Garcia-Garcia, S. | Search bioRxiv

bioRxiv2026

CMAS dampens anti-tumor immunity and associates with response to neoadjuvant immunotherapy in melanoma

Identifying immune-regulatory pathways to predict response is crucial for the efficacy of immune checkpoint blockade (ICB) immunotherapies. Sialylation is upregulated in tumor cells and modulates immune responses in cancer, yet its impact on patient clinical outcome and the spatial organization of the tumor microenvironment remain unclear. Here, using publicly available single-cell RNA sequencing data we show that expression of the sialylation master regulator CMAS in melanoma cells correlates with poorer patient survival. Using a murine melanoma model, we demonstrate that Cmas deletion in tumor cells severely impaired tumor growth and improved anti-tumor lymphoid and myeloid cell responses, increasing tumor cell-intrinsic susceptibility to interferon-gamma-, CD4+ T cell-, and macrophage-mediated killing. Single-cell spatial transcriptomics on neoadjuvant ICB-treated melanoma patient tumor biopsies revealed that CMAS expression in tumor cells inversely correlated with tumor cell proximity to and activation status of T cells and macrophages. Furthermore, expression of CMAS in tumor cells was increased in patients who did not respond to immunotherapy, compared to responders. Overall, our work identifies CMAS as a key modulator of tumor-immune dynamics associated with survival and response to neoadjuvant ICB immunotherapy in melanoma patients.

cancer biology↗

bioRxiv2026

Unmixing Spread Estimation Based on Residual Model in Spectral Flow Cytometry

Advances in spectral flow cytometry have enabled the simultaneous measurement of dozens of markers across millions of cells within a single experiment. Despite the increasing maximum perplexity achievable in spectral panels, panel design remains constrained. A central obstacle is signal spread-- unmixed fluorescence signal misattributed to unrelated channels--which reduces the resolution of cell populations. Here we introduce the Residual Model, a robust, scalable, and interpretable model-based approach for spread prediction during panel design. The Residual Model integrates statistical features derived from single-color controls and predicts spread under Ordinary Least Squares unmixing, the most widely used unmixing method. We demonstrate its reliable predictive performance across 141 single-color control samples measured on two instruments. To facilitate practical application, we developed the USERM R package, which implements the Residual Model and provides an out-of-box solution for interactive spread prediction and visualization.

immunology↗