Neuro-oncology in adolescents and young adults—an unmet need
Notice bibliographique
Résumé
See the article by Ng et al in this issue, pp. 851–863. The adolescent and young adult (AYA) age group has been recognized as an orphan group of patients with distinctive biology and unique age-related issues across many different types of cancer.1 This group typically encompasses patients aged 15–39 at time of initial cancer diagnosis. This is a unique time of development, transition, and novel milestones in life, with obvious implications for reaching these goals when facing a life-threatening diagnosis. In AYAs, the spectrum of cancer type and incidence is distinct when compared with pediatric and adult (≥40 y) age groups. Studies have shown worse outcomes for AYAs with hematologic and solid malignancies compared with the pediatric cohort with similar diagnoses.2 Care of these patients may be split between pediatric and adult centers given the age range encompassed in this population and often there are different treatment paradigms dividing these institutions. Toxicity may also be more severe in AYAs when treated with identical regimens compared with children.3 Retrospective studies have highlighted differences in outcomes when patients are treated on pediatric versus adult protocols in certain malignancies, but not all.4 These factors lead to variable treatment practices for AYAs depending on whether they are treated at a pediatric or adult institution. Lastly, these patients have historically had low rates of enrollment in clinical trials, limiting our understanding of outcome in AYAs.1 In primary central nervous system (PCNS) tumors, subgroup analysis of some tumors has suggested a biologic difference between tumors seen in AYAs versus their pediatric or adult counterparts. For example, in medulloblastoma, further subgroup analysis has revealed distinct subsets within each subgroup based on gene expression profiles and DNA methylation, with AYA patients enriched in the sonic hedgehog gamma and wingless beta subgroups along with a virtual absence of Group 3 tumors.5 In analysis of gliomas in the adult population, isocitrate dehydrogenase mutation tended to occur in younger age groups with a median age of 37 years.6 In analysis of pediatric low-grade glioma, BRAF fusions have been shown to have different break points in the adolescent cohort compared with younger children.6 In ependymoma of the posterior fossa (PF), most tumors in AYAs are in the PF-B subgroup with relatively few supratentorial tumors compared with the pediatric population.8 These biologic differences have implications for prognosis in all of these cohorts and suggest a need for a different therapeutic approach for the AYA population compared with a generic pediatric or adult approach. In this issue, Ng et al describe the nationwide incidence of histologically confirmed PCNS tumors entered into the French Brain Tumor Database (FBTDB) over a 6-year period.9 This report provides novel insight into the scope of brain tumors in the AYA population, with reliable data on the spectrum of different histological diagnoses. They found an overall crude rate (CR) of 8.15 cases per 100 000 person-years in the AYA population, with the lowest CR seen in the younger portion of the age group and CR steadily rising with each passing year through adolescence and young adulthood. World Health Organization (WHO) grade IV tumors become more common with aging through this time of life as well. The most common tumors were those of the neuroepithelial tissue, followed by tumors of the meninges and pituitary tumors. This clearly delineates a difference in the rates of different tumor types in AYAs compared with the pediatric or adult (≥40 y) population. This descriptive study provides a sound epidemiologic basis for further studies in this population. Compared with other epidemiologic descriptive studies, the authors note that the CR found in the FBTDB is slightly lower compared with that found in the Central Brain Tumor Registry of the United States (CBTRUS),10 with a CR adjusted to the US population of 8.21 per 100 000 versus 10.43 per 100 000, respectively. This is in part attributable to the requirement of histologic diagnosis for inclusion in FBTDB. Tumors such as pituitary tumors and germ cell tumors, which do not necessarily need a histologic diagnosis, will be omitted using this strategy. There may be other differences that explain the discrepancy in CR between CBTRUS and FBTDB, including different diagnostic strategies (radiologic vs histologic) and true differences between these populations. Further studies are needed to understand these subtleties in greater detail. While Ng et al have demonstrated an exhaustive histologic description of PCNS tumors in the AYA population, this report lacks treatment data, as well as both biological and survival insight. Given the different treatment approaches between pediatric and adult providers, it is likely that treatment of these tumors varied based on the location of therapy. No central review was performed on the tumor tissue, although the authors note that these diagnoses were made by experienced pathologists; however, this remains an obvious limitation. The histologic diagnosis was based on the 2007 WHO classification of CNS tumors, and with the updated 2016 classification more biologic insight may be possible for certain defined molecular subgroups within this registry. Given that the WHO classification only incorporates certain molecular entities, there remains an unmet need to further describe this population and understand the molecular underpinnings of these tumors. The FBTDB also lacks survival data, limiting our understanding of the prognosis of PCNS tumors in the AYA population. Treatment, molecular, and outcome data are required to understand where there is opportunity to improve outcome, and to plan rational clinical trials to better study AYAs. This report highlights not only the need for collaboration between pediatric and adult neuro-oncologists to manage these AYA patients appropriately, but also the need for development of specialists focused on this unique population. Clearly further study is needed to describe the biology and clinical outcomes in this population, but leaders in this field are also needed to invest in the design and implementation of clinical trials to improve outcomes. Clinical programs are needed to address the distinct clinical and psychosocial needs of AYAs. This suggests there is a large unmet need for the AYA population in both research and clinical care which needs to be urgently addressed. No funding supported this work. There is no conflict of interest for either author. Both authors contributed equally to the writing and revision of this work, and have approved the final version. This text is the sole product of these authors and no third party had input or gave support to its writing.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,029 | 0,021 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,004 |
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 ».