HGG-14. Immune checkpoint inhibitors in children with replication repair deficient high-grade gliomas: The South London Paediatric & TYA Neuro-Oncology Network experience.
Bibliographic record
Abstract
Abstract Background Pediatric high-grade gliomas (pHGG) are the most common intracranial tumors in children with replication repair deficiency (RRD) syndromes. The hypermutant phenotype constitutes a vulnerability to immune checkpoint inhibitors (ICI). However, responses are variable, and resistance mechanisms are not fully understood. Methods Single-institution retrospective review from children and adolescents diagnosed with HGG and molecularly confirmed RRD treated at the South London Paediatric and TYA Neuro-Oncology Network between January 2000 and December 2023. Clinical, histopathological, molecular, genetic, therapeutic and survival data were collected. Descriptive statistics, Kaplan-Meier and univariable Cox proportional hazard models were used. Results Six cases were identified (3 females). Median age was 9.6 years (range 2.74-11.02). Five supratentorial and one spinal HGG. Germline genetic testing confirmed heterozygous MMR gene variants (Lynch Syndrome) in 4 cases (67%) and homozygous (CMMRD syndrome) in 2 (33%). Tumour Mutational Burden was available for 3 patients, of whom two presented additional mutations in POLE/POLD2 and ultrahypermutant phenotype. Two children were treated with ICI upfront, two at relapse and two upfront and at relapse. One presented a synchronous Burkitt Lymphoma and received sequential rituximab and ICI after initial surgery; another developed a metachronous lymphoblastic lymphoma whilst on ICI for pHGG. Two cases developed hypothyroidism grade 2. Grade 3 toxicities included (n = 1 each): elevated AST/ALT and new onset of insulin-dependent diabetes. No grade 4 toxicities. Five cases (83%) died of disease: median OS 11.4 months (range 10.0-35.8). One patient remains in complete remission 26.5 months after initial diagnosis. The two patients with ultrahypermutant phenotype presented the longest survival. Conclusions Although ICI have a molecularly-driven rationale to treat RRD-HGG, the best agent, optimal drug combinations, and best predictive biomarkers yet remain to be elucidated. Collaboration with expert initiatives such as the International Replication Repair Deficiency Consortium (IRRDC) is essential to continue developing better tailored treatments for these children.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".