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

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

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

Summary of the stepwise development, adjustment and validation of the resistance gene list.

<p>Numbers relate to step 1–6 as illustrated in the figure. 1) First, The analysis starts by identification of candidate biomarkers by a sparse partial least squares algorithm (SPLS) to build a predictive gene list based on correlations with the GI50 values of the cell line panel, regarded as <i>“biomarker discovery”</i>. 2) Second, the candidate genes were trained to ensure weighted expression in the myeloma data set from the patient cohort in the clinical trial MRC Myeloma IX, to derive a gene signature model, predictive of resistance to melphalan – a step which is regarded as <i>biomarker weighting</i>. The weighting was performed by multivariate Cox regression with PFS as dependent variable and gene expression of the 19 genes as independent variables resulting in a weighted gene signature. 3) The weighted melphalan resistance gene signature is used to define a melphalan RI. 4) The signature is used to classify each tumour from the clinical trial HOVON65/GMMG-HD4 based on the individual GEP data. 5) The RIs were defined to be the linear predictor of the multivariate Cox regression, i.e. calculated for each individual clinical sample by a linear combination of the 19 gene expressions using the weights from the multivariate Cox regression model. 6) Finally, the molecular prediction of resistance to melphalan therapy was compared to the actual observed PFS and OS. – a step regarded as <i>implementation and evaluation</i>.</p

Effects of a peripherally acting µ-opioid receptor antagonist for the prevention of recurrent acute pancreatitis: study protocol for an investigator-initiated, randomized, placebo-controlled, double-blind clinical trial (PAMORA-RAP trial)

Abstract Background Acute and chronic pancreatitis constitute a continuum of inflammatory disease of the pancreas with an increasing incidence in most high-income countries. A subset of patients with a history of pancreatitis suffer from recurrence of acute pancreatitis attacks, which accelerate disease progression towards end-stage chronic pancreatitis with loss of exocrine and endocrine function. There is currently no available prophylactic treatment for recurrent acute pancreatitis apart from removing risk factors, which is not always possible. Pain is the primary symptom of acute pancreatitis, which induces the endogenous release of opioids. This may further be potentiated by opioid administration for pain management. Increased exposure to opioids leads to potentially harmful effects on the gastrointestinal tract, including, e.g. increased sphincter tones and decreased fluid secretion, which may impair pancreatic ductal clearance and elevate the risk for new pancreatitis attacks and accelerate disease progression. Peripherally acting µ-opioid receptor antagonists (PAMORAs) have been developed to counteract the adverse effects of opioids on the gastrointestinal tract. We hypothesize that the PAMORA naldemedine will reduce the risk of new pancreatitis attacks in patients with recurrent acute pancreatitis and hence decelerate disease progression. Methods The study is a double-blind, randomized controlled trial with allocation of patients to either 0.2 mg naldemedine daily or matching placebo for 12 months. A total of 120 outpatients will be enrolled from five specialist centres in Denmark and Sweden. The main inclusion criteria is a history of recurrent acute pancreatitis (minimum of two confirmed pancreatitis attacks). The primary endpoint is time to acute pancreatitis recurrence after randomization. Secondary outcomes include changes in quality of life, gastrointestinal symptom scores, new-onset diabetes, exocrine pancreatic insufficiency, disease severity, health care utilization, adherence to treatment, and frequency of adverse events. Exploratory outcomes are included for mechanistic linkage and include the progression of chronic pancreatitis-related findings on magnetic resonance imaging (MRI) and changes in circulating blood markers of inflammation and fibrosis. Discussion This study investigates if naldemedine can change the natural course of pancreatitis in patients with recurrent acute pancreatitis and improve patient outcomes. Trial registration EudraCT no. 2021–000069-34. ClinicalTrials.gov NCT04966559. Registered on July 8, 2021