The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Predicting Survival of DLBCL Patients in Pathway-Based Microarray Analysis

DLBCL 환자의 대사경로 정보를 이용한 생존예측

  • Lee, Kwang-Hyun (Department of Applied Statistics, Sejong University) ;
  • Lee, Sun-Ho (Department of Applied Statistics, Sejong University)
  • 이광현 (세종대학교 응용통계학) ;
  • 이선호 (세종대학교 응용통계학)
  • Received : 20100400
  • Accepted : 20100500
  • Published : 2010.08.31
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Abstract

Predicting survival from microarray data is not easy due to the problem of high dimensionality of data and the existence of censored observations. Also the limitation of individual gene analysis causes the shift of focus to the level of gene sets with functionally related genes. For developing a survival prediction model based on pathway information, the methods for selecting a supergene using principal component analysis and testing its significance for each pathway are discussed. Besides, the performance of gene filtering is compared.

마이크로어레이 실험 결과로부터 생존예측지표를 개발하는 일은 관찰 유전자수가 환자의 수보다 훨씬 많고 또 반응변수가 중도절단이 포함된 생존시간이기 때문에 어려운 작업이다. 또한 개별유전자 분석의 문제점이 대두되면서 동일한 대사기능을 수행하는 유전자들의 집합을 대상으로 분석하는 방법이 대두되고 있다. DLBCL 환자들의 마이크로어레이 유전자 발현 자료와 생존시간, 유전자들의 대사경로 정보를 바탕으로 생물학적 해석이 쉬운 생존예측지표를 찾고 그 정확성을 검정하는 pilot study를 실시하였다. 또한 유전자 걸러내기가 지표의 효율성에 미치는 영향력도 비교하여 보았다.

Keywords

References

  1. Adewale, A. J., Dinu, I., Potter, J. D., Liu, Q. and Yasui, Y. (2008). Pathway analysis of microarray data via regression, Journal of Computational Biology, 15, 269-277. https://doi.org/10.1089/cmb.2008.0002
  2. Bair, E. and Tibshirani, R. (2004). Semi-supervised methods to predict patient survival from gene Down-loaded from gene expression data, PLOS Biology, 2, 511-522.
  3. Brier, G. W. (1950). Verification of forecasts expressed in terms of probability, Monthly Weather Review, 78, 1-3. https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2
  4. Chen, X. and Wang, L. (2009). Integrating Biological Knowledge with Gene Expression Profiles for Survival Prediction of Cancer, Journal of Computational Biology, 16, 265-278. https://doi.org/10.1089/cmb.2008.12TT
  5. Chen, X., Wang, L., Smith, J. D. and Zhang, B. (2008). Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes, Bioinformatics, 24, 2479-2481.
  6. Dinu, I., Potter, J. D., Mueller, T., Liu, Q., Adewale, A. J., Jhangri, G. S., Einecke, G., Famulski, K. S., Halloran, P. and Yasui, Y. (2007). Improving gene set analysis of microarray data by SAM-GS, BMC Bioinformatics, 8, 242. https://doi.org/10.1186/1471-2105-8-242
  7. Goeman, J. J., Oosting, J., Cleton-Jansen, A. M., Anninga, J. K. and van Houwelingen, H. C. (2005). Testing association of a pathway with survival using gene expression data, Bioinformatics, 21, 1950-1957. https://doi.org/10.1093/bioinformatics/bti267
  8. Goeman, J. J., van de Geer, S. A., de Kort, F. and van Houwelingen, H. C. (2004). A global test for groups of genes: Testing association with a clinical outcome, Bioinformatics, 20, 93-99. https://doi.org/10.1093/bioinformatics/btg382
  9. Graf, E., Schmoor, C., Sauerbrei, W. and Schumacher, M. (1999). Assessment and comparison of prognostic classification schemes for survival data, Statistics in Medicine, 18, 2529-2545. https://doi.org/10.1002/(SICI)1097-0258(19990915/30)18:17/18<2529::AID-SIM274>3.0.CO;2-5
  10. Hastie, T., Tibshirani, R. and Friedman, J. H. (2001). The Elements of Statistical Learning, Data Mining, Inference, and Prediction, Springer-Verlag, New York.
  11. Heagerty, P. J., Lumley, T. and Pepe, M. S. (2000). Time-dependent ROC curves for censored survival data and a diagnostic marker, Biometrics, 56, 337-344. https://doi.org/10.1111/j.0006-341X.2000.00337.x
  12. Kerr M. and Churchill, G. (2001). Experimental design for gene expression microarrays, Biostatistics, 2, 183-201. https://doi.org/10.1093/biostatistics/2.2.183
  13. Kim, S. Y. and Volsky, D. J. (2005). PAGE: Parametric analysis of gene set enrichment, BMC Bioinformatics, 6, 14. https://doi.org/10.1186/1471-2105-6-14
  14. Ma, X. J., Wang, Z., Ryan, P. D., Isakoff, S. J., Barmettler, A., Fuller, A., Muir, B., Mohapatra, G., Salunga, R., Tuggle, J. T., Tran, Y., Tran, D., Tassin, A., Amon, P., Wang, W., Wang, W., Enright, E., Stecker, K., Estepa-Sabal, E., Smith, B., Younger, J., Balis, U., Michaelson, J., Bhan, A., Habin, K., Baer, T. M., Brugge, J., Haber, D. A., Erlander, M. G. and Sgroi, D. C. (2004). A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen, Cancer Cell, 5, 607-616. https://doi.org/10.1016/j.ccr.2004.05.015
  15. Mootha, V. K., Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstrale, M., Laurila, E., Houstis, N., Daly, M. J., Patterson, N., Mesirov, J. P., Golub, T. R., Tamayo, P., Spiegelman, B., Lander, E. S., Hirschhorn, J. N., Altshuler, D. and Groop, L. C. (2003). PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes, Nature Genetics, 34, 267-273. https://doi.org/10.1038/ng1180
  16. Rosenwald, A., Wright, G., Chan, W. C., Connors, J. M., Campo, E., Fisher, R. I., Gascoyne, R. D., Muller-Hermelink, H. K., Smeland, E. B., Giltnane, J. M., Hurt, E. M., Zhao, H., Averett, L., Yang, L., Wilson, W. H., Jaffe, E. S., Simon, R., Klausner, R. D., Powell, J., Duffey, P. L., Longo, D. L., Greiner, T. C., Weisenburger, D. D., Sanger, W. G., Dave, B. J., Lynch, J. C., Vose, J., Armitage, J. O., Montserrat, E., Lopez-Guillermo, A., Grogan, T. M., Miller, T. P., LeBlanc, M., Ott, G., Kvaloy, S., Delabie, J., Holte, H., Krajci, P., Stokke, T. and Staudt, L. M. (2002). The use of molecular profiling to predict survival after chemotherapy for diffuse large B-cell lymphoma, The New England Journal of Medicine, 346, 1937-1947. https://doi.org/10.1056/NEJMoa012914
  17. Simon, R., Radmacher, M. D., Dobbin, K. and McShane, L. M. (2003). Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification, Journal of National Cancer Institutes, 95, 14-18. https://doi.org/10.1093/jnci/95.1.14
  18. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L. Golub, T. R., Lander, E. S. and Mesirov, J. P. (2005). Gene set enrichment analysis:A knowledge-based approach for interpreting genome-wide expression profiles, PNAS, 102, 15545-15550. https://doi.org/10.1073/pnas.0506580102
  19. Tibshirani, R. (1997). The Lasso method for variable selection in the cox model, Statistics in Medicine, 16, 385-395. https://doi.org/10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3
  20. Tibshirani, R., Hastie, T. Narasimhan, B. and Chu, G. (2003). Class prediction by nearest shrunken centroids, with applications to DNA microarrays, Statistical Science, 18, 104-117. https://doi.org/10.1214/ss/1056397488
  21. Tusher, V. G., Tibshirani, R. and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response, PNAS, 98, 5116-5121. https://doi.org/10.1073/pnas.091062498