IBM and University Researchers to Develop Research Tools to Improve Cancer Patient Outcomes
News Feb 13, 2008
IBM; The Cancer Institute of New Jersey (CINJ), which is a Center of Excellence of the University of Medicine and Dentistry-Robert Wood Johnson Medical School; and Rutgers, The State University of New Jersey, have announced a collaborative research effort to develop diagnostic tools which can improve the accuracy of predicting patients’ responses to treatment and related clinical outcomes.
Through the use of advanced computer and imaging technologies that facilitate comparisons of cancerous tissues, cell and radiology studies, researchers and physicians expect to determine more accurate cancer prognoses, more personalized therapy planning and, subsequently, the discovery and development of new cancer drugs.
This project is a natural extension of the “Help Defeat Cancer” (HDC) project in which IBM’s World Community Grid was used to demonstrate the effectiveness of characterizing different types and stages of disease based upon the underlying staining patterns exhibited by digitally imaged cancer tissues. World Community Grid is a virtual supercomputer that gains its resources by thousands of volunteers donating their unused computer time.
Leveraging the experimental results gathered during the course of the HDC project, the team has recently received a $2.5-million grant through competitive funding from the National Institutes of Health.
The central objective of this project is to build a deployable, grid-enabled decision support system to help researchers, physicians and scientists to automatically analyze and classify imaged cancer specimens. It will be a useful tool for supporting the selection of personalized treatments for people with cancer based upon how patients with similar protein expression signatures and cancers have reacted to treatments.
The team is expanding the first phase of the project that studied breast, colon and head and neck cancers to include other cancers as well. From the World Community Grid project, CINJ created a reference library of expression signatures and demonstrated a reliable means for performing high-throughput analysis of tissue micro-arrays.
In addition, investigators at CINJ also are establishing a Center for High-Throughput Data Analysis for Cancer Research that will tap into computing resources and a Shared University Research Award provided by IBM.
The primary objective of the Center is to develop pattern recognition algorithms that can simultaneously take into consideration information contained in digitally archived cancer specimens, radiology images and proteomic and genomic data for improved assessment of disease onset and progression.
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