Biology subjects

Jour, G.

Publications and source records attributed to Jour, G..

3 recordsLinked to original sources

bioRxiv2026

NF1 Loss Remodels Tumor Niches for Immune Evasion

Genetic and transcriptional alterations in cancer cells shape their interactions with immune and stromal compartments, influencing tumor progression, immune evasion, and response to immune checkpoint inhibitors (ICI). Yet, how these interactions are organized within tumor architecture and linked to clinical outcomes remains unclear. Neurofibromin 1 (NF1) is a tumor suppressor gene that is frequently inactivated across multiple cancer types. NF1 loss-of-function mutations occur in up to 27% of melanoma cases and are associated with poor clinical outcomes. Here, we used spatial multi-omics analysis to uncover 12 meta-niches, each comprising distinctive cell types with distinct characteristics, in human melanoma tissues. We found that niches containing immunosuppressive cancer-associated fibroblasts (CAFs) and macrophages were significantly enriched in NF1 mutant melanoma (NF1Mut) tissues. In contrast, niches containing cytotoxic CD8 T cells were significantly diminished. NF1 loss correlates with increased epidermal growth factor signaling (EGFR) signaling and reduced antigen presentation in tissues with limited CD8 T cell infiltration in both human and mouse melanoma. We demonstrate that EGFR inhibition restores antigen presentation and activates immune responses in a syngeneic Nf1 knockdown model resistant to ICIs. These data, therefore, define functionally distinctive niches enriched in NF1Mut melanoma that likely contribute to their aggressive nature and nominate EGFR signaling as a specific target to reinvigorate ICI responses. We therefore link an understudied genetic driver to specific immune architectures and ultimately therapy resistance and suggest a therapeutic strategy expected to improve treatment outcomes in NF1Mut melanoma patients.

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↗

bioRxiv2019

A Deep Learning Approach for Rapid Mutational Screening in Melanoma

Image-based analysis as a rapid method for mutation detection can be advantageous in research or clinical settings when tumor tissue is limited or unavailable for direct testing. Here, we applied a deep convolutional neural network (CNN) to whole slide images of melanomas from 256 patients and developed a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for the presence of mutated BRAF in our test set (AUC=0.72) Model performance was cross-validated on melanoma images from The Cancer Genome Atlas (AUC=0.75). We confirm that the mutated BRAF genotype is linked to phenotypic alterations at the level of the nucleus through saliency mapping and pathomics analysis, which reveal that cells with mutated BRAF exhibit larger and rounder nuclei. Not only do these findings provide additional insights on how BRAF mutations affects tumor structural characteristics, deep learning-based analysis of histopathology images have the potential to be integrated into higher order models for understanding tumor biology, developing biomarkers, and predicting clinical outcomes.

pathology↗