Cancer Data Science Pulse

One of the most exciting developments of the past decade has been the success of methods broadly described as deep learning. While the roots of deep learning date back to early machine learning research of the 1950s, recent improvements in specialized computing hardware and the availability of labeled data have led to significant advances and have shattered performance benchmarks in tasks like image classification and language processing.

This is the second of a series of posts that discuss the principles underlying the three-year collaborative program “Joint Design of Advanced Computing Solutions for Cancer (JDACS4C).”

This is the first of a series of posts that discuss the pilot collaborative program “Joint Design of Advanced Computing Solutions for Cancer (JDACS4C)” being pursued by the National Cancer Institute (NCI) and the Department of Energy (DOE).

Biomedical research is evolving with an increasing emphasis on data science, e.g., data integration and storage, data privacy and security, data analytics and data representation, driven by the transformative technologies that have become the currency of genomics in precision medicine. In spite of numerous "beachhead" successes, however, the gap between data and clinical utility continues to grow.

In recent years, genomics has been described as a big data science on par with the likes of Twitter, YouTube, and the scientific pursuit of understanding the universe.

Precision medicine has quickly moved to the forefront of clinical research and practice, and is particularly pertinent to cancer since cancer is a disease of the genome. The need to accelerate discovery in cancer research has been further propelled by the Beau Biden Cancer Moonshot, challenging the community to make a decade's worth of progress in five years.

The recent weeks have been momentous as the high-performance computing (HPC) community embraced the challenge of precision medicine. The theme of this year's leading international supercomputing conference, SC16, was "HPC Matters" and it was evident that HPC matters to precision medicine and that precision medicine matters to the high-performance computing community.

"We are on the cusp of breakthroughs that will save lives, benefit all of humanity. But we have to work together." Vice President Joe Biden's words at the American Association for Cancer Research conference resonate as a clear call to action. When we collaborate and share our expertise, the cancer informatics community can bring a formidable wealth of knowledge and crucial skills to drive and facilitate cancer research.

There has been a lot of press in the past couple of months about the "Cancer Moonshot," first mentioned by Vice President Joe Biden in October 2015, and gaining steam recently with the President's State of the Union address and an initial recommendation of $1 billion of funding. The White House released a Fact Sheet highlighting the exciting and transformative goals of the Moonshot.

Researchers are using 3D printing to gain insights that contribute to advances in basic biomedical research and the development of precision medical therapies by creating 3D models of pathogens, tumors, normal tissues, cells, and biomolecules. Dr. Sriram Subramaniam, principal investigator in the Laboratory of Cell Biology at the NCI Center for Cancer Research (CCR), uses 3D printing as both an educational and a research tool.