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Correlative and spatial transcriptomic analysis of olaparib and durvalumab in patients with recurrent/refractory <i>IDH</i> -mutant gliomas.

2025· article· en· W4410818503 on OpenAlexaff
Xin Wang, Yosef Ellenbogen, Gurveer Gill, Vikas Patil, Thiago Pimentel Muniz, Mary Jane Lim-Fat, Andrew Gao, Ben X. Wang, A. Sorana Morrissy, Warren Mason, Gelareh Zadeh, Eric Xueyu Chen

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsOlaparibMedicineRefractory (planetary science)DurvalumabMutantCancer researchGliomaOncologyInternal medicineCancerBiologyGeneticsPoly ADP ribose polymeraseImmunotherapyGene

Abstract

fetched live from OpenAlex

2075 Background: Combination of immune checkpoint and PARP inhibition has potential synergistic effects in IDH mt gliomas in pre-clinical models. Durvalumab and olaparib demonstrated objective responses in a subset of patients (pts) with IDH mt gliomas (NCT03991832). We report mutational, transcriptomic, and spatial correlative analysis of pts samples from baseline and at time of progression. Methods: Pts with recurrent/refractory IDH mt gliomas received olaparib 300 mg twice daily and durvalumab 1500 mg IV every 4 weeks until disease progression as determined by RANO 2.0 criteria. Whole exome sequencing (WES, n = 28) and total RNA sequencing (RNA-seq, n = 21) were performed on baseline archival formalin-fixed, paraffin-embedded tumor samples. Baseline tumor microenvironment was characterized with multiplex-immunohistochemistry (n = 29). Matched responders (n = 4) and non-responders (n = 6) were further profiled using 10X Visium HD for spatial transcriptomics. An unsupervised deconvolution method was applied using consensus non-negative matrix factorization for de novo discovery of expression programs corresponding to cell types and cell states. Associations with objective response (OR) to therapy were determined using either Fisher’s exact test or rank-sum test. Results: In the 29 pts enrolled between January 2020–February 2023, median age was 40.5 (range 23–66) and 41% were female. The initial tumor grade was 2 (n = 9), 3 (n = 8), and 4 (n = 12). The OR rate was 14% (95% CI 3.9–32%), 1 complete response and 3 partial responses. All cases were mismatch repair proficient. The median tumor mutation burden (TMB) was 16.5, with TMB > 10 in 21 pts (75%). Baseline TMB was not associated with response. The most common co-mutations were TP53 (n = 21, 75%), ATRX (n = 20, 71%), ARID1A (n = 7, 25%), CIC (n = 4, 14%), and NF1 (n = 3, 11%), none were associated with response. There were no canonical mutations in BRCA1 , BRCA2 , or PALB2 . Pathway analysis on differentially expressed genes between responders and non-responders showed convergence on interferon signaling and inflammation among responders (p < 0.001). Lower pre-existing M2-polarized tumor associated macrophages/microglia (high expression of CD68, PDL1, CD163) was associated with response (p < 0.01). These findings were supported by metaprograms in the HD spatial data, which showed higher levels of CD8+ cytotoxic T-cells at baseline in responders. Conversely, M2-polarized macrophage/microglia were enriched in non-responders. Paired progression samples will additionally be presented. Conclusions: Responders to olaparib and durvalumab had decreased baseline M2-polarized macrophages/microglia and increased pre-existing immunogenicity (interferon signaling). Several spatially conserved expression metaprograms targeting baseline immune infiltration were associated with response. Clinical trial information: NCT03991832 .

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.032
GPT teacher head0.383
Teacher spread0.351 · 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 designObservational
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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