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

Development of a Precision Medicine Workflow in Hematological Cancers, Aalborg University Hospital, Denmark

Within recent years, many precision cancer medicine initiatives have been developed. Most of these have focused on solid cancers, while the potential of precision medicine for patients with hematological malignancies, especially in the relapse situation, are less elucidated. Here, we present a demographic unbiased and observational prospective study at Aalborg University Hospital Denmark, referral site for 10% of the Danish population. We developed a hematological precision medicine workflow based on sequencing analysis of whole exome tumor DNA and RNA. All steps involved are outlined in detail, illustrating how the developed workflow can provide relevant molecular information to multidisciplinary teams. A group of 174 hematological patients with progressive disease or relapse was included in a non-interventional and population-based study, of which 92 patient samples were sequenced. Based on analysis of small nucleotide variants, copy number variants, and fusion transcripts, we found variants with potential and strong clinical relevance in 62% and 9.5% of the patients, respectively. The most frequently mutated genes in individual disease entities were in concordance with previous studies. We did not find tumor mutational burden or micro satellite instability to be informative in our hematologic patient cohort

Increased expression of transcription factor TFAP2α correlates with chemosensitivity in advanced bladder cancer

<p>Abstract</p> <p>Background</p> <p>The standard treatment for patients with advanced transitional cell carcinoma of the bladder is platin based chemotherapy. Only approximately 50% of the patients respond to chemotherapy. Therefore, molecular predictive markers for identification of chemotherapy sensitive subgroups of patients are highly needed. We selected the transcription factor <it>TFAP2α </it>from a previously identified gene expression signature for chemotherapy response.</p> <p>Methods</p> <p><it>TFAP2α </it>expression and localization was assessed by immunohistochemistry using a tissue microarray (TMA) containing 282 bladder cancer tumors from patients with locally advanced (pT2-T4_band N_1-3) or metastatic (M₁) disease. All patients had received cisplatin containing chemotherapy. Furthermore, QPCR analysis of three <it>TFAP2α </it>isoforms was performed on tumor specimens of advanced muscle invasive bladder cancers (T2-4). Using the bladder cell lines T24 and SW780 the relation of <it>TFAP2α </it>and cisplatin and gemcitabine sensitivity as well as cell proliferation was examined using siRNA directed <it>TFAP2α </it>knockdown.</p> <p>Results</p> <p>TFAP2α protein expression was analyzed on a TMA with cores from 282 advanced bladder cancer tumors from patients treated with cisplatin based combinational chemotherapy. <it>TFAP2α </it>was identified as a strong independent predictive marker for a good response and survival after cisplatin-containing chemotherapy in patients with advanced bladder cancer. Strong TFAP2α nuclear and cytoplasmic staining predicted good response to chemotherapy in patients with lymph node metastasis, whereas weak TFAP2α nuclear staining predicted good response in patients without lymph node metastasis. In vitro studies showed that siRNA mediated knockdown of TFAP2α increased the proliferation of SW780 cells and rendered the cells less sensitive to cisplatin and gemcitabine. In contrast to that T24 bladder cells with mutated p53 showed to be more drug sensitive upon TFAP2α depletion.</p> <p>Conclusions</p> <p>High levels of nuclear and cytoplasmic TFAP2α protein were a predictor of increased overall survival and progression free survival in patients with advanced bladder cancer treated with cisplatin based chemotherapy. TFAP2α knockdown increased the proliferation of the SW780 bladder cells and reduced cisplatin and gemcitabine induced cell death. The inverse effect was observed in the <it>TP53 </it>mutated T24 cell line where TFAP2α silencing augmented cisplatin and gemcitabine sensitivity and did not stimulate proliferation.</p

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

Melphalan resistance gene index validation by clinical outcome.

<p>The individual RIs were assigned from gene expression data of the HOVON 65/GMMG-HD4 trial dividing tumor samples into groups of sensitive patients with low 0–25% RI, intermediate RI from 25–75% and resistant patients with the highest 75–100% RI. The impact of this assignment was subsequently evaluated with respect to PFS and overall OS as illustrated by log relative hazard for PFS (1A) and OS (1B) as a function of the individual RI levels. The P-values are the maximum likelihood tests for no RCS-association between log Relative Hazard and the RI and the dashed lines represent 95% confidence intervals. A landmark Kaplan-Meier analysis was performed from the time of HDM and we found that resistant, intermediate and sensitive patient groups had a median PFS of 18, 32 and 28 months, respectively (1C). The OS for the resistant group had a median of 45 months but not reached for the intermediate and resistant groups (1D) following a median observation time of 38 months. The P-values are the log-rank-test results for no difference between the estimated survival curves.</p