IMMU-07. COMBINED PD1 AND LAG3 INHIBITION IN PRECLINICAL MODELS AND PATIENTS WITH DNA REPLICATION REPAIR DEFICIENT GLIOBLASTOMA (RRD-GBM): AN IRRDC STUDY
Bibliographic record
Abstract
Abstract BACKGROUND RRD-GBM harbour high tumor mutation burden (TMB) and respond to anti-PD1 immune checkpoint inhibition (ICI). However, the majority ultimately progress, highlighting the need for combinatorial therapies for sustained immune-surveillance. METHODS We performed transcriptomic analyses of human RRD-GBM specimens for immune checkpoint expression, and accordingly, tested combined ICI in immunocompetent murine models. Based on these preclinical data, we treated refractory patients using a combination of anti-PD1 and anti-LAG3 through single-patient trials/ compassionate access. Complimentary immuno-genomic biomarker analyses including circulating tumor DNA (ctDNA) were performed to study mechanisms and track responses. RESULTS Human RRD-GBM (n=80) demonstrated high LAG3 expression, providing a strong rationale for targeting. We tested combined anti-PD1 and anti-LAG3 inhibition in three immunocompetent RRD-GBM murine models. In the anti-PD1-responsive (Nestin-Cre-MSH2LoxP/LoxP-POLES459F/+) model, combined inhibition resulted in universal tumor response and survival. In the anti-PD1 resistant models (Mlh1-/-/Nestin-Cre+/Trp53LoxP/LoxP and therapy-induced hypermutant ENU/Trp53-/- gliomas), the combination improved survival despite a lack of response to anti-PD1 monotherapy. Biologically, high LAG3 expression and exhaustion was observed in CD8 T-cells after treatment with anti-PD1, which was subsequently ablated by the addition of anti-LAG3. Serially transplanted mice showed response and improved survival to the combination, suggesting that resistance to anti-PD1 could be abrogated by the combination. Four patients with RRD-GBM who had failed anti-PD1 treatment were treated using the combination, resulting in objective radiological responses and prolonged ongoing survival in patients with RRD-GBM and high LAG3 expression. Tolerability was better than a previous study of combined CTLA4 and PD1 inhibition for similar patients. Correlation with paired immuno-genomic tumor analyses, flow-cytometry, T-cell receptor clonotype and CSF ctDNA are ongoing prospectively. CONCLUSION LAG3 is an effective target in refractory RRD-GBM. Combined inhibition with anti-PD1 inhibition demonstrated radiological response, prolonged survival and manageable toxicities in patients, and will be tested in future clinical trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".