Biology subjects

Cros, J.

Publications and source records attributed to Cros, J..

10 recordsLinked to original sources

bioRxiv2026

Distinct Progression Paths Underlie the Diversity of Pancreatic Papillary Cystic Neoplasms

Surgical decisions for intraductal papillary mucinous neoplasms (IPMNs), precursors of pancreatic cancer, depend on distinguishing low- from high-grade dysplasia, a distinction that molecular markers could refine. We microdissected 433 tumor regions from 135 patients and integrated transcriptomics, deep-learning histology, and KRAS/GNAS RNA-called variants. RNA programs and morphological motifs separated lesions by phenotype, intestinal versus others, rather than by dysplasia grade. Along gastric IPMN trajectories, low-grade lesions recapitulated the pit-chief polarity of normal gastric glands, whereas high-grade lesions were indistinguishable from pancreatobiliary lesions in morphology, RNA programs, and driver mutations. Intestinal IPMNs arose through two routes: a canonical GNAS-associated pathway and a KRAS-associated shift from low-grade gastric features, with lesions sharing driver mutations retaining common programs across phenotypes. Resolving these phenotypic trajectories clarified distinct progression paths and enabled phenotype-specific prediction of high-grade dysplasia in mucus aspirated from 94 resected lesions. These findings support a promising fluid-based assessment of IPMN status.

cancer biology↗

bioRxiv2025

Prediction of Gemcitabine sensitivity in resectable Pancreatic Cancer using a Glycation Stress Transcriptomic Signature

BACKGROUND & AIMSPancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer with limited response to systemic therapy. Gemcitabine (GEM) benefits only a subset of patients. Methylglyoxal (MG), a glycolysis byproduct, has been linked to tumor behavior and therapy response in PDAC, suggesting potential as a stratification marker. METHODSWe developed a metabolically informed gene signature (MG-GEM) integrating MG- related glycolytic stress with clinical outcomes. Using the Puleo cohort (n=309), differential expression analysis between tumors with high and low MG stress identified 365 genes. LASSO Cox regression selected 16 prognostic genes, combined into a weighted risk score for patient stratification. MG GEM was validated in internal and external cohorts, including PRODIGE 24/CCTG PA6. Molecular, transcriptomic, and immune features were compared between high and low MG GEM groups. Finally, predictive performance was evaluated against the GemPred signature. RESULTSMG GEM divided PDAC patients into distinct risk groups with marked differences in overall and disease-free survival among GEM treated patients (OS 11.7 vs 27.2 months; DFS 7.6 vs 17.8 months, both p<0.0001). High MG-GEM tumors showed enrichment for KRAS G12D and SMAD4 mutations, basal and activated stroma subtypes, glycolytic metabolism, and reduced immune infiltration. Low MG-GEM tumors showed KRAS G12V, classical and immune subtypes, cholesterogenic metabolism, and adaptive immune favourable signatures. MG-GEM independently predicted GEM-specific clinical outcomes, irrespective of GemPred signature, and significantly enhanced patient stratification when combined with it. Within the PRODIGE-24/TGCC PA6 cohort, MG-GEM exhibited prognostic relevance and selectively identified patients who derived a survival benefit from adjuvant GEM, but not from FOLFIRINOX. CONCLUSIONSThe 16 gene MG GEM signature predicts prognosis in resected PDAC, reflects glycolytic stress driven chemoresistance, surpassing conventional molecular classifications. As a metabolically informed signature, MG-GEM holds promise for guiding chemotherapy selection and informing KRAS-targeted combination strategies, meriting further prospective clinical validation.

cancer biology↗

bioRxiv2024

A robust workflow to benchmark deconvolution of multi-omic data

Tumour heterogeneity significantly affects cancer progression and therapeutic response, yet quantifying it from bulk molecular data remains challenging. Deconvolution algorithms, which estimate cell-type proportions in bulk samples, offer a potential solution. However, there is no consensus on the optimal algorithm for transcriptomic or methylomic data. Here, we present an unbiased evaluation framework for the first comprehensive comparison of deconvolution algorithms across both omic types, including reference-based and -free approaches. Our evaluation covers raw performance, stability, and computational efficiency under varying conditions, such as missing or additional cell types and diverse sample compositions. We apply this framework across multiple benchmark datasets, including a novel multi-omics dataset generated specifically for this study. To ensure transparency and re-usability, we have designed a reproducible work-flow using containerization and publicly available code. Our results highlight the strengths and limitations of various algorithms, and provides practical guidance for selecting the best method based on data type and analysis context. This benchmark sets a new standard for evaluating deconvolution methods and analysing tumour heterogeneity.

bioinformatics↗

bioRxiv2024

Traits related to pollination and mating result in diverse reproductive strategies in angiosperms

O_LIFlowering plants have many modes of sexual reproduction, notably varying from selfing to outcrossing, and from bisexual flowers to individuals with separate sexes (dioecy). These reproductive modes are associated with floral and life-history traits that have evolved together, making it difficult to interpret correlations between traits. C_LIO_LIWe analysed variation in 21 traits related to flowers, pollination, mating, sexual systems and life history from 361 species representative of flowering plant diversity. C_LIO_LIOutcrossing was mainly found among long-lived, large-stature plants, but hermaphroditic outcrossers and dioecious species appeared as largely non-overlapping strategies in the trait space. Level of floral investment was the main difference between these strategies, with dioecious species having generally smaller, less rewarding flowers, a pattern that also occurred in biotically pollinated species. C_LIO_LIThis multi-trait study shows that pollination can be achieved in many, often contrasting, ways. Despite extensive variation in reproductive traits, dioecy stands out as being linked to floral traits primarily, while correlations with lifespan and dispersal traits appear spurious. We provide a conceptual framework based on lifespan, floral investment, and sexual separation that can be used to integrate pollination, reproduction and plant growth in future research on plant evolution and ecology. C_LI

evolutionary biology↗

bioRxiv2024

Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology

ObjectiveThe transcriptomic classification of intrahepatic cholangiocarcinomas (iCCA) has been recently refined from two to five classes, associated with pathological features, targetable genetic alterations and survival. Despite its prognostic and therapeutic value, the classification is not routinely used in the clinic because of technical limitations, including insufficient tissue material or the cost of molecular analyses. Here, we assessed a self-supervised learning (SSL) model for predicting iCCA transcriptomic classes on whole-slide digital histological images (WSIs) DesignTranscriptomic classes defined from RNAseq data were available for all samples. The SSL method, called Giga-SSL, was used to train our model on a discovery set of 766 biopsy slides (n=137 cases) and surgical samples (n=109 cases) from 246 patients in a five-fold cross-validation scheme. The model was validated in The Cancer Genome Atlas (TCGA) (n= 29) and a French external validation set (n=32). ResultsOur model showed good to very good performance in predicting the four most frequent transcriptomic class in the discovery set (area under the curve [AUC]: 0.63-0.84), especially for the hepatic stem-like class (37% of cases, AUC 0.84). The model performed equally well in predicting these four transcriptomic classes in the two validation sets, with AUCs ranging from 0.76 to 0.80 in the TCGA set and 0.62 to 0.92 in the French external set. ConclusionWe developed and validated an SSL-based model for predicting iCCA transcriptomic classes on routine histological slides of biopsy and surgical samples, which may impact iCCA management by predicting prognosis and guiding the treatment strategy.

cancer biology↗

bioRxiv2023

Redefining phenotypic intratumor heterogeneity of pancreatic ductal adenocarcinoma: a bottom-up approach

BackgroundPancreatic ductal adenocarcinoma (PDAC) tumor inter-patient heterogeneity has been well described with two major prognostic subtypes (classical and basal-like). An important intra-patient heterogeneity has been reported but has not yet been extensively studied due to the lack of standardized, reproducible and easily accessible high throughput methods. Material and MethodsWe built an immunohistochemical (IHC) tool capable of differentiating RNA-defined classical and basal-like tumors by selecting relevant antibodies using a multi-step process. The successive stages of i) an in-silico selection from a review literature and a bulk transcriptome analysis of 309 PDACs, ii) a tumor-specific selection from 30 patient-derived xenografts followed by iii) the validation on tissue microarrays in 50 PDAC were conducted. We used our final IHC panel on two independent cohorts of resected PDAC (n=95, whole-slide, n=148, tissue microarrays) for external validation. After digitization and registration of pathology slides, we performed a tile-based-analysis in tumor and pre-neoplastic epithelial areas and a k-means clustering to identify relevant marker combinations. ResultsSequential marker selection led to the following panel: GATA6, CLDN18, TFF1, MUC16, S100A2, KRT17, PanBasal. Four different phenotypes were identified: 1 classical, 1 intermediate (KRT17+) and 2 basal-like (MUC16+ vs S100A2+) with specific biological properties. The presence of a minor basal contingent drastically reduced overall survival, even in classical predominant PDACs (HR=2.36, p=0.01). Analysis of preneoplastic lesions suggested that pancreatic carcinogenesis may follow a progressive evolution from classical toward a basal through an early intermediate phenotype. ConclusionOur IHC panel redefined and easily assessed the high degree of intra- and inter-tumoral heterogeneity of PDAC.

cancer biology↗

bioRxiv2023

Trogocytosis of cancer-associated fibroblasts promotes pancreatic cancer growth and immune suppression via phospholipid scramblase anoctamin 6 (ANO6).

In pancreatic ductal adenocarcinoma (PDAC), the fibroblastic stroma constitutes most of the tumor mass and is remarkably devoid of functional blood vessels. This raises an unresolved question of how PDAC cells obtain essential metabolites and water-insoluble lipids. We have found a critical role for cancer-associated fibroblasts (CAFs) in obtaining and transferring lipids from blood-borne particles to PDAC cells via trogocytosis of CAF plasma membranes. We have also determined that CAF-expressed phospholipid scramblase anoctamin 6 (ANO6) is an essential CAF trogocytosis regulator required to promote PDAC cell survival. During trogocytosis, cancer cells and CAFs form synapse-like plasma membranes contacts that induce cytosolic calcium influx in CAFs via Orai channels. This influx activates ANO6 and results in phosphatidylserine exposure on CAF plasma membrane initiating trogocytosis and transfer of membrane lipids, including cholesterol, to PDAC cells. Importantly, ANO6-dependent trogocytosis also supports the immunosuppressive function of pancreatic CAFs towards cytotoxic T cells by promoting transfer of excessive amounts of cholesterol. Further, blockade of ANO6 antagonizes tumor growth via disruption of delivery of exogenous cholesterol to cancer cells and reverses immune suppression suggesting a potential new strategy for PDAC therapy.

cancer biology↗

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↗

bioRxiv2020

DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

MotivationQuantification of tumor heterogeneity is essential to better understand cancer progressionand to adapt therapeutic treatments to patient specificities. ResultsWe present DECONbench, a web-based application to benchmark computational methods dedicated to quantify of cell-type heterogeneity in cancer. DECONbench includes benchmark datasets, computational methods and performance evaluation. It allows submission of new methods. Availability and implementationDECONbench is hosted on the open source codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/23660. Supplementary informationAdditional information is available online and on our website: https://cancer-heterogeneity.github.io/deconbench.html.

bioinformatics↗

bioRxiv2020

Establishment of a pancreatic adenocarcinoma molecular gradient (PAMG) that predicts the clinical outcome of pancreatic cancer

BACKGROUNDA significant gap in pancreatic ductal adenocarcinoma (PDAC) patients care is the lack of molecular parameters characterizing tumors and allowing a personalized treatment. The goal of this study was to examine whole PDAC transcriptomic profiles to define a signature that would predict aggressiveness and treatment responsiveness better than done until now. METHODS AND PATIENTSTumors were obtained from 76 consecutive resectable (n=40) or unresectable (n=36) tumors. PDAC were transplanted in mice to produce patient-drived xenografts (PDX). PDX were classified according to their histology into five groups, from highly undifferentiated to well differentiated. This classification resulted strongly associated with tumors aggressiveness. A PDAC molecular gradient (PAMG) was constructed from PDX transcriptomes recapitulating the five histological groups along a continuous gradient. The prognostic and predictive value for PMAG was evaluated in: i/ two independent series (n=598) of resected tumors; ii/ 60 advanced tumors obtained by diagnostic EUS-guided biopsy needle flushing and iii/ on 28 biopsies from mFOLFIRINOX treated metastatic tumors. RESULTSA unique transcriptomic signature (PAGM) was generated with significant and independent prognostic value. PAMG significantly improves the characterization of PDAC heterogeneity compared to non-overlapping classifications as validated in 4 independent series of tumors (e.g. 308 consecutive resected PDAC, HR=0.321 95% CI [0.207;0.5] and 60 locally-advanced or metastatic PDAC, HR=0.308 95% CI [0.113;0.836]). The PAMG signature is also associated with progression under mFOLFIRINOX treatment (Pearson correlation to tumor response: -0.67, p-value < 0.001). CONCLUSIONWe identified a transcriptomic signature (PAMG) that, unlike all other stratification schemas already proposed, classifies PDAC along a continuous gradient. It can be performed on formalin-fixed paraffin-embedded samples and EUS-guided biopsies showing a strong prognostic value and predicting mFOLFIRINOX responsiveness. We think that PAMG could unify all PDAC preexisting classifications inducing a shift in the actual paradigm of binary classifications towards a better characterization in a gradient. Trial RegistrationThe PaCaOmics study is registered at www.clinicaltrials.gov with registration number NCT01692873. The validation BACAP study is registered at www.clinicaltrials.gov with registration number NCT02818829.

cancer biology↗