Targeting the non-coding genome and temozolomide signature enables CRISPR-mediated glioma oncolysis
Glioblastoma (GBM) is the most common lethal primary brain cancer in adults. Despite treatment regimens including surgical resection, radiotherapy, and temozolomide (TMZ) chemotherapy, growth of residual tumor leads to therapy resistance and death. At recurrence, a quarter to a third of all gliomas have hypermutated genomes 5, with mutational burdens orders of magnitude greater than in normal tissue. Here, we quantified the mutational landscape progression in a patient's primary and recurrent GBM, and uncovered Cas9-targetable repeat elements. We show that CRISPR-mediated targeting of highly repetitive loci enables rapid elimination of GBM cells, an approach we term “Genome Shredding”. Importantly, in the patient's recurrent GBM, we identified unique repeat sequences with TMZ mutational signature and demonstrate that their CRISPR targeting enables cancer-specific cell ablation. “Cancer Shredding” leverages the non-coding genome and therapy-induced mutational signatures for targeted GBM cell depletion and provides an innovative paradigm to develop treatments for hypermutated glioma. Overall design: Validation of Cancer-Specific sgRNA Prediction through a CRISPR Screen in SF11411, a Patient-Derived Hyper-Mutated GBM Cell Line, and Normal Human Astrocytes (NHAs). Our objective is to identify sgRNAs that are specifically depleted in SF11411, a patient-derived hyper-mutated glioblastoma multiforme (GBM) cell line, compared to normal human astrocytes (NHAs) by utilizing a CRISPR library and next-generation sequencing (NGS). Both SF11411 and NHAs will be transduced with the CRISPR library, and samples will be collected at two time points: day 1 (T0) and day 28 (D28) post-transduction, equivalent to approximately 14 cell doublings. Subsequently, NGS will be employed to sequence the sgRNAs in both cell populations, and the resulting data will undergo analysis to pinpoint sgRNAs that exhibit specific depletion in SF11411 cells when compared to NHAs, thereby validating the prediction of cancer-specific sgRNAs.