Biology subjects

Emile, J.-F.

Publications and source records attributed to Emile, J.-F..

3 recordsLinked to original sources

bioRxiv2025

Mosaicism of BRAF V600E in Healthy Skin Predicts Neurodegeneration in Histiocytosis

Most histiocytoses harbor an oncogenic somatic alteration activating the MAP kinase pathway, the most frequent of which is BRAFV600E. Targeted therapy with BRAF or MEK inhibitors is highly effective, but neurodegeneration remains a severe complication. Recent data suggest that neurodegeneration may be secondary to mosaicism for BRAFV600E in microglia. In the prospective observational TARGET-HISTIO cohort study, we investigated extra-CNS mosaicism of BRAFV600E in a cohort of 124 adults with histiocytosis. Seeking widespread mosaicism, we analyzed healthy skin biopsies from areas without histiocytosis or melanocytic infiltration using high-sensitivity digital polymerase chain reaction. BRAFV600Ewas detected in healthy skin in 57/67 (85%) patients with BRAFV600Ehistiocytosis and was present in all second and third skin biopsies (n=6). BRAFV600Ewas absent from skin biopsies in all 37 patients with histiocytosis harboring another oncogenic mutation. BRAFV600E was also present in skin biopsies of four /20 patients with histiocytosis of unknown molecular status. Cells harboring BRAFV600E share characteristics with resident dermis macrophages. Only 58% of patients with BRAFV600E in skin also had BRAFV600E in blood leukocytes. Variant allele frequency in skin and blood leukocytes was 16 times lower than in histiocytosis, and frequency in skin was independent of treatment with BRAF/MEK inhibitors. None of the patients with wild type BRAF alleles in skin had neurodegeneration, whereas 22/61 (36%) of patients with BRAFV600E in skin had neurodegeneration. This study demonstrates that mosaicism of BRAFV600Ein healthy skin is frequent in patients with histiocytosis and is strongly associated with neurodegeneration. Key PointsLow allele frequency of the oncogene BRAF V600E is frequent in healthy skin of patients with histiocytosis. Patients without BRAF V600E in skin do not develop neurodegeneration, while 36% of those with BRAF V600E mosaicism do.

cancer biology↗

bioRxiv2024

A deep learning-based multiscale integration of spatial omics with tumor morphology.

Spatial Transcriptomics (spTx) offers unprecedented insights into the spatial arrangement of the tumor microenvironment, tumor initiation/progression and identification of new therapeutic target candidates. However, spTx remains complex and unlikely to be routinely used in the near future. Hematoxylin and eosin (H&E) stained histological slides, on the other hand, are routinely generated for a large fraction of cancer patients. Here, we present a novel deep learning-based approach for multiscale integration of spTx with tumor morphology (MISO). We trained MISO to predict spTx from H&E on a new unpublished dataset of 72 10X Genomics Visium samples, and derived a novel estimate of the upper bound on the achievable performance. We demonstrate that MISO enables near single-cell-resolution, spatially-resolved gene expression prediction from H&E. In addition, MISO provides an effective patient representation framework that enables downstream predictive tasks such as molecular phenotyping or MSI prediction.

bioinformatics↗

bioRxiv2022

PACpAInt: a deep learning approach to identify molecular subtypes of pancreatic adenocarcinoma on histology slides

Pancreatic ductal adenocarcinoma (PAC) is a highly heterogeneous and plastic tumor with different transcriptomic molecular subtypes that hold great prognostic and theranostic values. We developed PACpAInt, a multistep approach using deep learning models to determine tumor cell type and their molecular phenotype on routine histological preparation at a resolution enabling to decipher complete intratumor heterogeneity on a massive scale never achieved before. PACpAInt effectively identified molecular subtypes at the slide level in three validation cohorts and had an independent prognostic value. It identified an interslide heterogeneity within a case in 39% of tumors that impacted survival. Diving at the cell level, PACpAInt identified "pure" classical and basal-like main subtypes as well as an intermediary phenotype and hybrid tumors that co-carried both classical and basal-like phenotypes. These novel artificial intelligence-based subtypes, together with the proportion of basal-like cells within a tumor had a strong prognostic impact.

cancer biology↗