Biology subjects

Badoual, C.

Publications and source records attributed to Badoual, C..

5 recordsLinked to original sources

bioRxiv2026

Integrated inference of cancer gene expression from cell-free plasma chromatin

Gene expression is a defining determinant of tumor identity, behavior, and therapeutic response, yet remains challenging to measure noninvasively. Here, we introduce APEX (Associating Plasma Epigenomic features with eXpression), a framework for inferring expression from circulating cell-free chromatin. Trained on [~]270,000 gene-sample pairs from matched tumor RNA-seq and plasma cfChIP-seq across multiple cancers and validated on >15 unseen cancer subtypes, APEX accurately infers cancer gene expression across a range of tumor fractions and outperforms existing plasma-based approaches by integrating positional histone mark and DNA fragmentation patterns across promoters and gene bodies. Using plasma alone, APEX enables classification of prognostically relevant basal and classical pancreatic cancer subtypes and identifies plasma-inferred NECTIN4 expression as a biomarker of response to enfortumab vedotin in metastatic bladder cancer. Together, these findings establish APEX as a biopsy-free approach for profiling tumor transcriptional states and extend liquid biopsy beyond genomic alterations to clinically relevant gene expression programs.

genomics↗

bioRxiv2024

A new spatial multi-omics approach to deeply characterize human cancer tissue using a single tissue section

In the ever-changing world of digital pathology, being able to extract a maximum amount of information from a patient tissue sample is of paramount importance for better diagnosis, disease characterization, and therapeutic strategies. Recent technologies such as multiplex immunofluorescence imaging and spatial transcriptomic now enable a deep analysis of protein and gene expression while retaining the spatial context of the tissue. Here, we describe an innovative approach combining a 34-protein Phenocycler panel and transcriptome analysis using Visium on a single head and neck squamous cell carcinoma section. While protein analysis reveals the complexity of the immune phenotypes involved in the disease, transcriptome analysis reveals the intricate cellular states of cancer cells that coexist within the patients tumor. Finally, integrating both omics modalities, we uncover unique comparison of gene and protein expression of spatially resolved cellular subspaces.

cancer biology↗

bioRxiv2022

Diagnosis with Confidence: Deep Learning for Reliable Classification of Squamous Lesions of the Upper Aerodigestive Tract

BackgroundDiagnosis of head and neck (HN) squamous dysplasias and carcinomas is critical for patient care cure and follow-up. It can be challenging, especially for grading intraepithelial lesions. Despite recent simplification in the last WHO grading system, the inter- and intra-observer variability remains substantial, particularly for non-specialized pathologists, exhibiting the need for new tools to support pathologists. MethodsIn this study we investigated the potential of deep learning to assist the pathologist with automatic and reliable classification of HN lesions following the 2022 WHO classification system. We created, for the first time, a large-scale database of histological samples (>2000 slides) intended for developing an automatic diagnostic tool. We developed and trained a weakly supervised model performing classification from whole slide images (WSI). We evaluated our model on both internal and external test sets and we defined and validated a new confidence score to assess the predictions which can be used to identify difficult cases. ResultsOur model demonstrated high classification accuracy across all lesion types on both internal and external test sets (respectively average AUC: 0.878 (95% CI:[0.834-0.918]) and 0.886 (95% CI: [0.813-0.947])) and the confidence score allowed for accurate differentiation between reliable and uncertain predictions. ConclusionsOur results demonstrate that the model, associated with confidence measurements, can help in the difficult task of classifying head and neck squamous lesions by limiting variability and detecting ambiguous cases, taking us one step closer to a wider adoption of AI-based assistive tools.

bioinformatics↗

bioRxiv2022

Automatic grading of cervical biopsies by combining full and self-supervision

In computational pathology, predictive models from Whole Slide Images (WSI) mostly rely on Multiple Instance Learning (MIL), where the WSI are represented as a bag of tiles, each of which is encoded by a Neural Network (NN). Slide-level predictions are then achieved by building models on the agglomeration of these tile encodings. The tile encoding strategy thus plays a key role for such models. Current approaches include the use of encodings trained on unrelated data sources, full supervision or self-supervision. While self-supervised learning (SSL) exploits unlabeled data, it often requires large computational resources to train. On the other end of the spectrum, fully-supervised methods make use of valuable prior knowledge about the data but involve a costly amount of expert time. This paper proposes a framework to reconcile SSL and full supervision, showing that a combination of both provides efficient encodings, both in terms of performance and in terms of biological interpretability. On a recently organized challenge on grading Cervical Biopsies, we show that our mixed supervision scheme reaches high performance (weighted accuracy (WA): 0.945), outperforming both SSL (WA: 0.927) and transfer learning from ImageNet (WA: 0.877). We further shed light upon the internal representations that trigger classification results, providing a method to reveal relevant phenotypic patterns for grading cervical biopsies. We expect that the combination of full and self-supervision is an interesting strategy for many tasks in computational pathology and will be widely adopted by the field.

cancer biology↗

bioRxiv2018

HPV detection and genotyping of FFPE head and neck cancer biopsies by molecular testing to address new oropharyngeal squamous cell carcinoma classification based on HPV status

Recently, both the WHO/IARC (World Health Organisation/International Agency for Research on Cancer) and the American Joint Committee on Cancer (AJCC) have classified the oropharyngeal squamous cell carcinoma (OPSCC) on the basis of HPV status. For this purpose, the WHO/IARC recommended direct molecular HPV testing. In practice, formalin-fixed, paraffin-embedded (FFPE) biopsy specimens are frequently the only available samples. We herein compared in parallel two commercially available molecular assays that were firstly designed for cervical HPV detection and genotyping: Inno-Lipa(R) HPV genotyping extra II assay (Fujirebio, Gent, Belgium) (IL) and AnyplexTM II HPV 28 (Seegene, Seoul, South Korea) (AP28).\n\nBoth assays were carried out on the same DNA extracts obtained from prospectively collected FFPE biopsies from OPSCC origin and results were compared.\n\nA total of 55 samples were tested. By IL assay, chosen as reference assay, 27 (49.1%) biopsies were positive for HPV16, 10 (18.2%) were positive for HPV but negative for HPV16, and 18 (32.7%) were negative for HPV. A valid result with AP28 was obtained for 51 biopsy samples (92.7%). Among 37 HPV-positive samples by IL, 33 (89.2%) were positive by AP28. The agreement between both assays was good (Cohens {kappa} = 0.78). Among the six discrepancies between assays, always associated with low HPV16 viral load, four biopsies positive for HPV16 by IL could not be detected by AP28.\n\nTaken together, these observations demonstrate that both assays could be used in routine for HPV detection and genotyping on FFPE-biopsy samples of head and neck tumour.

molecular biology↗