Ye Zheng, Postdoctoral Research Fellow in the Vaccine and Infectious Disease and Basic Sciences Division at Fred Hutchinson Cancer Center and candidate for a faculty position in the Division of Biostatistics, will present:

“Statistical Modeling for Single-Cell Multi-Omics Integration: A Case Study in Car-T Cell Immunotherapy”

Abstract: CD19-directed chimeric antigen receptor (CAR)-T cell immunotherapy has been established as an effective treatment to redirect T cells’ activity against tumors, yielding unprecedented response rates in patients with relapsed or refractory B cell malignancies. However, a considerable proportion of patients still do not achieve durable complete responses. Single-cell multi-omics analyses with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) are leveraged to investigate the intrinsic genomic characteristics governing the proliferation capacity and neurotoxicity of CAR-T cell infusion products. I proposed a processing and analysis pipeline for the CAR-T cell genomic data. Specifically, to remove the batch effect of the CITE-seq data, a statistical normalization model, ADTnorm, is proposed, which also enables single-cell proteomics integration across studies. Moreover, to fulfill the goal of associating genomic features with patients’ response to CAR-T cell therapy, a tree-based model is constructed to identify the associated genomic features in a highly interpretable manner. Going beyond genomic feature detection, a statistical multi-omics integration model is developed to gain further insight into the gene regulation mechanisms. Such an integration model bridges single-cell three-dimensional chromatin structure measurements with transcriptomics and epigenomics profiles. Therefore, the integration model enables the construction of a multi-modal cellular network on which the gene cis-regulations and associations with clinical outcomes can be inferred.

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