Englander Institute for Precision Medicine

Recapitulation of patient-specific 3D chromatin conformation using machine learning.

TitleRecapitulation of patient-specific 3D chromatin conformation using machine learning.
Publication TypeJournal Article
Year of Publication2023
AuthorsXu D, Forbes ANeil, Cohen S, Palladino A, Karadimitriou T, Khurana E
JournalCell Rep Methods
Date Published2023 Sep 25
KeywordsCell Line, Chromatin, Chromatin Immunoprecipitation Sequencing, Humans, Regulatory Sequences, Nucleic Acid, RNA-Seq

Regulatory networks containing enhancer-gene edges define cellular states. Multiple efforts have revealed these networks for reference tissues and cell lines by integrating multi-omics data. However, the methods developed cannot be applied for large patient cohorts due to the infeasibility of chromatin immunoprecipitation sequencing (ChIP-seq) for limited biopsy material. We trained machine-learning models using chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) and high-throughput chromosome conformation capture combined with chromatin immunoprecipitation (HiChIP) data that can predict connections using only assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq data as input, which can be generated from biopsies. Our method overcomes limitations of correlation-based approaches that cannot distinguish between distinct target genes of given enhancers or between active vs. poised states in different samples, a hallmark of network rewiring in cancer. Application of our model on 371 samples across 22 cancer types revealed 1,780 enhancer-gene connections for 602 cancer genes. Using CRISPR interference (CRISPRi), we validated enhancers predicted to regulate ESR1 in estrogen receptor (ER)+ breast cancer and A1CF in liver hepatocellular carcinoma.

Alternate JournalCell Rep Methods
PubMed ID37673071
PubMed Central IDPMC10545938
Grant ListR01 CA218668 / CA / NCI NIH HHS / United States

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