Significance Analysis of Prognostic Signatures

A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that “random” gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show 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. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets. Author Summary: A major goal in biomedical research is to identify sets of genes (or “biological signatures”) associated with patient survival, as these genes could be targeted to aid in diagnosing and treating disease. A major challenge in using prognostic associations to identify biologically informative signatures is that in some diseases, “random” gene sets are associated with prognosis. To address this problem, we developed a new method called “Significance Analysis of Prognostic Signatures” (or “SAPS”) for the identification of biologically informative gene sets associated with patient survival. To test the effectiveness of SAPS, we use SAPS to perform a subtype-specific meta-analysis of prognostic signatures in large breast and ovarian cancer meta-data sets. This analysis represents the largest of its kind ever performed. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we demonstrate a striking similarity between prognostic pathways in ER negative breast cancer and ovarian cancer, suggesting new shared therapeutic targets for these aggressive malignancies. SAPS is a powerful new method for deriving robust prognostic biological pathways from clinically annotated genomic datasets.