Integrated profiling of human pancreatic cancer organoids reveals chromatin accessibility features associated with drug sensitivity (ATAC-Seq data).

Chromatin accessibility plays an essential role in controlling cellular identity and the therapeutic response of human cancers. However, the chromatin accessibility landscape and gene regulatory network of pancreatic cancer are largely uncharacterized. Here, we integrate the chromatin accessibility profiles of 84 pancreatic cancer organoid lines with whole-genome sequencing data, transcriptomic sequencing data and the results of drug sensitivity analysis of 283 epigenetic-related chemicals and 5 chemotherapeutic drugs. We identify distinct transcription factors that distinguish molecular subtypes of pancreatic cancer, predict numerous chromatin accessibility peaks associated with gene regulatory networks, discover novel regulatory noncoding mutations with potential as cancer drivers, and reveal the chromatin accessibility signatures associated with drug sensitivity. These results not only provide the chromatin accessibility atlas of pancreatic cancer but also suggest a systematic approach to comprehensively understand the gene regulatory network of pancreatic cancer in order to advance diagnosis and potential personalized medicine applications. For each PDPCO line, we prepared two sequencing libraries (technical replicates). ATAC-seq libraries were generated using a TruePrep DNA Library Prep Kit V2 for Illumina according to the manufacturer’s instructions. For each technical replicate, we selected the one with the higher QC score to quantify the chromatin accessibility of this sample. We used ChIP-seq-defined chromHMM states from the Roadmap Epigenomics Project to investigate the distribution of exocrine reproducible PDPCO peaks in the chromatin states. To investigate the similarity between the reproducible peaks in the PDPCO and pan-cancer data, we performed principal coordinates analysis, which takes a set of dissimilarities or distances as input and returns a set of points. We used the SCENIC workflow to identify the gene regulatory network (TF regulon) and scored the activity of the TF regulon. To identify the key regulators for specific peaks, we performed motif enrichment with HOMER. We also identified subtype-specific regulons by integrating RNA-seq and ATAC-seq data, links between ATAC-seq peak openness and targeted gene expression, and links between ATAC-seq peak openness and drug sensitivity. ***Please note that the raw data has been uploaded to a domestic repository National Omics Data Encyclopedia (NODE; https://www.biosino.org/node/index) with controlled access mechanisms under accession number OEP001744. the PDPCO_drug_screening.txt contains drug sensitivity AUC for each sample the transcriptomic sequencing data is repreasent in GEO under GSE194249.