Modeling intra-tumor protein expression heterogeneity in tissue microarray experiments

Statistics in Medicine

Volumn 27, Issue 11, 2008, Pages 1944-1959

  Shen, Ronglai ^a   Ghosh, Debashis ^b   Taylor, Jeremy M G ^c  

^a MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

^c UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Biomarker; Empirical Bayes; Joint modeling; Mixed effects; Tissue microarray; Varying replicate number (VRN)

Indexed keywords

2 METHYLACYL COENZYME A RACEMASE; BIOLOGICAL MARKER; DNA; MINICHROMOSOME MAINTENANCE PROTEIN 2;

ANALYTICAL ERROR; ARTICLE; BAYES THEOREM; CANCER RECURRENCE; CANCER SURVIVAL; CONTROLLED STUDY; DNA MICROARRAY; EMPIRICAL RESEARCH; HUMAN; HUMAN TISSUE; IMMUNOHISTOCHEMISTRY; MATHEMATICAL COMPUTING; MONTE CARLO METHOD; PROBABILITY; PROPORTIONAL HAZARDS MODEL; PROSTATE CANCER; PROTEIN EXPRESSION; PROTEOMICS; REGRESSION ANALYSIS; SIMULATION; SURVIVAL TIME; TISSUE MICROARRAY; TISSUE SPECIFICITY; VALIDATION STUDY;

BIOLOGICAL MARKERS; GENE EXPRESSION PROFILING; HUMANS; MALE; MODELS, STATISTICAL; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROSTATIC NEOPLASMS;

EID: 42949175116     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.3217     Document Type: Article

References (30)

1. Kononen J, Bubendorf L, Kallioniemi A, Barlund M, Schraml P, Leighton S, Torhorst J, Mihatsch MJ, Sauter G, Kallioniemi OP. Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nature Medicine 1998; 4:844-847.
2. Divito KA, Berger AJ, Camp RL, Dolled-Filhart M, Rimm DL, Kluger HM. Automated quantitative analysis of tissue microarrays reveals an association between high bcl-2 expression and improved outcome in melanoma. Cancer Research 2004; 64:8773-8777.
3. Seligson DB, Horvath S, Shi T, Yu H, Tze S, Grunstein M, Kurdistani SK. Global histone modification patterns predict risk of prostate cancer recurrence. Nature 2005; 435:1262-1266.
4. Rubin MA, Bismar TA, Andrèn O, Mucci L, Kim R, Shen R, Ghosh D, Wei J, Chinnaiyan AM, Adami H, Kantoff PJJ. Decreased α-methylacyl CoA racemase expression in localized prostate cancer is associated with an increased rate of biochemical recurrence and cancer-specific death. Cancer Epidemiology and Biomarkers Prevention 2005; 14:1424-1431.
5. Li C, Wang WH. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proceedings of the National Academy of Sciences of the United States of America 2001; 98:31-36.
6. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP. Summaries of Affymetrix genechip probe level data. Nucleic Acids Research 2003; 31:1-8.
7. Etzioni R, Hawley S, Billheimer D, True LD, Knudsen B. Analyzing patterns of staining in immunohistochemical studies: application to a study of prostate cancer recurrence. Cancer Epidemiology and Biomarkers Prevention 2005; 14:1040-1046.
8. Tsiatis AA, DeGruttola V, Wulfsohn MS. Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association 1995; 90:27-37.
9. Wulfsohn MS, Tsiatis AA. A joint modeling for survival and longitudinal data measured with error. Biometrics 1997; 53:330-339.
10. Faucett CJ, Thomas DC. Simultaneously modeling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. Statistics in Medicine 1996; 15:1663-1685.
11. Henderson R, Diggle P, Dobson A. Joint modeling of longitudinal measurements and event time data. Biostatistics 2000; 1:465-480.
12. Wang Y, Taylor JMG. Joint modeling longitudinal and event time data with application to acquired immunodeficiency syndrome. Journal of the American Statistical Association 2001; 96:895-905.
13. Xu J, Zeger SL. Joint analysis of longitudinal data comprising repeated measures and times to events. Applied Statistics 2001; 50:375-387.
14. Brown ER, Ibrahim JG. A Bayesian semiparametric joint hierarchical model for longitudinal and survival data. Biometrics 2003; 59:221-228.
15. Guo X, Carlin B. Separate and joint modeling of longitudinal and event time data using standard computer packages. The American Statistician 2004; 58:1-9.
16. Tadesse MG, Ibrahim JG, Gentleman R, Chiaretti S, Ritz J, Foa R. Bayesian error-in-variable survival model for the analysis of genechip arrays. Biometrics 2005; 61:488-497.
17. Prentice RL. Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika 1982; 69:331-342.
18. Carroll RJ, Ruppert D, Stefanski LA. Measurement Error in Nonlinear Models. Chapman & Hall: London, 1995.
19. Harville DA. Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association 1977; 72:320-338.
20. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982; 38:963-974.
21. Lindstrom MJ, Bates DM. Nonlinear mixed effects models for repeated measures data. Biometrics 1990; 46: 673-687.
22. McCulloch CE. Maximum likelihood variance components estimation for binary data. Journal of the American Statistical Association 1994; 89:330-335.
23. Tsiatis AA, Davidian M. Joint modeling of longitudinal and time-to-event data: an overview. Statistica Sinica 2004; 14:809-834.
24. Spiegelhalter D, Thomas A, Best N, Lunn D. WinBUGS 1.4 Manual, 2003. Available from: http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/ contents.shtml.
25. Thomas A. BRugs: An R interface to OpenBUGS. Version 1.0 User's Manual, 2004. Available from: http://mathstat.helsinki.fi/openbugs/.
26. Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM. Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Research 2002; 62:4427C33.
27. Bismar T, Demichelis F, Riva A, Kim R, Varambally S, He L. Defining aggressive prostate cancer using a 12-gene model. Neoplasia 2006; 8:59-68.
28. Horvath S, Liu X, Huang Y, Kim HL, Seligson DB. Forest-based methods for analyzing tissue microarray. ASA Proceedings of the Joint Statistical Meetings, 1863-1869. American Statistical Association: Alexandria, VA, 2003.
29. Liu X, Minin V, Huang Y, Seligson DB, Horvath S. Statistical methods for analyzing tissue microarray data. Journal of Biopharmaceutical Statistics 2004; 14:671-685.
30. Camp RL, Chung GG, Rimm DL. Automated subcellular localization and quantification of protein expression in tissue microarrays. Nature Medicine 2002; 8:1323-1328.