Comparison of gene-expression profiles in parallel bone marrow and peripheral blood samples in acute myeloid leukaemia by real-time polymerase chain reaction

Sakhinia, Ebrahim; Farahangpour, Mahboubeh; Tholouli, Eleni; Yin, Jiang-Bo; Hoyland, Judith A.; Byers, Richard

doi:10.1136/jcp.2005.031161

Cited by 21 publications

(10 citation statements)

References 7 publications

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“…10,11,13 There are very few studies that directly compared expression patterns in paired PB and BM samples in AML, but the available reports indicate a high degree of correlation between both sources. 13,27 Despite the important differences between the patient populations and the analytical methods, our gene-expression score was a significant predictor of survival in both the test and validation cohorts. Assessing the generalizability of a prognostic marker requires testing both its reproducibility in an independent patient sample as well as its transportability to patient cohorts with different geographic and methodologic background.…”

Section: Discussionmentioning

confidence: 91%

An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia

Metzeler¹,

Hummel

Bloomfield

et al. 2008

Blood

382

418

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Patients with cytogenetically normal acute myeloid leukemia (CN-AML) show heterogeneous treatment outcomes. We used gene-expression profiling to develop a gene signature that predicts overall survival (OS) in CN-AML. Based on data from 163 patients treated in the German AMLCG 1999 trial and analyzed on oligonucleotide microarrays, we used supervised principal component analysis to identify 86 probe sets (representing 66 different genes), which correlated with OS, and defined a prognostic score based on this signature. When applied to an independent cohort of 79 CN-AML patients, this continuous score remained a significant predictor for OS (hazard ratio [HR], 1.85; P = .002), event-free survival (HR = 1.73; P = .001), and relapse-free survival (HR = 1.76; P = .025). It kept its prognostic value in multivariate analyses adjusting for age, FLT3 ITD, and NPM1 status. In a validation cohort of 64 CN-AML patients treated on CALGB study 9621, the score also predicted OS (HR = 4.11; P < .001), event-free survival (HR = 2.90; P < .001), and relapse-free survival (HR = 3.14, P < .001) and retained its significance in a multivariate model for OS. In summary, we present a novel gene-expression signature that offers additional prognostic information for patients with CN-AML.

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Section: Discussionmentioning

confidence: 91%

An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia

Metzeler¹,

Hummel

Bloomfield

et al. 2008

Blood

382

418

View full text Add to dashboard Cite

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“…Thirdly, we demonstrated differential gene expression between paired BM and PB CD34 + myeloblasts from individual patients. The gene list was different from that reported by Sakhinia et al (2006), who compared the gene expression of non‐purified mononuclear cells from BM and PB of AML patients. Our results were also different from those reported by Gal et al (2006), where primitive CD34 + CD38 − and more differentiated CD34 + CD38 + myeloblasts were compared.…”

Section: Discussionmentioning

confidence: 78%

A comparative study of bone marrow and peripheral blood CD34⁺ myeloblasts in acute myeloid leukaemia

et al. 2009

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Summary To examine the differences between primitive bone marrow (BM) and peripheral blood (PB) myeloblasts in acute myeloid leukaemia (AML), we compared CD34+ myeloblasts of paired BM and PB samples from 14 AML patients in terms of surface phenotype, homing and engraftment in a xenogeneic transplantation model, and gene expression, based on microarray studies and quantitative polymerase chain reaction. While there was no significant difference in surface phenotypes between these two populations, in vivo assay showed significantly better homing potential of PB CD34+ cells than BM CD34+ cells. Significant correlation between homing and engraftment in AML samples was also noted. In addition, gene expression profiling of CD34+ cells from five paired BM and PB leukaemic samples showed that genes involved in G‐protein and prostaglandin signalling, chemotaxis and stress response, cell proliferation and apoptosis were down‐regulated in PB CD34+ myeloblasts. These data suggested that circulating primitive myeloblasts in AML are functionally different from those residing in the marrow compartment, and such differences may be partly regulated by the BM microenvironment.

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“…D26156 was reported to be a discriminatory gene between AML and ALL in Golub et al [35] and was also among the top ranking genes reported in Broberg [40] based on multiple gene ranking methods. Real time PCR measurement of D26156 was highly expressed from patients with AML [46]. The protein encoded by D26156 is a member of the large ATP-dependent chromatin remodeling complex SWI/SNF family of proteins, which have helicase and ATPase activities and are thought to regulate transcription of certain genes by altering the chromatin structure around those genes [47].…”

Section: Resultsmentioning

confidence: 99%

Improving accuracy for cancer classification with a new algorithm for genes selection

et al. 2012

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BackgroundEven though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BMSF). It not only overcomes the difficulty associated with the search schemes of traditional wrapper methods and overfitting problem in large dimensional search space but also takes potential gene interactions into account during gene selection. This method, coupled with Support Vector Machine (SVM) for implementation, often selects very small number of genes for easy model interpretability.ResultsWe applied our method to 9 two-class gene expression datasets involving human cancers. During the gene selection process, the set of genes to be kept in the model was recursively refined and repeatedly updated according to the effect of a given gene on the contributions of other genes in reference to their usefulness in cancer classification. The small number of informative genes selected from each dataset leads to significantly improved leave-one-out (LOOCV) classification accuracy across all 9 datasets for multiple classifiers. Our method also exhibits broad generalization in the genes selected since multiple commonly used classifiers achieved either equivalent or much higher LOOCV accuracy than those reported in literature.ConclusionsEvaluation of a gene’s contribution to binary cancer classification is better to be considered after adjusting for the joint effect of a large number of other genes. A computationally efficient search scheme was provided to perform effective search in the extensive feature space that includes possible interactions of many genes. Performance of the algorithm applied to 9 datasets suggests that it is possible to improve the accuracy of cancer classification by a big margin when joint effects of many genes are considered.

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Comparison of gene-expression profiles in parallel bone marrow and peripheral blood samples in acute myeloid leukaemia by real-time polymerase chain reaction

Cited by 21 publications

References 7 publications

An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia

An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia

A comparative study of bone marrow and peripheral blood CD34⁺ myeloblasts in acute myeloid leukaemia

Improving accuracy for cancer classification with a new algorithm for genes selection

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Comparison of gene-expression profiles in parallel bone marrow and peripheral blood samples in acute myeloid leukaemia by real-time polymerase chain reaction

Cited by 21 publications

References 7 publications

An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia

An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia

A comparative study of bone marrow and peripheral blood CD34+ myeloblasts in acute myeloid leukaemia

Improving accuracy for cancer classification with a new algorithm for genes selection

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A comparative study of bone marrow and peripheral blood CD34⁺ myeloblasts in acute myeloid leukaemia