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407 Relationships between lymphocyte counts and clinical outcomes in patients with recurrent glioblastoma treated with pembrolizumab

2025· article· W4415898975 on OpenAlexaff
Md. Al Amin, Sydney Schultz, Justin Tang, Hongzhi Dong, Nishika Karbhari, Jian Campian

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2025
Typearticle
Language
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPembrolizumabLymphocyteGlioblastomaImmunotherapyImmune system

Abstract

fetched live from OpenAlex

Background Recurrent glioblastoma (rGBM) remains uniformly lethal, with median survival measured in months despite multimodal therapy. Although pembrolizumab produces durable responses in other solid malignancies, activity in rGBM has been disappointing. Emerging data from melanoma, lung, and renal cancers suggest that treatment-induced or disease-related lymphopenia correlates with inferior outcomes on checkpoint inhibition. Because rGBM patients frequently receive lymphocyte-suppressive corticosteroids and chemoradiation, we asked whether a low pre-treatment absolute lymphocyte count (ALC) identifies a subgroup unlikely to benefit from pembrolizumab.Methods We performed a single-center retrospective study of adults with rGBM who received ≥1 dose of pembrolizumab between 1 May 2018 and 31 January 2025 and had lymphocyte counts available at our center. Demographics, MGMT status, prior bevacizumab exposure, dexamethasone use at initiation, and baseline ALC were abstracted from electronic records. Overall survival (OS) was defined as the time from the date of pembrolizumab initiation to the date of death from any cause or last follow up. Median OS (mOS) was calculated with Kaplan-Meier methods, and survival curves were compared with the log-rank test; an exploratory subgroup analysis stratified patients by bevacizumab exposure status. Low ALC was defined as <750 cells/mm 3 in this analysis.Results Forty-three patients met eligibility criteria (median age 59 years, 51% male) and 26% (n = 11) with MGMT methylation. Sixteen patients were bevacizumab-naïve and twenty-seven were bevacizumab-refractory. Across the entire cohort, baseline ALC<750 cells/mm 3 was associated with significantly shorter OS compared to baseline ALC≥750 cell/mm3 (3.4 vs 8.5 months, p=0.0048) with a slightly higher proportion of patients on dexamethasone in low ALC group (75% vs 52%). Among bevacizumab-naïve patients (n=16), mOS was 9.7 months; within this subgroup, patients with ALC<750 cells/mm3 demonstrated a trend toward shorter OS (4 vs. 11 months, p = 0.056) and had slightly higher dexamethasone exposure (80% vs. 55%). In the bevacizumab-refractory group, mOS was 4.3 months; again, low ALC trended toward a shorter OS (2.6 vs 7 months; p=0.075) with similar rates of dexamethasone use (73% vs 50%).Conclusions Baseline lymphopenia preceding pembrolizumab treatment is associated with shorter mOS in patients with rGBM. While the sample size was limited, the consistency of trends across subgroups highlights the need for further validation in larger patient cohorts.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.271
Teacher spread0.253 · 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
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