Biology subjects

Belleau, P.

Publications and source records attributed to Belleau, P..

5 recordsLinked to original sources

bioRxiv2026

A versatile set of computational tools for inference of genetic ancestry from human molecular data

Molecular properties of human diseases and conditions often exhibit dependence on the genetic ancestry of the donor. In order to make more human molecular data available for ancestry-informed research, we developed a set of software tools termed Robust Ancestry Inference using Data Synthesis (RAIDS) for inference of genetic ancestry from sequence data that pose challenges to genotyping. Here we describe a recent expansion and refinement of RAIDS, applicable to nucleic-acid sequence data resultant from a broad range of experimental strategies. These strategies currently include whole-genome, whole-exome, and targeted gene panel sequencing, RNA-Seq, including single-cell RNA-Seq, chromatin-accessibility (ATAC-Seq), and protein-binding (ChIP-Seq) assays. RAIDS inferential tools perform robustly in the presence of somatic nucleic-acid alterations caused by cancer and under the conditions of extreme non-uniformity of sequence read coverage, yielding both global ancestry and ancestral admixtures of the donor. RAIDS is available as an eponymous Bioconductor package.

bioinformatics↗

bioRxiv2026

Comprehensive Molecular Characterization of High-Grade Endometrial Cancer in an Ancestrally-Diverse Cohort

Endometrial cancer (EC) exhibits one of the most striking racial disparities in oncology with black women disproportionately affected by aggressive high-grade subtypes that have poorer outcomes. While social and environmental factors undoubtedly contribute, the molecular underpinnings of these disparities remain critically understudied. To bridge this knowledge gap, we performed matched tumor-normal whole-genome sequencing and tumor transcriptome sequencing on 71 predominantly high-grade EC patient samples from an ancestrally diverse cohort of women recruited at a large hospital system in the New York metropolitan area. Our analysis characterized the germline and somatic mutation landscape, identifying ancestry-associated molecular differences. Notably, focal amplification of the EVI1 transcription factor (encoded at the MECOM locus) was significantly more frequent in African ancestry patients and associated with poorer clinical outcomes in an external validation cohort. Additionally transcriptome analysis revealed decreased CD8+ T cell infiltration with increasing African ancestry, suggesting tumor immune microenvironment differences with potential therapeutic implications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/721962v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@12125b9org.highwire.dtl.DTLVardef@133c787org.highwire.dtl.DTLVardef@707af0org.highwire.dtl.DTLVardef@97615c_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThis study represents the most ancestrally diverse whole-genome sequencing characterization of high-grade endometrial cancer, with 62% of patients of African ancestry. C_LIO_LIMECOM focal amplification preferentially targets the oncogenic short isoform (EVI1) and is more frequent in patients of African ancestry. C_LIO_LIAfrican ancestry is associated with reduced CD8+ T cell infiltration and differential activation of immune and metabolic pathways in copy-number high endometrial tumors. C_LI

cancer biology↗

bioRxiv2026

A Patient-derived Organoid Platform for Uterine Carcinosarcoma that Emulates Disease Characteristics

Uterine carcinosarcoma (UCS) is a rare but extremely lethal endometrial cancer that metastasizes early and resists current treatment modalities. It is biphasic, built from malignant epithelial and mesenchymal cells. Genomic studies indicate that these tumors are clonal, and that the mesenchymal cells arise from the epithelial cells through cancer cell plasticity. This biology has been hard to study, because faithful patient-derived models are scarce. The gap is widened by inequity. Women of African ancestry carry the greatest burden of UCS, yet are underrepresented in existing models. To address this, we established patient-derived organoids (PDOs) from an ancestrally inclusive UCS cohort, alongside matched normal endometrial PDOs. The organoids reproduced the biphasic histology of the original tumors. Across four sequencing platforms, they retained the tumor mutation and copy-number landscape, remained stable across passages, and expanded for up to 28 months. At single-cell resolution, UCS PDOs captured both malignant compartments and traced continuous transcriptional trajectories along the epithelial-to-mesenchymal axis, capturing patient-specific cancer cell plasticity. The models also nominated candidate vulnerabilities in proof-of-concept therapeutic testing. UCS PDOs were enriched for CREB-family transcriptional programs, and CREB inhibition reduced their viability. Combined FGFR and YAP inhibition outperformed either agent alone. Together, this work delivers a histologically, genomically, and transcriptionally faithful, ancestrally inclusive, and lineage-resolved UCS organoid platform for studying cancer cell plasticity and its vulnerabilities in an aggressive and inequitably burdened cancer.

cancer biology↗

bioRxiv2022

Accurate and robust inference of genetic ancestry from cancer-derived molecular data across genomic platforms

Genetic ancestry-oriented cancer research requires the ability to perform accurate and robust genetic ancestry inference from existing cancer-derived data, including whole exomes, transcriptomes and targeted gene panels, very often in the absence of matching cancer-free genomic data. Here we examine the feasibility and accuracy of such computation. In order to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile, we have developed a data synthesis framework. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. Data synthesis is applicable to multiple profiling platforms and makes it possible to assess the performance of inference specifically for a given molecular profile, and separately for each continental-level ancestry. This ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. We further show that our inference procedure is accurate and robust in a wide range of sequencing depths. Testing our approach for three representative cancer types, and across three molecular profiling modalities, we demonstrate that global, continental-level ancestry of the patient can be inferred with high accuracy, as quantified by its agreement with the golden standard of the ancestry derived from matching cancer-free molecular data. Our study demonstrates that vast amounts of existing cancer-derived molecular data potentially are amenable to ancestry-oriented studies of the disease, without recourse to matching cancer-free genomes or patients self-identification by ancestry.

bioinformatics↗

bioRxiv2018

A type 2 diabetes disease module with a high collective influence for Cdk2 and PTPLAD1 is localized in endosomes

Despite the identification of many susceptibility genes our knowledge of the underlying mechanisms responsible for complex disease remains limited. Here, we identified a type 2 diabetes disease module in endosomes, and validate it for functional relevance on selected nodes. Using hepatic Golgi/endosomes fractions, we established a proteome of insulin receptor-containing endosomes that allowed the study of physical protein interaction networks on a type 2 diabetes background. The resulting collated network is formed by 313 nodes and 1147 edges with a topology organized around a few major hubs with Cdk2 displaying the highest collective influence. Overall, 88% of the nodes are associated with the type 2 diabetes genetic risk, including 101 new candidates. The Type 2 diabetes module is enriched with cytoskeleton and luminal acidification -dependent processes that are shared with secretion-related mechanisms. We identified new signaling pathways driven by Cdk2 and PTPLAD1 whose expression regulate the association of the insulin receptor with TUBA, TUBB, the actin component ACTB and the endosomal sorting markers Rab5c and Rab11a. Therefore, the interactome of internalized insulin receptors reveals the presence of a type 2 diabetes disease module enriched in new layers of feedback loops required for insulin signaling, clearance and islet biology.\n\nAuthor SummaryAccording to the local hypothesis each complex disease can be linked to a well-defined network called the disease module. A disease module can be defined by the topological properties of protein interaction networks. Given the complexity of the whole interaction map the existence of such disease modules remains largely to be tested. Here, we found a type 2 diabetes disease module in insulin receptor-containing endosomes. The disease module contains new pathways that are associated with both insulin signaling, clearance and secretion. Its co-functionality with islets biology may provide a mechanistic rationale for the exploration of personalized medicine and elaborate new drugs.

genomics↗