A SURVIV analysis of breast cancer isoforms developed at UCLA. Blue lines are associated with longer survival times and magenta lines with shorter survival times.
A new method developed by UCLA scientists could lead to a way to produce more reliable projections for survival time and treatment plans for people with cancer. The approach, which uses information about patients’ genetic sequences, is an innovative way of using biomedical big data — which glean patterns and trends from massive amounts of patient information — to achieve precision medicine, giving doctors the ability to better tailor their care for each patient.
The method 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 approach 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, which stands for “survival analysis of mRNA isoform variation,” is the first statistical method for conducting survival analysis on isoforms using RNA-seq data, says Yi Xing, PhD, associate professor of microbiology, immunology and molecular genetics and a member of the UCLA Institute for Quantitative and Computational Biosciences.
The researchers identified some 200 isoforms that are associated with survival time for people with breast cancer; some predict longer survival times, while others are linked to shorter times. Armed with that knowledge, scientists might eventually be able to target the isoforms associated with shorter survival times in order to suppress them and fight disease, Dr. Xing says.
The researchers evaluated the performance of survival predictors using a metric called C-index and found that across the six different types of cancer they analyzed, their isoform-based predictions performed consistently better than the conventional gene-based predictions. 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, Dr. Xing says.
The researchers studied tissues from 2,684 people with cancer, whose samples were part of the National Institutes of Health’s Cancer Genome Atlas, and they spent more than two years developing the algorithm for SURVIV. Dr. Xing notes that a human gene typically produces seven-to-10 isoforms. “We have just scratched the surface,” he says. “We will apply the method to much larger data sets, and we expect to learn a lot more.”
“SURVIV for Survival Analysis of mRNA Isoform Variation,” Nature Communications, June 9, 2016