Mass spectrometry (MS) can detect multiple myeloma–derived monoclonal proteins in the peripheral blood (PB) with high sensitivity, potentially serving as a PB assay for measurable residual disease (MRD). This study evaluated the significance of PB MS MRD negativity during posttransplant therapy in patients with newly diagnosed multiple myeloma. Serum samples from 138 patients treated in the phase 3 ATLAS trial of posttransplant maintenance with either carfilzomib, lenalidomide, and dexamethasone, or with lenalidomide alone were analyzed using EXENT MS methodology. We established feasibility of measuring MRD by MS in the PB in the posttransplant setting, despite unavailability of pretreatment calibration samples. There was high agreement between MRD by MS in the PB and paired bone marrow (BM) MRD results at the 10–5 threshold, assessed by either next-generation sequencing (NGS) or multiparameter flow cytometry (MFC) (70% and 67%, respectively). Agreement between PB MS and both BM MRD methods was lowest early after transplant and increased with time. MS negativity was associated with improved progression-free survival (PFS), which, in landmark analysis, reached statistical significance after 18 cycles after transplant. Combined PB/BM MRD negativity by MFC or NGS was associated with superior PFS compared with MRD negativity by only 1 modality. Sustained MS negativity carried similar prognostic performance to sustained BM MRD negativity at the 10–5 threshold. Overall, posttransplant MS assessment was feasible and provided additional prognostic information to BM MRD negativity. Further studies are needed to confirm the role and optimal timing of MS in disease evaluation algorithms. The ATLAS trial is registered at www.clinicaltrials.gov as #NCT02659293.

The aim of the study was to determine the levels of selected cytokines and chemokines in the serum of multiple myeloma (MM) patients treated with bortezomib-based regimens. A total of 71 MM patients were examined: 41 with primary refractory disease (17) or early relapse (28), and 30 who were bortezomib sensitive with no progression for at least six months. Patients who demonstrated CR or PR after bortezomib-based therapies longer than six months after treatment discontinuation were designated bortezomib sensitive. Serum cytokine levels were assayed with Bio-Rad Bio-Plex Pro Human Cytokine 27-Plex Assay on the MAGPIX Multiplex Reader and the Bio-Plex® 200 System (Bio-Rad). Higher levels of MIP-1α and lower levels of MIP-1β and IL-9 were associated with better responses to bortezomib-based treatment, and higher levels of IL-1ra and IL-8 were associated with bone involvement. MCP-1 was elevated in patients with hemoglobin<10g/dl compared to those without anemia. The levels of IL-8, MIP-1α, and TNF-α were significantly higher in patients with renal insufficiency. Only MIP-1α was elevated in patients with hypercalcemia compared to patients with normal calcium levels. In conclusion, distinct cytokines are involved in the pathogenesis of MM and may play a prominent role in the prediction of treatment response. However, a single measurement of serum cytokines should be interpreted with caution and further studies are needed.

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.

Proteasome inhibitors, like bortezomib, play a key role in the treatment of multiple myeloma (MM); however, most patients eventually relapse and eventually show multiple drug resistance, and the molecular mechanisms of this resistance remain unclear. The aim of our study is to assess the expression of previously described genes that may influence the resistance to bortezomib treatment at the mRNA level (ABCB1, CXCR4, MAF, MARCKS, POMP, PSMB5, RPL5, TXN, and XBP1) and prognosis of MM patients. mRNA expression was determined in 73 MM patients treated with bortezomib-based regimens (30 bortzomib-sensitive and 43 bortezomib-refractory patients) and 11 healthy controls. RPL5 was significantly down-regulated in multiple myeloma patients as compared with healthy controls. Moreover, POMP was significantly up-regulated in MM patients refractory to bortezomib-based treatment. In multivariate analysis, high expression of PSMB5 and CXCR and autologous stem cell transplantation were independent predictors of progression-free survival, and high expression of POMP and RPL5 was associated with shorter overall survival.

Hepatic complications are an acknowledged cause of mortality and morbidity among patients undergoing hematopoietic stem cell transplantation. In this study, we aimed to evaluate the potential role in the prediction of liver injury of five selected microRNAs (miRNAs)—miR-122-5p, miR-122-3p, miR-15b-5p, miR-99b-5p, and miR-125a-5p—in the setting of autologous hematopoietic stem cell transplantation (ASCT). A total of 66 patients were included in the study: 50 patients (75.8%) with multiple myeloma (MM) and 16 (24.2%) with lymphoma. Blood samples were collected after the administration of the conditioning regimen, on the day of transplant (day 0). The expression levels of selected miRNAs were quantified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) using the miRCURY LNA miRNA Custom PCR Panels (QIAGEN). In a multivariate logistic regression analysis adjusted for age, sex, and the administered conditioning regimen, two miRNAs, hsa-miR-122-5p (odds ratio, OR 2.10, 95% confidence interval, CI: 1.29–3.42, p = 0.0029) and hsa-miR-125a-5p (OR 0.27, 95% CI: 0.11–0.71, p = 0.0079), were independent for hepatic toxicity occurrence during the 14 days after transplant. Our model in 10-fold cross-validation preserved its diagnostic potential with a receiver operating characteristics area under the curve (ROC AUC) of 0.75, 95% CI: 0.63–0.88 and at optimal cut-off reached 72.0% sensitivity and 74.4% specificity. An elevated serum level of miR-122-5p and decreased level of miR-125a-5p on day 0 are independent risk factors for hepatotoxicity in ASCT recipients, showing promise in accurately predicting post-ASCT complications. Identifying patients susceptible to complications has the potential to reduce procedure costs and optimize the selection of inpatient or outpatient procedures.

A crucial phase of modern biomarker discovery studies is selecting the most promising features from high-throughput screening assays. Here, we present the OmicSelector - Docker-based web application and R package that facilitates the analysis of such experiments. OmicSelector provides a consistent and overfitting-resilient pipeline that integrates 94 feature selection approaches based on 25 distinct variable selection methods. It identifies and then ranks the best feature sets using 11 modeling techniques with hyperparameter optimization in hold-out or cross-validation. OmicSelector provides classification performance metrics for proposed feature sets, allowing researchers to choose the overfitting-resistant biomarker set with the highest diagnostic potential. Finally, it performs GPU-accelerated development, validation, and implementation of deep learning feedforward neural networks (up to 3 hidden layers, with or without autoencoders) on selected signatures. The application performs an extensive grid search of hyperparameters, including balancing and preprocessing of next-generation sequencing (e.g. RNA-seq, miRNA-seq) oraz qPCR data. The pipeline is applicable for determining candidate circulating or tissue miRNAs, gene expression data and methylomic, metabolomic or proteomic analyses. As a case study, we use OmicSelector to develop a diagnostic test for pancreatic and biliary tract cancer based on serum small RNA next-generation sequencing (miRNA-seq) data. The tool is open-source and available at https://biostat.umed.pl/OmicSelector/