[The haematological biobank--an infrastructure for quality assurance, development and research]. / Den hæmatologiske biobank er en infrastruktur til kvalitetssikring, udvikling og forskning.

Archived patient samples of biological material coupled to clinical databases are valuable and hold the potential to improve patient care, quality control, research and development. Biobanks form a catalytic infrastructure which ensures the registration, handling and storage of collected biological material. Here, we describe the Haematology Biobank and stress the significance of a registration database. Together, these form the basis for retrospective validation of clinical progress and prognostic evaluation of new diagnostic methods, which can therefore be more quickly implemented in the clinic.

Generation of a predictive melphalan resistance index by drug screen of B-cell cancer cell lines.

BACKGROUND: Recent reports indicate that in vitro drug screens combined with gene expression profiles (GEP) of cancer cell lines may generate informative signatures predicting the clinical outcome of chemotherapy. In multiple myeloma (MM) a range of new drugs have been introduced and now challenge conventional therapy including high dose melphalan. Consequently, the generation of predictive signatures for response to melphalan may have a clinical impact. The hypothesis is that melphalan screens and GEPs of B-cell cancer cell lines combined with multivariate statistics may provide predictive clinical information. MATERIALS AND METHODS: Microarray based GEPs and a melphalan growth inhibition screen of 59 cancer cell lines were downloaded from the National Cancer Institute database. Equivalent data were generated for 18 B-cell cancer cell lines. Linear discriminant analyses (LDA), sparse partial least squares (SPLS) and pairwise comparisons of cell line data were used to build resistance signatures from both cell line panels. A melphalan resistance index was defined and estimated for each MM patient in a publicly available clinical data set and evaluated retrospectively by Cox proportional hazards and Kaplan-Meier survival analysis. PRINCIPAL FINDINGS: Both cell line panels performed well with respect to internal validation of the SPLS approach but only the B-cell panel was able to predict a significantly higher risk of relapse and death with increasing resistance index in the clinical data sets. The most sensitive and resistant cell lines, MOLP-2 and RPMI-8226 LR5, respectively, had high leverage, which suggests their differentially expressed genes to possess important predictive value. CONCLUSION: The present study presents a melphalan resistance index generated by analysis of a B-cell panel of cancer cell lines. However, the resistance index needs to be functionally validated and correlated to known MM biomarkers in independent data sets in order to better understand the mechanism underlying the preparedness to melphalan resistance.

[Cancer stem cells in the haematopoietic system]. / Kraeftstamceller i det hæmopoietiske system.

Cancer is dependent on so-called cancer stem cells that initiate and maintain the cancer cell population. Stem cells are described in normal tissue as low-frequent, self-renewing cells with a multi- or pluripotent differentiation potential. The true characteristics of the cancer-initiating cells are still not entirely known, but it is obvious that identifying these cells will enable us to better understand the biology of cancer. In this article, we focus on normal haematopoietic stem cells and cancer stem cells in leukaemia and multiple myeloma.

[Use of translational database at clinical department]. / Brug af en translationel database i en klinisk afdeling.

In haematology it is assumed that integrative analysis of global gene expression, protein and cell profiles as well as clinical data will lead to the development of new diagnostic, prognostic and predictive methods. A translational database system registering and combining all data and clinical observations about the patient is therefore needed. It is expected that along with automated prediction and prognosis tools, such a database system may have the potential to assist the development of new machine-based diagnostic decision-making processes.

Multiparametric flow cytometry profiling of neoplastic plasma cells in multiple myeloma.

BACKGROUND AND AIM: The clinical impact of multiparametric flow cytometry (MFC) in multiple myeloma (MM) is still unclear and under evaluation. Further progress relies on multiparametric profiling of the neoplastic plasma cell (PC) compartment to provide an accurate image of the stage of differentiation. The primary aim of this study was to perform global analysis of CD expression on the PC compartment and subsequently to evaluate the prognostic impact. Secondary aims were to study the diagnostic and predictive impact. DESIGN AND METHODS: The design included a retrospective analysis of MFC data generated from diagnostic bone marrow (BM) samples of 109 Nordic patients included in clinical trials within NMSG. Whole marrow were analyzed by MFC for identification of end-stage CD45(-) /CD38(++) neoplastic PC and registered the relative numbers of events and mean fluorescence intensity (MFI) staining for CD19, CD20, CD27, CD28, CD38, CD44, CD45, CD56, and isotypes for cluster analysis. RESULTS: The median MFC-PC number was 15%, and the median light microscopy (LM)-PC number was 35%. However, the numbers were significant correlated and the prognostic value with an increased relative risk (95% CI) of 3.1 (1.7-5.5) and 2.9 (1.4-6.2), P < 0.0003 and P < 0.004 of MFC-PC and LM-PC counts, respectively. Unsupervised clustering based on global MFI assessment on PC revealed two clusters based on CD expression profiling. Cluster I with high intensity for CD56, CD38, CD45, right-angle light-scatter signal (SSC), forward-angle light-scatter signal (FSC), and low for CD28, CD19, and a Cluster II, with low intensity of CD56, CD38, CD45, SSC, FSC, and high for CD28, CD19 with a median survival of 39 months and 19 months, respectively (P = 0.02). CONCLUSIONS: The MFC analysis of MM BM samples produces diagnostic, prognostic, and predictive information useful in clinical practice, which will be prospectively validated within the European Myeloma Network (EMN). © 2010 International Clinical Cytometry Society.

Cancer stem cells and the cellular hierarchy in haematological malignancies.

Malignancies in the haematopoietic system seem to depend on a small subset of so-called cancer stem cells (CSC) for their continued growth and progression - this was first described as the "sleeper-feeder theory" for leukaemia. The leukaemia stem cell was the first of such subsets to be described although the origins of these cells have been difficult to dissect. Consequently, their biology is not fully elucidated, which also holds true for the normal-tissue counterparts. The stem cell concept describes stem cells to be of low frequency, self renewing and with multilineage potential based on phenomenology - a definition which may not hold strictly true for CSCs when studied in animals and humans in vivo and in vitro. Several studies have analysed the cellular hierarchy of the haematopoietic system by cell sorting of few and even single cells, tracking acquired genetic changes and performing transplantation model studies to document subsets within the differentiating hierarchy as potential CSC compartments. In leukaemia the CSC has been described in the bone marrow compartment of haematopoietic stem cells (HSC); however, in other bone marrow disorders like multiple myeloma it is likely that the cell of origin is a more differentiated cell, like post-germinal memory B cells or plasmablasts. Studies performed so far have even indicated that the genetic events may occur in different B cell subsets in accordance with the stepwise oncogenesis of the disease. Although our understanding of the nature and biology of these initiating cells remains unknown, the obvious existence of such cells has implications for understanding initial malignant transformation and disease metastasis or progression and, most important, the selection of individualised therapeutic strategies targeting the subsets harbouring the CSC function. In the present review on stem cells in haematological malignancies we have focused on two topics, first, describing the stem cell concept in health and disease, and its "phenomenology", and second, describing the CSC compartments in leukaemia and multiple myeloma.

Induction of adipocyte-like phenotype in human mesenchymal stem cells by hypoxia.

Human mesenchymal stem cells (hMSCs) have the capacity to differentiate along several pathways to form bone, cartilage, tendon, muscle, and adipose tissues. The adult hMSCs reside in vivo in the bone marrow in niches where oxygen concentration is far below the ambient air, which is the most commonly encountered laboratory condition. The study reported here was designed to determine whether oxygen has a role in the differentiation of hMSCs into adipocytes. Indeed, when exposed to atmosphere containing only 1% of oxygen, the formation of adipocyte-like phenotype with cytoplasmic lipid inclusions was observed. The effect of hypoxia on the expression of adipocyte-specific genes was determined by real-time reverse transcription polymerase chain reaction. Interestingly, neither of the two central regulators of adipogenesis--the transcription factors peroxisome proliferator-activated receptor gamma2 (PPAR-gamma2) and ADD1/SREBP1c-was induced. Furthermore, hypoxia did not have any effect on the transcription of early (lipoprotein lipase) or late (aP2) marker genes. By the same token, neither of the mature adipocyte-specific genes--leptin and adipophilin--was found responsive to the treatment. High level of induction, however, was observed with the PPAR-gamma-induced angiopoietin-related gene, PGAR. The lack of an adipocyte-specific transcription pattern thus indicates that despite accumulation of the lipid, true adipogenic differentiation did not take place. In conclusion, hypoxia appears to exert a potent lipogenic effect independent of PPAR-gamma2 maturation pathway.