Purpose

PD-L1 is upregulated in glioblastoma and supports immunosuppression. We evaluated PD-L1 blockade with durvalumab among glioblastoma cohorts and investigated potential biomarkers.

Patients and methods

MGMT unmethylated newly diagnosed patients received radiotherapy plus durvalumab (cohort A; n = 40). Bevacizumab-naïve, recurrent patients received durvalumab alone (cohort B; n = 31) or in combination with standard bevacizumab (cohort B2; n = 33) or low-dose bevacizumab (cohort B3; n = 33). Bevacizumab-refractory patients received durvalumab plus bevacizumab (cohort C; n = 22). Primary endpoints were: OS-12 (A), PFS-6 (B, B2, B3), and OS-6 (C). Exploratory biomarkers included: a systematic, quantitative, and phenotypic evaluation of circulating immune cells; tumor mutational burden (TMB); and tumor immune activation signature (IAS).

Results

No cohort achieved the primary efficacy endpoint. Outcome was comparable among recurrent, bevacizumab-naïve cohorts. No unexpected toxicities were observed. A widespread reduction of effector immune cell subsets was noted among recurrent patients compared with newly diagnosed patients that was partially due to dexamethasone use. A trend of increased CD8+Ki67+ T cells at day 15 was noted among patients who achieved the primary endpoint and were not on dexamethasone. Neither TMB nor IAS predicted outcome.

Conclusions

Patients with recurrent glioblastoma have markedly lower baseline levels of multiple circulating immune cell subsets compared with newly diagnosed patients. An early increase in systemic Ki67+CD8+ cells may warrant further evaluation as a potential biomarker of therapeutic benefit among patients with glioblastoma undergoing checkpoint therapy. Dexamethasone decreased immune cell subsets. PD-L1 blockade and combination with standard or reduced dose bevacizumab was ineffective.

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.