Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data

Abstract

AbstractAssay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) is a widely used technique to explore gene regulatory mechanisms. For most ATAC-Seq data from healthy and diseased tissues such as tumors, chromatin accessibility measurement represents a mixed signal from multiple cell types. In this work, we derive reliable chromatin accessibility marker peaks and reference profiles for all major cancer-relevant cell types. We then capitalize on the EPIC deconvolution framework (Racle et al. 2017) previously shown to accurately predict cell-type composition in tumor bulk RNA-Seq data and integrate our markers and reference profiles to EPIC to quantify cell-type heterogeneity in bulk ATAC-Seq data. Our EPIC-ATAC tool accurately predicts non-malignant and malignant cell fractions in tumor samples. When applied to a breast cancer cohort, EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes.