New algorithm combs cancer data for glioblastoma clues

brain imaging

A team comprising data scientists and cancer researchers has devised an algorithm that could eventually be used to determine the cause of brain tumors and identify new ways to treat them.

The researchers from Uppsala University, the University of Gothenburg, Chalmers University of Technology and the University of Freiburg created their algorithm aSICS (augmented sparse inverse covariance selection) to comb through a large group of data, finding what regulates glioblastomas through a specific transcriptional subtype called Annexin A2, or ANXA2.

They published results in the journal EBioMedicine, illustrating how aSICS takes data from brain tumors across a wide range of patients to find the key behind their growth.


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“According to the computer model, mesenchymal glioblastoma is partly caused by alterations in a gene called Annexin A2,” lead author Sven Nelander of Uppsala said. “To validate the relevance of this prediction we examined samples from patients and could show that mesenchymal glioblastoma have an increased activity of Annexin A2. Subsequently, we tested to inhibit the expression of Annexin A2 in cancer cells from patients and found that the cancer cells either died or changed to a less aggressive form.”

The use of big data in finding the causes of disease and possible treatment targets is becoming more and more prominent with the abundance of data that now exists.

This collaboration is a big part, in fact, of the U.S.’s “Cancer Moonshot” program announced by President Barack Obama at the beginning of the year, the aim of which is to double cancer research efforts (and results) over the next 5 years. In recommendations to the NIH by a panel of experts this month, networking among researchers and the creation of a “Tumor Atlas” played a major role.

“We confirmed ANXA2 as a key regulator of mesenchymal transformation and demonstrated its importance for viability, invasiveness and maintaining a mesenchymal gene signature,” the scientists write in their discussion. “The results thus show that integrative modeling can uncover a new mesenchymal modulator. Additional predictions provide a rich source for future investigation. Our results warrant further investigation of aSICS as a general tool to uncover cancer subtypes.”

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