Unsupervised consensus clustering algorithms can identify distinct classifications of histologically similar tumors based on machine learning algorithms. In this analysis, a small gene set distinguishes two inherent molecular subtypes of ccRCC (ccA and ccB), characterized by divergent biological pathways and a highly significant association with survival outcomes. This unique analysis provides a powerful method to discriminate molecular subgroups of tumors that may be informative of tumor biology or influence tumor behavior.
A fundamental problem in gene expression analysis of human tumors is the measurement of genetic noise in pairwise comparisons across thousands of independent and dependent variables. Our combined use of principal component analysis (PCA), consensus clustering, and LAD is robust and, more important, identifies stable clusters within patterns of gene expression. This method is highly reproducible and able to classify samples into molecular and clinically meaningful categories. Within these categories, “core clusters” are sets of nonoverlapping samples that are distinguishable from each other with high accuracy. This method of tumor analysis permits a refined assignment into gene expression-defined classifications and yields predictive gene signatures based on a manageable sized number of gene features. These properties permit the identification of limited sets of highly predictive molecular features (i.e., genes) useful for the classification of individual samples outside of the primary analysis. The extension of biomarker molecular profiles to small groups of genes, which can assign classification to individual tumors, is a major step forward toward the development of a clinically relevant biomarker. Ultimately, such a classification scheme will be applied with such measures as quantitative RT-PCR.
The clinical heterogeneity of ccRCC, coupled with previous gene expression studies,16,18,19,23
suggests that at least two molecular subtypes of ccRCC exist. We demonstrated that there are likely only
two primary subtypes of ccRCC stable under bootstrap analysis, although further subclassifications within these subtypes may be identified in much larger data sets, and rare tumors may represent unusual variants. Using the LAD predictions in the validation set, a third group of tumors shared pattern features with both ccA and ccB tumors. Such a third group, or other suggested classifications, may represent an intermediate manifestation of tumors undergoing progression from ccA to the ccB subtype or simply share common characteristics of both groups.
The subtypes ccA and ccB were associated with a significant difference in survival outcome, with ccA patients having a markedly better prognosis. While the continuous variable of LAD score proved to be an independent predictor of survival, the more immediately clinically useful dichotomous classification of ccA or ccB had a similar effect size and was statistically significant at the P = 0.1 level in the multivariable analysis. Future studies on larger numbers of patients are needed to validate the results of the preliminary multivariate analysis reported herein.
Pathway analysis showed that the better prognosis ccA group relatively overexpressed genes associated with hypoxia, angiogenesis, fatty acid metabolism, and organic acid metabolism, whereas ccB tumors overexpressed a more aggressive panel of genes that regulate EMT, the cell cycle, and wound healing. Intriguingly, ccA overexpresses genes associated with components of hypoxia and angiogenesis pathways, processes known to be broadly dysregulated in ccRCC. VHL
inactivation and subsequent activation of the hypoxia response pathway is so highly correlated with ccRCC that many of these pathways are expected to be upregulated in virtually all ccRCC tumors. As expected, using both training set tumors and LAD assigned gene expression arrays from Gordan et al
we identified VHL
inactivation in both clusters. Thus, ccB may have acquired additional genetic events that supplement VHL
pathway events, contributing to a more biologically immature and aggressive phenotype that overwhelms the signature associated with VHL
inactivation, which should be evaluated in future studies. In addition, it will be interesting in the future to determine if the key features that make up this classification are unique to ccRCC or if other histologic subtypes share the features of either the ccA or ccB classifications.
Finally, our small, robust panel of genes, whose expression levels can classify individual tumor samples into ccA and ccB subtypes with high accuracy, may provide a valuable resource for clinical decisions for patients following nephrectomy regarding frequency of surveillance or choices for adjuvant therapy in the future. This panel provides the basis for the development and validation by a prospective clinical trial to assign subtypes of ccRCC to individual tumor specimens for implementation in a prognostic algorithm.