Summary
Cellular heterogeneity is a fundamental property of glioblastoma (GBM) tumors and presents a major barrier for therapeutics. During the past six years we characterized this heterogeneity in glioblastoma, and in other types of glioma, using single cell RNA-seq (scRNA-seq). We found consistent patterns of heterogeneity across patients, such that each tumor harbors multiple subpopulations of cells that resemble neurodevelopmental cell types. The consistency of these cellular states across patients highlights their potential clinical significance. Yet, our understanding of how these common states are regulated and of how distinct states respond, individually or in combinations, to potential treatments, remain poorly understood. Furthermore, it is unclear if additional states of clinical significance remain to be uncovered. To address these challenges, we will perform extensive studies of GBM patient samples, animal models and gliomasphere culture models. First, we will dissect the regulation of the common cellular states (Aim 1). Single cell ATAC-seq of patient samples will be used to infer transcriptional regulators, Spatial Transcriptomics will be used to infer environmental interactions, and resulting predictions will be tested further in established model systems. Second, we will search for novel cellular states, including rare states or those specifically associated with invasion to the brain parenchyma (Aim 2). Third, we will examine strategies for treatment of heterogenous tumors that are composed of multiple states (Aim 3). We will screen for state-specific drug sensitivities, as well as for drugs that induce state transitions, and develop rational combinations to eliminate multiple co-existing states, while considering interactions among states. Taken together, these studies will considerably expand our understanding of cancer heterogeneity and develop strategies to target heterogeneous tumors.
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| Web resources: | https://cordis.europa.eu/project/id/101044318 |
| Start date: | 01-05-2022 |
| End date: | 30-04-2027 |
| Total budget - Public funding: | 2 000 000,00 Euro - 2 000 000,00 Euro |
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Original description
Cellular heterogeneity is a fundamental property of glioblastoma (GBM) tumors and presents a major barrier for therapeutics. During the past six years we characterized this heterogeneity in glioblastoma, and in other types of glioma, using single cell RNA-seq (scRNA-seq). We found consistent patterns of heterogeneity across patients, such that each tumor harbors multiple subpopulations of cells that resemble neurodevelopmental cell types. The consistency of these cellular states across patients highlights their potential clinical significance. Yet, our understanding of how these common states are regulated and of how distinct states respond, individually or in combinations, to potential treatments, remain poorly understood. Furthermore, it is unclear if additional states of clinical significance remain to be uncovered. To address these challenges, we will perform extensive studies of GBM patient samples, animal models and gliomasphere culture models. First, we will dissect the regulation of the common cellular states (Aim 1). Single cell ATAC-seq of patient samples will be used to infer transcriptional regulators, Spatial Transcriptomics will be used to infer environmental interactions, and resulting predictions will be tested further in established model systems. Second, we will search for novel cellular states, including rare states or those specifically associated with invasion to the brain parenchyma (Aim 2). Third, we will examine strategies for treatment of heterogenous tumors that are composed of multiple states (Aim 3). We will screen for state-specific drug sensitivities, as well as for drugs that induce state transitions, and develop rational combinations to eliminate multiple co-existing states, while considering interactions among states. Taken together, these studies will considerably expand our understanding of cancer heterogeneity and develop strategies to target heterogeneous tumors.Status
SIGNEDCall topic
ERC-2021-COGUpdate Date
09-02-2023
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