Combined Immunotherapy Improves Outcome for Replication-Repair-Deficient (RRD) High-Grade Glioma Failing Anti–PD-1 Monotherapy: A Report from the International RRD Consortium
Bibliographic record
Abstract
Immune checkpoint inhibition (ICI) is effective for replication-repair-deficient, high-grade gliomas (RRD-HGG). The clinical/biological impact of immune-directed approaches after failing ICI monotherapy is unknown. We performed an international study on 75 patients treated with anti-PD-1; 20 are progression free (median follow-up, 3.7 years). After second progression/recurrence (n = 55), continuing ICI-based salvage prolonged survival to 11.6 months (n = 38; P < 0.001), particularly for those with extreme mutation burden (P = 0.03). Delayed, sustained responses were observed, associated with changes in mutational spectra and the immune microenvironment. Response to reirradiation was explained by an absence of deleterious postradiation indel signatures (ID8). CTLA4 expression increased over time, and subsequent CTLA4 inhibition resulted in response/stable disease in 75%. RAS-MAPK-pathway inhibition led to the reinvigoration of peripheral immune and radiologic responses. Local (flare) and systemic immune adverse events were frequent (biallelic mismatch-repair deficiency > Lynch syndrome). We provide a mechanistic rationale for the sustained benefit in RRD-HGG from immune-directed/synergistic salvage therapies. Future approaches need to be tailored to patient and tumor biology.Hypermutant RRD-HGG are susceptible to checkpoint inhibitors beyond initial progression, leading to improved survival when reirradiation and synergistic immune/targeted agents are added. This is driven by their unique biological and immune properties, which evolve over time. Future research should focus on combinatorial regimens that increase patient survival while limiting immune toxicity. This article is featured in Selected Articles from This Issue, p. 201.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".