Cellular Evolution Upon Glioblastoma Recurrence At Single-Cell Resolution

Glioblastoma (GBM) is the most aggressive and prevalent type of primary brain cancer and has a poor prognosis with a median survival of approximately 1 year. The disease is managed best with a multimodal approach combining surgical resection, postoperative radiation therapy and adjuvant chemotherapy, however, even with the best medical treatment the majority of patients relapse following initial therapy. Moreover, once the disease recurs it is practically resistant to treatment and both patients and caregivers are left with very few second line treatment options in their arsenal which are only marginally efficient.

Substantial efforts have been made in recent years to characterize the molecular mechanisms governing GBM tumor evolution, including characterization of four GBM genomic subtypes by The Cancer Genome Atlas (TCGA) as well as dissection of the GBM heterogeneous intra-tumor ecosystem at the unprecedented resolution provided by single-cell sequencing technologies. However, despite these breakthroughs that laid the ground to understanding the biological mechanisms underlying this terrible disease and the forces driving it, a comprehensive understanding and characterization of the mechanisms responsible for recurrence and resistance to treatment are still lacking.

Our work tackles this problem at the single-cell resolution and to the best of our knowledge is first of its kind. Using frozen tumor sample pairs obtained from adult and pediatric GBM patients at two different time points - at initial diagnosis (the primary lesion) and at first disease recurrence - we perform single-nuclei RNA sequencing and computationally analyze the transcriptomes of thousands of cells to dissect the intra-tumor composition and uncover the differences between the transcriptomes of these matched samples. This unveils the cellular evolution upon recurrence that is driven by tremendous genetic and epigenetic changes, as well as by changes in the composition of the tumor microenvironment (TME), that these tumors undergo between the different time-points and enables characterizing better the mechanisms that govern resistance to treatment which will ultimately result in improved therapeutic outcomes for patients suffering from this horrible disease.