Citations

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.

We previously reported an extremely rare case of follicular dendritic cell sarcoma (FDCS) presented as a thyroid mass. Given the rarity of this disease, there are no personalized and molecularly targeted treatment options due to the lack of knowledge in the genomic makeup of the tumor. A 44-year-old white woman was diagnosed with an extranodal FDCS in thyroid. The patient underwent a total thyroidectomy, central compartment dissection, parathyroid re-implantation, and adjuvant radiation therapy. Tumor DNA sequencing of 236 genes by FoundationOne panel found truncating mutations in PTEN and missense mutations in RET and TP53. However, patient-matched germline DNA was not sequenced which is critical for identification of true somatic mutations. Furthermore, the FoundationOne panel doesn't measure genomic rearrangements which have been shown to be abundant in sarcomas and are associated with sarcoma tumorigenesis and progression. In the current study, we carried out comprehensive genomic sequencing of the tumor, adjacent normal tissues, and patient-matched blood, in an effort to understand the genomic makeup of this rare extranodal FDCS and to identify potential therapeutic targets. Eighty-one somatic point mutations were identified in tumor but not in adjacent normal tissues or blood. A clonal truncating mutation in the CLTCL1 gene, which stabilizes the mitotic spindle, was likely a driver mutation of tumorigenesis and could explain the extensive copy number aberrations (CNAs) and genomic rearrangements in the tumor including a chr15/chr17 local chromothripsis resulted in 6 expressed fusion genes. The fusion gene HDGFRP3→SHC4 led to a 200-fold increase in the expression of oncogene SHC4 which is a potential target of the commercial drug Dasatinib. Missense mutations in ATM and splice-site mutation in VEGFR1 were also detected in addition to the TP53 missense mutation reported by FoundationOne.

Abstract High grade serous ovarian cancer (HGSOC) is a complex disease in which initiation and progression have been associated with gene mutation, DNA methylation changes, genetic variation, and epigenetic processes. Variation in several susceptibility regions and cancer-typical global methylation patterns have been observed in HGSOC; however, this knowledge has not been compelling in understanding HGSOC intiation or progression. As ingetration of genomic, epigenomic, and transcriptomic data has increased mechanistic understanding in other cancers, we hypothesized that tumor methylation alone or in combination with germline genetic variation influences tumor gene expression in HGSOC. We examined three nested models using an Elastnic Net (ENET) penalized regression method while adjusting for somatic copy number (CNV): a) germline genotype and tumor DNA methylation (full model), b) genotype only, and c) DNA methylation only. We included 339 cases from The Cancer Genome Atlas (TCGA), 54 cases from Mayo Clinic, and 78 cases from the Australian Ovarian Cancer Study (AOCS). Genotyping and copy number calls on germline DNA, expression, methylation and copy number on somatic samples were collected and analyzed on different platforms separately at each study site. We excluded genes with low overall expression and thus analyzed a total of 11,922 genes available in three datasets ( Ensembl IDs, 500kb window up- and downstream). In general, combining genomic data in HGSOC did not reveal a role for germline genetic variation in altering gene expression. However, in methylation only models 79 genes were associated with differential expression in the TCGA cases (permutation multiple testing adjusted p-val <0.05), in the Mayo cases (unadjusted p-val <0.05) and AOCS cases (unadjusted p-val <0.05). A known tummor suppressor (FBXW7) was associated with differential expression in the three datasets at p-val <0.01. This work demonstrates the feasibility, utility, and statistical power of ENET gene-level analyses incoporating maximal genomic information. Citation Format: Yanina Natanzon, Madalene Earp, Julie M. Cunningham, Kimberly R. Kalli, Stacey J. Winham, Sebastian M. Armasu, Melissa C. Larson, Chen Wang, David D. Bowtell, Dale W. Garsed, Ellen Goode. Omics data integration analysis in high grade serous ovarian cancer: results from three studies [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 3368. doi:10.1158/1538-7445.AM2017-3368

Abstract Purpose: Here we assess whether molecular subtyping identifies biological features of tumors that correlate with survival and surgical outcomes of high-grade serous ovarian cancer (HGSOC). Experimental Design: Consensus clustering of pooled mRNA expression data from over 2,000 HGSOC cases was used to define molecular subtypes of HGSOCs. This de novo classification scheme was then applied to 381 Mayo Clinic HGSOC patients with detailed survival and surgical outcome information. Results: Five molecular subtypes of HGSOC were identified. In the pooled dataset, three subtypes were largely concordant with prior studies describing proliferative, mesenchymal, and immunoreactive tumors (concordance > 70%), and the group of tumors previously described as differentiated type was segregated into two new types, one of which (anti-mesenchymal) had downregulation of genes that were typically upregulated in the mesenchymal subtype. Molecular subtypes were significantly associated with overall survival (P < 0.001) and with rate of optimal surgical debulking (≤1 cm, P = 1.9E−4) in the pooled dataset. Among stage III-C or IV Mayo Clinic patients, molecular subtypes were also significantly associated with overall survival (P = 0.001), as well as rate of complete surgical debulking (no residual disease; 16% in mesenchymal tumors compared with >28% in other subtypes; P = 0.02). Conclusions: HGSOC tumors may be categorized into five molecular subtypes that associate with overall survival and the extent of residual disease following debulking surgery. Because mesenchymal tumors may have features that were associated with less favorable surgical outcome, molecular subtyping may have future utility in guiding neoadjuvant treatment decisions for women with HGSOC. Clin Cancer Res; 23(15); 4077–85. ©2017 AACR.

18 Background: Benign breast disease (BBD) is diagnosed in 1-2 million women/year in the US, and while these patients are known to be at substantially increased risk for subsequent development of breast cancer, existing models for risk assessment perform poorly at the individual level. Here, we describe a DNA-microarray-based transcriptional model for breast cancer risk prediction for patients with sclerosing adenosis (SA), a lesion found in ¼ of all BBD patients that is characterized by epithelial proliferation, disordered acinar architecture, and stromal fibrosis. Methods: A training set was developed from 86 patients diagnosed with sclerosing adenosis (SA), of which 27 subsequently developed cancer within 10 years (cases) and 59 remained cancer-free at 10 years (controls). A diagonal linear discriminate analysis (DLDA)-prediction model for prediction of cancer within 10 years (SA TTC10) was generated from transcriptional profiles of formalin fixed, paraffin-embedded (FFPE) biopsy-derived RNA. This model was tested on a separate validation case-control set composed of 65 SA patients. Results: The SA TTC10 gene signature model, composed of 35 gene features, achieved a clear and significant separation between case and control with receiver operating characteristic area under the curve of 0.913 in the training set and 0.836 in the validation set. Conclusions: Our results provide the first demonstration that benign breast tissue contains transcriptional alterations that indicate risk of breast cancer development, and that essential precursor biomarkers of malignancy are present many years prior to cancer development. Furthermore, the SA TTC10 gene signature model, which can be assessed on FFPE biopsies, constitutes a novel prognostic biomarker for patients with SA.

Abstract Purpose: Recent progress in understanding the molecular biology of epithelial ovarian cancer has not yet translated into individualized treatment for these women or improvements in their disease outcome. Gene expression has been utilized to identify distinct molecular subtypes, but there have been no reports investigating whether or not molecular subtyping is predictive of response to bevacizumab in ovarian cancer. Experimental Design: DASL gene expression arrays were performed on FFPE tissue from patients enrolled on the ICON7 trial. Patients were stratified into four TCGA molecular subtypes. Associations between molecular subtype and the efficacy of randomly assigned therapy with bevacizumab were assessed. Results: Molecular subtypes were assigned as follows: 122 immunoreactive (34%), 96 proliferative (27%), 73 differentiated (20%), and 68 mesenchymal (19%). In univariate analysis patients with tumors of proliferative subtype obtained the greatest benefit from bevacizumab with a median PFS improvement of 10.1 months [HR, 0.55 (95% CI, 0.34–0.90), P = 0.016]. For the mesenchymal subtype, bevacizumab conferred a nonsignificant improvement in PFS of 8.2 months [HR 0.78 (95% CI, 0.44–1.40), P = 0.41]. Bevacizumab conferred modest improvements in PFS for patients with immunoreactive subtype (3.8 months; P = 0.08) or differentiated subtype (3.7 months; P = 0.61). Multivariate analysis demonstrated significant PFS improvement in proliferative subtype patients only [HR, 0.45 (95% CI, 0.27–0.74), P = 0.0015]. Conclusions: Ovarian carcinoma molecular subtypes with the poorest survival (proliferative and mesenchymal) derive a comparably greater benefit from treatment that includes bevacizumab. Validation of our findings in an independent cohort could enable the use of bevacizumab for those patients most likely to benefit, thereby reducing side effects and healthcare cost. Clin Cancer Res; 23(14); 3794–801. ©2017 AACR.

AbstractIntroductionWe hypothesized that common Alzheimer's disease (AD)‐associated variants within the triggering receptor expressed on myeloid (TREM) gene cluster influence disease through gene expression.MethodsExpression microarrays on temporal cortex and cerebellum from ∼400 neuropathologically diagnosed subjects and two independent RNAseq replication cohorts were used for expression quantitative trait locus analysis.ResultsA variant within a DNase hypersensitive site 5′ of TREM2, rs9357347‐C, associates with reduced AD risk and increased TREML1 and TREM2 levels (uncorrected P = 6.3 × 10−3 and 4.6 × 10−2, respectively). Meta‐analysis on expression quantitative trait locus results from three independent data sets (n = 1006) confirmed these associations (uncorrected P = 3.4 × 10−2 and 3.5 × 10−3, Bonferroni‐corrected P = 6.7 × 10−2 and 7.1 × 10−3, respectively).DiscussionOur findings point to rs9357347 as a functional regulatory variant that contributes to a protective effect observed at the TREM locus in the International Genomics of Alzheimer's Project genome‐wide association study meta‐analysis and suggest concomitant increase in TREML1 and TREM2 brain levels as a potential mechanism for protection from AD.