breastCancerData: Sample data containing six datasets for gene expression,...

In survcomp: Performance Assessment and Comparison for Survival Analysis

Description

This dataset contains a subset of the gene expression, annotations and clinical data from 6 different datasets (see section details). The subsets contain the seven gene signature introduced by Desmedt et al. 2008.

Usage

data(breastCancerData)

Format

Six ExpressionSets. Example for 'mainz7g': eSet with 7 features and 200 samples, containing:

• exprs(mainz7g): Matrix containing gene expressions as measured by Affymetrix hgu133a technology (single-channel, oligonucleotides).

• fData(mainz7g): AnnotatedDataFrame containing annotations of Affy microarray platform hgu133a.

• pData(mainz7g): AnnotatedDataFrame containing Clinical information of the breast cancer patients whose tumors were hybridized.

• experimentalData(mainz7g): MIAME object containing information about the dataset.

• annotation(mainz7g): Name of the affy chip.

Details

This dataset represents subsets of the studies published by Schmidt et al. 2008 [mainz7g], Wang. et al. 2005 and Minn et al. 2007 [vdx7g], Miller et al. 2005 [upp7g], Sotiriou et al. 2006 [unt7g], Desmedt et al. 2007 and TRANSBIG [transbig7g], van't Veer et al. 2002 and van de Vijver et al. 2002 [nki7g]. Each subset contains the genes AURKA (also known as STK6, STK7, or STK15), PLAU (also known as uPA), STAT1, VEGF, CASP3, ESR1, and ERBB2, as introduced by Desmedt et al. 2008. The seven genes represent the proliferation, tumor invasion/metastasis, immune response, angiogenesis, apoptosis phenotypes, and the ER and HER2 signaling, respectively.

Source

mainz: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11121

transbig: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7390

upp: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3494

unt: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2990

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6532

vdx: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2034

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5327

nki: http://www.rii.com/publications/2002/vantveer.html

References

Marcus Schmidt, Daniel Boehm, Christian von Toerne, Eric Steiner, Alexander Puhl, Heryk Pilch, Hans-Anton Lehr, Jan G. Hengstler, Hainz Koelbl and Mathias Gehrmann (2008) "The Humoral Immune System Has a Key Prognostic Impact in Node-Negative Breast Cancer", Cancer Research, 68(13):5405-5413

Christine Desmedt, Fanny Piette, Sherene Loi, Yixin Wang, Francoise Lallemand, Benjamin Haibe-Kains, Giuseppe Viale, Mauro Delorenzi, Yi Zhang, Mahasti Saghatchian d Assignies, Jonas Bergh, Rosette Lidereau, Paul Ellis, Adrian L. Harris, Jan G. M. Klijn, John A. Foekens, Fatima Cardoso, Martine J. Piccart, Marc Buyse and Christos Sotiriou (2007) "Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series", Clinical Cancer Research, 13(11):3207-3214

Lance D. Miller, Johanna Smeds, Joshy George, Vinsensius B. Vega, Liza Vergara, Alexander Ploner, Yudi Pawitan, Per Hall, Sigrid Klaar, Edison T. Liu and Jonas Bergh (2005) "An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival" Proceedings of the National Academy of Sciences of the United States of America 102(38):13550-13555

Christos Sotiriou, Pratyaksha Wirapati, Sherene Loi, Adrian Harris, Steve Fox, Johanna Smeds, Hans Nordgren, Pierre Farmer, Viviane Praz, Benjamin Haibe-Kains, Christine Desmedt, Denis Larsimont, Fatima Cardoso, Hans Peterse, Dimitry Nuyten, Marc Buyse, Marc J. Van de Vijver, Jonas Bergh, Martine Piccart and Mauro Delorenzi (2006) "Gene Expression Profiling in Breast Cancer: Understanding the Molecular Basis of Histologic Grade To Improve Prognosis", Journal of the National Cancer Institute, 98(4):262-272

Y. Wang, J. G. Klijn, Y. Zhang, A. M. Sieuwerts, M. P. Look, F. Yang, D. Talantov, M. Timmermans, M. E. Meijer-van Gelder, J. Yu, T. Jatkoe, E. M. Berns, D. Atkins and J. A. Foekens (2005) "Gene-Expression Profiles to Predict Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer", Lancet, 365:671-679

Andy J. Minn, Gaorav P. Gupta, David Padua, Paula Bos, Don X. Nguyen, Dimitry Nuyten, Bas Kreike, Yi Zhang, Yixin Wang, Hemant Ishwaran, John A. Foekens, Marc van de Vijver and Joan Massague (2007) "Lung metastasis genes couple breast tumor size and metastatic spread", Proceedings of the National Academy of Sciences, 104(16):6740-6745

Laura J. van't Veer, Hongyue Dai, Marc J. van de Vijver, Yudong D. He, Augustinus A.M. Hart, Mao Mao, Hans L. Peterse, Karin van der Kooy, Matthew J. Marton, Anke T. Witteveen, George J. Schreiber, Ron M. Kerkhoven, Chris Roberts, Peter S. Linsley, Rene Bernards and Stephen H. Friend (2002) "Gene expression profiling predicts clinical outcome of breast cancer", Nature, 415:530-536

M. J. van de Vijver, Y. D. He, L. van't Veer, H. Dai, A. M. Hart, D. W. Voskuil, G. J. Schreiber, J. L. Peterse, C. Roberts, M. J. Marton, M. Parrish, D. Atsma, A. Witteveen, A. Glas, L. Delahaye, T. van der Velde, H. Bartelink, S. Rodenhuis, E. T. Rutgers, S. H. Friend and R. Bernards (2002) "A Gene Expression Signature as a Predictor of Survival in Breast Cancer", New England Journal of Medicine, 347(25):1999-2009

Examples

## load Biobase package
library(Biobase)
## load the dataset
data(breastCancerData)
########
## Example for the mainz7g dataset
########
## show the first 5 columns of the expression data
exprs(mainz7g)[ ,1:5]
## show the first 6 rows of the phenotype data
head(pData(mainz7g))
## show first 20 feature names
featureNames(mainz7g)
## show the experiment data summary
experimentData(mainz7g)
## show the used platform
annotation(mainz7g)
## show the abstract for this dataset
abstract(mainz7g)

Example output

Loading required package: survival
Loading required package: prodlim
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colMeans, colSums, colnames,
    dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
    rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

            MAINZ_BC6001 MAINZ_BC6002 MAINZ_BC6003 MAINZ_BC6004 MAINZ_BC6005
205225_at       8.493412    13.980538    11.629521    13.278576    13.808408
216836_s_at    14.153927    10.800657    10.825213    10.665231    10.155051
208079_s_at     9.828991     8.118580     8.366727     7.840417     8.633329
211668_s_at     9.271096     7.483367     8.199833     9.280243     8.674024
211527_x_at     7.924302     7.918804     4.047185     6.291389     7.298770
209969_s_at     7.323108     8.402956     8.781874     8.522883     9.212219
202763_at       8.841938     7.909849     8.132746     8.698890     8.476773
               samplename dataset series
MAINZ_BC6001 MAINZ_BC6001   MAINZ  MAINZ
MAINZ_BC6002 MAINZ_BC6002   MAINZ  MAINZ
MAINZ_BC6003 MAINZ_BC6003   MAINZ  MAINZ
MAINZ_BC6004 MAINZ_BC6004   MAINZ  MAINZ
MAINZ_BC6005 MAINZ_BC6005   MAINZ  MAINZ
MAINZ_BC6006 MAINZ_BC6006   MAINZ  MAINZ
                                                    id         filename size
MAINZ_BC6001  BC6001_r: Mainz_N0_untreated_frozen _001 GSM282373.CEL.gz  1.8
MAINZ_BC6002  BC6002_r: Mainz_N0_untreated_frozen _002 GSM282374.CEL.gz  2.5
MAINZ_BC6003  BC6003_r: Mainz_N0_untreated_frozen _003 GSM282375.CEL.gz  1.5
MAINZ_BC6004  BC6004_r: Mainz_N0_untreated_frozen _004 GSM282376.CEL.gz  1.2
MAINZ_BC6005 BC6005wdh: Mainz_N0_untreated_frozen _005 GSM282377.CEL.gz  2.4
MAINZ_BC6006    BC6006: Mainz_N0_untreated_frozen _006 GSM282378.CEL.gz  1.8
             age er grade pgr her2 brca.mutation e.dmfs t.dmfs node t.rfs e.rfs
MAINZ_BC6001  54  0     2  NA   NA            NA      1   2760    0    NA    NA
MAINZ_BC6002  54  1     3  NA   NA            NA      0   2130    0    NA    NA
MAINZ_BC6003  42  1     3  NA   NA            NA      1   1740    0    NA    NA
MAINZ_BC6004  71  1     2  NA   NA            NA      0   2040    0    NA    NA
MAINZ_BC6005  58  1     2  NA   NA            NA      0   3090    0    NA    NA
MAINZ_BC6006  61  1     2  NA   NA            NA      0   2790    0    NA    NA
             treatment tissue t.os e.os
MAINZ_BC6001         0      1   NA   NA
MAINZ_BC6002         0      1   NA   NA
MAINZ_BC6003         0      1   NA   NA
MAINZ_BC6004         0      1   NA   NA
MAINZ_BC6005         0      1   NA   NA
MAINZ_BC6006         0      1   NA   NA
[1] "205225_at"   "216836_s_at" "208079_s_at" "211668_s_at" "211527_x_at"
[6] "209969_s_at" "202763_at"  
Experiment data
  Experimenter name: MAINZ 
  Laboratory: Department of Obstetrics and Gynecology, Medical School, Johannes Gutenberg University, Mainz, Germany 
  Contact information: Mathias Gehrmann <[e-mail]> 
  Title: The humoral immune system has a key prognostic impact in node-negative breast cancer. 
  URL: GEO accession number: GSE11121 <http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11121> 
  PMIDs: 18593943 

  Abstract: A 258 word abstract is available. Use 'abstract' method.
[1] "hgu133a"
[1] "Schmidt et al. 2008. Background: Estrogen receptor (ER) expression and proliferative activity are established prognostic factors in breast cancer. In a search for additional prognostic motifs, we analyzed the gene expression patterns of 200 tumors of patients who were not treated by systemic therapy after surgery using a discovery approach. After performing hierarchical cluster analysis, we identified coregulated genes related to the biological process of proliferation, steroid hormone receptor expression, as well as B-cell and T-cell infiltration. We calculated metagenes as a surrogate for all genes contained within a particular cluster and visualized the relative expression in relation to time to metastasis with principal component analysis. Distinct pat- terns led to the hypothesis of a prognostic role of the immune system in tumors with high expression of proliferation- associated genes. In multivariate Cox regression analysis, the proliferation metagene showed a significant association with metastasis-free survival of the whole discovery cohort [hazard ratio (HR), 2.20; 95% confidence interval (95% CI), 1.40-3.46]. The B-cell metagene showed additional indepen- dent prognostic information in carcinomas with high prolif- erative activity (HR, 0.66; 95% CI, 0.46-0.97). A prognostic influence of the B-cell metagene was independently confirmed by multivariate analysis in a first validation cohort enriched for high-grade tumors (n = 286; HR, 0.78; 95% CI, 0.62-0.98) and a second validation cohort enriched for younger patients (n = 302; HR, 0.83; 95% CI, 0.7-0.97). Thus, we could show in three cohorts of untreated, node-negative breast cancer patients that the humoral immune system plays a pivotal role in metastasis-free survival of carcinomas of the breast."

survcomp documentation built on Nov. 8, 2020, 4:54 p.m.