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Decade of discovery: New precision tools to diagnose and treat cancer
By: Dr. David J. Foran, PhD
Rutgers Cancer Institute of New Jersey
3 Nov 2014   

It's week four of our 10th anniversary celebrations, and we're following up last week's childhood cancer feature by spotlighting another cancer project that's helped researchers develop powerful new tools to diagnose cancer and tailor treatments to individual patients, using big data and analytics.

When it comes to cancer, a doctor's diagnosis affects how aggressively a patient is treated, which medications might be appropriate and what levels of risk are justified. New precision medicine techniques are enabling physicians and scientists to refine diagnoses by identifying changes and patterns in individual cancers at unprecedented levels of granularity - ultimately improving treatment outcomes for patients.

A key tool for precision medicine is tissue microarray analysis. This enables investigators to analyze large batches of tissue sample images simultaneously, so they can look for patterns and identify cancer signatures. It also provides them with a deeper understanding of cancer biology and uncovers new sub-classifications of cancer and likely patient responses - all of which influence new courses of treatment and future drug design.

Tissue microarray analysis shows great promise, but it is not without its limitations. Pathologists typically examine the specimens visually, resulting in subjective interpretations and variations in diagnoses.

We realized that if this method of analysis could be automated using digital pattern recognition algorithms, we could improve accuracy and reveal new patterns across large sets of data. This would make it possible for researchers to determine a patient's type and stage of cancer more precisely, meaning they can prescribe therapies or combinations of treatments that are most likely to be effective.

To study the feasibility of automating tissue microarray analysis, we partnered with IBM's World Community Grid in 2006 to launch the Help Defeat Cancer project. At the time, we were pioneering a new approach that nobody else was investigating, and it was met with tremendous skepticism by many of our colleagues.

However, with the support of more than 200,000 World Community Grid volunteers from around the globe who donated over 2,900 years of their computing time, we were able to study over 100,000 patient tissue samples to search for cancer signatures.

Access to this vast computing power enabled our team to rapidly conduct this research under a much wider range of environmental conditions and to perform specimen analysis at much greater degrees of sensitivity.

Thanks to World Community Grid and the Help Defeat Cancer project, we demonstrated the success of using computer-based analysis to automatically investigate and classify cancer specimens based on expression signature patterns. We were able to develop a reference library of cancer signatures that can be used to systematically analyze and compare tissue samples across large patient cohorts.

Leveraging these experimental results, our team secured competitive funding from the National Institutes of Health (NIH) to build a clinical decision support system to automatically analyze and classify cancer specimens with improved diagnostic and prognostic accuracy. We used the core reference library of expression signatures generated through the Help Defeat Cancer project to demonstrate the proof-of-concept for the system.

These decision support tools are now being tested and refined by investigators from the Rutgers Cancer Institute of New Jersey, Stony Brook University School of Medicine, University of Pittsburgh Medical Center and Emory University. They are exploring how the tools can aid clinical decision-making, plus are pursuing further investigative research. Together, our ultimate aim is to refine these tools sufficiently so they can be certified for routine clinical use in diagnosing and treating patients.

Although the Help Defeat Cancer project has completed its research on World Community Grid, we continue to investigate the findings and they have contributed to some significant new beginnings. At Rutgers Cancer Institute of New Jersey, physicians and scientists - aided by high-performance computing resources - are analyzing genomes and human tissues, and identifying cancer patterns, faster than ever before.

In collaboration with our research partners at the Rutgers Discovery Informatics Institute (RDI2) and RUCDR Infinite Biologics (the world's largest university-based biorepository, located within the Human Genetics Institute of New Jersey), the Rutgers Cancer Institute is shaping a revolution in how best to determine cancer therapy for patients - a vast improvement from the time-intensive, trial-and-error approach that doctors have faced for years. To date, only a fraction of known cancer biomarkers have been examined. The long-term goal is to create a library of biomarkers and their expression patterns so that, in the future, physicians can consult the library to help diagnose cancer patients and provide them with the most effective treatment.

I would like to express my gratitude to Stanley Litow, Robin Willner, and Jen Crozier from IBM and to World Community Grid's Advisory Board for supporting the Help Defeat Cancer project. I'd also like to extend my special thanks to the IBM World Community Grid team members who contributed to the success of the project - I hope to have the opportunity to work with them again in the near future.

Additionally, I would like to acknowledge the NIH, Department of Defense and IBM for supporting this research - and give credit to those individuals from my laboratory and partnering institutions who were involved in the early experiments and the initial design and development of the imaging and computational tools, which we then used throughout the project. And, of course, a very big thank you to all the World Community Grid volunteers - without their support, our accomplishments with Help Defeat Cancer would not have been possible.

The Help Defeat Cancer project has completed its analysis on World Community Grid - but another innovative project, Mapping Cancer Markers, is currently running and needs your help. Help us celebrate a decade of discovery on World Community Grid by sharing this story and encouraging your friends to donate their unused computing power to cutting-edge cancer research.

Here’s to another decade of discovery.

To contribute to Mapping Cancer Markers go to your My Projects page and make sure the box is checked. Mapping Cancer Markers is dedicated to improving cancer treatment by identifying cancer biomarkers, which could help doctors detect cancer earlier and customize treatment.

Please visit the following pages to learn more:

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Dr. David J. Foran, PhD
Rutgers Cancer Institute of New Jersey
Chief Informatics Officer and Executive Director of Biomedical Informatics and Computational Imaging