Dataset Information

Pre-diagnosis insulin-like growth factor-I and risk of epithelial invasive ovarian cancer by histological subtypes: A collaborative re-analysis from the Ovarian Cancer Cohort Consortium.

ABSTRACT: PURPOSE:Biologic evidence suggests that the Insulin-like growth factor (IGF)-family may be involved in the etiology of epithelial invasive ovarian cancer (EOC). However, prospective studies investigating the role of IGF-I in ovarian carcinogenesis have yielded conflicting results. METHODS:We pooled and harmonized data from 6 case-control studies nested within the Ovarian Cancer Cohort Consortium to investigate the association between pre-diagnosis IGF-I concentrations and subsequent risk of EOC. We evaluated IGF-I concentrations and risk of EOC overall and by tumor subtype (defined by histology, grade, stage) in 1,270 cases and 2,907 matched controls. Multivariable conditional logistic regression models were used to calculate odds ratios (OR) and 95% confidence intervals (CI). RESULTS:Doubling of IGF-I concentration was associated with significantly lower risk of overall EOC [ORlog2?=?0.82; CI 0.72-0.93]. We observed no heterogeneity by tumor characteristics (e.g., histology, p het?=?0.62), menopausal status at blood collection (p het?=?0.79), or age at diagnosis (p het?=?0.60). CONCLUSIONS:These results suggest that IGF-I concentrations are inversely associated with EOC risk, independent of histological phenotype. Future prospective research should consider potential mechanisms for this association, including, considering other members of the IGF-family to better characterize the role of IGF-signaling in the etiology of EOC.

SUBMITTER: Ose J

PROVIDER: S-EPMC5447004 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Pre-diagnosis insulin-like growth factor-I and risk of epithelial invasive ovarian cancer by histological subtypes: A collaborative re-analysis from the Ovarian Cancer Cohort Consortium.

Ose Jennifer J Schock Helena H Poole Elizabeth M EM Lehtinen Matti M Visvanathan Kala K Helzlsouer Kathy K Buring Julie E JE Lee I-Min IM Tjønneland Anne A Boutron-Ruault Marie-Christine MC Trichopoulou Antonia A Mattiello Amalia A Onland-Moret N Charlotte NC Weiderpass Elisabete E Sánchez María-José MJ Idahl Annika A Travis Ruth C RC Rinaldi Sabina S Merritt Melissa A MA Wentzensen Nicolas N Tworoger Shelley S SS Kaaks Rudolf R Fortner Renée T RT

Cancer causes & control : CCC 20170216 5

<h4>Purpose</h4>Biologic evidence suggests that the Insulin-like growth factor (IGF)-family may be involved in the etiology of epithelial invasive ovarian cancer (EOC). However, prospective studies investigating the role of IGF-I in ovarian carcinogenesis have yielded conflicting results.<h4>Methods</h4>We pooled and harmonized data from 6 case-control studies nested within the Ovarian Cancer Cohort Consortium to investigate the association between pre-diagnosis IGF-I concentrations and subseque ...[more]

PMID: 28205047

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Project description:BackgroundEpithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70-80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics.MethodsNon-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2-10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models.ResultsApplication of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features.ConclusionsThis robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.

Project description:An increasing body of evidence shows that miR-34 family has tumor suppressive properties mediating apoptosis, cell cycle arrest and senescence. In ovarian cancer, miR34 family members were found to be under expressed. Particularly miR-34a has been revealed to be a direct transcriptional target of p53 which is frequently mutated in epithelial ovarian carcinomas especially in high grade serous cancer. Moreover, methylation of miR-34a CpG Islands was found to down-regulate miR-34a expression. The aim of this study was to investigate the clinical relevance of mir34a as well as its promoter methylation in a subset of 133 ovarian cancers with a special focus on the p53 mutation status, the dualistic type I and type II ovarian cancer model and the different histotypes.One hundred thirty-three epithelial ovarian cancers and 8 samples of healthy ovarian surface epithelium were retrospectively analysed for miR-34a expression with quantitative real-time reverse transcription PCR (qRT-PCR). Gene-specific DNA methylation was evaluated with MethyLight technique.Significantly lower miR-34a expression was found in ovarian cancers than in healthy ovarian epithelium (p = 0.002). The expression of miR-34a was found lower in type II than in type I cancers (p = 0.037), in p53 mutated as compared to p53 wild type cancers (p = 0.003) and in high grade compared to in low grade cancers (p = 0.028). In multivariate COX regression model low expressing miR-34a cancers exhibited a reduced PFS (p = 0.039) and OS (p = 0.018). In serous cancers low miR-34a levels showed a worse OS confirmed also in multivariate analysis (p = 0.022). miR-34a promoter methylation was found higher in type II cancers than in type I (p = 0.006). mir34a expression and promoter methylation showed an inverse correlation in cancer samples (p = 0.05).We demonstrated a clinical independent role of miR-34a in epithelial ovarian cancers. Moreover, we corroborated the correlation between miR-34a expression and its promoter methylation in a large set of ovarian cancers. The inverse association between miR-34a expression and grading, p53 mutation status and dualistic tumor type classification, together with its prognostic relevance may underline the tumor-suppressive character of miR-34a in ovarian cancer.

Dataset Information

Pre-diagnosis insulin-like growth factor-I and risk of epithelial invasive ovarian cancer by histological subtypes: A collaborative re-analysis from the Ovarian Cancer Cohort Consortium.

Publications

Pre-diagnosis insulin-like growth factor-I and risk of epithelial invasive ovarian cancer by histological subtypes: A collaborative re-analysis from the Ovarian Cancer Cohort Consortium.

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