Biology subjects

Rapsomaniki, M.

Publications and source records attributed to Rapsomaniki, M..

4 recordsLinked to original sources

bioRxiv2026

Spatial single-cell proteomics defines multicellular niches in the primary prostate cancer microenvironment

Prostate cancer displays substantial clinical and histopathological heterogeneity which is not fully captured by conventional Gleason grading. To resolve the spatial and phenotypic complexity of the prostate tumor microenvironment, we performed imaging mass cytometry using a prostate-tailored 34-plex antibody panel on a clinically annotated tissue microarray cohort of 195 patients of primary stage disease after radical prostatectomy (523 regions of interest; 2.19 million cells). We identified 34 distinct cell types spanning epithelial, endothelial, stromal and immune compartments, and further organized into 18 epithelial-dominated, cancer associated fibroblast-dominated, and immune-rich spatial niches. Within the epithelial compartment, we detected an ERGp53 luminal population whose abundance is independently associated with poor overall and progression-free survival. In the stroma, we defined extracellular matrix remodeling-related cancer associated fibroblast and smooth muscle cell lineages, including a periglandular CD105high niche with strong stromal-immune connectivity that is selectively associated with worse clinical outcome. Finally, cumulative immune niche burden correlated with histological inflammation and stratifies for worse patient survival. Together, these data provide a spatially resolved single-cell atlas of primary PCa and reveal stromal-immune-epithelial niches with prognostic relevance beyond Gleason grade.

cancer biology↗

bioRxiv2026

Deciphering spatial heterogeneity of patient-derived organoids of colorectal tumors for drug discovery

The spatial organization of tumors is critical for drug development, as spatial heterogeneity in cancer cells and the tumor microenvironment can present a significant barrier to effective therapies. Patient-derived tumor organoids (PDOs) offer promising platforms to study tumor biology and for drug development. Previous studies have demonstrated that PDOs can maintain the genomic/clonal heterogeneity of tumors, as well as expression heterogeneity. However, the spatial heterogeneity of PDOs and its correlation with the original tumors remains unclear. We propose an integrated pipeline combining spatial transcriptomics and phenotypic analyses of PDOs to capture and track spatial heterogeneity. This platform integrates multiplexed organoid cultures with imaging-based spatial transcriptomics and analysis at single organoid resolution. We validate the platform by comparing results with non-spatial single-cell transcriptomic data and spatial transcriptomic analysis of donor tumors. The combination of ex vivo PDOs and spatial transcriptomics could open novel avenues for developing drugs.

cancer biology↗

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↗