Biology subjects

Deschenes, A.

Publications and source records attributed to Deschenes, A..

11 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↗

bioRxiv2025

MYC-Driven Activation of USP39 Enhances SRSF1 Stability and Promotes PDAC Progression

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy primarily driven by oncogenic KRAS signaling. The splicing factor SRSF1 plays a key oncogenic role in PDAC, where its tightly regulated expression constrains KRAS-driven signaling under normal conditions, while its upregulation promotes tumorigenesis. SRSF1 expression is regulated in part by proteostasis. However, the precise mechanisms remain unclear. Here, we identify USP39 as a deubiquitinase that interacts with SRSF1 in an RNA-independent manner and stabilizes it by reducing ubiquitination. USP39 expression is elevated in PDAC tumors and precancerous lesions, and correlates with poor patient survival. USP39 knockdown suppresses PDAC cell proliferation and migration, effects that are partially rescued by SRSF1 overexpression. Mechanistically, we show that MYC directly activates USP39 transcription via E-box motifs within its exon 1b promoter, linking MYC-driven transcriptional regulation to post- translational stabilization of SRSF1. Together, these findings define a MYC-USP39-SRSF1 regulatory axis that integrates transcriptional and post-translational mechanisms in PDAC and highlight USP39 as a potential therapeutic target.

cancer biology↗

bioRxiv2025

Enhancing STED Microscopy via Fluorescence Lifetime Unmixing and Filtering in Two-Species SPLIT-STED

Simultaneous super-resolution imaging of multiple fluorophores remains a major challenge in STimulated Emission Depletion (STED) microscopy due to spectral overlap of STED-compatible fluorophores. The combination of STED microscopy and Fluorescence Lifetime Imaging Microscopy (FLIM) offers a powerful alternative for super-resolved, multiplexed imaging of biological samples but is hindered by lifetime convergence at high depletion powers. Here, we present an analysis method, two-species Separation of Photons by LIfetime Tuning (SPLIT)-STED, that uses a linear system of equations in phasor-based STED-FLIM to enhance both fluorophore unmixing and spatial resolution. It defines the fluorescence signal as a mixture of three lifetime components: the two target fluorophores and a short-lifetime contribution from undepleted peripheral fluorescence photons. Two-species SPLIT-STED disentangles overlapping lifetimes and selectively filters low-resolution signal. The method enables accurate unmixing of spectrally overlapping fluorophores and, by enhancing resolution through lifetime-based filtering, allows the use of lower depletion powers, thereby improving fluorescence lifetime separation.

bioinformatics↗

bioRxiv2025

A Self-Supervised Foundation Model for Robust and Generalizable Representation Learning in STED Microscopy

Foundation Models (FMs) have dramatically increased the potential and power of deep learning algorithms through general capacities over a variety of tasks. The performance increase they offer is obtained without elaborated specific trainings for domains such as natural language processing and computer vision. However, their application in specialized fields like biomedical imaging and fluorescence microscopy remains difficult due to distribution shifts and the scarcity of high-quality annotated datasets. The high cost of data acquisition and the requirement for in-domain expertise further exacerbate this challenge in microscopy. To address this we introduce STED-FM, a foundation model specifically designed for super-resolution STimulated Emission Depletion (STED) microscopy. STED-FM leverages a Vision Transformer architecture trained at scale with Masked Autoencoding on a new dataset of nearly one million STED images. STED-FM learns expressive latent representations without requiring extensive annotations, yielding robust performance across diverse downstream microscopy image analysis tasks. Unsupervised experiments demonstrate the discriminative structure of its learned latent space. These representations can be leveraged for multiple downstream applications, including fully supervised classification and segmentation with reduced annotation requirements. Moreover, STED-FM representations enhance the performance of deep learning-based image denoising and improve the quality of images generated by diffusion models, enabling latent attribute manipulation for the data-driven discovery of subtle nanostructures and phenotypes, as well as algorithmic super-resolution. Moreover, its powerful structure retrieval capabilities are integrated into automated STED microscopy acquisition pipelines, paving the way for smart microscopy. In sum, we demonstrate that STED-FM lays a robust foundation for state-of-the-art algorithms across a wide array of tasks, establishing it as a highly valuable and scalable resource for researchers in super-resolution microscopy.

bioinformatics↗

bioRxiv2024

Ductal pancreatic cancer interception by FGFR2 abrogation

Activating KRAS mutations are a key feature of pancreatic ductal adenocarcinoma (PDA) and drive tumor initiation and progression. However, mutant KRAS by itself is weakly oncogenic. The pathways that cooperate with mutant KRAS to induce tumorigenesis are less-defined. Analyzing organoids and murine and human pancreatic specimens, we found that the receptor tyrosine kinase FGFR2 was progressively up-regulated in mutant KRAS-driven metaplasia, pre-neoplasia and Classical PDA. Using genetic mouse models, we showed that FGFR2 supported mutant KRAS-driven transformation of acinar cells by promoting proliferation and MAPK pathway activation. FGFR2 abrogation significantly delayed tumor formation and extended the survival of these mice. Furthermore, we discovered that FGFR2 collaborated with EGFR and dual blockade of these receptor signaling pathways significantly reduced mutant KRAS-induced pre-neoplastic lesion formation. Together, our data have uncovered a pivotal role for FGFR2 in the early phases of pancreatic tumorigenesis, paving the way for future therapeutic applications of FGFR2 inhibitors for pancreatic cancer interception. STATEMENT OF SIGNIFICANCEMutant KRAS-expressing pancreatic intraepithelial neoplasias (PanINs), the precursor lesions of PDA, are prevalent in the average healthy adult but rarely advance to invasive carcinoma. Here, we discovered that FGFR2 promoted PDA progression by amplifying mutant KRAS signaling and that inactivation of FGFR2 intercepted disease progression.

cancer biology↗

bioRxiv2022

SPDEF promotes the classical subtype of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDA) samples reveal extensive cellular heterogeneity. Using single-cell RNA sequencing, we uncover multiple tumor cell populations distinguished by their differentiation state and associated with different stages of tumor progression in a mouse model of PDA. We identify Spdef as a factor required for tumorigenesis in pancreatic cancer cells of epithelial and mucinous nature. By comparative analysis of cell differentiation states in mice and humans, we find that the Spdef program is highly expressed by human PDAs of the classical subtype. Mouse and human PDA cells expressing elevated levels of Spdef are dependent upon this transcription factor for tumor progression in vivo. The tumor-promoting function of Spdef is recapitulated by two Spdef target genes that regulate protein folding and endoplasmic reticulum activity, Agr2 and Ern2/Ire1{beta}. These findings offer insights into the factors controlling differentiation states in PDA and identify new vulnerabilities in the most common subtype of pancreatic 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↗

bioRxiv2022

Loss of p53 tumor suppression function drives invasion and genomic instability in models of murine pancreatic cancer

Pancreatic ductal adenocarcinoma (PDA) is a deadly disease with few treatment options. There is an urgent need to better understand the molecular mechanisms that drive disease progression, with the ultimate aim of identifying early detection markers and clinically actionable targets. To investigate the transcriptional and morphological changes associated with pancreatic cancer progression, we analyzed the KrasLSLG12D/+; Trp53LSLR172H/+; Pdx1-Cre (KPC) mouse model. We have identified an intermediate cellular event during pancreatic carcinogenesis in the KPC mouse model of PDA that is represented by a subpopulation of tumor cells that express KrasG12D, p53R172H and one allele of wild-type Trp53. In vivo, these cells represent a histological spectrum of pancreatic intraepithelial neoplasia (PanIN) and acinar-to-ductal metaplasia (ADM) and rarely proliferate. Following loss of wild-type p53, these precursor lesions undergo malignant de-differentiation and acquire invasive features. We have established matched organoid cultures of pre-invasive and invasive cells from murine PDA. Expression profiling of the organoids led to the identification of markers of the pre-invasive cancer cells in vivo and mechanisms of disease aggressiveness.

cancer biology↗

bioRxiv2021

Task-Assisted GAN for Resolution Enhancement and Modality Translation in Fluorescence Microscopy

AbstractWe introduce a deep learning model that predicts super-resolved versions of diffraction-limited microscopy images. Our model, named Task- Assisted Generative Adversarial Network (TA-GAN), incorporates an auxiliary task (e.g. segmentation, localization) closely related to the observed biological nanostructures characterization. We evaluate how TA-GAN improves generative accuracy over unassisted methods using images acquired with different modalities such as confocal, brightfield (diffraction-limited), super-resolved stimulated emission depletion, and structured illumination microscopy. The generated synthetic resolution enhanced images show an accurate distribution of the F-actin nanostructures, replicate the nanoscale synaptic cluster morphology, allow to identify dividing S. aureus bacterial cell boundaries, and localize nanodomains in simulated images of dendritic spines. We expand the applicability of the TA-GAN to different modalities, auxiliary tasks, and online imaging assistance. Incorporated directly into the acquisition pipeline of the microscope, the TA-GAN informs the user on the nanometric content of the field of view without requiring the acquisition of a super-resolved image. This information is used to optimize the acquisition sequence, and reduce light exposure. The TA-GAN also enables the creation of domain-adapted labeled datasets requiring minimal manual annotation, and assists microscopy users by taking online decisions regarding the choice of imaging modality and regions of interest.

bioinformatics↗