Biology subjects

Mouron, S.

Publications and source records attributed to Mouron, S..

5 recordsLinked to original sources

bioRxiv2025

Generative AI Enables Breast Cancer Genomic Subtype Prediction from Histology Images

Breast cancer subtyping is essential for precision oncology, influencing prognosis, treatment selection, and clinical trial design. The Integrative Subtype Classification (IC) categorizes breast tumors into groups with distinct long-term outcomes based on genomic and correlated transcriptomic features. This method relies on sequencing data, which, despite decreasing costs, is not always available in research or clinical settings. Here we introduce PATH-IC, a computational pathology model that predicts ER+ breast cancer IC subtype risk of relapse categories from routine histology data. We enhance the current state-of-the-art computational pathology approach with BERGERON, which leverages generative AI to correct class imbalance and reduce overfitting, showing that synthetic data improves PATH-ICs performance by the equivalent of 41% more real training samples. PATH-IC achieves a testing AUROC of 0.814, with predictions correlating to Oncotype DX scores and long-term relapse risk. Using attention-based model interpretation and CRAWFORD, a novel embedding-to-image foundation model, we demonstrate that PATH-IC identifies expected tumor microenvironment patterns for IC subtypes and highlights heterochromatin condensation as a key feature of high-risk tumors. Matched single-cell spatial transcriptomics confirm IC subtype-specific gene expression patterns identified by PATH-IC, including active metabolic, proliferative, and proteostasis pathways in the high-risk group. PATH-IC advances computational pathology through generative AI, enabling subtype inference from histopathology data.

cancer biology↗

bioRxiv2025

QUICHE reveals structural definitions of anti-tumor responses in triple negative breast cancer

While recent innovations in spatial biology have driven new insights into how tissue organization is altered in disease, interpreting these datasets in a generalized and scalable fashion remains a challenge. Computational workflows for discovering condition-specific differences in tissue organization typically rely on pairwise comparisons or unsupervised clustering. In many cases, these approaches are computationally expensive, lack statistical rigor, and are insensitive to low-prevalence cellular niches that are nevertheless highly discriminative and predictive of patient outcomes. Here, we present QUICHE - an automated, scalable, and statistically robust method that can be used to discover cellular niches differentially enriched in spatial regions, longitudinal samples, or clinical patient groups. In contrast to existing methods, QUICHE combines local niche detection with interpretable statistical modeling using graph neighborhoods to detect differentially enriched cellular niches, even at low prevalence. Using in silico models and spatial proteomic imaging of human tissues, we demonstrate that QUICHE can accurately detect condition-specific cellular niches occurring at a frequency of 0.5% in fewer than 20% of patient samples, outperforming the next best method which required a patient prevalence of 60% for detection. To validate our approach and understand how tumor structure influences recurrence risk in triple negative breast cancer (TNBC), we used QUICHE to comprehensively profile the tumor microenvironment in a multi-center, spatial proteomics cohort consisting of primary surgical resections, analyzing over 2 million cells from 314 patients across 5 medical centers. We discovered cellular niches that were consistently enriched in key regions of the tumor microenvironment, including the tumor-immune border and extracellular matrix remodeling regions, as well as niches statistically-associated with patient outcomes, including recurrence status and recurrence-free survival. The majority of differential niches (74.2%) were specific to patients that did not relapse and formed a robust interconnected network enriched in monocytes, macrophages, APCs, and CD8T cells with tumor and stroma cells. In contrast, the interaction network for patients that relapsed was notably sparse and enriched in B cells, CD68 macrophages and neutrophils. We validated these findings using two independent cohorts, observing similar cellular interactions and predictive power. Collectively, these results suggest that salient, generalized profiles of productive anti-tumor immune responses are defined by a network of structural engagement between innate and adaptive immunity with tumor and stromal cells, rather than by any single specific cell population. We have made QUICHE freely available as a user-friendly open-source Python package at https://github.com/jranek/quiche.

bioinformatics↗

bioRxiv2023

Functional convergence underground? The scale-dependency of community assembly processes in European cave spiders

Understanding how species assemble into communities is a central tenet in ecology. One of the most elusive questions is the relative contribution of environmental filtering versus limiting similarity. Important advances in this area have been achieved by looking at communities through a functional lens (i.e., the traits they express), so as to derive principles valid across species pools. Yet, even using traits in lieu of taxonomy, the issue remains controversial because i) environmental filtering and limiting similarity often act simultaneously in shaping communities; and ii) their effect is scale-dependent. We exploited the experimental arena offered by caves, island-like natural laboratories characterized by largely constant environmental gradients and a limited diversity of species and interactions. Leveraging uniquely available data on distribution and traits for European cave spiders, we tested explicit hypotheses about variations in community assembly rules across ecological gradients and scales. We demonstrate that environmental filtering and limiting similarity shape cave communities acting on trait evolution in opposing directions. These effects are strongly scale dependent, varying along multiple environmental gradients. Conversely, the effect of geography on trait composition is weak, indicating that trait turnover in space happens primarily by substitution of species pursuing similar functions due to strong environmental filters. Our findings reconcile contrasted views about the relative importance of the two main mechanisms shaping patterns of biodiversity, and provide a conceptual foundation to account for scaling effects in the study of community assembly.

ecology↗

bioRxiv2021

RANK is an independent biomarker of poor prognosis in estrogen receptor-negative breast cancer and a therapeutic target in patient-derived xenografts

Despite strong preclinical data, the therapeutic benefit of the RANKL inhibitor denosumab in BC patients, beyond its bone-related effects, is unclear. Here, we investigated the prognostic value of RANK expression and its functionality in human BC. We analyzed RANK and RANKL expression in more than 1500 BC cases (777 being estrogen receptor-negative (ER-)) from four independent cohorts. We confirmed that RANK is more frequently expressed in ER- tumors, but it is also found in a subset of ER+ tumors. In ER- BC, RANK expression was independently associated with poor outcome, especially in postmenopausal patients and those who received adjuvant chemotherapy. Gene expression analyses unraveled distinct biology associated with RANK in relation to ER expression and menopause, and evidenced enhanced RANK activation in ER- postmenopausal tumors, together with regulation of metabolic pathways. Functional studies and transcriptomic analyses in ER- RANK+ patients-derived orthoxenografts demonstrated that activation of RANK signaling pathway promotes tumor cell proliferation and stemness, and regulates multiple biological processes including tumor immune surveillance and metabolism. Our results demonstrate that RANK expression is an independent poor prognosis biomarker in postmenopausal ER- BC patients and support the rational of using RANK pathway inhibitors in combination with chemotherapy in ER- BC.

cancer biology↗

bioRxiv2019

Estrogen deprivation triggers an immunosuppressive phenotype in breast cancer cells

Estrogen receptor (ER)-positive breast tumors are routinely treated with estrogen-depriving therapies. Despite their effectiveness, patients often progress into a more aggressive form of the disease. Through a chemical screen oriented to identify chemicals capable of inducing the expression of the immune-checkpoint ligand PD-L1, we found antiestrogens as hits. Subsequent validations confirmed that estrogen deprivation or ER depletion induces PD-L1 expression in ER-positive breast cancer cells, both in vitro and in vivo. Likewise, PD-L1 expression is increased in metastasis arising from breast cancer patients receiving adjuvant hormonal therapy for their local disease. Transcriptome analyses indicate that estrogen deprivation triggers a broad immunosuppressive program, not restricted to PD-L1. Accordingly, estrogen deprived MCF7 cells are resistant to T-cell mediated cell killing, in a manner that can be reverted by estradiol. Our study reveals that while antiestrogen therapies effectively limit tumor growth in ER-positive breast cancers, they also trigger a transcriptional program that favors immune evasion.

cancer biology↗