New Tool May Provide Deeper Understanding of Glioblastoma Multiforme

Researchers at Cornell University have developed a new tool to study genetic switches active in human glioblastoma tumors that drive the growth of the cancer.

Cornell University College of Veterinary Medicine researchers have developed a new tool to study genetic switches active in human glioblastoma multiforme (GBM) tumors that drive the growth of the cancer. Their work, published recently in Nature Genetics, identifies key switches in different tumor types, some of which are associated with how long a patient with glioblastoma survives.1

GBM is an aggressive cancer; most patients survive just 15 months after diagnosis even with treatment. The new technique provides information on which switch is turning on a tumor or a disease cell, thereby providing a starting point to think about how that switch can be turned off, according to study coauthor Charles Danko, PhD.

Lead author Tinyi Chu, PhD, a graduate fellow in the laboratory at Cornell’s Baker Institute for Animal Health, said the researchers had identified enhancers that regulate groups of genes that are characteristic of each known human GBM subtype and transcription factors that drive them. The paper details how the group found 3 transcription factors that control the expression of genes associated with clinical outcomes and can drive out-of-control growth of cancer cells: C/EBP, RAR, and NF-кB. The target genes of these 3 transcription factors are systematically correlated with poor clinical outcomes.

The researchers explain that many diseases, including cancer, have fundamental defects in how genes are used, not necessarily defects in the genes themselves. Genes make up only 2% of our genome, and transcription factors (switches) bind to the genome to turn those genes on and off, which trigger the cellular changes that cause disease.

The investigators used a technique called chromatin run-on (ChRO-seq), a new nascent transcription assay, to analyze the tumors and create a map of which switches are active and which genes they turn on. Using ChRO-seq data, the researchers classified the glioblastoma into subtypes based on which switches were active in different tumors compared with healthy brain tissue. The team identified 3 switches that will be tested to determine whether they can predict which glioblastoma patients survive longer, including 2 switches whose connections were previously unknown. It is hoped that these findings can eventually be used to create personalized treatment plans for patients or help to develop new therapies.

The researchers (along with colleagues at the State University of New York Upstate Medical University) analyzed 20 human glioblastoma samples from a tissue bank. They devised the new ChRO-seq protocol, which produces similar maps of transcription to PRO-seq in cell lines,2 but it can also be applied to solid tissue samples.

“Through this technique we have gained new insights into the molecular etiology of GBM,” the researchers note. “ChRO-seq has the potential to be used across the biomedical sciences to analyze regulatory programs that contribute to solid tumors and other tissues that have proved challenging to study using existing molecular tools.”

The investigators state that the insights they report were possible only because ChRO-seq is a more direct indicator of transcription factor activity than other tools applied previously in GBM. They conclude, “As the pharmacology for targeting diverse transcription factor families develops, the transcription factors reported here, as well as our strategies for finding them, should become more useful in nominating targeted therapies.”

Taken together, the analysis suggests that the 3 transcription factors detailed in the study work together to activate a shared regulatory program that controls inflammatory processes and correlates with poor clinical outcomes in GBM.


  1. Chu R, Rice EJ, Booth GT, et al. Chromatin run-on and sequencing maps the transcriptional regulatory landscape of glioblastoma multiforme. Nat Genet. 2018;50:1553-1564. doi: 10.1038/s41588-018-0244-3.
  2. Mahat DB, Kwak H, Booth GT, et al. Base-pair-resolution genome-wide mapping of active RNA polymerases using precision nuclear run-on (PRO-seq). Nat Protoc. 2016;11:1455-1476. doi: 10.1038/nprot.2016.086.