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. In this research, the UCLA scientists studied cancers of the breast, brain (glioblastoma multiforme, a highly malignant and aggressive form; and lower grade glioma, a less aggressive version), lung, ovary and kidney.
In addition, it may allow scientists to analyze people’s genetic sequences and determine which are lethal and which are harmless.
The new method analyzes various gene isoforms — combinations of genetic sequences that can produce an enormous variety of RNAs and proteins from a single gene — using data from RNA molecules in cancer specimens. That process, called RNA sequencing, or RNA-seq, reveals the presence and quantity of RNA molecules in a biological sample. In the method developed at UCLA, scientists analyzed the ratios of slightly different genetic sequences within the isoforms, enabling them to detect important but subtle differences in the genetic sequences. In contrast, the conventional analysis aggregates all of the isoforms together, meaning that the technique misses important differences within the isoforms.
SURVIV (for “survival analysis of mRNA isoform variation”) is the first statistical method for conducting survival analysis on isoforms using RNA-seq data, said senior author Yi Xing, a UCLA associate professor of microbiology, immunology and molecular genetics.
