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Record W3014007830 · doi:10.1093/neuonc/noaa079

Neuro-oncology in adolescents and young adults—an unmet need

2020· letter· en· W3014007830 on OpenAlexaff
Julie Bennett, Éric Bouffet

Bibliographic record

VenueNeuro-Oncology · 2020
Typeletter
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineOncologyPsychologyInternal medicine

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.029
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0290.021
Insufficient payload (model declined to judge)0.0130.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.287
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2020
Admission routes1
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