Research article
A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer
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* Corresponding author: Lodewyk FA Wessels l.wessels@nki.nl
1 Department of Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
2 Department of Surgery, Institut Curie, 6 rue d'Ulm, 75005 Paris, France
3 UMR 144, CNRS-Institut Curie, Molecular Oncology Team, 12 rue Lhomond, 75005 Paris, France
4 Bioinformatics and Statistics Group, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
5 Faculty of EEMCS, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands
6 Department of Molecular Biology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
7 Cancer Research UK, Cambridge Research Institute and Department of Oncology, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 ORE, UK
8 Department of Pathology, Academic Medical Center, Meibergdreef 9, 1100 DD Amsterdam The Netherlands
Breast Cancer Research 2008, 10:R93 doi:10.1186/bcr2192
Published: 13 November 2008Additional files
Additional file 1:
A Word file containing the supplementary Materials and methods section and four supplementary tables. Table S1 lists data for multivariate and univariate Cox regression analyses with DMFS and BCSS as the endpoints. Table S2 lists data for multivariate Cox regression analysis with selected clinical parameters – ER status based on immunohistochemistry (DMFS only), LN status (positive versus negative), histological grading (Elston Ellis I, II and III) – and the output of the nine gene expression classifiers as input and either DMFS and BCSS as clinical endpoints. Table S3 lists a performance analysis of the signatures on the complete set of 1,127 patients with dichotomous outcome labels of poor outcome and good outcome derived from DMFS and BCSS. Table S4 lists data for multivariate Cox regression analysis with selected clinical parameters – ER status based on immunohistochemistry, LN status (positive versus negative), histological grading (Elston Ellis I, II and III) – tumor size and the output of the Immune and RNA splicing modules gene signature (IR) or the 70-gene signature (NKI) as the input and DMFS and BCSS analysis as the clinical endpoint.
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Additional file 2:
An Adobe file containing a figure of the RNA splicing, Immune and 72 Proliferation gene annotations [Probe_ID, EntrezID, OMIM, Ensembl, UnigeneID, Representative Public ID, RefSeq Transcript ID, Gene Symbol, k-means metastasis, k-means no metastasis].
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Additional file 3:
An Adobe file containing a figure showing the nine signature Kaplan–Meier curves with BCSS as endpoints (S1a), showing the performance of the signatures on subgroups of the patient population (S1b (top panel)), and showing) the time-censoring performance analysis of the signatures (1b (bottom panel).
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Additional file 4:
An image file containing a figure showing heatmaps of the concordance of the nine classifiers across clinical subgroups among the 1,127 human breast tumor samples.
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Additional file 5:
An image file containing a figure showing the overlap and performance analysis of 403 samples with BCSS as the endpoint.
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Additional file 6:
An Excel file containing a figure showing the log-rank test P value on the Chin–Loi training set for each pairwise combination of the module classifiers.
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Additional file 7:
An Adobe file containing a figure showing the Kaplan–Meier plots on the dataset of van de Vijver and colleagues for the subgroups defined by the Nottingham Prognostic Index, St Gallen, and AdjuvantOnline! clinical staging systems – plots for the Immune/RNA splicing module classifier within each of the clinical subgroups for each staging system.
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