Identification of two poorly prognosed ovarian carcinoma subtypes associated with CHEK2 germ-line mutation and non-CHEK2 somatic mutation gene signatures

Abstract

High-grade serous ovarian cancer (HG-SOC), a major histologic type of epithelial ovarian cancer (EOC), is a poorly-characterized, heterogeneous and lethal disease where somatic mutations of TP53 are common and inherited loss-of-function mutations in BRCA1/2 predispose to cancer in 9.5–13% of EOC patients. However, the overall burden of disease due to either inherited or sporadic mutations is not known.

 

 

We performed bioinformatics analyses of mutational and clinical data of 334 HG-SOC tumor samples from The Cancer Genome Atlas to identify novel tumor-driving mutations, survival-significant patient subgroups and tumor subtypes potentially driven by either hereditary or sporadic factors.

We identified a sub-cluster of high-frequency mutations in 22 patients and 58 genes associated with DNA damage repair, apoptosis and cell cycle. Mutations of CHEK2, observed with the highest intensity, were associated with poor therapy response and overall survival (OS) of these patients (P = 8.00e-05), possibly due to detrimental effect of mutations at the nuclear localization signal. A 21-gene mutational prognostic signature significantly stratifies patients into relatively low or high-risk subgroups with 5-y OS of 37% or 6%, respectively (P = 7.31e-08). Further analysis of these genes and high-risk subgroup revealed 2 distinct classes of tumors characterized by either germline mutations of genes such as CHEK2, RPS6KA2 and MLL4, or somatic mutations of other genes in the signature.

Our results could provide improvement in prediction and clinical management of HG-SOC, facilitate our understanding of this complex disease, guide the design of targeted therapeutics and improve screening efforts to identify women at high-risk of hereditary ovarian cancers distinct from those associated with BRCA1/2 mutations.

Keywords: :

• CHEK2
• MLL4
• RPS6KA2
• biomarker
• cancer driver mutation
• cancer sub-type
• chemotherapy resistance
• diagnostics
• gene signature
• high-grade serous ovarian carcinoma
• mutations
• patient’s stratification
• prognosis
• survival analysis

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Acknowledgments

We acknowledge the TCGA Research Network for access to data. We thank Dr Kwoh Chee Keong, Dr Chandra Verma, and Prof Ted Hupp for useful discussions and suggestions. We also thank Dr Tang Zhiqun for assistance in the preprocessing of TCGA mRNA expression data.

The study was funded by Agency for Science, Technology and Research (A*STAR), Singapore. The funding agency had no role in study design, data collection and analysis, preparation of the manuscript or decision to publish.

Author Contributions

V.A.K. conceived and designed the study with input from other authors. A.V.I. performed clinical data interpretation and data analysis. G.F. analyzed the protein structure and performed molecular dynamics simulation. G.S.O. performed analysis and wrote the initial draft of the manuscript. All authors have read, edited and approved the final version of the manuscript. V.A.K. performed data analysis and overall supervision and is the guarantor of the study.