Three New Paradigms

By Sarah Greene Three New Paradigms The way we research cancer and present our data to the world is undergoing a major revolution. Open Science embraces open access publishing and advances the underlying concept a few light years. Cancer is very personal. That’s brought home in the lead paragraph of the lead story of this issue, Building a Better Mouse. The patient with lung cancer asks her doctor if she might qualify for a promising targeted t

Sarah Green
Apr 1, 2010

Three New Paradigms

The way we research cancer and present our data to the world is undergoing a major revolution.

Open Science embraces open access publishing and advances the underlying concept a few light years.

Cancer is very personal. That’s brought home in the lead paragraph of the lead story of this issue, Building a Better Mouse. The patient with lung cancer asks her doctor if she might qualify for a promising targeted therapy now in Phase III trials, and—after testing positive for the ALK mutation—her oncologist connects her with the responsible research team. We don’t get to hear the outcome of this story, but we’re inspired to ponder three new paradigms: participatory medicine, pharmacogenomics, and the open science movement.

Becoming experts on their own life-threatening diseases has only become plausible for a subset of the population with the advent of the Internet. Access to information is their lifeline—information...

But even an apparently single type of cancer is likely to have different causes in different patients. And even if the specific type and sub-type of a cancer can be diagnosed, the treatment must focus on what can be a very subtle difference from that individual’s “normal” cells. This brings us to the great new hope of pharmacogenomics and personalized medicine, where molecular profiling using microarrays and other technologies can lead to targeted therapies based on individual genomes. This underlies the development of increasingly cheaper genome sequencing technologies that will eventually make it feasible to decode each one of us.

Imagine the data, and imagine the science that must be invested in collecting, filtering, interpreting, and modeling data—already a petabyte a day—to achieve the ultimate goal of drug discovery. That is, if the data are accessible. This leads us to the third revolution—“Open Science.” Also known as Science Commons, it embraces open access publishing and advances the underlying concept a few light years.

The open science movement, like open access, is dedicated to making research findings, including raw data, available at no cost, but also to creating an infrastructure—through development of governance, software, and tools—that sparks generative research through collaboration. “Generative” refers to “a system’s capacity to produce unanticipated change through unfiltered contributions from broad and varied audiences,” as defined by Jonathan Zittrain in Harvard Law Review and quoted by John Wilbanks, a leader of open science at Creative Commons. So open science does more than just put information and data out there—it finds ways to use and build on them to drive science forward.

A growing number of activists and organizations are working in this arena, addressing issues of property rights as they impact patents, material objects such as stem cells, research results, and data—and creating open source software and tools such as Open Notebook Science. Sage Bionetworks, a nonprofit organization headed by pediatric oncologist Stephen Friend, is working to collect huge biomedical data sets and ensure that they are accessible, by establishing legal and research standards that will permit interoperability and collaborative platforms. In addition, Friend asserts, the Sage mission is to implement a “ pre competitive position for human disease biology,” whereby competition comes into play at the stage of drug development, not during the process of biomedical knowledge acquisition.

This issue also describes how the International Lung Cancer Consortium, made up of scientists from around the world, is gathering an enormous data set (genome-wide association data from more than 40,000 cases) to understand genetic factors behind lung cancer. Just one more example of getting a little help from our friends.

Interested in reading more?

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