Biology subjects

Elati, M.

Publications and source records attributed to Elati, M..

4 recordsLinked to original sources

bioRxiv2024

Sample-specific network analysis identifies gene coexpression patterns of immunotherapy response in advanced kidney cancer

Immunotherapies have recently emerged as a standard of care for advanced cancers, offering remarkable improvements in patient prognosis. However, only a small proportion of patients respond to the treatment, and no definitive molecular hallmark has been identified for clinical use in predicting treatment outcomes. Here, we propose a sample-specific weighted gene network approach to investigate the heterogeneity of patient clinical outcomes by leveraging multiscale network features derived from gene expression data. Our results show that patients exhibiting similar clinical benefits share comparable gene coexpression patterns. Increased gene connectivity and stronger negative gene-gene associations are also pivotal factors in patients with poor prognoses. Moreover, integrating pathway genes with topological measures enables the identification of the perturbed regulation of biological pathways associated with treatment responses. Additionally, sample-level network features enhance the prediction performance of gene expression values-based machine learning models. Collectively, our approach provides valuable guidance on the use of gene network information to stratify cancer patients and to optimize treatment strategies.

bioinformatics↗

bioRxiv2024

Transcriptional-program-based deciphering of the MET exon 14 skipping regulation network

The MET exon 14 skipping mutation (named METex14del) described in lung cancer leads to prolonged activation of signaling pathways and aberrant cell responses, but the link between HGF signaling and cell responses remains unclear. A putative regulatory network of influential regulators of target genes was constructed from the transcriptomes of lung cancer cell lines. Overlaying this reference network with transcriptomic data from METex14del-expressing cells, stimulated or not by HGF, revealed a major regulatory node consisting mainly of the transcription factors ETS1, FOSL1 and SMAD3. HGF activation of METex14del induced the phosphorylation of these master regulators and the expression of their predicted target genes in a RAS-ERK pathway-dependent manner. Furthermore, most of the transcription factors in the regulatory node are known regulators of epithelial-mesenchymal transition, consistent with their involvement in migration and invasion. New modeling with transcriptomic data from MEK inhibitor-treated METex14del cells validated the key role of RAS-ERK pathway regulators and their target genes in METex14del receptor activation. Thus, we report an original strategy to identify key transcriptional nodes associated with specific signaling pathways that may become novel therapeutic targets.

cancer biology↗

bioRxiv2024

A coregulatory influence map of glioblastoma heterogeneity and plasticity - From cells in vitro to tumors and back again

We present GBM-cRegMap, an online resource providing a comprehensive coregulatory influence network perspective on glioblastoma (GBM) heterogeneity and plasticity. Using representation learning algorithms, we derived two components of this resource: GBM-CoRegNet, a highly specific coregulatory network of tumor cells, and GBM-CoRegMap, a unified network influence map based on 1612 tumors from 16 studies. As a widely applicable closed-loop system connecting cellular models and tumors, GBM-cRegMap will provide the GBM research community with an easy-to-use web tool (https://gbm.cregmap.com) that maps any existing or newly generated transcriptomic "query" data to a reference coregulatory network and a large-scale manifold of disease heterogeneity. Using GBM-cRegMap, we demonstrated the synergy between the two components by refining the molecular classification of GBM, identifying potential key regulators, and aligning the transcriptional profiles of tumors and in vitro models. Through the amalgamation of a vast dataset, we validated the proneural (PN)-mesenchymal (MES) axis and identified three subclasses of classical (CL) tumors: astrocyte-like (CL-A), epithelial basal-like (CL-B), and cilium-rich (CL-C). We revealed the CL-C subclass, an intermediate state demonstrating the plasticity of GBM cells along the PN-MES axis under chemotherapy. We identified key regulators, such as PAX8, and NKX2.5, involved in TMZ resistance. Notably, NKX2.5, more expressed in higher-grade gliomas, negatively impacts patient survival and regulates genes involved in glucose metabolism. O_FIG O_LINKSMALLFIG WIDTH=152 HEIGHT=200 SRC="FIGDIR/small/609303v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@c7a401org.highwire.dtl.DTLVardef@e0681corg.highwire.dtl.DTLVardef@d31f55org.highwire.dtl.DTLVardef@ae350f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Inference of an integrative network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental and epigenetic factors. Systems biology approaches provide the means to study complex diseases through the integration of different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first make use of publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. The integrative network is then used as a template to analyze patients data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. We use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.

bioinformatics↗