A new sensitivity-preferred strategy to build prediction rules for therapy response of cancer patients using gene expression data

Computer Methods and Programs in Biomedicine

Volumn 100, Issue 2, 2010, Pages 132-139

  Jung, Klaus ^a   Grade, Marian ^a   Gaedcke, Jochen ^a   Jo, Peter ^a   Opitz, Lennart ^b   Becker, Heinz ^a   Ghadimi, B Michael ^a   Beißbarth, Tim ^a  

^a UNIVERSITY MEDICAL CENTER GÖTTINGEN   (Germany)

^b UNIVERSITY OF GÖTTINGEN   (Germany)

Author keywords

Class prediction; Gene expression profiling; Personalized medicine; Sensitivity; Specificity; Therapy response

Indexed keywords

CLASS PREDICTION; GENE EXPRESSION PROFILING; PERSONALIZED MEDICINE; SENSITIVITY; SPECIFICITY; THERAPY RESPONSE;

FORECASTING; GENE THERAPY;

GENE EXPRESSION;

ARTICLE; CANCER PATIENT; COLON CANCER; GENE EXPRESSION SYSTEM; PREDICTION; SENSITIVITY AND SPECIFICITY; SIMULATION; TREATMENT RESPONSE;

GENE EXPRESSION PROFILING; HUMANS; NEOPLASMS;

EID: 77956651540     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2010.03.016     Document Type: Article

References (14)

1. van't Veer L.J., Dai H., van de Vijver M.J., He Y.D., Hart A.A.M., Mao M., Peterse H.L., van der Kooy K., Marton M.J., Witteveen A.T., Schreiber G.J., Kerkhoven R.M., Roberts C., Linsley P.S., Bernards R., Friend S.H. Gene expression profiling predicts clinical outcome in breast cancer. Nature 2002, 415:530-536.
2. Oberthuer A., Berthold F., Warnat P., Hero B., Kahlert Y., Spitz R., Ernestus K., König R., Haas S., Eils R., Schwab M., Brors B., Westermann F., Fischer M. Customized oligonucleotide microarray gene expression-based classification of neuroblastoma patients outperforms current clinical risk stratification. Journal of Clinical Oncology 2006, 24:5070-5078.
3. Ghadimi B.M., Grade M., Difilippantonio M.J., Varma S., Simon R., Montagna C., Füzesi L., Langer C., Becker H., Liersch T., Ried T. Effectiveness of gene expression profiling for response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy. Journal of Clinical Oncology 2005, 23:1826-1838.
4. Sauer R., Becker H., Hohenberger W., Rödel C., Wittekind C., Fietkau R., Martus J., Tschmelitsch E., Hager E., Hess J.H., Karstens J.H., Liersch T., Schmidberger H., Raab R. Preoperative versus postoperative chemoradiotherapy for rectal cancer. New England Journal of Medicine 2004, 351:1731-1740. German Rectal Cancer Study Group.
5. Grade M., Hörmann P., Becker S., Hummon A.B., Wangsa D., Varma S., Simon R., Liersch T., Becker H., Difilippantonio M.J., Ghadimi B.M., Ried T. Gene expression profiling reveals a massive, aneuploidy-dependent transcriptional deregulation and distinct differences between lymph node-negative and lymph node-positive colon carcinomas. Cancer Research 2007, 67:41-56.
6. Youden W.J. Index for rating diagnostic tests. Cancer 1950, 3:32-35.
7. Pepe M.S., Longton G., Anderson G.L., Schummer M. Selecting differentially expressed genes from microarray experiments. Biometrics 2003, 59:133-142.
8. Smyth G.K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 2004, 3. (Article 3).
9. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series 1995, 57:289-300.
10. Simon R., Korn E.L., McShane L.M., Radmacher M.D., Wright G.W., Zhao Y. Class Prediction, in Design and Analysis of DNA Microarray Investigations 2003, Springer, New York, (Chapter 8).
11. Efron B., Tibshirani R.J. Cross-validation and Other Estimated of Prediction, in An Introduction to the Bootstrap 1993, Chapman & Hall, New York, Chap 17.
12. Dudoit S., Fridlyand J. Classification in microarray experiments. Statistical Analysis of Gene Expression Microarray Data 2003, Chapman & Hall, London, pp. 111-114. T. Speed (Ed.).
13. Lim T.S., Loh W.Y., Shih Y.S. A comparison of prediction accuracy, complexity, and training time of thorty-three old and new classification algorithms. Machine Learning 2000, 40:203-229.
14. Tibshirani R, Hastie T., Narasimhan B., Chu G. Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Statistical Science 2003, 18:104-117.