MicroRNAs in B-cells: from normal differentiation to treatment of malignancies.

MicroRNAs (miRNAs) are small non-coding RNAs that play important post-transcriptional regulatory roles in a wide range of biological processes. They are fundamental to the normal development of cells, and evidence suggests that the deregulation of specific miRNAs is involved in malignant transformation due to their function as oncogenes or tumor suppressors. We know that miRNAs are involved in the development of normal B-cells and that different B-cell subsets express specific miRNA profiles according to their degree of differentiation. B-cell-derived malignancies contain transcription signatures reminiscent of their cell of origin. Therefore, we believe that normal and malignant B-cells share features of regulatory networks controlling differentiation and the ability to respond to treatment. The involvement of miRNAs in these processes makes them good biomarker candidates. B-cell malignancies are highly prevalent, and the poor overall survival of patients with these malignancies demands an improvement in stratification according to prognosis and therapy response, wherein we believe miRNAs may be of great importance. We have critically reviewed the literature, and here we sum up the findings of miRNA studies in hematological cancers, from the development and progression of the disease to the response to treatment, with a particular emphasis on B-cell malignancies.

Stable phenotype of B-cell subsets following cryopreservation and thawing of normal human lymphocytes stored in a tissue biobank.

BACKGROUND: Cryopreservation is an acknowledged procedure to store vital cells for future biomarker analyses. Few studies, however, have analyzed the impact of the cryopreservation on phenotyping. METHODS: We have performed a controlled comparison of cryopreserved and fresh cellular aliquots prepared from individual healthy donors. We studied circulating B-cell subset membrane markers and global gene expression, respectively by multiparametric flow cytometry and microarray data. Extensive statistical analysis of the generated data tested the concept that "overall, there are no phenotypic differences between cryopreserved and fresh B-cell subsets." Subsequently, we performed an uncontrolled comparison of tonsil tissue samples. RESULTS: By multiparametric flow analysis, we documented no significant changes following cryopreservation of subset frequencies or membrane intensity for the differentiation markers CD19, CD20, CD22, CD27, CD38, CD45, and CD200. By gene expression profiling following cryopreservation, across all samples, only 16 out of 18708 genes were significantly up or down regulated, including FOSB, KLF4, RBP7, ANXA1 or CLC, DEFA3, respectively. Implementation of cryopreserved tissue in our research program allowed us to present a performance analysis, by comparing cryopreserved and fresh tonsil tissue. As expected, phenotypic differences were identified, but to an extent that did not affect the performance of the cryopreserved tissue to generate specific B-cell subset associated gene signatures and assign subset phenotypes to independent tissue samples. CONCLUSIONS: We have confirmed our working concept and illustrated the usefulness of vital cryopreserved cell suspensions for phenotypic studies of the normal B-cell hierarchy; however, storage procedures need to be delineated by tissue-specific comparative analysis.

Stable Phenotype Of B-Cell Subsets Following Cryopreservation and Thawing of Normal Human Lymphocytes Stored in a Tissue Biobank.

Background Cryopreservation is an acknowledged procedure to store vital cells for future biomarker analyses. Few studies, however, have analyzed the impact of the cryopreservation on phenotyping. Methods We have performed a controlled comparison of cryopreserved and fresh cellular aliquots prepared from individual healthy donors. We studied circulating B-cell subset membrane markers and global gene expression, respectively by multiparametric flow cytometry and microarray data. Extensive statistical analysis of the generated data tested the concept that "overall, there are phenotypic differences between cryopreserved and fresh B-cell subsets". Subsequently, we performed a consecutive uncontrolled comparison of tonsil tissue samples. Results By multiparametric flow analysis, we documented no significant changes following cryopreservation of subset frequencies or membrane intensity for the differentiation markers CD19, CD20, CD22, CD27, CD38, CD45, and CD200. By gene expression profiling following cryopreservation, across all samples, only 16 out of 18708 genes were significantly up or down regulated, including FOSB, KLF4, RBP7, ANXA1 or CLC, DEFA3, respectively. Implementation of cryopreserved tissue in our research program allowed us to present a performance analysis, by comparing cryopreserved and fresh tonsil tissue. As expected, phenotypic differences were identified, but to an extent that did not affect the performance of the cryopreserved tissue to generate specific B-cell subset associated gene signatures and assign subset phenotypes to independent tissue samples. Conclusions We have confirmed our working concept and illustrated the usefulness of vital cryopreserved cell suspensions for phenotypic studies of the normal B-cell hierarchy; however, storage procedures need to be delineated by tissue specific comparative analysis. © 2014 Clinical Cytometry Society.

Human B-cell cancer cell lines as a preclinical model for studies of drug effect in diffuse large B-cell lymphoma and multiple myeloma.

Drug resistance in cancer refers to recurrent or primary refractory disease following drug therapy. At the cellular level, it is a consequence of molecular functions that ultimately enable the cell to resist cell death-one of the classical hallmarks of cancer. Thus, drug resistance is a fundamental aspect of the cancer cell phenotype, in parallel with sustained proliferation, immortality, angiogenesis, invasion, and metastasis. Here we present a preclinical model of human B-cell cancer cell lines used to identify genes involved in specific drug resistance. This process includes a standardized technical setup for specific drug screening, analysis of global gene expression, and the statistical considerations required to develop resistance gene signatures. The state of the art is illustrated by the first-step classical drug screen (including the CD20 antibody rituximab, the DNA intercalating topoisomerase II inhibitor doxorubicin, the mitotic inhibitor vincristine, and the alkylating agents cyclophosphamide and melphalan) along with the generation of gene lists predicting the chemotherapeutic outcome as validated retrospectively in clinical trial datasets. This B-cell lineage-specific preclinical model will allow us to initiate a range of laboratory studies, with focus on specific gene functions involved in molecular resistance mechanisms.

Exposure time independent summary statistics for assessment of drug dependent cell line growth inhibition.

BACKGROUND: In vitro generated dose-response curves of human cancer cell lines are widely used to develop new therapeutics. The curves are summarised by simplified statistics that ignore the conventionally used dose-response curves' dependency on drug exposure time and growth kinetics. This may lead to suboptimal exploitation of data and biased conclusions on the potential of the drug in question. Therefore we set out to improve the dose-response assessments by eliminating the impact of time dependency. RESULTS: First, a mathematical model for drug induced cell growth inhibition was formulated and used to derive novel dose-response curves and improved summary statistics that are independent of time under the proposed model. Next, a statistical analysis workflow for estimating the improved statistics was suggested consisting of 1) nonlinear regression models for estimation of cell counts and doubling times, 2) isotonic regression for modelling the suggested dose-response curves, and 3) resampling based method for assessing variation of the novel summary statistics. We document that conventionally used summary statistics for dose-response experiments depend on time so that fast growing cell lines compared to slowly growing ones are considered overly sensitive. The adequacy of the mathematical model is tested for doxorubicin and found to fit real data to an acceptable degree. Dose-response data from the NCI60 drug screen were used to illustrate the time dependency and demonstrate an adjustment correcting for it. The applicability of the workflow was illustrated by simulation and application on a doxorubicin growth inhibition screen. The simulations show that under the proposed mathematical model the suggested statistical workflow results in unbiased estimates of the time independent summary statistics. Variance estimates of the novel summary statistics are used to conclude that the doxorubicin screen covers a significant diverse range of responses ensuring it is useful for biological interpretations. CONCLUSION: Time independent summary statistics may aid the understanding of drugs' action mechanism on tumour cells and potentially renew previous drug sensitivity evaluation studies.

Validation and implementation of a method for microarray gene expression profiling of minor B-cell subpopulations in man.

BACKGROUND: This report describes a method for the generation of global gene expression profiles from low frequent B-cell subsets by using fluorescence-activated cell sorting and RNA amplification. However, some of the differentiating compartments involve a low number of cells and therefore it is important to optimize and validate each step in the procedure. METHODS: Normal lymphoid tissues from blood, tonsils, thymus and bone marrow were immunophenotyped by the 8-colour Euroflow panel using multiparametric flow cytometry. Subsets of B-cells containing cell numbers ranging from 800 to 33,000 and with frequencies varying between 0.1 and 10 percent were sorted, subjected to mRNA purification, amplified by the NuGEN protocol and finally analysed by the Affymetrix platform. RESULTS: Following a step by step strategy, each step in the workflow was validated and the sorting/storage conditions optimized as described in this report. First, an analysis of four cancer cell lines on Affymetrix arrays, using either 100 ng RNA labelled with the Ambion standard protocol or 1 ng RNA amplified and labelled by the NuGEN protocol, revealed a significant correlation of gene expressions (r ≥ 0.9 for all). Comparison of qPCR data in samples with or without amplification for 8 genes showed that a relative difference between six cell lines was preserved (r ≥ 0.9). Second, a comparison of cells sorted into PrepProtect, RNAlater or directly into lysis/binding buffer showed a higher yield of purified mRNA following storage in lysis/binding buffer (p < 0.001). Third, the identity of the B-cell subsets validated by the cluster of differentiation (CD) membrane profile was highly concordant with the transcriptional gene expression (p-values <0.001). Finally, in normal bone marrow and tonsil samples, eight evaluated genes were expressed in accordance with the biology of lymphopoiesis (p-values < 0.001), which enabled the generation of a gene-specific B-cell atlas. CONCLUSION: A description of the implementation and validation of commercially available kits in the laboratory has been examined. This included steps for cell sorting, cell lysis/stabilization, RNA isolation, RNA concentration and amplification for microarray analysis. The workflow described in this report will enable the generation of microarray data from minor sorted B-cell subsets.

Reproducible probe-level analysis of the Affymetrix Exon 1.0 ST array with R/Bioconductor.

The presence of different transcripts of a gene across samples can be analysed by whole-transcriptome microarrays. Reproducing results from published microarray data represents a challenge owing to the vast amounts of data and the large variety of preprocessing and filtering steps used before the actual analysis is carried out. To guarantee a firm basis for methodological development where results with new methods are compared with previous results, it is crucial to ensure that all analyses are completely reproducible for other researchers. We here give a detailed workflow on how to perform reproducible analysis of the GeneChip®Human Exon 1.0 ST Array at probe and probeset level solely in R/Bioconductor, choosing packages based on their simplicity of use. To exemplify the use of the proposed workflow, we analyse differential splicing and differential gene expression in a publicly available dataset using various statistical methods. We believe this study will provide other researchers with an easy way of accessing gene expression data at different annotation levels and with the sufficient details needed for developing their own tools for reproducible analysis of the GeneChip®Human Exon 1.0 ST Array.

Cell of origin associated classification of B-cell malignancies by gene signatures of the normal B-cell hierarchy.

Recent findings have suggested biological classification of B-cell malignancies as exemplified by the "activated B-cell-like" (ABC), the "germinal-center B-cell-like" (GCB) and primary mediastinal B-cell lymphoma (PMBL) subtypes of diffuse large B-cell lymphoma and "recurrent translocation and cyclin D" (TC) classification of multiple myeloma. Biological classification of B-cell derived cancers may be refined by a direct and systematic strategy where identification and characterization of normal B-cell differentiation subsets are used to define the cancer cell of origin phenotype. Here we propose a strategy combining multiparametric flow cytometry, global gene expression profiling and biostatistical modeling to generate B-cell subset specific gene signatures from sorted normal human immature, naive, germinal centrocytes and centroblasts, post-germinal memory B-cells, plasmablasts and plasma cells from available lymphoid tissues including lymph nodes, tonsils, thymus, peripheral blood and bone marrow. This strategy will provide an accurate image of the stage of differentiation, which prospectively can be used to classify any B-cell malignancy and eventually purify tumor cells. This report briefly describes the current models of the normal B-cell subset differentiation in multiple tissues and the pathogenesis of malignancies originating from the normal germinal B-cell hierarchy.

Multiparametric flow cytometry for identification and fluorescence activated cell sorting of five distinct B-cell subpopulations in normal tonsil tissue.

The purpose of this study was to establish a procedure capable of isolating distinct B-cell subpopulations from human tonsils as a basis for subsequent molecular analyses. Overall, 5 distinct B-cell subpopulations were purified from fresh tonsils based on their fluorescence surface marker expression: naive B cells, centroblasts, centrocytes, memory B cells, and plasmablasts. The immunophenotypic identity of the subpopulations was verified by quantitative real-time reverse transcriptase-polymerase chain reaction using the proliferation marker MKI-67 and 6 B-cell-associated differentiation markers (BACH2, BCL6, PAX5, IRF4, PRDM1, and XBP1). Furthermore, within the centroblast compartment, large and small centroblasts could be distinguished and large centroblasts were shown to proliferate with a morphologic appearance of a "centroblast"-like cell but with lower gene expression of the germinal center markers BCL6 and BACH2 vs small centroblasts. This study has established a detailed and fast procedure for simultaneous sorting of up to 5 distinct maturation-associated B-cell subpopulations from human tonsils.

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.