| Literature DB >> 22287481 |
Lionel M L Chow1, Suzanne J Baker.
Abstract
High-grade astrocytoma remains a significant challenge to the clinician and researcher alike. Intense study of the molecular pathogenesis of these tumors has allowed identification of frequent genetic alterations and critical core pathways in this disease. The use of novel mouse genetic tools to study the consequence of specific mutations in brain has led to the development of multiple representative genetically engineered mouse models that provided novel insights into gliomagenesis. As we learn more about the biology of high-grade astrocytoma from the study of these models, we anticipate that our improved understanding will eventually lead to greater success in clinical trials and improved outcome for patients.Entities:
Mesh:
Year: 2012 PMID: 22287481 PMCID: PMC3292893 DOI: 10.18632/oncotarget.425
Source DB: PubMed Journal: Oncotarget ISSN: 1949-2553
Figure 1Frequency of pathway mutations in high-grade astrocytoma
The frequency of mutations affecting intermediates of several pathways is indicated in parentheses. Proteins whose genes are amplified or have an activating mutation are represented in red while those whose genes are deleted or have an inactivating mutation are in blue. The proteins are grouped according to the core pathways in which they function (dotted lines). The IDH1 mutant proteins (IDH1*) possess a neomorphic catalytic activity which is depicted. The frequencies of mutations are derived from references 14 and 22.
Comparison of genetically engineered mouse models
| Cre Driver Line/Viral Driver Construct | Targeted Genes | Tumor Histology | Core Pathway Mutation/Dysregulation | Tumor Initiating Cell | Tumor Location | Expression Subgroups | Reference |
|---|---|---|---|---|---|---|---|
| HGA | mutation | adult NPC or astrocyte | proliferative and non-proliferative zones | PN, Prolif, Mes | 42 | ||
| HGA | mutation | ||||||
| HGA, PNET, ONB | dysregulation | NA | |||||
| Adeno- | HGA | dysregulation | postnatal SVZ cells | SVZ | NA | 43 | |
| PNET | NA | ||||||
| PNET | NA | ||||||
| HGA, MB | dysregulation | early NPC or OPC | proliferative zones | NA | 46 | ||
| Adeno- | HGA | NA | glial cells | striatum | NA | 47 | |
| RCAS- | HGA | NA | nestin positive cells | proliferative and non-proliferative zones | NA | 48 | |
| HGA | dysregulation | early SVZ cells | SVZ | NA | 52 | ||
| HGA | dysregulation | early SVZ cells | SVZ | NA | 51 | ||
| HGA | NA | adult NPC | proliferative zones | NA | 53 | ||
| HGA | NA | ||||||
| Adeno- | HGA | NA | adult SVZ cells | SVZ | NA | 53 | |
| HGA | NA | ||||||
| Retroviral | HGA | NA | adult OPC | white matter | PN | 61 | |
| HGA | NA | ||||||
| HGA | NA | early OPC | proliferative zones | PN | 63 |
Abbreviations: SVZ – subventricular zone; HGA – high-grade glioma; PNET – primitive neuroectodermal tumor; ONB – olfactory neuroblastoma; MB – medulloblastoma; NA – not assessed; NPC – neural progenitor cell; OPC – oligodendrocyte progenitor cell; PN – Proneural; Prolif – Proliferative; Mes – Mesenchymal
Figure 2Framework for translational research using genetically engineered mouse models (GEMMs) for high-grade astrocytoma (HGA)
Tumors arising in HGA GEMMs are subjected to genome-wide analyses (array comparative genomic hybridization, gene expression, microRNA expression). The status of signaling pathways are determined by western blots or immunohistochemistry. These data are analyzed integratively and can be compared to similar data obtained from human HGAs to identify critical driver mutations, pathways and biomarkers of disease. Candidate genes can be validated in novel GEMMs while biomarkers can be queried either retrospectively or prospectively in clinical trials. Identified pathways with known inhibitors can be tested in the GEMMs using various readouts of efficacy. Promising agents or combinations are carried forward into clinical trials. These in turn will lead to banking and further studies of HGA tumor samples.