A new algorithm makes use of three separate criteria to more accurately identify prognostic signatures associated with patients’ survival.

In recent years, researchers believed that select sets of genes might reveal cancer patients’ prognoses. However, a study published last year examining breast cancer cases found that most of these “prognostic signatures” were no more accurate than random gene sets in determining cancer prognoses. While many saw this as a disappointment, one research team saw this as an opportunity to identify gene sets that could yield more significant prognostic value.

The team developed SAPS (Significance Analysis of Prognostic Signatures), their new algorithm. In the new study, published in PLoS Computational Biology (2013; doi:10.1371/journal.pcbi.1002875), the SAPS algorithm was applied to gene expression profiling data from 19 published breast cancer studies (approximately 3,800 patients) and 12 published gene expression profiling studies in ovarian cancer (approximately 1,700 patients).

When the investigators used SAPS to analyze these previously identified prognostic signatures in breast and ovarian cancer, they found 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,” said Andrew Beck of Beth Israel Deaconess Medical Center and Harvard Medical School. “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,” said 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 anticancer 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,” said Beck. “We also hope the method proves widely useful to other researchers.” To that end, the team would like to create a Web-accessible tool to enable investigators with little knowledge of statistical software and programming to identify gene sets significantly associated with patient outcomes in other diseases.