UC San Francisco has received a National Cancer Institute grant of $5 million over the next five years to lead a massive effort to integrate the data from all experimental models across all types of cancer. The web-based repository is an important step in moving the fight against cancer toward precision medicine.
The goal is to accelerate cancer research to improve the way we diagnose, treat and conduct further research on the disease. The resulting database, called the Oncology Models Forum (OMF), will be accessible to researchers through the National Institutes of Health, to encourage scientists to use existing validated cancer models, rather than creating new ones.
"There are incredible new discoveries happening in cancer research today, such as detecting cancer cells and DNA in the blood stream, and even harnessing the immune system to fight cancers," said Atul Butte, MD, PhD, director of the Institute for Computational Health Sciences at UCSF and principal investigator for the grant. "These research methodologies generate enormous amounts of data that can and should be harnessed by researchers and engineers to yield new drugs and diagnostics."
Cell lines and mice have been placeholders for studying human cancer for decades, resulting in thousands of mouse models for all cancer types. While results from those studies are chronicled in scientific papers and journals, it is difficult to know how relevant the data from these experimental systems are to the actual research and development of drugs and diagnostics in actual human cancers.
This is particularly important, Butte said, because there can be a gap of up to 10 years between the early basic science discoveries from experimental systems and the actual clinical trial of the drug candidates that are developed from that science, with many drug candidates failing in those clinical trials. As a result, it is critically important to ensure that early scientific discoveries are in fact relevant to human cancers, to provide every possible hope that the eventual drugs developed from those discoveries will work in clinical trials and be available to cancer patients.
The project aims to create an online cache of molecular data that oncologists and cancer researchers could use to validate the current models that best translate to humans, make predictions about the disease and move toward a collaborative, precision medicine approach to cancer. Ultimately, Butte said, the effort also has the potential to create computer-based cancer models that greatly reduce the need for using animals in research.
The project, led by the UCSF Institute for Computational Health Sciences, will collaborate with Alejandro Sweet-Cordero, MD, Julien Sage, PhD, and Nigam Shah, PhD, at Stanford University, who will provide support with the latest genetically-engineered cancer models, as well as standardized nomenclatures. It also will include bioinformatics specialists from the Northrop Grumman Corp., who will help build and maintain the online database.
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