High CXCR4 expression impairs rituximab response and the prognosis of R-CHOP-treated diffuse large B-cell lymphoma patients.

Survival of diffuse large B-cell lymphoma (DLBCL) patients has improved by inclusion of rituximab. Refractory/recurrent disease caused by treatment resistance is, however, a major problem. Determinants of rituximab sensitivity are not fully understood, but effect of rituximab are enhanced by antagonizing cell surface receptor CXCR4. In a two-step strategy, we tested the hypothesis that prognostic value of CXCR4 in DLBCL relates to rituximab treatment, due to a hampering effect of CXCR4 on the response of DLBCL cells to rituximab. First, by investigating the prognostic impact of CXCR4 mRNA expression separately for CHOP (n=181) and R-CHOP (n=233) cohorts and, second, by assessing the interaction between CXCR4 and rituximab in DLBCL cell lines. High CXCR4 expression level was significantly associated with poor outcome only for R-CHOP-treated patients, independent of IPI score, CD20 expression, ABC/GCB and B-cell-associated gene signature (BAGS) classifications. s. For responsive cell lines, inverse correlation was observed between rituximab sensitivity and CXCR4 surface expression, rituximab induced upregulation of surface-expressed CXCR4, and growth-inhibitory effect of rituximab increased by plerixafor, supporting negative impact of CXCR4 on rituximab function. In conclusion, CXCR4 is a promising independent prognostic marker for R-CHOP-treated DLBCL patients, possibly due to inverse correlation between CXCR4 expression and rituximab sensitivity.

Molecular classification of tissue from a transformed non-Hogkin's lymphoma case with unexpected long-time remission.

BACKGROUND: The concept of precision medicine in cancer includes individual molecular studies to predict clinical outcomes. In the present N = 1 case we retrospectively have analysed lymphoma tissue by exome sequencing and global gene expression in a patient with unexpected long-term remission following relaps. The goals were to phenotype the diagnostic and relapsed lymphoma tissue and evaluate its pattern. Furthermore, to identify mutations available for targeted therapy and expression of genes to predict specific drug effects by resistance gene signatures (REGS) for R-CHOP as described at http://www.hemaclass.org. We expected that such a study could generate therapeutic information and a frame for future individual evaluation of molecular resistance detected at clinical relapse. CASE PRESENTATION: The patient was diagnosed with a transformed high-grade non-Hodgkin lymphoma stage III and treated with conventional R-CHOP [rituximab (R), cyclophosphamide (C), doxorubicin (H), vincristine (O) and prednisone (P)]. Unfortunately, she suffered from severe toxicity but recovered during the following 6 months' remission until biopsy-verified relapse. The patient refused second-line combination chemotherapy, but accepted 3 months' palliation with R and chlorambucil. Unexpectedly, she obtained continuous complete remission and is at present >9 years after primary diagnosis. Molecular studies and data evaluation by principal component analysis, mutation screening and copy number variations of the primary and relapsed tumor, identified a pattern of branched lymphoma evolution, most likely diverging from an in situ follicular lymphoma. Accordingly, the primary diagnosed transformed lymphoma was classified as a diffuse large B cell lymphoma (DLBCL) of the GCB/centrocytic subtype by "cell of origin BAGS" assignment and R sensitive and C, H, O and P resistant by "drug specific REGS" assignment. The relapsed DLBCL was classified as NC/memory subtype and R, C, H sensitive but O and P resistant. CONCLUSIONS: Thorough analysis of the tumor DNA and RNA documented a branched evolution of the two clinical diagnosed tFL, most likely transformed from an unknown in situ lymphoma. Classification of the malignant tissue for drug-specific resistance did not explain the unexpected long-term remission and potential cure. However, it is tempting to consider the anti-CD20 immunotherapy as the curative intervention in the two independent tumors of this case.

hemaClass.org: Online One-By-One Microarray Normalization and Classification of Hematological Cancers for Precision Medicine.

BACKGROUND: Dozens of omics based cancer classification systems have been introduced with prognostic, diagnostic, and predictive capabilities. However, they often employ complex algorithms and are only applicable on whole cohorts of patients, making them difficult to apply in a personalized clinical setting. RESULTS: This prompted us to create hemaClass.org, an online web application providing an easy interface to one-by-one RMA normalization of microarrays and subsequent risk classifications of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin and chemotherapeutic sensitivity classes. Classification results for one-by-one array pre-processing with and without a laboratory specific RMA reference dataset were compared to cohort based classifiers in 4 publicly available datasets. Classifications showed high agreement between one-by-one and whole cohort pre-processsed data when a laboratory specific reference set was supplied. The website is essentially the R-package hemaClass accompanied by a Shiny web application. The well-documented package can be used to run the website locally or to use the developed methods programmatically. CONCLUSIONS: The website and R-package is relevant for biological and clinical lymphoma researchers using affymetrix U-133 Plus 2 arrays, as it provides reliable and swift methods for calculation of disease subclasses. The proposed one-by-one pre-processing method is relevant for all researchers using microarrays.

Multiplex polymerase chain reaction-based prognostic models in diffuse large B-cell lymphoma patients treated with R-CHOP.

We present a multiplex analysis for genes known to have prognostic value in an attempt to design a clinically useful classification model in patients with diffuse large B-cell lymphoma (DLBCL). Real-time polymerase chain reaction was used to measure transcript levels of 28 relevant genes in 194 de novo DLBCL patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone). Including International Prognostic Index (IPI) as a variable in a penalized Cox regression, we investigated the association with disease progression for single genes or gene combinations in four models. The best model was validated in data from an online available R-CHOP treated cohort. With progression-free survival (PFS) as primary endpoint, the best performing IPI independent model incorporated the LMO2 and HLADQA1 as well as gene interactions for GCSAMxMIB1, GCSAMxCTGF and FOXP1xPDE4B. This model assigned 33% of patients (n = 60) to poor outcome with an estimated 3-year PFS of 40% vs. 87% for low risk (n = 61) and intermediate (n = 60) risk groups (P < 0·001). However, a simpler, IPI independent model incorporated LMO2 and BCL2 and assigned 33% of the patients with a 3-year PFS of 35% vs. 82% for low risk group (P < 0·001). We have documented the impact of a few single genes added to IPI for assignment in new drug trials.

Unaccounted uncertainty from qPCR efficiency estimates entails uncontrolled false positive rates.

BACKGROUND: Accurate adjustment for the amplification efficiency (AE) is an important part of real-time quantitative polymerase chain reaction (qPCR) experiments. The most commonly used correction strategy is to estimate the AE by dilution experiments and use this as a plug-in when efficiency correcting the Δ Δ C q . Currently, it is recommended to determine the AE with high precision as this plug-in approach does not account for the AE uncertainty, implicitly assuming an infinitely precise AE estimate. Determining the AE with such precision, however, requires tedious laboratory work and vast amounts of biological material. Violation of the assumption leads to overly optimistic standard errors of the Δ Δ C q , confidence intervals, and p-values which ultimately increase the type I error rate beyond the expected significance level. As qPCR is often used for validation it should be a high priority to account for the uncertainty of the AE estimate and thereby properly bounding the type I error rate and achieve the desired significance level. RESULTS: We suggest and benchmark different methods to obtain the standard error of the efficiency adjusted Δ Δ C q using the statistical delta method, Monte Carlo integration, or bootstrapping. Our suggested methods are founded in a linear mixed effects model (LMM) framework, but the problem and ideas apply in all qPCR experiments. The methods and impact of the AE uncertainty are illustrated in three qPCR applications and a simulation study. In addition, we validate findings suggesting that MGST1 is differentially expressed between high and low abundance culture initiating cells in multiple myeloma and that microRNA-127 is differentially expressed between testicular and nodal lymphomas. CONCLUSIONS: We conclude, that the commonly used efficiency corrected quantities disregard the uncertainty of the AE, which can drastically impact the standard error and lead to increased false positive rates. Our suggestions show that it is possible to easily perform statistical inference of Δ Δ C q , whilst properly accounting for the AE uncertainty and better controlling the false positive rate.

Alternative pre-mRNA splicing leads to potential biomarkers in diffuse large B-cell lymphoma - a systematic review.

INTRODUCTION: The study of potential biomarkers in haematological malignancies has gained momentum in past decades. We compiled a systematic review to outline potential biomarkers based on alternative pre-mRNA splicing that were suggested to be clinically useful for the diagnosis, prognosis and response to therapeutics in diffuse large B-cell lymphoma (DLBCL). METHODS: A comprehensive search of the literature in PubMed, Embase and Scopus was performed and supplemented with screening of reference lists. Only articles concerning potential biomarkers originating from reports on alternative pre-mRNA splicing were included. The contributions of these studies will help develop personalised medicine. Therefore, the clinical utility of the potential biomarkers was evaluated. RESULTS: A total of 16 studies were included of which eight described seven different potential diagnostic biomarkers. Eight studies reported two potential prognostic biomarkers for CD44, its spliced mRNA variants and the resulting proteins that were the most frequently reported. Furthermore, two studies reported two proteins originating from alternative pre-mRNA splicing as potentially predictive biomarkers. CONCLUSIONS: Alternative pre-mRNA splicing is a promising potential diagnostic, prognostic and predictive biomarker for the identification of pathogenic impacts in DLBCL. The use of these potential biomarkers in the clinical management of DLBCL awaits prospective clinical validation supporting its potential to contribute to the shift towards more personalised medicine.

High miR-34a expression improves response to doxorubicin in diffuse large B-cell lymphoma.

The standard treatment for patients with diffuse large B-cell lymphoma (DLBCL) is the immunochemotherapy-based R-CHOP regimen (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone). Resistance to treatment, intrinsic or acquired, is observed in approximately 40% of patients with DLBCL, who thus require novel interventions to survive. To identify biomarkers for cytotoxic response assessment, microRNAs (miRNAs) associated with doxorubicin sensitivity were determined by combining global miRNA expression profiling with systematic dose-response screens in 15 human DLBCL cell lines. One candidate, miR-34a, was tested in functional in vitro studies and in vivo in a retrospective clinical cohort. High expression of miR-34a was observed in cell lines sensitive to doxorubicin, and upregulation of miR-34a is documented here to increase doxorubicin sensitivity in in vitro lentiviral transduction assays. High expression of miR-34a had a prognostic impact using overall survival as outcome. With risk stratification of DLBCL samples based on resistance gene signatures (REGS), doxorubicin-responsive samples had statistically significant upregulated miR-34a expression. Classification of the DLBCL samples into subset-specific B cell-associated gene signatures (BAGS) revealed differentiation-specific expression of miR-34a. Our data further support FOXP1 as a target of miR-34a, suggesting that downregulation of FOXP1 may sensitize DLBCL cells to doxorubicin. We conclude that miRNAs, in particular miR-34a, may have clinical utility in DLBCL patients as both predictive and prognostic biomarkers.

Predicting response to multidrug regimens in cancer patients using cell line experiments and regularised regression models.

BACKGROUND: Patients suffering from cancer are often treated with a range of chemotherapeutic agents, but the treatment efficacy varies greatly between patients. Based on recent popularisation of regularised regression models the goal of this study was to establish workflows for pharmacogenomic predictors of response to standard multidrug regimens using baseline gene expression data and origin specific cell lines. The proposed workflows are tested on diffuse large B-cell lymphoma treated with R-CHOP first-line therapy. METHODS: First, B-cell cancer cell lines were tested successively for resistance towards the chemotherapeutic components of R-CHOP: cyclophosphamide (C), doxorubicin (H), and vincristine (O). Second, baseline gene expression data were obtained for each cell line before treatment. Third, regularised multivariate regression models with cross-validated tuning parameters were used to generate classifier and predictor based resistance gene signatures (REGS) for the combination and individual chemotherapeutic drugs C, H, and O. Fourth, each developed REGS was used to assign resistance levels to individual patients in three clinical cohorts. RESULTS: Both classifier and predictor based REGS, for the combination CHO, were of prognostic value. For patients classified as resistant towards CHO the risk of progression was 2.33 (95% CI: 1.6, 3.3) times greater than for those classified as sensitive. Similarly, an increase in the predicted CHO resistance index of 10 was related to a 22% (9%, 36%) increased risk of progression. Furthermore, the REGS classifier performed significantly better than the REGS predictor. CONCLUSIONS: The regularised multivariate regression models provide a flexible workflow for drug resistance studies with promising potential. However, the gene expressions defining the REGSs should be functionally validated and correlated to known biomarkers to improve understanding of molecular mechanisms of drug resistance.

Diffuse large B-cell lymphoma classification system that associates normal B-cell subset phenotypes with prognosis.

PURPOSE: Current diagnostic tests for diffuse large B-cell lymphoma use the updated WHO criteria based on biologic, morphologic, and clinical heterogeneity. We propose a refined classification system based on subset-specific B-cell-associated gene signatures (BAGS) in the normal B-cell hierarchy, hypothesizing that it can provide new biologic insight and diagnostic and prognostic value. PATIENTS AND METHODS: We combined fluorescence-activated cell sorting, gene expression profiling, and statistical modeling to generate BAGS for naive, centrocyte, centroblast, memory, and plasmablast B cells from normal human tonsils. The impact of BAGS-assigned subtyping was analyzed using five clinical cohorts (treated with cyclophosphamide, doxorubicin, vincristine, and prednisone [CHOP], n = 270; treated with rituximab plus CHOP [R-CHOP], n = 869) gathered across geographic regions, time eras, and sampling methods. The analysis estimated subtype frequencies and drug-specific resistance and included a prognostic meta-analysis of patients treated with first-line R-CHOP therapy. RESULTS: Similar BAGS subtype frequencies were assigned across 1,139 samples from five different cohorts. Among R-CHOP-treated patients, BAGS assignment was significantly associated with overall survival and progression-free survival within the germinal center B-cell-like subclass; the centrocyte subtype had a superior prognosis compared with the centroblast subtype. In agreement with the observed therapeutic outcome, centrocyte subtypes were estimated as being less resistant than the centroblast subtype to doxorubicin and vincristine. The centroblast subtype had a complex genotype, whereas the centrocyte subtype had high TP53 mutation and insertion/deletion frequencies and expressed LMO2, CD58, and stromal-1-signature and major histocompatibility complex class II-signature genes, which are known to have a positive impact on prognosis. CONCLUSION: Further development of a diagnostic platform using BAGS-assigned subtypes may allow pathogenetic studies to improve disease management.

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.

A model system for assessing and comparing the ability of exon microarray and tag sequencing to detect genes specific for malignant B-cells.

BACKGROUND: Malignant cells in tumours of B-cell origin account for 0.1% to 98% of the total cell content, depending on disease entity. Recently, gene expression profiles (GEPs) of B-cell lymphomas based on microarray technologies have contributed significantly to improved sub-classification and diagnostics. However, the varying degrees of malignant B-cell frequencies in analysed samples influence the interpretation of the GEPs. Based on emerging next-generation sequencing technologies (NGS) like tag sequencing (tag-seq) for GEP, it is expected that the detection of mRNA transcripts from malignant B-cells can be supplemented. This study provides a quantitative assessment and comparison of the ability of microarrays and tag-seq to detect mRNA transcripts from malignant B-cells. A model system was established by eight serial dilutions of the malignant B-cell lymphoma cell line, OCI-Ly8, into the embryonic kidney cell line, HEK293, prior to parallel analysis by exon microarrays and tag-seq. RESULTS: We identified 123 and 117 differentially expressed genes between pure OCI-Ly8 and HEK293 cells by exon microarray and tag-seq, respectively. There were thirty genes in common, and of those, most were B-cell specific. Hierarchical clustering from all dilutions based on the differentially expressed genes showed that neither technology could distinguish between samples with less than 1% malignant B-cells from non-B-cells. A novel statistical concept was developed to assess the ability to detect single genes for both technologies, and used to demonstrate an inverse proportional relationship with the sample purity. Of the 30 common genes, the detection capability of a representative set of three B-cell specific genes--CD74, HLA-DRA, and BCL6 - was analysed. It was noticed that at least 5%, 13% and 22% sample purity respectively was required for detection of the three genes by exon microarray whereas at least 2%, 4% and 51% percent sample purity of malignant B-cells were required for tag-seq detection. CONCLUSION: A sample purity-dependent loss of the ability to detect genes for both technologies was demonstrated. Taq-seq, in comparison to exon microarray, required slightly less malignant B-cells in the samples analysed in order to detect the two most abundantly expressed of the selected genes. The results show that malignant cell frequency is an important variable, with fundamental impact when interpreting GEPs from both technologies.

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.