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Background: Multiple myeloma remains an incurable disease with multiple relapses due to residual myeloma cells in the bone marrow of patients after therapy. Presence of small number of cancer cells in the body after cancer treatment, called minimal residual disease, has been shown to be prognostic for progression-free and overall survival. However, for multiple myeloma, it is unclear whether patients attaining minimal residual disease negativity may be candidates for treatment discontinuation. We investigated, if longitudinal flow cytometry-based monitoring of minimal residual disease (flow-MRD) may predict disease progression earlier and with higher sensitivity compared to biochemical assessments., Methods: Patients from the Nordic countries with newly diagnosed multiple myeloma enrolled in the European-Myeloma-Network-02/Hovon-95 (EMN02/HO95) trial and undergoing bone marrow aspiration confirmation of complete response, were eligible for this Nordic Myeloma Study Group (NMSG) substudy. Longitdudinal flow-MRD assessment of bone marrow samples was performed to identify and enumerate residual malignant plasma cells until observed clinical progression., Results: Minimal residual disease dynamics were compared to biochemically assessed changes in serum free light chain and M-component. Among 20 patients, reaching complete response or stringent complete response during the observation period, and with ≥3 sequential flow-MRD assessments analysed over time, increasing levels of minimal residual disease in the bone marrow were observed in six cases, preceding biochemically assessed disease and clinical progression by 5.5 months and 12.6 months (mean values), respectively. Mean malignant plasma cells doubling time for the six patients was 1.8 months (95% CI, 1.4-2.3 months). Minimal malignant plasma cells detection limit was 4 × 10-5., Conclusions: Flow-MRD is a sensitive method for longitudinal monitoring of minimal residual disease dynamics in multiple myeloma patients in complete response. Increasing minimal residual disease levels precedes biochemically assessed changes and is an early indicator of subsequent clinical progression., Trial Registration: NCT01208766., (© 2022. The Author(s).)

Background: Diffuse large B-cell lymphoma (DLBCL) is the most frequent lymphoid neoplasm among adults,and approximately 30-40% of patients will experience relapse while 5-10% will suffer from primary refractory disease caused by different mechanisms, including treatment-induced resistance. For refractory and relapsed DLBCL (rrDLBCL) patients, early detection and understanding of the mechanisms controlling treatment resistance are of great importance to guide therapy decisions. Here, we have focused on genetic variations in immune surveillance genes in diagnostic DLBCL (dDLBCL) and rrDLBCL patients to elaborate on the suitability of new promising immunotherapies., Methods: Biopsies from 30 dDLBCL patients who did not progress or relapse during follow up and 17 rrDLBCL patients with refractory disease or who relapsed during follow up were analyzed by whole-exome sequencing, including matched individual germline samples to include only somatic genetic variants in downstream analysis of a curated list of 58 genes involved in major immune surveillance pathways., Results: More than 70% of both dDLBCLs and rrDLBCLs harbored alterations in immune surveillance genes, but rrDLBCL tumor samples have a lower number of genes affected compared to dDLBCL tumor samples. Increased gene mutation frequencies in rrDLBCLs were observed in more than half of the affected immune surveillance genes than dDLBCLs., Conclusion: Genetic variants in the antigen-presenting genes affect a higher number of rrDLBCL patients supporting an important role for these genes in tumor progression and development of refractory disease and relapse., (© 2021. The Author(s).)

Background: Treatment resistance is a major clinical challenge of diffuse large B-cell lymphoma (DLBCL) where approximately 40% of the patients have refractory disease or relapse. Since DLBCL is characterized by great clinical and molecular heterogeneity, the purpose of the present study was to investigate whether miRNAs associated to single drug components of R-CHOP can improve robustness of individual markers and serve as a prognostic classifier., Methods: Fifteen DLBCL cell lines were tested for sensitivity towards single drug compounds of the standard treatment R-CHOP: rituximab (R), cyclophosphamide (C), doxorubicin (H), and vincristine (O). For each drug, cell lines were ranked using the area under the dose-response curve and grouped as either sensitive, intermediate or resistant. Baseline miRNA expression data were obtained for each cell line in untreated condition, and differential miRNA expression analysis between sensitive and resistant cell lines identified 43 miRNAs associated to growth response after exposure towards single drugs of R-CHOP. Using the Affymetrix HG-U133 platform, expression levels of miRNA precursors were assessed in 701 diagnostic DLBCL biopsies, and miRNA-panel classifiers predicting disease progression were build using multiple Cox regression or random survival forest. Classifiers were validated and ranked by repeated cross-validation., Results: Prognostic accuracies were assessed by Brier Scores and time-varying area under the ROC curves, which revealed better performance of multivariate Cox models compared to random survival forest models. The Cox model including miR-146a, miR-155, miR-21, miR-34a, and miR-23a~miR-27a~miR-24-2 cluster performed the best and successfully stratified GCB-DLBCL patients into high- and low-risk of disease progression. In addition, combination of the Cox miRNA-panel and IPI substantially increased prognostic performance in GCB classified patients., Conclusion: As a proof of concept, we found that expression data of drug associated miRNAs display prognostic utility and adding these to IPI improves prognostic stratification of GCB-DLBCL patients treated with R-CHOP.

Background: Diffuse large B-cell lymphoma (DLBCL) is an aggressive disease with variable clinical outcome, accounting for at least 25-30 % of adult non-Hodgkin lymphomas. Approximately one third of DLBCL patients are not cured by the currently used treatment regimen, R-CHOP. Hence, new treatment strategies are needed. Antagonizing the CXCR4 receptor might be promising since the CXCR4-CXCL12 axis is implicated in several aspects of tumor pathogenesis as well as in protection from chemotherapeutic response. In Burkitt lymphoma, the CXCR4 antagonist plerixafor has already been shown to enhance the therapeutic effect of rituximab, the immunotherapeutic agent of R-CHOP; but this is yet to be confirmed for DLBCL. We, therefore, investigated the effect of plerixafor on DLBCL cellular response to rituximab., Methods: In this in vitro study, human DLBCL cell lines were treated with rituximab and/or plerixafor, concomitantly or in sequence. The trypan blue exclusion method and MTS-based assays were used to evaluate cellular proliferation, whereas flow cytometry was used for assessment of apoptosis status and CXCR4 surface expression level. Linear mixed effects models were used to assess statistical significance., Results: We observed that simultaneous addition of plerixafor and rituximab resulted in a significant decrease in DLBCL cellular proliferation, compared to monotherapeutic response. The effect was dose-dependent, and concomitant administration was observed to be superior to sequential drug administration. Accordingly, the fraction of apoptotic/dead cells significantly increased following addition of plerixafor to rituximab treatment. Furthermore, exposure of DLBCL cells to plerixafor resulted in a significant decrease in CXCR4 fluorescence intensity., Conclusions: Based on our results, implying that the anti-proliferative/pro-apoptotic effect of rituximab on DLBCL cells can be synergistically enhanced by the CXCR4 antagonist plerixafor, addition of plerixafor to the R-CHOP regimen can be suggested to improve treatment outcome for DLBCL patients.

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.

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.

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.

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.

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.

Background: Human natural killer (NK) cells are the key contributors of innate immune response and the effector functions of these cells are enhanced by cytokines such as interleukine 2 (IL2). We utilized genome-wide transcriptional profiling to identify gene expression signatures and pathways in resting and IL2 activated NK cell isolated from peripheral blood of healthy donors., Results: Gene expression profiling of resting NK cells showed high expression of a number of cytotoxic factors, cytokines, chemokines and inhibitory and activating surface NK receptors. Resting NK cells expressed many genes associated with cellular quiescence and also appeared to have an active TGFbeta (TGFB1) signaling pathway. IL2 stimulation induced rapid downregulation of quiescence associated genes and upregulation of genes associated with cell cycle progression and proliferation. Numerous genes that may enhance immune function and responsiveness including activating receptors (DNAM1, KLRC1 and KLRC3), death receptor ligand (TNFSF6 (FASL) and TRAIL), chemokine receptors (CX3CR1, CCR5 and CCR7), interleukin receptors (IL2RG, IL18RAB and IL27RA) and members of secretory pathways (DEGS1, FKBP11, SSR3, SEC61G and SLC3A2) were upregulated. The expression profile suggested PI3K/AKT activation and NF-kappaB activation through multiple pathways (TLR/IL1R, TNF receptor induced and TCR-like possibly involving BCL10). Activation of NFAT signaling was supported by increased expression of many pathway members and downstream target genes. The transcription factor GATA3 was expressed in resting cells while T-BET was upregulated on activation concurrent with the change in cytokine expression profile. The importance of NK cells in innate immune response was also reflected by late increased expression of inflammatory chemotactic factors and receptors and molecules involved in adhesion and lymphocyte trafficking or migration., Conclusion: This analysis allowed us to identify genes implicated in cellular quiescence and the cytokines and cytotoxic factors ready for immediate immune response. It also allowed us to observe the sequential immunostimulatory effects of IL2 on NK cells improving our understanding of the biology and molecular mediators behind NK cell activation.

Background: There are three major B-cell compartments in peripheral lymphoid organs: the germinal center (GC), the mantle zone (MNZ) and the marginal zone (MGZ). Unique sets of B-cells reside in these compartments, and they have specific functional roles in humoral immune response. MNZ B cells are naive cells in a quiescent state and may participate in GC reactions upon proper stimulation. The adult splenic MGZ contains mostly memory B cells and is also known to provide a rapid response to particulate antigens. The GC B-cells proliferate rapidly and undergo selection and affinity maturation. The B-cell maturational process is accompanied by changes in the expression of cell-surface and intracellular proteins and requires signals from the specialized microenvironments., Results: We performed laser microdissection of the three compartments for gene expression profiling by cDNA microarray. The transcriptional program of the GC was dominated by upregulation of genes associated with proliferation and DNA repair or recombination. The MNZ and MGZ showed increased expression of genes promoting cellular quiescence. The three compartments also revealed distinct repertoires of apoptosis-associated genes, chemokines and chemokine receptors. The MNZ and GC showed upregulation of CCL20 and CCL18 respectively. The MGZ was characterized by high expression of many chemokines genes e.g. CXCL12, CCL3, CCL14 and IFN-associated genes, consistent with its role in rapid response to infections. A stromal signature was identified including genes associated with macrophages or with synthesis of extracellular matrix and genes that influenced lymphocyte migration and survival. Differentially expressed genes that did not belong to the above categories include the well characterized BCL6 and CD10 and many others whose function is not known., Conclusions: Transcriptional profiling of B-cell compartments has identified groups of genes involved in critical molecular and cellular events that affect proliferation, survival migration, and differentiation of the cells. The gene expression study of normal B-cell compartments may additionally contribute to our understanding of the molecular abnormalities of the corresponding lymphoid tumors.