BIOM-91. RELATIONSHIPS BETWEEN LYMPHOCYTE COUNTS AND CLINICAL OUTCOMES IN PATIENTS WITH GLIOMAS TREATED WITH PEMBROLIZUMAB
Bibliographic record
Abstract
Abstract BACKGROUND Pembrolizumab elicits durable responses in many solid tumors, yet activity in gliomas has been limited. Lymphopenia has been reported in association with reduced survival and a shorter time to progression on checkpoint inhibition in other solid tumors. We evaluated whether low absolute lymphocyte count (ALC) is related to worse outcomes in pembrolizumab-treated patients with gliomas. METHODS In this single-center retrospective study, we identified adults with gliomas who received ≥1 dose of pembrolizumab and had available lymphocyte counts at our center between 05/01/2018 and 1/31/2025. Patients’ demographics, dexamethasone use, baseline ALC (before initiation of pembrolizumab), and clinical outcomes were collected. Overall Survival (OS) was estimated using Kaplan–Meier analysis from the date of starting pembrolizumab to the date of death. RESULTS Ninety patients met eligibility criteria (glioblastoma [GBM] n = 44, astrocytoma n = 20, oligodendroglioma n = 13, meningioma n = 8, other n = 5); 62% were male and 31% (n = 28) harbored IDH-mutant tumors. Across all histological subtypes, baseline ALC<750 cells/mm³ was associated with significantly shorter OS compared to baseline ALC≥750 cells/mm³ (3.7 vs 7.7 months, P = 0.0063) with similar dexamethasone exposure (63% vs 52%). In the recurrent GBM subgroup (n = 43; MGMT-methylated 26%), ALC≥750 cells/mm³ correlated with longer OS (8.5 vs 3.4 months, P = 0.0065) with a slightly lower proportion of patients on dexamethasone in high ALC group (52% vs 75%). Conversely, among patients with IDH-mutant gliomas (n = 28), no significant difference in OS was observed based on ALC levels (7.8 vs. 7.3 months, P = 0.58) with similar dexamethasone use (57% and 43% in high and low ALC groups, respectively). CONCLUSION Baseline lymphopenia preceding pembrolizumab treatment is associated with shorter OS in patients with GBM, but not in IDH-mutant gliomas. These associations require further validation in additional patient cohorts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".