Germline genetic regulation of the colorectal tumor immune microenvironment.

OBJECTIVE: To evaluate the contribution of germline genetics to regulating the briskness and diversity of T cell responses in CRC, we conducted a genome-wide association study to examine the associations between germline genetic variation and quantitative measures of T cell landscapes in 2,876 colorectal tumors from participants in the Molecular Epidemiology of Colorectal Cancer Study (MECC). METHODS: Germline DNA samples were genotyped and imputed using genome-wide arrays. Tumor DNA samples were extracted from paraffin blocks, and T cell receptor clonality and abundance were quantified by immunoSEQ (Adaptive Biotechnologies, Seattle, WA). Tumor infiltrating lymphocytes per high powered field (TILs/hpf) were scored by a gastrointestinal pathologist. Regression models were used to evaluate the associations between each variant and the three T-cell features, adjusting for sex, age, genotyping platform, and global ancestry. Three independent datasets were used for replication. RESULTS: We identified a SNP (rs4918567) near RBM20 associated with clonality at a genome-wide significant threshold of 5 × 10- 8, with a consistent direction of association in both discovery and replication datasets. Expression quantitative trait (eQTL) analyses and in silico functional annotation for these loci provided insights into potential functional roles, including a statistically significant eQTL between the T allele at rs4918567 and higher expression of ADRA2A (P = 0.012) in healthy colon mucosa. CONCLUSIONS: Our study suggests that germline genetic variation is associated with the quantity and diversity of adaptive immune responses in CRC. Further studies are warranted to replicate these findings in additional samples and to investigate functional genomic mechanisms.

Differences in tumor-associated T-cell receptor repertoires between early-onset and average-onset colorectal cancer.

The incidence of colorectal cancer (CRC) among individuals younger than age 50 (early-onset CRC [EOCRC]) has substantially increased, and yet the etiology and molecular mechanisms underlying this alarming rise remain unclear. We compared tumor-associated T-cell repertoires between EOCRC and average-onset CRC (AOCRC) to uncover potentially unique immune microenvironment-related features by age of onset. Our discovery cohort included 242 patients who underwent surgical resection at Cleveland Clinic from 2000 to 2020. EOCRC was defined as younger than age 50 years at diagnosis (N = 126) and AOCRC as 60 years of age or older (N = 116). T-cell receptor (TCR) abundance and clonality were measured by immunosequencing of tumors. Logistic regression models were used to evaluate the associations between TCR repertoire features and age of onset, adjusting for sex, race, tumor location, and stage. Findings were replicated in 152 EOCRC and 1984 AOCRC cases from the Molecular Epidemiology of Colorectal Cancer Study. EOCRC tumors had significantly higher TCR diversity compared with AOCRC tumors in the discovery cohort (odds ratio [OR] = 0.44, 95% confidence interval [CI] = 0.32 to 0.61, P < .0001). This association was also observed in the replication cohort (OR = 0.74, 95% CI = 0.62 to 0.89, P = .0013). No significant differences in TCR abundance were observed between EOCRC and AOCRC in either cohort. Higher TCR diversity, suggesting a more diverse intratumoral T-cell response, is more frequently observed in EOCRC than AOCRC. Further studies are warranted to investigate the role of T-cell diversity and the adaptive immune response more broadly in the etiology and outcomes of EOCRC.

Heterozygote advantage at HLA class I and II loci and reduced risk of colorectal cancer.

Objective: Reduced diversity at Human Leukocyte Antigen (HLA) loci may adversely affect the host's ability to recognize tumor neoantigens and subsequently increase disease burden. We hypothesized that increased heterozygosity at HLA loci is associated with a reduced risk of developing colorectal cancer (CRC). Methods: We imputed HLA class I and II four-digit alleles using genotype data from a population-based study of 5,406 cases and 4,635 controls from the Molecular Epidemiology of Colorectal Cancer Study (MECC). Heterozygosity at each HLA locus and the number of heterozygous genotypes at HLA class -I (A, B, and C) and HLA class -II loci (DQB1, DRB1, and DPB1) were quantified. Logistic regression analysis was used to estimate the risk of CRC associated with HLA heterozygosity. Individuals with homozygous genotypes for all loci served as the reference category, and the analyses were adjusted for sex, age, genotyping platform, and ancestry. Further, we investigated associations between HLA diversity and tumor-associated T cell repertoire features, as measured by tumor infiltrating lymphocytes (TILs; N=2,839) and immunosequencing (N=2,357). Results: Individuals with all heterozygous genotypes at all three class I genes had a reduced odds of CRC (OR: 0.74; 95% CI: 0.56-0.97, p= 0.031). A similar association was observed for class II loci, with an OR of 0.75 (95% CI: 0.60-0.95, p= 0.016). For class-I and class-II combined, individuals with all heterozygous genotypes had significantly lower odds of developing CRC (OR: 0.66, 95% CI: 0.49-0.87, p= 0.004) than those with 0 or one heterozygous genotype. HLA class I and/or II diversity was associated with higher T cell receptor (TCR) abundance and lower TCR clonality, but results were not statistically significant. Conclusion: Our findings support a heterozygote advantage for the HLA class-I and -II loci, indicating an important role for HLA genetic variability in the etiology of CRC.

TRM: a powerful two-stage machine learning approach for identifying SNP-SNP interactions.

Studies have shown that interactions of single nucleotide polymorphisms (SNPs) may play an important role in understanding the causes of complex disease. We have proposed an integrated machine learning method that combines two machine-learning methods-Random Forests (RF) and Multivariate Adaptive Regression Splines (MARS)-to identify a subset of important SNPs and detect interaction patterns more effectively and efficiently. In this two-stage RF-MARS (TRM) approach, RF is first applied to detect a predictive subset of SNPs, and then MARS is used to identify the interaction patterns. We evaluated the TRM performances in four models. RF variable selection was based on out-of-bag classification error rate (OOB) and variable important spectrum (IS). Our results support that RF(OOB) had better performance than MARS and RF(IS) in detecting important variables. This study demonstrates that TRM(OOB) , which is RF(OOB) plus MARS, has combined the strengths of RF and MARS in identifying SNP-SNP interactions in a scenario of 100 candidate SNPs. TRM(OOB) had greater true positive rate and lower false positive rate compared with MARS, particularly for searching interactions with a strong association with the outcome. Therefore, the use of TRM(OOB) is favored for exploring SNP-SNP interactions in a large-scale genetic variation study.

ABO blood group and risk of epithelial ovarian cancer within the Ovarian Cancer Association Consortium.

PURPOSE: Previous studies have examined the association between ABO blood group and ovarian cancer risk, with inconclusive results. METHODS: In eight studies participating in the Ovarian Cancer Association Consortium, we determined ABO blood groups and diplotypes by genotyping 3 SNPs in the ABO locus. Odds ratios and 95 % confidence intervals were calculated in each study using logistic regression; individual study results were combined using random effects meta-analysis. RESULTS: Compared to blood group O, the A blood group was associated with a modestly increased ovarian cancer risk: (OR: 1.09; 95 % CI: 1.01-1.18; p = 0.03). In diplotype analysis, the AO, but not the AA diplotype, was associated with increased risk (AO: OR: 1.11; 95 % CI: 1.01-1.22; p = 0.03; AA: OR: 1.03; 95 % CI: 0.87-1.21; p = 0.76). Neither AB nor the B blood groups were associated with risk. Results were similar across ovarian cancer histologic subtypes. CONCLUSION: Consistent with most previous reports, the A blood type was associated modestly with increased ovarian cancer risk in this large analysis of multiple studies of ovarian cancer. Future studies investigating potential biologic mechanisms are warranted.

Meta-analysis of 8q24 for seven cancers reveals a locus between NOV and ENPP2 associated with cancer development.

BACKGROUND: Human chromosomal region 8q24 contains several genes which could be functionally related to cancer, including the proto-oncogene c-MYC. However, the abundance of associations around 128 Mb on chromosome 8 could mask the appearance of a weaker, but important, association elsewhere on 8q24. METHODS: In this study, we completed a meta-analysis of results from nine genome-wide association studies for seven types of solid-tumor cancers (breast, prostate, pancreatic, lung, ovarian, colon, and glioma) to identify additional associations that were not apparent in any individual study. RESULTS: Fifteen SNPs in the 8q24 region had meta-analysis p-values < 1E-04. In particular, the region consisting of 120,576,000-120,627,000 bp contained 7 SNPs with p-values < 1.0E-4, including rs6993464 (p = 1.25E-07). This association lies in the region between two genes, NOV and ENPP2, which have been shown to play a role in tumor development and motility. An additional region consisting of 5 markers from 128,478,000 bp - 128,524,000 (around gene POU5F1B) had p-values < 1E-04, including rs6983267, which had the smallest p-value (p = 6.34E-08). This result replicates previous reports of association between rs6983267 and prostate and colon cancer. CONCLUSIONS: Further research in this area is warranted as these results demonstrate that the chromosomal region 8q24 may contain a locus that influences general cancer susceptibility between 120,576 and 120,630 kb.

Variation at 8q24 and 9p24 and risk of epithelial ovarian cancer.

The chromosome 8q24 region (specifically, 8q24.21.a) is known to harbor variants associated with risk of breast, colorectal, prostate, and bladder cancers. In 2008, variants rs10505477 and rs6983267 in this region were associated with increased risk of invasive ovarian cancer (p < 0.01); however, three subsequent ovarian cancer reports of 8q24 variants were null. Here, we used a multi-site case-control study of 940 ovarian cancer cases and 1,041 controls to evaluate associations between these and other single-nucleotide polymorphisms (SNPs) in this 8q24 region, as well as in the 9p24 colorectal cancer associated-region (specifically, 9p24.1.b). A total of 35 SNPs from previous reports and additional tagging SNPs were assessed using an Illumina GoldenGate array and analyzed using logistic regression models, adjusting for population structure and other potential confounders. We observed no association between genotypes and risk of ovarian cancer considering all cases, invasive cases, or invasive serous cases. For example, at 8q24 SNPs rs10505477 and rs6983267, analyses yielded per-allele invasive cancer odds ratios of 0.95 (95% confidence interval (CI) 0.82-1.09, p trend 0.46) and 0.97 (95% CI 0.84-1.12, p trend 0.69), respectively. Analyses using an approach identical to that of the first positive 8q24 report also yielded no association with risk of ovarian cancer. In the 9p24 region, no SNPs were associated with risk of ovarian cancer overall or with invasive or invasive serous disease (all p values > 0.10). These results indicate that the SNPs studied here are not related to risk of this gynecologic malignancy and that the site-specific nature of 8q24.21.a associations may not include ovarian cancer.

A comparison in association and linkage genome-wide scans for alcoholism susceptibility genes using single-nucleotide polymorphisms.

We conducted genome-wide linkage scans using both microsatellite and single-nucleotide polymorphism (SNP) markers. Regions showing the strongest evidence of linkage to alcoholism susceptibility genes were identified. Haplotype analyses using a sliding-window approach for SNPs in these regions were performed. In addition, we performed a genome-wide association scan using SNP data. SNPs in these regions with evidence of association (P

Haplotypic structure of the X chromosome in the COGA population sample and the quality of its reconstruction by extant software packages.

BACKGROUND: The haplotypes of the X chromosome are accessible to direct count in males, whereas the diplotypes of the females may be inferred knowing the haplotype of their sons or fathers. Here, we investigated: 1) the possible large-scale haplotypic structure of the X chromosome in a Caucasian population sample, given the single-nucleotide polymorphism (SNP) maps and genotypes provided by Illumina and Affimetrix for Genetic Analysis Workshop 14, and, 2) the performances of widely used programs in reconstructing haplotypes from population genotypic data, given their known distribution in a sample of unrelated individuals. RESULTS: All possible unrelated mother-son pairs of Caucasian ancestry (N = 104) were selected from the 143 families of the Collaborative Study on the Genetics of Alcoholism pedigree files, and the diplotypes of the mothers were inferred from the X chromosomes of their sons. The marker set included 313 SNPs at an average density of 0.47 Mb. Linkage disequilibrium between pairs of markers was computed by the parameter D', whereas for measuring multilocus disequilibrium, we developed here an index called D*, and applied it to all possible sliding windows of 5 markers each. Results showed a complex pattern of haplotypic structure, with regions of low linkage disequilibrium separated by regions of high values of D*. The following programs were evaluated for their accuracy in inferring population haplotype frequencies: 1) ARLEQUIN 2.001; 2) PHASE 2.1.1; 3) SNPHAP 1.1; 4) HAPLOBLOCK 1.2; 5) HAPLOTYPER 1.0. Performances were evaluated by Pearson correlation (r) coefficient between the true and the inferred distribution of haplotype frequencies. CONCLUSION: The SNP haplotypic structure of the X chromosome is complex, with regions of high haplotype conservation interspersed among regions of higher haplotype diversity. All the tested programs were accurate (r = 1) in reconstructing the distribution of haplotype frequencies in case of high D* values. However, only the program PHASE realized a high correlation coefficient (r > 0.7) in conditions of low linkage disequilibrium.

Comparison of type I error for multiple test corrections in large single-nucleotide polymorphism studies using principal components versus haplotype blocking algorithms.

Although permutation testing has been the gold standard for assessing significance levels in studies using multiple markers, it is time-consuming. A Bonferroni correction to the nominal p-value that uses the underlying pair-wise linkage disequilibrium (LD) structure among the markers to determine the number of effectively independent tests has recently been proposed. We propose using the number of independent LD blocks plus the number of independent single-nucleotide polymorphisms for correction. Using the Collaborative Study on the Genetics of Alcoholism LD data for chromosome 21, we simulated 1,000 replicates of parent-child trio data under the null hypothesis with two levels of LD: moderate and high. Assuming haplotype blocks were independent, we calculated the number of independent statistical tests using 3 haplotype blocking algorithms. We then compared the type I error rates using a principal components-based method, the three blocking methods, a traditional Bonferroni correction, and the unadjusted p-values obtained from FBAT. Under high LD conditions, the PC method and one of the blocking methods were slightly conservative, whereas the 2 other blocking methods exceeded the target type I error rate. Under conditions of moderate LD, we show that the blocking algorithm corrections are closest to the desired type I error, although still slightly conservative, with the principal components-based method being almost as conservative as the traditional Bonferroni correction.

Investigation of altering single-nucleotide polymorphism density on the power to detect trait loci and frequency of false positive in nonparametric linkage analyses of qualitative traits.

Genome-wide linkage analysis using microsatellite markers has been successful in the identification of numerous Mendelian and complex disease loci. The recent availability of high-density single-nucleotide polymorphism (SNP) maps provides a potentially more powerful option. Using the simulated and Collaborative Study on the Genetics of Alcoholism (COGA) datasets from the Genetics Analysis Workshop 14 (GAW14), we examined how altering the density of SNP marker sets impacted the overall information content, the power to detect trait loci, and the number of false positive results. For the simulated data we used SNP maps with density of 0.3 cM, 1 cM, 2 cM, and 3 cM. For the COGA data we combined the marker sets from Illumina and Affymetrix to create a map with average density of 0.25 cM and then, using a sub-sample of these markers, created maps with density of 0.3 cM, 0.6 cM, 1 cM, 2 cM, and 3 cM. For each marker set, multipoint linkage analysis using MERLIN was performed for both dominant and recessive traits derived from marker loci. Our results showed that information content increased with increased map density. For the homogeneous, completely penetrant traits we created, there was only a modest difference in ability to detect trait loci. Additionally, as map density increased there was only a slight increase in the number of false positive results when there was linkage disequilibrium (LD) between markers. The presence of LD between markers may have led to an increased number of false positive regions but no clear relationship between regions of high LD and locations of false positive linkage signals was observed.

Identification of tag single-nucleotide polymorphisms in regions with varying linkage disequilibrium.

We compared seven different tagging single-nucleotide polymorphism (SNP) programs in 10 regions with varied amounts of linkage disequilibrium (LD) and physical distance. We used the Collaborative Studies on the Genetics of Alcoholism dataset, part of the Genetic Analysis Workshop 14. We show that in regions with moderate to strong LD these programs are relatively consistent, despite different parameters and methods. In addition, we compared the selected SNPs in a multipoint linkage analysis for one region with strong LD. As the number of selected SNPs increased, the LOD score, mean information content, and type I error also increased.

Description of the data from the Collaborative Study on the Genetics of Alcoholism (COGA) and single-nucleotide polymorphism genotyping for Genetic Analysis Workshop 14.

The data provided to the Genetic Analysis Workshop 14 (GAW 14) was the result of a collaboration among several different groups, catalyzed by Elizabeth Pugh from The Center for Inherited Disease Research (CIDR) and the organizers of GAW 14, Jean MacCluer and Laura Almasy. The DNA, phenotypic characterization, and microsatellite genomic survey were provided by the Collaborative Study on the Genetics of Alcoholism (COGA), a nine-site national collaboration funded by the National Institute of Alcohol and Alcoholism (NIAAA) and the National Institute of Drug Abuse (NIDA) with the overarching goal of identifying and characterizing genes that affect the susceptibility to develop alcohol dependence and related phenotypes. CIDR, Affymetrix, and Illumina provided single-nucleotide polymorphism genotyping of a large subset of the COGA subjects. This article briefly describes the dataset that was provided.

Common Genetic Variation In Cellular Transport Genes and Epithelial Ovarian Cancer (EOC) Risk.

BACKGROUND: Defective cellular transport processes can lead to aberrant accumulation of trace elements, iron, small molecules and hormones in the cell, which in turn may promote the formation of reactive oxygen species, promoting DNA damage and aberrant expression of key regulatory cancer genes. As DNA damage and uncontrolled proliferation are hallmarks of cancer, including epithelial ovarian cancer (EOC), we hypothesized that inherited variation in the cellular transport genes contributes to EOC risk. METHODS: In total, DNA samples were obtained from 14,525 case subjects with invasive EOC and from 23,447 controls from 43 sites in the Ovarian Cancer Association Consortium (OCAC). Two hundred seventy nine SNPs, representing 131 genes, were genotyped using an Illumina Infinium iSelect BeadChip as part of the Collaborative Oncological Gene-environment Study (COGS). SNP analyses were conducted using unconditional logistic regression under a log-additive model, and the FDR q<0.2 was applied to adjust for multiple comparisons. RESULTS: The most significant evidence of an association for all invasive cancers combined and for the serous subtype was observed for SNP rs17216603 in the iron transporter gene HEPH (invasive: OR = 0.85, P = 0.00026; serous: OR = 0.81, P = 0.00020); this SNP was also associated with the borderline/low malignant potential (LMP) tumors (P = 0.021). Other genes significantly associated with EOC histological subtypes (p<0.05) included the UGT1A (endometrioid), SLC25A45 (mucinous), SLC39A11 (low malignant potential), and SERPINA7 (clear cell carcinoma). In addition, 1785 SNPs in six genes (HEPH, MGST1, SERPINA, SLC25A45, SLC39A11 and UGT1A) were imputed from the 1000 Genomes Project and examined for association with INV EOC in white-European subjects. The most significant imputed SNP was rs117729793 in SLC39A11 (per allele, OR = 2.55, 95% CI = 1.5-4.35, p = 5.66x10-4). CONCLUSION: These results, generated on a large cohort of women, revealed associations between inherited cellular transport gene variants and risk of EOC histologic subtypes.

Genetic susceptibility and dietary patterns in lung cancer.

Cigarette smoking is the dominant risk factor for lung cancer, but only a minority of smokers ever develops tumors. Though genetic susceptibility is likely to explain some of the variability in risk, results from previous studies of genetic polymorphisms have been inconclusive. As diet may also affect the risk of lung cancer, it is possible that the degree of risk produced by smoking and genetic susceptibility varies, depending on diet. To assess this hypothesis, we conducted a case-control study to examine the effect of cigarette smoking, dietary patterns and variation in genes involved in phase II metabolism. A total of 254 individuals with lung cancer and 184 healthy controls were recruited for the study. To identify persons with similar dietary patterns, cluster analysis was performed using nutrient densities of four major dietary constituents: protein, carbohydrate, animal fat, and dietary fiber. Two groups of individuals were identified with distinct dietary patterns: (1) a group (n=241) with a high intake of animal fat and protein and a low intake of carbohydrates and dietary fiber (the 'unhealthy' pattern) and (2) a group (n=197) with a high intake of fiber and carbohydrate and a low intake of protein and animal fat (the 'healthy' pattern) [corrected]. On stratified analysis, several genotype/dietary pattern combinations were found to affect risk of lung cancer. Smokers who were not homozygous for the most common GSTP1 allele and had a healthy dietary pattern were at significantly lower risk than smokers who were homozygous for the GSTP1 common allele and who had an unhealthy dietary pattern (OR=0.16, 95%CI: 0.04-0.57). Among smokers who were GSTM1 null, persons with a healthy dietary pattern were at lower risk than persons with an unhealthy dietary pattern (OR: 0.46, 95%CI: 0.21-1.01). Among smokers with an unhealthy dietary patterns, persons with a His/His genotype in the exon 3 polymorphism of EPHX1 were at significantly lower risk that persons who were not homozygous. These data suggest that dietary factors may affect the risk imposed by genetic susceptibility at detoxification loci. Adjustments using dietary pattern may be useful in elucidating the effects of polymorphisms in genes responsible for carcinogen metabolism.

Upper airway and its surrounding structures in obese and nonobese patients with sleep-disordered breathing.

OBJECTIVES/HYPOTHESIS: The objective was to understand the pathophysiological relationship between obesity and sleep-disordered breathing by using cephalometry with the Muller maneuver. STUDY DESIGN: A prospective study. METHODS: One hundred habitually snoring men were evaluated for sleep-disordered breathing at the Sleep Center of Chang Gung Memorial Hospital (Taipei, Taiwan). Each subject received overnight polysomnography and two lateral cephalograms at the end-expiration phase (L1) and the Muller maneuver (L2), respectively, to evaluate the facial skeleton and the upper airway and its surrounding structures (soft palate, tongue, epiglottis, and hyoid bone). After excluding 14 patients from the study because of jaw opening during cephalometry, 86 (39 nonobese and 47 obese) patients with sleep-disordered breathing were enrolled. RESULTS: Patients with varying degrees of obesity significantly differed in terms of the facial skeleton and the structure and function of the upper airway and its surrounding structures. The Muller maneuver caused dynamic changes in the hypopharyngeal airway and position of the tongue, and these dynamic changes were related to the pathogenesis of sleep-disordered breathing for the two groups (nonobese and obese patients). The regression model generated for the nonobese group revealed that the apnea hypopnea index was significantly related to the pharyngeal length (L2) and the soft palate thickness (L1). In contrast, the regression model generated for the obese group revealed that the apnea hypopnea index was significantly related to the soft palate (length [L1] and dynamic position change), the hyoid position (vertical [L1] and horizontal [L2]), the tongue (dynamic position change), and body mass index. CONCLUSION: Cephalometry with the Muller maneuver may provide further insight into the pathogenesis of sleep-disordered breathing for the two groups of patients (nonobese and obese patients).

Safety and chemopreventive effect of Polyphenon E in preventing early and metastatic progression of prostate cancer in TRAMP mice.

Prostate cancer treatment is often accompanied by untoward side effects. Therefore, chemoprevention to reduce the risk and inhibit the progression of prostate cancer may be an effective approach to reducing disease burden. We investigated the safety and efficacy of Polyphenon E, a green tea extract, in reducing the progression of prostate cancer in transgenic adenocarcinoma of the mouse prostate (TRAMP) mice. A total of 119 male TRAMP and 119 C57BL/6J mice were treated orally with one of 3 doses of Polyphenon E (200, 500, and 1,000 mg/kg/day) in drinking water ad libitum replicating human achievable doses. Baseline assessments were performed before treatments. Safety and efficacy assessments during treatments were performed when mice were 12, 22, and 32 weeks old. The number and size of tumors in treated TRAMP mice were significantly decreased compared with untreated animals. In untreated 32 weeks old TRAMP mice, prostate carcinoma metastasis to distant sites was observed in 100% of mice (8/8), compared with 13% of mice (2/16) treated with high-dose Polyphenon E during the same period. Furthermore, Polyphenon E treatment significantly inhibited metastasis in TRAMP mice in a dose-dependent manner (P = 0.0003). Long-term (32 weeks) treatment with Polyphenon E was safe and well tolerated with no evidence of toxicity in C57BL/6J mice. Polyphenon E is an effective chemopreventive agent in preventing the progression of prostate cancer to metastasis in TRAMP mice. Polyphenon E showed no toxicity in these mouse models. Our findings provide additional evidence for the safety and chemopreventive effect of Polyphenon E in preventing metastatic progression of prostate cancer.

Identification and molecular characterization of a new ovarian cancer susceptibility locus at 17q21.31.

Epithelial ovarian cancer (EOC) has a heritable component that remains to be fully characterized. Most identified common susceptibility variants lie in non-protein-coding sequences. We hypothesized that variants in the 3' untranslated region at putative microRNA (miRNA)-binding sites represent functional targets that influence EOC susceptibility. Here, we evaluate the association between 767 miRNA-related single-nucleotide polymorphisms (miRSNPs) and EOC risk in 18,174 EOC cases and 26,134 controls from 43 studies genotyped through the Collaborative Oncological Gene-environment Study. We identify several miRSNPs associated with invasive serous EOC risk (odds ratio=1.12, P=10(-8)) mapping to an inversion polymorphism at 17q21.31. Additional genotyping of non-miRSNPs at 17q21.31 reveals stronger signals outside the inversion (P=10(-10)). Variation at 17q21.31 is associated with neurological diseases, and our collaboration is the first to report an association with EOC susceptibility. An integrated molecular analysis in this region provides evidence for ARHGAP27 and PLEKHM1 as candidate EOC susceptibility genes.

GWAS meta-analysis and replication identifies three new susceptibility loci for ovarian cancer.

Genome-wide association studies (GWAS) have identified four susceptibility loci for epithelial ovarian cancer (EOC), with another two suggestive loci reaching near genome-wide significance. We pooled data from a GWAS conducted in North America with another GWAS from the UK. We selected the top 24,551 SNPs for inclusion on the iCOGS custom genotyping array. We performed follow-up genotyping in 18,174 individuals with EOC (cases) and 26,134 controls from 43 studies from the Ovarian Cancer Association Consortium. We validated the two loci at 3q25 and 17q21 that were previously found to have associations close to genome-wide significance and identified three loci newly associated with risk: two loci associated with all EOC subtypes at 8q21 (rs11782652, P = 5.5 × 10(-9)) and 10p12 (rs1243180, P = 1.8 × 10(-8)) and another locus specific to the serous subtype at 17q12 (rs757210, P = 8.1 × 10(-10)). An integrated molecular analysis of genes and regulatory regions at these loci provided evidence for functional mechanisms underlying susceptibility and implicated CHMP4C in the pathogenesis of ovarian cancer.

Phase I trial of vorinostat combined with bevacizumab and CPT-11 in recurrent glioblastoma.

A phase I study was conducted to determine the dose-limiting toxicities (DLT) and maximum tolerated dose (MTD) for the combination of vorinostat with bevacizumab and CPT-11 in recurrent glioblastoma. Vorinostat was combined with bevacizumab and CPT-11 and was escalated using a standard 3 + 3 design. Vorinostat was escalated up to 2 actively investigated doses of this compound or until the MTD was identified on the basis of DLTs. Correlative science involving proteomic profiling of serial patient plasma samples was performed. Nineteen patients were treated. The MTD of vorinostat was established at 400 mg on days 1-7 and 15-21 every 28 days when combined with bevacizumab and CPT-11. Common toxicities were fatigue and diarrhea. DLTs included fatigue, hypertension/hypotension, and central nervous system ischemia. Although the MTD was established, CPT-11 dose reductions were common early in therapy. High-dose vorinostat had an improved progression-free survival and overall survival when compared with low-dose vorinostat. Serum proteomic profiling identified IGFBP-5 and PDGF-AA as markers for improved PFS and recurrence, respectively. A MTD for the combination of vorinostat with bevacizumab and CPT-11 has been established, although it has poor long-term tolerability. With the increased toxicities associated with CPT-11 coupled with its unclear clinical significance, investigating the efficacy of vorinostat combined with bevacizumab alone may represent a more promising strategy to evaluate in the context of a phase II clinical trial.