Biology subjects

Mezzano, V.

Publications and source records attributed to Mezzano, V..

5 recordsLinked to original sources

bioRxiv2025

Population analysis and immunologic landscape of melanoma in people living with HIV

PurposeTo dissect the clinical and immunological features of people living with HIV (PLWH) diagnosed with melanoma, who have consistently shown worse outcomes than HIV-negative individuals (PLw/oH) with the same cancer. Experimental DesignWe analyzed electronic health records from 1,087 PLWH and 394,437 PLw/oH with melanoma. Demographic and clinical characteristics were compared. Spatial immune transcriptomics (72 immune-related genes) was performed on melanoma tumor samples (n=11), with downstream validation using multiplex immunofluorescence (n=15 PLWH, n=14 PLw/oH). ResultsPLWH were diagnosed at a younger age, had greater representation of Hispanic and Black individuals, and showed reduced survival. They also had a markedly increased risk of brain metastases. PLWH experienced significant delays in initiating immune checkpoint inhibitor (ICI) therapy and had worse post-ICI survival, even after balancing covariates. Spatial transcriptomics revealed a more immunosuppressive tumor microenvironment in PLWH, with increased transcription of immune checkpoints (PD1, LAG3) and reduced antigen-presentation markers (HLA-DRB, B2M), with distinct spatial distributions in tumors and surrounding microenvironments. Multiplex immunofluorescence demonstrated features of an exhausted CD8 T cell compartment, including enrichment of PD1intLAG3- and PD1intLAG3 subpopulations, and a significant accumulation of myeloid-derived suppressor cells (CD11b HLA-DR- CD33). ConclusionsMelanoma in PLWH is associated with distinct clinical and immunological features, including delayed ICI treatment, reduced survival, and an immunosuppressive microenvironment with exhausted CD8 T cells and expanded myeloid-derived suppressor cells. These findings suggest that chronic HIV infection may impair antitumor immunity in melanoma. Targeting the pathways identified here may improve therapeutic responses and outcomes in this population. Statement of translational relevanceThis study reveals critical barriers to effective melanoma treatment in people living with HIV (PLWH). Despite receiving immune checkpoint inhibitors (ICIs), PLWH face delayed therapy initiation, a greater likelihood of brain metastases, and significantly higher long-term mortality, even after adjusting for demographic covariates. Transcriptional immune profiling further uncovers a tumor microenvironment enriched in immunosuppressive myeloid-derived suppressor cells and CD8 T cell populations with features of exhaustion. These findings suggest that poorer outcomes in PLWH stem not only from delayed care, but also from distinct targetable mechanisms of immune dysfunction. For example, strategies to reverse MDSC accumulation in the tumor or tailored ICI regimens could enhance immune responsiveness and improve treatment efficiency. By defining the clinical and immunological features of this population, this work highlights opportunities for precision immunotherapy tailored to PLWH with melanoma, with direct implications for improving survival and reducing disparities.

cancer biology↗

bioRxiv2024

Characterization of tumor heterogeneity through segmentation-free representation learning

The interaction between tumors and their microenvironment is complex and heterogeneous. Recent developments in high-dimensional multiplexed imaging have revealed the spatial organization of tumor tissues at the molecular level. However, the discovery and thorough characterization of the tumor microenvironment (TME) remains challenging due to the scale and complexity of the images. Here, we propose a self-supervised representation learning framework, CANVAS, that enables discovery of novel types of TMEs. CANVAS is a vision transformer that directly takes high-dimensional multiplexed images and is trained using self-supervised masked image modeling. In contrast to traditional spatial analysis approaches which rely on cell segmentations, CANVAS is segmentation-free, utilizes pixel-level information, and retains local morphology and biomarker distribution information. This approach allows the model to distinguish subtle morphological differences, leading to precise separation and characterization of distinct TME signatures. We applied CANVAS to a lung tumor dataset and identified and validated a monocytic signature that is associated with poor prognosis.

cancer biology↗

bioRxiv2023

MetFinder: a neural network-based tool for automated quantitation of metastatic burden in histological sections from animal models

Diagnosis of most diseases relies on expert histopathological evaluation of tissue sections by an experienced pathologist. By using standardized staining techniques and an expanding repertoire of markers, a trained eye is able to recognize disease-specific patterns with high accuracy and determine a diagnosis. As efforts to study mechanisms of metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate effects on tumor burden resulting from specific interventions. However, current methods of quantifying tumor burden are low in either resolution or throughput. Artificial neural networks, which can perform in-depth image analyses of tissue sections, provide an opportunity for automated recognition of consistent histopathological patterns. In order to increase the outflow of data collection from preclinical studies, we trained a deep neural network for quantitative analysis of melanoma tumor content on histopathological sections of murine models. This AI-based algorithm, made freely available to academic labs through a web-interface called MetFinder, promises to become an asset for researchers and pathologists interested in accurate, quantitative assessment of metastasis burden.

cancer biology↗

bioRxiv2023

Tumor-intrinsic LKB1-LIF signaling axis establishes a myeloid niche to promote immune evasion and tumor growth

Tumor mutations can influence the surrounding microenvironment leading to suppression of anti-tumor immune responses and thereby contributing to tumor progression and failure of cancer therapies. Here we use genetically engineered lung cancer mouse models and patient samples to dissect how LKB1 mutations accelerate tumor growth by reshaping the immune microenvironment. Comprehensive immune profiling of LKB1-mutant vs wildtype tumors revealed dramatic changes in myeloid cells, specifically enrichment of Arg1+ interstitial macrophages and SiglecFHi neutrophils. We discovered a novel mechanism whereby autocrine LIF signaling in Lkb1-mutant tumors drives tumorigenesis by reprogramming myeloid cells in the immune microenvironment. Inhibiting LIF signaling in Lkb1-mutant tumors, via gene targeting or with a neutralizing antibody, resulted in a striking reduction in Arg1+ interstitial macrophages and SiglecFHi neutrophils, expansion of antigen specific T cells, and inhibition of tumor progression. Thus, targeting LIF signaling provides a new therapeutic approach to reverse the immunosuppressive microenvironment of LKB1-mutant tumors.

cancer biology↗

bioRxiv2022

Type I interferon and MAVS signaling restricts chikungunya virus heart infection and cardiac tissue damage

Chikungunya virus (CHIKV) infection has been associated with severe cardiac manifestations, yet, how CHIKV infection leads to heart disease remains unknown. Here, we leveraged both mouse models and human primary cells to define the mechanisms of CHIKV heart infection. We found that CHIKV actively replicates in cardiac fibroblasts and is cleared without significant tissue damage through the induction of a local type-I interferon response from both infected and non-infected cardiac cells. Importantly, signaling through the mitochondrial antiviral-signaling protein (MAVS) is required for viral clearance from the heart. In the absence of MAVS, persistent infection leads to focal myocarditis and major vessel vasculitis persisting for up to 60 days post-infection, suggesting CHIKV can lead to vascular inflammation and potential long-lasting cardiovascular complications. This study provides a model of CHIKV cardiac infection and mechanistic insight into CHIKV-induced heart disease, underscoring the importance of monitoring cardiac function in patients with CHIKV infections.

microbiology↗