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Cabello-Aguilar, S.

Publications and source records attributed to Cabello-Aguilar, S..

3 recordsLinked to original sources

Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR

The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different bulk datasets, including new Visium liver metastasis ST data, with experimental validation of selected interactions. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.

bioinformatics↗

SingleCellSignalR: Inference of intercellular networks from single-cell transcriptomics

Single-cell transcriptomics offers unprecedented opportunities to infer the ligand-receptor interactions underlying cellular networks. We introduce a new, curated ligand-receptor database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted ligand-receptor interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison with related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.

bioinformatics↗

The molecular landscape and microenvironment of salivary duct carcinoma reveal new therapeutic opportunities

Salivary duct carcinoma (SDC) is a rare and aggressive salivary gland cancer subtype with poor prognosis. The mutational landscape of SDC has been described rather exhaustively; yet, with respect to functional genomics and tumor microenvironment, little is known. In this study, transcriptomics and proteomics were combined to obtain the first characterization of the pathways deregulated in SDC. The data revealed the importance of Notch, TGB-{beta}, and interferon-{gamma} signaling. After associating computational biology, immunohistochemistry, multiplexed immunofluorescence, and digital imaging the first steps towards charting the cellular network within the microenvironment was initiated. According to immune infiltrate, two well-defined groups of tumors were observed, novel SDC immune checkpoints were discovered, and the key role played by macrophages and potentially NK cells in immunosuppression was shown. Furthermore, a clear trend between recurrence-free survival and M2 macrophage abundance was apparent. Independently, a measure of desmoplastic stromal reaction as determined by -SMA abundance, was also shown. Altogether, these many findings open new perspectives for understanding and treating SDC. Before applying an immunotherapy, classical patient stratification according to immune infiltrate should be taken into account. Moreover, the microenvironment offers new potential targets including macrophages or NK cells, or even fibroblasts or hyaluronic acid. Related therapies that have been developed against, e.g., pancreatic tumors could inspire equivalent therapy for SDC.\n\nAdditional informationO_LIFinancial support: MA (1 grant, GIRCI SOOM API-K 2016-811-DRC-AC), JC (2 grants, Fondation ARC PJA 20141201975, Labex EpiGenMed ANR 10-LABX-0012), AT (2 grants, Gunma University GIAR Research Program for Omics-Based Medical Science, Labex MabImprove ANR 10-LABX-0053 starting grant), ET (1 grant, SIRIC Montpellier Cancer Grant INCa_Inserm_DGOS_12553).\nC_LIO_LINo conflict of interest\nC_LIO_LI5408 words, 1 table, and 4 figures\nC_LI\n\nStatement of translational relevanceBased on the presence or absence of an immune infiltrate, two groups of SDC were identified. These have the potential to provide a rationale for therapy and clinical trial enrolment. Two novel immune checkpoints that could be targeted were also identified; namely, CTLA-4/CD86 and TIM-3/galectin-9. Both showed the important contribution that macrophages and NK cells have in immunosuppression. Treatments that induce reprogramming or elimination of these cells could be considered. Moreover, the importance of the desmoplastic stroma was stressed. The stroma acts as a physical barrier against therapy suggesting that strategies developed against pancreatic tumors could inspire SDC treatments. For SDC devoid of immune infiltrate, components of the stroma including fibroblasts or hyaluronic acid could be targeted, e.g., in combination with drugs against immune checkpoints or mutated genes. Finally, evidence that Notch and TGF-{beta} signaling are prevalent in SDC was obtained. This translates into additional therapeutic options.

cancer biology↗