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Biomedical subjects

Guangsheng Pei

Publications and source records attributed to Guangsheng Pei.

2 recordsLinked to original sources

First-in-human use of recombinant IL-7 to potentiate antigen-specific T cell therapy: a single patient case study.

Clinical trials of adoptive cellular therapy demonstrate that a key characteristic associated with durable responses is in vivo expansion and persistence of transferred T cells. Strategies to develop a less differentiated, stem/memory population in the infusion product and peri-infusional regimens to promote the maintenance of desired T cell states following adoptive transfer would be desirable. Endogenous T cell therapy studies have routinely achieved memory T cells enriched for expression of interleukin (IL)-7 receptor; to eliminate the conventional requirement for immunosuppressive lymphodepletion and its attendant life-threatening toxicities, we performed the first-in-human use of IL-7 in combination with adoptively transferred antigen-specific memory CD8 T cells in a patient with refractory metastatic uveal melanoma. Single-cell immune repertoire profiling of serial peripheral blood sampling revealed substantial in vivo proliferation and expansion of a stem cell memory population in the endogenous T cell therapy product that achieved a >79% predominance of total circulating T cells by 3 weeks post-infusion in this non-lymphodepleted recipient. Although the patient's disease ultimately progressed, these findings demonstrate safety and proof of concept for an IL-7 treatment regimen for expansion of adoptively transferred T cells in vivo and induced memory differentiation in a heavily pretreated patient with refractory solid malignancy.

Humans

Deep generative neural network for accurate drug response imputation.

Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outcome. In this study, we develop a deep variational autoencoder (VAE) model to compress thousands of genes into latent vectors in a low-dimensional space. We then demonstrate that these encoded vectors could accurately impute drug response, outperform standard signature-gene based approaches, and appropriately control the overfitting problem. We apply rigorous quality assessment and validation, including assessing the impact of cell line lineage, cross-validation, cross-panel evaluation, and application in independent clinical data sets, to warrant the accuracy of the imputed drug response in both cell lines and cancer samples. Specifically, the expression-regulated component (EReX) of the observed drug response achieves high correlation across panels. Using the well-trained models, we impute drug response of The Cancer Genome Atlas data and investigate the features and signatures associated with the imputed drug response, including cell line origins, somatic mutations and tumor mutation burdens, tumor microenvironment, and confounding factors. In summary, our deep learning method and the results are useful for the study of signatures and markers of drug response.

Antineoplastic Agents