Genes Can Predict Prognosis of Cancer Patients

A new algorithm was developed that makes use of three specific criteria to more accurately identify prognostic signatures associated with the cancer patient's survival.

Called Significance Analysis of Prognostic Signatures (SAPS), the new method was designed by investigators at Beth Israel Deaconess Medical Center (BIDMC; Boston, MA, USA), the Dana-Farber Cancer Institute (Boston, MA, USA), and the Institut de Recherches Cliniques de Montréal (IRCM; Montreal, QC, Canada). Their results, the largest analysis of its kind ever performed, are reported in the January 24, 2013 on-line issue of the journal PLOS Computational Biology.

The scientific team applied the SAPS algorithm to gene expression profiling data from the study's senior author Benjamin Haibe-Kains, PhD, director of the Bioinformatics and Computational Genomics Laboratory at IRCM and an 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.

Prof. Beck said, "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 Prof. 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 indicated 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 Prof. Beck. "We also hope the method proves widely useful to other researchers." 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 different diseases.

Soon the team plans to release a software package, which includes all the code and corresponding documentation of their analysis pipeline. Prof. Beck and his team are currently working to validate the prognostic signatures they identified in breast and ovarian cancers further, with the hope of bringing them closer to the clinic through the development of new diagnostics and treatments. "We are also extending our approach to other common cancers that lack robust prognostic signatures," he noted.

Related Links:
Beth Israel Deaconess Medical Center
Dana-Farber Cancer Institute
Institut de Recherches Cliniques de Montréal