Rade, M. | Search bioRxiv

bioRxiv2026

A CAR T cell-amplified suppressive B cell circuit in AML

Immunotherapy in acute myeloid leukemia (AML) is constrained by a hostile bone marrow niche. Here we identify atypical memory B cells (Batyp) as a suppressive population within this microenvironment. Across single cell and flow cytometry cohorts, Batyp were expanded in AML, and their molecular and phenotypic profiles resembled those of atypical B cells associated with chronic inflammatory conditions. Batyp were polarized within the AML microenvironment, and this was amplified by CAR T cell-AML interactions. Mechanistically, polarization was dependent on Toll-like receptor, interferon and CD70-CD27 signaling. Batyp suppressed T cell and CAR T cell responses via PD-1- and TGF-beta signaling, and were enriched in non-responding patients with AML and myeloma. CD180-directed CAR T cells, targeting an antigen shared by Batyp and AML blasts, resisted suppression in vitro and cleared disease in B cell-humanized xenografts while sparing hematopoietic stem cells, with B cell engagement further enhancing their expansion. CAR T cells thus reinforce their own suppressive niche through Batyp polarization, a constraint that can be relieved by dual blast- and B cell-targeting strategies.

immunology↗

bioRxiv2023

A time-resolved meta-analysis of consensus gene expression profiles during human T-cell activation

BackgroundThe coordinated transcriptional regulation of activated T-cells is based on the complex dynamic behavior of signaling networks. Given an external stimulus, T-cell gene expression is characterized by impulse and sustained patterns over the course. Here, we analyzed the temporal pattern of activation across different T-cell populations to develop consensus gene signatures for T-cell activation. MethodsWe applied a meta-analysis of anti-CD3/CD28 induced CD4+ T-cell activation kinetics of publicly available transcriptomewide time series using a random effects model. We used non-negative matrix factorization, an unsupervised deconvolution method, to infer changes in biological patterns over time. For verification and to further map a wider variety of the T-cell landscape, we performed a time series of transcriptome-wide RNA sequencing on activated blood T-cells. Lastly, we matched the identified consensus biomarker signatures to single-cell RNA sequencing (scRNA-Seq) data of autologous anti-CD19 chimeric antigen receptor (CAR) T-cells from 24 patients with large B cell lymphoma (LBCL) to characterize activation status of the cell product before infusion. ResultsWe identified time-resolved gene expression profiles comprising 521 genes of up to 10 disjunct time points during activation and different polarization conditions. The gene signatures include central transcriptional regulators of T-cell activation, representing successive waves as well as sustained patterns of induction. They cover early, intermediate, and late response expression rates across multiple T-cell populations, thus defining consensus biomarker signatures for T-cell activation. Intermediate and late response activation signatures in CAR T-cell infusion products were correlated to immune effector cell-associated neurotoxicity syndrome. ConclusionIn conclusion, we describe temporally resolved gene expression patterns across T-cell populations. These biomarker signatures are a valuable source for e.g., monitoring transcriptional changes during T-cell activation with a reasonable number of genes, annotating T-cell states in single-cell transcriptome studies or assessing dysregulated functions of human T-cell immunity.

bioinformatics↗