A conceptual and methodological framework for investigating etiologic heterogeneity

Abstract

Cancer has traditionally been studied using the disease site of origin as the organizing framework. However, recent advances in molecular genetics have begun to challenge this taxonomy, as detailed molecular profiling of tumors has led to discoveries of subsets of tumors that have profiles that possess distinct clinical and biological characteristics. This is increasingly leading to research that seeks to investigate whether these subtypes of tumors have distinct etiologies. However, research in this field has been opportunistic and anecdotal, typically involving the comparison of distributions of individual risk factors between tumors classified on the basis of candidate tumor characteristics. The purpose of this article is to place this area of investigation within a more general conceptual and analytic framework, with a view to providing more efficient and practical strategies for designing and analyzing epidemiologic studies to investigate etiologic heterogeneity. We propose a formal definition of etiologic heterogeneity and show how classifications of tumor subtypes with larger etiologic heterogeneities inevitably possess greater disease risk predictability overall. We outline analytic strategies for estimating the degree of etiologic heterogeneity among a set of subtypes and for choosing subtypes that optimize the heterogeneity, and we discuss technical challenges that require further methodologic research. We illustrate the ideas by using a pooled case-control study of breast cancer classified by expression patterns of genes known to define distinct tumor subtypes.

Keywords: cancer epidemiology; clustering; etiologic heterogeneity.

Figure 1

The figure displays conceptually the interplay of genetic and pathologic tumor characteristics (denoted by {y_i}) and risk profiles (denoted by {x_i}) that are used interactively to determine the set of subtypes that best characterize etiologic heterogeneity.

Figure 2

The incremental explained variation is plotted against the distance metric for every possible two-class configuration of the 16 distinct permutations of the four binary markers, excluding only configurations in which one subtype contains fewer than 5% of the cases (green dots). The red dots represent local maxima obtained from k-means clustering. The blue dot corresponds to the classification ER+ versus ER−.

Figure 3

This figure is similar to Figure 2, except that the green dots represent a random sample of 50,000 candidate configurations. The blue dot corresponds to the widely used four-class system: luminal A, luminal B, HER2-enriched and triple negative.