Genomics browser for cancer researchers
A Cancer Genomics Browser developed by researchers at the University of California, Santa Cruz, provides a new way to visualise and analyse data from studies aimed at improving cancer treatment by unravelling the complex genetic roots of the disease.
The browser consists of a suite of web-based tools designed to help researchers find patterns in the huge amounts of clinical and genomic data being gathered in large-scale cancer studies. Medical researchers hope to identify genetic signatures and other biomarkers in cancer cells that can be used to predict how individual patients will respond to different therapies throughout the course of their treatment.
A paper describing the Cancer Genomics Browser has been published in the April issue of Nature Methods by a team based at the Jack Baskin School of Engineering at UCSC. Co-author David Haussler, professor of biomolecular engineering, said development of the browser was driven by the needs of cancer researchers, who are now using powerful technologies for genome analysis and DNA sequencing in their efforts to understand cancer at the molecular level.
"Each of these tests gives millions of measurements, and the result is a bad case of data overload," Haussler said. "We've built the cancer browser so that researchers can upload their data and use a variety of software tools to visualise and interpret their results."
To get a user's perspective on the browser as it took shape, Haussler's team worked closely with Dr Laura Esserman, professor of surgery and radiology at UC San Francisco, and Marc Lenburg, associate professor of pathology and laboratory medicine at Boston University School of Medicine. Esserman and Lenburg, both co-authors of the paper, are involved in the I-SPY Trial, a multi-institutional collaboration aimed at identifying biomarkers to predict the most effective therapies for patients with advanced breast cancer.
"What is amazing about the browser is that it allows us to combine complex molecular data and clinical observations, and provides insights into how we can truly improve treatment and outcomes," said Esserman, director of the Carol Franc Buck Breast Care Center and associate director of the Breast Oncology Program at the Helen Diller Family Comprehensive Cancer Center at UCSF.
Many different types of genomic changes can have clinical significance, including insertions, deletions and other changes in the DNA sequence, such as changes in the number of copies of a gene. Moreover, microarrays and high-throughput methods for measuring proteins make it possible to see how these genomic alterations interfere with the cell's normal workings.
The browser was developed by a team of scientists at UCSC's Center for Biomolecular Science and Engineering (CBSE), an interdisciplinary centre housed in the Baskin School of Engineering and directed by Haussler.
The public browser site hosts a growing body of publicly available cancer genomic data, and the browser is also being used on confidential, prepublication data by several groups involved in clinical trials and cancer genomics research, Wang said.
The Cancer Genomics Browser is a natural extension of the UCSC Genome Browser, a widely used platform for accessing and visualising genomic data. The UCSC Genome Browser now averages one million page requests every week. It displays data and annotations in linear tracks that parallel the DNA sequences of the dozens of genomes in the browser.
But this type of display doesn't work well with clinical data from large numbers of patients. And clinical databases don't handle genomic data very well. The Cancer Genomics Browser is able to integrate these different types of data into a single interactive display.
The Cancer Genomics Browser represents data as ‘heatmaps', in which colours represent the values of key variables. Genomic and clinical data are displayed side by side, and researchers can group and sort the data on the basis of any feature of interest, such as age, gender, response to therapy, oestrogen-receptor status of breast cancers, and so on. Because humans excel at visual pattern recognition, correlations in the data tend to jump out as the user manipulates the browser display.
Standard statistical tools are integrated into the browser so that users can perform quantitative analyses. The browser's developers hope to improve these capabilities in the future.
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