University of California Los Angeles (UCLA) scientists have developed a new technique utilising biomedical big data on various cancers to provide more accurate estimations of patient survival time and the potential response to treatment.

The technique, called Survival Analysis of mRNA Isoform Variation (SURVIV), processes large amounts of patient data for patterns and trends in cancer genetic sequences to help improve the ability of doctors in providing tailored healthcare to cancer patients.

The SURVIV technique y analyses various patient cancer RNA gene isoforms, which are combinations of genetic sequences capable of producing a large variety of different RNA and proteins from a single gene. The process is known as RNA sequencing (RNA-seq), which reveals the presence and quantity of RNA molecules in a given sample.

While conventional methods simply aggregate the different isoforms, SURVIV studies the ratios of different gene sequences in isoforms to detect subtle but important differences.

The researchers, who studied cancers of the breast, brain, lung, ovary and kidney, believe that the technique will allow doctors to make more accurate predictions for patients with different types of cancers.

It may also allow scientists to analyse the genetic sequences from each patient to determine how dangerous each cancer type really is.

The research project studied cancer tissue samples from 2,684 people, part of the National Institutes of Health’s Cancer Genome Atlas, with the research team having worked over two years to develop the algorithm behind the SURVIV technique.

The research team has identified around 200 different isoforms in breast cancer that are believed to affect the lifespan of a patient in different ways. The team believes that this knowledge may eventually be used to target specific lifespan-reducing isoforms to help suppress and treat the disease.

When evaluating the performance of the SURVIV survival predictors across the six different cancer types using the C-Index metric, the researchers found that Isoform-based predictions were consistently more accurate than conventional gene-based predictions.

The result was surprising, as it suggests that Isoform ratios are a more robust indicator than the overall cancer gene presence.

“Our finding suggests that isoform ratios provide a more robust molecular signature of cancer patients in large-scale RNA-seq datasets,” said UCLA Associate Professor of Microbiology, Immunology and Molecular Genetics, Yi Xing.

“In cancer, sometimes a single gene produces two isoforms, one of which promotes metastasis and one of which represses metastasis,” Xing continued. “Understanding the differences between the two is extremely important in combatting cancer.”

Xing added that they plan to apply the method to much larger data sets.

The research paper was published in June 2016 within the journal Nature Communications.

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