Predicting Risk for the Appearance of Melanoma

VOLUME NUMBER AUGUST J OURNAL OF C LINICAL O NCOLOGY E D I T O R I A L Predicting Risk for the Appearance of Melanoma Frank L. Meyskens Jr, Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, CA David F. Ransohoff, University of North Carolina at Chapel Hill, Chapel Hill, NC The incidence rate of cutaneous melanoma is rapidly increas- ing and because the disease frequently manifests itself in young people, the economic, social, and morbidity costs are high. The etiologic factors contributing to this phenomenon are complex and include hereditary and familial contributions, a complex rela- tionship with sunlight and other ultraviolet-light exposure (such as sunlamps), and more recently, a reconsideration of the role of metal and other substances that bind melanin. 1 Identifying indi- viduals at high risk of a malignancy is an important overall goal of cancer control. Although these individuals are limited to only a small subset of the population, the potential patients at risk should be easily identifiable in most cases, with an appropriate surveil- lance program instituted and a plan for intervention developed. At the other extreme is the use of age as the major consideration for enhanced surveillance, as patient age at about 50 years is a major threshhold for an increase in many cancers. For the most part, surveillance programs for breast, colon, and prostate cancers begin at this age, 3 notwithstanding arguments for starting earlier. The overall recommendations for surveillance of cutaneous melanoma represent a different type of challenge, as the etiologic factors contributing to this disease are complex. A familial contri- bution is present in many cases, but is poorly understood, and the patterns of incidence are sex- and age-dependent. However, factors that may seem to be correlated with risk may or may not be important in assessing the risk for progression to frank malignancy. The model by Fears et al 2 in this issue of the Journal of Clinical Oncology suggests that the many risk factors that are commonly assessed can be collapsed into two broader categories: host interaction with sunlight and the number of melanocytes (ie, moles) present at a particular time point in the participant’s life. The current predictor is based on the model by Gail et al, 4 which is used widely to calculate the risk of breast cancer. Such models are derived from two types of data. The relative risks associated with specific possible predictors are estimated from case-control study results, as is in this report about melanoma. 2 However, because case-control studies describe only relative risk, absolute risk must be calculated by combining those results with rates in a group obtained from population-based studies, such as the Surveillance, Epidemiology, and End Results program (SEER). The risk estimates that such models provide can be used for two related, but different, purposes. For a research purpose, to help design clinical trials, the “risk of. . . cancer in various subgroups” 6 can be assessed by such models in order to select groups with higher risks, resulting in trials that are more efficiently powered, using smaller numbers of participants. The use of models for this pur- pose has been clearly demonstrated. For a clinical purpose, to discriminate among individual per- sons, those “who will develop. . . cancer from those who will not,” 5 risk estimates can be “important for decisions made at the individ- ual level for clinical decision making and screening.” 6 The prob- lem, though, is that risk estimates, even if accurate, may not necessarily be useful in clinical decision making. Utility depends on the degree of absolute risk indicated by a model and whether that degree is high or low enough to support a specific clinical decision. An underlying problem is that the absolute risk of cancer is gener- ally so low that a higher risk indicated by a model may still be low in absolute terms. As noted by Rockhill et al, “the probability that a person with a certain risk factor profile will develop even a rela- tively common cancer. . . [is] low because the lifetime risk of developing common cancers [is] low. . . . Therefore, most individ- uals will remain cancer-free over a considerable period of time; most cancers will arise among individuals from the population with close to an average individual risk.” 6 The situation, further discussed by Rockhill et al 7 and by Gail and Costantino, 5 is inher- ent to many risk models of cancer. As noted by Fears et al, for the melanoma model, even for high-risk “patient 6” who has an esti- mated relative risk of 24.2, the 5-year absolute risk of developing melanoma may be less than 1%. 2 That degree of risk, though numerically low, would also seem to be high enough to justify the authors’ suggestion that such individuals undergo complete skin examination, counseling, or surveillance. 2 This model raises two important issues for clinicians and researchers to consider. First, what absolute risk in any individual should trigger increased surveillance or further assessment? The recent development of sophisticated noninvasive technology such as diffuse optical spectroscopy that is nevertheless simply applied may make the options more attractive in the future by providing an alternative to biopsy, but the technology needs to be validated before that can occur. 8 Second, in the currently assessed popula- tion, many thousands of at-risk individuals will need to be entered onto a study to assure sufficient power to attain a difference in a reasonable time period in a chemoprevention trial. 7 As emphasized by these investigators, their model is not meant to identify current melanoma cases and is not meant to be used in patients with a prior melanoma or nonmelanoma skin cancer or in patients with a first-degree relative with melanoma. These latter two groups, how- ever, are likely to be good candidates for chemoprevention trials and to require considerably fewer candidates to attain sufficient Journal of Clinical Oncology, Vol 24, No 22 (August 1), 2006: pp 3522-3523 DOI: 10.1200/JCO.2006.06.8163; published online ahead of print at www.jco.org on May 25, 2006 Information downloaded from jco.ascopubs.org and provided by at UNIV OF CALIFORNIA IRVINE on December 22, 2014 Copyright © 2006 American from Society of Clinical Oncology. All rights reserved.