected at 5/25 loci, including TERT, EGFR, CCDC26, CDKN2B, and RTEL1. The strongest association was at rs55705857 (CCDC26, OR=2.7, p=7.49x10-17). For GBM (222 familial cases), significant associations were detected at 6/26 loci (TERT, EGFR, CDKN2B, TP53 and RTEL1), while in non-GBM (205 familial cases) significant associations were detected at 3/25 loci (LRIG1, CCDC26, PHLDB1). These SNPs were further examined using a case-only approach comparing familial to non-familial cases, and there was no significant difference in allele frequencies by family history status. There was a strong correlation between log(OR) for familial cases only versus non-familial cases (adjusted R2=0.88). CONCLUSIONS: In this analysis we identified a significant association between familial glioma and five common risk loci previously identified by glioma GWAS. This provides further evidence of shared pathways of genetic risk and gliomagenesis between familial and non-familial glioma. Further exploration is necessary to determine the overall contribution of common genetic variation to risk of familial glioma. Citation Format: Quinn T. Ostrom, Georgina Armstrong, Christopher I. Amos, Jonine L. Bernstein, Elizabeth B. Claus, Jeanette E. Eckel-Passow, Dora Il'yasova, Christoffer Johansen, Daniel H. Lachance, Rose K. Lai, Ryan T. Merrell, Sara H. Olson, Joellen H. Schildkraut, Sanjay S. Shete, Richard S. Houlston, Robert B. Jenkins, Margaret R. Wrensch, Beatrice Melin, Jill S. Barnholtz-Sloan, Melissa L. Bondy. Previously identified common glioma risk SNPs are associated with familial glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4173.

Background: Glioblastoma (GBM) is the most common and lethal primary malignant brain tumor in adults. The heterogeneity of the disease leads to significant variability in response to standard therapy (surgery plus concurrent radiation and temozolomide). An accurate and reliable predictor of patient prognosis represents an unmet need to improve the care of GBM patients. While protein markers are an effective readout of cellular function, proteomics has rarely been utilized in GBM prognostic marker discovery. Experimental Procedures: GBM patients were prospectively recruited (Ohio Brain Tumor Study) and proteomics discovery using liquid chromatography mass spectrometry analysis (LC MS/MS) was performed in a discovery set of 27 patients including 13 short-term survivors (< 9 months, STS) and 14 long-term survivors (>= 18 months, LTS). Statistically significant proteins were evaluated in two independent datasets, including in18 samples micro-dissected from multiple tumor areas of 6 GBM patients. Results: Proteomics discovery identified 11,941 peptides in 2,495 unique proteins, with 172 proteins exhibiting significant dysregulation when comparing STS and LTS. Proteins involved in glycolysis/TCA cycle were up-regulated in STS compared to LTS by examination of individual targets as well as upon application of a novel protein and peptide pathway enrichment analysis. Validation of these dysregulated proteins and other protein markers from the literature in our first validation set (18 samples micro-dissected from n=6 patients) demonstrated that they were very unevenly distributed throughout individual patients' tumors. Several proteins overcame the heterogeneous nature of the tumors and were both prognostic markers differentially expressed between LTS and STS, as well as potential drug targets owing to their even distribution throughout the tumor. Protein abundance of significant marker proteins were verified in a second independent validation set using Western immunoblots. Conclusion: The current study verified the importance of metabolism in GBM pathology and demonstrated the heterogeneous nature of GBMs at the protein level. Citation Format: Lindsay C. Stetson, Quinn T. Ostrom, Peter Liao, Andrew E. Sloan, Mark R. Chance, Jill S. Barnholtz-Sloan. Heterogeneous distribution of prognostic protein markers in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4220.

Glioma incidence is highest in non-Hispanic Whites, where it occurs ~2x as frequently compared with other race/ethnicity groups. Glioma GWAS to date have included European ancestry populations only, and it is unknown whether variants identified by these analyses are associated with glioma in non- European ancestry populations. African Americans and Hispanics are admixed populations with varying proportions of European ancestry. While global ancestry may be similar within admixed groups, the proportion of European ancestry at each allele can vary across the genome. As glioma is more common in European ancestry populations, the presence of increased local European ancestry in these admixed populations could be used to identify glioma risk loci. Here we assessed whether excess European ancestry at established risk loci (Melin et al, Nature Genetics, 2017) was associated with glioma risk in non-European ancestry populations. Global ancestry was estimated using fastStructure, and local ancestry was estimated using RFMix. Both methods used 1,000 genomes project reference populations (African: YRI; European: CEU; East Asian: CHB/JPT; and Native American: CLM/PEL/MXL). We evaluated differences in local European ancestry between cases and controls using logistic regression conditioned on global European ancestry within 500kb of 25 previously identified risk variants among individuals with ≥50% African ancestry, and ≥30% Native American ancestry for all gliomas, and for grade IV glioblastoma (GBM) and grade II-III non-GBM. There were 347 individuals (184 cases and 163 controls) with ≥50% global African ancestry, and 277 individuals (153 cases and 124 controls) with ≥30% global American ancestry. There was no significant difference in proportion of global European ancestry between cases and controls with ≥50% global African ancestry (cases: 18.2%, controls: 17.7%, p=0.6834), and no significant difference in proportion of global European ancestry between cases and controls with ≥30% global American ancestry (cases: 51.1%, controls: 49.0%, p=0.2123). Among individuals with >50% African ancestry, we observed a nominally significant association between all glioma and increased local European ancestry at 7p11.2 (EGFR, pmin=0.0070) and between GBM and increased local European ancestry at 22q13.1 (CSNK1E, pmin=0.0098), both near SNPs previously associated with glioblastoma in majority European-ancestry populations. The dataset used for this analysis represents the largest collection of genotyped non-European glioma cases. These results suggest that glioma risk in African Americans may be associated with an increased local European ancestry variants at glioma risk loci previously identified in majority European ancestry populations (7p11.2 and 22q13.1). Citation Format: Quinn T. Ostrom, Kathleen M. Egan, L. Burt Nabors, Travis Gerke, Reid C. Thompson, Jeffrey J. Olson, Renato LaRocca, Sajeel Chowdhary, Jeanette E. Eckel-Passow, Georgina Armstrong, John K. Wiencke, Christopher I. Amos, Jonine L. Bernstein, Elizabeth B. Claus, Dora Il'yasova, Christoffer Johansen, Daniel H. Lachance, Rose Lai, Ryan T. Merrell, Sara H. Olson, Siegal Sadetzki, Joellen Schildkraut, Sanjay Shete, Richard S. Houlston, Robert B. Jenkins, Beatrice Melin, Melissa L. Bondy, Jill S. Barnholtz-Sloan. Evaluating glioma risk associated with extent of European admixture in African-Americans and Latinos [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 233.

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 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

BACKGROUND: Glioma accounts for ~27% of all primary brain tumors and is responsible for ~13,000 cancer-related deaths in the US each year. Glioma tumors can be broadly classified into glioblastoma (GBM) and lower-grade non-GBM. Typically gliomas have a poor prognosis irrespective of medical care, with the most common form, GBM, having a five-year survival rate of only 5%. While genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, individual studies have had limited power to identify risk loci. METHODS: We performed the largest glioma GWAS to date, comprising a meta-analysis of six existing GWAS (6,405 cases, 14,100 controls) as well as new GWAS from the Glioma International Case Control Consortium (GICC; 4,572 cases and 3,286 controls) and University of California, San Francisco (UCSF)-Mayo (1,519 cases, 804 controls), totaling 12,496 cases (6,191 classified as GBM, 5,819 as non-GBM) and 18,190 controls. RESULTS: We identified five new risk loci for GBM at 1p31.3 (rs12752552; near JAK1, P=2.04×10-9, odds ratio (OR)=1.22), 11q14.1 (rs11233250; P=9.95×10-10, OR=1.24), 16p13.3 (rs2562152; near MPG, P=1.93x10-8, OR=1.21), 16q12.1 (rs10852606; HEATR3, P=1.29×10-11, OR=1.18), 22q13.1 (rs2235573; P=1.76×10-10, OR=1.15) and eight for non-GBM at 1q32.1 (rs4252707; MDM4, P=3.34×10-9, OR=1.19), 1q44 (rs12076373; AKT3, P=2.63×10-10, OR=1.23), 2q33.3 (rs7572263; near IDH1, P=2.18×10-10, OR=1.20), 3p14.1 (rs11706832; LRIG1, P=7.66×10-9, OR=1.15), 10q24.33 (rs11598018; OBFC1, P=3.39×10-8, OR=1.14), 11q21 (rs7107785; P=3.87×10-10, OR=1.16), 14q12 (rs10131032; P=5.07x10-11, OR=1.33) and 16p13.3 (rs3751667; P=2.61×10-9, OR=1.18). Case-only analyses confirmed the specificity of 11q14.1, 16p13.3 and 22q13.1 associations for GBM and 1q44, 2q33.3, 3p14.1, 11q21 and 14q12 for non-GBM tumors. In the combined meta-analysis, among previously published glioma risk SNPs, those for all glioma at 17p13.1 (TP53), GBM at 5p15.33 (TERT), 7p11.2 (EGFR), 9p21.3 (CDKN2B-AS1) and 20q13.33 (RTEL1) and for non-GBM at 8q24.21 (CCDC26), 11q23.2, 11q23.3 (PHLDB1) and 15q24.2 (ETFA) showed even greater evidence for association. SNPs at 10q25.2 and 12q12.1 for non-GBM tumors retained genome-wide significance (i.e. P CONCLUSIONS: Our findings substantiate genetic susceptibility to GBM and non-GBM glioma being highly distinct, consistent with their distinctive molecular profiles presumably resulting from different etiological pathways. Functional analyses should lead to further insights into the biological basis of the different glioma histologies. Such information can inform gene discovery initiatives and therefore have a measurable impact on the successful development of new therapeutic agents. Citation Format: Ben Kinnersley, Beatrice S. Melin, Jill S. Barnholtz-Sloan, Margaret R. Wrensch, Christoffer Johansen, Dora Il’yasova, Quinn Ostrom, and members of GICC, Karim Labreche, Jeanette E. Eckel-Passow, Paul A. Decker, Marianne Labussière, Ahmed Idbaih, Khe Hoang-Xuan, Anna-Luisa Di Stefano, Karima Mokhtari, Jean-Yves Delattre, Pilar Galan, Konstantinos Gousias, Johannes Schramm, Minouk J. Schoemaker, Sarah J. Fleming, Stefan Herms, Stefanie Heilmann, Marcus M. Nöthen, Heinz-Erich Wichmann, Stefan Schreiber, Anthony Swerdlow, Mark Lathrop, Matthias Simon, Marc Sanson, Preetha Rajaraman, Stephen Chanock, Martha Linet, Zhaoming Wang, Meredith Yeager, Rose K. Lai, Elizabeth B. Claus, Sara H. Olson, Robert B. Jenkins, Richard S. Houlston, Melissa L. Bondy. Genome-wide association study of glioma reveals specific differences in genetic susceptibility to glioblastoma and non-glioblastoma [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 1302. doi:10.1158/1538-7445.AM2017-1302

Despite the general poor prognosis for patients with GBM, a proportion survives well beyond the median survival of 12-14 months following diagnosis. To elucidate molecular features associated with disproportionately protracted survival, we conducted deep genomic comparative analysis of a cohort of patients receiving standard therapy (surgery plus concurrent radiation and temozolomide) wherein “GBM outliers” were identified: patients who responded (long-term survivor, LTS) versus those who failed rapidly (short-term survivor, STS). The datasets enabled interrogation for signatures indicative of tumor vulnerability. Whole genomic, transcriptomic and epigenetic analyses of 18 patients, including 8 LTS with an average 30 months overall survival (OS) and 10 STS with an average of 7 months OS were performed to capture single nucleotide variants (SNVs), indels, translocations, intra-chromosomal rearrangements, copy number variants, along with DNA methylation and mRNA expression. LTS and STS cases showed equal proportion of 7p11.2 (EGFR) amplification and 9p21.3 (CDKN2A) deletion. However, LTS GBM showed frequent chromosomal gains in 4q12 (PDGFRA and KIT) and 12q14.1 (CDK4) and deletion in 19q13.33 (BAX, BCAT2 and CD33), whereas, STS GBM showed frequent deletion in 9p11.2 (FOXD4L2 and AQP7P3) and 22q11.21 (HIC2). In addition, LTS GBM showed a 2-fold increased copy number alteration (specifically deletion) as compared to STS GBM. By gene expression analysis, supervised clustering using the CIN70 signature (prognostic) showed an increased expression in STS GBM. Overall, whole genome methylation analyses showed that STS GBM tumors harbor more hypomethylation in probes situated in -200 bp of transcription start site (TSS) and both exon 1 and 5’UTR region. A methylation signature consist of 89 probes distantly separate LTS from STS GBM tumors. We posit that genomic instability (broadly inclusive) is associated with vulnerability of GBM to standard therapy; conversely, genomic instability coupled with genetic and epigenetic signatures may identify patients where up-front entry into alternative, targeted regimens would be a preferred, more-efficacious management. Supported by a grant from the Ben & Catherine Ivy Foundation. Citation Format: Sen Peng, Harshil Dhurv, Brock Armstrong, Jeffrey Kiefer, Bodour Salhia, Julianna Ross, Christophe Legendre, Selene Virk, Andrew E. Sloan, Quinn T. Ostrom, Jill Barnholtz-Sloan, Nhan L. Tran, Michael E. Berens. Integrated genomic analysis of survival outliers in glioblastoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 129.

Varying possibilities of tumor relapse for lower grade glioma (LGG, WHO grade II and III) and glioblastoma (GBM, WHO grade IV) have led to heterogenous clinical outcomes for patients. Our current study aims to establish a molecular profile of recurrence of matched primary and recurrent LGG (n = 28) and recurrent GBM (n = 24) tumor samples. The Cancer Genome Atlas (TCGA) has comprehensively profiled these matched primary/recurrent tumor sets; whole genomes, coding exomes, methylomes, and transcriptomes have been sequenced, and the samples have undergone targeted profiling of the proteome. While unsupervised analysis techniques have not led to a clear recurrence signature, supervised analysis methods have revealed interesting patterns. Protein profiling has shown that recurrent gliomas retain the overall molecular signature of their primary counterpart, but the DNA damage response, apoptosis and RTK pathways are downregulated in the recurrent gliomas, in contrast to RAS/MAPK, PI3K/AKT, and EMT pathways, which are upregulated. Whole genome sequencing and rearrangement analysis have revealed increased genomic complexity among most recurrent gliomas as well as new fusions of interest in recurrent LGG samples (PTPRZ1-MET and involving ATRX). Using genome-wide Illumina HumanMethylation 450K data we observed that 78.6% of LGGs showed depletion of DNA methylation at recurrence and 50% of GBM tumors showed an enrichment of DNA methylation at recurrence. Patient centric enrichment analysis allowed us to discover a candidate biological subgroup characterized by a subset of LGG recurrences (50%) exhibiting an aberrant CpG methylation loss at inintergenic opensea regions when compared with canonical CpG islands and shores (fold > 1.3 and confidence intervals of 99%). Importantly, inspection of CpG sites significantly hypomethylated at openseas showed that this pronounced epigenetic signature maps to candidate TSS distal and hypomethylated enhancers. The gene-targets of these hypomethylated CpG sites show a corresponding up-regulation of expression. Pathway analysis has demonstrated that these upregulated genes are involved in cellular growth and proliferation, cellular function and maintenance, and cell cycle regulation. Our results provide evidence that DNA methylation may represent a stable signature of glioma recurrence and that the crosstalk between DNA hypomethylation at openseas and chromosomal instability may be involved in glioma recurrence. We plan to further integrate our findings between data types and correlate with treatment and patient clinical outcome. Citation Format: Lindsay C. Stetson, Camila Ferreira de Souza, Tathiane Maistro Malta, Thais Sarraf Sabedot, Quinn Ostrom, Peter Liao, Daniela Pretti da Cunha Tirapelli, Luciano Neder, Carlos Gilberto Carlotti, Rehan Akbani, Sofie Salama, Laila Poisson, Daniel Brat, The Cancer Genome Atlas Network, Houtan Noushmehr, Jill Barnholtz-Sloan. Multi-omic profiling of gliomas reveals distinct DNA methylation changes at tumor recurrence. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 780.