Immune checkpoint inhibitors targeting programmed cell death 1 (PD1) or its ligand (PD-L1) showed activity in several cancer types. We performed immunohistochemistry for CD3, CD8, CD20, HLA-DR, phosphatase and tensin homolog (PTEN), PD-1, and PD-L1 and pyrosequencing for assessment of the O6-methylguanine-methyltransferase (MGMT) promoter methylation status in 135 glioblastoma specimens (117 initial resection, 18 first local recurrence). PD-L1 gene expression was analyzed in 446 cases from The Cancer Genome Atlas. Diffuse/fibrillary PD-L1 expression of variable extent, with or without interspersed epithelioid tumor cells with membranous PD-L1 expression, was observed in 103 of 117 (88.0%) newly diagnosed and 13 of 18 (72.2%) recurrent glioblastoma specimens. Sparse-to-moderate density of tumor-infiltrating lymphocytes (TILs) was found in 85 of 117 (72.6%) specimens (CD3+ 78/117, 66.7%; CD8+ 52/117, 44.4%; CD20+ 27/117, 23.1%; PD1+ 34/117, 29.1%). PD1+ TIL density correlated positively with CD3+ (P < .001), CD8+ (P < .001), CD20+ TIL density (P < .001), and PTEN expression (P = .035). Enrichment of specimens with low PD-L1 gene expression levels was observed in the proneural and G-CIMP glioblastoma subtypes and in specimens with high PD-L1 gene expression in the mesenchymal subtype (P = 5.966e-10). No significant differences in PD-L1 expression or TIL density between initial and recurrent glioblastoma specimens or correlation of PD-L1 expression or TIL density with patient age or outcome were evident. TILs and PD-L1 expression are detectable in the majority of glioblastoma samples but are not related to outcome. Because the target is present, a clinical study with specific immune checkpoint inhibitors seems to be warranted in glioblastoma.

Tumor infiltrating lymphocytes (TILs) and programmed death ligand 1 (PD-L1) are targets of immune checkpoint inhibitors. Forty-three World Health Organization (WHO) grade II/III gliomas (39 IDH-mutant [mut], 4 IDH-wildtype [wt]) and 14 IDH-mut glioblastomas (GBM) were analyzed for TIL (CD3+; PD1+) infiltration and PD-L1 expression. Results were compared with the data of a previously published series of 117 IDH-wt glioblastomas. PD-L1 gene expression levels were evaluated in 677 diffuse gliomas grades II–IV from The Cancer Genome Atlas (TCGA) database. TIL and PD-L1 expression were observed in approximately half of WHO grade II/III gliomas. IDH-wt status was associated with significantly higher TIL infiltration and PD-L1 expression among all (grades II–IV) cases (n = 174, P < 0.001) and within the cohort of glioblastomas (n = 131, P < 0.001). In low-grade glioma (LGG) and glioblastoma cohorts of TCGA, significantly higher PD-L1 gene expression levels were evident in IDH-wt compared with IDH-mut samples (LGG: N = 516; P = 1.933e-11, GBM: N = 161; P < 0.009). Lower PD-L1 gene expression was associated with increased promoter methylation (Spearman correlation coefficient −0.36; P < 0.01) in the LGG cohort of TCGA. IDH-mut gliomas had higher PD-L1 gene promoter methylation levels than IDH-wt gliomas (P < 0.01). The immunological tumor microenvironment of diffuse gliomas differs in association with IDH mutation status. IDH-wt gliomas display a more prominent TIL infiltration and higher PD-L1 expression than IDH-mut cases. Mechanistically this may be at least in part due to differential PD-L1 gene promoter methylation levels. Our findings may be relevant for immune modulatory treatment strategies in glioma patients.

Intraoperative identification of anaplastic foci in diffusely infiltrating gliomas (DIG) with non-significant contrast-enhancement on MRI is indispensible to avoid histopathological undergrading and subsequent treatment failure. Recently, we found that 5-aminolevulinic acid (5-ALA) induced protoporphyrin IX (PpIX) fluorescence can visualize areas with increased proliferative and metabolic activity in such gliomas intraoperatively. As treatment of DIG is predominantely based on histopathological World Health Organisation (WHO) parameters, we analyzed whether PpIX fluorescence can detect anaplastic foci according to these criteria. We prospectively included DIG patients with non-significant contrast-enhancement that received 5-ALA prior to resection. Intraoperatively, multiple samples from PpIX positive and negative intratumoral areas were collected using a modified neurosurgical microscope. In all samples, histopathological WHO criteria and proliferation rate were assessed and correlated to the PpIX fluorescence status. A total of 215 tumor specimens were collected in 59 patients. Of 26 WHO grade III gliomas, 23 cases (85%) showed focal PpIX fluorescence, whereas 29 (91%) of 33 WHO grade II gliomas were PpIX negative. In intratumoral areas with focal PpIX fluorescence, mitotic rate, cell density, nuclear pleomorphism, and proliferation rate were significantly higher than in non-fluorescing areas. The positive predictive value of focal PpIX fluorescence for WHO grade III histology was 85%. Our study indicates that 5-ALA induced PpIX fluorescence is a powerful marker for intraoperative identification of anaplastic foci according to the histopathological WHO criteria in DIG with non-significant contrast-enhancement. Therefore, application of 5-ALA optimizes tissue sampling for precise histopathological diagnosis independent of brain-shift.

Gliomas are the most common types of tumors in the brain. While the definite diagnosis is routinely made ex vivo by histopathologic and molecular examination, diagnostic work-up of patients with suspected glioma is mainly done by using magnetic resonance imaging (MRI). Nevertheless, L-S-methyl-¹¹C-methionine (¹¹C-MET) Positron Emission Tomography (PET) holds a great potential in characterization of gliomas. The aim of this study was to establish machine learning (ML) driven survival models for glioma built on ¹¹C-MET-PET, ex vivo and patient characteristics. Methods: 70 patients with a treatment naïve glioma, who had a positive ¹¹C-MET-PET and histopathology-derived ex vivo feature extraction, such as World Health Organization (WHO) 2007 tumor grade, histology and isocitrate dehydrogenase (IDH1-R132H) mutation status were included. The ¹¹C-MET-positive primary tumors were delineated semi-automatically on PET images followed by the feature extraction of tumor-to-background ratio based general and higher-order textural features by applying five different binning approaches. In vivo and ex vivo features, as well as patient characteristics (age, weight, height, body-mass-index, Karnofsky-score) were merged to characterize the tumors. Machine learning approaches were utilized to identify relevant in vivo, ex vivo and patient features and their relative weights for 36 months survival prediction. The resulting feature weights were used to establish three predictive models per binning configuration based on a combination of: in vivo/ex vivo and clinical patient information (M36IEP), in vivo and patient-only information (M36IP), and in vivo only (M36I). In addition a binning-independent ex vivo and patient-only (M36EP) model was created. The established models were validated in a Monte Carlo (MC) cross-validation scheme. Results: Most prominent ML-selected and -weighted features were patient and ex vivo based followed by in vivo features. The highest area under the curve (AUC) values of our models as revealed by the MC cross-validation were: 0.9 (M36IEP), 0.87 (M36EP), 0.77 (M36IP) and 0.72 (M36I). Conclusion: Survival prediction of glioma patients based on amino acid PET using computer-supported predictive models based on in vivo, ex vivo and patient features is highly accurate.

This study aimed to assess the correlation of temporal muscle thickness (TMT), measured on routine cranial magnetic resonance (MR) images, with lumbar skeletal muscles obtained on computed tomography (CT) images in brain metastasis patients to establish a new parameter estimating skeletal muscle mass on brain MR images. We retrospectively analyzed the cross-sectional area (CSA) of skeletal muscles at the level of the third lumbar vertebra on computed tomography scans and correlated these values with TMT on MR images of the brain in two independent cohorts of 93 lung cancer and 61 melanoma patients (overall: 154 patients) with brain metastases. Pearson correlation revealed a strong association between mean TMT and CSA in lung cancer and melanoma patients with brain metastases (0.733; p<0.001). The two study cohorts did not differ significantly in patient characteristics, including age (p = 0.661), weight (p = 0.787), and height (p = 0.123). However, TMT and CSA measures differed significantly between male and female patients in both lung cancer and melanoma patients with brain metastases (p<0.001). Our data indicate that TMT, measured on routine cranial MR images, is a useful surrogate parameter for the estimation of skeletal muscle mass in patients with brain metastases. Thus, TMT may be useful for prognostic assessment, treatment considerations, and stratification or a selection factor for clinical trials in patients with brain metastases. Further studies are needed to assess the association between TMT and clinical frailty parameters, and the usefulness of TMT in patients with primary brain tumors.