Predicting relapse prior to transplantation in chronic myeloid leukemia by integrating expert knowledge and expression data

Bioinformatics

Volumn 28, Issue 6, 2012, Pages 823-830

  Yeung, K Y ^a   Gooley, T A ^b   Zhang, A ^b   Raftery, A E ^a   Radich, J P ^b   Oehler, V G ^b  

^a UNIVERSITY OF WASHINGTON   (United States)

^b Fred Hutchinson Cancer Research Center   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; CHRONIC MYELOID LEUKEMIA; DISEASE COURSE; DNA MICROARRAY; GENETICS; HEMATOPOIETIC STEM CELL TRANSPLANTATION; HUMAN; MYELOID LEUKEMIA; POLYMERASE CHAIN REACTION; RECURRENT DISEASE;

ALGORITHMS; BAYES THEOREM; DISEASE PROGRESSION; HEMATOPOIETIC STEM CELL TRANSPLANTATION; HUMANS; LEUKEMIA, MYELOGENOUS, CHRONIC, BCR-ABL POSITIVE; LEUKEMIA, MYELOID, CHRONIC-PHASE; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; POLYMERASE CHAIN REACTION; RECURRENCE;

EID: 84859031209     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts059     Document Type: Article

References (58)

1. Abeel, T. et al. (2010) Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics, 26, 392-398.
2. Alon, U. et al. (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl Acad. Sci. USA, 96, 6745-6750.
3. Bessarabova, M. et al. (2011) Functional synergies yet distinct modulators affected by genetic alterations in common human cancers. Cancer Res., 71, 3471-3481.
4. Branford, S. et al. (2003) Detection of BCR-ABLmutations in patients withCMLtreated with imatinib is virtually always accompanied by clinical resistance, and mutations in the ATP phosphate-binding loop (P-loop) are associated with a poor prognosis. Blood, 102, 276-283.
5. Brier, G. W. (1950) Verification of forecasts expressed in terms of probability. Mon. Weather Rev., 78, 1-3.
6. Chen, P. C. et al. (2009) A new regularized least squares support vector regression for gene selection. BMC Bioinform., 10, 44.
7. Drier, Y. and Domany, E. (2011) Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes? PLoS One, 6, e17795.
8. Druker, B. J. et al. (2001) Efficacy and safety of a specific inhibitor of the BCRABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med., 344, 1031-1037.
9. Druker, B. J. et al. (2006) Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N. Engl. J. Med., 355, 2408-2417.
10. Dupuy, A. and Simon, R. M. (2007) Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J. Natl Cancer Inst., 99, 147-157.
11. Ein-Dor, L. et al. (2005) Outcome signature genes in breast cancer: is there a unique set? Bioinformatics, 21, 171-178.
12. Ein-Dor, L. et al. (2006) Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc. Natl Acad. Sci. USA, 103, 5923-5928.
13. Faderl, S. et al. (1999) Chronic myelogenous leukemia: biology and therapy. Ann. Intern. Med., 131, 207-219.
14. Furnival, G. M. andWilson, R. W. (1974) Regression by leaps and bounds. Technometrics, 16, 499-511.
15. Gratwohl, A. et al. (1998) Risk assessment for patients with chronic myeloid leukaemia before allogeneic blood or marrow transplantation. Chronic Leukemia Working Party of the European Group for Blood and Marrow Transplantation. Lancet, 352, 1087-1092.
16. Hansen, J. A. et al. (1998) Bone marrow transplants from unrelated donors for patients with chronic myeloid leukemia. N. Engl. J. Med., 338, 962-968.
17. Hasford, J. et al. (1996) Analysis and validation of prognostic factors for CML. German CML Study Group. Bone Marrow Transplant., 17 (Suppl. 3), S49-S54.
18. Hoeting, J. A. et al. (1999) Bayesian model averaging: a tutorial. Stat. Sci., 14, 382-401.
19. Hosmer, D. W. and Lemeshow, S. (2000) Applied Logistic Regression. Wiley, New York.
20. Huttenhower, C. et al. (2009) Exploring the human genome with functional maps. Genome Res., 19, 1093-1106.
21. Ioannidis, J. P. (2005) Microarrays and molecular research: noise discovery? Lancet, 365, 454-455.
22. Jamieson, C. H. et al. (2004) Granulocyte-macrophage progenitors as candidate leukemic stem cells in blast-crisis CML. N. Engl. J. Med., 351, 657-667.
23. Kass, R. E. and Raftery, A. E. (1995) Bayes factors. J. Am. Stat. Assoc., 90, 773-795.
24. Lee, K. E. et al. (2003) Gene selection: a Bayesian variable selection approach. Bioinformatics, 19, 90-97.
25. Linghu, B. et al. (2009) Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol., 10, R91.
26. Madigan, D. and Raftery, A. (1994) Model selection and accounting for model uncertainty in graphical models using Occam's window. J. Am. Stat. Assoc., 89, 1335-1346.
27. McWeeney, S. K. et al. (2010) A gene expression signature of CD34+ cells to predict major cytogenetic response in chronic-phase chronic myeloid leukemia patients treated with imatinib. Blood, 115, 315-325.
28. Melo, J. V. and Barnes, D. J. (2007) Chronic myeloid leukaemia as a model of disease evolution in human cancer. Nat. Rev. Cancer, 7, 441-453.
29. Michiels, S. et al. (2005) Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet, 365, 488-492.
30. Mitchell, T. (1997) Machine Learning. McGraw Hill, New York.
31. Mohty, M. et al. (2007) The polycomb group BMI1 gene is a molecular marker for predicting prognosis of chronic myeloid leukemia. Blood, 110, 380-383.
32. Mohty, M. et al. (2008) Association between BMI-1 expression, acute graft-versushost disease, and outcome following allogeneic stem cell transplantation from HLA-identical siblings in chronic myeloid leukemia. Blood, 112, 2163-2166.
33. Neviani, P. et al. (2005) The tumor suppressor PP2A is functionally inactivated in blast crisis CML through the inhibitory activity of the BCR/ABL-regulated SET protein. Cancer Cell, 8, 355-368.
34. Nguyen, D. V. and Rocke, D. M. (2002) Tumor classification by partial least squares using microarray gene expression data. Bioinformatics, 18, 39-50.
35. Oehler, V. G. et al. (2009a) The preferentially expressed antigen in melanoma (PRAME) inhibits myeloid differentiation in normal hematopoietic and leukemic progenitor cells. Blood, 114, 3299-3308.
36. Oehler, V. G. et al. (2009b) The derivation of diagnostic markers of chronic myeloid leukemia progression from microarray data. Blood, 114, 3292-3298.
37. Passweg, J. R. et al. (2004)Validation and extension of the EBMTRisk Score for patients with chronic myeloid leukaemia (CML) receiving allogeneic haematopoietic stem cell transplants. Br. J. Haematol., 125, 613-620.
38. Pavlu, J. et al. (2010) Optimizing patient selection for myeloablative allogeneic hematopoietic cell transplantation in chronic myeloid leukemia in chronic phase. Blood, 115, 4018-4020.
39. Quintas-Cardama, A. and Cortes, J. (2009) Molecular biology of bcr-abl1-positive chronic myeloid leukemia. Blood, 113, 1619-1630.
40. Radich, J. P. et al. (1995) Polymerase chain reaction detection of the BCR-ABL fusion transcript after allogeneic marrow transplantation for chronic myeloid leukemia: results and implications in 346 patients. Blood, 85, 2632-2638.
41. Radich, J. P. et al. (2001) The significance of bcr-abl molecular detection in chronic myeloid leukemia patients "late," 18 months or more after transplantation. Blood, 98, 1701-1707.
42. Radich, J. P. et al. (2003) HLA-matched related hematopoietic cell transplantation for chronic-phase CML using a targeted busulfan and cyclophosphamide preparative regimen. Blood, 102, 31-35.
43. Radich, J. P. et al. (2006) Gene expression changes associated with progression and response in chronic myeloid leukemia. Proc. Natl Acad. Sci. USA, 103, 2794-2799.
44. Raftery, A. E. (1995) Bayesian model selection in social research (with discussion). Sociol. Method., 25, 111-196.
45. Ramaswamy, S. et al. (2001) Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl Acad. Sci. USA, 98, 15149-15154.
46. Ren, R. (2005) Mechanisms of BCR-ABL in the pathogenesis of chronic myelogenous leukaemia. Nat. Rev. Cancer, 5, 172-183.
47. Sawyers, C. L. et al. (2002) Imatinib induces hematologic and cytogenetic responses in patients with chronic myelogenous leukemia in myeloid blast crisis: results of a phase II study. Blood, 99, 3530-3539.
48. Shah, N. P. (2008) Advanced CML: therapeutic options for patients in accelerated and blast phases. J. Natl Compr. Canc. Netw., 6 (Suppl. 2), S31-S36.
49. Sokal, J. E. et al. (1984) Prognostic discrimination in "good-risk" chronic granulocytic leukemia. Blood, 63, 789-799.
50. Stirewalt, D. L. et al. (2008) Identification of genes with abnormal expression changes in acute myeloid leukemia. Genes Chromosomes Cancer, 47, 8-20.
51. Tibshirani, R. (1996) Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. B, 58, 267-288.
52. Tusher, V. G. et al. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA, 98, 5116-5121.
53. van't Veer, L. J. et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530-536.
54. Xu, J. Z. andWong, C. W. (2010) Hunting for robust gene signature from cancer profiling data: sources of variability, different interpretations, and recent methodological developments. Cancer Lett., 296, 9-16.
55. Yang, A. J. and Song, X. Y. (2010) Bayesian variable selection for disease classification using gene expression data. Bioinformatics, 26, 215-222.
56. Yeung, K. Y. et al. (2005) Bayesian model averaging: development of an improved multiclass, gene selection and classification tool for microarray data. Bioinformatics, 21, 2394-2402.
57. Yong, A. S. et al. (2006) Molecular profiling of CD34+ cells identifies low expression of CD7, along with high expression of proteinase 3 or elastase, as predictors of longer survival in patients with CML. Blood, 107, 205-212.
58. Zhao, C. et al. (2009) Hedgehog signalling is essential for maintenance of cancer stem cells in myeloid leukaemia. Nature, 458, 776-779.