Phenotype stability under dynamic brain-tumor environment stimuli maps glioblastoma progression in patients

Abstract

Although tumor invasiveness is known to drive glioblastoma (GBM) recurrence, current approaches to treatment assume a fairly simple GBM phenotype transition map. We provide new analyses to estimate the likelihood of reaching or remaining in a phenotype under dynamic, physiologically likely perturbations of stimuli (“phenotype stability”). We show that higher stability values of the motile phenotype (Go) are associated with reduced patient survival. Moreover, induced motile states are capable of driving GBM recurrence. We found that the Dormancy and Go phenotypes are equally represented in advanced GBM samples, with natural transitioning between the two. Furthermore, Go and Grow phenotype transitions are mostly driven by tumor-brain stimuli. These are difficult to regulate directly, but could be modulated by reprogramming tumor-associated cell types. Our framework provides a foundation for designing targeted perturbations of the tumor-brain environment, by assessing their impact on GBM phenotypic plasticity, and is corroborated by analyses of patient data.

Glioma cells survival depends both on fatty acid oxidation and on functional carnitine transport by SLC22A5

“…The fact that glioblastoma cells with a high level of SLC22A5 in the plasma membrane, and overall high SLC22A5 expression, more actively oxidize fatty acids can be the reason why patients with high SLC22A5 expression have worse prognosis when compared with those with low SLC22A5 expression (Fink et al., 2019). Moreover, our detection of apoptosis upon inhibition of FAO and SLC22A5 indicates that the phenotype of glioma cells switched the “grow” to “apoptosis” phenotype (Rajapakse, Herreada, & Lavi, 2020).…”

Glucose fluxes in glycolytic and oxidative pathways detected in vivo by deuterium magnetic resonance spectroscopy reflect proliferation in mouse glioblastoma

“…Accordingly, metabolic plasticity represents a key element for cancer cell survival, adaptation, and proliferation in a rapidly shifting microenvironment ( Fendt et al, 2020 , Gillies et al, 2012 , Lehuédé et al, 2016 ), with a pivotal role for mitochondrial reprogramming between e.g. invasive and proliferative states (the latter supported by oxidative metabolism ( Li et al, 2020 )), which are present in GBM ( Rajapakse et al, 2020 , Xie et al, 2014 ). Altogether, our observations strengthen the relevance of DGE 2 H-MRS for in vivo detection of GBM dependencies on oxidative metabolism at any given progression stage.…”

A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma

“…In a recent study, Rajapakse et al used computational modelling to define cancer cell states and allocate active and dormant cancer cell types within these different tumour regions. 80 In contrast, in this work, we combined publicly available scRNA-seq data (using the CellKb platform) and our own experiments to determine the gene signatures expressed by different cell types in the microenvironment and how these relate to different tumour compartments. Although spatial transcriptomics 81 – 83 and multiplex immunofluorescence imaging 74 , 75 approaches can offer similar outcomes to that presented here, these techniques are still prohibitively expensive and time-consuming.…”