Biology subjects

Roger, P.

Publications and source records attributed to Roger, P..

6 recordsLinked to original sources

bioRxiv2021

SPARC in cancer-associated fibroblasts is an independent poor prognostic factor in non-metastatic triple-negative breast cancer and exhibits pro-tumor activity

PurposeTriple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype and lacks specific targeted therapeutics. The current mechanistic evidence from cell-based studies suggests that the matricellular protein SPARC has a tumor-promoting role in TNBC; however, data on the clinical relevance of SPARC expression/secretion by tumor and stromal cells in TNBC are limited. Experimental DesignThis study analyzed the prognostic value of tumor and stromal cell SPARC expression in a large series of 148 patients with non-metastatic TNBC and long follow-up (median: 5.4 years). Fibrosis, tumor-associated macrophage (TAM) infiltration, tumor-infiltrating lymphocyte (TIL) density, PD-L1 and PD-1 expression were assessed. Tumor and stromal cell SPARC expression was studied by immunofluorescence, western blotting, and meta-analysis of published single-cell mRNA sequencing data. The biological role of fibroblast-secreted SPARC was analyzed using cell adhesion, wound healing, Transwell-based motility and invasion, and tumor spheroid assays. ResultsSPARC expression was detected in cancer cells (42.4%), cancer-associated fibroblasts (CAFs) (88.1%), TAMs (77.1%), endothelial cells (75.2%), and TILs (9.8%). Recurrence-free survival was significantly lower in patients with SPARC-expressing CAFs. SPARC expression in CAFs was an independent prognostic factor in multivariate analysis. Tumor and stromal cell SPARC expression was observed in TNBC cytosols, patient-derived xenografts, and cell lines. SPARC was expressed by different CAF subsets, including myofibroblasts and inflammatory CAFs. Fibroblast-secreted SPARC inhibited TNBC cell adhesion and stimulated their migration and invasion. ConclusionsSPARC expression in CAFs is an independent predictor of recurrence-free survival in TNBC. Patients with SPARC-expressing CAFs could be eligible for anti-SPARC-targeted therapy. Statement of translational relevanceHere, we identified a subgroup of patients with triple-negative breast cancer (TNBC) with worse prognosis and eligible for therapies that target extracellular matrix proteins in the tumor stroma. Specifically, we found that expression of the matricellular protein SPARC in cancer-associated fibroblasts (CAFs) is an independent prognostic marker of poor outcome in TNBC. Furthermore, we showed that in TNBC, SPARC is expressed by different CAF subpopulations, including myofibroblasts and inflammatory fibroblasts that are involved/associated with many tumor-related processes. We then found that SPARC secreted by fibroblasts has a pro-tumor-promoting role by inhibiting TNBC cell adhesion and stimulating their motility and invasiveness. Overall, our results support the need to consider SPARC expressed/secreted by CAFs as a novel therapeutic target in TNBC in the context of treatments to modulate the tumor stroma.

cancer biology↗

bioRxiv2020

ISA API: An open platform for interoperable life science experimental metadata

BackgroundThe Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open-source community specifications and software tools for enabling discovery, exchange and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab - a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, a JSON serialization ISA-JSON was developed. ResultsIn this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python object classes. We describe the ISA API feature set, early adopters and its growing user community. ConclusionsThe ISA API provides users with rich programmatic metadata handling functionality to support automation, a common interface and an interoperable medium between the two ISA formats, as well as with other life science data formats required for depositing data in public databases.

bioinformatics↗

bioRxiv2020

Cathepsin D exacerbates SPARC-driven aggressiveness by limited proteolysis in triple-negative breast cancer

Extracellular matrix (ECM) remodeling by proteases results in the release of protein fragments that promote tumor progression and metastasis. The protease cathepsin D (cath-D), a marker of poor prognosis in triple-negative breast cancer (TNBC), is aberrantly secreted in the tumor microenvironment. Using degradomic analyses by TAILS, we discovered that the matricellular protein SPARC is a substrate of extracellular cath-D. In vitro, cath-D induced limited proteolysis of SPARC C-terminal extracellular Ca2+ binding domain at acidic pH, leading to the production of SPARC fragments (34-, 27-, 16-, 9-, and 6-kDa). Similarly, cath-D secreted by TNBC cells cleaved fibroblast- and cancer cell-derived SPARC at the tumor pericellular acidic pH. SPARC cleavage also occurred in TNBC tumors. Among these fragments, only the 9-kDa SPARC fragment inhibited TNBC cell adhesion and spreading on fibronectin, and stimulated their migration, endothelial transmigration, and invasion. Our study establishes a novel crosstalk between proteases and matricellular proteins in the tumor microenvironment through limited proteolysis of SPARC, revealing a novel targetable 9-kDa bioactive SPARC fragment for new TNBC treatments.

cancer biology↗

bioRxiv2018

Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud

Making reproducible, auditable and scalable data-processing analysis workflows is an important challenge in the field of bioinformatics. Recently, software containers and cloud computing introduced a novel solution to address these challenges. They simplify software installation, management and reproducibility by packaging tools and their dependencies. In this work we implemented a cloud provider agnostic and scalable container orchestration setup for the popular Galaxy workflow environment. This solution enables Galaxy to run on and offload jobs to most cloud providers (e.g. Amazon Web Services, Google Cloud or OpenStack, among others) through the Kubernetes container orchestrator. AvailabilityAll code has been contributed to the Galaxy Project and is available (since Galaxy 17.05) at https://github.com/galaxyproject/ in the galaxy and galaxy-kubernetes repositories. https://public.phenomenal-h2020.eu/ is an example deployment. Suppl. InformationSupplementary Files are available online. Contactpmoreno@ebi.ac.uk, European Molecular Biology Laboratory, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK, Tel: +44-1223-494267, Fax: +44-1223-484696.

bioinformatics↗

bioRxiv2018

PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

BackgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organisms metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent - and sometimes incompatible - analysis methods that are difficult to connect into a useful and complete data analysis solution.\n\nFindingsThe PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the projects continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm.\n\nConclusionsPhenoMeNal constitutes a keystone solution in cloud infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and omics research domains.

bioinformatics↗

bioRxiv2017

Interoperable and scalable metabolomics data analysis with microservices

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed in parallel using the Kubernetes container orchestrator. The access point is a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.

bioinformatics↗