Thursday, September 8, 2016

Scientists find new clues about early detection, personalized treatment of ovarian cancer

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Scientists find new clues about early detection, personalized treatment of ovarian cancer -

Scientists at the A * STAR Institute of Medical Biology (IMB ) and the Institute of Bioinformatics (BII) have found new clues about early detection and personalized treatment for ovarian cancer, currently one of the most difficult cancers to diagnose early because of the lack of symptoms that are unique to the disease

There are three predominant cancers affecting women. - Breast, ovarian and breast cancer. Of the three, ovarian cancer is the biggest concern because it is usually diagnosed only in an advanced stage because of the lack of clear early warning symptoms. Successful treatment is difficult at this late stage, resulting in high mortality rates. ovarian cancer increased prevalence in Singapore, and other countries recently developed. It is now the fifth most common cancer among women in Singapore, with 280 cases diagnosed each year and 0 deaths per year.

Identification previous Ovarian Cancer

IMB scientists have successfully identified a biomarker for ovarian stem cells, which can allow earlier detection of cancer of the ovary and thus allow treatment at an early stage of the disease.

team identified a molecule, known as LGR5 name, on a subset of cells in the surface epithelium of the ovary. LGR5 was previously used to identify stem cells in other tissues, including the intestine and stomach, but this is the first time scientists were able to locate this important biomarker in the ovary. In doing so they have uncovered a new population of epithelial stem cells in the ovary that 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 more effective treatment at an early stage of disease (see Appendix A). These results were published online in Nature Cell Biology in July 2014.

Bioinformatics analysis to develop personalized treatment

Among the different types of ovarian cancers detected high quality serous ovarian carcinoma (HG-SOC) is the most common epithelial cancers of the ovary. It also proved to be one of ovarian cancer the deadliest, with only 30 percent of these patients survived more than five years after diagnosis. HG-SOC remains poorly understood, with a lack of identified biomarkers for clinical use from diagnosis to prognosis of patient survival.

By applying bioinformatics analysis on major cancer genomics data, scientists BII 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), was identified as an effective prognostic marker for survival. HG-SOC patients with mutations in this gene have succumbed to the disease within five years of diagnosis, perhaps because CHEK2 mutations have been associated with poor response to anti-cancer treatments (see Appendix B). These results were published in the cell cycle in July 2014.

The mortality after diagnosis currently remains high, that patients receive similar treatment options of chemotherapy and radiotherapy, despite the diversity of cells tumor in the tumor and in tumor samples. With these results, personalized medicine for ovarian cancer could be developed, with a targeted treatment that would be optimized for subgroups of patients.

Prof Sir David Lane, chief scientist, A * STAR, said: "These results show how the various A * STAR research institutes offer their expertise in the development of new approaches to examine the various aspects the same disease that has not been studied successfully before, such as ovarian cancer. the various capacities and knowledge of our scientists allows us to study holistically the disease, from diagnosis to treatment. "


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