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Biomedical data and supercomputing analysis reveal links between Alzheimer's and cancer

When comparing brain tissue from Alzheimer's and GBM patients, the microarray profiles show significantly reversed signaling activities. Nodes enriched with down-regulated genes in Alzheimer's disease are shown in blue in the node face, with darker blue indicating larger fold changes. Up-regulated genes in GBM are shown in the node boundary in red. Image courtesy Stephen Wong. Cover image courtesy Oxford Gene Technology.

A team of researchers, led by scientists at Houston Methodist Research Institute (HMRI) in Texas, US, has discovered a new link between Alzheimer's disease and glioblastoma multiform (GBM), the most aggressive form of brain cancer. "This is the first time we have found, that at the molecular mechanism level, there are linkages between the two diseases," says Stephen Wong, lead investigator and a medical researcher and bioengineer at HMRI.

Cells regulate their growth and reproduction by sending signals inward, from receptors at their surface to their nucleus, which contains genetic material. Wong and his team sought out the molecular pathways that the two diseases may share. "No one understands why this link is there, in a biological sense," Wong says. "And that's the reason we did this study. I think we are among the first to study it this way.

By identifying which genes were active in both diseases, the researchers can map those genes to known pathways through a process called pathway analysis. Wong and colleagues formed a working list of common pathways and narrowed that list with cell culture validation tests, as well as tests in live mice. "Once you identify the mechanism - the particular pathway - you can use that information to design a new therapeutic strategy," explains Wong.

"Although GBM and Alzheimer's both affect nearly 50% of the population between the ages of 65 and 85, the body itself has very fine regulation, at a very detailed level within the individual signaling pathways, to make these two diseases exclude each other," says study co-author Hong Zhao.

The team found that in brain cancer, the ERK/MAPK cell signal pathway is up-regulated (meaning it results in increased gene and protein expression). Reciprocally, in Alzheimer's disease, the angiopoietin signaling pathway is up-regulated. "We identified when one signal pathway is up, it's good for one thing, but bad for the other," Wong says. The group's findings appear in the open access journal Scientific Reports by the Nature Publishing Group.

To analyze and compare data from thousands of genes, and narrow the search for common cell signaling pathways between the two diseases, the scientists used the Lonestar and Stampede clusters at the Texas Advanced Computing Center (TACC) at The University of Texas at Austin, US. Using a DNA microarray, the HMRI team can reveal both the active and inactive genes shared between two samples, and thus find the common genes expressed in both.

A look at how DNA microarrays and the process of gene expression profiling, protein microarrays, and comparative genomic hybridization microarrays work. Video courtesy Genome British Columbia.

"It's almost like using a big data approach to address these interesting problems," adds Wong. He says the microarray data sets were fairly manageable, covering 1,091 GBM and 524 Alzheimer's subjects. "The gene sequencing data size would easily be a thousand-fold larger than the microarray data in the reported study," Wong says. "Which means the need to use TACC's Lonestar and Stampede clusters for number crunching is even more eminent."

Both Lonestar and Stampede are part of the US National Science Foundation's Extreme Science and Engineering Discovery Environment (XSEDE). The research is supported by grants from the National Institutes of Health (NIH), and a gift from the Ting Tsung and Wei Fong Chao Foundation.

The research remains in the early stages of understanding the biological links between brain cancer and Alzheimer's disease. Deeper understanding of cell signal regulation could eventually help determine the best treatment options for patients or inspire new drug designs.

"For instance, if important molecules which cause GBM are discovered, they could be developed into drugs and used for the Alzheimer's disease treatment," says study co-author and postdoctoral researcher Xiaoping Zhu of HMRI. "The drug developing process could be shortened compared with the de novo drug discovery," she adds.

"Reversely, some drugs were developed for targeting Alzheimer's, but the clinical trials showed unexpected results; in rare cases the drugs induced the cancer occurrence in patients," Zhao says. "Still, there is sharing of some signaling pathways between these two diseases, and thus the studies to reveal the relationship of these two diseases at the transcriptional molecular level are important."

Wong says his team is going one step further by analyzing recently released, fine-grained (computationally costly) Alzheimer's disease and brain tumor gene sequencing data. "Conventionally, scientific research focuses on one particular protein or one gene. Such a strategy does not scale up for complex diseases like cancer and neurodegeneration.

"We're at the tip of the iceberg. Leveraging the availability of big biomedical data and supercomputing, we're going to dig deeper to delineate crosstalk between different pathways to identify the promising, druggable targets to cure either of these two devastating diseases, or both. It is a fresh, cost-effective strategy, a big data analytic approach, to enable us to find this mechanism. We are witnessing a new era of digital biology."

To read Jorge Salazar'scomplete article, visit the Texas Advanced Computing Center website.

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