Posted September 16, 2015
David D.L. Bowtell, Ph.D., University of Melbourne, Australia

David D.L. Bowtell, Ph.D. The treatment of any disease requires understanding "what's under the hood." Recently, researchers and doctors alike are recognizing that ovarian cancer is not a single disease, but a collection of molecularly and clinically diverse diseases. The individual manifestation of epithelial ovarian cancer is determined by a combination of genetic and environmental factors. In order to improve treatment success, as well as diagnosis, it is important to understand essential molecular mechanisms controlling the growth of different types of ovarian cancer.

When an ovarian tumor is surgically removed, a pathologist will classify it based on cellular features observed under the microscope and this information is combined with data collected during surgery about the extent of spread in the patient to determine treatment and likely survival. The most common types of ovarian cancer include high-grade serous (HGSC), low-grade serous, endometrioid, clear cell, and mucinous. High-grade serous epithelial ovarian cancer is particularly important, accounting for over 70% of ovarian cancer deaths in the United States and other Western countries.

Dr. David Bowtell previously identified, in a study funded by the OCRP, different subtypes of ovarian tumors at the genetic level through gene expression profiling, four of which were HGSC subtypes: C1, C2, C4, and C5.1 Importantly, these subtypes had very distinct biological features and correlated to patient outcome. Being able to separate subtypes of tumors is the first essential step to understanding and identifying essential molecular mechanisms that control the growth of the cancer - the Achilles' heels of each disease.

Dr. Bowtell, at the University of Melbourne and the Peter MacCallum Cancer Center in Australia, recognized the opportunity to utilize these subtype classifications in the clinical setting of individualized patient treatments. Supported by the FY11 OCRP Translational Leverage Award mechanism, he led a team included Dr. Susan Ramus, University of Southern California, and Dr. Martin Kobel, University of Calgary Canada, which sought to develop a more affordable and standardized subtype test for transition to patient care. The team compared four platforms on formalin-fixed, paraffin-embedded tissue samples provided by the Australian Ovarian Cancer Study. By evaluating the previously mentioned subtype classifications, they identified the minimal gene signature to consist of 48 genes including 9 control and 39 differentially-expressed genes, which could be assayed using a Nanostring RNA classifier. They achieved consistent and greater than 80% accuracy in subtype classification of hundreds of samples analyzed at all three locations using this signature and method. Furthermore, they are optimistic about a seven-marker immunohistochemical panel for subtype classification that has been 82% effective in classification but requires more testing. Overall, these results are promising as both methods require minimal material and are cost effective. These methods offer a realistic option of furthering HGSC characterizations, which is especially important for the development of subtype-targeted clinical trials for more effective treatments.


Leong HS, Galletta L, Etemadmoghadam D, George J, Australian Ovarian Cancer Study, Kobel M, Ramus SJ, and Bowtell D. 2015. Efficient molecular subtype classification of high-grade serous ovarian cancer. The Journal of Pathology 236(3):272-277.

[1] Tothill RW, Tinker AV, George J, Brown R, Fox SB, Lade S, Johnson DS, Trivett MK, Etemadmoghadam D, Locandro B, Traficante N, Fereday S, Hung JA, Chiew YE, Haviv I, Australian Ovarian Cancer Study Group, Gertig D, DeFazio A, and Bowtell DD. 2008. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clinical Cancer Research 15(16):5198-5208.


Public and Technical Abstracts: Molecular Subtypes of High-Grade Serous Ovarian Cancer: Leveraging of TCGA, OCAC, OTT, and AOCS Genome and Genomic Datasets

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