Using microarrays to study the microenvironment in tumor biology: The crucial role of statistics

Seminars in Cancer Biology

Volumn 18, Issue 5, 2008, Pages 305-310

  Baker, Stuart G ^a   Kramer, Barnett S ^b  

^a NATIONAL CANCER INSTITUTE   (United States)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

Bonferroni; Class prediction; Cluster analysis; Differential expression; False discovery rate; Sample size

Indexed keywords

BREAST CANCER; CANCER GROWTH; CLUSTER ANALYSIS; EPITHELIUM CELL; GENE EXPRESSION; GENE IDENTIFICATION; HUMAN; MICROENVIRONMENT; PROSTATE CANCER; REVIEW; STROMA CELL; TISSUE MICROARRAY; WOUND HEALING;

BREAST; BREAST NEOPLASMS; CLUSTER ANALYSIS; EPITHELIAL CELLS; FEMALE; GENE EXPRESSION; HUMANS; MALE; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROSTATE; PROSTATIC NEOPLASMS; STROMAL CELLS;

EID: 48349141827     PISSN: 1044579X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.semcancer.2008.03.001     Document Type: Review

References (24)

1. Zhao H.J., Ramos C.F., Brooks J.D., and Peehl D.M. Distinctive gene expression of prostatic stromal cells cultured from diseased versus normal tissues. J Cell Physiol 210 (2007) 111-121
2. Finak G., Sadekova S., Pepin F., Hallett M., Meterissian S., Halwani F., Khetani K., Souleimanova M., Zabolotny B., Omeroglu A., and Park M. Gene expression signatures of morphologically normal breast tissue identify basal-like tumors. Breast Cancer Res 8 (2006) R58
3. Chang H.Y., Sneddon J.B., Alizadeh A.A., Sood R., West R.B., Montgomery K., Chi J.T., van de Rijn M., Botstein D., and Brown P.O. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol 2 (2004) 206-214
4. Dupuy A., and Simon R.M. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 99 (2007) 147-157
5. Tusher V.G., Tibshirani R., and Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98 (2001) 5116-5121
6. Bretz F., Landgrebe J., and Brunner E. Multiplicity issues in microarray experiments. Methods Inf Med 44 (2005) 431-437
7. Allison D.B., Cui X.Q., Page G.P., and Sabripour M. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 1 (2006) 55-65
8. Gustano A., Calza S., and Pawitan Y. Identification of differentially expressed genes and false discovery rate in microarray studies. Curr Opin Lipidol 18 (2007) 187-193
9. Hunter D.J., and Kraft P. Drinking from the fire hose-statistical issues in genomewide association studies. N Engl J Med 357 (2007) 436-439
10. Eisen M.B., Spellman P.T., Brown P.O., and Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci 95 (1998) 1483-14868
11. Suzuki R., and Shimodaira H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22 (2006) 1540-1542
12. Michiels S., Koscielny S., and Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 365 (2005) 488-492
13. Dudoit S., Fridlyand J., and Speed T.P. Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 97 (2002) 77-87
14. Baker SG, Kramer BS. Identifying genes that contribute most to good classification in microarrays. BMC Bioinformatics 2006;7:407. http://www.biomedcentral.com/1471-2105/7/407.
15. Tibsharini R., Hastie T., Narasimhan B., and Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA 99 (2002) 6567-6572
16. Dabney A.R. Classification of microarrays to the nearest centroids. Bioinformatics 21 (2005) 4125-4148
17. Simon R., Radmach M.D., Dobbin K., and McShane L. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 95 (2003) 14-17
18. Ein-Dor L., Kela I., Getz G., Givol D., and Domany E. Outcome signature genes in breast cancer: is there a unique set?. Bioinformatics 21 (2005) 171-178
19. Hand D.J. Classifier technology and the illusion of progress. Stat Sci 21 (2006) 1-14
20. Ransahoff D.F. Bias as a threat to the validity of cancer molecular marker research. Nat Rev Cancer 5 (2005) 142-149
21. Dobbin K.K., and Simon R.M. Sample size planning for developing classifiers using high-dimensional DNA microarray data. Biostatistics 8 (2007) 101-117
22. Baker SG, Kramer BS, Srivastava, S. Markers for early detection of cancer: statistical issues for nested case-control studies. BMC Med Res Methodol 2002;2:4. http://www.biomedcentral.com/1471-2288/2/4/.
23. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield C.D., and Lander E.S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531-537
24. Baker SG, Kramer BS. Paradoxes in carcinogenesis: new opportunities for research directions. BMC Cancer 2007;7:151 (06 Aug 2007). http://www.biomedcentral.com/1471-2407/7/151.