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Record W4404256951 · doi:10.1093/neuonc/noae165.0605

IMMU-12. TRANSLATING COMBINED PD1 AND LAG3 INHIBITION FROM PRECLINICAL MODELS TO PATIENTS WITH REFRACTORY, DNA REPLICATION REPAIR DEFICIENT (RRD) GLIOBLASTOMA: AN IRRDC STUDY

2024· article· en· W4404256951 on OpenAlexaff
Anirban Das, Owen Crump, Olha Kos, Nuno M. Nunes, Lucie Stengs, Amanda Li, Adrian Levine, Yoshiko Nakano, Katharine O’Flaherty, Alexander Stein, Gadi Abebe‐Campino, Annika Bronsema, Vanessa Bianchi, Melissa Edwards, Stergios Zacharoulis, Birgit Ertl-Wagner, Daniel A. Morgenstern, Trevor J. Pugh, Pamela S. Ohashi, Éric Bouffet, Cynthia Hawkins, Peter B. Dirks, Uri Tabori

Bibliographic record

VenueNeuro-Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreHospital for Sick Children
Fundersnot available
KeywordsGlioblastomaRefractory (planetary science)Replication (statistics)Cancer researchDNA repairChemistryMedicineDNABiologyVirologyBiochemistryAstrobiology

Abstract

fetched live from OpenAlex

Abstract BACKGROUND AND AIMS RRD-glioblastoma harbour high tumor mutation burden (TMB) and respond to anti-PD1 immune-checkpoint inhibition (ICI). However, not all respond, and the majority progress, highlighting the need for combinatorial therapies for sustained immune-surveillance. METHODS We performed transcriptomic analyses of human RRD-glioblastoma 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+anti-LAG3 through single-patient trial/compassionate access. Complimentary immuno-genomic biomarker analyses including circulating tumor DNA (ctDNA) were performed to investigate mechanisms and track responses. RESULTS Human RRD-glioblastoma (n=80) demonstrated high LAG3 expression, providing a strong rationale for therapeutic targeting. We tested combined anti-PD1+anti-LAG3 inhibition in three immunocompetent RRD-glioblastoma murine models. In the anti-PD1-responsive (Nestin-CreMSH2LoxP/LoxP-POLES459F/+) model, anti-PD1+anti-LAG3 resulted in universal tumor response and superior survival to ICI-monotherapy. In the anti-PD1 resistant models (Mlh1-/-/NestinCre+/Trp53LoxP/LoxP and therapy-induced hypermutant ENU/Trp53-/- gliomas), anti-PD1+antiLAG3 improved survival, overcoming the lack of response to ICI-monotherapy. Biologically, high LAG3 expression and immune-exhaustion observed in CD8 T-cells after treatment with anti-PD1 was ablated following the addition of anti-LAG3. Serially transplanted, post-anti-PD1 treated tumors showed response, confirming, in-vivo, that resistance to anti-PD1 could be abrogated by anti-PD1+anti-LAG3. Seven children with refractory RRD-glioblastoma who had progressed after anti-PD1 treatment were treated using anti-PD1+anti-LAG3, resulting in objective radiological responses and prolonged ongoing survival. Tolerability was better than a previous study of combined CTLA4 and PD1 inhibition for similar patients. Paired immuno-genomic tumor analyses, serial blood flow-cytometry, T-cell receptor clonotype, and CSF ctDNA analyses provided novel insights into the mechanisms of immunological invigoration and first-in-human, radiological responses. CONCLUSIONS LAG3 is an effective target in refractory RRD-glioblastoma. Combined inhibition with anti-PD1 inhibition demonstrated radiological response, prolonged survival and manageable toxicities in patients, and unearthered mechanisms of immune-responses. The combination will now be tested in biomarker-driven clinical trials in RRD-glioblastoma and other immune-inflamed solid tumors.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.038
GPT teacher head0.325
Teacher spread0.286 · 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 designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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