Molecular Genetic Approaches and Potential New Therapeutic Strategies for Pediatric Diffuse Intrinsic Pontine Glioma
Notice bibliographique
Résumé
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
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Prédiction machine sur la base complète
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».