Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Agora Cancer Research Center, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Geneva, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Agora Cancer Research Center, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Geneva, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
Maryline Falquet
Swiss Cancer Center Leman (SCCL), Geneva, Switzerland; Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland; Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Geneva Center for Inflammation Research, Geneva, Switzerland
Camilla Jandus
Swiss Cancer Center Leman (SCCL), Geneva, Switzerland; Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland; Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Geneva Center for Inflammation Research, Geneva, Switzerland
Department of Oncology, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Agora Cancer Research Center, Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Geneva, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
Assay 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 most non-malignant cell types frequently observed in the microenvironment of human tumors. We then integrate these data into the EPIC deconvolution framework (Racle et al., 2017) 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 human breast cancer cohort, EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes.
