A molecular and computational diagnostic approach identifies FOXP3, ICOS, CD52 and CASP1 as the most informative biomarkers in acute graft-versus-host disease

Haematologica

Volumn 97, Issue 10, 2012, Pages 1532-1538

  Cuzzola, Maria ^a   Fiasché, Maurizio ^b   Iacopino, Pasquale ^c   Messina, Giuseppe ^a   Martino, Massimo ^a   Console, Giuseppe ^a   Fedele, Roberta ^a   Massi, Daniela ^d   Recchia, Anna Grazia ^e   Irrera, Giuseppe ^a   Morabito, Fortunato ^e  

^a Transplant Regional Center of Stem Cells and Cellular Therapy   (Italy)

^b POLITECNICO DI MILANO   (Italy)

^c NATIONAL CANCER INSTITUTE GIOVANNI PAOLO II   (Italy)

^d UNIVERSITY OF FLORENCE   (Italy)

^e Azienda Ospedaliera Cosenza   (Italy)

Author keywords

Computational method; Gene expression profile; Graft versus host disease; Stem cell transplantation

Indexed keywords

CD52 ANTIGEN; CYCLOPHOSPHAMIDE; CYCLOSPORIN A; INDUCIBLE T CELL COSTIMULATOR LIGAND; INFLIXIMAB; INTERLEUKIN 1BETA CONVERTING ENZYME; LYMPHOCYTE ANTIBODY; MESSENGER RNA; METHOTREXATE; MYCOPHENOLIC ACID 2 MORPHOLINOETHYL ESTER; PREDNISONE; TACROLIMUS; THYMOCYTE ANTIBODY; TRANSCRIPTION FACTOR FOXP3;

ACUTE GRAFT VERSUS HOST DISEASE; ADOLESCENT; ADULT; AGED; ALLOGENEIC HEMATOPOIETIC STEM CELL TRANSPLANTATION; ARTICLE; CELL INTERACTION; CONTROLLED STUDY; DISEASE ASSOCIATION; FEMALE; GENE EXPRESSION; GENE EXPRESSION PROFILING; HUMAN; HUMAN TISSUE; IMMUNE RESPONSE; IMMUNOHISTOCHEMISTRY; MAJOR CLINICAL STUDY; MALE; PROSPECTIVE STUDY; REAL TIME POLYMERASE CHAIN REACTION; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY;

ACUTE DISEASE; ADOLESCENT; ADULT; AGED; ALGORITHMS; ANTIGENS, CD; ANTIGENS, NEOPLASM; BIOLOGICAL MARKERS; CASPASE 1; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; FEMALE; FORKHEAD TRANSCRIPTION FACTORS; GENE EXPRESSION PROFILING; GENETIC PREDISPOSITION TO DISEASE; GLYCOPROTEINS; GRAFT VS HOST DISEASE; HEMATOPOIETIC STEM CELL TRANSPLANTATION; HUMANS; INDIVIDUALIZED MEDICINE; INDUCIBLE T-CELL CO-STIMULATOR PROTEIN; MALE; MIDDLE AGED; MODELS, BIOLOGICAL; TRANSPLANTATION, HOMOLOGOUS; YOUNG ADULT;

EID: 84867274643     PISSN: 03906078     EISSN: 15928721     Source Type: Journal    
DOI: 10.3324/haematol.2011.059980     Document Type: Article

References (32)

1. Ferrara JL. Advances in the clinical management of GVHD. Best Pract Res Clin Haematol. 2008;21(4):677-682.
2. Choi SW, Levine JE, Ferrara JL. Pathogenesis and management of graft-versus-host disease. Immunol Allergy Clin North Am. 2010;30(1):75-101.
3. Paczesny S, Levine JE, Braun TM, Ferrara JL. Plasma biomarkers in graft-versus-host disease: a new era? Biol Blood Marrow Transplant 2009;15 (Suppl 1):33-38.
4. Paczesny S, Krijanovski OI, Braun TM, Choi SW, Clouthier SG, Kuick R, et al. A biomarker panel for acute graft-versus-host disease. Blood. 2009;113(2):273-278.
5. Paczesny S, Braun TM, Levine JE, Hogan J, Crawford J, Coffing B, et al. Elafin is a biomarker of graft-versus-host disease of the skin. Sci Transl Med. 2010;2(13):13ra2.
6. August KJ, Chiang KY, Bostick RM, Flanders WD, Waller EK, Langston A, et al. Biomarkers of immune activation to screen for severe, acute GVHD. Bone Marrow Transplant. 2011;46(4):601-604.
7. Baron C, Somogyi R, Greller LD, Rineau V, Wilkinson P, Cho CR, et al. Prediction of graft-versus-host disease in humans by donor gene-expression profiling. PloS Med. 2007;4(1):e23.
8. Buzzeo MP, Yang J, Casella G, Reddy V. A preliminary gene expression profile of acute graft-versus-host disease. Cell Transplant. 2008;17(5):489-494.
9. Paczesny S, Hanauer D, Sun Y, Reddy P. New perspectives on the biology of acute GVHD. Bone Marrow Transplant. 2010;45(1):1-11.
10. Fiasché M, Verma A, Cuzzola M, Morabito FC, Irrera G. Incremental - adaptive - knowledge based - learning for informative rules extraction in classification analysis of aGvHD. In: L. Iliadis, C. Jayne, editors. IFIP Advances in Information and Communication Technology, Engineering Applications of Neural Networks. Berlin Heidelberg: Springer-Verlag; 2011;363:361-371.
11. Fiasché M, Cuzzola M, Iacopino P, Kasabov N, Morabito FC. Personalized modeling based gene selection for acute GvHD gene expression data analysis: a computational framework proposed. Australian Journal of Intelligent Information Processing Systems, Machine Learning Applications. 2010;12(4 Part II):13-18.
12. Przepiorka D, Weisdorf D, Martin P, Klingemann HG, Beatty P, Hows J, et al. Consensus conference on acute GVHD grading. Bone Marrow Transplant. 1994;15(6): 825-828.
13. Rowlings PA, Przepiorka D, Klein JP, Gale RP, Passweg JR, Henslee-Downey PJ, et al. IBMTR severity index for grading acute graft-versus-host disease: retrospective comparison with Gluckberg grade. Br J Haematol. 1997;97(4):855-864.
14. Song Q, Kasabov N. TWNFI-Transductive weighted neuro-fuzzy inference system and applications for personalized modelling. Neural Netw. 2006;19(10):159-196.
15. Kasabov N. Evolving Connectionist Systems: The Knowledge Engineering Approach. 2nd ed. London: Springer; 2007.
16. Kasabov N. Global, local and personalized modelling and profile discovery in bioinformatics: an integrated approach. Pattern Recognit Lett. 2007;28(6):673-685.
17. Verma A, Fiasché M, Cuzzola M, Morabito FC, Irrera G. Knowledge Discovery and Risk Prediction for Chronic Diseases: An Integrated Approach. In: L. Iliadis, C. Jayne, editors. IFIP Advances in Information and Communication Technology, Engineering Applications of Neural Networks. Berlin Heidelberg: Springer-Verlag; 2011. Vol. 363, p. 270-279. DOI: 10.1007/978-3-642-23957- 1_31.
18. Verma A, Fiasché M, Cuzzola M, Iacopino P, Morabito FC, Kasabov N. Ontology Based Personalized Modelling for Type 2 Diabetes Risk Analysis: An Integrated Approach. In: C.S. Leung, M. Lee, J.H. Chan, editors. LNCS, Neural Information Processing. Berlin Heidelberg: Springer-Verlag; 2009. Vol.5864 (Part II), p. 360-366. DOI: 1007/978-3-642-10684-2_40
19. Fiasché M, Cuzzola M, Irrera G, Iacopino P, Morabito FC. Advances in medical decision support systems for acute graft-versus-host disease: molecular and computational intelligence joint approaches. Front Biol. 2011;6(4): 263-273.
20. Fiasché M, Cuzzola M, Messina G, Irrera G, Morabito FC. Personalized modelling based medical decision support system over gene expression data: a new framework proposed. In: Apolloni B, Bassis S, Esposito A, Morabito FC, editors. Frontiers in Artificial Intelligence and Applications, Neural Nets WIRN11. Amsterdam Netherlands; 2011; 234:186-194.
21. Fiasché M, Cuzzola M, Fedele R, Iacopino P, Morabito FC. Machine learning and personalized modeling based gene selection for acute GvHD gene expression data analysis. In: Diamantaras K, Duch W, Iliadis LS, editors. LNCS Artificial Neural Networks - ICANN 2010. Berlin Heidelberg: Springer- Verlag; 2010;6352 (Part I):217-223.
22. Fiasché M, Verma A, Cuzzola M, Iacopino P, Kasabov N, Morabito FC. Discovering diagnostic gene targets for early diagnosis of acute GvHD using methods of computational intelligence on gene expression data. Journal of Artificial Intelligence and Soft Computing Research 2011;1(1):81-89.
23. Singh MK, Scott TF, La Framboise WA, Hu FZ, Post JC, Ehrlich GD. Gene expression changes in peripheral blood mononuclear cells from multiple sclerosis patients undergoing beta-interferon therapy. J Neurol Sci. 2007;258(1-2):52-59.
24. Socié G, Blazar BR. Acute graft-versus-host disease: from the bench to the bedside. Blood. 2009;114(20):4327-4336.
25. Spivey TL, Uccellini L, Ascierto ML, Zoppoli G, De Giorgi V, Lucia Gemma Delogu LG, et al. Gene expression profiling in acute allograft rejection: challenging the immunologic constant of rejection hypothesis. J Transl Med. 2011;9:174.
26. Busca A. The use of monoclonal antibodies for the treatment of graft-versus-host disease following allogeneic stem cell transplantation. Expert Opin Biol Ther. 2011;11 (6):687-697.
27. Watanabe T, Masuyama J, Sohma Y, Inazawa H, Horie K, Kojima K, et al. CD52 is a novel costimulatory molecule for induction of CD4+ regulatory T cells. Clin Immunol. 2006;120(3):247-259.
28. Foley JE, Mariotti J, Ryan K, Eckhaus M, Fowler DH. Th2 cell therapy of established acute graft-versus-host disease requires IL-4 and IL-10 and is abrogated by IL-2 or hosttype antigen-presenting cells. Biol Blood Marrow Transplant. 2008;14(9):959-972.
29. Hutloff A, Dittrich AM, Beier KC, Eljaschewitsch B, Kraft R, Anagnostopoulos I, Kroczek RA. ICOS is an inducible T-cell co-stimulator structurally and functionally related to CD28. Nature. 1999;397(6716): 263-266.
30. Ogawa S, Nagamatsu G, Watanabe M, Watanabe S, Hayashi T, Horita S et al. Opposing effects of anti-activation-inducible lymphocyte-immunomodulatory molecule/ inducible costimulator antibody on the development of acute versus chronic graftversus- host disease. J Immunol. 2001;167 (10):5741-5748.
31. Witsch EJ, Peiser M, Hutloff A, Büchner K, Dorner BG, Jonuleit H, et al. ICOS and CD28 reversely regulate IL-10 on re-activation of human effector T cells with mature dendritic cells. Eur J Immunol. 2002;32(9): 2680-2686.
32. Taylor PA, Panoskaltsis-Mortari A, Freeman GJ, Sharpe AH, Noelle RJ, Rudensky AY, et al. Targeting of inducible costimulator (ICOS) expressed on alloreactive T cells down-regulates graft-versus-host disease (GVHD) and facilitates engraftment of allogeneic bone marrow (BM). Blood. 2005;105 (8):3372-3380.