The flow cytometry-defined light chain cytoplasmic immunoglobulin index and an associated 12-gene expression signature are independent prognostic factors in multiple myeloma

Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles

Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): FGFR3, MAF, IGHA2, IGHV1-69, and GRB14. Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity.

Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients

Gene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype-tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10^-7 either in EBV-transformed B-lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression-free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4-rs1992292, TBRG4-rs2287535 and ENTPD1-rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta-analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4-rs1992292 is associated with OS (hazard ratio = 1.14, 95% confidence interval 1.04-1.26, P = .007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.

High expression of chaperonin-containing TCP1 subunit 3 may induce dismal prognosis in multiple myeloma

The prognosis role of CCT3 in MM and the possible pathways it involved were studied in our research. By analyzing ten independent datasets (including 48 healthy donors, 2220 MM, 73 MGUS, and 6 PCL), CCT3 was found to express higher in MM than healthy donors, and the expression level was gradually increased from MGUS, SMM, MM to PCL (all P < 0.01). By analyzing three independent datasets (GSE24080, GSE2658, and GSE4204), we found that CCT3 was a significant indicator of poor prognosis (all P < 0.01). KEGG and GSEA analysis showed that CCT3 expression was associated with JAK-STAT3 pathway, Hippo signaling pathway, and WNT signaling pathway. In addition, different expressed genes analysis revealed MYC, which was one of the downstream genes regulated by JAK-STAT3 pathway, was upregulated in MM. This confirms that JAK-STAT3 signaling pathway may promote the progress of disease which was regulated by CCT3 expression. Our study revealed that CCT3 may play a supporting role at the diagnosis of myeloid, and high expression of CCT3 suggested poor prognosis in MM. CCT3 expression may promote the progression of MM mainly by regulating MYC through JAK-STAT3 signaling pathway.

Prognostic Biomarkers in the Progression From MGUS to Multiple Myeloma: A Systematic Review

Multiple myeloma (MM), characterized by malignant plasma cells in the bone marrow, is consistently preceded by asymptomatic premalignant stage monoclonal gammopathy of undetermined significance (MGUS). These MGUS patients have an annual risk of 1% to progress to MM. Clinical, imaging, and genomic (genetic and epigenetic) factors were identified, whose presence increased the risk of progression from MGUS to MM. In this systematic review we summarize the currently identified clinical, imaging, and genomic biomarkers suggested to increase the progression risk or shown to be differentially expressed/present between both cohorts of patients. Despite the wide range of proposed markers, there are still no reliable biomarkers to individually predict which MGUS patient will progress to MM and which will not. Research on biomarkers in the progression from MGUS to MM will give more insight in the unknown pathogenesis of this hematological malignancy. This would improve research by elucidating new pathways and potential therapeutic targets as well as clinical management by closer follow-up and earlier treatment of high-risk MGUS patients.

Rediscovering Beta-2 Microglobulin As a Biomarker across the Spectrum of Kidney Diseases

There is currently an unmet need for better biomarkers across the spectrum of renal diseases. In this paper, we revisit the role of beta-2 microglobulin (β₂M) as a biomarker in patients with chronic kidney disease and end-stage renal disease. Prior to reviewing the numerous clinical studies in the area, we describe the basic biology of β₂M, focusing in particular on its role in maintaining the serum albumin levels and reclaiming the albumin in tubular fluid through the actions of the neonatal Fc receptor. Disorders of abnormal β₂M function arise as a result of altered binding of β₂M to its protein cofactors and the clinical manifestations are exemplified by rare human genetic conditions and mice knockouts. We highlight the utility of β₂M as a predictor of renal function and clinical outcomes in recent large database studies against predictions made by recently developed whole body population kinetic models. Furthermore, we discuss recent animal data suggesting that contrary to textbook dogma urinary β₂M may be a marker for glomerular rather than tubular pathology. We review the existing literature about β₂M as a biomarker in patients receiving renal replacement therapy, with particular emphasis on large outcome trials. We note emerging proteomic data suggesting that β₂M is a promising marker of chronic allograft nephropathy. Finally, we present data about the role of β₂M as a biomarker in a number of non-renal diseases. The goal of this comprehensive review is to direct attention to the multifaceted role of β₂M as a biomarker, and its exciting biology in order to propose the next steps required to bring this recently rediscovered biomarker into the twenty-first century.