Singapore—Scientists at A*STAR’s Institute of Medical Biology (IMB) and the Bioinformatics Institute (BII) have found new clues to early detection and personalised treatment of ovarian cancer, currently one of the most difficult cancers to diagnose early due to the lack of symptoms that are unique to the illness.
There are three predominant cancers that affect women – breast, ovarian and womb cancer. Of the three, ovarian cancer is of the greatest concern as it is usually diagnosed only at an advanced stage due to the absence of clear early warning symptoms. Successful treatment is difficult at this late stage, resulting in high mortality rates. Ovarian cancer has increased in prevalence in Singapore as well as other developed countries recently. It is now the fifth most common cancer in Singapore amongst women, with about 280 cases diagnosed annually and 90 deaths per year[1].
Identifying Ovarian Cancer Earlier
IMB scientists have successfully identified a biomarker of ovarian stem cells, which may allow for earlier detection of ovarian cancer and thus allow treatment at an early stage of the illness.
The team has identified a molecule, known as Lgr5, on a subset of cells in the ovarian surface epithelium[2]. Lgr5 has been previously used to identify stem cells in other tissues including the intestine and stomach, but this is the first time that scientists have successfully located this important biomarker in the ovary. In doing so, they have unearthed a new population of epithelial stem cells in the ovary which produce Lgr5 and control the development of the ovary. Using Lgr5 as a biomarker of ovarian stem cells, ovarian cancer can potentially be detected earlier, allowing for more effective treatment at an early stage of the illness (see Annex A). These findings were published online in Nature Cell Biology in July 2014.
Bioinformatics Analysis to Develop Personalised Treatment
Of the different types of ovarian cancers detected, high-grade serous ovarian carcinoma (HG-SOC) is the most prevalent of epithelial ovarian cancers[3]. It has also proven to be one of the most lethal ovarian cancers, with only 30 per cent of such patients surviving more than five years after diagnosis[4]. HG-SOC remains poorly understood, with a lack of biomarkers identified for clinical use, from diagnosis to prognosis of patient survival rates.
By applying bioinformatics analysis on big cancer genomics data[5], BII scientists were able to identify genes whose mutation status could be used for prognosis and development of personalized treatment for HG-SOC.
The gene, Checkpoint Kinase 2 (CHEK2), has been identified as an effective prognostic marker of patient survival. HG-SOC patients with mutations in this gene succumbed to the disease within five years of diagnosis, possibly because CHEK2 mutations were associated with poor response to existing cancer therapies (see Annex B). These findings were published in Cell Cycle in July 2014.
Mortality after diagnosis currently remains high, as patients receive similar treatment options of chemotherapy and radiotherapy despite the diverse nature of tumour cells within tumours and across different tumour samples. With these findings, personalised medicine for ovarian cancer could be developed, with targeted treatment that would be optimised for subgroups of patients.
Prof Sir David Lane, Chief Scientist, A*STAR, said, “These findings show how the various research institutes at A*STAR offer their expertise in developing new approaches to examine different aspects of the same disease that have not been successfully studied before, such as ovarian cancer. The diverse capabilities and knowledge of our scientists allows us to investigate diseases holistically, from diagnosis to treatment.”
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[1] Singapore Cancer Registry Interim Annual Registry Report – Trends in Cancer Incidence in Singapore, 2007-2011
[2] Ovarian surface epithelium refers to the tissue covering the ovary.
[3] Epithelial ovarian cancer occurs when cancer cells form in the tissue covering the ovary.
[4] Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. 2011
[5] 9083 genes and their mutational patterns were examined from the retrospective data of 334 HG-SOC tumor samples provided by The Cancer Genome Atlas Research Network for study by the research and clinical community
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Annex B – findings at A*STAR’s bioinformatics institute hold potential for personalised treatment of major type ovarian cancer
Scientists at A*STAR’s Bioinformatics Institute have identified genes whose mutation status could be used for prognosis and development of personalized treatment for high-grade serous ovarian carcinoma (HG-SOC), a major type of ovarian cancer.
Besides identification of CHEK2 as a biomarker for poor prognosis, the team of scientists also identified a prognostic signature comprising 21 genes that could be used to stratify patients diagnosed with HG-SOC into subgroups with high- or low- risk of mortality within five years of diagnosis. Patients identified as high-risk had a five-year survival rate of 6%, and appeared to be twice as likely to exhibit resistance to therapy in contrast to those in the low-risk group. Besides determining the effectiveness of treatments, this prognostic signature would allow patients with poor prognosis to be identified even in the absence of mutations in CHEK2 by determining the mutational status of the other 20 genes in the profile.
These findings advance understanding of HG-SOC and could improve prediction and clinical management of this complex disease. The team’s findings could also lead to development of new diagnostic or prognostic tests for women with inherited risk of ovarian cancer, or those whose genes contain mutations associated with poor prognosis and drug resistance.
Dr Vladimir Kuznetsov, Head of BII’s Research Division and Senior Principal Investigator who led the study, said “Mutations are genetic events that initiate and drive cancer. We hope to continue our success in using these rare mutations and analysis of big data to address the challenges of screening, diagnosis, prognosis and treatment prediction of various diseases, including HG-SOC.”
Dr Frank Eisenhaber, Executive Director of BII, said, “These findings demonstrate the importance of bioinformatics and the use of statistical models and computational genomics for the analysis of big biomedical data. These tools allow us to stratify patients into relevant subgroups and open avenues for development of diagnostic and prognostic kits for ovarian cancer, providing great promise for the future of personalised medicine for cancer.”
BII will now further develop its study, having established research collaborations with clinical doctors and researchers locally. The group will continue its focus on developing and validating several next-generation biomarkers for ovarian cancer, using computational and experimental methods.
Notes to Editor:
The research findings described in this media release can be found in:
1. the Nature Cell BiologyLgr5 marks stem/progenitor cells in ovary and tubal epithelia” by Annie Ng1, Shawna Tan1, Gurmit Singh1, Pamela Rizk1, Yada Swathi1, Tuan Zea Tan2, Ruby Yun-Ju Huang2,3, Marc Leushacke1 and Nick Barker1,4,5,6 Journal, under the title, “
1A*STAR Institute of Medical Biology, Singapore
2Cancer Science Institute of Singapore, National University of Singapore, Singapore
3Department of Obstetrics & Gynaecology, National University Hospital, Singapore
4Centre for Regenerative Medicine, University of Edinburgh, UK
5Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
2. the journal Cell Cycle, under the title “Identification of two poorly prognosed ovarian carcinoma subtypes associated with CHEK2 germline mutation and non-CHEK2 somatic mutation gene signatures” by Ghim Siong Ow1, Anna V Ivshina1, Gloria Fuentes1,2, and Vladimir A Kuznetsov1,3,4.
1Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore;
2Centre for Life Science Technologies (CLST), Riken, Saitama, Japan;
3Division of Software & Information Systems, School of Computer Engineering, Nanyang Technological University, Singapore;
4School for Integrative Science and Engineering, National University of Singapore, Singapore
Full text of the paper in Cell Cycle can be accessed online from:
https://www.landesbioscience.com/journals/cc/article/29271/
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