Reducing ovarian cancer death rates through screening

In this issue of Cancer, Havrilesky and colleagues argue that the biologic diversity of ovarian cancer means that it will be even harder than we expected to save lives through screening. Instead of the 15% to 20% reduction in ovarian cancer mortality that otherwise might be expected, those authors estimate that deaths would fall only by 11% from their current levels if postmenopausal women in the United States underwent annual screening with current technology. They envision a screening regimen about as sensitive (accurate in detecting cancer) and as specific (accurate in registering the absence of cancer) as that offered currently in the large, randomized United Kingdom trial and the Gynecologic Oncology Group (GOG) trial of high-risk women. That algorithm uses serial values of cancer antigen 125 (CA 125) to estimate risk, and the providers refer women with elevated risk for an ultrasound examination. Havrilesky et al reach their sobering conclusion by reasoning that one major subset of ovarian cancers differs profoundly from the other in how quickly the cancers move from stage I to stage IV. They assume that the more indolent phenotype typically spends 24 months in stage I and 12 months in stage II, in stark contrast to the more aggressive phenotype, which they assume spends 8 months in stage I and 5 months in stage II. When either the indolent or aggressive phenotype eventually reaches stage III, it is assumed that both spend 6 months on average at stage III followed by 2 months in stage IV. Logically, the shorter transit times of the more aggressive cancers make them harder to identify by screening at intervals and, thus, harder to intercept early enough to save lives. ‘‘Length bias’’ is the technical term for this general phenomenon in all screening programs. In ovarian cancer, if the mutation spectrum (tumor protein 53 [TP53] vs the v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog [KRAS], the B-Raf proto-oncogene serine/threonine-protein kinase [BRAF], phosphatase and tensin homolog [PTEN], and beta-catenin), the distribution of grades within stage, and other features stem from 2 major and distinctive phenotypes, as the authors suggest, then we should expect more modest gains from screening than would be expected otherwise. Are these estimates of potential benefit realistic with current technologies? What future developments could improve the potential for lives saved? The 11% estimate appears to be slightly pessimistic, because the supporting mathematical model underestimated the accuracy of the first wave of positive screens in the UK trial. The model also underestimated the fraction of detected cancers in early stages. In addition, it is impossible to avoid uncertainty in the estimation process, because it is almost certain that more than 2 ovarian cancer phenotypes exist. The maximum mortality benefit could be smaller or larger, depending on the proclivity of each phenotype to shed specific markers early, the appearance on ultrasound, and the response to therapy. In short, we cannot calculate the best currently achievable reduction in mortality, but this modeling effort suggests the realistic range of possible mortality benefits. Setting 1 year as the appropriate interval between screenings warrants especially close evaluation. The authors observe that intervals of screening>12 months offer some advantage in a reduced opportunity for false-positive results. A 2-year interval would improve the predictive power of each positive screen and substantially reduce the total number of false-positive screen results a woman could expect over her decades in the screening program.