Biology subjects

Coosemans, A.

Publications and source records attributed to Coosemans, A..

4 recordsLinked to original sources

bioRxiv2026

Macrophage-instructed GSDME couples glioblastoma cell-state plasticity with inflammatory cell death

Glioblastoma (GBM) cell states reflect spatial microenvironmental interactions. Here, using COMET spatial proteomics and RNAscope across multiregional human GBM tissue, spanning tumor cores with pseudopalisading regions, infiltrative margins and peripheral regions, together with multiplex spatial profiling of 202 specimens from 33 patients with matched primary and recurrent tumors, we identify full-length gasdermin E (GSDME-FL) as a macrophage-instructed regulator of malignant cell plasticity. Integration with single-cell transcriptomics and functional perturbation shows that GSDME-high tumor cells localize to macrophage-rich perivascular niches and are associated with delayed recurrence and longer patient survival. Mechanistically, macrophage-derived S100A4 activates EGFR-Sp1 signaling to induce GSDME-FL in neighboring GBM cells. GSDME-FL restrains hypoxia-associated mesenchymal plasticity and shapes macrophage-induced tumor state transitions independently of caspase activation. During immunogenic cell death (ICD), cleaved GSDME promotes pre-lytic swelling, early ATP efflux, and the release of canonical ICD-associated cytokines and chemokines. NLRP3 signaling further supports ATP release and licenses macrophage phagocytosis of dying GBM cells, whereas NINJ1-dependent membrane rupture enables terminal HMGB1 release. Vaccination with ICD-treated glioma cells elicits tumor rejection in a prophylactic intracranial challenge model. Notably, co-expression of GSDME and NINJ1 in macrophage-rich perivascular niches provides a spatial correlate of pathway convergence in patient tumors. Thus, our findings define how a spatial macrophage niche induces GSDME-FL, thereby coupling malignant cell state plasticity to the inflammatory properties of GBM cell death.

cancer biology↗

bioRxiv2024

Alleviating cell-free DNA sequencing biases with optimal transport

Cell-free DNA (cfDNA) is a rich source of biomarkers for various (patho)physiological conditions. Recent developments have used Machine Learning on large cfDNA data sets to enhance the detection of cancers and immunological diseases. Preanalytical variables, such as the library preparation protocol or sequencing platform, are major confounders that influence such data sets and lead to domain shifts (i.e., shifts in data distribution as those confounders vary across time or space). Here, we present a domain adaptation method that builds on the concept of optimal transport, and explicitly corrects for the effect of such preanalytical variables. Our approach can be used to merge cohorts representative of the same population but separated by technical biases. Moreover, we also demonstrate that it improves cancer detection via Machine Learning by alleviating the sources of variation that are not of biological origin. Our method also improves over the widely used GC-content bias correction, both in terms of bias removal and cancer signal isolation. These results open perspectives for the downstream analysis of larger data sets through the integration of cohorts produced by different sequencing pipelines or collected in different centers. Notably, the approach is rather general with the potential for application to many other genomic data analysis problems.

bioinformatics↗

bioRxiv2022

LARP1 regulates metabolism and mTORC1 activity in cancer

The protein mammalian target of rapamycin (mTOR) is a master regulator of cell homeostasis. Although mTOR is aberrantly overactivated in 70% ovarian cancers, mTOR cascade inhibitors (such as those blocking the kinase activity of mTOR itself or upstream kinases PI3K/AKT) have demonstrated disappointing activity in ovarian cancer clinical trials. These findings indicate that, despite its pivotal role in normal cells, hyperactivated mTOR does not act as a master regulator of metabolism in this cancer context. Surprisingly, we have identified that the RNA binding protein LARP1, a known phospho-target of mTORC1 and activator of ribosomal biogenesis, is responsible for metabolic reprogramming in mTOR-dysregulated cancers. LARP1 post-transcriptionally regulates the expression of several hundred rate-limiting enzymes involved in multiple aspects of metabolism, including glycolysis and oxidative phosphorylation. Through this mechanism LARP1 sustains ATP production and mTORC1 localisation on the lysosome, thereby activating cell proliferation despite the scarcity of extracellular nutrients. Our findings show that, by sustaining global cellular metabolism in response to growth factor signalling, LARP1 has a central post-transcriptional role in controlling mTORC1 localisation and driving cancer progression, a key cancer hallmark.

cancer biology↗

bioRxiv2021

Towards more accurate preclinical glioblastoma modelling: reverse translation of clinical standard of care in a glioblastoma mouse model.

IntroductionContrarily to clinical trials, where glioblastoma (GBM) patients are usually heavily pre-treated (surgery, radiotherapy (RT) and temozolomide (TMZ)) before receiving the experimental therapies, in preclinical GBM studies experimental treatments have mostly been administered as monotherapy or combined with a very limited part of the clinical standard of care. This discrepancy can explain the failed clinical translation of preclinically successful treatments. This study is aimed at evaluating the feasibility and survival impact of the clinical standard of care in glioma-bearing mice. Methods. Neurospheres CT-2A tumor-bearing mice were treated with fluorescence-guided tumor resection, focal RT and oral TMZ. ResultsThe implementation of postoperative intensive care treatment reduced surgical mortality by approximately 50% and increased experimental cost-effectiveness. Partial and total tumor resection significantly prolonged survival, the latter showing the strongest effect. Mice treated with surgery combined with RT or with RT and TMZ survived significantly longer than untreated controls. ConclusionsImplementing the current GBM clinical standard of care in preclinical research is feasible and it leads to results in line with those obtained in patients. Systematic preclinical evaluation of the efficacy of experimental therapies in combination with standard of care treatments could improve the translational impact of next generation GBM animal studies.

neuroscience↗