Profiling the Perpetrator

In the classic Western movie genre, the bad guys wear black hats. Identifying all bad guys isn’t always as easy, so the US Federal Bureau of Investigation’s Violent Criminal Apprehension Program (ViCAP) assembles a psychological profile to catch serial killers. More recently, television shows like CSI feature sophisticated profiling techniques (DNA analysis, gas chromatography, and so on) to supplement conventional ballistics and common sense detective work to trap nefarious villains. Molecular profiling of epithelial ovarian cancers holds promise that goes beyond identifying the most aggressive tumors. Gene expression signatures may also be a cornerstone to understanding the root causes of ovarian cancer, and to designing pathway-specific targeted therapies. Clinicians and scientists have long searched for the fundamental molecular alterations that differentiate an ovarian cancer cell from its normal counterpart. Until recently, our predictions have relied on descriptive clinical and tumor characteristics such as patient age, tumor histology and stage, and extent of surgical cytoreduction. In vivo surrogates of chemosensitivity, such as time to CA125 normalization, and genetic alterations, such as mutations in BRCA1 or BRCA2 genes, have recently factored into our discussions of prognosis with patients and their families. In this issue of the Journal of Clinical Oncology, Spentzos and colleagues describe an Ovarian Cancer Prognostic Profile (OCPP), a 115-gene signature that seems to be an independent prognostic determinant of outcome for women with epithelial ovarian cancer. Their data imply that the OCPP is a better predictor of patients’ overall survival than any of the currently available clinical criteria, and they suggest that gene profiling may have an important role in identifying patients for future investigational therapies. Using 68 frozen tumor specimens collected at two institutions, the authors performed microarray hybridization using Affymetrix U95A2 GeneChips (Affymetrix, Santa Clara, CA). The OCPP was derived from gene expression analysis of one-half the samples (the training set) using supervised statistical methods of pattern recognition, class prediction, and leave-one-out cross validation. Importantly, survival was analyzed as a continuous variable rather than dichotomizing the cohort around an arbitrary time point. As such, samples from women at the extremes of overall survival in the training set were used in the first step of developing the prognostic profile. The shortest survival was 4 months, and the longest was 73 months; a minimum of 32 months separated these groups. After optimizing the class predictors, the expression profile was refined, and the final predictive signature was developed using the multigene patterns with the highest accuracy for predicting survival. The final 115-gene predictor profile, the OCPP, was validated using the remaining half of the patient samples. Labels of “favorable” or “unfavorable” were assigned to each of the tumors in this independent validation set based on their OCPP. Kaplan-Meier analysis demonstrated statistically different overall survival rates between these two groups that also seem to be clinically important. In the favorable group, the median survival had not yet been reached at 47 months, while in the unfavorable group, the median survival was only 30 months, almost a year and a half shorter. Furthermore, there was a definite plateau in the favorable survival curve, suggesting that there is a cohort of ovarian cancer patients with a more indolent disease course and a 70% 5-year survival. When patients were segregated by traditional clinical prognosticators, the OCPP continued to discriminate between favorable and unfavorable outcomes. Examining only stage III/IV patients, only patients with grade 3 tumors, or only those who had optimal surgical cytoreduction, the molecular signature was able to further refine our ability to accurately predict patient survival. In multivariate analysis, controlling for age, stage, grade, and debulking status, the OCPP demonstrated independent prognostic value, with a hazard ratio for death of 3.6 when all 68 patients were included in the calculation. So, how can we best use this information at the current time? Eighty-one percent of patients with an unfavorable JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 22 NUMBER 23 DECEMBER 1 2004