Molecular Scores to Predict Ovarian Cancer Outcomes: A Worthy Goal, but Not Ready for Prime Time
A partir de données portant sur 511 patientes atteintes d'un cancer de l'ovaire, cette étude évalue l'intérêt d'une signature basée sur l'expression de 23 gènes pour prédire la réponse à un traitement à base de sels de platine
Ovarian carcinoma has the highest mortality of all gynecological cancers. The American Cancer Society estimates that in 2012, about 22 280 new cases of ovarian cancer will be diagnosed, and 15 500 women will die of ovarian cancer in the United States (1). Despite achieving high rates of remission following radical surgery and platinum-based chemotherapy, most women relapse and ultimately die of chemoresistant disease. Advances in chemotherapy lengthen survival for women with advanced-stage (stages III and IV) disease but have not changed the likelihood of cure. Biomarkers, such as the rate of decline of serum cancer antigen 125 (CA-125, also known as mucin-16) level or the absolute CA-125 nadir, can be predictors of progression-free and overall survivals (2–4); however, when faced with a slowly declining level of CA-125 during primary treatment, the oncologist has few effective alternatives. With almost 80% of primary ovarian cancers initially responding to platinum-based therapy, a prospective biomarker would need to be highly predictive of treatment failure because alternative treatments would be purely experimental.
The advent of high-throughput molecular profiling technologies has led to numerous studies that defined ovarian carcinoma phenotypes by various profiles, including mRNA, protein, microRNA, DNA copy number, etc. In unsupervised analyses, the molecular profiles are used to sort the carcinomas into different categories by statistical algorithms without assumptions about the type or number categories present. In contrast, in supervised analyses, the investigator defines the characteristics of interest (such as response to chemotherapy) in a test set and determines which subset components of the molecular profile are differentially expressed, thereby defining a signature for the phenotype of interest. Because of the large number of comparisons made, statistically significant associations can occur by chance, and the “novel” signature will invariably perform well within the test set from which it was derived. Therefore, to assess …
Journal of the National Cancer Institute , éditorial en libre accès, 2012