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      The Value of Serum MicroRNA Expression Signature in Predicting Refractoriness to Bortezomib-Based Therapy in Multiple Myeloma Patients

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          Abstract

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          The proteasome inhibitor bortezomib is currently commonly used for the treatment of multiple myeloma (MM). MicroRNAs (miRNAs) are small non-coding RNAs that play a crucial role in messenger RNA silencing and post-transcriptional regulation of gene expression. In MM, the expression of several miRNAs is markedly dysregulated suggesting their role in MM pathogenesis and drug resistance. The aim of our study was to assess miRNA expression patterns in the serum of MM patients treated with bortezomib. We have identified 21 serum miRNAs differentially expressed in patients refractory to bortezomib-based chemotherapy. A miRNAs-based prediction model was developed to assess the probability of refractoriness to bortezomib. Our findings, indicating the differential expression of miRNAs between bortezomib-refractory and bortezomib-sensitive patients, suggest that these circulating miRNAs may play an important role in personalized treatment of MM patients.

          Abstract

          Bortezomib is the first-in-class proteasome inhibitor, commonly used in the treatment of multiple myeloma (MM). The mechanisms underlying acquired bortezomib resistance in MM are poorly understood. Several cell-free miRNAs have been found to be aberrantly regulated in MM patients. The aim of this pilot study was to identify a blood-based miRNA signature that predicts bortezomib-based therapy efficacy in MM patients. Thirty MM patients treated with bortezomib-based regimens were studied, including 19 with refractory disease and 11 who were bortezomib sensitive. Serum miRNA expression patterns were identified with miRCURY LNA miRNA miRNome PCR Panels I+II (Exiqon/Qiagen). Univariate analysis found a total of 21 miRNAs to be differentially expressed in patients with MM according to bortezomib sensitivity. Multivariate logistic regression was created and allowed us to discriminate refractory from sensitive patients with a very high AUC of 0.95 (95%CI: 0.84–1.00); sensitivity, specificity and accuracy were estimated as 0.95, 0.91, and 0.93. The model used expression of 3 miRNAs: miR-215-5p, miR-181a-5p and miR-376c-3p. This study is the first to demonstrate that serum expression of several miRNAs differs between patients who are bortezomib refractory and those who are sensitive which may prove useful in studies aimed at overcoming drug resistance in MM treatment.

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          MicroRNAs: genomics, biogenesis, mechanism, and function.

          MicroRNAs (miRNAs) are endogenous approximately 22 nt RNAs that can play important regulatory roles in animals and plants by targeting mRNAs for cleavage or translational repression. Although they escaped notice until relatively recently, miRNAs comprise one of the more abundant classes of gene regulatory molecules in multicellular organisms and likely influence the output of many protein-coding genes.
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            Adjusting batch effects in microarray expression data using empirical Bayes methods.

            Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
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              Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

              The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.
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                Author and article information

                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                09 September 2020
                September 2020
                : 12
                : 9
                : 2569
                Affiliations
                [1 ]Department of Experimental Hematology, Medical University of Lodz, 93-510 Lodz, Poland; pawel.robak@ 123456umed.lodz.pl (P.R.); piotr.smolewski@ 123456umed.lodz.pl (P.S.)
                [2 ]Department of Clinical Genetics, Medical University of Lodz, 92-213 Lodz, Poland; izabela.drozdz@ 123456umed.lodz.pl
                [3 ]Laboratory of Personalized Medicine, Bionanopark, Lodz, 93-465 Lodz, Poland; d.jarych@ 123456bionanopark.pl (D.J.); e.weglowska@ 123456bionanopark.pl (E.W.)
                [4 ]Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland; damian.mikulski@ 123456stud.umed.lodz.pl (D.M.); konrad.stawiski@ 123456stud.umed.lodz.pl (K.S.); wojciech_fendler@ 123456dfci.harvard.edu (W.F.)
                [5 ]Department of Hematology, Medical University of Lodz, 93-510 Lodz, Poland; monika.siemieniuk-rys@ 123456umed.lodz.pl (M.S.-R.); malgorzata.misiewicz@ 123456umed.lodz.pl (M.M.)
                [6 ]Department of Medical Biochemistry, Medical University of Lodz, 92-215 Lodz, Poland; janusz.szemraj@ 123456umed.lodz.pl
                Author notes
                [* ]Correspondence: robaktad@ 123456csk.umed.lodz.pl ; Tel.: +48-42-689-51-91; Fax: +48 42-689-51-92
                Author information
                https://orcid.org/0000-0003-1303-7163
                https://orcid.org/0000-0002-2806-2583
                https://orcid.org/0000-0002-6550-3384
                https://orcid.org/0000-0002-3411-6357
                Article
                cancers-12-02569
                10.3390/cancers12092569
                7565855
                32916955
                9cd68b60-940c-4069-978d-8575205e4eaf
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 22 July 2020
                : 03 September 2020
                Categories
                Article

                bortezomib,efficacy,multiple myeloma,resistance,sensitivity,refractory,microrna,mir-215-5p,mir-181a-5p,mir-376c-3p

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