A novel DNA damage response signature of IDH-mutant grade II and grade III astrocytoma at transcriptional level

Molecular markers related to patient outcome in patients with IDH-mutant astrocytomas grade 2 to 4: A systematic review

BACKGROUND

Grading and classification of IDH-mutant astrocytomas has shifted from solely histology towards histology combined with molecular diagnostics. In this systematic review, we give an overview of all currently known clinically relevant molecular markers within IDH-mutant astrocytomas grade 2 to 4.

METHODS

A literature search was performed in five electronic databases for English original papers on patient outcome with respect to a molecular marker as determined by DNA/RNA sequencing, micro-arrays, or DNA methylation profiling in IDH-mutant astrocytomas grade 2 to 4. Papers were included if molecular diagnostics were performed on tumour tissue of at least 15 IDH-mutant astrocytoma patients, and if the investigated molecular markers were not limited to the diagnostic markers MGMT, ATRX, TERT, and/or TP53.

RESULTS

The literature search identified 4508 unique articles, published between August 2012 and December 2021, of which ultimately 44 articles were included. Numerous molecular markers from these papers were significantly correlated to patient outcome. The associations between patient outcome and non-canonical IDH mutations, PI3K mutations, high expression of MSH2, high expression of RAD18, homozygous deletion of CDKN2A/B, amplification of PDGFRA, copy number neutral loss of chromosomal arm 17p, loss of chromosomal arm 19q, the G-CIMP-low DNA methylation cluster, high total CNV, and high tumour mutation burden were confirmed in multiple studies.

CONCLUSIONS

Multiple genetic and epigenetic markers are associated with survival in IDH-mutant astrocytoma patients. Commonly affected are the RB signalling pathway, the RTK-PI3K-mTOR signalling pathway, genomic stability markers, and (epigenetic) gene regulation.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.