Axis 1: Inter- and intracellular network inference
The analysis of large-scale molecular biology data is often highly complex, due to the number of molecules measured and the limited number of samples involved. Contextualizing data using molecular interaction networks is a classic approach to facilitating the extraction of relevant and reliable information. We combine the development of methods for inferring such networks, as well as for embedding datasets in existing or newly determined networks.
At present, our efforts are mainly focused on predictive aspects. We have published and are continuing to build a series of tools designed to reconstruct cellular ligand-receptor networks characterizing the ecosystem of cells making up a tissue (Cabello-Aguilar, Nucleic Acids Res, 2020 ; Villemin, Nucleic Acids Res, 2023). These methods apply to bulk, single-cell and spatial transcriptomics data. They are available as R libraries and involve data integration, graph-based algorithms, and statistical modeling.
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We are also working on methods for reconstructing molecular networks from perturbation data. To this end, we have developed a new formulation of the Modular Response Analysis (MRA) approach by revisiting the analysis of MRA equations and applying multilinear regression techniques (least squares, lasso, STEP, etc.). This has defined a whole new family of methods that are much less sensitive to noise and can model networks of 10-1000 nodes. Here too, R code has been made available (Jimenez-Dominguez, Sci Rep, 2020 ; Borg, Bioinformatics, 2023).
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We also have strong expertise in the application and creation of methods for projecting and integrating data over networks (graphs) such as random walks (Dwivedi, Malaria J, 2017 ; Müllner, Mol Syst Biol, 2015 ; Blomen, Science, 2015 ; Rix, PLoS ONE, 2013 ; Burkard, PLoS Comput Biol, 2010). Developments in this area are on hold, but we'll be coming back to it shortly.
Axis 2: Functional systems biology
The lab is involved in the analysis of numerous data sets in collaboration with our biologist and clinician partners. This work naturally relies on the application of our own methods, but also of any relevant tools developed by the community. The model study generally consists of a small or medium-sized cohort of a rare tumor, or one that answers a particular question (response to a therapy, patient sub-type, access to a new technology, etc.).
Typically, our analyses aim to provide an informative description of the tumor entity, as well as criteria for stratifying patients, understanding a mechanism of resistance or non-response, or suggesting new therapeutic opportunities. Recent examples include salivary gland ductal carcinoma (Alame, Theranostics, 2020), colorectal liver metastases (Giguelay, Theranostics, 2022) and pancreatic adenocarcinoma (Souche, Endoscopy, 2021).
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We have several studies of this type underway (breast, colon, ovarian, pancreatic and various blood cell cancers) with various partners, including the Turtoi, Maraver, Djiane and Gongora labs at IRCM, the translational research unit at ICM (Evelyne Crapez), and numerous clinicians, including William Jacot, Didier Pourquier, Thibault Mazard at the ICM, Valérie Costes-Martineau, Eric Assenat at Montpellier University Hospital, Francine Garnache-Ottou, Florian Renosi at EFS Besançon, Eric Tartour at APHP, Olivier Adotévi at Besançon University Hospital, etc.
Axis 3: Modelling
The lab has extensive experience in the application of numerical analysis and computational statistical methods.
Proteomics is a key application area for us (Colinge, Proteomics, 2003; Breitwieser, J Proteome Res, 2011). In this domain, we are currently working on modeling the dynamics (turnover) of proteins in vivo (Lehmann, Anal Chem, 2019) with the teams of Sylvain Lehmann and Christophe Hirtz (CHU Montpellier, IRMB and INM). We are starting research on modeling variation in protein complexes in collaboration with Serge Roche (IGMM), an old familiar theme (Stukalov, J Proteome Res, 2012; Varjosalo, Nat Methods, 2013; Huber, Nat Methods, 2015; Mellacheruvu, Nat Methods, 2015) that we are revisiting with enthusiasm.
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Circulating DNA analysis is a field of application for machine learning methods that enable cancer detection from a blood sample (Tanos, Adv Sci, 2020). We are collaborating with Alain Thierry's lab(IRCM), which is a leader in the field.
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Finally, in parallel with our projects in spatial transcriptomics, we are applying spatial statistics techniques to tumor nanomechanics, i.e., the variation of tumor tissue stiffness (Crapez, Sci Rep, 2022). Project in collaboration with the ICM translational research unit (Evelyne Crapez), Thibault Mazard (ICM clinician) and Christine Bénistant (Milhiet CBS lab).
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Located in the Parc Euromédecine, the Institut de Recherche en Cancérologie de Montpellier (U1194) is located on the Val d'Aurelle Campus (ICM, Institut régional du Cancer Montpellier/ Val d'Aurelle).
U1194 Research Centre supported by Inserm, the University of Montpellier and the Institut du Cancer de Montpellier (ICM)
Axis 1: Multi-OMICS integrative projects to characterize resistant or aggressive tumor cellular networks
Owing to previous expertise in data integration over molecular networks, we decided to establish collaboration with pathologists to develop projects aimed at characterizing rare, aggressive tumors for which small numbers of patient samples are available. By means of extensive data integration we try to compensate for the smaller number of replicates available (20 to 30 typically) thus ensuring confidence in the observations. In particular, we are interested in potential targets to kill or resensitize such tumors to chemotherapy. We established a pipeline of bioinformatics procedures and a methodological toolbox to support such projects. This work includes novel methods inferring cell-cell interactions taking place within the tumour microenvironment (TME), for instance in the case where single cell transcriptomes are available.
We also apply our multi-omics framework to colorectal metastases mapping spatial heterogeneity or e.g. to lung adenocarcinoma following tumor development and resistance emergence in a preclinical model model.
Multi-omics projects involve multiple collaborations with IRCM teams (Maraver, Turtoi, Cavaillès, Pèlegrin) as well as national and international collaborators including clinicians.
Axis 2: Modular response analysis (MRA)
While Axis 1 comprises the inference of intra cellular networks by integrating various pathway databases as well as a cell-cell interaction database that we develop internally, it is limited to mapping existing network biology knowledge to fresh data. We hence initiated a dual research axis around perturbations experiments. We develop methods to learn the quantitative coupling, i.e. weighted directional networks, linking a set of molecules of interest that can be transcripts or proteins. The procedure involves performing quantitative measures on the latter molecule activity depending on their nature (expression, activation, etc.) and mathematical modelling. It has the advantage of being generally applicable and to limit the number of required experiments. We implement and develop new methods around MRA (Santra et al., 2018), which we apply to transcriptional networks (collaboration with the Cavaillès Team) and pancreatic cancer kinase receptor signalling and dynamics upon antibody therapy (collaboration with the Pèlegrin Team).
Axis 3: Computational proteomics and mass spectrometry
The team has a strong expertise in many topics of computational proteomics (identification, quantitation, PTMs, AP-MS data filtering and modelling) and continues collaboration with several teams locally and internationally. Our main project at the time is the assembly of computational and mathematical models for a ground-breaking proteomics technology (proteome wide SILK, pwSILK for short) that allows determining protein turnover in human in vivo and proteome wide (collaboration with the Lehmann and Hirtz Teams and at IRMB, Montpellier). We established automated and fully parallelized pipelines for processing pwSILK complex data, which include a novel differential equation-based model coupled with statistical procedures. We showed that our new mathematical model is able to extract degradation rates from data generated by us and others, following different protocols, reproducibly and consistently. Recently, we could determine the degradation rates of ~200 proteins in ventricular CSF in vivo, an unprecedented result in the field since such data were available for only 6 proteins so far. More projects are coming combining multiple biofluids with the ambition to explore a new class of biomarkers related to protein clearance abnormalities or biological barriers defects. We also have plans to apply pwSILK to cancer metabolism.
Institut de Recherche en Cancérologie de