Optimization of Preanalytical Variables for cfDNA Processing and Detection of ctDNA in Archival Plasma Samples

DNA released from cells into the peripheral blood is known as cell-free DNA (cfDNA), representing a promising noninvasive source of biomarkers that could be utilized to manage Diffuse Large B-Cell Lymphoma (DLBCL), among other diseases. The procedure for purification and handling of cfDNA is not yet standardized, and various preanalytical variables may affect the yield and analysis of cfDNA, including the purification kits, blood collection tubes, and centrifugation regime. Therefore, we aimed to investigate the impact of these preanalytical variables on the yield of cfDNA by comparing three different purification kits DNeasy Blood & Tissue Kit (Qiagen), QIAamp Circulating Nucleic Acid Kit (Qiagen), and Quick-cfDNA Serum & Plasma Kit (Zymo Research). Two blood collection tubes (BCTs), EDTA-K2 and Cell-Free DNA (Streck), stored at four different time points before plasma was separated and cfDNA purified, were compared, and for EDTA tubes, two centrifugation regimes at 2000 × g and 3000 × g were tested. Additionally, we have tested the utility of long-term archival blood samples from DLBCL patients to detect circulating tumor DNA (ctDNA). We observed a higher cfDNA yield using the QIAamp Circulating Nucleic Acid Kit (Qiagen) purification kit, as well as a higher cfDNA yield when blood samples were collected in EDTA BCTs, with a centrifuge regime at 2000 × g. Moreover, ctDNA detection was feasible from archival plasma samples with a median storage time of nine years

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 ΔΔC(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 ΔΔC(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 ΔΔC(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 ΔΔC(q), whilst properly accounting for the AE uncertainty and better controlling the false positive rate

Stringent or nonstringent complete remission and prognosis in acute myeloid leukemia:a Danish population-based study

Key Points sCR and non-sCR in acute myeloid leukemia are associated with different prognoses. Three lineage blood cell recoveries have independent impacts on prognosis.</jats:p

Global myeloma research clusters, output, and citations: A bibliometric mapping and clustering analysis

Background: International collaborative research is a mechanism for improving the development of dis-ease- specific therapies and for improving health at the population level. However, limited data are available to assess the trends in research output related to orphan diseases. Methods and Findings: We used bibliometric mapping and clustering methods to illustrate the level of fragmentation in myeloma research and the development of collaborative efforts. Publication data from Thomson Reuters Web of Science were retrieved for 2005-2009 and followed until 2013. We created a database of multiple myeloma publications, and we analysed impact and coauthorship density to identify scientific collaborations, developments, and international key players over time. The global annual publication volume for studies on multiple myeloma increased from 1,144 in 2005 to 1,628 in 2009, which represents a 4

Proof of the concept to use a malignant B cell line drug screen strategy for identification and weight of melphalan resistance genes in multiple myeloma

In a conceptual study of drug resistance we have used a preclinical model of malignant B-cell lines by combining drug induced growth inhibition and gene expression profiling. In the current report a melphalan resistance profile of 19 genes were weighted by microarray data from the MRC Myeloma IX trial and time to progression following high dose melphalan, to generate an individual melphalan resistance index. The resistance index was subsequently validated in the HOVON65/GMMG-HD4 trial data set to prove the concept. Biologically, the assigned resistance indices were differentially distributed among translocations and cyclin D expression classes. Clinically, the 25% most melphalan resistant, the intermediate 50% and the 25% most sensitive patients had a median progression free survival of 18, 32 and 28 months, respectively (log-rank P-value  = 0.05). Furthermore, the median overall survival was 45 months for the resistant group and not reached for the intermediate and sensitive groups (log-rank P-value  = 0.003) following 38 months median observation. In a multivariate analysis, correcting for age, sex and ISS-staging, we found a high resistance index to be an independent variable associated with inferior progression free survival and overall survival. This study provides clinical proof of concept to use in vitro drug screen for identification of melphalan resistance gene signatures for future functional analysis

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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-015-1237-6) contains supplementary material, which is available to authorized users