Using Big Data, Scientists Discover Biomarkers that Could Help Give Cancer Patients Better Survival Estimates

Using Big Data, Scientists Discover Biomarkers that Could Help Give Cancer Patients Better Survival Estimates

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.

Read Also:
Apple aims to up its AI smarts with iCloud user data in iOS 10.3

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.

Read Also:
What is predictive analytics and how could you use it?


HR & Workforce Analytics Summit 2017 San Francisco

19
Jun
2017
HR & Workforce Analytics Summit 2017 San Francisco

$200 off with code DATA200

Read Also:
The Role Of Business Intelligence In Social Media Marketing

M.I.E. SUMMIT BERLIN 2017

20
Jun
2017
M.I.E. SUMMIT BERLIN 2017

15% off with code 7databe

Read Also:
Can Big Data Help Us Travel Better?

Sentiment Analysis Symposium

27
Jun
2017
Sentiment Analysis Symposium

15% off with code 7WDATA

Read Also:
Connecting Anything to Anything: How the Mulesoft Team Got a Commodore 64 to Tweet

Data Analytics and Behavioural Science Applied to Retail and Consumer Markets

28
Jun
2017
Data Analytics and Behavioural Science Applied to Retail and Consumer Markets

15% off with code 7WDATA

Read Also:
7 Keys To Building A Successful Big Data Infrastructure

AI, Machine Learning and Sentiment Analysis Applied to Finance

28
Jun
2017
AI, Machine Learning and Sentiment Analysis Applied to Finance

15% off with code 7WDATA

Read Also:
The New Data Scientist Venn Diagram

Leave a Reply

Your email address will not be published. Required fields are marked *