ERK1/2 Phosphorylation Predicts Survival in Recurrent Glioblastoma Following Intracerebral and Adjuvant PD-1/CTLA-4 Immunotherapy: A REMARK-guided Analysis

Abstract

Purpose: Evidence suggests that MAPK pathway activation, as measured by ERK1/2 phosphorylation (p-ERK), predicts overall survival (OS) in patients with recurrent glioblastoma receiving anti-PD-1 therapy. We aimed to validate these findings in independent cohorts.

Experimental design: In a 24-patient clinical trial on recurrent glioblastoma and high-grade gliomas, we examined the link between p-ERK levels and OS. Patients received intravenous nivolumab, followed by maximal safe resection and an intracerebral injection of either ipilimumab alone or combined with nivolumab. Biweekly adjuvant nivolumab was then administered up to five times (NCT03233152). Using REporting recommendations for tumor MARKER prognostic studies (REMARK) criteria, we conducted independent analyses for p-ERK quantification and statistical evaluations. Additional comparative analysis included prior cohorts, totaling 65 patients. Cox proportional hazards models and meta-analysis were employed to assess p-ERK as a predictive biomarker after immunotherapy.

Results: Lower median p-ERK+ cell density was observed compared with prior studies, likely due to variable tissue processing across cohorts. Nonetheless, high p-ERK was associated with prolonged OS, particularly in isocitrate dehydrogenase wild-type glioblastomas (P = 0.036). Median OS for high and low p-ERK patients were 55.6 and 30 weeks, respectively. Multivariable analysis reinforced p-ERK's significance in survival prediction (P = 0.011). Upon p-ERK normalization across cohorts (n = 65), meta-analysis supported the survival benefit of elevated tumor p-ERK levels (P = 0.0424).

Conclusions: This study strengthens the role of p-ERK as a predictive biomarker for OS in patients with glioblastoma on immune checkpoint blockade. Future research should focus on further validation in prospective trials and the standardization of preanalytical variables influencing p-ERK quantification.

Figure 1.. Analysis workflow and application of the REMARK criteria.

a, Diagram showing the selection of glioblastoma patient samples and formation of groups based on p-ERK cell density. b, Survival analysis workflow using the REMARK criteria for both complete patient cohort and wild-type IDH glioblastoma patients. The blue arrow represents the transfer of tumor samples and the pink arrows represent the transfer of data. *Patients from the phase I clinical trial with tissue available for analysis.

Figure 2.. Survival outcomes of high-grade glioma patients.

Kaplan-Meier curve showing the OS of analyzed glioblastoma patients.

Figure 3.. Comparison of p-ERK values across independent glioblastoma cohorts.

a, Distribution of p-ERK cell density values for three different glioblastoma patient cohorts (NU/CU cohort from Arrieta et al, UCLA cohort from Cloughesy et al, and UZ Brussels). b, Intensity of p-ERK staining in endothelial cells from 3 glioblastomas at different periods of ischemic time and glioblastomas across the three cohorts. Each dots represent p-ERK intensity on individual endothelial cells within the same samples. P values by two-sided Kruskal Wallis test with post-hoc Dunn’s multiple comparison test.

Figure 4.. Association of p-ERK with survival in glioblastoma patients treated with immune checkpoint blockade.

a, OS comparison between high and low p-ERK groups in the complete glioblastoma patient cohort. b, OS comparison between high and low p-ERK groups in wild-type IDH glioblastoma patients. c, Overall survival comparison among high, medium, and low p-ERK groups in the exploratory analysis.

Figure 5.. Multivariable analysis of p-ERK and covariates influencing survival trends in recurrent glioma patients receiving immune checkpoint blockade.

a, Forest plots showing the association of steroids, IDH mutation, age, and p-ERK after adjusting the multivariable Cox model with cohorts considered as a covariate (N= 65 recurrent glioma patients). b, Forest plot showing the results from the meta-analysis employing a random-effects model in three different cohorts (UZ Brussels, NU/CU, and UCLA; N= 65 recurrent glioma patients). c, Forest plots showing the association of steroids, age, and p-ERK after adjusting the multivariable Cox model with cohorts considered as a covariate (N= 56 recurrent glioma patients). Results are presented as HR (95% CI) in a, b, and c. P value by two-sided Wald test in a and c.