Researchers at UCLA developed a new statistical method that provides more accurate estimates on how long a cancer patient has to live.
The tool, called Survival Analysis of mRNA Isoform Variation, looks at mRNA isoform ratios in RNA-sequencing data to predict survival times. The team behind the method spent more than two years developing the algorithm.
The study, published in the journal Nature Communications, includes samples from more than 2,600 patients with six different types of cancer. After analyzing the patients' molecular and clinical profiles, the scientists found new biomarkers that surround cancer prognosis and treatment. Some isoforms were found to predict longer survival while others indicated shorter lengths.
"[W]e found that isoform-based predictions work consistently better than the conventional gene-based predictions in predicting survival time," said Yi Xing, PhD, lead author of the study.
According to Dr. Xing, it could take one to three years before the technology is used in clinical settings. He plans on applying the method to a larger data set across more types of cancer to develop more dependable isoform-based patient survival predictions.
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