Activation priming and cytokine polyfunctionality modulate the enhanced functionality of low-affinity CD19 CAR T cells.

We recently described a low-affinity second-generation CD19 chimeric antigen receptor (CAR) CAT that showed enhanced expansion, cytotoxicity, and antitumor efficacy compared with the high-affinity (FMC63-based) CAR used in tisagenlecleucel, in preclinical models. Furthermore, CAT demonstrated an excellent toxicity profile, enhanced in vivo expansion, and long-term persistence in a phase 1 clinical study. To understand the molecular mechanisms behind these properties of CAT CAR T cells, we performed a systematic in vitro characterization of the transcriptomic (RNA sequencing) and protein (cytometry by time of flight) changes occurring in T cells expressing low-affinity vs high-affinity CD19 CARs following stimulation with CD19-expressing cells. Our results show that CAT CAR T cells exhibit enhanced activation to CD19 stimulation and a distinct transcriptomic and protein profile, with increased activation and cytokine polyfunctionality compared with FMC63 CAR T cells. We demonstrate that the enhanced functionality of low-affinity CAT CAR T cells is a consequence of an antigen-dependent priming induced by residual CD19-expressing B cells present in the manufacture.

Taming Cell-to-Cell Heterogeneity in Acute Myeloid Leukaemia With Machine Learning.

Acute Myeloid Leukaemia (AML) is a phenotypically and genetically heterogenous blood cancer characterised by very poor prognosis, with disease relapse being the primary cause of treatment failure. AML heterogeneity arise from different genetic and non-genetic sources, including its proposed hierarchical structure, with leukemic stem cells (LSCs) and progenitors giving origin to a variety of more mature leukemic subsets. Recent advances in single-cell molecular and phenotypic profiling have highlighted the intra and inter-patient heterogeneous nature of AML, which has so far limited the success of cell-based immunotherapy approaches against single targets. Machine Learning (ML) can be uniquely used to find non-trivial patterns from high-dimensional datasets and identify rare sub-populations. Here we review some recent ML tools that applied to single-cell data could help disentangle cell heterogeneity in AML by identifying distinct core molecular signatures of leukemic cell subsets. We discuss the advantages and limitations of unsupervised and supervised ML approaches to cluster and classify cell populations in AML, for the identification of biomarkers and the design of personalised therapies.