New Bioinformatics Methods Makes Waves in Cancer Research

A team of computational scientists and cancer experts have joined together to devise their own bioinformatics software that evaluates how successful current strategies are at identifying cancer-driving mutations, and distinguishing them from benign mutations in cancer cells.

Investigators stressed the importance of assessing how well these genetic research methods work because of their potential value in developing treatments to maintain cancer. The findings were published in the Proceedings of the National Academy of Sciences.

“Identifying the genes that cause cancer when altered is often challenging, but is critical for directing research along the most fruitful course,” said co-author Bert Vogelstein. “This paper established novel ways to judge the techniques used to identify true cancer-causing genes and should considerably facilitate advances in this field in the future.”

One of the greatest challenges investigators faced in the study was the lack of a widely-accepted consensus on what qualifies as a cancer driver gene.

“People have lists of what they consider to be cancer driver genes, but there’s no official reference guide, no gold standard,” said lead author Collin J. Tokheim.

Despite this challenge, the investigators were able to develop a machine-learning-based method for cancer driver gene prediction, as well as a framework for evaluating and comparing other prediction methods.

In the study, the evaluation tool was applied to 8 existing cancer driver gene prediction methods, but the results were not complete reassuring.

“Our conclusion is that these methods still need to get better,” Tokheim said. “We’re sharing our methodology publicly, and it should help others to improve their systems for identifying cancer driver genes.”