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The standard treatment in glioblastoma includes maximal safe resection followed by concomitant radiotherapy plus chemotherapy and adjuvant temozolomide. The first follow‐up study to evaluate treatment response is performed 1 month after concomitant treatment, when contrast‐enhancing regions may appear that can correspond to true progression or pseudoprogression. We retrospectively evaluated 31 consecutive patients at the first follow‐up after concomitant treatment to check whether the metabolic pattern assessed with multivoxel MRS was predictive of treatment response 2 months later. We extracted the underlying metabolic patterns of the contrast‐enhancing regions with a blind‐source separation method and mapped them over the reference images. Pattern heterogeneity was calculated using entropy, and association between patterns and outcomes was measured with Cramér's V. We identified three distinct metabolic patterns—proliferative, necrotic, and responsive, which were associated with status 2 months later. Individually, 70% of the patients showed metabolically heterogeneous patterns in the contrast‐enhancing regions. Metabolic heterogeneity was not related to the regions' size and only stable patients were less heterogeneous than the rest. Contrast‐enhancing regions are also metabolically heterogeneous 1 month after concomitant treatment. This could explain the reported difficulty in finding robust pseudoprogression biomarkers. [ABSTRACT FROM AUTHOR]

The tumor microenvironment in glioblastoma (GB) is considered to be "cold", i.e., the fraction of cytotoxic T cells, for instance, is low. Instead, macrophages are the major immune cell population in GB, which stem either from tissue response (resident microglia) or recruitment of macrophages from the periphery, thereby undergoing tumor-dependent "imprinting" mechanisms by which macrophages can adapt a tumor-supportive phenotype. In this regard, it is important to describe the nature of macrophages associated with GB, in particular under therapy conditions using the gold standard chemotherapy drug temozolomide (TMZ). Here, we explored the suitability of combining information from in vivo magnetic resonance spectroscopic (MRS) approaches (metabolomics) with in vitro molecular analyses to assess therapy response and characterize macrophage populations in mouse GB using an isogenic GL261 model. For macrophage profiling, expression levels of matrix metalloproteinases (MMPs) and A disintegrin and metalloproteinases (ADAMs) were determined, since their gene products affect macrophage–tumor cell communication by extensive cleavage of immunomodulatory membrane proteins, such as PD-L1. In tumor mice with an overall therapy response, expression of genes encoding the proteases ADAM8, ADAM10, and ADAM17 was increased and might contribute to the immunosuppressive phenotype of GB and immune cells. In tumors responding to therapy, expression levels of ADAM8 were upregulated by TMZ, and higher levels of PD-L1 were correlated significantly. Using a CRISPR/Cas9 knockout of ADAM8 in GL261 cells, we demonstrated that soluble PD-L1 (sPD-L1) is only generated in the presence of ADAM8. Moreover, primary macrophages from WT and ADAM8-deficient mice showed ADAM8-dependent release of sPD-L1, independent of the macrophage polarization state. Since ADAM8 expression is induced in responding tumors and PD-L1 shedding is likely to decrease the anti-tumor activities of T-cells, we conclude that immunotherapy resistance is caused, at least in part, by the increased presence of proteases, such as ADAM8. [ABSTRACT FROM AUTHOR]

Despite the success of automated pattern recognition methods in problems of human brain tumor diagnostic classification, limited attention has been paid to the issue of automated data quality assessment in the field of MRS for neuro‐oncology. Beyond some early attempts to address this issue, the current standard in practice is MRS quality control through human (expert‐based) assessment. One aspect of automatic quality control is the problem of detecting artefacts in MRS data. Artefacts, whose variety has already been reviewed in some detail and some of which may even escape human quality control, have a negative influence in pattern recognition methods attempting to assist tumor characterization. The automatic detection of MRS artefacts should be beneficial for radiology as it guarantees more reliable tumor characterizations, as well as the development of more robust pattern recognition‐based tumor classifiers and more trustable MRS data processing and analysis pipelines. Feature extraction methods have previously been used to help distinguishing between good and bad quality spectra to apply subsequent supervised pattern recognition techniques. In this study, we apply feature extraction differently and use a variant of a method for blind source separation, namely Convex Non‐Negative Matrix Factorization, to unveil MRS signal sources in a completely unsupervised way. We hypothesize that, while most sources will correspond to the different tumor patterns, some of them will reflect signal artefacts. The experimental work reported in this paper, analyzing a combined short and long echo time 1H‐MRS database of more than 2000 spectra acquired at 1.5T and corresponding to different tumor types and other anomalous masses, provides a first proof of concept that points to the possible validity of this approach. [ABSTRACT FROM AUTHOR]