The online version of this article (doi:10.1186/1471-2288-14-138) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
CBB conceived the study, directed the statistical analyses and wrote the article. VES and ECZ conducted the statistical analyses and helped draft the manuscript. HF provided advice on the epidemiology of kidney cancer. AA and RS provided input on bioinformatics. JKM, MEN, WKR, SS, PT, JAK and TKC supervised data collection at individual institutions and helped write the manuscript. AAH and JJH helped conceive the project, organized data collection at MSKCC and helped write the article. All authors read and approved the final manuscript.
The etiologic heterogeneity of cancer has traditionally been investigated by comparing risk factor frequencies within candidate sub-types, defined for example by histology or by distinct tumor markers of interest. Increasingly tumors are being profiled for molecular features much more extensively. This greatly expands the opportunities for defining distinct sub-types. In this article we describe an exploratory analysis of the etiologic heterogeneity of clear cell kidney cancer. Data are available on the primary known risk factors for kidney cancer, while the tumors are characterized on a genome-wide basis using expression, methylation, copy number and mutational profiles.
We use a novel clustering strategy to identify sub-types. This is accomplished independently for the expression, methylation and copy number profiles. The goals are to identify tumor sub-types that are etiologically distinct, to identify the risk factors that define specific sub-types, and to endeavor to characterize the key genes that appear to represent the principal features of the distinct sub-types.
The analysis reveals strong evidence that gender represents an important factor that distinguishes disease sub-types. The sub-types defined using expression data and methylation data demonstrate considerable congruence and are also clearly correlated with mutations in important cancer genes. These sub-types are also strongly correlated with survival. The complexity of the data presents many analytical challenges including, prominently, the risk of false discovery.
Genomic profiling of tumors offers the opportunity to identify etiologically distinct sub-types, paving the way for a more refined understanding of cancer etiology.
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- Genomic investigation of etiologic heterogeneity: methodologic challenges
Colin B Begg
Venkatraman E Seshan
Emily C Zabor
Jodi K Maranchie
Matthew E Nielsen
W Kimryn Rathmell
Jose A Karam
Toni K Choueiri
A Ari Hakimi
James J Hsieh
- BioMed Central
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