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Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences

2023; Springer Science+Business Media; Volume: 11; Issue: 1 Linguagem: Inglês

10.1186/s40478-023-01587-w

2051-5960

Wesley Wang, Jonah Domingo C. Tugaoen, Paolo Fadda, Amanda E. Toland, Qin Ma, J Brad Elder, Pierre Giglio, Pierre Giglio, Shirley Ong, Clement Pillainayagam, Justin Gornanovich, Megan Gould, Judith Lima, Russell R. Lonser, Brad Elder, Douglas A. Hardesty, Timothy Lucas, Saman Ahmadian, Peter Kobalka, Diana Thomas, Wayne Slone, Arnab Chakravarti, Raju R. Raval, Sasha Beyer, Joshua D. Palmer, Dukagjin M. Blakaj, Erica Dawson, Erica H. Bell, José Javier Otero,

Single-cell and spatial transcriptomics

Abstract 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.