New computer method used to identify brain cancer gene

In collaboration with the Press Association

US scientists have developed a new computer-based method of analysing biological data, and used it to unearth the genetic roots of an aggressive form of brain cancer known as ‘mesenchymal’ glioblastoma.

"This method offers an attractive way to make sense of what may be an otherwise overwhelming amount of genomic information - Dr Chris Jones, Cancer Research UK

Columbia University Medical Center (CUMC) researchers combined the new method, or ‘algorithm’, with traditional computer models to look at the underlying causes of a form of glioblastoma in 250 patients with the condition.

The algorithm called DIGGIT (Driver-Gene Inference by Genetical-Genomic Information Theory) identified that about half of the tumours were caused by the loss of a gene called KLHL9.

By reintroducing the protein that is usually created by a functioning KLHL9 gene into mice with the disease, the scientists found they were able to shrink tumours, highlighting a new potential strategy to treat the disease.

The discovery is “really interesting” and has never been observed before, according to Dr Chris Jones, a Cancer Research UK-funded brain tumour researcher based at The Institute of Cancer Research – who was not involved in the study.

“Recent research has shown that, rather than being a single disease, glioblastoma is actually several different subtypes. For some of these, we have a good idea of the key genes which drive tumour growth, but in others we have little clue.

“Identifying what drives the remaining mesenchymal tumours will be a priority, as will determining whether a similar approach can work for other types of cancer. If found to be the case, this method offers an attractive way to make sense of what may be an otherwise overwhelming amount of genomic information. And this, in turn, could help identify genetic alterations that could form targets for new drugs,” he added.

Writing in the journal Cell, study leader Professor Andrea Califano said: "This algorithm adds a new dimension to our ability to identify the genetic causes of complex disease. When combined with other tools that our lab has developed, it will help identify many more genes that hold potential as genetic biomarkers of disease progression and targets for treatment.”


  • Chen J., Flaminia Talos, Harshil Dhruv, Gabrielle E. Rieckhof, Archana Iyer, Kristin L. Diefes, Kenneth Aldape, Michael Berens, Michael M. Shen & Andrea Califano & (2014). Identification of Causal Genetic Drivers of Human Disease through Systems-Level Analysis of Regulatory Networks, Cell, 159 (2) 402-414. DOI: