Gradient of Developmental and Injury Response transcriptional states defines functional vulnerabilities underpinning glioblastoma heterogeneity

Richards, Laura M.; Whitley, Owen; MacLeod, Graham; Cavalli, Florence M.G.; Coutinho, Fiona J.; Jaramillo, Julia E.; Svergun, N.; Riverin, Mazdak; Croucher, Danielle; Kushida, Michelle; Yu, Kenny; Guilhamon, Paul; Rastegar, Naghmeh; Ahmadi, M. Amir; Bhatti, Jasmine K.; Bozek, Danielle; Li, Naijin; Lee, Lilian; Che, Clare; Luis, Erika; Park, Nicole I.; Xu, Zhiyu; Ketela, Troy; Moore, Richard A.; Marra, Marco A.; Spears, Julian; Cusimano, Michael D.; Das, Sunit; Bernstein, Mark; Haibe‐Kains, Benjamin; Lupien, Mathieu; Luchman, H. Artee; Weiss, Samuel; Angers, Stéphane; Dirks, Peter B.; Bader, Gary D.; Pugh, Trevor J.

doi:10.1038/s43018-020-00154-9

Cited by 180 publications

(254 citation statements)

References 77 publications

Supporting

Mentioning

222

Contrasting

Order By: Relevance

“…Our identified modules encompass two major groups involving developmental and inflammatory/hypoxia-associated transcriptional programs, later referred to as “reactive states”. In contrast to the recent described injury response signature 2 of glioblastoma, our data indicate that two distinct subtypes of reactive states coexist and emerge spatially segregated from each other, Extended Data Figure 5a-b . The first module revealed a strong enrichment in glycolysis-related pathways and those involved in the response to reduced oxygen-levels (false discovery rate [FDR] < 0.01, hypergeometric test), therefore named as “Reactive Hypoxia” Extended Data Figure 5a .…”

Section: Articlecontrasting

confidence: 99%

“…To evaluate whether malignant transcriptomes resulted from somatic alterations, we estimated copy number variations (CNVs) from the average expression of genes in large chromosomal regions within each spot, which confirmed the typical gain in chromosome 7 and/or loss in chromosome 10 in the majority of malignant spots, Figure 1c-d and Extended Data Figure 1-3 . The high number of individual copy-number alterations and mutational profiles are assumed to drive patient-specific transcriptional regulation 1 resulting in individual clusters of transcriptomes, similar to results seen in other studies 1,2 .…”

Section: Articlesupporting

confidence: 71%

“…Additionally, tumor cells obtained from aged cortex slices were more abundant in the terminal-reactive population, Figure 4h . Next we performed differential gene expression analysis to decipher the impact of an aged-environment on tumor cells which revealed an up-regulation of genes associated with INF gamma response ( IFIT3, IFI6 and IFIT1 ), Extended Data Figure 10f , and a loss of known markers of development programs ( H2AFZ also referred to as H2AZ1 ) 2 . Diffusion map re-embedding of the scRNA-seq data indicated a major branching for developmental and reactive programs.…”

Section: Articlementioning

confidence: 99%

See 2 more Smart Citations

Spatiotemporal heterogeneity of glioblastoma is dictated by microenvironmental interference

Ravi

Will

Kueckelhaus

et al. 2021

Preprint

View full text Add to dashboard Cite

Glioblastomas are highly malignant tumors of the central nervous system. Evidence suggests that these tumors display large intra- and inter-patient heterogeneity hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies. However, the source for dynamic reorganization of cellular states within their spatial context remains elusive. Here, we in-depth characterized glioblastomas by spatially resolved transcriptomics, metabolomics and proteomics. By deciphering exclusive and shared transcriptional programs across patients, we inferred that glioblastomas develop along defined neural lineages and adapt to inflammatory or metabolic stimuli reminiscent of reactive transformation in mature astrocytes. Metabolic profiling and imaging mass cytometry supported the assumption that tumor heterogeneity is dictated by microenvironmental alterations. Analysis of copy number variation (CNV) revealed a spatially cohesive organization of subclones associated with reactive transcriptional programs, confirming that environmental stress gives rise to selection pressure. Deconvolution of age-dependent transcriptional programs in malignant and non-malignant specimens identified the aging environment as the major driver of inflammatory transformation in GBM, suggesting that tumor cells adopt transcriptional programs similar to inflammatory transformation in astrocytes. Glioblastoma stem cells implanted into human neocortical slices of varying age levels, independently confirmed that the ageing environment dynamically shapes the intratumoral heterogeneity towards reactive transcriptional programs. Our findings provide insights into the spatial architecture of glioblastoma, suggesting that both locally inherent tumor as well as global alterations of the tumor microenvironment shape its transcriptional heterogeneity. Global age-related inflammation in the human brain is driving distinct transcriptional transformation in glioblastomas, which requires an adjustment of the currently prevailing glioma models.

show abstract

Section: Articlecontrasting

confidence: 99%

Section: Articlesupporting

confidence: 71%

Section: Articlementioning

confidence: 99%

See 1 more Smart Citation

Spatiotemporal heterogeneity of glioblastoma is dictated by microenvironmental interference

Ravi

Will

Kueckelhaus

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…an implementation of some of the discussed algorithms on the patient-derived single-cell RNA sequencing (scRNA-seq) count matrices from Neftel et al 2 and Richards et al, 3 the clinical relevance of the presented tools, and the insights of the paper are strongly emphasized. Thus, the patient-centered section on cancer networks (section ''Cancer networks'') is to be treated as the heart of the paper.…”

mentioning

confidence: 99%

“…The ''few pediatric GBM samples'' results correspond to six patient samples with N = 846 cells analyzed. The ''few adult GBM samples'' correspond to three adult patient samples with N = 409 cells analyzed from the count matrix found in Neftel et al 2 Finally, the adult glioblastoma stem cell (GSC) analysis correspond to sample BT127_L (N = 500 random cells) from the scRNA-seq counts obtained from Richards et al 3 The source datasets (gene expression count matrices) for these data used to demonstrate some of the algorithms herein are available as .csv and .txt files in the Broad Single Cell Portal links provided in the data and code availability section at the end of this review. The source codes for all discussed algorithms are available in the links provided at the end of the paper and their corresponding citations.…”

mentioning

confidence: 99%

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Uthamacumaran

2021

Patterns

View full text Add to dashboard Cite

Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. The review provides an overview of dynamical systems theory to steer cancer research in pattern science. While most of our current tools in network medicine rely on statistical correlation methods, causality inference remains primitively developed. As such, a survey of attractor reconstruction methods and machine algorithms for the detection of causal structures applicable in experimentally derived time series cancer datasets is presented. A toolbox of complex systems approaches are discussed for reconstructing the signaling state space of cancer networks, interpreting causal relationships in their time series gene expression patterns, and assisting clinical decision making in computational oncology. As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted. ll

show abstract

Traumatic Brain Injury and Subsequent Risk of Brain Cancer in US Veterans of the Iraq and Afghanistan Wars

Stewart,

Howard,

Poltavskiy

et al. 2024

JAMA Netw Open

View full text Add to dashboard Cite

ImportanceWhile brain cancer is rare, it has a very poor prognosis and few established risk factors. To date, epidemiologic work examining the potential association of traumatic brain injury (TBI) with the subsequent risk of brain cancer is conflicting. Further data may be useful.ObjectiveTo examine whether a history of TBI exposure is associated with the subsequent development of brain cancer.Design, Setting, and ParticipantsA retrospective cohort study was conducted from October 1, 2004, to September 20, 2019, and data analysis was performed between January 1 and June 26, 2023. The median follow-up for the cohort was 7.2 (IQR, 4.1-10.1) years. Veterans Affairs (VA) and Department of Defense (DoD) administrative data on 1 919 740 veterans from the Long-Term Impact of Military-Relevant Brain Injury Consortium–Chronic Effects of Neurotrauma Consortium were included.ExposureThe main exposure of interest was TBI severity (categorized as mild, moderate or severe [moderate/severe], and penetrating).Main Outcomes and MeasuresThe outcome of interest was the development of brain cancer based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) or International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnostic codes in either the DoD/VA medical records or from the National Death Index.ResultsAfter 611 107 exclusions (predominately for no encounter during the study period), a cohort including 1 919 740 veterans was included, most of whom were male (80.25%) and non-Hispanic White (63.11%). Median age at index date was 31 (IQR, 25-42) years. The cohort included 449 880 individuals with TBI (mild, 385 848; moderate/severe, 46 859; and penetrating, 17 173). Brain cancer occurred in 318 individuals without TBI (0.02%), 80 with mild TBI (0.02%), 17 with moderate/severe TBI (0.04%), and 10 or fewer with penetrating TBI (≤0.06%). After adjustment, moderate/severe TBI (adjusted hazard ratio [AHR], 1.90; 95% CI, 1.16-3.12) and penetrating TBI (AHR, 3.33; 95% CI, 1.71-6.49), but not mild TBI (AHR, 1.14; 95% CI, 0.88-1.47), were associated with the subsequent development of brain cancer.Conclusions and RelevanceIn this cohort study of veterans of the Iraq and Afghanistan wars, moderate/severe TBI and penetrating TBI, but not mild TBI, were associated with the subsequent development of brain cancer.

show abstract

Gradient of Developmental and Injury Response transcriptional states defines functional vulnerabilities underpinning glioblastoma heterogeneity

Cited by 180 publications

References 77 publications

Spatiotemporal heterogeneity of glioblastoma is dictated by microenvironmental interference

Spatiotemporal heterogeneity of glioblastoma is dictated by microenvironmental interference

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Traumatic Brain Injury and Subsequent Risk of Brain Cancer in US Veterans of the Iraq and Afghanistan Wars

Contact Info

Product

Resources

About