Mutual regulation of TGFβ-induced oncogenic EMT, cell cycle progression and the DDR

Schuhwerk, Harald; Brabletz, Thomas

doi:10.1016/j.semcancer.2023.11.009

Cited by 3 publications

(4 citation statements)

References 305 publications

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“…Intrigued by the considerable number of DEGs exclusively in LysM Ctrl or LysM ΔZeb1 BMDMs, we performed GO term analyses on these genes (Table S2). IL-4-induced exclusive DEGs did not retrieve significant GO terms in LysM Ctrl BMDMs but indicated moderately altered cell cycling in LysM ΔZeb1 BMDMs, consistent with the multifaceted role of ZEB1 in cell cycle control, which we addressed systematically recently (Schuhwerk et al 2022, Schuhwerk and Brabletz 2023). In the LPS condition, LysM Ctrl BMDMs showed altered cellular respiration and ribonucleotide biosynthesis, while, interestingly, protein translation, ubiquitination and transport were strongly deregulated in LysM ΔZeb1 BMDMs on the mRNA level, with major players in protein trafficking, such as Rab family members as well as Vamp3 being affected (Fig.…”

Section: Resultssupporting

confidence: 77%

“…Mechanistically, ZEB1 assists inflammatory polarization, influences cytokine expression, is supporting their full translation rate and is required for their secretion. Altogether, this fine-tuning role of ZEB1 is distinct from its well-established one as a key driver of oncogenic (TGFβ-induced) EMT in tumor cells, yet adds a translationally relevant facet of ZEB1-linked cellular plasticity (Krebs et al 2017, Caramel et al 2018, Feldker et al 2020, Brabletz et al 2021, Schuhwerk and Brabletz 2023).…”

Section: Discussionmentioning

confidence: 94%

“…The partial activation of the developmental process EMT in cancer cells is well-known to induce invasiveness and cellular plasticity, enabling adaptation to environmental challenges, such as posed by cancer therapies or faced during the metastatic cascade (Dongre and Weinberg 2019, Yang et al 2020, Brabletz et al 2021, Schuhwerk and Brabletz 2023). The core EMT-inducing transcription factor (EMT-TF) ZEB1 regulates the expression of a wide range of target genes influencing cell adhesion, differentiation, metabolism, therapy resistance, proliferation and the DNA damage response to promote plasticity and malignancy (Stemmler et al 2019, Dongre and Weinberg 2019, Brabletz et al 2021, Schuhwerk and Brabletz 2023).…”

Section: Introductionmentioning

confidence: 99%

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Macrophages foster adaptive anti-tumor immunity by ZEB1-dependent cytotoxic T cell chemoattraction

Fuchs,

Armstark,

van Roey

et al. 2024

Preprint

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Tumor-associated macrophages (TAMs) shape the tumor microenvironment (TME) and exert a decisive impact on anti-tumor immunity. Understanding TAM function is therefore critical to understand anti-tumor immune responses and to design immunotherapies. Here, we describe the transcription factor ZEB1, a well-known driver of epithelial-to-mesenchymal transition, as an intrinsic regulator of TAM function in adaptive anti-tumor immunity. By combining cell type-specific deletion of Zeb1 with syngeneic models of colorectal and pancreatic cancer, we discovered an unexpected function of ZEB1 in the TAM-mediated control of T cell trafficking. ZEB1 supports secretion of a subset of chemokines including CCL2 and CCL22 by promoting their transcription and translation as well as by safeguarding protein processing. ZEB1 thereby elevates cytotoxic T cell (CTL) recruitmentin vitroandin vivoand fosters immunosurveillance during tumor as well as lung metastatic outgrowth. Our study spotlights ZEB1 as a crucial facilitator of adaptive anti-tumor immunity and uncovers a potential therapeutic window of opportunity for cytokine-guided enhancement of CTL infiltration into tumors and metastases.

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Section: Resultssupporting

confidence: 77%

Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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Macrophages foster adaptive anti-tumor immunity by ZEB1-dependent cytotoxic T cell chemoattraction

Fuchs,

Armstark,

van Roey

et al. 2024

Preprint

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“…5g). These pathways can interact with each other and are associated with the prognosis and treatment response of breast cancer [28,29].…”

Section: Stereomm Dissects Breast Cancer Heterogeneity and Identifies...mentioning

confidence: 99%

StereoMM: A Graph Fusion Model for Integrating Spatial Transcriptomic Data and Pathological Images

Luo,

Teng,

Tang

et al. 2024

Preprint

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Spatially resolved omics technologies generating multimodal and high-throughput data lead to the urgent need for advanced analysis to allow the biological discoveries by comprehensively utilizing information from multi-omics data. The H&E image and spatial transcriptomic data indicate abundant features which are different and complementary to each other. AI algorithms can perform nonlinear analysis on these aligned or unaligned complex datasets to decode tumoral heterogeneity for detecting functional domain. However，the interpretability of AI-generated outcomes for human experts is a problem hindering application of multi-modal analysis in clinic. We presented a machine learning based toolchain called StereoMM, which is a graph fusion model that can integrate gene expression, histological images, and spatial location. StereoMM firstly performs information interaction on transcriptomic and imaging features through the attention module, guaranteeing explanations for its decision-making processes. The interactive features are input into the graph autoencoder together with the graph of spatial position, so that multimodal features are fused in a self-supervised manner. Here, StereoMM was subjected to mouse brain tissue, demonstrating its capability to discern fine tissue architecture, while highlighting its advantage in computational speed. Utilizing data from Stereo-seq of human lung adenosquamous carcinoma and 10X Visium of human breast cancer, we showed its superior performance in spatial domain recognition over competing software and its ability to reveal tumor heterogeneity. The fusion approach for imaging and gene expression data within StereoMM aids in the more accurate identification of domains, unveils critical molecular features, and elucidates the connections between different domains, thereby laying the groundwork for downstream analysis.

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OmeSim: a genetics-based nonlinear simulator for in-between-ome and phenotype

Long,

Zhang

2024

Preprint

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MotivationDeciphering genetic basis of complex traits via genotype-phenotype association studies is a long-standing theme in genetics. The availability of molecular omics data (such as transcriptome) has enabled researchers to utilize “in-between-omes” in association studies, for instance transcriptome-wide association study. Although many statistical tests and machine learning models integrating omics in genetic mapping are emerging, there is no standard way to simulate phenotype by genotype with the role of in-between-omes incorporated. Moreover, the involvement of in-between-omes usually bring substantial nonlinear architecture (e.g., co-expression network), that may be non-trivial to simulate. As such, rigorous power estimations, a critical step to test novel models, may not be conducted fairly.ResultsTo address the gap between emerging methods development and the unavailability of adequate simulators, we developed OmeSim, a phenotype simulator incorporating genetics, an in-between-ome (e.g., transcriptome), and their complex relationships including nonlinear architectures. OmeSim outputs detailed causality graphs together with original data, correlations, and associations structures between phenotypic traits and omes terms as comprehensive gold-standard datasets for the verifications of novel tools integrating an in-between-ome in genotype-phenotype association studies. We expect OmeSim to enable rigorous benchmarking for the future multi-omics integrations.Availabilityhttps://github.com/zhoulongcoding/OmeSimContactqingrun.zhang@ucalgary.ca

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Mutual regulation of TGFβ-induced oncogenic EMT, cell cycle progression and the DDR

Cited by 3 publications

References 305 publications

Macrophages foster adaptive anti-tumor immunity by ZEB1-dependent cytotoxic T cell chemoattraction

Macrophages foster adaptive anti-tumor immunity by ZEB1-dependent cytotoxic T cell chemoattraction

StereoMM: A Graph Fusion Model for Integrating Spatial Transcriptomic Data and Pathological Images

OmeSim: a genetics-based nonlinear simulator for in-between-ome and phenotype

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