Centrosome associated genes pattern for risk sub-stratification in multiple myeloma

miRNA-seq identification and clinical validation of CD138+ and circulating miR-25 in treatment response of multiple myeloma

BACKGROUND

Despite significant advancements in multiple myeloma (MM) therapy, the highly heterogenous treatment response hinders reliable prognosis and tailored therapeutics. Herein, we have studied the clinical utility of miRNAs in ameliorating patients' management.

METHODS

miRNA-seq was performed in bone marrow CD138+ plasma cells (PCs) of 24 MM and smoldering MM (sMM) patients to analyze miRNAs profile. CD138+ and circulating miR-25 levels were quantified using in house RT-qPCR assays in our screening MM/sMM cohort (CD138+ plasma cells n = 167; subcohort of MM peripheral plasma samples n = 69). Two external datasets (Kryukov et al. cohort n = 149; MMRF CoMMpass study n = 760) served as institutional-independent validation cohorts. Patients' mortality and disease progression were assessed as clinical endpoints. Internal validation was performed by bootstrap analysis. Clinical benefit was estimated by decision curve analysis.

RESULTS

miRNA-seq highlighted miR-25 of CD138+ plasma cells to be upregulated in MM vs. sMM, R-ISS II/III vs. R-ISS I, and in progressed compared to progression-free patients. The analysis of our screening cohort highlighted that CD138+ miR-25 levels were correlated with short-term progression (HR = 2.729; p = 0.009) and poor survival (HR = 4.581; p = 0.004) of the patients; which was confirmed by Kryukov et al. cohort (HR = 1.878; p = 0.005) and MMRF CoMMpass study (HR = 1.414; p = 0.039) validation cohorts. Moreover, multivariate miR-25-fitted models contributed to superior risk-stratification and clinical benefit in MM prognostication. Finally, elevated miR-25 circulating levels were correlated with poor survival of MM patients (HR = 5.435; p = 0.021), serving as a potent non-invasive molecular prognostic tool.

CONCLUSIONS

Our study identified miR-25 overexpression as a powerful independent predictor of poor treatment outcome and post-treatment progression, aiding towards modern non-invasive disease prognosis and personalized treatment decisions.

Molecular subtypes based on centrosome-related genes can predict prognosis and therapeutic responsiveness in patients with low-grade gliomas

Background

Abnormalities in centrosome regulatory genes can induce chromosome instability, cell differentiation errors, and tumorigenesis. However, a limited number of comprehensive analyses of centrosome-related genes have been performed in low-grade gliomas (LGG).

Methods

LGG data were extracted from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. The ConsensusClusterPlus” R package was used for unsupervised clustering. We constructed a centrosome-related genes (CRGs) signature using a random forest model, lasso Cox model, and multivariate Cox model, and quantified the centrosome-related risk score (centS). The prognostic prediction efficacy of centS was evaluated using a Receiver Operating Characteristic (ROC) curve. Immune cell infiltration and genomic mutational landscapes were evaluated using the ESTIMATE algorithm, single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm, and “maftools” R package, respectively. Differences in clinical features, isocitrate dehydrogenase (IDH) mutation, 1p19q codeletion, O6-methylguanine-DNA methyltransferase promoter (MGMTp) methylation, and response to antitumor therapy between the high- and low-centS groups were explored. “pRRophetic” R packages were used for temozolomide (TMZ) sensitivity analysis. qRT-PCR verified the differential expression of the centrosomal gene team, the core of which is CEP135, between LGG cells and normal cells.

Results

Two distinct CRG-based clusters were identified using consensus unsupervised clustering analysis. The prognosis, biological characteristics, and immune cell infiltration of the two clusters differed significantly. A well-performing centS signature was developed to predict the prognosis of patients with LGG based on 12 potential CRGs. We found that patients in the high-centS group showed poorer prognosis and lower proportion of IDH mutation and 1p19q codeletion compared to those in the low-centS group. Furthermore, patients in the high-centS group showed higher sensitivity to TMZ, higher tumor mutation burden, and immune cell infiltration. Finally, we identified a centrosomal gene team whose core was CEP135, and verified their differential expression between LGG cells and normal glial cells.

Conclusion

Our findings reveal a novel centrosome-related signature for predicting the prognosis and therapeutic responsiveness of patients with LGG. This may be helpful for the accurate clinical treatment of LGG.

Comprehensive small RNA-sequencing of primary myeloma cells identifies miR-105-5p as a predictor of patient survival

BACKGROUND

Small RNAs (sRNAs), a heterogenous group of non-coding RNAs, are emerging as promising molecules for cancer patient risk stratification and as players in tumour pathogenesis. Here, we have studied microRNAs (miRNAs) and other sRNAs in relation to survival and disease severity in multiple myeloma.

METHODS

We comprehensively characterised sRNA expression in multiple myeloma patients by performing sRNA-sequencing on myeloma cells isolated from bone marrow aspirates of 86 myeloma patients. The sRNA expression profiles were correlated with the patients' clinical data to investigate associations with survival and disease subgroups, by using cox proportional hazards (coxph) -models and limma-voom, respectively. A publicly available sRNA dataset was used as external validation (n = 151).

RESULTS

We show that multiple miRNAs are differentially expressed between ISS Stage I and III. Interestingly, we observed the downregulation of seven different U2 spliceosomal RNAs, a type of small nuclear RNAs in severe disease stages. Further, by a discovery-based approach, we identified miRNA miR-105-5p as a predictor of poor overall survival (OS) in multiple myeloma. Multivariate analysis showed that miR-105-5p predict OS independently of established disease markers.

CONCLUSIONS

Overexpression of miR-105-5p in myeloma cells correlates with reduced OS, potentially improving prognostic risk stratification in multiple myeloma.

miRNA-seq and clinical evaluation in multiple myeloma: miR-181a overexpression predicts short-term disease progression and poor post-treatment outcome

BACKGROUND

Despite significant advances in multiple myeloma (MM) therapy, disease relapse and treatment resistance remain major obstacles in clinical management. Herein, we have studied the clinical utility of miRNAs in improving patients' risk-stratification and prognosis.

METHODS

miRNA-seq was performed in CD138+ plasma cells of MM, smoldering multiple myeloma (sMM) and monoclonal gammopathy of undetermined significance (MGUS) patients. The screening MM cohort consisted of 138 patients. miRNA levels of CD138+ plasma cells were quantified by RT-qPCR following 3'-end RNA polyadenylation. Disease progression and patients' death were used as clinical end-point events. Internal validation was conducted by bootstrap analysis. Clinical net benefit on disease prognosis was assessed by decision curve analysis. Kruykov et al. 2016 served as validation cohort (n = 151).

RESULTS

miRNA-seq highlighted miR-181a to be upregulated in MM vs. sMM/MGUS, and R-ISS III vs. I patients. Screening and validation cohorts confirmed the significantly higher risk for short-term progression and worse survival of the patients overexpressing miR-181a. Multivariate models integrating miR-181a with disease established markers led to superior risk-stratification and clinical benefit for MM prognosis.

CONCLUSIONS

CD138+ overexpression of miR-181a was strongly correlated with inferior disease outcome and contributed to superior prediction of MM patients early progression, supporting personalised prognosis and treatment decisions.

PSMB7 Is a Key Gene Involved in the Development of Multiple Myeloma and Resistance to Bortezomib

Multiple myeloma (MM), the second most commonly diagnosed hematologic neoplasm, is the most significant clinical manifestation in a series of plasma cell (PC) dyscrasia. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM (SMM), approximately 1% or 10% of which, respectively, can progress to MM per year, are the premalignant stages of MM. The overall survival (OS) of MM is significantly improved by the introduction of proteasome inhibitors (PIs), but almost all MM patients eventually relapse and resist anti-MM drugs. Therefore, it is crucial to explore the progression of MM and the mechanisms related to MM drug resistance. In this study, we used weighted gene co-expression network analysis (WGCNA) to analyze the gene expression of the dynamic process from normal plasma cells (NPC) to malignant profiling PC, and found that the abnormal gene expression was mainly concentrated in the proteasome. We also found that the expression of one of the proteasomal subunits PSMB7 was capable of distinguishing the different stages of PC dyscrasia and was the highest in ISS III. In the bortezomib (BTZ) treated NDMM patients, higher PSMB7 expression was associated with shorter survival time, and the expression of PSMB7 in the BTZ treatment group was significantly higher than in the thalidomide (Thai) treatment group. In summary, we found that PSMB7 is the key gene associated with MM disease progression and drug resistance.

A centromere-associated gene score for rapid determination of risk in multiple myeloma

Risk stratification in patients with multiple myeloma (MM) remains a challenge. As clinicopathological characteristics have been proven deficient for accurately defining risk stratification, molecular markers have gradually become the focus of interests. This study investigated the expressions of centromere-associated genes in MM patients, their potential as prognostic markers, and their roles in disease progression. Several cohorts of 2301 MM patients were enrolled and gene expression profiling (GEP) was used to screen for CENP-A through CENP-W. Correlations between centromere-associated genes and clinicopathological characteristics, proliferative activity and recurrence of MM patients were analyzed. Clinically, CENP-E/H/K/L/N/U/W expressions were present at high-risk MM, which were even stronger elevated in patients with high tumor burden and recurrence. Mechanistically, CENP-E/H/K/L/N/U/W and FOXM1 were positively expressed in MM patients, which play synergistic or additive effects in clinical outcome. Furthermore, CENP-E/H/K/L/N/U/W were used to construct a centromere-associated gene score (CGS) model, which proved to be strongly prognostic values in several independent cohorts compared to usual clinical prognostic parameters using multivariate Cox analysis. Patients in the CGS low-risk group were significantly related to better clinical outcome than those in high-risk group. In this study, we provided proof-of-concept that CENP-E/H/K/L/N/U/W have critical roles in MM patients' progression and prognosis. The CGS model validated in different datasets clearly indicated novel risk stratification for personalized anti-MM treatments.

Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.

Prognostic model for multiple myeloma progression integrating gene expression and clinical features

BACKGROUND

Multiple myeloma (MM) is a hematological cancer caused by abnormal accumulation of monoclonal plasma cells in bone marrow. With the increase in treatment options, risk-adapted therapy is becoming more and more important. Survival analysis is commonly applied to study progression or other events of interest and stratify the risk of patients.

RESULTS

In this study, we present the current state-of-the-art model for MM prognosis and the molecular biomarker set for stratification: the winning algorithm in the 2017 Multiple Myeloma DREAM Challenge, Sub-Challenge 3. Specifically, we built a non-parametric complete hazard ranking model to map the right-censored data into a linear space, where commonplace machine learning techniques, such as Gaussian process regression and random forests, can play their roles. Our model integrated both the gene expression profile and clinical features to predict the progression of MM. Compared with conventional models, such as Cox model and random survival forests, our model achieved higher accuracy in 3 within-cohort predictions. In addition, it showed robust predictive power in cross-cohort validations. Key molecular signatures related to MM progression were identified from our model, which may function as the core determinants of MM progression and provide important guidance for future research and clinical practice. Functional enrichment analysis and mammalian gene-gene interaction network revealed crucial biological processes and pathways involved in MM progression. The model is dockerized and publicly available at https://www.synapse.org/#!Synapse:syn11459638. Both data and reproducible code are included in the docker.

CONCLUSIONS

We present the current state-of-the-art prognostic model for MM integrating gene expression and clinical features validated in an independent test set.

Development and Validation of a 9-Gene Prognostic Signature in Patients With Multiple Myeloma

Background: Multiple myeloma (MM) is one of the most common types of hematological malignance, and the prognosis of MM patients remains poor.

Objective: To identify and validate a genetic prognostic signature in patients with MM.

Methods: Co-expression network was constructed to identify hub genes related with International Staging System (ISS) stage of MM. Functional analysis of hub genes was conducted. Univariate Cox proportional hazard regression analysis was conducted to identify genes correlated with the overall survival (OS) of MM patients. Least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression model was used to minimize overfitting and construct a prognostic signature. The prognostic value of the signature was validated in the test set and an independent validation cohort.

Results: A total of 758 hub genes correlated with ISS stage of MM patients were identified, and these hub genes were mainly enriched in several GO terms and KEGG pathways involved in cell proliferation and immune response. Nine hub genes (HLA-DPB1, TOP2A, FABP5, CYP1B1, IGHM, FANCI, LYZ, HMGN5, and BEND6) with non-zero coefficients in the LASSO Cox regression model were used to build a 9-gene prognostic signature. Relapsed MM and ISS stage III MM was associated with high risk score calculated based on the signature. Patients in the 9-gene signature low risk group was significantly associated with better clinical outcome than those in the 9-gene signature high risk group in the training set, test, and validation set.

Conclusions: We developed a 9-gene prognostic signature that might be an independent prognostic factor in patients with MM.

Regularization and grouping -omics data by GCA method: A transcriptomic case

The paper presents the application of Grade Correspondence Analysis (GCA) and Grade Correspondence Cluster Analysis (GCCA) for ordering and grouping -omics datasets, using transcriptomic data as an example. Based on gene expression data describing 256 patients with Multiple Myeloma it was shown that the GCA method could be used to find regularities in the analyzed collections and to create characteristic gene expression profiles for individual groups of patients. GCA iteratively permutes rows and columns to maximize the tau-Kendall or rho-Spearman coefficients, which makes it possible to arrange rows and columns in such a way that the most similar ones remain in each other’s neighbourhood. In this way, the GCA algorithm highlights regularities in the data matrix. The ranked data can then be grouped using the GCCA method, and after that aggregated in clusters, providing a representation that is easier to analyze–especially in the case of large sets of gene expression profiles. Regularization of transcriptomic data, which is presented in this manuscript, has enabled division of the data set into column clusters (representing genes) and row clusters (representing patients). Subsequently, rows were aggregated (based on medians) to visualise the gene expression profiles for patients with Multiple Myeloma in each collection. The presented analysis became the starting point for characterisation of differentiated genes and biochemical processes in which they are involved. GCA analysis may provide an alternative analytical method to support differentiation and analysis of gene expression profiles characterising individual groups of patients.

MDM2/X inhibitors under clinical evaluation: perspectives for the management of hematological malignancies and pediatric cancer

The two murine double minute (MDM) family members MDM2 and MDMX are at the center of an intense clinical assessment as molecular target for the management of cancer. Indeed, the two proteins act as regulators of P53, a well-known key controller of the cell cycle regulation and cell proliferation that, when altered, plays a direct role on cancer development and progression. Several evidence demonstrated that functional aberrations of P53 in tumors are in most cases the consequence of alterations on the MDM2 and MDMX regulatory proteins, in particular in patients with hematological malignancies where TP53 shows a relatively low frequency of mutation while MDM2 and MDMX are frequently found amplified/overexpressed. The pharmacological targeting of these two P53-regulators in order to restore or increase P53 expression and activity represents therefore a strategy for cancer therapy. From the discovery of the Nutlins in 2004, several compounds have been developed and reported with the ability of targeting the P53-MDM2/X axis by inhibiting MDM2 and/or MDMX. From natural compounds up to small molecules and stapled peptides, these MDM2/X pharmacological inhibitors have been extensively studied, revealing different biological features and different rate of efficacy when tested in in vitro and in vivo experimental tumor models. The data/evidence coming from the preclinical experimentation have allowed the identification of the most promising molecules and the setting of clinical studies for their evaluation as monotherapy or in therapeutic combination with conventional chemotherapy or with innovative therapeutic protocols in different tumor settings. Preliminary results have been recently published reporting data about safety, tolerability, potential side effects, and efficacy of such therapeutic approaches. In this light, the aim of this review is to give an updated overview about the state of the art of the clinical evaluation of MDM2/X inhibitor compounds with a special attention to hematological malignancies and to the potential for the management of pediatric cancers.