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Integrative genomics approach identifies molecular features associated with early-stage ovarian carcinoma histotypes.

ABSTRACT: Ovarian cancer comprises multiple subtypes (clear-cell (CCC), endometrioid (EC), high-grade serous (HGSC), low-grade serous (LGSC), and mucinous carcinomas (MC)) with differing molecular and clinical behavior. However, robust histotype-specific biomarkers for clinical use have yet to be identified. Here, we utilized a multi-omics approach to identify novel histotype-specific genetic markers associated with ovarian carcinoma histotypes (CCC, EC, HGSC, and MC) using DNA methylation, DNA copy number alteration and RNA sequencing data for 96 primary invasive early-stage (stage I and II) ovarian carcinomas. More specifically, the DNA methylation analysis revealed hypermethylation for CCC in comparison with the other histotypes. Moreover, copy number imbalances and novel chromothripsis-like rearrangements (n?=?64) were identified in ovarian carcinoma, with the highest number of chromothripsis-like patterns in HGSC. For the 1000 most variable transcripts, underexpression was most prominent for all histotypes in comparison with normal ovarian samples. Overall, the integrative approach identified 46 putative oncogenes (overexpressed, hypomethylated and DNA gain) and three putative tumor suppressor genes (underexpressed, hypermethylated and DNA loss) when comparing the different histotypes. In conclusion, the current study provides novel insights into molecular features associated with early-stage ovarian carcinoma that may improve patient stratification and subclassification of the histotypes.

SUBMITTER: Engqvist H

PROVIDER: S-EPMC7224294 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Integrative genomics approach identifies molecular features associated with early-stage ovarian carcinoma histotypes.

Engqvist Hanna H Parris Toshima Z TZ Biermann Jana J Rönnerman Elisabeth Werner EW Larsson Peter P Sundfeldt Karin K Kovács Anikó A Karlsson Per P Helou Khalil K

Scientific reports 20200514 1

Ovarian cancer comprises multiple subtypes (clear-cell (CCC), endometrioid (EC), high-grade serous (HGSC), low-grade serous (LGSC), and mucinous carcinomas (MC)) with differing molecular and clinical behavior. However, robust histotype-specific biomarkers for clinical use have yet to be identified. Here, we utilized a multi-omics approach to identify novel histotype-specific genetic markers associated with ovarian carcinoma histotypes (CCC, EC, HGSC, and MC) using DNA methylation, DNA copy numbe ...[more]

PMID: 32409713

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Project description:BackgroundBreast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets.ResultsOur results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods.ConclusionThe integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers there exists solid evidence for involvement in the development of breast carcinomas. Our approach also unraveled the complex regulatory interactions comprising the identified key drivers. These genomic drivers could be further investigated in the wet lab as potential candidates for new drug targets. This integrative approach can be applied in a similar fashion to other cancer types, complex diseases, or for studying cellular differentiation processes.

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Integrative genomics approach identifies molecular features associated with early-stage ovarian carcinoma histotypes.

Publications

Integrative genomics approach identifies molecular features associated with early-stage ovarian carcinoma histotypes.

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