Haist, M. | bioRxiv Science

bioRxiv2025

Spatially organized inflammatory myeloid-CD8+ T cell aggregates linked to Merkel-cell Polyomavirus driven Reorganization of the Tumor Microenvironment

Merkel cell carcinoma (MCC) is an aggressive skin cancer with high propensity for metastasis, caused by Merkel-cell-polyomavirus (MCPyV), or chronic UV-light-exposure. How MCPyV spatially modulates immune responses within the tumor microenvironment and how such are linked to patient outcomes remains unknown. We interrogated the cellular and transcriptional landscapes of 60 MCC-patients using a combination of multiplex proteomics, in-situ RNA-hybridization, and spatially oriented transcriptomics. We identified a spatial co-enrichment of activated CD8+ T-cells and CXCL9+PD-L1+ macrophages at the invasive front of virus-positive MCC. This spatial immune response pattern was conserved in another virus-positive tumor, HPV+ head-and-neck cancer. Importantly, we show that virus-negativity correlated with high risk of metastasis through low CD8+ T-cell infiltration and the enrichment of cancer-associated-fibroblasts at the tumor boundary. By contrast, responses to immune-checkpoint blockade (ICB) were independent of viral-status but correlated with the presence of a B-cell-enriched spatial contexts. Our work is the first to reveal distinct immune-response patterns between virus-positive and virus-negative MCC and their impact on metastasis and ICB-response.

immunology↗

bioRxiv2024

Constitutive expression of IκBζ promotes tumor growth and immunotherapy resistance in melanoma

BackgroundI{kappa}B{zeta}, a rather unknown co-regulator of NF-{kappa}B, is mostly inducibly expressed and can either activate or repress a specific subset of NF-{kappa}B target genes. While its role as a transcriptional regulator of various cytokines and chemokines in immune cells has been revealed, I{kappa}B{zeta}s function in solid cancer remains unclear. MethodsWe investigated I{kappa}B{zeta} expression in melanoma, and assessed its impact on target gene expression, tumor growth, and response to immunotherapy in melanoma cell lines, mouse models, and patient samples. ResultsUnlike in other cell types, I{kappa}B{zeta} protein was found to be constitutively expressed in a subfraction of melanoma cell lines, and around 35% of melanoma cases. This atypical expression pattern of I{kappa}B{zeta} did not correlate with its mRNA levels or known driver mutations, but instead seemed to result from changes in its post-transcriptional or post-translational regulation. Deleting constitutively expressed I{kappa}B{zeta} abrogated the activity and chromatin association of STAT3 and p65, leading to reduced expression of the pro-proliferative cytokines IL-1{beta} and IL-6 in melanoma cells. Consequently, loss of tumor-derived I{kappa}B{zeta} suppressed self-sustained melanoma cell growth both in vitro and in vivo. Additionally, constitutive I{kappa}B{zeta} expression suppressed the induction of the chemokines CXCL9, CXCL10, and CCL5, which impaired the recruitment of NK and CD8+ T-cells to the tumor, causing resistance to -PD-1 immunotherapy in mice. Furthermore, the expression of tumor-derived I{kappa}B{zeta} also correlated with the absence of CD8+ T-cells in human melanoma samples and progressive disease during immunotherapy. ConclusionWe propose that tumor-derived I{kappa}B{zeta} could serve as a new therapeutic target and prognostic marker that characterizes melanoma with high tumor cell proliferation, cytotoxic T- and NK-cell exclusion, and unfavorable immunotherapy responses. Targeting I{kappa}B{zeta} expression might open up a new therapy option to re-establish the recruitment of cytotoxic cells, thereby resensitizing for immunotherapy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/613946v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@76389aorg.highwire.dtl.DTLVardef@17e3f1eorg.highwire.dtl.DTLVardef@161d491org.highwire.dtl.DTLVardef@1ca9f2d_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

SPACEc: A Streamlined, Interactive Python Workflow for Multiplexed Image Processing and Analysis

Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface.

bioinformatics↗