Challenges to curing primary brain tumours

Aldape, Kenneth; Brindle, Kevin M.; Chesler, Louis; Chopra, Rajesh; Gajjar, Amar; Gilbert, Mark R.; Gottardo, Nicholas G.; Gutmann, David H.; Hargrave, Darren; Holland, Eric C.; Jones, David; Joyce, Johanna A.; Kearns, Pamela; Kieran, Mark W.; Mellinghoff, Ingo K.; Merchant, Melinda S.; Pfister, Stefan M.; Pollard, Steven M.; Ramaswamy, Vijay; Rich, Jeremy; Robinson, Giles; Rowitch, David H.; Sampson, John H.; Taylor, Michael D.; Workman, Paul; Gilbertson, Richard J.

doi:10.1038/s41571-019-0177-5

Cited by 689 publications

(556 citation statements)

References 90 publications

Supporting

Mentioning

555

Contrasting

Order By: Relevance

“…Glioma comprise different subtypes of glial tumors including astrocytoma and glioblastoma multiforme (GBM). Thereby, the latter group is the most aggressive type and to date, non-curable [48,49]. Astrocytoma derive from star-shaped glia cells that are called astrocytes [50].…”

Section: Signaling Backgroundmentioning

confidence: 99%

Open Data for Differential Network Analysis in Glioma

Jean-Quartier¹,

Jeanquartier²,

Holzinger³

2020

IJMS

View full text Add to dashboard Cite

The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. By using selected exemplary tools and open-access resources for cancer research and differential network analysis, we highlight disturbed signaling components in brain cancer subtypes of glioma.

show abstract

Section: Signaling Backgroundmentioning

confidence: 99%

Open Data for Differential Network Analysis in Glioma

Jean-Quartier¹,

Jeanquartier²,

Holzinger³

2020

IJMS

View full text Add to dashboard Cite

show abstract

“…In addition, most lower-grade gliomas (grade II and III, LGG) will progress to glioblastoma (grade IV, GBM) in less than 10 years [4, 7, 8]. At present, the reasons for glioma recurrence or malignant progression may be as follows: 1) infiltrative tumour cells cannot be completely removed by neurosurgical resection [9, 10]; 2) retained tumour cells cannot be effectively suppressed by limited postoperative treatment options [3, 11, 12]; 3) multiple lesions may develop [13, 14]; 4) cell cloning is rapid under chemotherapy and/or radiotherapy [7, 15]; 5) the adaptive tumour microenvironment permits tumour cells [16, 17]; and 6) limited data resources lead to limited research. Therefore, it is essential to collect clinical specimens and generate genomic data for the glioma research community.…”

Section: Introductionmentioning

confidence: 99%

Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data for Chinese Glioma Patients

Zhao

Zhang

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

1Gliomas are the most common and malignant intracranial tumours in adults. Recent studies have 2 shown that functional genomics greatly aids in the understanding of the pathophysiology and 3 therapy of glioma. However, comprehensive genomic data and analysis platforms are relatively 4 limited. In this study, we developed the Chinese Glioma Genome Atlas (CGGA, 5 http://www.cgga.org.cn), a user-friendly data portal for storage and interactive exploration of multi-6 dimensional functional genomic data that includes nearly 2,000 primary and recurrent glioma 7 samples from Chinese cohorts. CGGA currently provides access to whole-exome sequencing (286 8 samples), messenger RNA sequencing (1,018 samples) and microarray (301 samples), DNA 9 methylation microarray (159 samples), and microRNA microarray (198 samples) data, as well as 10 detailed clinical data (e.g., WHO grade, histological type, critical molecular genetic information, 11 age, sex, chemoradiotherapy status and survival data). In addition, we developed an analysis tool to 12 allow users to browse mutational, mRNA/microRNA expression, and DNA methylation profiles and 13 perform survival and correlation analyses of specific glioma subtypes. CGGA greatly reduces the 14 barriers between complex functional genomic data and glioma researchers who seek rapid, intuitive, 15 and high-quality access to data resources and enables researchers to use these immeasurable data 16 sources for biological research and clinical application. Importantly, the free provision of data will 17 allow researchers to quickly generate and provide data to the research community. 18 19

show abstract

“…Glioblastoma (GBM) is the most frequent and most aggressive primary brain tumor 1,2 . Despite decades of intensive research, average survival time remains at 12-15 months from diagnosis 3 . Surgical resection of GBM tumors is rarely complete because the tumor aggressively infiltrates the brain, with cells interconnecting via long membrane protrusions (microtubes) 4 .…”

Section: Introductionmentioning

confidence: 99%

Modeling glioblastoma invasion using human brain organoids and single-cell transcriptomics

Krieger

Tirier

Park

et al. 2019

Preprint

View full text Add to dashboard Cite

AbstractGlioblastoma multiforme (GBM) are devastating neoplasms with high invasive capacity. GBM has been difficult to study in vitro. Therapeutic progress is also limited by cellular heterogeneity within and between tumors. To address these challenges, we present an experimental model using human cerebral organoids as a scaffold for patient-derived glioblastoma cell invasion. By tissue clearing and confocal microscopy, we show that tumor cells within organoids extend a network of long microtubes, recapitulating the in vivo behavior of GBM. Single-cell RNA-seq of GBM cells before and after co-culture with organoid cells reveals transcriptional changes implicated in the invasion process that are coherent across patient samples, indicating that GBM cells reactively upregulate genes required for their dispersion. Functional therapeutic targets are identified by an in silico receptor-ligand pairing screen detecting potential interactions between GBM and organoid cells. Taken together, our model has proven useful for studying GBM invasion and transcriptional heterogeneity in vitro, with applications for both pharmacological screens and patient-specific treatment selection at a time scale amenable to clinical practice.

show abstract

Challenges to curing primary brain tumours

Cited by 689 publications

References 90 publications

Open Data for Differential Network Analysis in Glioma

Open Data for Differential Network Analysis in Glioma

Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data for Chinese Glioma Patients

Modeling glioblastoma invasion using human brain organoids and single-cell transcriptomics

Contact Info

Product

Resources

About