BACKGROUND: Gene signatures (Indicator genes) in bone marrow that provide more precise prognostication in haematological malignancy have been identified by microarray expression studies. It would be beneficial to measure these diagnostic signatures in peripheral blood. AIMS: To determine the degree of correspondence of gene expression for a set of Indicator genes between bone marrow and peripheral blood in acute myeloid leukaemia (AML). METHODS: Parallel bone marrow aspirate and peripheral blood samples were obtained from 19 patients diagnosed with AML and mononuclear cells isolated from both sample types. mRNA was globally amplified by polyadenylated real-time polymerase chain reaction (polyA RT-PCR); the expression of 15 AML Indicator genes, identified from previous microarray studies, was measured by RT-PCR. All values were normalised to the mean expression of three housekeeping genes (IF2-beta, GAP and RbS9) and were statistically compared using SPSS software. RESULTS: No significant difference in expression between bone marrow and peripheral blood was observed for 10 of the genes (leptin receptor, CD33, adipsin, proteoglycan 1, MB-1, cyclin D3, hSNF2b, proteasome iota, HkrT-1 and E2A), indicating its possible use in monitoring disease activity in peripheral blood samples, whereas c-myb, HOXA9, LYN, cystatin c and LTC4s showed significantly different expression between bone marrow and peripheral blood samples. CONCLUSION: These results indicate a possible use for the method in monitoring AML in peripheral blood by RT-PCR measurement of Indicator genes. In addition, the initial use of polyA PCR facilitates translation to very small clinical samples, including fractionated cell populations, of particular importance for monitoring haematological malignancy.
BACKGROUND: Gene signatures (Indicator genes) in bone marrow that provide more precise prognostication in haematological malignancy have been identified by microarray expression studies. It would be beneficial to measure these diagnostic signatures in peripheral blood. AIMS: To determine the degree of correspondence of gene expression for a set of Indicator genes between bone marrow and peripheral blood in acute myeloid leukaemia (AML). METHODS: Parallel bone marrow aspirate and peripheral blood samples were obtained from 19 patients diagnosed with AML and mononuclear cells isolated from both sample types. mRNA was globally amplified by polyadenylated real-time polymerase chain reaction (polyA RT-PCR); the expression of 15 AML Indicator genes, identified from previous microarray studies, was measured by RT-PCR. All values were normalised to the mean expression of three housekeeping genes (IF2-beta, GAP and RbS9) and were statistically compared using SPSS software. RESULTS: No significant difference in expression between bone marrow and peripheral blood was observed for 10 of the genes (leptin receptor, CD33, adipsin, proteoglycan 1, MB-1, cyclin D3, hSNF2b, proteasome iota, HkrT-1 and E2A), indicating its possible use in monitoring disease activity in peripheral blood samples, whereas c-myb, HOXA9, LYN, cystatin c and LTC4s showed significantly different expression between bone marrow and peripheral blood samples. CONCLUSION: These results indicate a possible use for the method in monitoring AML in peripheral blood by RT-PCR measurement of Indicator genes. In addition, the initial use of polyA PCR facilitates translation to very small clinical samples, including fractionated cell populations, of particular importance for monitoring haematological malignancy.
Authors: T R Golub; D K Slonim; P Tamayo; C Huard; M Gaasenbeek; J P Mesirov; H Coller; M L Loh; J R Downing; M A Caligiuri; C D Bloomfield; E S Lander Journal: Science Date: 1999-10-15 Impact factor: 47.728
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Authors: Klaus H Metzeler; Manuela Hummel; Clara D Bloomfield; Karsten Spiekermann; Jan Braess; Maria-Cristina Sauerland; Achim Heinecke; Michael Radmacher; Guido Marcucci; Susan P Whitman; Kati Maharry; Peter Paschka; Richard A Larson; Wolfgang E Berdel; Thomas Büchner; Bernhard Wörmann; Ulrich Mansmann; Wolfgang Hiddemann; Stefan K Bohlander; Christian Buske Journal: Blood Date: 2008-08-20 Impact factor: 22.113
Authors: Suhaida A Selamat; Brian S Chung; Luc Girard; Wei Zhang; Ying Zhang; Mihaela Campan; Kimberly D Siegmund; Michael N Koss; Jeffrey A Hagen; Wan L Lam; Stephen Lam; Adi F Gazdar; Ite A Laird-Offringa Journal: Genome Res Date: 2012-05-21 Impact factor: 9.043