Biology subjects

Dewaele, M.

Publications and source records attributed to Dewaele, M..

5 recordsLinked to original sources

bioRxiv2022

A TCF4/BRD4-dependent regulatory network confers cross-resistance to targeted and immune checkpoint therapy in melanoma

Primary resistance drastically limits the clinical success of immune checkpoint blockade (ICB) in melanoma. Resistance to ICB may also develop when tumours relapse after targeted therapy. To identify cancer cell-intrinsic mechanisms driving resistance to ICB, we generated single-cell RNA-sequencing (scRNA-seq) data from a prospective longitudinal cohort of patients on ICB therapy, including an early time point obtained after only one cycle of treatment. Comparing these data with murine scRNA-seq datasets, we established a comprehensive view of the cellular architecture of the treatment-naive melanoma ecosystem, and defined 6 evolutionarily conserved melanoma transcriptional metaprograms (Melanocytic or MEL, Mesenchymal-like or MES, Neural Crest-like, Antigen Presentation, Stress (hypoxia response) and Stress (p53 response)). Spatial multi-omics revealed a non-random geographic distribution of cell states that is, at least partly, driven by the tumour microenvironment. The single-cell data allowed unambiguous discrimination between melanoma MES cells and cancer-associated fibroblasts both in silico and in situ, a long-standing challenge in the field. Importantly, two of the melanoma transcriptional metaprograms were associated with divergent clinical responses to ICB. While the Antigen Presentation cell population was more abundant in tumours from patients who exhibited a clinical response to ICB, MES cells were significantly enriched in early on-treatment biopsies from non-responders, and their presence significantly predicted lack of response. Critically, we identified TCF4 (E2-2) as a master regulator of the MES program and suppressor of both MEL and Antigen Presentation programs. Targeting TCF4 expression in MES cells either genetically or pharmacologically using a bromodomain inhibitor increased immunogenicity and sensitivity to targeted therapy. This study describes an increasingly complex melanoma transcriptional landscape and its rapid evolution under ICB. It also identifies a putative biomarker of early response to ICB and an epigenetic therapeutic strategy that increases both immunogenicity of ICB-refractory melanoma and their sensitivity to targeted therapy.

cancer biology↗

bioRxiv2020

The IRE1 Inhibitor KIRA6 Curtails The Inflammatory Trait Of Immunogenic Anticancer Treatments By Targeting HSP60 Independent Of IRE1

Cellular stress evoked by immunogenic anticancer treatments engaging the unfolded protein response (UPR) can elicit inflammation with conflicting therapeutic outcomes. To define cell-autonomous mechanisms coupling the UPR to molecular mediators of inflammation, we profiled the transcriptome of cancer cells responding to immunogenic or weakly immunogenic-treatments. Bioinformatics-driven pathway analysis indicated that immunogenic treatments instigated NF-{kappa}B/AP-1-inflammatory pathways, which were abolished by the IRE1-kinase inhibitor KIRA6. Cell-free fractions of chemotherapy and KIRA6 co-treated cancer cells were deprived of pro-inflammatory/chemoattractant factors and failed to mobilize innate immune cells. Strikingly, these potent KIRA6 anti-inflammatory effects were found to be independent of IRE1. Generation of a KIRA6-clickable photoaffinity probe, mass spectrometry and co-immunoprecipitation analysis identified cytosolic HSP60 as a KIRA6 off-target in the NF-{kappa}B pathway. In sum, our study unravels that inflammation evoked by immunogenic treatments is curtailed by KIRA6 independently of IRE1 and further suggests great caution in interpreting the anti-inflammatory action of IRE1 chemical inhibitors.

cancer biology↗

bioRxiv2020

A neural crest stem cell-like state drives nongenetic resistance to targeted therapy in melanoma

The ability to predict the future behaviour of an individual cancer is crucial for precision cancer medicine and, in particular, for the development of strategies that prevent acquisition of resistance to anti-cancer drugs. Therapy resistance, which often develops from a heterogeneous pool of drug-tolerant cells known as minimal residual disease (MRD), is thought to mainly occur through acquisition of genetic alterations. Increasing evidence, however, indicates that drug resistance might also be acquired though nongenetic mechanisms. A key emerging question is therefore whether specific molecular and/or cellular features of the MRD ecosystem determine which of these two distinct resistance trajectories will eventually prevail. We show herein that, in melanoma exposed to MAPK-therapeutics, the presence of a neural crest stem cell (NCSC) subpopulation in MRD concurred with the rapid development of resistance through nongenetic mechanisms. Emergence of this drug-tolerant population in MRD relies on a GDNF-dependent autocrine and paracrine signalling cascade, which activates the AKT survival pathway in a Focal-adhesion kinase-(FAK) dependent manner. Ablation of this subpopulation through inhibition of FAK/SRC-signalling delayed relapse in patient-derived tumour xenografts. Strikingly, all tumours that eventually escaped this treatment exhibited resistance-conferring genetic alterations and increased sensitivity to ERK-inhibition. These findings firmly establish that nongenetic reprogramming events contribute to therapy resistance in melanoma and identify a clinically-compatible approach that abrogates such a trajectory. Importantly, these data demonstrate that the cellular composition of MRD deterministically imposes distinct drug resistance evolutionary paths and highlight key principles that may permit more effective pre-emptive therapeutic interventions.

cancer biology↗

bioRxiv2019

Single-cell gene regulatory network analysis reveals new melanoma cell states and transition trajectories during phenotype switching

Melanoma is notorious for its cellular heterogeneity, which is at least partly due to its ability to transition between alternate cell states. Similarly to EMT, melanoma cells with a melanocytic phenotype can switch to a mesenchymal-like phenotype. However, scattered emerging evidence indicates that additional, intermediate state(s) may exist. In order to search for such new melanoma states and decipher their underlying gene regulatory network (GRN), we extensively studied ten patient-derived melanoma cultures by single-cell RNA-seq of >39,000 cells. Although each culture exhibited a unique transcriptome, we identified shared gene regulatory networks that underlie the extreme melanocytic and mesenchymal cell states, as well as one (stable) intermediate state. The intermediate state was corroborated by a distinct open chromatin landscape and governed by the transcription factors EGR3, NFATC2, and RXRG. Single-cell migration assays established that this \"transition\" state exhibits an intermediate migratory phenotype. Through a dense time-series sampling of single cells and dynamic GRN inference, we unraveled the sequential and recurrent arrangement of transcriptional programs at play during phenotype switching that ultimately lead to the mesenchymal cell state. We provide the scRNA-Seq data with 39,263 melanoma cells on our SCope platform and the ATAC-seq data on a UCSC hub to jointly serve as a resource for the melanoma field. Together, this exhaustive analysis of melanoma cell state diversity indicates that additional states exists between the two extreme melanocytic and mesenchymal-like states. The GRN we identified may serve as a new putative target to prevent the switch to mesenchymal cell state and thereby, acquisition of metastatic and drug resistant potential.

genomics↗

bioRxiv2017

SCENIC: Single-Cell Regulatory Network Inference And Clustering

Single-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

bioinformatics↗