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Enregistrement W2977486434 · doi:10.1093/neuonc/noz174

Finding a four-leaf clover—identifying long-term survivors in IDH-wildtype glioblastoma

2019· letter· en· W2977486434 sur OpenAlexaff
Derek Wong, Stephen Yip

Notice bibliographique

RevueNeuro-Oncology · 2019
Typeletter
Langueen
DomaineMedicine
ThématiqueGlioma Diagnosis and Treatment
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésTerm (time)GlioblastomaWild typeBiologyType (biology)GeneticsOncologyMedicineCancer researchGeneEcologyPhysicsAstronomy

Résumé

récupéré en direct d'OpenAlex

See the article by Burgenske et al. in this issue, pp. 1458–1469. Glioblastoma (GBM) is the most commonly diagnosed primary brain tumor and is associated with the most dismal clinical outcomes of all brain tumors. Patients face a median survival of 9 months with standard of care surgery and adjuvant chemo-radiotherapy increasing survival to 15 months, a modest improvement.1 Standard of care for GBM has remained stagnant for the past two decades, involving a combination of surgery, radiation, and a small selection of chemotherapeutic agents despite significant discoveries in the genomic, biological, and clinical understanding of the disease. One major advancement was the discovery of recurrent heterozygous isocitrate dehydrogenase 1 and 2 (IDH1/2) mutations in infiltrating gliomas and the significance on prognosis and impact on realignment of glass-based diagnoses.2 These mutations occur in a vast proportion of World Health Organization (WHO) grades II and III infiltrating gliomas regardless of morphology and are considered to be “neomorphic” in nature. It results in the subversion of cellular metabolic pathways in the aberrant overproduction of 2-hydroxyglutarate, a potent oncometabolite, which leads to epigenome-wide dysfunction.3 Despite its central role in gliomagenesis, IDH-mutant gliomas exhibit favorable prognoses that supersede traditional histologic grading.4 Malignant transformation of IDH-mutant grades II/III gliomas to secondary GBM is virtually inevitable and these exhibit better survival compared with IDH-wildtype de novo or primary GBM.2,5 Survival for IDH-wildtype GBM remains dismal. The search for therapeutic targets within IDH-wildtype GBMs has remained challenging due to the extraordinary amount of intra- and intertumoral heterogeneity, reflected in their molecular and clinical profiles.6 Prior research has shed light on the genomic drivers of IDH-wildtype GBM such as amplification of EGFR/PDGFRa/PTEN loss, and TP53 mutation.7 While integrated genomic analyses have identified 4 clinical subtypes based on molecular signatures,8 single cell sequencing has revealed that even within one GBM, several populations driven by different genomic events can exist in cohort,9 suggesting that no one single event drives each GBM. Although the survival rates of GBM remain unfavorable, a small percentage of patients have exhibited extraordinary response to treatment and disease-free survival, some surviving over 10 years.1 Therefore, there is very strong motivation to collate and profile GBM patients who have survived beyond the usual course of the disease. Numerous studies, beyond the scope of this editorial, have catalogued and profiled the clinical features and genomic landscapes of these GBM long-term survivors (LTS). However, beyond the usual metrics of age, performance status, O6-methylguanine-DNA methyltransferase promoter methylation, and IDH1/2 mutation status which predicts superior clinical status in GBM, there is little known about the molecular features of GBM LTS. This is even more remarkable in an IDH-wildtype population, which is normally associated with universally poor outcome. In a report titled “Molecular Profiling of Long-Term IDH-Wildtype Glioblastoma Survivors,” Burgenske et al10 aimed to identify the biological differences between patients who survive over 5 years, or LTS, versus patients who succumb to their disease within 2 years, or short-term survivors (STS). The authors assembled an impressive cohort of clinical and molecular data of 12 pretreated IDH-wildtype GBM LTS utilizing targeted next-generation sequencing, copy number profiling, and transcriptomic and global methylation microarray analysis. Using a gene panel of 50 commonly mutated glioma genes and copy number microarray, the authors found no significant enrichment of any genomic events between STS and LTS patients, supporting the notion that GBM is heterogeneous both intra- and intertumorally. No discernible differences in methylation profiles were also detected. However, at the transcriptomic level, LTS patients showed a propensity for increased sphingomyelin and ceramide-related metabolic pathways, while STS patients exhibited increased nucleotide excision repair and cell cycling pathways. Clinically, the authors found that LTS patients were more likely to be younger at diagnosis, female, and to not have been given the current gold standard Stupp regimen of concurrent radiotherapy with temozolomide followed by adjuvant temozolomide. Altogether, the lack of any defining molecular feature useful for predicting LTS remains a disappointment. However, discovery of unique transcriptomic signatures associated with the STS and LTS cohorts may provide promising leads for future research. While the authors have curated a remarkable cohort and generated a large amount of molecular data, further and more in-depth interrogation of this cohort may lead to more clinically informative biomarkers. A more exhaustive analysis of the genomic landscape of LTS using whole exome or even whole genome sequencing would provide a more comprehensive overview of genomic aberrations which may affect survival. Profiling of germline single nucleotide variants may further define the constitutional fabric of this subset of patients. Lastly, proteomic and metabolomic analyses may shed light on pathways that augment the effectiveness of current therapies leading to LTS. Hopefully, granular analyses of GBM LTS patients will enable us to pinpoint specific prognostic and possibly therapeutic clinical markers.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,018

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0040,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,045
Tête enseignante GPT0,319
Écart entre enseignants0,274 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2019
Routes d'admission1
Résumé présentnon

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