Defining Cancer Subtypes With Distinctive Etiologic Profiles: An Application to the Epidemiology of Melanoma

ABSTRACT

We showcase a novel analytic strategy to identify subtypes of cancer that possess distinctive causal factors, that is, subtypes that are “etiologically” distinct. The method involves the integrated analysis of two types of study design: an incident series of cases with double primary cancers with detailed information on tumor characteristics that can be used to define the subtypes; a case-series of incident cases with information on known risk factors that can be used to investigate the specific risk factors that distinguish the subtypes. The methods are applied to a rich melanoma dataset with detailed information on pathologic tumor factors, and comprehensive information on known genetic and environmental risk factors for melanoma. Identification of the optimal subtyping solution is accomplished using a novel clustering analysis that seeks to maximize a measure that characterizes the distinctiveness of the distributions of risk factors across the subtypes and that is a function of the correlations of tumor factors in the case-specific tumor pairs. This analysis is challenged by the presence of extensive missing data. If successful, studies of this nature offer the opportunity for efficient study design to identify unknown risk factors whose effects are concentrated in defined subtypes. Supplementary materials for this article are available online.

KEYWORDS:

• Case–control study
• Etiologic heterogeneity
• K-means clustering
• Logistic regression
• Multiple imputation

Acknowledgment

We are also grateful to Kathleen Conway, Sharon Edmiston, Eloise Parrish, Audrey Alexander, and Honglin Hap who performed the BRAF and NRAS genotyping, to Lynn From and Klaus Busam who performed the pathology review, and to the GEM team of investigators for access to the data.

Funding

The research was supported by the National Cancer Institute, awards CA163251, CA008748, CA112243, CA112524, and U01CA81830. We are also grateful to Kathleen Conway, Sharon Edmiston, Eloise Parrish, Audrey Alexander, and Honglin Hao who performed the BRAF and NRAS genotyping, to Lynn From and Klaus Busam who performed the pathology review, and to the GEM team of investigators for access to the data.