Epigenomic Deconvolution of Breast Tumors Reveals Metabolic Coupling between Constituent Cell Types

Vitor Onuchic,Ryan J Hartmaier,David N Boone,Michael L Samuels,Ronak Y Patel,Wendy M White,Vesna D Garovic,Steffi Oesterreich,Matt E Roth,Adrian V Lee,Aleksandar Milosavljevic

doi:10.1016/j.celrep.2016.10.057

Abstract

Cancer progression depends on both cell-intrinsic processes and interactions between different cell types. However, large-scale assessment of cell type composition and molecular profiles of individual celltypes within tumors remains challenging. To address this, we developed epigenomic deconvolution (EDec), an insilico method that infers cell type composition of complex tissues as well as DNA methylation and gene transcription profiles of constituent cell types. By applying EDec to The Cancer Genome Atlas (TCGA) breast tumors, we detect changes in immune cell infiltration related to patient prognosis, and a striking change in stromal fibroblast-to-adipocyte ratio across breast cancer subtypes. Furthermore, we show that a less adipose stroma tends to display lower levels of mitochondrial activity and to be associated with cancerous cells with higher levels of oxidative metabolism. These findings highlight the role of stromal composition in the metabolic coupling between distinct cell types within tumors.

Highlights

Molecular profiling of breast tumors has led to their categorization into different subtypes with distinct risks and underlying biology
Cancer progression depends on both cell-intrinsic processes and interactions between different cell types
We developed Epigenomic Deconvolution (EDec), an in silico method that infers cell type composition of complex tissues as well as DNA methylation and gene transcription profiles of constituent cell types

Summary

Introduction

Molecular profiling of breast tumors has led to their categorization into different subtypes with distinct risks and underlying biology. Most molecular profiling studies to date have been performed on bulk tissue samples, ignoring the complexity of the breast tissue, with its multiple cell types and the interactions between them. Laser capture microdissection (LCM), cell sorting, and other physical methods to isolate cell types from solid tumors for molecular profiling are technically challenging, and severely limit throughput (Debey et al, 2004). The ability of these methods to infer cell type composition of solid tumors and interpret the states of constituent cell types is limited, hampering the study of cellular states and cellular interactions within the tumor microenvironment

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Conclusion