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New method identifies genes that can predict prognoses of cancer patients
Date:1/25/2013

ished gene expression profiling studies in ovarian cancer (including data from approximately 1700 patients).

When the investigators used SAPS to analyze these previously identified prognostic signatures in breast and ovarian cancer, they found that only a small subset of the signatures that were considered statistically significant by standard measurements also achieved statistical significance when evaluated by SAPS.

"Our work shows that when using prognostic associations to identify biological signatures that drive cancer progression, it is important to not rely solely on a gene set's association with patient survival," says Beck. "A gene set may appear to be important based on its survival association, when in reality it does not perform significantly better than random genes. This can be a serious problem, as it can lead to false conclusions regarding the biological and clinical significance of a gene set."

By using SAPS, Beck and his colleagues found that they could overcome this problem. "The SAPS procedure ensures that a significant prognostic gene set is not only associated with patient survival but also performs significantly better than random gene sets," says Beck. His team revealed new prognostic signatures in subtypes of breast cancer and ovarian cancer and demonstrated a striking similarity between signatures in estrogen receptor negative breast cancer and ovarian cancer, suggesting new shared therapeutic targets for these aggressive malignancies.

The findings also indicate that the prognostic signatures identified with SAPS will not only help predict patient outcomes but might also help in the development of new anti-cancer drugs. "We hope that markers identified in our analysis will provide new insights into the biological pathways driving cancer progression in breast and ovarian cancer subtypes, and will one day lead to improvements in targeted diagnostics and therapeutics," says Beck. "We also hope the method
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Contact: Bonnie Prescott
bprescot@bidmc.harvard.edu
617-667-7306
Beth Israel Deaconess Medical Center
Source:Eurekalert

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