Rapsomaniki, M. | Search bioRxiv

bioRxiv2025

Characterization of Chemoresistant Cell Populations Improves Risk Stratification and Therapy Prediction in Pediatric AML

Most pediatric acute myeloid leukemia (pAML) patients achieve complete remission after chemotherapy, yet relapse is common, with nearly 40% ultimately dying of the disease. Prognosis is currently assessed using cytogenetic biomarkers and measurable residual disease after the first chemotherapy cycle, with the highest risk patients referred for stem cell transplantation (SCT) at first remission. Because aggressive therapies such as SCT are highly toxic, yet cures after relapse are rare, accurate early risk prediction is essential for improving outcomes. To address this need, we analyzed paired diagnosis-relapse samples from 33 pAML patients at single-cell resolution and identified chemoresistant cell populations whose abundance at diagnosis significantly improved risk prediction. Incorporating the detection of these cell populations into our risk model revealed a previously unrecognized patient subgroup with a 5-year event-free survival rate below 40%. Although this subgroup represents only 20% of pAML cases, it accounted for half of the deaths among patients who do not receive SCT at first remission. Moreover, molecular characterization of these chemoresistant cell populations uncovered potential therapeutic targets and candidate interventions relevant to most high-risk patients, paving the way for more effective targeted treatments for high-risk pAML patients.

cancer biology↗

bioRxiv2024

Modeling CAR Response at the Single-Cell Level Using Conditional Optimal Transport

Chimeric Antigen Receptor (CAR) T cell therapy is a promising area of cancer immunotherapy. However, many challenges such as loss of persistence, T cell exhaustion, and therapy associated toxicities hamper further advancement of CAR T cell therapy. Therefore, recent efforts have focused on designing improved CARs that show better therapeutic characteristics. However, it is unfeasible to test all CAR variants in lab-based assays as CARs consist of multiple intracellular signalling domains. This results in over 100000 possible variants. We leverage computational modeling to navigate this vast combinatorial space by learning the relationship between CAR design and T cell functionality, thereby proposing promising CAR T cell designs. CAR T cells expressing different variants can be viewed as cells that underwent different perturbations. Neural Optimal Transport is an upcoming field that can model single cell perturbations and predict unseen cells and conditions. In this work we leverage the conditional Monge Gap to model the response to CAR expression at a single-cell level and generate gene expression of cells that express an unseen CAR design. We show that CAR OT (CAROT) significantly outperforms the baseline for gene expression prediction for in-distribution CAR variants, with distinct gene expression patterns per CAR that capture biological characteristics. When predicting unseen CAR variants, we demonstrate promising results in terms of gene expression prediction and show the model learns gene expression patterns linked to domains in the training set. This work demonstrates that optimal transport may support discovery and development of new CAR T cell designs.

synthetic biology↗