Extreme structural heterogeneity rewires glioblastoma chromosomes to sustain patient-specific transcriptional programs

Abstract

Glioblastoma multiforme (GBM) encompasses brain malignancies marked by phenotypic and transcriptional heterogeneity thought to render these tumors aggressive, resistant to therapy, and inevitably recurrent. However, little is known about how the spatial organization of GBM genomes underlies this heterogeneity and its effects. Here, we compiled a cohort of 28 patient-derived glioblastoma stem cell-like lines (GSCs) known to reflect the properties of their tumor-of-origin; six of these were primary-relapse tumor pairs from the same patient. We generated and analyzed kbp-resolution chromosome conformation capture (Hi-C) data from all GSCs to systematically map >3,100 standalone and complex structural variants (SVs) and the >6,300 neoloops arising as a result. By combining Hi-C, histone modification, and gene expression data with chromatin folding simulations, we explain how the pervasive, uneven, and idiosyncratic occurrence of neoloops sustains tumor-specific transcriptional programs via the formation of new enhancer-promoter contacts. We also show how even moderately recurrent neoloops can help us infer patient-specific vulnerabilities. Together, our data provide a resource for dissecting GBM biology and heterogeneity, as well as for informing therapeutic approaches.

The genome in space and time comes of age

“…Importantly, they demonstrate that, without a priori information of the 3D structure of the genome, it is difficult to predict the effect of the identified SV. However, using Hi-C, they revealed SV promoting formation of neo-loops that sustained tumor-specific program via new enhancer–promoter interaction [ 12 ]. Such neo-loops could help inferring patient-specific vulnerabilities and provide a better diagnostic for glioblastoma patients.…”

Pervasive structural heterogeneity rewires glioblastoma chromosomes to sustain patient-specific transcriptional programs

“…All code used to analyze Hi-C, RNA-seq, WGS, and CUT&Tag data 82 is available at https://github.com/xieting0603/GBM; the custom code used to perform simulations 83 is available at https://github.com/ marianoimperatore/MeanFieldChromatin.git.…”