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Enregistrement W2133643739 · doi:10.1200/jco.2011.37.8661

Molecular Genetic Approaches and Potential New Therapeutic Strategies for Pediatric Diffuse Intrinsic Pontine Glioma

2011· letter· en· W2133643739 sur OpenAlexaff
Cynthia Hawkins, Ute Bartels, Éric Bouffet

Notice bibliographique

RevueJournal of Clinical Oncology · 2011
Typeletter
Langueen
DomaineMedicine
ThématiqueGlioma Diagnosis and Treatment
Établissements canadiensHospital for Sick Children
Organismes subventionnairesnon disponible
Mots-clésMedicineClinical trialAnaplastic astrocytomaPediatric cancerGliomaDiseaseOncologyAstrocytomaCancerInternal medicinePediatricsCancer research

Résumé

récupéré en direct d'OpenAlex

Diffuse intrinsic pontine gliomas (DIPG) are tumors that diffusely involve the brainstem and appear almost exclusively during childhood and adolescence. This devastating cancer is the main cause of brain tumor–related death in children, with a median survival time of less than 1 year for the majority of affected patients. Because of the location, surgical resection is not an option for this disease, and diagnosis is currently based on clinical findings and radiologic appearance. Radiation is the mainstay of therapy but is largely palliative, and decades of clinical trials of numerous chemotherapeutic regimens have not led to an improvement in outcome for these patients. DIPGs usually histologically resemble high-grade astrocytic tumors (anaplastic astrocytoma or glioblastoma [GBM], WHO grade 3/4). Thus many pediatric clinical trials over the past several decades have been based on agents with activity in adult GBM, although they have failed to show any benefit in pediatric DIPGs. However, we are now entering an era in which molecular data specific to pediatric DIPG are becoming available, thus potentially overcoming one of the roadblocks to smarter trial design and improved patient outcome. Although still relatively limited when compared with those from large-scale genomic studies of adult cancer, several important conclusions can be drawn from the data available for DIPGs so far that can help inform future clinical trials. First, consistent with previous data, Paugh et al report differences at both the copy number and expression level that distinguish pediatric DIPG from both adult and pediatric supratentorial GBM. This confirms that DIPG must be considered as a separate biologic entity for the purposes of clinical trial design. Second, receptor tyrosine kinases (RTKs) appear to be upregulated at the genomic or expression level (or both) in the majority of pediatric DIPGs. The most common recurrent focal gain in pediatric DIPG encompasses PDGFRA, occurring at the genomic level in at least 30% of DIPGs, with an even larger number showing overexpression at the RNA and protein levels. Gain of EGFR does not appear to be a frequent event in pediatric DIPG. However, two other RTKs are reported by Paugh et al to frequently show gains in DIPGs: MET and IGF1R. Interestingly, most, but not all, of the DIPGs showing gain of these RTKs also show gain of PDGFRA (64% and 88% of tumors showing gain of MET or IGF1R, respectively, have a concomitant gain of PDGFRA). Using fluorescence in situ hybridization (FISH), Paugh et al were able to demonstrate that this represented both cases where the same cells showed amplification of both genes and cases where different clones within the same tumor showed amplification of either PDGFRA or MET. Further, their FISH studies uncovered cases with RTK amplification that were missed by single nucleotide polymorphism (SNP) array analysis and vice versa. This raises important questions related to use of these data for clinical trials: (1) If we are to use targeted agents, what method should be used to identify gain of the target (FISH v SNP array)? (2) Should we be looking for gain/amplification of the target at the genomic level or expression of the target at the protein level? (3) What about tumor heterogeneity? If we use biopsy samples (the only real option for real-time, biology-based stratification for clinical trials), will they be representative of the tumor as a whole? The study by Paugh et al suggests substantial tumor heterogeneity at the genomic level. Will this be the same at the protein level? How many cells within the tumor need to express the RTK before a response to a targeted inhibitor might be anticipated? Can we expect a bystander effect, or will we, as suggested by Paugh et al, simply be allowing outgrowth of tumor cells lacking that particular RTK? In addition to these open biologic questions, the availability of these data raises further clinical questions. Can this information translate into a clinical breakthrough after 30 years of unsuccessful clinical trials? Can molecular biology really help to identify the best drug out of a growing number of targeted therapeutics currently under development? Without any doubt, the collection of material from postmortem samples has generated immense hope in the neuro-oncology community, offering insight into critical pathways involved in DIPG growth. The lack of tissue material has certainly been one of the major limiting factors for the development of innovative clinical trials. However, it is unlikely that this new information alone will be sufficient to change the outcome of this deadly disease. These findings need to be linked to other recent breakthroughs in DIPG research, such as the generation of PDGF-induced brainstem glioma models and orthotopic DIPG xenograft models that can potentially be used as preclinical tools for the testing of novel molecules with or without concomitant radiation. Together, these findings will provide new insights into DIPG pathogenesis and may ultimately result in successful therapeutic avenues to treat DIPG. Some recent trials of biologic modifiers have already identified a subset of patient with JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L S

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,009

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,133
Tête enseignante GPT0,380
Écart entre enseignants0,247 · 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'étudeSans objet
Domainenon disponible
GenreÉditorial

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

En bref

Citations9
Publié2011
Routes d'admission1
Résumé présentoui

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