Cell-type specific subtyping of epigenomes improves prognostic stratification of cancer

Abstract

BackgroundMost molecular classifications of cancer are based on bulk-tissue profiles that measure an average over many distinct cell-types. As such, cancer subtypes inferred from transcriptomic or epigenetic data are strongly influenced by cell-type composition and do not necessarily reflect subtypes defined by cell-type specific cancer-associated alterations, which could lead to suboptimal cancer classifications. MethodsTo address this problem, we here propose the novel concept of cell-type specific combinatorial clustering (CELTYC), which aims to group cancer samples by the molecular alterations they display in specific cell-types. We illustrate this concept in the context of DNA methylation data of liver and kidney cancer, deriving in each case novel cancer subtypes and assessing their prognostic relevance against current state-of-the-art prognostic models. ResultsIn both liver and kidney cancer, we reveal improved cell-type specific prognostic models, not discoverable using standard methods. In the case of kidney cancer, we show how combinatorial indexing of epithelial and immune-cell clusters define improved prognostic models driven by synergy of high mitotic age and altered cytokine signaling. We validate the improved prognostic models in independent datasets, and identify underlying cytokine-immune-cell signatures driving poor outcome. ConclusionsIn summary, cell-type specific combinatorial clustering is a valuable strategy to help dissect and improve current prognostic classifications of cancer in terms of the underlying cell-type specific epigenetic and transcriptomic alterations.