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Remarkable progress has been achieved in the characterization and isolation of primitive hematopoietic stem cells (HSC). HSC represent a very small subset of hematopoietic cells and provide self-renewal, possess differentiation capacity and allow a constant supply of the entire hematopoietic cell spectrum. Until recently, CD34 has been used as a convenient marker for HSC, since CD34[sup +] cells have been shown to possess colony-forming potential in short-term assays, maintain long-term colony-forming potential in in vitro cultures and allow the expression and differentiation of blood cells from different hematopoietic lineages in in vivo models. Clinical and experimental protocols have targeted CD34[sup +] cells enriched by a variety of selection models and have readily used these for transplantation, purging and gene therapies and targets for future organ replacement. Recent studies in murine and human models, however, have indicated that CD34[sup -] HSC exist as well, which possess engraftment potential and distinct HSC characteristics. These studies challenge the dogma that HSC are uniformly found in the CD34[sup +] subset, and question whether primitive HSC are CD34[sup +] or CD34[sup -]. In this review, results on murine and human CD34[sup +] and CD34[sup -] HSC, differences between them and their possible interactions are examined. [ABSTRACT FROM AUTHOR]

State-of-the art selection methods fail to identify weak but cumulative effects of features found in many high-dimensional omics datasets. Nevertheless, these features play an important role in certain diseases. We present Netboost, a three-step dimension reduction technique. First, a boosting-based filter is combined with the topological overlap measure to identify the essential edges of the network. Second, sparse hierarchical clustering is applied on the selected edges to identify modules and finally module information is aggregated by the first principal components. We demonstrate the application of the newly developed Netboost in combination with CoxBoost for survival prediction of DNA methylation and gene expression data from 180 acute myeloid leukemia (AML) patients and show, based on cross-validated prediction error curve estimates, its prediction superiority over variable selection on the full dataset as well as over an alternative clustering approach. The identified signature related to chromatin modifying enzymes was replicated in an independent dataset, the phase II AMLSG 12-09 study. In a second application we combine Netboost with Random Forest classification and improve the disease classification error in RNA-sequencing data of Huntington's disease mice. Netboost is a freely available Bioconductor R package for dimension reduction and hypothesis generation in high-dimensional omics applications.

Purpose: In patients with acute myeloblastic leukemia with t(8;21) or inv(16) aberrations (core binding factor [CBF] leukemias), minimal residual disease (MRD) can be sensitively detected during and after chemotherapy by use of molecular methods. However, the prognostic impact of qualitative MRD detection is still under debate. In this study, the prognostic value of MRD quantification in patients with CBF leukemias was assessed., Patients and Methods: We quantified MRD at various time points during and after therapy by real-time reverse transcriptase polymerase chain reaction (RT-PCR) for AML1/MTG8 and CBFB/MYH11 in 37 patients with CBF leukemias treated within a multicenter trial., Results: At initial diagnosis, the patients showed a heterogenous fusion gene expression relative to glyceraldehyde 3-phosphate dehydrogenase with a variation of more than two log steps. According to MRD status during/after therapy, two groups of patients were separated. Of the 26 patients who had MRD levels of less than 1% in relation to initial diagnosis at all time points tested after induction chemotherapy, only two experienced relapse after a median follow-up of 19 months. Of the 11 patients who had a sample with an MRD level >/= 1% at least at one time point after induction therapy, 10 experienced relapse, with a median remission duration of 10 months (P <.001). The median interval between the informative MRD sample and clinical relapse in these patients was 3 months., Conclusion: MRD quantification by real-time RT-PCR allows the identification of patients with a high risk of relapse among the CBF leukemias.