DOI: 10.5061/dryad.mk471
title: Data from: Significance Analysis of Prognostic Signatures

description: 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.
description: Breast Cancer
 DataBreast cancer data. This
 R-workspace contains the objects: dat, dat.st, event, st, and
 time.Breast.zipOvary_NonAngio_GSEA_ResultsResults from GSEA Analysis in Non-Angiogenic subtype of ovarian cancerOvary_NonAngio.zipBreast_Global_GSEA_ResultsResults from GSEA Analysis in Global Breast Cancer AnalysisBreast_Global.zipBreast_Her2_GSEA_ResultsResults from GSEA Analysis in HER2+ subtype of breast cancerBreast_Her2.zipOvary_Angio_GSEA_ResultsResults from GSEA Analysis in Angiogenic subtype of ovarian cancerOvary_Angio.zipBreast_ERHigh_GSEA_ResultsResults from GSEA Analysis in ER+ high proliferation subtype of breast cancerBreast_ERHigh.zipBreast_ERNegHer2Neg_GSEA_ResultsResults from GSEA Analysis in ER Neg HER2 Neg subtype of breast cancerBreast_ERNegHer2Neg.zipOvary_Global_GSEA_ResultsResults from GSEA Analysis in Global ovarian cancer analysisOvary_Global.zipBreast_ERLow_GSEA_ResultsResults from GSEA Analysis in ER+ low proliferation subtype of breast cancerBreast_ERLow.zipOvarian Cancer DataOvarian cancer data. This R-workspace contains the objects: dat, dat.st, event, st, and time.Ovary.zipBreast.Ps.OnPermutedData.RDataBreast.Ps.OnPermutedData.RData contains the results of performing SAPS using permuted gene sets on the breast data. P_enrich, p_pure,p_rand are each 8 x 10000 x 6 arrays with P_enrich,P_pure, and P_random p values from permuted gene setsOvary.Ps.OnPermutedData.RDataReadMe, Ovary.Ps.OnPermutedData.RData. Ovary.Ps.OnPermutedData.RData contains the results of performing SAPS using permuted gene sets on the ovarian data. P_enrich, p_pure,p_rand are arrays with P_enrich,P_pure, and P_random p values from permuted gene sets.FinalOutput_BreastFinalOutput_Breast.RData contains the results from the subtype-specific analysis in breast cancer, including the results of the permutation-based procedure to compute p values and q values for the SAPSscores.FinalOutput_OvaryFinalOutput_Ovary.RData contains the results from the traditional scaled data set in ovarian cancer, including the results of the permutation-based procedure to compute p values and q values for the SAPSScores.molsigdb.v3.0.entrezForR.txtmolsigdb.v3.0.entrezForR contains the molsigdb, downloaded from the Broad Institute. The file is used to read the molsigdb.v3.0 gene sets into R.BreastOutput_TradScaledBreastOutput_TradScaled.RData is an R-workspace contains the objects: allPs, allPs.adj, sumTable. These were generated from applying the SAPS method to the breast cancer meta-data set scaled by transforming each feature into its Z score across all patients in a data-set prior to merging across data-sets.BreastOutput_SubScaledBreastOutput_SubScaled.RData is an R-workspace contains the objects: allPs, allPs.adj, sumTable. These were generated from applying the SAPS method to the breast cancer meta-data set scaled by transforming each feature into its Z score across all patients within a breast cancer subtype data-set prior to merging across data-sets.BreastSubtypeSpecScaleRankDirBreastSubtypeSpecScaleRankDir contains the ranked gene lists of concordance indices used to perform the GSEA in breast cancer.OvaryOutput_TradScaledOvaryOutput_TradScaled.RData contains the objects: allPs, allPs.adj, sumTable.BreastOutput_TradScaled.RData. These were generated from applying the SAPS method to the ovarian cancer meta-data set scaled by transforming each feature into its Z score across all patients in a data-set prior to merging across data-sets.OvaryOutput_SubScaledOvaryOutput_SubScaled.RData is an R-workspace contains the objects: allPs, allPs.adj, sumTable. These were generated from applying the SAPS method to the ovarian cancer meta-data set scaled by transforming each feature into its Z score across all patients within a ovarian cancer subtype data-set prior to merging across data-sets.OvaryTradScaleRankDirOvaryTradScaleRankDircontains the ranked gene lists of concordance indices used to perform the GSEA in ovarian cancer.BreastOvary_HCv2BreastOvary_HCv2.zip – This zip directory contains files to generate Figure 10 (Hierarchical clustering of breast and ovarian cancer subtypes based on SAPS scores) using JavaTreeView (http://jtreeview.sourceforge.net/)runSAPSonPermutedDatarunSAPSonPermutedData.R – This R script generates the P_pure, P_random, and P_enrichment on random gene sets. This "biologically null" set of SAPS scores is used to compute the SAPS_q_values on the msigdb gene sets.sapssaps.R – This R script provides R commands for loading data, applying the SAPS method, and generating the SAPS p values. The script is interactive, and the user must specify the working directory, and if the analysis is on the ovarian or breast data.sapsFiguressapsFigures.R – This R script generates the figures, tables, and file used for clusteringcomputeSAPS.Permute.PValue.RcomputeSAPS.Permute.PValue.R – This script generates permutation-based p and q values for the SAPSscores obtained in breast and ovarian cancer.