How much change lies ahead for cancer science? Orders of magnitude. Below, from the Cancer Moonshot page, an excerpt from an example of the vast computing power being used to discern the molecular level characteristics of the many diseases we call cancer.

“WHY SUPERCOMPUTERS?
To find a cure, we need to find patterns we haven’t seen before. The challenge is not a lack of relevant data — we have more than ever before. The challenge is accessing that data, and processing it to find patterns that tell us something about what causes cancer, or how to fight it more effectively. With supercomputers, we can find answers to questions that are practically impossible to solve with the human eye. In this case, supercomputers are excellent tools for analyzing genomic and molecular datasets, patient records, family histories and other complex information related to cancer.
Energy Department supercomputing is pushing the intersection of big data analytics, machine learning and modeling and simulation. Today our top supercomputers can make over 20 million billion calculations per second. Over the past 20 years, our determination to maintain our nuclear weapons stockpile without testing drove the Energy Department to develop computers that could model nuclear processes down to tiny fractions of a second. That meant raising the processing speed of the world’s best computers by a factor of 10,000. Last year we invested $550 million into new high-performance computing centers, bringing multiple National Labs together to increase our computing capabilities a further five- to seven-fold.

These future computers will be an entirely new breed. Not only are they fast, they handle big data in entirely new ways. They open avenues for techniques in artificial intelligence, data science and simulations that can tease out new insights. These centers lay the groundwork for advanced “exascale” computing, which can perform a billion billion calculations per second — 20 to 40 times what we’re talking about today. And that will be necessary to adequately understand the complexities of cancer.”