Mesenchymal stromal cells derived from acute myeloid leukemia bone marrow exhibit aberrant cytogenetics and cytokine elaboration.

Bone marrow-derived mesenchymal stromal cells (BM-MSCs) play a fundamental role in the BM microenvironment (BME) and abnormalities of these cells may contribute to acute myeloid leukemia (AML) pathogenesis. The aim of the study was to characterize the cytokine and gene expression profile, immunophenotype and cytogenetics of BM-MSCs from AML patients compared to normal BM-MSCs from healthy donors. AML BM-MSCs showed decreased monocyte chemoattractant protein-1 levels compared to normal BM-MSCs. AML BM-MSCs expressed similar ß1 integrin, CD44, CD73, CD90 and E-cadherin compared to normal BM-MSCs. Cytogenetic analysis revealed chromosomal aberrations in AML BM-MSCs, some overlapping with and others distinct from their corresponding AML blasts. No significant difference in gene expression was detected between AML BM-MSCs compared to normal BM-MSCs; however, comparing the differences between AML and MSCs from AML patients with the differences between normal hematopoietic cells and normal MSCs by Ingenuity pathway analysis showed key distinctions of the AML setting: (1) upstream gene regulation by transforming growth factor beta 1, tumor necrosis factor, tissue transglutaminase 2, CCAAT/enhancer binding protein alpha and SWItch/Sucrose NonFermentable related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4; (2) integrin and interleukin 8 signaling as overrepresented canonical pathways; and (3) upregulation of transcription factors FBJ murine osteosarcoma viral oncogene homolog and v-myb avian myeloblastosis viral oncogene homolog. Thus, phenotypic abnormalities of AML BM-MSCs highlight a dysfunctional BME that may impact AML survival and proliferation.

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

MOTIVATION: Selecting a small number of signature genes for accurate classification of samples is essential for the development of diagnostic tests. However, many genes are highly correlated in gene expression data, and hence, many possible sets of genes are potential classifiers. Because treatment outcomes are poor in advanced chronic myeloid leukemia (CML), we hypothesized that expression of classifiers of advanced phase CML when detected in early CML [chronic phase (CP) CML], correlates with subsequent poorer therapeutic outcome. RESULTS: We developed a method that integrates gene expression data with expert knowledge and predicted functional relationships using iterative Bayesian model averaging. Applying our integrated method to CML, we identified small sets of signature genes that are highly predictive of disease phases and that are more robust and stable than using expression data alone. The accuracy of our algorithm was evaluated using cross-validation on the gene expression data. We then tested the hypothesis that gene sets associated with advanced phase CML would predict relapse after allogeneic transplantation in 176 independent CP CML cases. Our gene signatures of advanced phase CML are predictive of relapse even after adjustment for known risk factors associated with transplant outcomes.