Ovarian cancer is one of the most deadly cancers. It has a five year survival rate below 30% and a high rate of recurrence.
It is a variable disease, with many different biological underpinnings that make it difficult to treat.
Researchers led by Roel Verhaak at MD Anderson Cancer Center examined gene expression patterns in ovarian cancer tumor samples to identify gene expression signatures that correlated with patient prognosis.
Using these data, they developed a model, Classification of Ovarian Cancer (CLOVAR), that could accurately classify ovarian cancer subtypes and predict patient outcomes.
This new classification system may be useful in determining which treatments will be most effective for a given patient.
TITLE: Prognostically relevant gene expression signatures of high grade serous ovarian carcinoma
Journal of Clinical Investigation