The strategy used by Google to decide which pages are relevant for a search query can also be used to determine which proteins in a patient's cancer are relevant for the disease progression. Researchers from Dresden University of Technology, Germany, have used a modified version of Google's PageRank algorithm to rank about 20,000 proteins by their genetic relevance to the progression of pancreatic cancer. In their study, published in PLoS Computational Biology, they found seven proteins that can help to assess how aggressive a patient's tumor is and guide the clinician to decide if that patient should receive chemotherapy or not.
The researcher's own version of the Google algorithm has been used in this study to find new cancer biomarkers, which are molecules produced by cancer cells. Biomarkers can help to detect cancer earlier in body fluids or directly in the cancer tissue obtained in an operation or biopsy. Finding these biomarkers is often difficult and time consuming. Another problem is that markers found in different studies for the same types of cancer almost never overlap.
This problem has been circumvented using the Google strategy, which takes into account the content of a web page and also how these pages are connected via hyperlinks. With this strategy as the model, the authors made use of the fact that proteins in a cell are connected through a network of physical and regulatory interactions; the 'protein Facebook' so to speak.
"Once we added the network information in our analysis, our biomarkers became more reproducible," said Christof Winter, the paper's first author. Using this network information and the Google Algorithm, a significant overlap was found with an earlier study from the University of North Carolina. There, a connection was made with a protein which can assess aggressiveness in pancreatic cancer.
Although the new biomarkers seem to mark an improvement over currently used diagnostic tools, they are far from perfect and still need to be validated in a larger follow-up study before they can be used in clinical practice. It remains an open problem to turn these insights into novel drugs which slow down cancer progression. A first step in this direction is the group's cooperation with the Dresden-based biotech company RESprotect, who are running a clinical trial on a pancreas cancer drug.
TU Dresden is a leading German university, whose Center for Regenerative Therapies was awarded excellence status in the national excellence initiative. The work was a cooperation between the bioinformatics group of Prof. Dr. Michael Schroeder and the medical groups of Dr. Christian Pilarsky and Prof. Robert Grützmann.
FINANCIAL DISCLOSURE: Funding was provided by the Roland Ernst Stiftung für Gesundheitswesen, MeDDrive TU Dresden, the EU project Ponte and the national projects GoOn, Format, and GeneCloud. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
COMPETING INTERESTS: The authors have declared that no competing interests exist.
CITATION: Winter C, Kristiansen G, Kersting S, Roy J, Aust D, et al. (2012) Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes. PLoS Comput Biol 8(5): e1002511. doi:10.1371/journal.pcbi.1002511
PLEASE ADD THIS LINK TO THE FREELY AVAILABLE ARTICLE IN ONLINE VERSIONS OF YOUR REPORT (the link will go live when the embargo ends): http://www.
Christof Winter: (firstname.lastname@example.org)
Michael Schroeder: email@example.com
Phone: +49 351 463 400 60
Technische Universität Dresden
Deparment of Bioinformatics
This press release refers to an upcoming article in PLoS Computational Biology. The release is provided by journal staff, or by the article authors and/or their institutions. Any opinions expressed in this release or article are the personal views of the journal staff and/or article contributors, and do not necessarily represent the views or policies of PLoS. PLoS expressly disclaims any and all warranties and liability in connection with the information found in the releases and articles and your use of such information.
PLoS Journals publish under a Creative Commons Attribution License which permits free reuse of all materials published with the article, so long as the work is cited (e.g., Brinkworth RSA, O'Carroll DC (2009) Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology. PLoS Comput Biol 5(11): e1000555. doi:10.1371/journal.pcbi.1000555). No prior permission is required from the authors or publisher. For queries about the license, please contact the relative journal contact indicated here: http://www.
About PLoS Computational Biology
PLoS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLoS Computational Biology are open access. Everything is immediately available subject only to the condition that the original authorship and source are properly attributed. Copyright is retained.
About the Public Library of Science
The Public Library of Science (PLoS) is a non-profit organization of scientists and physicians committed to making the world's scientific and medical literature a freely available public resource. For more information, visit http://www.