Data from: 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.