Titre
A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Zinn, P.O.
Auteure/Auteur
Sathyan, P.
Auteure/Auteur
Mahajan, B.
Auteure/Auteur
Bruyere, J.
Auteure/Auteur
Hegi, M.
Auteure/Auteur
Majumder, S.
Auteure/Auteur
Colen, R.R.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
1932-6203
Statut éditorial
Publié
Date de publication
2012
Volume
7
Numéro
8
Première page
e41522
Langue
anglais
Notes
Publication types: Journal ArticlePublication Status: ppublish. Author Contributions : conceived and designed the experiments: POZ RRC. Performed the experiments: POZ PS BM JB RRC. Analyzed the data: POZ PS BM JB RRC. Contributed reagents/materials/analysis tools: POZ MH SM RRC. Wrote the paper: POZ PS RRC.
Résumé
BACKGROUND: Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission.
METHODS: We quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N = 64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis.
RESULTS: VAK-A and VAK-B classes showed significant median survival differences in discovery (P = 0.007) and validation sets (P = 0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P = 0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients.
CONCLUSIONS: The non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.
METHODS: We quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N = 64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis.
RESULTS: VAK-A and VAK-B classes showed significant median survival differences in discovery (P = 0.007) and validation sets (P = 0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P = 0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients.
CONCLUSIONS: The non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.
PID Serval
serval:BIB_93B5D844F165
PMID
Open Access
Oui
Date de création
2012-09-20T17:11:04.398Z
Date de création dans IRIS
2025-05-20T21:04:52Z
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Nom
BIB_93B5D844F165.P001.pdf
Version du manuscrit
preprint
Taille
600.27 KB
Format
Adobe PDF
PID Serval
serval:BIB_93B5D844F165.P001
URN
urn:nbn:ch:serval-BIB_93B5D844F1655
Somme de contrôle
(MD5):1c822edd993dec6e7d060deb7239b620