Linking epigenetic signature and metabolic phenotype in <i>IDH</i> mutant and <i>IDH</i> wildtype diffuse glioma

Braun, Yannick; Filipski, Katharina; Bernatz, Simon; Baumgarten, Peter; Roller, Bastian; Zinke, Jenny; Zeiner, Pia S.; Ilina, Elena I.; Senft, Christian; Ronellenfitsch, Michael; Plate, Karl H.; Bähr, Oliver; Hattingen, Elke; Steinbach, Joachim P.; Mittelbronn, Michel; Harter, Patrick N.

doi:10.1111/nan.12669

Cited by 5 publications

(4 citation statements)

References 56 publications

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“…The prognostic importance of IDH mutation is independent of other known prognostic factors, including age, grade, and MGMT methylation status. IDH mutations promote a metabolic reprogramming mainly due to the accumulation of the oncometabolite 2- hydroxyglutarate (2-HG), which, in turn, induces the epigenetic silencing of several genes from the glycolytic pathway ( 193 , 194 ). Moreover, IDH1 mut is associated with a distinct hypoxia/angiogenesis transcriptome signature and stabilization of HIF-1a levels in glioma cells ( 195 ), important autophagy regulators (see above).…”

Section: Autophagy Activation As a Response To Internal Stimulimentioning

confidence: 99%

A Key Pathway to Cancer Resilience: The Role of Autophagy in Glioblastomas

et al. 2021

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There are no effective strategies for the successful treatment of glioblastomas (GBM). Current therapeutic modalities effectively target bulk tumor cells but leave behind marginal GBM cells that escape from the surgical margins and radiotherapy field, exhibiting high migratory phenotype and resistance to all available anti-glioma therapies. Drug resistance is mostly driven by tumor cell plasticity: a concept associated with reactivating transcriptional programs in response to adverse and dynamic conditions from the tumor microenvironment. Autophagy, or “self-eating”, pathway is an emerging target for cancer therapy and has been regarded as one of the key drivers of cell plasticity in response to energy demanding stress conditions. Many studies shed light on the importance of autophagy as an adaptive mechanism, protecting GBM cells from unfavorable conditions, while others recognize that autophagy can kill those cells by triggering a non-apoptotic cell death program, called ‘autophagy cell death’ (ACD). In this review, we carefully analyzed literature data and conclude that there is no clear evidence indicating the presence of ACD under pathophysiological settings in GBM disease. It seems to be exclusively induced by excessive (supra-physiological) stress signals, mostly from in vitro cell culture studies. Instead, pre-clinical and clinical data indicate that autophagy is an emblematic example of the ‘dark-side’ of a rescue pathway that contributes profoundly to a pro-tumoral adaptive response. From a standpoint of treating the real human disease, only combinatorial therapy targeting autophagy with cytotoxic drugs in the adjuvant setting for GBM patients, associated with the development of less toxic and more specific autophagy inhibitors, may inhibit adaptive response and enhance the sensibility of glioma cells to conventional therapies.

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Section: Autophagy Activation As a Response To Internal Stimulimentioning

confidence: 99%

A Key Pathway to Cancer Resilience: The Role of Autophagy in Glioblastomas

et al. 2021

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“…In addition, cyclic immunohistochemistry benefits from the speed and simplicity of brightfield microscopy for data collection, making the collection of entire tissue sections a trivial cost compared to other high dimensional imaging modalities. However, such large datasets currently require an expert data scientist to bring together separate tools for each step of image pre‐processing, registration, and segmentation, or expensive proprietary software such as HALO or Visiopharm (Braun et al, 2021; Tsujikawa et al, 2017). An area of particular and growing interest in the field of multiplexed imaging of solid tissues and tumor samples is the spatial relationships between cells that are expert‐ or machine‐identified as different populations within the tissue.…”

Section: Introductionmentioning

confidence: 99%

Alignment, segmentation and neighborhood analysis in cyclic immunohistochemistry data using CASSATT

Brockman

Khurana

Bartkowiak

et al. 2023

Cytometry Part B Clinical

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Cyclic immunohistochemistry (cycIHC) uses sequential rounds of colorimetric immunostaining and imaging for quantitative mapping of location and number of cells of interest. Additionally, cycIHC benefits from the speed and simplicity of brightfield microscopy, making the collection of entire tissue sections and slides possible at a trivial cost compared to other high dimensional imaging modalities. However, large cycIHC datasets currently require an expert data scientist to concatenate separate open‐source tools for each step of image pre‐processing, registration, and segmentation, or the use of proprietary software. Here, we present a unified and user‐friendly pipeline for processing, aligning, and analyzing cycIHC data ‐ Cyclic Analysis of Single‐Cell Subsets and Tissue Territories (CASSATT). CASSATT registers scanned slide images across all rounds of staining, segments individual nuclei, and measures marker expression on each detected cell. Beyond straightforward single cell data analysis outputs, CASSATT explores the spatial relationships between cell populations. By calculating the log odds of interaction frequencies between cell populations within tissues and tissue regions, this pipeline helps users identify populations of cells that interact—or do not interact—at frequencies that are greater than those occurring by chance. It also identifies specific neighborhoods of cells based on the assortment of neighboring cell types that surround each cell in the sample. The presence and location of these neighborhoods can be compared across slides or within distinct regions within a tissue. CASSATT is a fully open source workflow tool developed to process cycIHC data and will allow greater utilization of this powerful staining technique.

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“…In addition, cyclic immunohistochemistry benefits from the speed and simplicity of brightfield microscopy for data collection, making the collection of entire tissue sections a trivial cost compared to other high dimensional imaging modalities. However, such large datasets currently require an expert data scientist to bring together separate tools for each step of image pre-processing, registration, and segmentation, or expensive proprietary software such as HALO or Visiopharm (1,2). An area of particular and growing interest in the field of multiplexed imaging of solid tissues and tumor samples is the spatial relationships between cells that are expert- or machine-identified as different populations within the tissue.…”

Section: Introductionmentioning

confidence: 99%

Alignment, Segmentation and Neighborhood Analysis in Cyclic Immunohistochemistry Data Using CASSATT

Brockman

Khurana

Bartkowiak

et al. 2022

Preprint

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Cyclic immunohistochemistry (cycIHC) uses sequential rounds of colorimetric immunostaining and imaging for quantitative mapping of location and number of cells of interest. In addition, cycIHC benefits from the speed and simplicity of brightfield microscopy for data collection, making the collection of entire tissue sections and slides possible at a trivial cost compared to other high dimensional imaging modalities. However, large cycIHC datasets (greater than 50 GB) currently require an expert data scientist to concatenate separate open-source tools for each step of image pre-processing, registration, and segmentation, or the use of proprietary software. Here, we present a unified and user-friendly pipeline for processing, aligning, and analyzing cycIHC data - Cyclic Analysis of Single-Cell Subsets and Tissue Territories (CASSATT). CASSATT registers scanned slide images across all rounds of staining, segments individual nuclei, and measures marker expression on each detected cell. Beyond straightforward single cell data analysis outputs, CASSATT explores the spatial relationships between cell populations. By calculating the logodds of interaction frequencies between cell populations within tissues and tissue regions, this pipeline helps users identify populations of cells that interact - or do not interact - at frequencies that are greater than those occurring by chance. It also identifies specific neighborhoods of cells based on the assortment of neighboring cell types that surround each cell in the sample. The presence and location of these neighborhoods can be compared across slides or within distinct regions within a tissue. CASSATT was first tested using a newly generated cycIHC dataset consisting of six GBM tissue sections processed through eight cycles of AEC based IHC staining. Further validation was completed on a previously published lung cancer tissue microarray dataset consisting of 107 cores processed through eighteen cycles of staining and imaging. CASSATT is a fully open-source workflow tool developed to process cycIHC data and will allow greater utilization of this powerful staining technique.

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Linking epigenetic signature and metabolic phenotype in IDH mutant and IDH wildtype diffuse glioma

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 5 publications

References 56 publications

A Key Pathway to Cancer Resilience: The Role of Autophagy in Glioblastomas

A Key Pathway to Cancer Resilience: The Role of Autophagy in Glioblastomas

Alignment, segmentation and neighborhood analysis in cyclic immunohistochemistry data using CASSATT

Alignment, Segmentation and Neighborhood Analysis in Cyclic Immunohistochemistry Data Using CASSATT

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