Systematic computation with functional gene-sets among leukemic and hematopoietic stem cells reveals a favorable prognostic signature for acute myeloid leukemia

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages 1-21

  Yang, Xinan Holly ^a   Li, Meiyi ^b   Wang, Bin ^a   Zhu, Wanqi ^c   Desgardin, Aurelie ^a   Onel, Kenan ^a   de Jong, Jill ^a   Chen, Jianjun ^c   Chen, Luonan ^b   Cunningham, John M ^a  

^a UNIVERSITY OF CHICAGO   (United States)

^b INSTITUTE OF BIOCHEMISTRY AND CELL BIOLOGY   (China)

^c UNIVERSITY OF CHICAGO   (United States)

Author keywords

AML outcome; Dynamic network biomarker; Functional gene set; Leukemic stem cell; Relative effect analysis

Indexed keywords

BIOINFORMATICS; BIOLOGY; CELLS; COMPLEX NETWORKS; COMPUTATIONAL EFFICIENCY; CYTOLOGY; DIAGNOSIS; DISEASES; GENES;

ACUTE MYELOID LEUKEMIA; AML OUTCOME; BIOLOGICAL DISCOVERIES; COMPUTATIONAL FRAMEWORK; DYNAMIC NETWORK; FUNCTIONAL GENES; HEMATOPOIETIC STEM CELLS; RELATIVE EFFECTS;

STEM CELLS;

TRANSCRIPTOME;

ACUTE MYELOBLASTIC LEUKEMIA; ALGORITHM; CANCER STEM CELL; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENETICS; HEMATOPOIETIC STEM CELL; HUMAN; METABOLISM; MORTALITY; PROGNOSIS;

ALGORITHMS; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; HEMATOPOIETIC STEM CELLS; HUMANS; LEUKEMIA, MYELOID, ACUTE; NEOPLASTIC STEM CELLS; PROGNOSIS; TRANSCRIPTOME;

EID: 84925775190     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0510-7     Document Type: Article

References (52)

1. Ishikawa F, Yoshida S, Saito Y, Hijikata A, Kitamura H, Tanaka S, et al. Chemotherapy-resistant human AML stem cells home to and engraft within the bone-marrow endosteal region. Nat Biotechnol. 2007;25(11):1315-21.
2. Guzman ML, Rossi RM, Karnischky L, Li X, Peterson DR, Howard DS, et al. The sesquiterpene lactone parthenolide induces apoptosis of human acute myelogenous leukemia stem and progenitor cells. Blood. 2005;105(11):4163-9.
3. Horton SJ, Huntly BJ. Recent advances in acute myeloid leukemia stem cell biology. Haematologica. 2012;97(7):966-74.
4. Lapidot T, Sirard C, Vormoor J, Murdoch B, Hoang T, Caceres-Cortes J, et al. A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature. 1994;367(6464):645-8.
5. Wiseman DH, Greystoke BF, Somervaille TC. The variety of leukemic stem cells in myeloid malignancy. Oncogene. 2014;33(24):3091-8.
6. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50.
7. Yang X, Regan K, Huang Y, Zhang Q, Li J, Seiwert TY, et al. Single sample expression-anchored mechanisms predict survival in head and neck cancer. PLoS Comput Biol. 2012;8(1):e1002350.
8. Yang X, Li H, Regan K, Li J, Huang Y, Lussier YA. Towards mechanism classifiers: expression-anchored gene ontology signature predicts clinical outcome in lung adenocarcinoma patients. AMIA Annu Symp Proc. 2012;2012:1040-9.
9. Tarca AL, Bhatti G, Romero R. A comparison of gene set analysis methods in terms of sensitivity, prioritization and specificity. PLoS One. 2013;8(11):e79217.
10. Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics. 2010;26(12):i237-45.
11. Segal E, Friedman N, Koller D, Regev A. A module map showing conditional activity of expression modules in cancer. Nat Genet. 2004;36(10):1090-8.
12. Drier Y, Sheffer M, Domany E. Pathway-based personalized analysis of cancer. Proc Natl Acad Sci U S A. 2013;110(16):6388-93.
13. Xiong Q, Ancona N, Hauser ER, Mukherjee S, Furey TS. Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets. Genome Res. 2012;22(2):386-97.
14. Yang X, Bentink S, Scheid S, Spang R. Similarities of ordered gene lists. J Bioinform Comput Biol. 2006;4(3):693-708.
15. Chen L, Liu R, Liu ZP, Li M, Aihara K. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci Rep. 2012;2:342.
16. Li M, Zeng T, Liu R, Chen L. Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis. Brief Bioinform. 2014;15(2):229-43.
17. Yang X, Vasudevan P, Parekh V, Penev A, Cunningham JM. Bridging cancer biology with the clinic: relative expression of a GRHL2-mediated gene-set pair predicts breast cancer metastasis. PLoS One. 2013;8(2):e56195.
18. Yang X, Ai X, Cunningham JM. Computational prognostic indicators for breast cancer. Cancer Manage Res. 2014;6:301-12.
19. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739-40.
20. Lottaz C, Yang X, Scheid S, Spang R. OrderedList-a bioconductor package for detecting similarity in ordered gene lists. Bioinformatics. 2006;22(18):2315-6.
21. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.
22. Gentles AJ, Plevritis SK, Majeti R, Alizadeh AA. Association of a leukemic stem cell gene expression signature with clinical outcomes in acute myeloid leukemia. JAMA. 2010;304(24):2706-15.
23. de Jonge HJ, Woolthuis CM, Vos AZ, Mulder A, van den Berg E, Kluin PM, et al. Gene expression profiling in the leukemic stem cell-enriched CD34+ fraction identifies target genes that predict prognosis in normal karyotype AML. Leukemia. 2011;25(12):1825-33.
24. Eppert K, Takenaka K, Lechman ER, Waldron L, Nilsson B, van Galen P, et al. Stem cell gene expression programs influence clinical outcome in human leukemia. Nat Med. 2011;17(9):1086-93.
25. Ohashi S, Sakashita G, Ban R, Nagasawa M, Matsuzaki H, Murata Y, et al. Phospho-regulation of human protein kinase Aurora-A: analysis using anti-phospho-Thr288 monoclonal antibodies. Oncogene. 2006;25(59):7691-702.
26. Yang J, Ikezoe T, Nishioka C, Nobumoto A, Udaka K, Yokoyama A. CD34(+)/CD38(-) acute myelogenous leukemia cells aberrantly express Aurora kinase A. Int J Cancer. 2013;133(11):2706-19.
27. Kojima K, Shimanuki M, Shikami M, Andreeff M, Nakakuma H. Cyclin-dependent kinase 1 inhibitor RO-3306 enhances p53-mediated Bax activation and mitochondrial apoptosis in AML. Cancer Sci. 2009;100(6):1128-36.
28. Graham SM, Vass JK, Holyoake TL, Graham GJ. Transcriptional analysis of quiescent and proliferating CD34+ human hemopoietic cells from normal and chronic myeloid leukemia sources. Stem Cells. 2007;25(12):3111-20.
29. Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T, et al. Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'. Nat Genet. 2005;37(3):225-32.
30. Boultwood J, Lewis S, Wainscoat JS. The 5q-syndrome. Blood. 1994;84(10):3253-60.
31. Lai F, Godley LA, Joslin J, Fernald AA, Liu J, Espinosa 3rd R, et al. Transcript map and comparative analysis of the 1.5-Mb commonly deleted segment of human 5q31 in malignant myeloid diseases with a del(5q). Genomics. 2001;71(2):235-45.
32. Myllykangas S, Himberg J, Bohling T, Nagy B, Hollmen J, Knuutila S. DNA copy number amplification profiling of human neoplasms. Oncogene. 2006;25(55):7324-32.
33. Wouters BJ, Lowenberg B, Erpelinck-Verschueren CA, van Putten WL, Valk PJ, Delwel R. Double CEBPA mutations, but not single CEBPA mutations, define a subgroup of acute myeloid leukemia with a distinctive gene expression profile that is uniquely associated with a favorable outcome. Blood. 2009;113(13):3088-91.
34. Cancer Genome Atlas Research N. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368(22):2059-74.
35. Metzeler KH, Hummel M, Bloomfield CD, Spiekermann K, Braess J, Sauerland MC, et al. An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia. Blood. 2008;112(10):4193-201.
36. Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol. 2011;7(10):e1002240.
37. Shen Y, Zhu YM, Fan X, Shi JY, Wang QR, Yan XJ, et al. Gene mutation patterns and their prognostic impact in a cohort of 1185 patients with acute myeloid leukemia. Blood. 2011;118(20):5593-603.
38. Illmer T, Thiede C, Fredersdorf A, Stadler S, Neubauer A, Ehninger G, et al. Activation of the RAS pathway is predictive for a chemosensitive phenotype of acute myelogenous leukemia blasts. Clin Cancer Res. 2005;11(9):3217-24.
39. Becker MW, Jordan CT. Leukemia stemness signatures step toward the clinic. Cell Stem Cell. 2011;9(3):185-6.
40. Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 2012;8(2):e1002375.
41. Shih AH, Abdel-Wahab O, Patel JP, Levine RL. The role of mutations in epigenetic regulators in myeloid malignancies. Nat Rev Cancer. 2012;12(9):599-612.
42. Itonaga H, Imanishi D, Wong YF, Sato S, Ando K, Sawayama Y, et al. Expression of myeloperoxidase in acute myeloid leukemia blasts mirrors the distinct DNA methylation pattern involving the downregulation of DNA methyltransferase DNMT3B. Leukemia. 2014;28(7):1459-66.
43. Leong AS-Y, Cooper K, Leong FJW-M. Manual of Diagnostic Antibodies for Immunohistology Entnode. London: Greenwich Medical Media Ltd; 1999.
44. Zhang H, Mi JQ, Fang H, Wang Z, Wang C, Wu L, et al. Preferential eradication of acute myelogenous leukemia stem cells by fenretinide. Proc Natl Acad Sci U S A. 2013;110(14):5606-11.
45. Meloni G, Foa R, Vignetti M, Guarini A, Fenu S, Tosti S, et al. Interleukin-2 may induce prolonged remissions in advanced acute myelogenous leukemia. Blood. 1994;84(7):2158-63.
46. Noordermeer SM, Sanders MA, Gilissen C, Tonnissen E, van der Heijden A, Dohner K, et al. High BRE expression predicts favorable outcome in adult acute myeloid leukemia, in particular among MLL-AF9-positive patients. Blood. 2011;118(20):5613-21.
47. Li Z, Herold T, He C, Valk PJ, Chen P, Jurinovic V, et al. Identification of a 24-gene prognostic signature that improves the European LeukemiaNet risk classification of acute myeloid leukemia: an international collaborative study. J Clin Oncol. 2013;31(9):1172-81.
48. Miller JA, Cai C, Langfelder P, Geschwind DH, Kurian SM, Salomon DR, et al. Strategies for aggregating gene expression data: the collapseRows R function. BMC Bioinformatics. 2011;12:322.
49. Hansen KD, Irizarry RA, Wu Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics. 2012;13(2):204-16.
50. Lim WK, Wang K, Lefebvre C, Califano A. Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks. Bioinformatics. 2007;23(13):i282-8.
51. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc B (Methodological). 1995;57(1):289-300.
52. Li J, Lenferink AE, Deng Y, Collins C, Cui Q, Purisima EO, et al. Identification of high-quality cancer prognostic markers and metastasis network modules. Nat Commun. 2010;1:34.