bims-malgli 2023-12-10 papers

Issue of 2023‒12‒10
four papers selected by
Oltea Sampetrean, Keio University

Clin Cancer Res. 2023 Dec 07.

Functional Contribution and Clinical Implication of Cancer-Associated Fibroblasts in Glioblastoma.

Phillip M Galbo, Anne Tranberg Madsen, Yang Liu, Mou Peng, Yao Wei, Michael J Ciesielski, Robert A Fenstermaker, Sarah Graff, Cristina Montagna, Jeffrey E Segall, Simone Sidoli, Xingxing Zang, Deyou Zheng.

PURPOSE: The abundance and biological contribution of cancer-associated fibroblasts (CAFs) in glioblastoma are poorly understood. Here, we aim to uncover its molecular signature, cellular roles, and potential tumorigenesis implications.EXPERIMENTAL DESIGN: We first applied single-cell RNA sequencing and bioinformatics analysis to identify and characterize stromal cells with CAF transcriptomic features in human glioblastoma tumors. Then, we performed functional enrichment analysis and in vitro assays to investigate their interactions with malignant glioblastoma cells.
RESULTS: We found that CAF abundance was low but significantly correlated with tumor grade, poor clinical outcome, and activation of extracellular matrix remodeling using three large cohorts containing bulk RNA-sequencing data and clinical information. Proteomic analysis of a glioblastoma-derived CAF line and its secretome revealed fibronectin (FN1) as a critical candidate factor mediating CAF functions. This was validated using in vitro cellular models, which demonstrated that CAF-conditioned media and recombinant FN1 could facilitate the migration and invasion of glioblastoma cells. In addition, we showed that CAFs were more abundant in the mesenchymal-like state (or subtype) than in other states of glioblastomas. Interestingly, cell lines resembling the proneural state responded to the CAF signaling better for the migratory and invasive phenotypes.
CONCLUSIONS: Overall, this study characterized the molecular features and functional impacts of CAFs in glioblastoma, alluding to novel cell interactions mediated by CAFs in the glioblastoma microenvironment.

DOI: https://doi.org/10.1158/1078-0432.CCR-23-0493

iScience. 2023 Dec 15. 26(12): 108353

TIGIT contributes to the regulation of 4-1BB and does not define NK cell dysfunction in glioblastoma.

Kyle B Lupo, Sandra Torregrosa-Allen, Bennett D Elzey, Sagar Utturkar, Nadia A Lanman, Aaron A Cohen-Gadol, Veronika Slivova, MacKenzie McIntosh, Karen E Pollok, Sandro Matosevic.

TIGIT is a receptor on human natural killer (NK) cells. Here, we report that TIGIT does not spontaneously induce inhibition of NK cells in glioblastoma (GBM), but rather acts as a decoy-like receptor, by usurping binding partners and regulating expression of NK activating ligands and receptors. Our data show that in GBM patients, one of the underpinnings of unresponsiveness to TIGIT blockade is that by targeting TIGIT, NK cells do not lose an inhibitory signal, but gains the potential for new interactions with other, shared, TIGIT ligands. Therefore, TIGIT does not define NK cell dysfunction in GBM. Further, in GBM, TIGIT+ NK cells are hyperfunctional. In addition, we discovered that 4-1BB correlates with TIGIT expression, the agonism of which contributes to TIGIT immunotherapy. Overall, our data suggest that in GBM, TIGIT acts as a regulator of a complex network, and provide new clues about its use as an immunotherapeutic target.

Keywords: Biological sciences; Cancer; Health sciences; Immunology

DOI: https://doi.org/10.1016/j.isci.2023.108353

Acta Neuropathol Commun. 2023 Dec 04. 11(1): 192

Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences.

James Cancer Center Integrated Neuro-Oncology Team

Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence.

Keywords: Clinical decision-making; Glioblastoma; Novel enhancement; Pathology informatics; Pseudo-progression

DOI: https://doi.org/10.1186/s40478-023-01587-w

Neurooncol Adv. 2023 Jan-Dec;5(1):5(1): vdad134

Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures.

Anna Giczewska, Krzysztof Pastuszak, Megan Houweling, Kulsoom U Abdul, Noa Faaij, Laurine Wedekind, David Noske, Thomas Wurdinger, Anna Supernat, Bart A Westerman.

Background: In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays.Methods: We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15).
Results: Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time.
Conclusions: Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma.

Keywords: convolutional networks; drug combination; glioblastoma; image processing; synergistic effect

DOI: https://doi.org/10.1093/noajnl/vdad134