Impact of the H3K27M mutation on survival in pediatric high-grade glioma: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE Pediatric high-grade gliomas (pHGGs), including diffuse intrinsic pontine glioma, present a prognostic challenge given their lethality and rarity. A substitution mutation of lysine for methionine at position 27 in histone H3 (H3K27M) has been shown to be highly specific to these tumors. Data are accumulating regarding the poor outcomes of patients with these tumors; however, the quantification of pooled outcomes has yet to be done, which could assist in prioritizing management. The aim of this study was to quantitatively pool data in the current literature on the H3K27M mutation as an independent prognostic factor in pHGG. METHODS Searches of seven electronic databases from their inception to March 2018 were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data were extracted and pooled using a meta-analysis of proportions. Meta-regression was used to identify potential sources of heterogeneity. RESULTS Six observational studies satisfied the selection criteria for inclusion. They reported the survival outcomes of a pooled cohort of 474 pHGG patients, with 258 (54%) and 216 (46%) patients positive and negative, respectively, for the H3K27M mutation. Overall, the presence of the mutation was independently and significantly associated with a worse prognosis (HR 3.630, p < 0.001). Overall survival was significantly shorter (by 2.300 years; p = 0.008) when the H3K27M mutation was present in pHGG. Meta-regression did not identify any study covariates of heterogeneous concern. CONCLUSIONS According to the current literature, pHGG patients positive for the H3K27M mutation are more than 3 times more susceptible to succumbing to this disease by more than 2 years, compared to patients negative for the mutation. More robust outcome data are required to improve our quantitative understanding of this pathological entity in order to assist in prioritizing clinical management. Future larger prospective studies are required to overcome inherent biases in the current literature to validate the quantitative findings of this study. ABBREVIATIONS CI = confidence interval; GRADE = Grades of Recommendation Assessment, Development and Evaluation; HR = hazard ratio; MD = mean difference; NOS = Newcastle-Ottawa Scale; OS = overall survival; pHGG = pediatric high-grade glioma; PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RE = random effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".