A team led by Houston Methodist Research Institute (HMRI) scientists has found that Alzheimer's disease and cancer share a pathway in gene transcription, a process essential for cell reproduction and growth. They published their findings in December 2013 in the open access journal Scientific Reports by the Nature Publishing Group.
The scientists used the Lonestar and Stampede supercomputers at the Texas Advanced Computing Center (TACC) at The University of Texas at Austin to analyze and compare data from thousands of genes and to narrow the search for common cell signaling pathways of the two diseases. The Lonestar and Stampede systems are part of the Extreme Science and Engineering Discovery Environment (XSEDE), a single virtual system that scientists use to interactively share computing resources, data and expertise. XSEDE is funded by the National Science Foundation (NSF) through award ACI-1053575. The research is supported by a gift from the T.T. and W.F. Chao Foundation, and by grants from the National Institutes of Health (NIH).
Lead investigator Stephen Wong, a medical researcher and bioengineer with HMRI, said his study showed a new link between Alzheimer's disease, the most prevalent form of neurodegenerative disease, and glioblastoma multiform (GBM), the most aggressive form of brain cancer.
"This is the first time people have found that, at the molecular mechanism level, there are linkages between the two diseases," Wong said.
A 2012 study in Taiwan and a 2013 study in Italy of public health data had shown an inverse association between Alzheimer's disease, a severe degeneration of the brain's nerve cells, and cancer, where cells grow out of control.
"No one understands why this link is there, in a biological sense," Wong said. "And that's the reason we did this study. I think we are among the first to study it this way."
Cells regulate their growth and reproduction by sending signals inward from receptors at their surface to the nucleus containing its genetic material. Wong and his team sought the molecular pathways the two diseases might share. By finding which genes were active in the two diseases, the active genes could be mapped to known pathways through a process called pathway analysis. Wong and colleagues formed a working list of common pathways and narrowed that list further with validation tests in cell cultures and with live mice.
"Once you identify the mechanism, the particular pathway, we can use that information to design a new therapeutic strategy," Wong said.
The first step in finding the common genes expressed in each disease involved using a DNA microarray to reveal the active and inactive genes shared between the two samples of brain cancer vs. Alzheimer's disease.
"We identified when one signal pathway is up, it's good for one thing but bad for the other," Wong said.
His team found that the ERK/MAPK cell signal pathway is up-regulated in brain cancer. Reciprocally, the Angiopoietin Signaling pathway is up-regulated in Alzheimer's disease. Further tests showed the suppression of tumor growth in the cells of mice with Alzheimer's was mediated by the ERK-AKT-p21-cell cycle pathway and anti-angiogenesis pathway.
"Although GBM and Alzheimer's both affect nearly 50 percent for aged population between 65 and 85 years of age, 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," said study co-author Hong Zhao, HMRI. "Different kinds of cells, like Alzheimer's disease cells or cancer cells, have very fine and elaborated regulations on the general molecular signaling pathways, which depend on the cells' response to the microenvironments."
The HMRI team analyzed large amounts of microarray data of Alzheimer's disease and brain tumors in this study.
This analysis included gene annotation, pathway expansion, enrichment analysis and more.
"The results guided our further studies with cell cultures and in live mice. It's almost like using a big data approach to address these interesting problems," Wong added.
The gene sequencing data of brain tumors came from The Cancer Genome Atlas at NIH, an effort to sequence all kinds of different cancers. The data is available for researchers on the web. Wong's gene sequencing data for Alzheimer's disease came from the Alzheimer's Disease Neuroimaging Initiative, also funded by the NIH.
"The gene sequencing data size would easily be 1000-fold larger than the microarray data in the reported study," Wong said, "which means the need to use TACC's Lonestar and Stampede supercomputing clusters for number crunching is even more eminent."
Wong said the microarray data sets were fairly manageable, with microarray data covering 1091 GBM and 524 AD subjects.
"We derived more than 2,000 significant genes," he explained, "and 15 gene ontology terms were identified as significantly changed in both diseases."
Scientists use gene ontology terms to represent how attributes of gene products relate to on another. Most of these 15 gene ontology terms, said Wong, share a group of genes including MAPK.
"It's the gene we performed careful biological validation in cell assays and animal models."
The research remains at an early stage in fully understanding the biological links between cancer and Alzheimer's disease. Deeper understanding of the cell signal regulation could eventually help guide doctors in making the best choice of treatment options for patients or to inspire new drug designs.
"For instance, if some important molecules are discovered which caused GBM, maybe they could be developed into some drugs and used for the Alzheimer's disease treatment, which inspire new drugs development," said 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 added.
"Reversely, some drugs were developed for targeting Alzheimer's, but the clinical trials showed unexpected results that in rare cases the drugs induced the cancer occurrence in the patients." Zhao said, "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 said his team is going one step further by analyzing much more fine-grained and computationally costly gene sequencing data of Alzheimer's disease and brain tumors that was just recently released.
"Conventionally, scientific research focuses on one particular protein or one gene," Wong said.
"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."
Wong currently holds a NIH grant that is part of the larger Integrative Cancer Biology Program.
According to the NIH, this program attempts to unravel the biological complexity of cancer by applying a "systems biology" approach using a variety of Big Data and computational modeling techniques to uncover new understanding and connections associated with the development and management of cancer. As these processes start to unravel, it's understandable that connections will be seen across diseases.
The NIH program officer for the grant, Dan Gallahan, deputy director of the Division of Cancer Biology with the National Cancer Institute remarked: "This work of Dr. Wong's is quite exciting in that it shows connections between two of the most intractable diseases in modern society. And while our focus is on cancer, the great hope is that as we make these connections we can leverage that knowledge to find new targets and opportunities that can provide meaningful intervention for either disease."
The Texas Advanced Computing Center (TACC) at The University of Texas at Austin is one of the leading centers of computational excellence in the United States. The center's mission is to enable discoveries that advance science and society through the application of advanced computing technologies. To fulfill this mission, TACC identifies, evaluates, deploys, and supports powerful computing, visualization, and storage systems and software. TACC's staff experts help researchers and educators use these technologies effectively, and conduct research and development to make these technologies more powerful, more reliable, and easier to use. TACC staff also help encourage, educate, and train the next generation of researchers, empowering them to make discoveries that change the world.