Project Status and Findings:
Information about this project is provided on the web pages below and by the project scientists on the Help Defeat Cancer website. For the latest status report, please go to the Help Defeat Cancer status report. To comment or ask questions about this project, please submit a post in the Help Defeat Cancer Forum.
What is Cancer?
Cancer is a generic term for a group of diseases that can affect any part of the body. According to the World Health Organization, cancer causes 7 million deaths each year, or 12.5% of deaths worldwide. More than 11 million people are diagnosed with cancer every year, and it is estimated that there will be 16 million new cases every year by 2020.
Cancer develops when cells in one part of the body begin to grow out of control, often leading to invasion of other tissues, either directly or by traveling to other parts of the body where they begin to grow and replace normal tissue through a process called metastasis.
Cancer cells develop as a result of damage to DNA. Most of the time when DNA becomes damaged the body is able to repair it, but in cancer cells, damaged DNA is not fixed. Damaged DNA can be hereditary or can be caused by carcinogens, by exposure to radioactive materials, or by certain viruses that insert their DNA into the human genome.
Subclasses of Cancer
Within broad categories of cancer – such as breast, liver, or lung, there are subclasses of cancer. Breast cancer, for example, is a broad category that consists of a number of subclasses (including intraductal, lobular, medullary, colloid), which exhibit variation in terms of aggressiveness and which require specific treatments and drug regimens. So rather than looking at breast cancer as a single disease, doctors must treat it as a multitude of diseases, each requiring targeted therapies.
Not all subclasses of cancer have been identified. As new drugs and treatments become available – each targeting specific clinical profiles, it is becoming increasingly important to be able to distinguish subclasses of cancers. To classify different cancers and identify new subclasses, researchers are conducting gene and protein profiling analysis in order to identify which signatures correspond to which specific cancer. More and more subclasses are emerging as scientists continue to gain a better understanding of the underlying mechanisms of disease progression.
A relatively new investigative tool called tissue microarrays (TMA) holds great promise in helping doctors in selecting proper treatment strategies and providing accurate prognosis for cancer patients. Although TMA is not currently being used by doctors to render primary diagnoses, it does make it possible for researchers to determine the specific type and stage of cancer present and systematically investigate which therapies or combinations of treatments are most likely to be effective for each kind of cancer based upon the known outcomes of individual patients. Specific courses of treatment can then be prescribed for actual cancer patients based on whether a specific biomarker is present or not.
Much of the difficulty in rendering consistent evaluation of expression patterns in cancer tissue microarrays arises from subjective impressions of observers. It has been shown that when characterizations are based upon computer-aided analysis, objectivity, reproducibility and sensitivity improve considerably. Professor David J. Foran's laboratory at The Cancer Institute of New Jersey, UMDNJ – Robert Wood Johnson Medical School leads a collaborative project with a group of investigators at Rutgers University and the University of Pennsylvania. Together they have developed a web-based, robotic prototype for automatically imaging, analyzing, archiving and sharing digitized tissue microarrays. Utilizing a combination of sophisticated image processing and pattern recognition strategies, the system can automatically analyze and characterize expression patterns in cancer tissue microarrays. Through funding from the National Institutes of Health, contracts 5R01LM007455-03 from the National Library of Medicine and 1R01EB003587-01A2 from the National Institute of Biomedical Imaging and Bioengineering, these researchers have begun analyzing breast cancer and will soon proceed to evaluate protein and molecular expression patterns in head and neck cancers.
Currently, when doctors diagnose patients with cancer, they make determinations about the type of cancer and its stage based upon microscopic evaluation of specimens, through consultations with their peers and by utilizing a host of ancillary tests. The diagnosis that is ultimately rendered will affect how aggressively a patient is treated, which medications might be appropriate, and what levels of risk are justified.
Although TMA is not currently being used by doctors to render primary diagnoses, it does make it possible for researchers to determine the specific type and stage of cancer present and systematically investigate which therapies or combinations of treatments are most likely to be effective for each kind of cancer based upon the known outcomes of individual patients. Specific courses of treatment can then be prescribed for actual cancer patients based on whether a specific biomarker is present or not.
TMA also gives researchers improved understanding of cancer biology and uncovers new sub-classifications of cancer that will then point to new courses of treatment, and allows unparalleled insight into which patient populations are most likely to respond to a given treatment regimen, while also providing information needed for future drug design.
Beyond the impact that TMA's are likely to have in the area of drug discovery and improved therapy planning, they offer several advantages over traditional specimen preparation by maximizing limited tissue resources since such small amounts of a biopsy are used and reducing costs for conducting investigative research.
World Community Grid and Tissue Microarrays
Currently, the primary methods used to evaluate tissue microarrays involve manual, interactive review of samples during which they are subjectively evaluated and scored. An alternate, but less utilized strategy is to sequentially digitize specimens for subsequent semi-quantitative assessment. Both procedures ultimately involve the interactive evaluation of TMA samples, which is a slow, tedious process that is prone to human error. Much of the difficulty in rendering consistent evaluation of expression patterns in cancer tissue microarrays is due to subjective impressions of observers.
IBM's World Community Grid will enable the most computationally expensive components of the software to run at optimal speed, thereby increasing the accuracy and sensitivity with which expression calculations and pattern recognition procedures can be conducted. By harnessing the collective computational power of World Community Grid, researchers will be able to analyze a larger set of cancer tissue specimens and conduct experiments using a much broader ensemble of biomarkers and stains than is possible using traditional computer resources.
To date, only a fraction of the known 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 them in rendering diagnoses and providing the most effective treatment for patients with cancer.
In the absence of World Community Grid, TMA's are processed in individual or small batches. Using World Community Grid, analysis can be carried out for hundreds of arrays in parallel, allowing multiple experiments to be conducted simultaneously. This added level of speed and sophistication could potentially enable investigators to detect and track subtle changes in measurable parameters, thereby facilitating discovery of prognostic clues, which are not apparent by human inspection or traditional analysis alone and could advance the fields of cancer biology, drug discovery and therapy planning.
For more about the agent running the Help Defeat Cancer project, click here.
Click Here to listen to an interview of Dr. David Foran (Help Defeat Cancer scientist)
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