Abstract:The epithelial to mesenchymal transition (EMT) is a key cellular process underlying cancer progression, with multiple intermediate states whose molecular hallmarks remain poorly characterised. To fill this gap, we present a method to robustly evaluate EMT transformation in individual tumours based on transcriptomic signals. We apply this approach to explore EMT trajectories in 7180 tumours of epithelial origin and identify three macro-states with prognostic and therapeutic value, attributable to epithelial, hy… Show more
“…We uncovered heterogeneous relationships across breast tissue slides with other key immune cells such as NK cells, NKT cells and T cells, highlighting the multifaceted interplay between these components. T-cells have been shown to induce EMT in breast cancer 67,68 , and this relationship has further been highlighted in bulk transcriptomics 69 and smaller scale spatial transcriptomic analyses 34,70 however there is also evidence showing the exclusion of these cells, linked to the relationship between EMT, macrophages and CAFs promoting an immune suppressed environment 64,51,71 . Indeed, our findings uncover notable associations between EMT hotspots and immune suppression, alongside signatures indicative of a response to checkpoint therapy, building upon evidence that EMT may offer crucial insights for existing strategies in immunotherapy 72,11 .…”
Spatial transcriptomics is revolutionising our ability to explore intratissue heterogeneity in cancer, but methods that can effectively capture cancer cell niches and explore their relationships with the tumour microenvironment at various spatial scales remain limited. Here we present SpottedPy, a Python package designed to identify tumour hotspots and map spatial interactions within the cancer ecosystem. We employ SpottedPy to examine epithelial-mesenchymal plasticity in breast cancer and highlight locally stable niches associated with angiogenic and hypoxic regions, and shielded by myCAFs, macrophages and perivascular cell populations. Hybrid and mesenchymal hotspot distribution followed transformation gradients within the tissue reflecting progressive immunosuppression. Our method offers the flexibility to explore spatial relationships at different scales, from immediate neighbours to broader tissue modules, providing new insights into the spatial dynamics of the tumour microenvironment.
“…We uncovered heterogeneous relationships across breast tissue slides with other key immune cells such as NK cells, NKT cells and T cells, highlighting the multifaceted interplay between these components. T-cells have been shown to induce EMT in breast cancer 67,68 , and this relationship has further been highlighted in bulk transcriptomics 69 and smaller scale spatial transcriptomic analyses 34,70 however there is also evidence showing the exclusion of these cells, linked to the relationship between EMT, macrophages and CAFs promoting an immune suppressed environment 64,51,71 . Indeed, our findings uncover notable associations between EMT hotspots and immune suppression, alongside signatures indicative of a response to checkpoint therapy, building upon evidence that EMT may offer crucial insights for existing strategies in immunotherapy 72,11 .…”
Spatial transcriptomics is revolutionising our ability to explore intratissue heterogeneity in cancer, but methods that can effectively capture cancer cell niches and explore their relationships with the tumour microenvironment at various spatial scales remain limited. Here we present SpottedPy, a Python package designed to identify tumour hotspots and map spatial interactions within the cancer ecosystem. We employ SpottedPy to examine epithelial-mesenchymal plasticity in breast cancer and highlight locally stable niches associated with angiogenic and hypoxic regions, and shielded by myCAFs, macrophages and perivascular cell populations. Hybrid and mesenchymal hotspot distribution followed transformation gradients within the tissue reflecting progressive immunosuppression. Our method offers the flexibility to explore spatial relationships at different scales, from immediate neighbours to broader tissue modules, providing new insights into the spatial dynamics of the tumour microenvironment.
“…Complementarily, we here show that JUNB/AP1 acts as a counterpart to promote a favorable CLA phenotypic identity in PDAC. Of note, JUNB/AP1-mediated transcriptional programs can also confer tumor-promoting functions in other cancer types [39][40][41] ; JUN/AP1 TFs are highly context dependent and may co-operate for target gene transcription [41][42][43] or oppose one another 44 Altogether, this string of insights provides a potential mechanistic foundation for several recent studies that showed a high degree of heterogeneity in the neoplastic and stromal immune compartments in human PDAC, including hybrid/intermediate/coexpressor CLA/BL subtype states that exist in naive and therapy-treated PDAC tumors [10][11][12][13][14][15][16]20,36,37 . We propose that extrinsic regional TNF-α plays an essential role in destabilizing CLA neoplastic cell identity by promoting BL cJUN/AP1-mediated transcriptional programs.…”
Section: Discussionmentioning
confidence: 96%
“…Here, we investigated the role of neoplastic AP1-mediated epigenetic and transcriptional programs in shaping the local inflammatory TiME, which in turn is critical for intratumoral subtype plasticity and PDAC aggressiveness [10][11][12][13][14][15]20,36,37 . We report that AP1 transcription factors (JUNB/AP1 vs. cJUN/AP1) hold a dichotomous role in maintaining both the plasticity and stability of CLA and BL neoplastic cells via intrinsic epigenetic and transcriptional regulation of lineage gene expression as well as extrinsic inflammatory processes.…”
Section: Discussionmentioning
confidence: 99%
“…However, it has become clear that the CLA and BL subtypes are not discrete states of individual PDAC tumors, but rather co-exist and contribute to significant intratumoral heterogeneity that is poorly understood. Multi-scale transcriptomic and imaging-based profiling has revealed a widespread co-existence of CLA and BL subtypes within PDAC patient tumors, as well as hybrid/co-expressor states that are implicated as transition mechanism between the subtype extremes [10][11][12][13][14][15] , emphasizing the complex tumor cell plasticity. Moreover, the extent of subtype co-existence increases in advanced disease, especially in metastatic samples 13,16,17 .…”
Pancreatic ductal adenocarcinoma (PDAC) displays a high degree of spatial subtype heterogeneity. This intratumoral co-existence of classical and basal-like programs is evident in multi-scale transcriptomic and spatial analyses of resected, advanced-stage and chemotherapy-treated specimens and reciprocally linked to a diverse stromal immune microenvironment as well as worse clinical outcome. However, the underlying mechanisms of intratumoral subtype heterogeneity remain largely unclear. Here, by combining preclinical models, multi-center clinical, bulk and compartment-specific transcriptomic, proteomic, and bioimaging data from human specimens, we identified an interplay between neoplastic intrinsic AP1 transcription factor dichotomy and extrinsic CD68+macrophages as a driver of intratumoral subtype co-existence along with an immunosuppressive tumor microenvironment with T cell exclusion. Our ATAC-, ChIP-, and RNA-seq analyses revealed that JUNB/AP1- and HDAC-mediated epigenetic programs repress pro-inflammatory immune signatures in tumor cells, antagonizing cJUN/AP1 signaling to favor a therapy-responsive classical neoplastic identity. Through the tumor microenvironment, this dichotomous regulation was further amplified via regional macrophage populations. Moreover, CD68+/TNF-α+cells associated with a reactive phenotype and reduced CD8+T cell infiltration in human PDAC tumors. Consequently, combined anti-TNF-α immunotherapy and chemotherapy resulted in reduced macrophage counts and promoted CD3+/CD8+T cell infiltration in basal-like PDAC, leading to improved survival in preclinical murine models. We conclude that tumor cell intrinsic epigenetic programs, together with extrinsic microenvironmental cues, facilitate intratumoral subtype heterogeneity and disease progression.
“…EMT can be tracked with transcriptomic methods, which are costly and don't integrate all parameters, including the morphological aspect or single-cell variation. [13] Each cell, even in homogenous condition, can be modified or modify itself from its population. [14] Single cell variation is a necessity in organism, but also a major issue when it comes to treatment, especially in highly heterogeneous malignancy like ovarian cancer.…”
Epithelial to Mesenchymal Transition (EMT) is highly plastic with a program where cells lose adhesion and become more motile. EMT heterogeneity is one of the factors for disease progression and chemoresistance in cancer. Omics characterizations are costly and challenging to use. We developed single cell phenomics with easy to use wide-field fluorescence microscopy. We analyse over 70000 cells and combined 51 features. Our simplistic pipeline allows efficient tracking of EMT plasticity, with a single statistical metric. We discriminate four high EMT plasticity cancer cell lines along the EMT spectrum. We test two cytokines, inducing EMT in all cell lines, alone or in combination. The single cell EMT metrics demonstrate the additive effect of cytokines combination on EMT independently of cell line EMT spectrum. Single cell phenomics is uniquely suited to characterize the cellular heterogeneity in response to complex microenvironment, and show potential for drug testing assays.
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