Common data determinants of recurrent cancer are broken, mislead researchers

Jan. 2, 2013 — In order to study the effectiveness or cost effectiveness of treatments for recurrent cancer, you first have to discover the patients in medical databases who have recurrent cancer. Generally studies do this with billing or treatment codes -- certain codes should identify who does and does not have recurrent cancer. A recent study published in the journal Medical Care shows that the commonly used data determinants of recurrent cancer may be misidentifying patients and potentially leading researchers astray.

"For example, a study might look in a database for all patients who had chemotherapy and then another round of chemotherapy more than six months after the first, imagining that a second round defines recurrent disease. Or a study might look in a database for all patients with a newly discovered secondary tumor, imagining that all patients with a secondary tumor have recurrent disease. Our study shows that both methods are leave substantial room for improvement," says Debra Ritzwoller, PhD, health economist at the Kaiser Permanente Colorado Institute for Health Research and investigator at the University of Colorado Cancer Center.

The study used two unique datasets derived from HMO/Cancer Research Network and CanCORS/Medicare to check if the widely used algorithms in fact discovered the patients with recurrent disease that the algorithms were designed to detect. They did not. For example, a newly diagnosed secondary cancer may not mark a recurrence but may instead be a new cancer entirely; a second, later round of chemotherapy may be needed for continuing control of the de novo cancer, and not to treat recurrence.

"Basically, these algorithms don't work for all cancer sites in many datasets commonly used for cancer research," says Ritzwoller.

For example, to discover recurrent prostate cancer, no combination of billing codes used in this large data set pointed with sensitivity and specificity to patients whom notes in the data showed had recurrent disease. The highest success of the widely used algorithms was predicting patients with recurrent lung, colorectal and breast cancer, with success rates only between 75 and 85 percent.

"We need to know who in these data sets has recurrent disease. Then we can do things like look at which treatments lead to which outcomes," Ritzwoller says. Matching patients to outcomes can help to decide who gets what treatment, and can help optimize costs in health care systems.

In a forthcoming paper, Ritzwoller and colleagues will suggest algorithms to replace these that have now proved inadequate.

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The above story is reprinted from materials provided by University of Colorado Denver. The original article was written by Garth Sundem.

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Journal Reference:

Michael J. Hassett, Debra P. Ritzwoller, Nathan Taback, Nikki Carroll, Angel M. Cronin, Gladys V. Ting, Deb Schrag, Joan L. Warren, Mark C. Hornbrook, Jane C. Weeks. Validating Billing/Encounter Codes as Indicators of Lung, Colorectal, Breast, and Prostate Cancer Recurrence Using 2 Large Contemporary Cohorts. Medical Care, 2012; : 1 DOI: 10.1097/MLR.0b013e318277eb6f

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Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

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Google goes cancer: Search engine algorithm finds cancer biomarkers

ScienceDaily (May 17, 2012) — 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.

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The above story is reprinted from materials provided by Public Library of Science.

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Journal Reference:

Christof Winter, Glen Kristiansen, Stephan Kersting, Janine Roy, Daniela Aust, Thomas Knösel, Petra Rümmele, Beatrix Jahnke, Vera Hentrich, Felix Rückert, Marco Niedergethmann, Wilko Weichert, Marcus Bahra, Hans J. Schlitt, Utz Settmacher, Helmut Friess, Markus Büchler, Hans-Detlev Saeger, Michael Schroeder, Christian Pilarsky, Robert Grützmann. Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes. PLoS Computational Biology, 2012; 8 (5): e1002511 DOI: 10.1371/journal.pcbi.1002511

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Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

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Video games lead to new paths to treat cancer, other diseases

ScienceDaily (Feb. 16, 2012) — Anqi Zou never thought she would thank video gamers for showing her the way to exciting discoveries in molecular biology.

But here she is, acknowledging that the technology she uses to show the inner workings of cells was originally perfected to create realistic images on gaming screens worldwide.

No matter. Sam Cho and his students are using graphics processing units -- also called GPUs or graphics cards -- to explore the biomolecular processes in the cell and take on challenges, including a cure for cancer.

"We have hijacked the same technology that creates the detailed gaming scenes on your computer screen to perform molecular-dynamic simulations," Cho said.

Zou is helping Cho push the limits of GPU-optimized cell simulations. This Mathematical Business and Computational Science major is comparing the data provided by GPU and non-GPU simulations.

"Because of the powerful computational ability of these GPU devices that are usually used for gaming, I couldn't help registering for Dr. Cho's GPU programming course," she said. "Halfway through the semester, I was much impressed by the computational performance of the GPUs, and I approached Dr. Cho about working on a research project related to GPU programming."

For his most recent published study, Cho, an assistant professor of physics and computer science, simulated the folding and unfolding of a critical RNA molecule component of the human telomerase enzyme. This enzyme lengthens DNA strands during cell division. It's what makes tumors continue to grow.

Knowing how human telomerase works could lead to cancer therapies that essentially obliterate tumors, Cho said.

His research findings appear in the Journal of the American Chemical Society.

Now, Cho and his research assistants are looking at a much larger cell system -- the bacterial ribosome -- to see what they can uncover through GPU-optimized molecular dynamic simulations. The graphics cards were donated by Nvidia, the company that invented the GPU; Cho developed a new GPU programming course so he could teach Wake students how to use the cards.

The benefit of the GPU-optimized simulations is that they are much quicker to perform. The ribosome simulation, for example, would take more than 40 years on a standard computer. Using GPUs, Cho and his students will see results in a few months.

The end goal is to map the ribosome's functions so researchers can develop antibiotics to specifically kill bacteria.

And that would be an amazing accomplishment, thanks in large part to videogamers, Cho said.

"If it wasn't for gamers who kept buying these GPUs, the prices wouldn't have dropped, and we couldn't have used them for science," he said.

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The above story is reprinted from materials provided by Wake Forest University, via Newswise. The original article was written by Alicia Roberts.

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Journal Reference:

Shi Biyun, Samuel S. Cho, D. Thirumalai. Folding of Human Telomerase RNA Pseudoknot Using Ion-Jump and Temperature-Quench Simulations. Journal of the American Chemical Society, 2011; 133 (50): 20634 DOI: 10.1021/ja2092823

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Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

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Google goes cancer: Search engine algorithm finds cancer biomarkers

ScienceDaily (May 17, 2012) — 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.

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The above story is reprinted from materials provided by Public Library of Science.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Christof Winter, Glen Kristiansen, Stephan Kersting, Janine Roy, Daniela Aust, Thomas Knösel, Petra Rümmele, Beatrix Jahnke, Vera Hentrich, Felix Rückert, Marco Niedergethmann, Wilko Weichert, Marcus Bahra, Hans J. Schlitt, Utz Settmacher, Helmut Friess, Markus Büchler, Hans-Detlev Saeger, Michael Schroeder, Christian Pilarsky, Robert Grützmann. Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes. PLoS Computational Biology, 2012; 8 (5): e1002511 DOI: 10.1371/journal.pcbi.1002511

Note: If no author is given, the source is cited instead.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here