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Improved Prediction Of Prostate Cancer Outcome With New Model

Date:
February 4, 2008
Source:
BioMed Central
Summary:
Scientists are another step closer to understanding why some people suffer from life-threatening diseases such as cancer. A new model may enable more accurate prediction of the risk of prostate cancer progression. By combining the Gleason score (a pathological score given to prostate cancer based on its microscopic appearance) with structured data from biomarker assessments, the researchers have developed a model for predicting the likelihood of prostate cancer virulence.

Scientists are another step closer to understanding why some people suffer from life-threatening diseases such as cancer. New research reveals a model that may enable more accurate prediction of the risk of prostate cancer progression. By combining the Gleason score (a pathological score given to prostate cancer based on its microscopic appearance) with structured data from biomarker assessments, the researchers have developed a model for predicting the likelihood of prostate cancer virulence.

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The research team from the University of Texas M. D. Anderson Cancer Center, led by Dr. Timothy McDonnell, used tissue microarrays with specimens covering the spectrum of low to high grade prostate cancer, to address several questions of clinical and pathological interest. The biomarkers evaluated in this study comprised well-characterized cell cycle and cell death regulators known to be variably expressed by prostate cancers. Significant differences in the molecular signatures were found among these varying grades of tumors, and a biostatistical model was developed using a limited number of molecular markers to enable a more accurate prediction of the risk of prostate cancer progression.

"We believe these findings will be of potential benefit to a substantial portion of the patient population diagnosed with early prostate cancer by providing valuable information regarding the risk of disease progression" says McDonnell. "With this type of information patients could be more appropriately managed based on their individual risk profile."

Healthcare professionals are increasingly turning to the use of genomic techniques to understand why some people are predisposed to certain conditions such as infections, addictions, and illnesses like diabetes, heart disease, and schizophrenia, while others are not. By employing these techniques, researchers and physicians could soon offer patients a more tailored and individual medical treatment program.

Journal reference: Biomarker Expression Patterns That Correlate With High Grade Features in Treatment Naive, Organ-confined Prostate Cancer. Timothy J McDonnell, Nikhil S Chari, Jeong Hee Cho-Vega, Patricia Troncoso, Xuemei Wang, Carlos E Bueso-Ramos, Kevin Coombes, Shawn Brisbay, Remigio Lopez, George Prendergast, Christopher Logothetis and Kim-Anh Do. BMC Medical Genomics (in press) http://www.biomedcentral.com/1755-8794/1/1/abstract


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The above story is based on materials provided by BioMed Central. Note: Materials may be edited for content and length.


Cite This Page:

BioMed Central. "Improved Prediction Of Prostate Cancer Outcome With New Model." ScienceDaily. ScienceDaily, 4 February 2008. <www.sciencedaily.com/releases/2008/01/080131082240.htm>.
BioMed Central. (2008, February 4). Improved Prediction Of Prostate Cancer Outcome With New Model. ScienceDaily. Retrieved December 22, 2014 from www.sciencedaily.com/releases/2008/01/080131082240.htm
BioMed Central. "Improved Prediction Of Prostate Cancer Outcome With New Model." ScienceDaily. www.sciencedaily.com/releases/2008/01/080131082240.htm (accessed December 22, 2014).

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