Biology subjects

Petitprez, F.

Publications and source records attributed to Petitprez, F..

9 recordsLinked to original sources

bioRxiv2026

Deciphering the association of spatial landscapes of DCIS with recurrence as invasive breast cancer

Stromal cell composition has been proposed as a potential biomarker in ductal carcinoma in situ (DCIS) for assessing the risk of subsequent recurrence or progression to invasive breast cancer (IBC). However, the characteristics of the tumor microenvironment within DCIS associated with these outcomes remain poorly understood. Here, we perform spatial transcriptomic analyses of 49 primary DCIS specimens from patients with no recurrence, DCIS recurrence, or recurrence as IBC. Cell mapping reveals spatially organized stromal cell gradients within DCIS, with enrichment of TREM2+ macrophages and myofibroblastic cancer-associated fibroblasts (myCAFs) in tumor-proximal niches. Notably, the abundance of TREM2+ macrophages near tumor nests is associated with subsequent recurrence as IBC. Moreover, ligand-receptor pair analysis identifies GRN--SORT1, SPP1--CD44, and LGALS9--CD44 interactions as spatially plausible signaling interactions between TREM2+ macrophages and epithelial cells. Finally, these findings are validated at the protein level by multiplex immunostaining of serial sections. This study characterizes the spatial landscape of the DCIS tumor microenvironment associated with subsequent recurrence, providing a basis for developing biomarkers of disease progression and identifying potential therapeutic targets.

cancer biology↗

bioRxiv2026

Local Tumor Microenvironment Niches Correlate With Survival And Immunotherapy Response In Human Glioblastoma

Background: Glioblastoma (GBM) is an aggressive form of primary brain cancer. Recent efforts to characterize GBM using single-cell or spatial transcriptomics have revealed tremendous intra-tumoral heterogeneity among malignant cells and across different tumor areas. However, most studies have focused on malignant cells, and the spatial and cellular heterogeneity of the tumor microenvironment (TME) remains poorly understood. Consequently, it is unclear how TME compositions and organizations influence clinical outcomes for patients. Results: Spatial transcriptomics on 25 tumors, integrated with single-cell RNA sequencing and histological analyses, enabled the estimation of cellular composition within the TME across 46,000 spots (55-m wide). This analysis identified spatial associations between mesenchymal-like cancer cells and monocyte-derived macrophages and revealed six unique niches (N1-N6) characterized by distinct cellular composition and biological pathway activations. Deconvolution of large-scale bulk RNA-seq datasets using a spatial transcriptomics reference revealed that tumor niche composition significantly associated with patient survival and response to immunotherapy. Specifically, the N1 niche, characterized by the enrichment of mesenchymal-like cancer cells and monocyte-derived macrophages alongside hypoxic signature, was associated with lower overall survival. In contrast, the N5 niche, characterized by the enrichment of oligodendrocyte progenitor-like cancer cells and microglia-derived tumor-associated macrophages, was associated with longer patients survival. Analysis of data from patients treated with immunotherapy identified that niches N1 and N3, characterized by mixed mesenchymal-like and astrocyte-like cells, were associated with poor response to anti-PD-1 immune checkpoint inhibitors. Conclusions: Our results demonstrate that GBM exhibits significant spatial heterogeneity within the TMEs, comprising distinct niche classes, and that the specific niche compositions are associated with overall survival and immunotherapy responsiveness. Our results suggest incorporation of TME niche categories as biomarkers for risk stratification and therapeutic decisions for GBM patients.

cancer biology↗

bioRxiv2022

Community assessment of methods to deconvolve cellular composition from bulk gene expression

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

bioinformatics↗

bioRxiv2020

webMCP-counter: a web interface for transcriptomics-based quantification of immune and stromal cells in heterogeneous human or murine samples

SummaryWe previously reported MCP-counter and mMCP-counter, methods that allow precise estimation of the immune and stromal composition of human and murine samples from bulk transcriptomic data, but they were only distributed as R packages. Here, we report webMCP-counter, a user-friendly web interface to allow all users to use these methods, regardless of their proficiency in the R programming language. Availability and ImplementationFreely available from http://134.157.229.105:3838/webMCP/. Website developed with the R package shiny. Source code available from GitHub: https://github.com/FPetitprez/webMCP-counter.

immunology↗

bioRxiv2020

Genetic determinism of spontaneous masculinisation in XX female rainbow trout: new insights using medium throughput genotyping and whole-genome sequencing

Rainbow trout has a male heterogametic (XY) sex determination system controlled by a major sex-determining gene, sdY. Unexpectedly, a few phenotypically masculinised fish are regularly observed in all-female farmed trout stocks. To better understand the genetic determinism underlying spontaneous maleness in XX-rainbow trout, we recorded the phenotypic sex of 20,210 XX-rainbow trout from a French farm at 10 and 15 months post-hatching. The masculinisation rate was 1.45%. We performed two genome-wide association studies (GWAS) using both medium-throughput genotyping (31,811 SNPs) and whole-genome sequencing (WGS, 8.7 million SNPs) on a subsample of 1,139 individuals classified as females, intersex or males. The genomic heritability of maleness ranged between 0.48 and 0.62 depending on the method and number of SNPs used for estimation. At the 31K SNPs level, we detected four QTLs on three chromosomes (Omy1, Omy12 and Omy20). Using WGS information, we narrowed down the positions of the two QTLs detected on Omy1 to 96 kb and 347 kb respectively, with the second QTL explaining up to 14% of the total genetic variance of maleness. Within this QTL, we detected three putative candidate genes, fgfa8, cyp17a1 and an uncharacterised protein (LOC110527930), which might be involved in spontaneous maleness of XX-female rainbow trout.

genomics↗

bioRxiv2020

The murine Microenvironment Cell Population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression

Quantifying tissue-infiltrating immune and stromal cells provides clinically relevant information for various diseases, notably cancer. While numerous methods allow to quantify immune or stromal cells in human tissue samples based on transcriptomic data, very few are available for mouse studies. Here, we introduce murine Microenvironment Cell Population counter (mMCP-counter), a method based on highly specific transcriptomic markers that allow to accurately quantify 12 immune and 4 stromal murine cell populations. We validated mMCP-counter with flow cytometry data. We also showed that mMCP-counter outperforms existing methods. We showed in mouse models of mesothelioma and kidney cancer that mMCP-counter quantification scores are predictive of response to immune checkpoint blockade Finally, we illustrated mMCP-counters potential to analyze immune impacts of Alzheimers disease. mMCP-counter is available as an R package from GitHub: https://github.com/cit-bioinfo/mMCP-counter.

bioinformatics↗

bioRxiv2019

MS.liverK: an R package for transcriptome-based computation of molecular subtypes and functional signatures in liver cancer

SummaryLiver cancer is a highly heterogeneous disease in terms of etiology, tissue and cellular morphology, tumor molecular characteristics, microenvironment composition and prognosis. Several studies, based on tumor gene-expression profiling (GEP) data, have dissected the molecular heterogeneity of liver cancer. They resulted in various tools, either delineating homogeneous tumor subtypes or calculating molecular scores of prognostic or biological functions. Here, we present MS.liverK, an easy-to-use R package providing a comprehensive implementation of these tools, for research use. Availability and implementationThe MS.liverK R package is available from GitHub (https://github.com/cit-bioinfo/MS.liverK).

bioinformatics↗

bioRxiv2018

Comprehensive evaluation of cell-type quantification methods for immuno-oncology

MotivationThe composition and density of immune cells in the tumor microenvironment profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining, or single-cell sequencing is often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing. ResultsWe developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of [~]11,000 cells from the tumor microenvironment to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and [~]1,800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures. AvailabilityA snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv). Contactg.sturm@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Gene nucleotide composition accurately predicts expression and is linked to topological chromatin domains

Gene expression is orchestrated by distinct regulatory regions (e.g. promoters, enhancers, UTRs) to ensure a wide variety of cell types and functions. A challenge is to identify which regulatory regions are active, what are their associated features and how they work together in each cell type. Several approaches have tackled this problem by modeling gene expression based on epigenetic marks (e.g. ChIP-seq, methylation, DNase hypersensitivity), with the ultimate goal of identifying driving genomic regions and mutations that are clinically relevant in particular in precision medicine. However, these models rely on experimental data, which are limited to specific samples (even often to cell lines) and cannot be generated for all regulators and all patients. In addition, we show here that, although these approaches are accurate in predicting gene expression, their biological interpretation can be misleading. Finally these methods are not designed to capture potential regulation instructions present at the sequence level, before the binding of regulators or the opening of the chromatin. We develop here a method for predicting mRNA levels based solely on sequence features collected from distinct regulatory regions, which is as accurate as methods based on experimental data. Our approach confirms the importance of nucleotide composition in predicting gene expression and ranks regulatory regions according to their contribution. It also unveils strong influence of gene body sequence, in particular introns. We further provide evidence that the contribution of nucleotide content can be linked to co-regulations associated with genome 3D architecture and to associations of genes within topologically associated domains.

bioinformatics↗