Researchers have developed a new method to identify gene sets that could yield more significant prognostic value for cancer patients than before. Investigators at Beth Israel Deaconess Medical Center (BIDMC), the Dana-Farber Cancer Institute, and the Institut de Recherches Cliniques de Montréal (IRCM) developed SAPS (significance analysis of prognostic signatures), a new algorithm to identify prognostic signatures associated with patient survival.
“SAPS makes use of three specific criteria,” explains study leader Andrew Beck, M.D., director of the molecular epidemiology research laboratory at BIDMC and assistant professor of pathology at Harvard Medical School. “First, the gene set must be enriched for genes that are associated with survival. In addition, the gene set must separate patients into groups that show survival differences. Lastly, it must also perform significantly better than sets of random genes at these tasks.”
In the new study, the scientific team applied the SAPS algorithm to gene expression profiling data from the study’s senior author Benjamin Haibe-Kains, Ph.D., director of the bioinformatics and computational genomics laboratory at IRCM and assistant research professor at the University of Montreal. The first collection of data was obtained from 19 published breast cancer studies (including approximately 3,800 patients), and the second included 12 published gene expression profiling studies in ovarian cancer (including data from approximately 1,700 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 Dr. 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, Dr. 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,” he says. 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 diseases. In fact, they found that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer.
“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 Dr. Beck.
Their results are reported in the January 24 online issue of the journal PLOS Computational Biology. The study is called “Significance Analysis of Prognostic Signatures”.