Fridley, Brooke L.

Abstract Background: Endometrioid carcinoma (EC) and clear cell carcinoma (CC) histotypes of epithelial ovarian cancer are understudied compared with the more common high-grade serous carcinomas (HGSC). We therefore sought to characterize EC and CC transcriptomes in relation to HGSC. Methods: Following bioinformatics processing and gene abundance normalization, differential expression analysis of RNA sequence data collected on fresh-frozen tumors was completed with nonparametric statistical analysis methods (55 ECs, 19 CCs, 112 HGSCs). Association of gene expression with progression-free survival (PFS) was completed with Cox proportional hazards models. Eight additional multi-histotype expression array datasets (N = 852 patients) were used for replication. Results: In the discovery set, tumors generally clustered together by histotype. Thirty-two protein-coding genes were differentially expressed across histotype (P < 1 × 10−10) and showed similar associations in replication datasets, including MAP2K6, KIAA1324, CDH1, ENTPD5, LAMB1, and DRAM1. Nine genes associated with PFS (P < 0.0001) showed similar associations in replication datasets. In particular, we observed shorter PFS time for CC and EC patients with high gene expression for CCNB2, CORO2A, CSNK1G1, FRMD8, LIN54, LINC00664, PDK1, and PEX6, whereas, the converse was observed for HGSC patients. Conclusions: The results suggest important histotype differences that may aid in the development of treatment options, particularly those for patients with EC or CC. Impact: We present replicated findings on transcriptomic differences and how they relate to clinical outcome for two of the rarer ovarian cancer histotypes of EC and CC, along with comparison with the common histotype of HGSC. Cancer Epidemiol Biomarkers Prev; 27(9); 1101–9. ©2018 AACR.

High-grade serous ovarian cancer (HGSOC) is a complex disease in which initiation and progression have been associated with copy number alterations, epigenetic processes, and, to a lesser extent, germline variation. We hypothesized that, when summarized at the gene level, tumor methylation and germline genetic variation, alone or in combination, influence tumor gene expression in HGSOC. We used Elastic Net (ENET) penalized regression method to evaluate these associations and adjust for somatic copy number in 3 independent data sets comprising tumors from more than 470 patients. Penalized regression models of germline variation, with or without methylation, did not reveal a role in HGSOC gene expression. However, we observed significant association between regional methylation and expression of 5 genes ( WDPCP, KRT6C, BRCA2, EFCAB13, and ZNF283). CpGs retained in ENET model for BRCA2 and ZNF283 appeared enriched in several regulatory elements, suggesting that regularized regression may provide a novel utility for integrative genomic analysis.

ABSTRACTX‐chromosome inactivation (XCI) epigenetically silences transcription of an X chromosome in females; patterns of XCI are thought to be aberrant in women's cancers, but are understudied due to statistical challenges. We develop a two‐stage statistical framework to assess skewed XCI and evaluate gene‐level patterns of XCI for an individual sample by integration of RNA sequence, copy number alteration, and genotype data. Our method relies on allele‐specific expression (ASE) to directly measure XCI and does not rely on male samples or paired normal tissue for comparison. We model ASE using a two‐component mixture of beta distributions, allowing estimation for a given sample of the degree of skewness (based on a composite likelihood ratio test) and the posterior probability that a given gene escapes XCI (using a Bayesian beta‐binomial mixture model). To illustrate the utility of our approach, we applied these methods to data from tumors of ovarian cancer patients. Among 99 patients, 45 tumors were informative for analysis and showed evidence of XCI skewed toward a particular parental chromosome. For 397 X‐linked genes, we observed tumor XCI patterns largely consistent with previously identified consensus states based on multiple normal tissue types. However, 37 genes differed in XCI state between ovarian tumors and the consensus state; 17 genes aberrantly escaped XCI in ovarian tumors (including many oncogenes), whereas 20 genes were unexpectedly inactivated in ovarian tumors (including many tumor suppressor genes). These results provide evidence of the importance of XCI in ovarian cancer and demonstrate the utility of our two‐stage analysis.

Abstract Introduction: In females, X-chromosome inactivation (XCI) epigenetically silences transcription of one copy of the X chromosome. Which chromosome is silenced is randomly selected, and is tissue- and cell-specific. While some genes are known to escape XCI under normal conditions, aberrant XCI patterns are thought to occur in female-specific cancers, although the role of XCI in ovarian tumorigenesis and progression is largely unknown. The process of XCI is complex, and integration of gene expression, DNA methylation, and copy number data can inform the XCI status of individual genes and chromosome-wide XCI patterns for individual patients. Methods: We evaluated gene- and chromosome-level patterns of XCI by integrating RNA sequence, copy number alteration, genotype, and DNA methylation data to study XCI escape patterns in tumor samples from 99 ovarian cancer patients. We measured allele-specific expression (ASE) for 397 X-linked genes to identify the active alleles for each tumor. Combining ASE data with knowledge of copy number status, we used a Bayesian beta-binomial mixture model to estimate which genes escaped XCI for each patient, and validated our findings using DNA methylation data. To assess global XCI patterns, we performed cluster analyses on the ASE and methylation data, after adjusting for loss of heterozygosity. We examined the relationship between the clusters and clinical factors, including overall survival and time to recurrence. Results: DNA promoter methylation demonstrated inverse regional correlations with ASE. Cluster analyses using ASE and methylation data demonstrated evidence of two tumor clusters, representing normal XCI and global XCI dysregulation. The dysregulated XCI cluster (N=52) was associated with lower X-inactive specific transcript expression as expected (p<0.01). Patients with XCI dysregulated tumors were higher grade, stage, serous histology and were sub-optimally debulked (p<0.05). These patients also had shorter overall survival time (HR=1.87, p=0.02) and time to recurrence (HR=2.34, p<0.01), although associations were attenuated after covariate adjustment. In 45 tumor samples with sufficient data, we observed escape patterns largely consistent with previous reports of multiple tissue types. When comparing tumor to normal ovarian tissue, eight genes (CXorf23, CXorf36, BRWD3, ELF4, SLITRK4, GABRE, CLCN4, SH3BGRL) showed putative escape in the tumor and two genes (RBBP7, OFD1) showed discrepant tumor inactivation. Conclusions: We identified discrepant gene-level XCI tumor classifications compared to normal tissue and identified a group of patients with chromosome-wide XCI dysregulation associated with worse clinical prognosis. This provides evidence of the role of XCI in ovarian cancer and highlights the need to integrate multiple genomic data types to study XCI. Citation Format: Stacey J. Winham, Nicholas B. Larson, Sebastian M. Armasu, Zachary C. Fogarty, Melissa C. Larson, Kimberly R. Kalli, Kate Lawrenson, Simon Gayther, Brooke L. Fridley, Ellen L. Goode. Integrative analyses of gene expression, DNA methylation, genotype and copy number alterations characterize X-chromosome inactivation in ovarian 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 2420. doi:10.1158/1538-7445.AM2017-2420

AbstractLarge-scale genotyping studies have identified over 70 single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. However, knowledge regarding genetic risk factors associated with the prognosis is limited. The aim of this study was therefore to investigate the prognostic effect of nine known breast cancer risk SNPs. BC patients (n = 1687) randomly sampled in an adjuvant, randomized phase III trial (SUCCESS A study) were genotyped for nine BC risk SNPs: rs17468277(CASP8), rs2981582(FGFR2), rs13281615(8q24), rs3817198(LSP1), rs889312(MAP3K1), rs3803662(TOX3), rs13387042(2q35), rs4973768(SLC4A7), rs6504950(COX11). Cox proportional hazards models were used to test the SNPsʼ association with overall survival (OS) and progression-free survival (PFS). Additional analyses were carried out for molecular subgroups. rs3817198 in LSP1 (lymphocyte-specific protein 1) was the only SNP that significantly influenced OS (p = 0.01) and PFS (p < 0.01) in the likelihood ratio test comparing the genetic survival model with the clinical survival model. In the molecular subgroups, triple-negative patients with two minor alleles in rs3817198 had a much better prognosis relative to OS (adjusted HR 0.03; 95% CI 0.002 – 0.279) and PFS (HR 0.09; 95% CI 0.02 – 0.36) than patients with the common alleles. The same effect on PFS was shown for patients with luminal A tumors (HR 0.19; 95% CI 0.05 – 0.84), whereas patients with luminal B tumors had a poorer PFS with two minor alleles (HR 2.13; 95% CI 1.02 – 4.40). The variant in rs3817198 has a prognostic effect particularly in the subgroup of patients with triple-negative BC, suggesting a possible link with immunomodulation and BC.