Literature DB >> 18781520

A predictive risk probability approach for microarray data with survival as an endpoint.

Dung-Tsa Chen1, Michael J Schell, James J Chen, William J Fulp, Steven Eschrich, Timothy Yeatman.   

Abstract

Gene expression profiling has played an important role in cancer risk classification and has shown promising results. Since gene expression profiling often involves determination of a set of top rank genes for analysis, it is important to evaluate how modeling performance varies with the number of selected top ranked genes incorporated in the model. We used a colon data set collected at Moffitt Cancer Center as an example of the study, and ranked genes based on the univariate Cox proportional hazards model. A set of top ranked genes was selected for evaluation. The selection was done by choosing the top k ranked genes for k = 1 to 12,500. An analysis indicated a considerable variation of classification outcomes when the number of top ranked genes was changed. We developed a predictive risk probability approach to accommodate this variation by identifying a range number of top ranked genes. For each number of top ranked genes, the procedure classifies each patient as having high risk (score = 1) or low risk (score = 0). The categorizations are then averaged, giving a risk score between 0 and 1, thus providing a ranking for the patient's need for further treatment. This approach was applied to the colon data set and demonstrated the strength of this approach by three criteria: First, a univariate Cox proportional hazards model showed a highly statistically significant level (log-rank chi(2) statistics = 110 with p-value <10(-16)) for the predictive risk probability classification. Second, the survival tree model used the risk probability to partition patients into five risk groups showing a good separation of survival curves (log-rank chi(2) statistics = 215). In addition, utilization of the risk group status identified a small set of risk genes that may be practical for biological validation. Third, analysis of resampling the risk probability suggested the variation pattern of the log-rank chi(2) in the colon cancer data set was unlikely caused by chance.

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Year:  2008        PMID: 18781520      PMCID: PMC2717790          DOI: 10.1080/10543400802277967

Source DB:  PubMed          Journal:  J Biopharm Stat        ISSN: 1054-3406            Impact factor:   1.051


  12 in total

1.  Significance analysis of microarrays applied to the ionizing radiation response.

Authors:  V G Tusher; R Tibshirani; G Chu
Journal:  Proc Natl Acad Sci U S A       Date:  2001-04-17       Impact factor: 11.205

2.  Summaries of Affymetrix GeneChip probe level data.

Authors:  Rafael A Irizarry; Benjamin M Bolstad; Francois Collin; Leslie M Cope; Bridget Hobbs; Terence P Speed
Journal:  Nucleic Acids Res       Date:  2003-02-15       Impact factor: 16.971

Review 3.  Gene expression phenotypes of oncogenic signaling pathways.

Authors:  Erich S Huang; Esther P Black; Holly Dressman; Mike West; Joseph R Nevins
Journal:  Cell Cycle       Date:  2003 Sep-Oct       Impact factor: 4.534

4.  Predicting the clinical status of human breast cancer by using gene expression profiles.

Authors:  M West; C Blanchette; H Dressman; E Huang; S Ishida; R Spang; H Zuzan; J A Olson; J R Marks; J R Nevins
Journal:  Proc Natl Acad Sci U S A       Date:  2001-09-18       Impact factor: 11.205

5.  Linking gene expression data with patient survival times using partial least squares.

Authors:  Peter J Park; Lu Tian; Isaac S Kohane
Journal:  Bioinformatics       Date:  2002       Impact factor: 6.937

6.  The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma.

Authors:  Andreas Rosenwald; George Wright; Wing C Chan; Joseph M Connors; Elias Campo; Richard I Fisher; Randy D Gascoyne; H Konrad Muller-Hermelink; Erlend B Smeland; Jena M Giltnane; Elaine M Hurt; Hong Zhao; Lauren Averett; Liming Yang; Wyndham H Wilson; Elaine S Jaffe; Richard Simon; Richard D Klausner; John Powell; Patricia L Duffey; Dan L Longo; Timothy C Greiner; Dennis D Weisenburger; Warren G Sanger; Bhavana J Dave; James C Lynch; Julie Vose; James O Armitage; Emilio Montserrat; Armando López-Guillermo; Thomas M Grogan; Thomas P Miller; Michel LeBlanc; German Ott; Stein Kvaloy; Jan Delabie; Harald Holte; Peter Krajci; Trond Stokke; Louis M Staudt
Journal:  N Engl J Med       Date:  2002-06-20       Impact factor: 91.245

7.  A gene-expression signature as a predictor of survival in breast cancer.

Authors:  Marc J van de Vijver; Yudong D He; Laura J van't Veer; Hongyue Dai; Augustinus A M Hart; Dorien W Voskuil; George J Schreiber; Johannes L Peterse; Chris Roberts; Matthew J Marton; Mark Parrish; Douwe Atsma; Anke Witteveen; Annuska Glas; Leonie Delahaye; Tony van der Velde; Harry Bartelink; Sjoerd Rodenhuis; Emiel T Rutgers; Stephen H Friend; René Bernards
Journal:  N Engl J Med       Date:  2002-12-19       Impact factor: 91.245

8.  Partial least squares proportional hazard regression for application to DNA microarray survival data.

Authors:  Danh V Nguyen; David M Rocke
Journal:  Bioinformatics       Date:  2002-12       Impact factor: 6.937

9.  Semi-supervised methods to predict patient survival from gene expression data.

Authors:  Eric Bair; Robert Tibshirani
Journal:  PLoS Biol       Date:  2004-04-13       Impact factor: 8.029

10.  Repeated observation of breast tumor subtypes in independent gene expression data sets.

Authors:  Therese Sorlie; Robert Tibshirani; Joel Parker; Trevor Hastie; J S Marron; Andrew Nobel; Shibing Deng; Hilde Johnsen; Robert Pesich; Stephanie Geisler; Janos Demeter; Charles M Perou; Per E Lønning; Patrick O Brown; Anne-Lise Børresen-Dale; David Botstein
Journal:  Proc Natl Acad Sci U S A       Date:  2003-06-26       Impact factor: 12.779

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