Prognostic value of cyclin D2 mRNA expression in newly diagnosed multiple myeloma treated with high-dose chemotherapy and tandem autologous stem cell transplantations

The prognostic significance of ubiquitination-related genes in multiple myeloma by bioinformatics analysis

BACKGROUND

Immunoregulatory drugs regulate the ubiquitin-proteasome system, which is the main treatment for multiple myeloma (MM) at present. In this study, bioinformatics analysis was used to construct the risk model and evaluate the prognostic value of ubiquitination-related genes in MM.

METHODS AND RESULTS

The data on ubiquitination-related genes and MM samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The consistent cluster analysis and ESTIMATE algorithm were used to create distinct clusters. The MM prognostic risk model was constructed through single-factor and multiple-factor analysis. The ROC curve was plotted to compare the survival difference between high- and low-risk groups. The nomogram was used to validate the predictive capability of the risk model. A total of 87 ubiquitination-related genes were obtained, with 47 genes showing high expression in the MM group. According to the consistent cluster analysis, 4 clusters were determined. The immune infiltration, survival, and prognosis differed significantly among the 4 clusters. The tumor purity was higher in clusters 1 and 3 than in clusters 2 and 4, while the immune score and stromal score were lower in clusters 1 and 3. The proportion of B cells memory, plasma cells, and T cells CD4 naïve was the lowest in cluster 4. The model genes KLHL24, HERC6, USP3, TNIP1, and CISH were highly expressed in the high-risk group. AICAr and BMS.754,807 exhibited higher drug sensitivity in the low-risk group, whereas Bleomycin showed higher drug sensitivity in the high-risk group. The nomogram of the risk model demonstrated good efficacy in predicting the survival of MM patients using TCGA and GEO datasets.

CONCLUSIONS

The risk model constructed by ubiquitination-related genes can be effectively used to predict the prognosis of MM patients. KLHL24, HERC6, USP3, TNIP1, and CISH genes in MM warrant further investigation as therapeutic targets and to combat drug resistance.

The SAGA acetyltransferase module is required for the maintenance of MAF and MYC oncogenic gene expression programs in multiple myeloma

Despite recent advances in therapeutic treatments, multiple myeloma (MM) remains an incurable malignancy. Epigenetic factors contribute to the initiation, progression, relapse, and clonal heterogeneity in MM, but our knowledge on epigenetic mechanisms underlying MM development is far from complete. The SAGA complex serves as a coactivator in transcription and catalyzes acetylation and deubiquitylation. Analyses of datasets in the Cancer Dependency Map Project revealed many SAGA components are selective dependencies in MM. To define SAGA-specific functions, we focused on ADA2B, the only subunit in the lysine acetyltransferase (KAT) module that specifically functions in SAGA. Integration of RNA-seq, ATAC-seq, and CUT&RUN results identified pathways directly regulated by ADA2B include MTORC1 signaling, MYC, E2F, and MM-specific MAF oncogenic programs. We discovered that ADA2B is recruited to MAF and MYC gene targets, and that MAF shares a majority of its targets with MYC in MM cells. Furthermore, we found the SANT domain of ADA2B is required for interaction with both GCN5 and PCAF acetyltransferases, incorporation into SAGA, and ADA2B protein stability. Our findings uncover previously unknown SAGA KAT module-dependent mechanisms controlling MM cell growth, revealing a vulnerability that might be exploited for future development of MM therapy.

Quantification of cyclin D1 and D2 proteins in multiple myeloma identifies different expression patterns from those revealed by gene expression profiling

Upregulation of a cyclin D gene determined by expression microarrays is an almost universal event in multiple myeloma (MM), but this finding has not been properly confirmed at the protein level. For this reason, we carried out a quantitative analysis of cyclin D proteins using a capillary electrophoresis nanoimmunoassay in newly diagnosed MM patients. Exclusive expression of cyclin D1 and D2 proteins was detected in 54 of 165 (33%) and 30 of 165 (18%) of the MM patients, respectively. Of note, cyclin D1 or D2 proteins were undetectable in 41% of the samples. High levels of cyclin D1 protein were strongly associated with the presence of t(11;14) or 11q gains. Cyclin D2 protein was detected in all the cases bearing t(14;16), but in only 24% of patients with t(4;14). The presence of cyclin D2 was associated with shorter overall survival (hazard ratio =2.14; P=0.017), although patients expressing cyclin D2 protein, but without 1q gains, had a favorable prognosis. In conclusion, although one of the cyclins D is overexpressed at the mRNA level in almost all MM patients, in approximately half of the patients this does not translate into detectable protein. This suggests that cyclins D could not play an oncogenic role in a proportion of patients with MM (clinicaltrials gov. identifier: NCT01916252).

CTPS1 is a novel therapeutic target in multiple myeloma which synergizes with inhibition of CHEK1, ATR or WEE1

Targeting nucleotide biosynthesis is a proven strategy for the treatment of cancer but is limited by toxicity, reflecting the fundamental nucleotide requirement of dividing cells. The rate limiting step in de novo pyrimidine synthesis is of interest, being catalyzed by two homologous enzymes, CTP synthase 1 (CTPS1) and CTPS2, that could be differentially targeted. Herein, analyses of publicly available datasets identified an essential role for CTPS1 in multiple myeloma (MM), linking high expression of CTPS1 (but not CTPS2) with advanced disease and poor outcomes. In cellular experiments, CTPS1 knockout induced apoptosis of MM cell lines. Exposure of MM cells to STP-B, a novel and highly selective pharmacological inhibitor of CTPS1, inhibited proliferation, induced S phase arrest and led to cell death by apoptosis. Mechanistically, CTPS1 inhibition by STP-B activated DNA damage response (DDR) pathways and induced double-strand DNA breaks which accumulated in early S phase. Combination of STP-B with pharmacological inhibitors of key components of the DDR pathway (ATR, CHEK1 or WEE1) resulted in synergistic growth inhibition and early apoptosis. Taken together, these findings identify CTPS1 as a promising new target in MM, either alone or in combination with DDR pathway inhibition.

Comprehensive analysis of prognosis of cuproptosis-related oxidative stress genes in multiple myeloma

Introduction: Multiple myeloma (MM) is a highly heterogeneous hematologic malignancy. The patients’ survival outcomes vary widely. Establishing a more accurate prognostic model is necessary to improve prognostic precision and guide clinical therapy.

Methods: We developed an eight-gene model to assess the prognostic outcome of MM patients. Univariate Cox analysis, Least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses were used to identify the significant genes and construct the model. Other independent databases were used to validate the model.

Results: The results showed that the overall survival of patients in the high-risk group was signifificantly shorter compared with that of those in the low-risk group. The eight-gene model demonstrated high accuracy and reliability in predicting the prognosis of MM patients.

Discussion: Our study provides a novel prognostic model for MM patients based on cuproptosis and oxidative stress. The eight-gene model can provide valid predictions for prognosis and guide personalized clinical treatment. Further studies are needed to validate the clinical utility of the model and explore potential therapeutic targets.

A novel prognostic model based on pyroptosis-related genes for multiple myeloma

BACKGROUND

Multiple myeloma (MM) is an incurable and relapse-prone disease with apparently prognostic heterogeneity. At present, the risk stratification of myeloma is still incomplete. Pyroptosis, a type of programmed cell death, has been shown to regulate tumor growth and may have potential prognostic value. However, the role of pyroptosis-related genes (PRGs) in MM remains undetermined. The aims of this study were to identify potential prognostic biomarkers and to construct a predictive model related to PRGs.

METHODS

Sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Non-negative matrix factorization (NMF) was performed to identify molecular subtype screening. LASSO regression was used to screen for prognostic markers, and then a risk score model was constructed. The Maxstat package was utilized to calculate the optimal cutoff value, according to which patients were divided into a high-risk group and a low-risk group, and the survival curves were plotted using the Kaplan-Meier (K-M) method. Nomograms and calibration curves were established using the rms package.

RESULTS

A total of 33 PRGs were extracted from the TCGA database underlying which 4 MM molecular subtypes were defined. Patients in cluster 1 had poorer survival than those in cluster 2 (p = 0.035). A total of 9 PRGs were screened out as prognostic markers, and the predictive ability of the 9-gene risk score for 3-year survival was best (AUC = 0.658). Patients in the high-risk group had worse survival than those in the low-risk group (p < 0.001), which was consistent with the results verified by the GSE2658 dataset. The nomogram constructed by gender, age, International Staging System (ISS) stage, and risk score had the best prognostic predictive performance with a c-index of 0.721.

CONCLUSION

Our model could enhance the predictive ability of ISS staging and give a reference for clinical decision-making. The new, prognostic, and pyroptosis-related markers screened out by us may facilitate the development of novel risk stratification for MM.

CLINICAL TRIAL REGISTRATION

Not applicable.

UBE2C promotes leptomeningeal dissemination and is a therapeutic target in brain metastatic disease

Background

Despite current improvements in systemic cancer treatment, brain metastases (BM) remain incurable, and there is an unmet clinical need for effective targeted therapies.

Methods

Here, we sought common molecular events in brain metastatic disease. RNA sequencing of thirty human BM identified the upregulation of UBE2C, a gene that ensures the correct transition from metaphase to anaphase, across different primary tumor origins.

Results

Tissue microarray analysis of an independent BM patient cohort revealed that high expression of UBE2C was associated with decreased survival. UBE2C-driven orthotopic mouse models developed extensive leptomeningeal dissemination, likely due to increased migration and invasion. Early cancer treatment with dactolisib (dual PI3K/mTOR inhibitor) prevented the development of UBE2C-induced leptomeningeal metastases.

Conclusions

Our findings reveal UBE2C as a key player in the development of metastatic brain disease and highlight PI3K/mTOR inhibition as a promising anticancer therapy to prevent late-stage metastatic brain cancer.

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made.

Systematic Pan-Cancer Analysis Identifies CDK1 as an Immunological and Prognostic Biomarker

Cyclin-dependent kinase 1 (CDK1) plays an important role in cancer development, progression, and the overall process of tumorigenesis. However, no pan-cancer analysis has been reported for CDK1, and the predictive role of CDK1 in immune checkpoint inhibitors (ICIs) therapy response remains unexplored. Thus, in this study, we first investigated the potential oncogenic role of CDK1 in 33 tumors by multidimensional bioinformatics analysis based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Bioinformatic analysis and immunohistochemical experiments confirmed that CDK1 is significantly upregulated in most common cancers and is strongly associated with prognosis. Further analysis indicated that CDK1 may influence tumor immunity mainly by mediating the degree of tumor infiltration of immune-associated cells, and the effect of CDK1 on immunity is diverse across tumor types in tumor microenvironment. CDK1 was also positively correlated with tumor mutational burden (TMB) and microsatellite instability (MSI) in certain cancer types, linking its expression to the assessment of possible treatment response. The results of the pan-cancer analysis study showed that the CDK1 gene was positively associated with the expression of three classes of RNA methylation regulatory proteins, and affects RNA function through multiple mechanisms of action and plays an important role in the posttranscriptional regulation of the tumor microenvironment. These findings shed light on the role of the CDK1 gene in cancer progression and provide information to further study the CDK1 gene as a potential target for pan-cancer.

AATF/Che-1 localizes to paraspeckles and suppresses R-loops accumulation and interferon activation in Multiple Myeloma

Several kinds of stress promote the formation of three-stranded RNA:DNA hybrids called R-loops. Insufficient clearance of these structures promotes genomic instability and DNA damage, which ultimately contribute to the establishment of cancer phenotypes. Paraspeckle assemblies participate in R-loop resolution and preserve genome stability, however, the main determinants of this mechanism are still unknown. This study finds that in Multiple Myeloma (MM), AATF/Che-1 (Che-1), an RNA-binding protein fundamental to transcription regulation, interacts with paraspeckles via the lncRNA NEAT1_2 (NEAT1) and directly localizes on R-loops. We systematically show that depletion of Che-1 produces a marked accumulation of RNA:DNA hybrids. We provide evidence that such failure to resolve R-loops causes sustained activation of a systemic inflammatory response characterized by an interferon (IFN) gene expression signature. Furthermore, elevated levels of R-loops and of mRNA for paraspeckle genes in patient cells are linearly correlated with Multiple Myeloma progression. Moreover, increased interferon gene expression signature in patients is associated with markedly poor prognosis. Taken together, our study indicates that Che-1/NEAT1 cooperation prevents excessive inflammatory signaling in Multiple Myeloma by facilitating the clearance of R-loops. Further studies on different cancer types are needed to test if this mechanism is ubiquitously conserved and fundamental for cell homeostasis.

Identification of key genes and miRNA-mRNA regulatory networks associated with bone marrow immune microenvironment regulations in multiple myeloma by integrative bioinformatics analysis

The deregulation of microRNAs (miRNAs) and genes in the bone marrow microenvironment have been involved with the pathogenesis of multiple myeloma (MM). However, the exploration of miRNA-mRNA regulatory networks in MM remains lacking. We used GSE125363, GSE125361, GSE47552, GSE2658, GSE136324, GSE16558, and GSE13591 datasets for this bioinformatics study. We identified 156 downregulated and 13 upregulated differentially expressed miRNAs (DEmiRs) in MM. The DEmiRs are associated with the enrichment of pathways mainly involved with cancers, cellular signaling, and immune regulations. We identified 112 hub genes associated with five significant clusters in MM. Moreover, we identified 9 upregulated hub genes (such as IGF1, RPS28, UBA52, CDKN1A, and CDKN2A) and 52 downregulated hub genes (such as TP53, PCNA, BRCA1, CCNB1, and MSH2) in MM that is targeted by DEmiRs. The expression of DEmiRs targeted two hub genes (CDKN2A and TP53) are correlated with the survival prognosis of MM patients. Furthermore, the expression level of CDKN2A is correlated with immune signatures, including CD4+ Regulatory T cells, T cell exhaustion, MHC Class I, immune checkpoint genes, macrophages, neutrophils, and TH2 cells in the TME of MM. Finally, we revealed the consistently deregulated expression level of key gene CDKN2A and its co-regulatory DEmiRs, including hsa-mir-192, hsa-mir-10b, hsa-mir-492, and hsa-mir-24 in the independent cohorts of MM. Identifying key genes and miRNA-mRNA regulatory networks may provide new molecular insights into the tumor immune microenvironment in MM.

AKT signaling restrains tumor suppressive functions of FOXO transcription factors and GSK3 kinase in multiple myeloma

The phosphatidylinositide-3 kinases and the downstream mediator AKT drive survival and proliferation of multiple myeloma (MM) cells. AKT signaling is active in MM and has pleiotropic effects; however, the key molecular aspects of AKT dependency in MM are not fully clear. Among the various downstream AKT targets are the Forkhead box O (FOXO) transcription factors (TFs) and glycogen synthase kinase 3 (GSK3), which are negatively regulated by AKT signaling. Here we show that abrogation of AKT signaling in MM cells provokes cell death and cell cycle arrest, which crucially depends on both FOXO TFs and GSK3. Based on gene expression profiling, we defined a FOXO-repressed gene set that has prognostic significance in a large cohort of patients with MM, indicating that AKT-mediated gene activation is associated with inferior overall survival. We further show that AKT signaling stabilizes the antiapoptotic myeloid cell leukemia 1 (MCL1) protein by inhibiting FOXO- and GSK3-mediated MCL1 turnover. In concordance, abrogation of AKT signaling greatly sensitized MM cells for an MCL1-targeting BH3-mimetic, which is currently in clinical development. Taken together, our results indicate that AKT activity is required to restrain the tumor-suppressive functions of FOXO and GSK3, thereby stabilizing the antiapoptotic protein MCL1 in MM. These novel insights into the role of AKT in MM pathogenesis and MCL1 regulation provide opportunities to improve targeted therapy for patients with MM.

Che-1/AATF-induced transcriptionally active chromatin promotes cell proliferation in multiple myeloma

Multiple myeloma (MM) is a hematologic malignancy produced by a clonal expansion of plasma cells and characterized by abnormal production and secretion of monoclonal antibodies. This pathology exhibits an enormous heterogeneity resulting not only from genetic alterations but also from several epigenetic dysregulations. Here we provide evidence that Che-1/AATF (Che-1), an interactor of RNA polymerase II, promotes MM proliferation by affecting chromatin structure and sustaining global gene expression. We found that Che-1 depletion leads to a reduction of "active chromatin" by inducing a global decrease of histone acetylation. In this context, Che-1 directly interacts with histones and displaces histone deacetylase class I members from them. Strikingly, transgenic mice expressing human Che-1 in plasma cells develop MM with clinical features resembling those observed in the human disease. Finally, Che-1 downregulation decreases BRD4 chromatin accumulation to further sensitize MM cells to bromodomain and external domain inhibitors. These findings identify Che-1 as a promising target for MM therapy, alone or in combination with bromodomain and external domain inhibitors.

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.

CDK6 Is a Potential Prognostic Biomarker in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a threatening hematological malignant disease in which new successful approaches in therapy are needed. Cyclin-dependent kinase 6 (CDK6), a regulatory enzyme of the cell cycle that plays an important role in leukemogenesis and the maintenance of leukemia stem cells (LSC), has the potential to predict the prognosis of AML. By analyzing public databases, we observed that the messenger RNA (mRNA) levels of CDK6 were significantly overexpressed in AML cell lines and non-acute promyelocytic leukemia (non-APL) AML patients when compared to healthy donors. Furthermore, CDK6 expression was significantly reduced in AML patients who achieved complete remission (CR) compared to that at the time of diagnosis in our validated cohort. The expression of CDK6 was tightly correlated with peripheral blood blasts, French-American-British (FAB) subtypes, CCAAT-enhancer-binding protein α (CEBPA) mutation, and chromosomal abnormalities of t(8;21). However, the clinical significance and effects of CDK6 expression on the prognosis of non-APL AML patients remain uncertain. We found that CDK6 expression was inversely correlated with overall survival (OS) among non-APL AML patients using the Kaplan-Meier analysis. CDK6 was also found to be positively associated with genes identified to contribute to the development of leukemia, including CCND2, DNMT3B, SOX4, and IKZF2, as well as being negatively associated with anticancer microRNAs, including miR-187, miR-9, miR-582, miR708, and miR-362. In summary, our study revealed that CDK6 might be a potential diagnostic and prognostic biomarker in non-APL AML patients.

A same‐day assay predicts apoptotic response to combined BCL‐2 and MCL‐1 BH3‐mimetic targeting in multiple myeloma cells

Recent advances in treatment options for multiple myeloma (MM) have positive impact on patient survival. However, there is a short fall of rapid and reliable assays that can predict patient response to novel agents. The anti‐apoptotic proteins B‐cell lymphoma‐2 (BCL‐2) and myeloid cell leukaemia‐1 (MCL‐1), are necessary for MM survival, although most myelomas are more dependent on MCL‐1. BCL‐2 inhibition alone yields significant cytotoxicity in only a minority of cases, therefore targeting both proteins simultaneously, is a therapeutic option. Venetoclax and S63845 are BCL‐2 and MCL‐1 targeting BH3‐mimetics which have demonstrated apoptotic synergy in MM. We investigated whether a novel short‐term flow cytometric cytochrome c release assay could predict response to dual BH3‐mimetic targeting in MM cells. Six human myeloma cell lines (HMCL) and seven primary samples were treated with venetoclax and S63845 alone or in combination. The 4‐hour assay confirmed the drug combination was synergistic in all HMCL tested. Annexin‐V data at 48 hours corresponded with 4‐hour response verifying the assay as a predictor of drug sensitivity. All primary samples responded to the drug combination, including samples with 1q gain and t(4;14) translocation. Normal stem cells were unaffected by the drug combination. We have developed a novel assay with the potential to predict response to therapy in MM cells.

Genomic analysis of multiple myeloma using targeted capture sequencing in the Japanese cohort

Previous genomic studies have revealed the genomic landscape of myeloma cells. Although some of the genomic abnormalities shown are believed to be correlated to the molecular pathogenesis of multiple myeloma and/or clinical outcome, these correlations are not fully understood. The aim of this study is to elucidate the correlation between genomic abnormalities and clinical characteristics by targeted capture sequencing in the Japanese multiple myeloma cohort. We analysed 154 patients with newly diagnosed multiple myeloma. The analysis revealed that the study cohort consisted of a less frequent hyperdiploid subtype (37·0%) with relatively high frequencies of KRAS mutation (36·4%) and IGH-CCND1 translocation (26·6%) compared with previous reports. Moreover, our targeted capture sequencing strategy was able to detect rare IGH-associated chromosomal translocations, such as IGH-CCND2 and IGH-MAFA. Interestingly, all 10 patients harboured MAX mutations accompanied by 14q23 deletion. The patients with del(17p) exhibited an unfavourable clinical outcome, and the presence of KRAS mutation was associated with shorter survival in patients with multiple myeloma, harbouring IGH-CCND1. Thus, our study provides a detailed landscape of genomic abnormalities, which may have potential clinical application for patients with multiple myeloma.

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.

Regulation of miR-34b/c-targeted gene expression program by SUMOylation

The miR-34 family of microRNAs suppresses the expression of proteins involved in pluripotency and oncogenesis. miR-34 expression is frequently reduced in cancers; however, the regulation of their expression is not well understood. We used genome-wide miRNA profiling and mechanistic analysis to show that SUMOylation regulates miR-34b/c expression, which impacts the expression of c-Myc and other tested miR-34 targets. We used site-directed mutagenesis and other methods to show that protein kinase B (also known as Akt) phosphorylation of FOXO3a plays an important role in SUMOylation-dependent expression of miR-34b/c. This study reveals how the miR-34-targeted gene expression program is regulated by SUMOylation and shows that SUMOylation need not regulate target proteins through direct modification, but instead can act through the expression of their targeting miRNAs.

Prediction and prognostic significance of BCAR3 expression in patients with multiple myeloma

Background

Multiple myeloma (MM) is the plasma cell tumor, which is characterized by clonal proliferation of tumor cells, with high risk of progression to renal impairment, bone damage and amyloidosis. Although the survival rate of patients with MM has improved in the past decade, most people inevitably relapse. The treatment and prognosis of MM are still urgent problems. Breast Cancer Antiestrogen Resistance 3 (BCAR3) is a protein-coding gene that is associated with many tumors. However, there have been few studies on the relationship of BCAR3 and MM.

Methods

We analyzed 1878 MM patients (1930 samples) from 7 independent datasets. First, we compared the BCAR3 expression level of MM patients in different stages and MM patients with different amplification of 1q21. Second, we analyzed BCAR3 expression levels in MM patients with different molecular subtypes. Finally, we explored the event-free survival rate (EFS) and overall survival rate (OS) of MM patients with high or low BCAR3 expression, including patients before and after relapse, and their therapeutic responses to bortezomib and dexamethasone.

Results

The expression of BCAR3 showed a decreasing trend in stages I, II and III (P = 0.00068). With the increase of 1q21 amplification level, the expression of BCAR3 decreased (P = 0.022). Patients with high BCAR3 expression had higher EFS and OS (EFS: P < 0.0001, OS: P < 0.0001). The expression of BCAR3 gene before relapse was higher than that after relapse (P = 0.0045). BCAR3 is an independent factor affecting prognosis (EFS: P = 5.17E−03; OS: P = 3.33E−04).

Conclusion

We found that high expression level of BCAR3 predicted better prognosis of MM patients. Low expression of BCAR3 at diagnosis can predict early relapse. BCAR3 is an independent prognostic factor for MM. BCAR3 can be used as a potential biomarker.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1728-8) contains supplementary material, which is available to authorized users.

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.

Systematic expression alteration analysis of master reprogramming factor OCT4 and its three pseudogenes in human cancer and their prognostic outcomes

OCT4 is a master transcription factor that regulates the pluripotency of pluripotent stem cells and cancer stem cells along with other factors, including SOX2, KLF4, and C-MYC. Three different transcripts, OCT4A, OCT4B, and OCT4B1, are known to be generated by alternative splicing and eight OCT4 pseudogenes have been found in the human genome. Among them, we examined OCT4 and three pseudogenes (POU5F1P1, POU5F1P3, and POU5F1P4) because of their high expression possibility in cancer. In addition, previous studies indicated that OCT4 expression is augmented in cervical cancer and associated with poor prognosis, whereas OCT4 is down-regulated and correlated with good clinical outcomes in breast cancer. Because of these conflicting reports, we systematically evaluated whether expression of OCT4 and its pseudogenes can serve as oncogenic markers in various human cancers using the Oncomine database. Moreover, copy number alterations and mutations in OCT4 gene and its pseudogenes were analyzed using cBioPortal and the relationship between expression of OCT4 and pseudogenes and survival probability of cancer patients were explored using Kaplan-Meier plotter, OncoLnc, PROGgeneV2, and PrognoScan databases. Multivariate survival analysis was further conducted to determine the risk of the expression of the occurrence of OCT4 and its pseudogenes on certain cancer types using data from the Kaplan-Meier plotter. Overall, an association between expression of OCT4 and pseudogenes and cancer prognosis were established, which may serve as a therapeutic target for various human cancers.

Protein arginine methyltransferase 5 has prognostic relevance and is a druggable target in multiple myeloma

Arginine methyltransferases critically regulate cellular homeostasis by modulating the functional outcome of their substrates. The protein arginine methyltransferase 5 (PRMT5) is an enzyme involved in growth and survival pathways promoting tumorigenesis. However, little is known about the biologic function of PRMT5 and its therapeutic potential in multiple myeloma (MM). In the present study, we identified and validated PRMT5 as a new therapeutic target in MM. PRMT5 is overexpressed in patient MM cells and associated with decreased progression-free survival and overall survival. Either genetic knockdown or pharmacological inhibition of PRMT5 with the inhibitor EPZ015666 significantly inhibited growth of both cell lines and patient MM cells. Furthermore, PRMT5 inhibition abrogated NF-κB signaling. Interestingly, mass spectrometry identified a tripartite motif-containing protein 21 TRIM21 as a new PRMT5-partner, and we delineated a TRIM21-dependent mechanism of NF-κB inhibition. Importantly, oral administration of EPZ015666 significantly decreased MM growth in a humanized murine model of MM. These data both demonstrate the oncogenic role and prognostic relevance of PRMT5 in MM pathogenesis, and provide the rationale for novel therapies targeting PRMT5 to improve patient outcome.

Inhibition of thioredoxin activates mitophagy and overcomes adaptive bortezomib resistance in multiple myeloma

Background

Although current chemotherapy using bortezomib (Velcade) against multiple myeloma in adults has achieved significant responses and even remission, a majority of patients will develop acquired resistance to bortezomib. Increased thioredoxin level has been reported to be associated with carcinogenesis; however, the role of thioredoxin in bortezomib drug resistance of myeloma remains unclear.

Methods

We generated several bortezomib-resistant myeloma cell lines by serially passaging with increased concentrations of bortezomib over a period of 1.5 years. Thioredoxin expression was measured by real-time PCR and western blot.

Results

The role of thioredoxin in the survival of bortezomib-resistant myeloma cells was determined by specific shRNA knockdown in vitro and in vivo. Thioredoxin inhibitor (PX12) was used to determine the effectiveness of thioredoxin inhibition in the treatment of bortezomib-resistant myeloma cells. The effect of thioredoxin inhibition on mitophagy induction was examined. The correlation of thioredoxin expression with patient overall survival was interrogated. Thioredoxin expression was significantly upregulated in bortezomib-resistant myeloma cells and the change correlated with the increase of bortezomib concentration. Thioredoxin gene knockdown using specific shRNA sensitized bortezomib-resistant myeloma cells to bortezomib efficiency in vitro and in vivo. Similarly, pharmacological inhibition with PX12 inhibited the growth of bortezomib-resistant myeloma cells and overcame bortezomib resistance in vitro and in vivo. Furthermore, inhibition of thioredoxin resulted in the activation of mitophagy and blockage of mitophagy prevented the effects of PX12 on bortezomib-resistant myeloma cells, indicating that mitophagy is the important molecular mechanism for the induction of cell death in bortezomib-resistant myeloma cells by PX12. Moreover, inhibition of thioredoxin resulted in downregulation of phosphorylated mTOR and ERK1/2. Finally, thioredoxin was overexpressed in primary myeloma cells isolated from bortezomib-resistant myeloma patients and overexpression of thioredoxin correlated with poor overall survival in patients with multiple myeloma.

Conclusions

Our findings demonstrated that increased thioredoxin plays a critical role in bortezomib resistance in multiple myeloma through mitophagy inactivation and increased mTOR and ERK1/2 phosphorylation. Thioredoxin provides a potential target for clinical therapeutics against multiple myeloma, particularly for bortezomib-resistant/refractory myeloma patients.

Electronic supplementary material

The online version of this article (10.1186/s13045-018-0575-7) contains supplementary material, which is available to authorized users.

The small GTPase RhoU lays downstream of JAK/STAT signaling and mediates cell migration in multiple myeloma

Multiple myeloma is a post-germinal center B-cell neoplasm, characterized by the proliferation of malignant bone marrow plasma cells, whose survival and proliferation is sustained by growth factors and cytokines present in the bone marrow microenvironment. Among them, IL-6 triggers the signal downstream of its receptor, leading to the activation of the JAK/STAT pathway. The atypical GTPase RhoU lays downstream of STAT3 transcription factor and could be responsible for mediating its effects on cytoskeleton dynamics. Here we demonstrate that RHOU is heterogeneously expressed in primary multiple myeloma cells and significantly modulated with disease progression. At the mRNA level, RHOU expression in myeloma patients correlated with the expression of STAT3 and its targets MIR21 and SOCS3. Also, IL-6 stimulation of human myeloma cell lines up-regulated RHOU through STAT3 activation. On the other hand, RhoU silencing led to a decrease in cell migration with the accumulation of actin stress fibers, together with a decrease in cyclin D2 expression and in cell cycle progression. Furthermore, we found that even though lenalidomide positively regulated RhoU expression leading to higher cell migration rates, it actually led to cell cycle arrest probably through a p21 dependent mechanism. Lenalidomide treatment in combination with RhoU silencing determined a loss of cytoskeletal organization inhibiting cell migration, and a further increase in the percentage of cells in a resting phase. These results unravel a role for RhoU not only in regulating the migratory features of malignant plasma cells, but also in controlling cell cycle progression.

Transcription factor MafB may play an important role in secondary hyperparathyroidism

The transcription factor MafB is essential for development of the parathyroid glands, the expression of which persists after morphogenesis and in adult parathyroid glands. However, the function of MafB in adult parathyroid tissue is unclear. To investigate this, we induced chronic kidney disease (CKD) in wild-type and MafB heterozygote (MafB+/-) mice by feeding them an adenine-supplemented diet, leading to secondary hyperparathyroidism. The elevated serum creatinine and blood urea nitrogen levels in heterozygous and wild-type mice fed the adenine-supplemented diet were similar. Interestingly, secondary hyperparathyroidism, characterized by serum parathyroid hormone elevation and enlargement of parathyroid glands, was suppressed in MafB+/- mice fed the adenine-supplemented diet compared to similarly fed wild-type littermates. Quantitative RT-PCR and immunohistochemical analyses showed that the increased expression of parathyroid hormone and cyclin D2 in mice with CKD was suppressed in the parathyroid glands of heterozygous CKD mice. A reporter assay indicated that MafB directly regulated parathyroid hormone and cyclin D2 expression. To exclude an effect of a developmental anomaly in MafB+/- mice, we analyzed MafB tamoxifen-induced global knockout mice. Hypocalcemia-stimulated parathyroid hormone secretion was significantly impaired in MafB knockout mice. RNA-sequencing analysis indicated PTH, Gata3 and Gcm2 depletion in the parathyroid glands of MafB knockout mice. Thus, MafB appears to play an important role in secondary hyperparathyroidism by regulation of parathyroid hormone and cyclin D2 expression. Hence, MafB may represent a new therapeutic target in secondary hyperparathyroidism.

MUC1-C is a target in lenalidomide resistant multiple myeloma

Lenalidomide (LEN) acts directly on multiple myeloma (MM) cells by inducing cereblon-mediated degradation of interferon regulatory factor 4, Ikaros (IKZF)1 and IKZF3, transcription factors that are essential for MM cell survival. The mucin 1 (MUC1) C-terminal transmembrane subunit (MUC1-C) oncoprotein is aberrantly expressed by MM cells and protects against reactive oxygen species (ROS)-mediated MM cell death. The present studies demonstrate that targeting MUC1-C with GO-203, a cell-penetrating peptide inhibitor of MUC1-C homodimerization, is more than additive with LEN in downregulating the WNT/β-catenin pathway, suppressing MYC, and inducing late apoptosis/necrosis. We show that the GO-203/LEN combination acts by synergistically increasing ROS and, in turn, suppressing β-catenin. LEN resistance has been linked to activation of the WNT/β-catenin→CD44 pathway. In this regard, our results further demonstrate that targeting MUC1-C is effective against LEN-resistant MM cells. Moreover, GO-203 resensitized LEN-resistant MM cells to LEN treatment in association with suppression of β-catenin and CD44. Targeting MUC1-C also resulted in downregulation of CD44 on the surface of primary MM cells. These findings, and the demonstration that expression of MUC1 and CD44 significantly correlate in microarrays from primary MM cells, provide support for combining GO-203 with LEN in the treatment of MM and in LEN-resistance.

Proteomic characterization of human multiple myeloma bone marrow extracellular matrix

The extracellular matrix (ECM) is a major component of the tumor microenvironment, contributing to the regulation of cell survival, proliferation, differentiation and metastasis. In multiple myeloma (MM), interactions between MM cells and the bone marrow (BM) microenvironment, including the BM ECM, are critical to the pathogenesis of the disease and the development of drug resistance. Nevertheless, composition of the ECM in MM and its role in supporting MM pathogenesis has not been reported. We have applied a novel proteomic-based strategy and defined the BM ECM composition in patients with monoclonal gammopathy of undetermined significance (MGUS), newly diagnosed and relapsed MM compared with healthy donor-derived BM ECM. In this study, we show that the tumor ECM is remodeled at the mRNA and protein levels in MGUS and MM to allow development of a permissive microenvironment. We further demonstrate that two ECM-affiliated proteins, ANXA2 and LGALS1, are more abundant in MM and high expression is associated with a decreased overall survival. This study points to the importance of ECM remodeling in MM and provides a novel proteomic pipeline for interrogating the role of the ECM in cancers with BM tropism.

A gene expression inflammatory signature specifically predicts multiple myeloma evolution and patients survival

Multiple myeloma (MM) is closely dependent on cross-talk between malignant plasma cells and cellular components of the inflammatory/immunosuppressive bone marrow milieu, which promotes disease progression, drug resistance, neo-angiogenesis, bone destruction and immune-impairment. We investigated the relevance of inflammatory genes in predicting disease evolution and patient survival. A bioinformatics study by Ingenuity Pathway Analysis on gene expression profiling dataset of monoclonal gammopathy of undetermined significance, smoldering and symptomatic-MM, identified inflammatory and cytokine/chemokine pathways as the most progressively affected during disease evolution. We then selected 20 candidate genes involved in B-cell inflammation and we investigated their role in predicting clinical outcome, through univariate and multivariate analyses (log-rank test, logistic regression and Cox-regression model). We defined an 8-genes signature (IL8, IL10, IL17A, CCL3, CCL5, VEGFA, EBI3 and NOS2) identifying each condition (MGUS/smoldering/symptomatic-MM) with 84% accuracy. Moreover, six genes (IFNG, IL2, LTA, CCL2, VEGFA, CCL3) were found independently correlated with patients' survival. Patients whose MM cells expressed high levels of Th1 cytokines (IFNG/LTA/IL2/CCL2) and low levels of CCL3 and VEGFA, experienced the longest survival. On these six genes, we built a prognostic risk score that was validated in three additional independent datasets. In this study, we provide proof-of-concept that inflammation has a critical role in MM patient progression and survival. The inflammatory-gene prognostic signature validated in different datasets clearly indicates novel opportunities for personalized anti-MM treatment.

Targeting the SR-B1 Receptor as a Gateway for Cancer Therapy and Imaging

Malignant tumors display remarkable heterogeneity to the extent that even at the same tissue site different types of cells with varying genetic background may be found. In contrast, a relatively consistent marker the scavenger receptor type B1 (SR-B1) has been found to be consistently overexpressed by most tumor cells. Scavenger Receptor Class B Type I (SR-BI) is a high density lipoprotein (HDL) receptor that facilitates the uptake of cholesterol esters from circulating lipoproteins. Additional findings suggest a critical role for SR-BI in cholesterol metabolism, signaling, motility, and proliferation of cancer cells and thus a potential major impact in carcinogenesis and metastasis. Recent findings indicate that the level of SR-BI expression correlate with aggressiveness and poor survival in breast and prostate cancer. Moreover, genomic data show that depending on the type of cancer, high or low SR-BI expression may promote poor survival. This review discusses the importance of SR-BI as a diagnostic as well as prognostic indicator of cancer to help elucidate the contributions of this protein to cancer development, progression, and survival. In addition, the SR-B1 receptor has been shown to serve as a potential gateway for the delivery of therapeutic agents when reconstituted high density lipoprotein nanoparticles are used for their transport to cancer cells and tumors. Opportunities for the development of new technologies, particularly in the areas of cancer therapy and tumor imaging are discussed.

Proteomic Characterization of the World Trade Center dust-activated mdig and c-myc signaling circuit linked to multiple myeloma

Several epidemiological studies suggested an increased incidence rate of multiple myeloma (MM) among first responders and other individuals who exposed to World Trade Center (WTC) dust. In this report, we provided evidence showing that WTC dust is potent in inducing mdig protein and/or mRNA in bronchial epithelial cells, B cells and MM cell lines. An increased mdig expression in MM bone marrow was observed, which is associated with the disease progression and prognosis of the MM patients. Through integrative genomics and proteomics approaches, we further demonstrated that mdig directly interacts with c-myc and JAK1 in MM cell lines, which contributes to hyperactivation of the IL-6-JAK-STAT3 signaling important for the pathogenesis of MM. Genetic silencing of mdig reduced activity of the major downstream effectors in the IL-6-JAK-STAT3 pathway. Taken together, these data suggest that WTC dust may be one of the key etiological factors for those who had been exposed for the development of MM by activating mdig and c-myc signaling circuit linked to the IL-6-JAK-STAT3 pathway essential for the tumorigenesis of the malignant plasma cells.

UCHL1 is a biomarker of aggressive multiple myeloma required for disease progression

The success of proteasome inhibition in multiple myeloma highlights the critical role for the ubiquitin-proteasome system (UPS) in this disease. However, there has been little progress in finding more specific targets within the UPS involved in myeloma pathogenesis. We previously found the ubiquitin hydrolase UCH-L1 to be frequently over-expressed in B-cell malignancies, including myeloma, and showed it to be a potent oncogene in mice. Here we show that UCH-L1 is a poor prognostic factor that is essential for the progression of myeloma. We found high levels of UCHL1 to predict early progression in newly diagnosed patients; a finding reversed by the inclusion of bortezomib. We also found high UCHL1 levels to be a critical factor in the superiority of bortezomib over high-dose dexamethasone in relapsed patients. High UCHL1 partially overlaps with, but is distinct from, known genetic risks including 4p16 rearrangement and 1q21 amplification. Using an orthotopic mouse model, we found UCH-L1 depletion delays myeloma dissemination and causes regression of established disease. We conclude that UCH-L1 is a biomarker of aggressive myeloma that may be an important marker of bortezomib response, and may itself be an effective target in disseminated disease.

IQGAP1 Scaffold-MAP Kinase Interactions Enhance Multiple Myeloma Clonogenic Growth and Self-Renewal

Despite improved outcomes in newly diagnosed multiple myeloma, virtually all patients relapse and ultimately develop drug-resistant disease. Aberrant RAS/MAPK signaling is activated in the majority of relapsed/refractory multiple myeloma patients, but its biological consequences are not fully understood. Self-renewal, as defined by the long-term maintenance of clonogenic growth, is essential for disease relapse, and we examined the role of RAS/MAPK activation on multiple myeloma self-renewal by targeting IQ motif-containing GTPase-activating protein 1 (IQGAP1), an intracellular scaffold protein required for mutant RAS signaling. We found that loss of IQGAP1 expression decreased MAPK signaling, cell-cycle progression, and tumor colony formation. Similarly, a peptide mimicking the WW domain of IQGAP1 that interacts with ERK inhibited the clonogenic growth and self-renewal of multiple myeloma cell lines and primary clinical specimens in vitro as well as tumor-initiating cell frequency in immunodeficient mice. During multiple myeloma progression, self-renewal may be enhanced by aberrant RAS/MAPK signaling and inhibited by targeting IQGAP1. Mol Cancer Ther; 15(11); 2733-9. ©2016 AACR.

CGDM: collaborative genomic data model for molecular profiling data using NoSQL

MOTIVATION

High-throughput molecular profiling has greatly improved patient stratification and mechanistic understanding of diseases. With the increasing amount of data used in translational medicine studies in recent years, there is a need to improve the performance of data warehouses in terms of data retrieval and statistical processing. Both relational and Key Value models have been used for managing molecular profiling data. Key Value models such as SeqWare have been shown to be particularly advantageous in terms of query processing speed for large datasets. However, more improvement can be achieved, particularly through better indexing techniques of the Key Value models, taking advantage of the types of queries which are specific for the high-throughput molecular profiling data.

RESULTS

In this article, we introduce a Collaborative Genomic Data Model (CGDM), aimed at significantly increasing the query processing speed for the main classes of queries on genomic databases. CGDM creates three Collaborative Global Clustering Index Tables (CGCITs) to solve the velocity and variety issues at the cost of limited extra volume. Several benchmarking experiments were carried out, comparing CGDM implemented on HBase to the traditional SQL data model (TDM) implemented on both HBase and MySQL Cluster, using large publicly available molecular profiling datasets taken from NCBI and HapMap. In the microarray case, CGDM on HBase performed up to 246 times faster than TDM on HBase and 7 times faster than TDM on MySQL Cluster. In single nucleotide polymorphism case, CGDM on HBase outperformed TDM on HBase by up to 351 times and TDM on MySQL Cluster by up to 9 times.

AVAILABILITY AND IMPLEMENTATION

The CGDM source code is available at https://github.com/evanswang/CGDM.

CONTACT

y.guo@imperial.ac.uk.

MUC1-C drives MYC in multiple myeloma

Multiple myeloma (MM) cell lines and primary tumor cells are addicted to the MYC oncoprotein for survival. Little is known, however, about how MYC expression is upregulated in MM cells. The mucin 1 C-terminal subunit (MUC1-C) is an oncogenic transmembrane protein that is aberrantly expressed in MM cell lines and primary tumor samples. The present studies demonstrate that targeting MUC1-C with silencing by clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 editing or with the GO-203 inhibitor is associated with downregulation of MYC messenger RNA and protein. The results show that MUC1-C occupies the MYC promoter and thereby activates the MYC gene by a β-catenin/transcription factor 4 (TCF4)-mediated mechanism. In this way, MUC1-C (1) increases β-catenin occupancy on the MYC promoter, (2) forms a complex with β-catenin and TCF4, and, in turn, (3) drives MYC transcription. Analysis of MM cells using quantitative real-time reverse transcription polymerase chain reaction arrays further demonstrated that silencing MUC1-C is associated with downregulation of MYC target genes, including CCND2, hTERT, and GCLC Analysis of microarray data sets further demonstrated that MUC1 levels positively correlate with MYC expression in MM progression and in primary cells from over 800 MM patients. These findings collectively provide convincing evidence that MUC1-C drives MYC expression in MM.

Four genes predict high risk of progression from smoldering to symptomatic multiple myeloma (SWOG S0120)

Multiple myeloma is preceded by an asymptomatic phase, comprising monoclonal gammopathy of uncertain significance and smoldering myeloma. Compared to the former, smoldering myeloma has a higher and non-uniform rate of progression to clinical myeloma, reflecting a subset of patients with higher risk. We evaluated the gene expression profile of smoldering myeloma plasma cells among 105 patients enrolled in a prospective observational trial at our institution, with a view to identifying a high-risk signature. Baseline clinical, bone marrow, cytogenetic and radiologic data were evaluated for their potential to predict time to therapy for symptomatic myeloma. A gene signature derived from four genes, at an optimal binary cut-point of 9.28, identified 14 patients (13%) with a 2-year therapy risk of 85.7%. Conversely, a low four-gene score (< 9.28) combined with baseline monoclonal protein < 3 g/dL and albumin ≥ 3.5 g/dL identified 61 patients with low-risk smoldering myeloma with a 5.0% chance of progression at 2 years. The top 40 probe sets showed concordance with indices of chromosome instability. These data demonstrate high discriminatory power of a gene-based assay and suggest a role for dysregulation of mitotic checkpoints in the context of genomic instability as a hallmark of high-risk smoldering myeloma.

High dimensional biological data retrieval optimization with NoSQL technology

Background

High-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data.

Results

In this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB.

Conclusions

The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data.

Optimising parallel R correlation matrix calculations on gene expression data using MapReduce

Background

High-throughput molecular profiling data has been used to improve clinical decision making by stratifying subjects based on their molecular profiles. Unsupervised clustering algorithms can be used for stratification purposes. However, the current speed of the clustering algorithms cannot meet the requirement of large-scale molecular data due to poor performance of the correlation matrix calculation. With high-throughput sequencing technologies promising to produce even larger datasets per subject, we expect the performance of the state-of-the-art statistical algorithms to be further impacted unless efforts towards optimisation are carried out. MapReduce is a widely used high performance parallel framework that can solve the problem.

Results

In this paper, we evaluate the current parallel modes for correlation calculation methods and introduce an efficient data distribution and parallel calculation algorithm based on MapReduce to optimise the correlation calculation. We studied the performance of our algorithm using two gene expression benchmarks. In the micro-benchmark, our implementation using MapReduce, based on the R package RHIPE, demonstrates a 3.26-5.83 fold increase compared to the default Snowfall and 1.56-1.64 fold increase compared to the basic RHIPE in the Euclidean, Pearson and Spearman correlations. Though vanilla R and the optimised Snowfall outperforms our optimised RHIPE in the micro-benchmark, they do not scale well with the macro-benchmark. In the macro-benchmark the optimised RHIPE performs 2.03-16.56 times faster than vanilla R. Benefiting from the 3.30-5.13 times faster data preparation, the optimised RHIPE performs 1.22-1.71 times faster than the optimised Snowfall. Both the optimised RHIPE and the optimised Snowfall successfully performs the Kendall correlation with TCGA dataset within 7 hours. Both of them conduct more than 30 times faster than the estimated vanilla R.

Conclusions

The performance evaluation found that the new MapReduce algorithm and its implementation in RHIPE outperforms vanilla R and the conventional parallel algorithms implemented in R Snowfall. We propose that MapReduce framework holds great promise for large molecular data analysis, in particular for high-dimensional genomic data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new algorithm as a basis for optimising high-throughput molecular data correlation calculation for Big Data.

Pyk2 promotes tumor progression in multiple myeloma

Proline-rich tyrosine kinase 2 (Pyk2) is a member of the focal adhesion kinase family that has been recently linked to tumor development. However, its role in modulating multiple myeloma (MM) biology and disease progression remains unexplored. We first demonstrated that patients with MM present with higher expression of Pyk2 compared with healthy individuals. By using loss-of-function approaches, we found that Pyk2 inhibition led to reduction of MM tumor growth in vivo as well as decreased cell proliferation, cell-cycle progression, and adhesion ability in vitro. In turn, overexpression of Pyk2 promoted the malignant phenotype, substantiated by enhanced tumor growth and reduced survival. Mechanistically, inhibition of Pyk2 reduced activation of Wnt/β-catenin signaling by destabilizing β-catenin, leading to downregulation of c-Myc and Cyclin D1. Furthermore, treatment of MM cells with the FAK/Pyk2 inhibitor VS-4718 effectively inhibited MM cell growth both in vitro and in vivo. Collectively, our findings describe the tumor-promoting role of Pyk2 in MM, thus providing molecular evidence for a novel tyrosine kinase inhibitor as a new therapeutic option in MM.

Hypodiploid multiple myeloma is characterized by more aggressive molecular markers than non-hyperdiploid multiple myeloma

Multiple myeloma can be categorized into hyperdiploid or non-hyperdiploid myeloma based on the number of chromosomes found in the tumor clone. Among the non-hyperdiploid myelomas, the hypodiploid subtype has the most aggressive clinical phenotype, but the genetic differences between groups are not completely defined. In order to understand the genetic background of hypodiploid multiple myeloma better, we compared the genomic (array-based comparative genomic hybridization) and transcriptomic (gene expression profiling) background of 49 patients with hypodiploid myeloma with 50 other non-hyperdiploid and 125 hyperdiploid myeloma patients. There were significant chromosomal and gene expression differences between hyperdiploid patients and non-hyperdiploid and hypodiploid patients. Non-hyperdiploid and hypodiploid patients shared most of the chromosomal abnormalities; nevertheless a subset of these abnormalities, such as monosomies 13, 14 and 22, was markedly increased in hypodiploid patients. Furthermore, deletions of 1p, 12p, 16q and 17p, all associated with poor outcome or progression in multiple myeloma, were significantly enriched in hypodiploid patients. Molecular risk-stratification indices reinforce the worse prognosis associated with hypodiploid multiple myeloma compared with non-hyperdiploid multiple myeloma. Gene expression profiling clustered hypodiploid and non-hyperdiploid subgroups closer than hyperdiploid myeloma but also highlighted the up-regulation of CCND2, WHSC1/MMSET and FGFR3 in the hypodiploid subtype. In summary, hypodiploid multiple myeloma is genetically similar to non-hyperdiploid multiple myeloma but characterized by a higher prevalence of genetic alterations associated with poor outcome and disease progression. It is provocative to hypothesize that hypodiploid multiple myeloma is an advanced stage of non-hyperdiploid multiple myeloma.

Correlation between eight-gene expression profiling and response to therapy of newly diagnosed multiple myeloma patients treated with thalidomide-dexamethasone incorporated into double autologous transplantation

We performed a molecular study aimed at identifying a gene expression profile (GEP) signature predictive of attainment of at least near complete response (CR) to thalidomide-dexamethasone (TD) as induction regimen in preparation for double autologous stem cell transplantation in 112 younger patients with newly diagnosed multiple myeloma. A GEP supervised analysis was performed on a training set of 32 patients, allowing to identify 157 probe sets differentially expressed in patients with CR versus those failing CR to TD. We then generated an eight-gene GEP signature whose performance was subsequently validated in a training set of 80 patients. A correct prediction of response to TD was found in 71 % of the cases analyzed. The eight genes were downregulated in patients who achieved CR to TD. Comparisons between post-autotransplantation outcomes of the 44 non-CR-predicted patients and of the 36 CR-predicted patients showed that this latter subgroup had a statistically significant benefit in terms of higher rate of CR after autotransplant(s) and longer time to progression, event-free survival, and overall survival. These results can be an important first step to identify at diagnosis those patients who will respond more favourably to a particular treatment strategy.

Monitoring bortezomib therapy in multiple myeloma: screening of cyclin D1, D2, and D3 via reliable real-time polymerase chain reaction and association with clinico-pathological features and outcome

Cyclins D1, D2, and D3 (CCND1, 2, 3) are regulated by proteasomal degradation. Their overexpression in multiple myeloma (MM) has prognostic value. We performed this pilot study to analyze a possible association between CCND1-3 overexpression and response to treatment with the proteasome inhibitor bortezomib, since a specific prognostic marker for bortezomib response has not been reported, but would be ideal to predict who benefits most from bortezomib in times of several potentially efficient therapeutic options. Bone marrow (BM) specimens of 20/47 consecutive patients were available for reliable CCND1-3 analyses by real-time PCR. With CCND1 overexpression in 9/20 patients, the risk for progression after bortezomib treatment was significantly decreased (HR 0.102, 95% CI 0.021-0.498, p = 0.0048) and progression-free survival substantially prolonged (p = 0.0011). Our study is the first to suggest that overexpressed CCND1 in MM is an independent prognostic marker associated with a more durable response to bortezomib. These preliminary results warrant a larger study.

A small-molecule inhibitor of D-cyclin transactivation displays preclinical efficacy in myeloma and leukemia via phosphoinositide 3-kinase pathway

D-cyclins are universally dysregulated in multiple myeloma and frequently overexpressed in leukemia. To better understand the role and impact of dysregulated D-cyclins in hematologic malignancies, we conducted a high-throughput screen for inhibitors of cyclin D2 transactivation and identified 8-ethoxy-2-(4-fluorophenyl)-3-nitro-2H-chromene (S14161), which inhibited the expression of cyclins D1, D2, and D3 and arrested cells at the G(0)/G(1) phase. After D-cyclin suppression, S14161 induced apoptosis in myeloma and leukemia cell lines and primary patient samples preferentially over normal hematopoietic cells. In mouse models of leukemia, S14161 inhibited tumor growth without evidence of weight loss or gross organ toxicity. Mechanistically, S14161 inhibited the activity of phosphoinositide 3-kinase in intact cells and the activity of the phosphoinositide 3-kinases α, β, δ, and γ in a cell-free enzymatic assay. In contrast, it did not inhibit the enzymatic activities of other related kinases, including the mammalian target of rapamycin, the DNA-dependent protein kinase catalytic subunit, and phosphoinositide-dependent kinase-1. Thus, we identified a novel chemical compound that inhibits D-cyclin transactivation via the phosphoinositide 3-kinase/protein kinase B signaling pathway. Given its potent antileukemia and antimyeloma activity and minimal toxicity, S14161 could be developed as a novel agent for blood cancer therapy.

The molecular characterization and clinical management of multiple myeloma in the post-genome era

Cancer-causing mutations disrupt coordinated, precise programs of gene expression that govern cell growth and differentiation. Microarray-based gene-expression profiling (GEP) is a powerful tool to globally analyze these changes to study cancer biology and clinical behavior. Despite overwhelming genomic chaos in multiple myeloma (MM), expression patterns within tumor samples are remarkably stable and reproducible. Unique expression patterns associated with recurrent chromosomal translocations and ploidy changes defined molecular classes with differing clinical features and outcomes. Combined molecular techniques also dissected two distinct, reproducible forms of hyperdiploid disease and have molecularly defined MM with high risk for poor clinical outcome. GEP is now used to risk-stratify patients with newly diagnosed MM. Groups with high-risk features are evident in all GEP-defined MM classes, and GEP studies of serial samples showed that risk increases over time, with relapsed disease showing dramatic GEP shifts toward a signature of poor outcomes. This suggests a common mechanism of disease evolution and potentially reflects preferential expansion of therapy-resistant cells. Correlating GEP-defined disease class and risk with outcomes of therapeutic regimens reveals class-specific benefits for individual agents, as well as mechanistic insights into drug sensitivity and resistance. Here, we review modern genomics contributions to understanding MM pathogenesis, prognosis, and therapy.

NOTCH is part of the transcriptional network regulating cell growth and survival in mouse plasmacytomas

Aside from Myc-activating translocations characteristic of plasmacytomas (PCT), little is known about genetic factors and signaling pathways responsible for the development of spontaneous B-cell lineage lymphomas of mice. Here, we characterized the transcriptional profiles of PCT, centroblastic diffuse large B-cell lymphomas (CBL), and high-grade splenic marginal zone B-cell lymphoma (MZL++) using high-throughput quantitative reverse transcription-PCR. Expression profiles of CBL and MZL++ were strikingly similar and quite unlike that of PCT. Among the genes expressed at significantly higher levels by PCT were a number involved in NOTCH signaling, a finding supported by gene set enrichment analyses of microarray data. To investigate the importance of this pathway, NOTCH signaling was blocked in PCT cell lines by treatment with a gamma-secretase inhibitor (GSI) or transduction of a dominant-negative mutant of MAML1. These treatments resulted in reduced expression of NOTCH transcriptional targets in association with impaired proliferation and increased apoptosis. GSI treatment of transformed plasma cells in a primary PCT also induced apoptosis. These results integrate NOTCH activation with oncogenic signaling pathways downstream of translocated Myc in the pathogenesis of mouse PCT, two signaling pathways also implicated in development of human multiple myeloma and T-cell lymphoblastic lymphoma.

Cyproheptadine displays preclinical activity in myeloma and leukemia

D-cyclins are regulators of cell division that act in a complex with cyclin-dependent kinases to commit cells to a program of DNA replication. D-cyclins are overexpressed in many tumors, including multiple myeloma and leukemia, and contribute to disease progression and chemoresistance. To better understand the role and impact of D-cyclins in hematologic malignancies, we conducted a high throughput screen for inhibitors of the cyclin D2 promoter and identified the drug cyproheptadine. In myeloma and leukemia cells, cyproheptadine decreased expression of cyclins D1, D2, and D3 and arrested these cells in the G(0)/G(1) phase. After D-cyclin suppression, cyproheptadine induced apoptosis in myeloma and leukemia cell lines and primary patient samples preferentially over normal hematopoietic cells. In mouse models of myeloma and leukemia, cyproheptadine inhibited tumor growth without significant toxicity. Cyproheptadine-induced apoptosis was preceded by activation of the mitochondrial pathway of caspase activation and was independent of the drug's known activity as an H1 histamine and serotonin receptor antagonist. Thus, cyproheptadine represents a lead for a novel therapeutic agent for the treatment of malignancy. Because the drug is well tolerated and already approved in multiple countries for clinical use as an antihistamine and appetite stimulant, it could be moved directly into clinical trials for cancer.

Upregulation of translational machinery and distinct genetic subgroups characterise hyperdiploidy in multiple myeloma

Karyotypic instability, including numerical and structural chromosomal aberrations, represents a distinct feature of multiple myeloma (MM). About 40-50% of patients display hyperdiploidy, defined by recurrent trisomies of non-random chromosomes. To molecularly characterise hyperdiploid (H) and nonhyperdiploid (NH) MM, we analysed the gene expression profiles of 66 primary tumours, and used fluorescence in situ hybridisation to investigate the major chromosomal alterations. The differential expression of 225 genes mainly involved in protein biosynthesis, transcriptional machinery and oxidative phosphorylation distinguished the 28 H-MM from the 38 NH-MM cases. The 204 upregulated genes in H-MM mapped mainly to the chromosomes involved in hyperdiploidy, and the 29% upregulated genes in NH-MM mapped to 16q. The identified transcriptional fingerprint was robustly validated on a publicly available gene expression dataset of 64 MM cases; and the global expression modulation of regions on the chromosomes involved in hyperdiploidy was verified using a self-developed non-parametric statistical method. H-MM could be further divided into two distinct molecular and transcriptional entities, characterised by the presence of trisomy 11 and 1q-extracopies/chromosome 13 deletion respectively. These data reinforce the importance of combining molecular cytogenetics and gene expression profiling to define a genomic framework for the study of MM pathogenesis and clinical management.