Ovarian cancers are among the most difficult cancers to diagnose and treat. There are no early stage diagnostics for ovarian cancer and by the time they are discovered, they may not respond to standard cancer treatments.  But a new research from A*STAR's Institute of Medical Biology (IMB) and the Bioinformatics Institute (BII) have found new clues to early detection and personalized treatments of ovarian cancer, thus offering hope to millions of women who might be susceptible to it. Their findings were published online in Nature Cell Biology in July 2014.

Ovarian cancer has the distinction of being the most lethal cancer to affect the female reproductive system. It starts in the two ovaries that are located in the uterus. Currently it is the fifth most common cancer affecting women in Singapore, while The American Cancer Society estimates that in 2014, about 21,980 new cases of ovarian cancer will be diagnosed and 14,270 American women will die of it.

Statistics from around the world show that only 19 percent of the all cancer cases are detected before the cancer spreads outside the ovaries, when it is most responsive to treatments. Until now, women had to rely on their own instincts and prevalence of symptoms such as bloating, pelvic cramps, back pain, which gradually worsen as the disease progresses.

New research

At IMB, scientists have identified a biomarker, a molecule known as Lgr5 in the ovarian stem cells, according to a press release. With this biomarker they are optimistic that ovarian cancers can be detected early.

The molecule Lgr5 is present in the ovarian surface epithelium. While it has been found to be present in the stem cells of other tissues such as intestine and stomach, this is the first time that scientists have located it in the ovary. Lgr5 was found to be produced by an important subset of epithelial cells that 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.

 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 and thus treated at an earlier stage.

Personalized treatment with the help of bioinformatics analysis

Currently more than 30 types of ovarian cancers have been known to affect women, and of these high-grade serous ovarian carcinoma (HG-SOC) is the most prevalent of type. The prognosis for this type of cancer is also very bleak with only 30 per cent of patients surviving more than five years after diagnosis. Again, the lack of biomarkers for early detection makes this a poorly understood illness.

By applying bioinformatics analysis on big cancer genomics data, BII scientists identified a set of genes and their mutational status that 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. The primary role of CHEK2 is to act as a tumor suppressor by keeping the uncontrolled growth of cells in check which may result due to damage in the DNA.

Scientists in the current study found that 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. 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 tumor cells within tumors and across different tumor samples.

The scientists hope that with the new findings, personalized medicine and targeted treatments can be developed to treat subgroups of ovarian cancer patients.

 

Source: Ng A, Tan S, Singh G, et.al. Lgr5 marks stem/progenitor cells in ovary and tubal epithelia. Nature Cell Biology. 2014.