Integrative Cancer Biology Program is Born

The National Cancer Institute launched the Integrative Cancer Biology Program (ICBP) in October 2004, providing a total of $15 million to nine multidisciplinary centers. The goal: to use predictive cancer modeling to better understand how the disease develops and progresses.

 

“Only high-level computation can handle the explosion of information that we’ve seen in the last ten years as a result of genomics, proteomics and molecular imaging,” says Daniel Gallahan, PhD, associate director of the Division of Cancer Biology at the NCI. “Cancer is such a complex problem that we really have to approach it with all the tools in our arsenal. By modeling how cancer develops from initiation to metastasis, we hope to predict and better understand the cancer process.”

 

Until now, cancer researchers have used computation only in a fragmented way. “Hard-core modeling hasn’t been addressed in the cancer community,” Gallahan says. “There has been some modeling of cell migration, some statistical analysis of microarrays, and some modeling of risk factors and predictors, but nothing at the level that we’re taking it to with the ICBP.”

 

Making the leap to more complex computation means that the cancer biologists who head up each of the nine centers had to enlist experts from other fields. “All of these grant applications had to include computation on an equal footing with biology,” Gallahan says.

 

Initially, the projects will be taking the steps necessary to integrate vast amounts of genomic, proteomic, imaging, and other data so that they are usable. Each center will then develop computational methods to make models that address a specific set of biological problems.

 

The nine centers cover the gamut of the cancer process—from initiation through signaling, DNA repair, tumor progression, invasion, angiogenesis and metastasis. One center, at Harvard, will be doing three-dimensional modeling of the tumor itself.

 

In principle, the ICBP should first lead to models at each step of the cancer process, but ultimately, Gallahan says, these should become modules that can be integrated. “Once these models are available in a modular way, we would then piece them together and look at how the cell transforms,” he says. “By increasing our understanding of the cancer process, the models will help us identify and design better prevention and treatment strategies.”

 



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