People with cancer are often told by their doctors approximately how long they have to live, and how well they will respond to treatments, but what if there were a way to improve the accuracy of doctors’ predictions? A new method developed by UCLA scientists could eventually lead to a way to do just that, using data about patients’ genetic sequences to produce more reliable projections for survival time and how they might respond to possible treatments. The technique is an innovative way of using biomedical big data — which gleans patterns and trends from massive amounts of patient information — to achieve precision medicine — giving doctors the ability to better tailor their care for each individual patient. The approach is likely to enable doctors to give more accurate predictions for people with many types of cancers.
The result was surprising because it suggests, contrary to conventional wisdom, that isoform ratios provide a more robust molecular signature of cancer patients than overall gene abundance, said Xing, director of UCLA’s bioinformatics doctoral program and a member of the UCLA Institute for Quantitative and Computational Biosciences.