Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib

Role of BACH1 in multiple myeloma

OBJECTIVE

Examine Bach1 protein expression in bone marrow biopsy specimens obtained from newly diagnosed multiple myeloma (NDMM) and iron deficiency anemia (IDA) patients. Conduct a thorough analysis to explore the potential connection between Bach1 and the onset as well as treatment response of NDMM.

METHODS

This study investigated Bach1 expression in bone marrow biopsy tissues from NDMM and IDA patients. Immunohistochemical staining and Image-pro Plus software were utilized for quantitatively obtaining the expression level of Bach1 protein. Arrange Bach1 expression levels from high to low, and use its median expression level as the threshold. Samples with Bach1 expression level above the median are categorized as the high-expression group, while those below the median are categorized as the low-expression group. Under this grouping, a detailed discussion was conducted to explore relationship of the Bach1 expression level with the patients' gender, ISS stage, and survival rate based on the Bortezomib (Btz) therapy.

RESULTS

Our experiment indicates that the expression level of Bach1 in NDMM patients is significantly higher than in IDA patients. Furthermore, we discovered that patients in the high-expression group exhibit better prognosis compared to those in the low-expression group after Btz-treatment. Bioinformatics analysis further confirms this conclusion.

CONCLUSION

By categorizing Bach1 expression level as high and low, our study offers a unique perspective on understanding the relationship between Bach1 and NDMM.

Loss of GABARAP mediates resistance to immunogenic chemotherapy in multiple myeloma

ABSTRACT

Immunogenic cell death (ICD) is a form of cell death by which cancer treatments can induce a clinically relevant antitumor immune response in a broad range of cancers. In multiple myeloma (MM), the proteasome inhibitor bortezomib is an ICD inducer and creates durable therapeutic responses in patients. However, eventual relapse and resistance to bortezomib appear inevitable. Here, by integrating patient transcriptomic data with an analysis of calreticulin (CRT) protein interactors, we found that GABA type A receptor-associated protein (GABARAP) is a key player whose loss prevented tumor cell death from being perceived as immunogenic after bortezomib treatment. GABARAP is located on chromosome 17p, which is commonly deleted in patients with high risk MM. GABARAP deletion impaired the exposure of the eat-me signal CRT on the surface of dying MM cells in vitro and in vivo, thus reducing tumor cell phagocytosis by dendritic cells and the subsequent antitumor T-cell response. Low GABARAP was independently associated with shorter survival in patients with MM and reduced tumor immune infiltration. Mechanistically, we found that GABARAP deletion blocked ICD signaling by decreasing autophagy and altering Golgi apparatus morphology, with consequent defects in the downstream vesicular transport of CRT. Conversely, upregulating autophagy using rapamycin restored Golgi morphology, CRT exposure, and ICD signaling in GABARAPKO cells undergoing bortezomib treatment. Therefore, coupling an ICD inducer, such as bortezomib, with an autophagy inducer, such as rapamycin, may improve patient outcomes in MM, in which low GABARAP in the form of del(17p) is common and leads to worse outcomes.

Prioritizing Disease Diagnosis in Neonatal Cohorts through Multivariate Survival Analysis: A Nonparametric Bayesian Approach

Understanding the intricate relationships between diseases is critical for both prevention and recovery. However, there is a lack of suitable methodologies for exploring the precedence relationships within multiple censored time-to-event data, resulting in decreased analytical accuracy. This study introduces the Censored Event Precedence Analysis (CEPA), which is a nonparametric Bayesian approach suitable for understanding the precedence relationships in censored multivariate events. CEPA aims to analyze the precedence relationships between events to predict subsequent occurrences effectively. We applied CEPA to neonatal data from the National Health Insurance Service, identifying the precedence relationships among the seven most commonly diagnosed diseases categorized by the International Classification of Diseases. This analysis revealed a typical diagnostic sequence, starting with respiratory diseases, followed by skin, infectious, digestive, ear, eye, and injury-related diseases. Furthermore, simulation studies were conducted to demonstrate CEPA suitability for censored multivariate datasets compared to traditional models. The performance accuracy reached 76% for uniform distribution and 65% for exponential distribution, showing superior performance in all four tested environments. Therefore, the statistical approach based on CEPA enhances our understanding of disease interrelationships beyond competitive methodologies. By identifying disease precedence with CEPA, we can preempt subsequent disease occurrences and propose a healthcare system based on these relationships.

A novel anoikis-related signature predicts prognosis risk and treatment responsiveness in diffuse large B-cell lymphoma

BACKGROUND

Although anoikis plays a role in cancer metastasis and aggressiveness, it has rarely been reported in diffuse large B cell lymphoma (DLBCL).

METHODS

We obtained RNA sequencing data and matched clinical data from the GEO database. An anoikis-related genes (ARGs)-based risk signature was developed in GSE10846 training cohort and validated in three other cohorts. Additionally, we predicted half-maximal inhibitory concentration (IC50) of drugs based on bioinformatics method and obtained the actual IC50 to some chemotherapy drugs via cytotoxicity assay.

RESULTS

The high-risk group, as determined by our signature, was associated with worse prognosis and an immunosuppressive environment in DLBCL. Meanwhile, the nomogram based on eight variables had more accurate ability in forecasting the prognosis than the international prognostic index in DLBCL. The prediction of IC50 indicated that DLBCL patients in the high-risk group were more sensitive to doxorubicin, IPA-3, lenalidomide, gemcitabine, and CEP.701, while patients in the low-risk group were sensitive to cisplatin and dasatinib. Consistent with the prediction, cytotoxicity assay suggested the higher sensitivity to doxorubicin and gemcitabine and the lower sensitivity to dasatinib in the high-risk group in DLBCL.

CONCLUSION

The ARG-based signature may provide a promising direction for prognosis prediction and treatment optimization for DLBCL patients.

PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses

Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC (Pathway-Based Attention Convolution neural network), which integrates a deep learning framework and attention mechanism to address the complex biological pathway information, thereby provide a biology function-based robust drug responsiveness prediction model. PBAC has four layers: gene-pathway layer, attention layer, convolution layer and fully connected layer. PBAC improves the performance of predicting drug responsiveness by focusing on important pathways, helping us understand the mechanism of drug action in diseases. We validated the PBAC model using data from four chemotherapy drugs (Bortezomib, Cisplatin, Docetaxel and Paclitaxel) and 11 immunotherapy datasets. In the majority of datasets, PBAC exhibits superior performance compared to traditional machine learning methods and other research approaches (area under curve = 0.81, the area under the precision-recall curve = 0.73). Using PBAC attention layer output, we identified some pathways as potential core cancer regulators, providing good interpretability for drug treatment prediction. In summary, we presented PBAC, a powerful tool to predict drug responsiveness based on the biology pathway information and explore the potential cancer-driving pathways.

Transient loss of Polycomb components induces an epigenetic cancer fate

Although cancer initiation and progression are generally associated with the accumulation of somatic mutations1,2, substantial epigenomic alterations underlie many aspects of tumorigenesis and cancer susceptibility3-6, suggesting that genetic mechanisms might not be the only drivers of malignant transformation7. However, whether purely non-genetic mechanisms are sufficient to initiate tumorigenesis irrespective of mutations has been unknown. Here, we show that a transient perturbation of transcriptional silencing mediated by Polycomb group proteins is sufficient to induce an irreversible switch to a cancer cell fate in Drosophila. This is linked to the irreversible derepression of genes that can drive tumorigenesis, including members of the JAK-STAT signalling pathway and zfh1, the fly homologue of the ZEB1 oncogene, whose aberrant activation is required for Polycomb perturbation-induced tumorigenesis. These data show that a reversible depletion of Polycomb proteins can induce cancer in the absence of driver mutations, suggesting that tumours can emerge through epigenetic dysregulation leading to inheritance of altered cell fates.

Proteasome Inhibitors in Multiple Myeloma: Biological Insights on Mechanisms of Action or Resistance Informed by Functional Genomics

During the last 20 years, proteasome inhibitors have been a cornerstone for the therapeutic management of multiple myeloma (MM). This review highlights how MM research has evolved over time in terms of our understanding of the mechanistic basis for the pronounced clinical activity of proteasome inhibitors in MM, compared with the limited clinical applications of this drug class outside the setting of plasma cell dyscrasias.

Enhancing prognostic power in multiple myeloma using a plasma cell signature derived from single-cell RNA sequencing

Multiple myeloma (MM) is a heterogenous plasma cell malignancy, for which the established prognostic models exhibit limitations in capturing the full spectrum of outcome variability. Leveraging single-cell RNA-sequencing data, we developed a novel plasma cell gene signature. We evaluated and validated the associations of the resulting plasma cell malignancy (PBM) score with disease state, progression and clinical outcomes using data from five independent myeloma studies consisting of 2115 samples (1978 MM, 65 monoclonal gammopathy of undetermined significance, 35 smoldering MM, and 37 healthy controls). Overall, a higher PBM score was significantly associated with a more advanced stage within the spectrum of plasma cell dyscrasias (all p < 0.05) and a shorter overall survival in MM (hazard ratio, HR = 1.72; p < 0.001). Notably, the prognostic effect of the PBM score was independent of the International Staging System (ISS) and Revised ISS (R-ISS). The downstream analysis further linked higher PBM scores with the presence of cytogenetic abnormalities, TP53 mutations, and compositional changes in the myeloma tumor immune microenvironment. Our integrated analyses suggest the PBM score may provide an opportunity for refining risk stratification and guide decisions on therapeutic approaches to MM.

ClinicalOmicsDB: exploring molecular associations of oncology drug responses in clinical trials

Matching patients to optimal treatment is challenging, in part due to the limited availability of real-world clinical datasets for predictive biomarker identification. The growing integration of omics profiling into clinical trials presents a new opportunity to tackle this challenge. Here, we introduce ClinicalOmicsDB, a web application for exploring molecular associations of oncology drug responses in clinical trials. This database includes transcriptomic data from 40 clinical trial studies, with 5913 patients spanning 11 cancer types. These studies include 67 treatment arms with a variety of chemotherapy, targeted therapy and immunotherapy drugs, and their combinations, which we organize based on an established ontology for easier navigation. The web application provides users with three options to explore molecular associations of oncology drug responses, focusing on studies, treatments or genes, respectively. Gene set analysis further connects treatment response to pathway activity and tumor microenvironment attributes. The user-friendly web interface of ClinicalOmicsDB streamlines interactive analysis. A Rust-based backend speeds up response time, and application programming interfaces and an R package enable programmatic access. We use three case studies to demonstrate the utility of this resource in human cancer studies. ClinicalOmicsDB is freely available at http://trials.linkedomics.org/.

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.

Identification of a novel ferroptosis-related gene signature associated with retinal degeneration induced by light damage in mice

Background

Neurodegenerative retinal diseases such as retinitis pigmentosa are serious disorders that may cause irreversible visual impairment. Ferroptosis is a novel type of programmed cell death, and the involvement of ferroptosis in retinal degeneration is still unclear. This study aimed to investigate the related ferroptosis genes in a mice model of retinal degeneration induced by light damage.

Methods

A public dataset of GSE10528 deriving from the Gene Expression Omnibus database was analyzed to identify the differentially expressed genes (DEGs). Gene set enrichment analysis between light damage and control group was conducted. The differentially expressed ferroptosis-related genes (DE-FRGs) were subsequently identified by intersecting the DEGs with a ferroptosis genes dataset retrieved from the FerrDb database. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were further performed using the DE-FRGs. A protein-protein interaction (PPI) network was constructed to identify hub ferroptosis-related genes (HFRGs). The microRNAs (miRNAs)-HFRGs, transcription factors (TFs)-HFRGs networks as well as target drugs potentially interacting with HFRGs were analyzed utilizing bioinformatics algorithms.

Results

A total of 932 DEGs were identified between the light damage and control group. Among these, 25 genes were associated with ferroptosis. GO and KEGG analyses revealed that these DE-FRGs were mainly enriched in apoptotic signaling pathway, response to oxidative stress and autophagy, ferroptosis, necroptosis and cytosolic DNA-sensing pathway. Through PPI network analysis, six hub ferroptosis-related genes (Jun, Stat3, Hmox1, Atf3, Hspa5 and Ripk1) were ultimately identified. All of them were upregulated in light damage retinas, as verified by the GSE146176 dataset. Bioinformatics analyses predicated that 116 miRNAs, 23 TFs and several potential therapeutic compounds might interact with the identified HFRGs.

Conclusion

Our study may provide novel potential biomarkers, therapeutic targets and new insights into the ferroptosis landscape in retinal neurodegenerative diseases.

CTIVA: Censored time interval variable analysis

Traditionally, datasets with multiple censored time-to-events have not been utilized in multivariate analysis because of their high level of complexity. In this paper, we propose the Censored Time Interval Analysis (CTIVA) method to address this issue. It estimates the joint probability distribution of actual event times in the censored dataset by implementing a statistical probability density estimation technique on the dataset. Based on the acquired event time, CTIVA investigates variables correlated with the interval time of events via statistical tests. The proposed method handles both categorical and continuous variables simultaneously-thus, it is suitable for application on real-world censored time-to-event datasets, which include both categorical and continuous variables. CTIVA outperforms traditional censored time-to-event data handling methods by 5% on simulation data. The average area under the curve (AUC) of the proposed method on the simulation dataset exceeds 0.9 under various conditions. Further, CTIVA yields novel results on National Sample Cohort Demo (NSCD) and proteasome inhibitor bortezomib dataset, a real-world censored time-to-event dataset of medical history of beneficiaries provided by the National Health Insurance Sharing Service (NHISS) and National Center for Biotechnology Information (NCBI). We believe that the development of CTIVA is a milestone in the investigation of variables correlated with interval time of events in presence of censoring.

RNA-sequencing based first choice of treatment and determination of risk in multiple myeloma

Background

Immunotherapeutic targets in multiple myeloma (MM) have variable expression height and are partly expressed in subfractions of patients only. With increasing numbers of available compounds, strategies for appropriate choice of targets (combinations) are warranted. Simultaneously, risk assessment is advisable as patient's life expectancy varies between months and decades.

Methods

We first assess feasibility of RNA-sequencing in a multicenter trial (GMMG-MM5, n=604 patients). Next, we use a clinical routine cohort of untreated symptomatic myeloma patients undergoing autologous stem cell transplantation (n=535, median follow-up (FU) 64 months) to perform RNA-sequencing, gene expression profiling (GEP), and iFISH by ten-probe panel on CD138-purified malignant plasma cells. We subsequently compare target expression to plasma cell precursors, MGUS (n=59), asymptomatic (n=142) and relapsed (n=69) myeloma patients, myeloma cell lines (n=26), and between longitudinal samples (MM vs. relapsed MM). Data are validated using the independent MMRF CoMMpass-cohort (n=767, FU 31 months).

Results

RNA-sequencing is feasible in 90.8% of patients (GMMG-MM5). Actionable immune-oncological targets (n=19) can be divided in those expressed in all normal and >99% of MM-patients (CD38, SLAMF7, BCMA, GPRC5D, FCRH5, TACI, CD74, CD44, CD37, CD79B), those with expression loss in subfractions of MM-patients (BAFF-R [81.3%], CD19 [57.9%], CD20 [82.8%], CD22 [28.4%]), aberrantly expressed in MM (NY-ESO1/2 [12%], MUC1 [12.7%], CD30 [4.9%], mutated BRAF V600E/K [2.1%]), and resistance-conveying target-mutations e.g., against part but not all BCMA-directed treatments. Risk is assessable regarding proliferation, translated GEP- (UAMS70-, SKY92-, RS-score) and de novo (LfM-HRS) defined risk scores. LfM-HRS delineates three groups of 40%, 38%, and 22% of patients with 5-year and 12-year survival rates of 84% (49%), 67% (18%), and 32% (0%). R-ISS and RNA-sequencing identify partially overlapping patient populations, with R-ISS missing, e.g., 30% (22/72) of highly proliferative myeloma.

Conclusion

RNA-sequencing based assessment of risk and targets for first choice treatment is possible in clinical routine.

Immunomodulatory Effect of Proteasome Inhibitors via the Induction of Immunogenic Cell Death in Myeloma Cells

Several anti-cancer drugs are known to have immunomodulatory effects, including immunogenic cell death (ICD) of cancer cells. ICD is a form of apoptosis which is caused by the release of damage-associated molecular patterns (DAMPs), the uptake of cancer antigens by dendritic cells, and the activation of acquired immunity against cancer cells. ICD was originally reported in solid tumors, and there have been few reports on ICD in multiple myeloma (MM). Here, we showed that proteasome inhibitors, including carfilzomib, induce ICD in myeloma cells via an unfolded protein response pathway distinct from that in solid tumors. Additionally, we demonstrated the potential impact of ICD on the survival of patients with myeloma. ICD induced by proteasome inhibitors is expected to improve the prognosis of MM patients not only by its cytotoxic effects, but also by building strong immune memory response against MM cells in combination with other therapies, such as chimeric antigen receptor-T cell therapy.

GPER1 Activation Exerts Anti-Tumor Activity in Multiple Myeloma

G protein-coupled estrogen receptor 1 (GPER1) activation is emerging as a promising therapeutic strategy against several cancer types. While GPER targeting has been widely studied in the context of solid tumors, its effect on hematological malignancies remains to be fully understood. Here, we show that GPER1 mRNA is down-regulated in plasma cells from overt multiple myeloma (MM) and plasma cell leukemia patients as compared to normal donors or pre-malignant conditions (monoclonal gammopathy of undetermined significance and smoldering MM); moreover, lower GPER1 expression associates with worse overall survival of MM patients. Using the clinically applicable GPER1-selective agonist G-1, we demonstrate that the pharmacological activation of GPER1 triggered in vitro anti-MM activity through apoptosis induction, also overcoming the protective effects exerted by bone marrow stromal cells. Noteworthy, G-1 treatment reduced in vivo MM growth in two distinct xenograft models, even bearing bortezomib-resistant MM cells. Mechanistically, G-1 upregulated the miR-29b oncosuppressive network, blunting an established miR-29b-Sp1 feedback loop operative in MM cells. Overall, this study highlights the druggability of GPER1 in MM, providing the first preclinical framework for further development of GPER1 agonists to treat this malignancy.

Deep learning of 2D-Restructured gene expression representations for improved low-sample therapeutic response prediction

Clinical outcome prediction is important for stratified therapeutics. Machine learning (ML) and deep learning (DL) methods facilitate therapeutic response prediction from transcriptomic profiles of cells and clinical samples. Clinical transcriptomic DL is challenged by the low-sample sizes (34-286 subjects), high-dimensionality (up to 21,653 genes) and unordered nature of clinical transcriptomic data. The established methods rely on ML algorithms at accuracy levels of 0.6-0.8 AUC/ACC values. Low-sample DL algorithms are needed for enhanced prediction capability. Here, an unsupervised manifold-guided algorithm was employed for restructuring transcriptomic data into ordered image-like 2D-representations, followed by efficient DL of these 2D-representations with deep ConvNets. Our DL models significantly outperformed the state-of-the-art (SOTA) ML models on 82% of 17 low-sample benchmark datasets (53% with >0.05 AUC/ACC improvement). They are more robust than the SOTA models in cross-cohort prediction tasks, and in identifying robust biomarkers and response-dependent variational patterns consistent with experimental indications.

Shutting off the fuel supply to target metabolic vulnerabilities in multiple myeloma

Pathways that govern cellular bioenergetics are deregulated in tumor cells and represent a hallmark of cancer. Tumor cells have the capacity to reprogram pathways that control nutrient acquisition, anabolism and catabolism to enhance their growth and survival. Tumorigenesis requires the autonomous reprogramming of key metabolic pathways that obtain, generate and produce metabolites from a nutrient-deprived tumor microenvironment to meet the increased bioenergetic demands of cancer cells. Intra- and extracellular factors also have a profound effect on gene expression to drive metabolic pathway reprogramming in not only cancer cells but also surrounding cell types that contribute to anti-tumor immunity. Despite a vast amount of genetic and histologic heterogeneity within and between cancer types, a finite set of pathways are commonly deregulated to support anabolism, catabolism and redox balance. Multiple myeloma (MM) is the second most common hematologic malignancy in adults and remains incurable in the vast majority of patients. Genetic events and the hypoxic bone marrow milieu deregulate glycolysis, glutaminolysis and fatty acid synthesis in MM cells to promote their proliferation, survival, metastasis, drug resistance and evasion of immunosurveillance. Here, we discuss mechanisms that disrupt metabolic pathways in MM cells to support the development of therapeutic resistance and thwart the effects of anti-myeloma immunity. A better understanding of the events that reprogram metabolism in myeloma and immune cells may reveal unforeseen vulnerabilities and advance the rational design of drug cocktails that improve patient survival.

A novel glycolysis-related gene signature for predicting the prognosis of multiple myeloma

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM's great heterogeneity and incurability, risk assessment and treatment choices are still difficult. Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes. Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study. Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients.

Mass Cytometry reveals unique phenotypic patterns associated with subclonal diversity and outcomes in multiple myeloma

Multiple myeloma (MM) remains an incurable plasma cell (PC) malignancy. Although it is known that MM tumor cells display extensive intratumoral genetic heterogeneity, an integrated map of the tumor proteomic landscape has not been comprehensively evaluated. We evaluated 49 primary tumor samples from newly diagnosed or relapsed/refractory MM patients by mass cytometry (CyTOF) using 34 antibody targets to characterize the integrated landscape of single-cell cell surface and intracellular signaling proteins. We identified 13 phenotypic meta-clusters across all samples. The abundance of each phenotypic meta-cluster was compared to patient age, sex, treatment response, tumor genetic abnormalities and overall survival. Relative abundance of several of these phenotypic meta-clusters were associated with disease subtypes and clinical behavior. Increased abundance of phenotypic meta-cluster 1, characterized by elevated CD45 and reduced BCL-2 expression, was significantly associated with a favorable treatment response and improved overall survival independent of tumor genetic abnormalities or patient demographic variables. We validated this association using an unrelated gene expression dataset. This study represents the first, large-scale, single-cell protein atlas of primary MM tumors and demonstrates that subclonal protein profiling may be an important determinant of clinical behavior and outcome.

FAM46C-mediated tumor heterogeneity predicts extramedullary metastasis and poorer survival in multiple myeloma

Cancers originate from a single cell according to Nowell's theory of clonal evolution. The enrichment of the most aggressive clones has been developed and the heterogeneity arises for genomic instability and environmental selection. Multiple myeloma (MM) is a multiple relapse plasma cell cancer generated from bone marrow. Although there were accumulating researches in multiple myeloma pathogenesis, the heterogeneity remains poorly understood. The participants enrolled in this study were 4 EMP+ (EMP, Extramedullary plasmacytoma) and 2 EMP- primarily untreated MM patients. Single cell RNA sequencing and analysis were conducted for the single cell suspension, which was sorted by flow cytometry from peripheral blood mononuclear cells or bone marrow cells. In our research, the results of single cell RNA sequencing show that FAM46C determines MM tumor heterogeneity predicting extramedullary metastasis by influencing RNA stability. Further, we integrated and analyzed 2280 multiple myeloma samples from 7 independent datasets, which uncover that FAM46C mediated tumor heterogeneity predicts poorer survival in multiple myeloma.

A Machine Learning Model to Predict Survival and Therapeutic Responses in Multiple Myeloma

Multiple myeloma (MM) is a highly heterogeneous hematologic tumor. Ubiquitin proteasome pathways (UPP) play a vital role in its initiation and development. We used cox regression analysis and least absolute shrinkage and selector operation (LASSO) to select ubiquitin proteasome pathway associated genes (UPPGs) correlated with the overall survival (OS) of MM patients in a Gene Expression Omnibus (GEO) dataset, and we formed this into ubiquitin proteasome pathway risk score (UPPRS). The association between clinical outcomes and responses triggered by proteasome inhibitors (PIs) and UPPRS were evaluated. MMRF CoMMpass was used for validation. We applied machine learning algorithms to MM clinical and UPPRS in the whole cohort to make a prognostic nomogram. Single-cell data and vitro experiments were performed to unravel the mechanism and functions of UPPRS. UPPRS consisting of 9 genes showed a strong ability to predict OS in MM patients. Additionally, UPPRS can be used to sort out the patients who would gain more benefits from PIs. A machine learning model incorporating UPPRS and International Staging System (ISS) improved survival prediction in both datasets compared to the revisions of ISS. At the single-cell level, high-risk UPPRS myeloma cells exhibited increased cell adhesion. Targeted UPPGs effectively inhibited myeloma cells in vitro. The UPP genes risk score is a helpful tool for risk stratification in MM patients, particularly those treated with PIs.

Unraveling the Role of Peroxisome Proliferator-Activated Receptor β/Δ (PPAR β/Δ) in Angiogenesis Associated with Multiple Myeloma

Growing evidence suggests a role for peroxisome proliferator-activated receptor β/δ (PPAR β/δ) in the angiogenesis, growth, and metastasis of solid tumors, but little is known about its role in multiple myeloma (MM). Angiogenesis in the bone marrow (BM) is characteristic of disease transition from monoclonal gammopathy of undetermined significance (MGUS) to MM. We examined the expression and function of PPAR β/δ in endothelial cells (EC) from the BM of MGUS (MGEC) and MM (MMEC) patients and showed that PPAR β/δ was expressed at higher levels in MMEC than in MGEC and that the overexpression depended on myeloma plasma cells. The interaction between myeloma plasma cells and MMEC promoted the release of the PPAR β/δ ligand prostaglandin I2 (PGI2) by MMEC, leading to the activation of PPAR β/δ. We also demonstrated that PPAR β/δ was a strong stimulator of angiogenesis in vitro and that PPAR β/δ inhibition by a specific antagonist greatly impaired the angiogenic functions of MMEC. These findings define PGI2-PPAR β/δ signaling in EC as a potential target of anti-angiogenic therapy. They also sustain the use of PPAR β/δ inhibitors in association with conventional drugs as a new therapeutic approach in MM.

Targeting the fatty acid binding proteins disrupts multiple myeloma cell cycle progression and MYC signaling

Multiple myeloma is an incurable plasma cell malignancy with only a 53% 5-year survival rate. There is a critical need to find new multiple myeloma vulnerabilities and therapeutic avenues. Herein, we identified and explored a novel multiple myeloma target: the fatty acid binding protein (FABP) family. In our work, myeloma cells were treated with FABP inhibitors (BMS3094013 and SBFI-26) and examined in vivo and in vitro for cell cycle state, proliferation, apoptosis, mitochondrial membrane potential, cellular metabolism (oxygen consumption rates and fatty acid oxidation), and DNA methylation properties. Myeloma cell responses to BMS309403, SBFI-26, or both, were also assessed with RNA sequencing (RNA-Seq) and proteomic analysis, and confirmed with western blotting and qRT-PCR. Myeloma cell dependency on FABPs was assessed using the Cancer Dependency Map (DepMap). Finally, MM patient datasets (CoMMpass and GEO) were mined for FABP expression correlations with clinical outcomes. We found that myeloma cells treated with FABPi or with FABP5 knockout (generated via CRISPR/Cas9 editing) exhibited diminished proliferation, increased apoptosis, and metabolic changes in vitro. FABPi had mixed results in vivo, in two pre-clinical MM mouse models, suggesting optimization of in vivo delivery, dosing, or type of FABP inhibitors will be needed before clinical applicability. FABPi negatively impacted mitochondrial respiration and reduced expression of MYC and other key signaling pathways in MM cells in vitro. Clinical data demonstrated worse overall and progression-free survival in patients with high FABP5 expression in tumor cells. Overall, this study establishes the FABP family as a potentially new target in multiple myeloma. In MM cells, FABPs have a multitude of actions and cellular roles that result in the support of myeloma progression. Further research into the FABP family in MM is warrented, especially into the effective translation of targeting these in vivo.

A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications

Drug response prediction is an important problem in personalized cancer therapy. Among various newly developed models, significant improvement in prediction performance has been reported using deep learning methods. However, systematic comparisons of deep learning methods, especially of the transferability from preclinical models to clinical cohorts, are currently lacking. To provide a more rigorous assessment, the performance of six representative deep learning methods for drug response prediction using nine evaluation metrics, including the overall prediction accuracy, predictability of each drug, potential associated factors and transferability to clinical cohorts, in multiple application scenarios was benchmarked. Most methods show promising prediction within cell line datasets, and TGSA, with its lower time cost and better performance, is recommended. Although the performance metrics decrease when applying models trained on cell lines to patients, a certain amount of power to distinguish clinical response on some drugs can be maintained using CRDNN and TGSA. With these assessments, we provide a guidance for researchers to choose appropriate methods, as well as insights into future directions for the development of more effective methods in clinical scenarios.

Proteasome Inhibitors Silence Oncogenes in Multiple Myeloma through Localized Histone Deacetylase 3 (HDAC3) Stabilization and Chromatin Condensation

Proteasome inhibitors have become the standard of care for multiple myeloma (MM). Blocking protein degradation particularly perturbs the homeostasis of short-lived polypeptides such as transcription factors and epigenetic regulators. To determine how proteasome inhibitors directly impact gene regulation, we performed an integrative genomics study in MM cells. We discovered that proteasome inhibitors reduce the turnover of DNA-associated proteins and repress genes necessary for proliferation through epigenetic silencing. Specifically, proteasome inhibition results in the localized accumulation of histone deacetylase 3 (HDAC3) at defined genomic sites, which reduces H3K27 acetylation and increases chromatin condensation. The loss of active chromatin at super-enhancers critical for MM, including the super-enhancer controlling the proto-oncogene c-MYC, reduces metabolic activity and cancer cell growth. Epigenetic silencing is attenuated by HDAC3 depletion, suggesting a tumor-suppressive element of this deacetylase in the context of proteasome inhibition. In the absence of treatment, HDAC3 is continuously removed from DNA by the ubiquitin ligase SIAH2. Overexpression of SIAH2 increases H3K27 acetylation at c-MYC-controlled genes, increases metabolic output, and accelerates cancer cell proliferation. Our studies indicate a novel therapeutic function of proteasome inhibitors in MM by reshaping the epigenetic landscape in an HDAC3-dependent manner. As a result, blocking the proteasome effectively antagonizes c-MYC and the genes controlled by this proto-oncogene.

The OTUD6B-LIN28B-MYC axis determines the proliferative state in multiple myeloma

Deubiquitylases (DUBs) are therapeutically amenable components of the ubiquitin machinery that stabilize substrate proteins. Their inhibition can destabilize oncoproteins that may otherwise be undruggable. Here, we screened for DUB vulnerabilities in multiple myeloma, an incurable malignancy with dependency on the ubiquitin proteasome system and identified OTUD6B as an oncogene that drives the G1/S‐transition. LIN28B, a suppressor of microRNA biogenesis, is specified as a bona fide cell cycle‐specific substrate of OTUD6B. Stabilization of LIN28B drives MYC expression at G1/S, which in turn allows for rapid S‐phase entry. Silencing OTUD6B or LIN28B inhibits multiple myeloma outgrowth in vivo and high OTUD6B expression evolves in patients that progress to symptomatic multiple myeloma and results in an adverse outcome of the disease. Thus, we link proteolytic ubiquitylation with post‐transcriptional regulation and nominate OTUD6B as a potential mediator of the MGUS‐multiple myeloma transition, a central regulator of MYC, and an actionable vulnerability in multiple myeloma and other tumors with an activated OTUD6B‐LIN28B axis.

Allosteric HSP70 inhibitors perturb mitochondrial proteostasis and overcome proteasome inhibitor resistance in multiple myeloma

Proteasome inhibitor (PI) resistance remains a central challenge in multiple myeloma. To identify pathways mediating resistance, we first mapped proteasome-associated genetic co-dependencies. We identified heat shock protein 70 (HSP70) chaperones as potential targets, consistent with proposed mechanisms of myeloma cells overcoming PI-induced stress. We therefore explored allosteric HSP70 inhibitors (JG compounds) as myeloma therapeutics. JG compounds exhibited increased efficacy against acquired and intrinsic PI-resistant myeloma models, unlike HSP90 inhibition. Shotgun and pulsed SILAC mass spectrometry demonstrated that JGs unexpectedly impact myeloma proteostasis by destabilizing the 55S mitoribosome. Our data suggest JGs have the most pronounced anti-myeloma effect not through inhibiting cytosolic HSP70 proteins but instead through mitochondrial-localized HSP70, HSPA9/mortalin. Analysis of myeloma patient data further supports strong effects of global proteostasis capacity, and particularly HSPA9 expression, on PI response. Our results characterize myeloma proteostasis networks under therapeutic pressure while motivating further investigation of HSPA9 as a specific vulnerability in PI-resistant disease.

The Concilium of Information Processing Networks of Chemical Oscillators for Determining Drug Response in Patients With Multiple Myeloma

It can be expected that medical treatments in the future will be individually tailored for each patient. Here we present a step towards personally addressed drug therapy. We consider multiple myeloma treatment with drugs: bortezomib and dexamethasone. It has been observed that these drugs are effective for some patients and do not help others. We describe a network of chemical oscillators that can help to differentiate between non-responsive and responsive patients. In our numerical simulations, we consider a network of 3 interacting oscillators described with the Oregonator model. The input information is the gene expression value for one of 15 genes measured for patients with multiple myeloma. The single-gene networks optimized on a training set containing outcomes of 239 therapies, 169 using bortezomib and 70 using dexamethasone, show up to 71% accuracy in differentiating between non-responsive and responsive patients. If the results of single-gene networks are combined into the concilium with the majority voting strategy, then the accuracy of predicting the patient’s response to the therapy increases to ∼ 85%.

Identification of a drug-response gene in multiple myeloma through longitudinal single-cell transcriptome sequencing

Despite recent therapeutic advances for multiple myeloma (MM), relapse is very common. Here, we conducted longitudinal single-cell transcriptome sequencing (scRNA-seq) of MM cells from a patient with relapsed MM, treated with multiple anti-myeloma drugs. We observed five subclusters of MM cells, which appeared and/or disappeared in response to the therapeutic pressure, and identified cluster 3 which emerged during lenalidomide treatment and disappeared after proteasome inhibitor (PI) treatment. Among the differentially expressed genes in cluster 3, we found a candidate drug-response gene; pellino E3 ubiquitin-protein ligase family member 2 (PELI2), which is responsible for PI-induced cell death in in vitro assay. Kaplan-Meier survival analysis of database revealed that higher expression of PELI2 is associated with a better prognosis. Our integrated strategy combining longitudinal scRNA-seq analysis, in vitro functional assay, and database analysis would facilitate the understanding of clonal dynamics of MM in response to anti-myeloma drugs and identification of drug-response genes.

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Longitudinal scRNA-seq reveals clonal dynamics of MM under therapeutic pressure

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PELI2 is identified as a candidate proteasome inhibitors (PI)-sensitive gene from the PI-sensitive cluster

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Overexpression of PELI2 sensitizes PI to an MM cell line in the cytotoxic assay

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In database analysis, high expression of PELI2 is associated with a better prognosis

Inhibition of the Protein Arginine Methyltransferase PRMT5 in High-Risk Multiple Myeloma as a Novel Treatment Approach

Multiple myeloma (MM) is an incurable clonal plasma cell malignancy. Subsets of patients have high-risk features linked with dismal outcome. Therefore, the need for effective therapeutic options remains high. Here, we used bio-informatic tools to identify novel targets involved in DNA repair and epigenetics and which are associated with high-risk myeloma. The prognostic significance of the target genes was analyzed using publicly available gene expression data of MM patients (TT2/3 and HM cohorts). Hence, protein arginine methyltransferase 5 (PRMT5) was identified as a promising target. Druggability was assessed in OPM2, JJN3, AMO1 and XG7 human myeloma cell lines using the PRMT5-inhibitor EPZ015938. EPZ015938 strongly reduced the total symmetric-dimethyl arginine levels in all cell lines and lead to decreased cellular growth, supported by cell line dependent changes in cell cycle distribution. At later time points, apoptosis occurred, as evidenced by increased AnnexinV-positivity and cleavage of PARP and caspases. Transcriptome analysis revealed a role for PRMT5 in regulating alternative splicing, nonsense-mediated decay, DNA repair and PI3K/mTOR-signaling, irrespective of the cell line type. PRMT5 inhibition reduced the expression of upstream DNA repair kinases ATM and ATR, which may in part explain our observation that EPZ015938 and the DNA-alkylating agent, melphalan, have combinatory effects. Of interest, using a low-dose of mTOR-inhibitor, we observed that cell viability was partially rescued from the effects of EPZ015938, indicating a role for mTOR-related pathways in the anti-myeloma activity of EPZ015938. Moreover, PRMT5 was shown to be involved in splicing regulation of MMSET and SLAMF7, known genes of importance in MM disease. As such, we broaden the understanding of the exact role of PRMT5 in MM disease and further underline its use as a possible therapeutic target.

Metabolic Vulnerabilities in Multiple Myeloma

Multiple myeloma (MM) remains an incurable malignancy with eventual emergence of refractory disease. Metabolic shifts, which ensure the availability of sufficient energy to support hyperproliferation of malignant cells, are a hallmark of cancer. Deregulated metabolic pathways have implications for the tumor microenvironment, immune cell function, prognostic significance in MM and anti-myeloma drug resistance. Herein, we summarize recent findings on metabolic abnormalities in MM and clinical implications driven by metabolism that may consequently inspire novel therapeutic interventions. We highlight some future perspectives on metabolism in MM and propose potential targets that might revolutionize the field.

Increased Expression of SETDB1 Predicts Poor Prognosis in Multiple Myeloma

Several genes on chromosome 1q21 region predict a high risk of multiple myeloma (MM); however, the underlying molecular pathology remains elusive. Overexpression, amplification, or activation of SET Domain Bifurcated 1 (SETDB1), which is located on 1q21, is closely associated with poor prognosis of many human solid malignancies. In our study, upregulation of SETDB1 might indicate an unfavorable prognosis of MM using bioinformatics analysis from GEO databases and MMRF-CoMMpass. Here, increased SETDB1 expression was observed in the plasma cells from newly diagnosed multiple myeloma patients compared to those from the normal controls. Meanwhile, SETDB1 overexpression was the result of increased copy numbers of SETDB1 gene. In MM patients, the Kaplan-Meier analysis was employed to demonstrate that increased SETDB1 expression was associated with shorter overall survival (OS) and event-free survival (EFS). Besides, we conducted multifactorial cox regression analysis to state that SETDB1 expression was an independent biomarker for OS and EFS. MM patients with higher SETDB1 expression showed higher levels of beta-2 microglobulin (β2M), lactate dehydrogenase (LDH), and bone marrow biopsy plasma cells (BMPC) and lower levels of haemoglobin (HGB). Functional enrichment analysis suggested that SETDB1 could promote cell cycle progression in myeloma. Finally, we observed that SETDB1 was distinctly correlated with tumor immunity in MM. SETDB1 expression in myeloma cells was positively correlated with CD56dim natural killer cells but negatively correlated with infiltrating levels of type17 T helper cells, effector memory CD8 T cells, activated dendritic cells, and natural killer T cells from whole bone marrow (WBM) biopsies. Taken together, these results indicated that SETDB1 could be used as a novel biomarker for predicting the prognosis of MM patients.

Estrogen-Responsive Gene MAST4 Regulates Myeloma Bone Disease

Our previous data showed that young female multiple myeloma (MM) patients had a low frequency of osteolytic lesions. Based on this clinical observation, we found that estrogen cell signaling played a regulatory role in MM bone disease (MMBD), and the estrogen-responsive gene microtubule-associated serine/threonine kinase family member 4 (MAST4) was a critical factor. The presence of estrogen in cell cultures promoted MAST4 expression in MM cells, while knocking down estrogen receptor 1 (ESR1) inhibited MAST4 expression. Chromatin immunoprecipitation assay suggested a binding site of ESR1 on the MAST4 promoter. Bisphosphonates, such as zoledronic acid (ZOL), which was widely used in MMBD control, could stimulate MAST4 expression in MM cells by promoting ESR1 expression. MAST4 interacted with phosphatase and tensin homolog (PTEN), therefore regulating the PI3K-Akt-mTOR pathway and the expression of downstream cytokines, such as CCL2/3/4. MAST4 knockdown (MAST4-KD) or ESR1 knockdown (ESR1-KD) MM cells had repressed PTEN activity, elevated PI3K-Akt-mTOR activity, and increased CCL2/3/4 expressions. Coculture of MAST4-KD or ESR1-KD MM cells with pre-osteoclasts (pre-OCs) stimulated OC formation in vitro, whereas neutralizing antibodies of CCL2/3/4 attenuated such stimulation. In mouse models, mice inoculated with MAST4-KD or ESR1-KD MM cells had severer MMBD than control knockdown (CTR-KD). The correlations between MAST4 and ESR1 expressions in MMBD, as well as related cell signaling pathways, were confirmed in analyses using gene expression profiles (GEPs) of patients' MM cells. The negative correlation of MAST4 expression and occurrence of MMBD was further validated by patients' immunohistochemical tissue array. Overall, our data suggested that estrogen cell signaling negatively regulated MMBD through MAST4. © 2022 American Society for Bone and Mineral Research (ASBMR).

FTO promotes multiple myeloma progression by posttranscriptional activation of HSF1 in an m6A-YTHDF2-dependent manner

N⁶-methyladenosine (m⁶A), as the most pervasive internal modification of eukaryotic mRNA, plays a crucial role in various cancers, but its role in multiple myeloma (MM) pathogenesis has not yet been investigated. In this study, we revealed significantly decreased m⁶A methylation in plasma cells (PCs) from MM patients and showed that the abnormal m⁶A level resulted mainly from upregulation of the demethylase fat mass and obesity-associated protein (FTO). Gain- and loss-of-function studies demonstrated that FTO plays a tumor-promoting and pro-metastatic role in MM. Combined m⁶A and RNA sequencing (RNA-seq) and subsequent validation and functional studies identified heat shock factor 1 (HSF1) as a functional target of FTO-mediated m⁶A modification. FTO significantly promotes MM cell proliferation, migration, and invasion by targeting HSF1/HSPs in a YTHDF2-dependent manner. FTO inhibition, especially when combined with bortezomib (BTZ) treatment, synergistically inhibited myeloma bone tumor formation and extramedullary spread in NOD-Prkdc^em26Cd52il2rg^em26Cd22/Nju (NCG) mice. We demonstrated the functional importance of m⁶A demethylase FTO in MM progression, especially in promoting extramedullary myeloma (EMM) formation, and proposed the FTO-HSF1/HSP axis as a potential novel therapeutic target in MM.

Epigenomic reprogramming via HRP2-MINA dictates response to proteasome inhibitors in multiple myeloma with t(4;14) translocation

The chromosomal t(4;14) (p16;q32) translocation drives high expression of histone methyltransferase nuclear SET domain–containing 2 (NSD2) and plays vital roles in multiple myeloma (MM) evolution and progression. However, the mechanisms of NSD2-driven epigenomic alterations in chemoresistance to proteasome inhibitors (PIs) are not fully understood. Using a CRISPR/Cas9 sgRNA library in a bone marrow–bearing MM model, we found that hepatoma-derived growth factor 2 (HRP2) was a suppressor of chemoresistance to PIs and that its downregulation correlated with a poor response and worse outcomes in the clinic. We observed suppression of HRP2 in bortezomib-resistant MM cells, and knockdown of HRP2 induced a marked tolerance to PIs. Moreover, knockdown of HRP2 augmented H3K27me3 levels, consequentially intensifying transcriptome alterations promoting cell survival and restriction of ER stress. Mechanistically, HRP2 recognized H3K36me2 and recruited the histone demethylase MYC-induced nuclear antigen (MINA) to remove H3K27me3. Tazemetostat, a highly selective epigenetic inhibitor that reduces H3K27me3 levels, synergistically sensitized the anti-MM effects of bortezomib both in vitro and in vivo. Collectively, these results provide a better understanding of the origin of chemoresistance in patients with MM with the t(4;14) translocation and a rationale for managing patients with MM who have different genomic backgrounds.

Bone Marrow Stroma-Induced Transcriptome and Regulome Signatures of Multiple Myeloma

Multiple myeloma (MM) is a hematological cancer with inevitable drug resistance. MM cells interacting with bone marrow stromal cells (BMSCs) undergo substantial changes in the transcriptome and develop de novo multi-drug resistance. As a critical component in transcriptional regulation, how the chromatin landscape is transformed in MM cells exposed to BMSCs and contributes to the transcriptional response to BMSCs remains elusive. We profiled the transcriptome and regulome for MM cells using a transwell coculture system with BMSCs. The transcriptome and regulome of MM cells from the upper transwell resembled MM cells that coexisted with BMSCs from the lower chamber but were distinctive to monoculture. BMSC-induced genes were enriched in the JAK2/STAT3 signaling pathway, unfolded protein stress, signatures of early plasma cells, and response to proteasome inhibitors. Genes with increasing accessibility at multiple regulatory sites were preferentially induced by BMSCs; these genes were enriched in functions linked to responses to drugs and unfavorable clinic outcomes. We proposed JUNB and ATF4::CEBPβ as candidate transcription factors (TFs) that modulate the BMSC-induced transformation of the regulome linked to the transcriptional response. Together, we characterized the BMSC-induced transcriptome and regulome signatures of MM cells to facilitate research on epigenetic mechanisms of BMSC-induced multi-drug resistance in MM.

Risk Alleles for Multiple Myeloma Susceptibility in ADME Genes

The cause of multiple myeloma (MM) remains largely unknown. Several pieces of evidence support the involvement of genetic and multiple environmental factors (i.e., chemical agents) in MM onset. The inter-individual variability in the bioactivation, detoxification, and clearance of chemical carcinogens such as asbestos, benzene, and pesticides might increase the MM risk. This inter-individual variability can be explained by the presence of polymorphic variants in absorption, distribution, metabolism, and excretion (ADME) genes. Despite the high relevance of this issue, few studies have focused on the inter-individual variability in ADME genes in MM risk. To identify new MM susceptibility loci, we performed an extended candidate gene approach by comparing high-throughput genotyping data of 1936 markers in 231 ADME genes on 64 MM patients and 59 controls from the CEU population. Differences in genotype and allele frequencies were validated using an internal control group of 35 non-cancer samples from the same geographic area as the patient group. We detected an association between MM risk and ADH1B rs1229984 (OR = 3.78; 95% CI, 1.18–12.13; p = 0.0282), PPARD rs6937483 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), SLC28A1 rs8187737 (OR = 11.33; 95% CI, 1.43–89.59; p = 0.005), SLC28A2 rs1060896 (OR = 6.58; 95% CI, 1.42–30.43; p = 0.0072), SLC29A1 rs8187630 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), and ALDH3A2 rs72547554 (OR = 2.46; 95% CI, 0.64–9.40; p = 0.0293). The prognostic value of these genes in MM was investigated in two public datasets showing that shorter overall survival was associated with low expression of ADH1B and SLC28A1. In conclusion, our proof-of-concept findings provide novel insights into the genetic bases of MM susceptibility.

RARγ activation sensitizes human myeloma cells to carfilzomib treatment through the OAS-RNase L innate immune pathway

Proteasome inhibitors (PIs) such as bortezomib (Btz) and carfilzomib (Cfz) are highly efficacious for patients with multiple myeloma (MM). However, relapses are frequent, and acquired resistance to PI treatment emerges in most patients. Here, we performed a high-throughput screen of 1855 Food and Drug Administration (FDA)-approved drugs and identified all-trans retinoic acid (ATRA), which alone has no antimyeloma effect, as a potent drug that enhanced MM sensitivity to Cfz-induced cytotoxicity and resensitized Cfz-resistant MM cells to Cfz in vitro. ATRA activated retinoic acid receptor (RAR)γ and interferon-β response pathway, leading to upregulated expression of IRF1. IRF1 in turn initiated the transcription of OAS1, which synthesized 2-5A upon binding to double-stranded RNA (dsRNA) induced by Cfz and resulted in cellular RNA degradation by RNase L and cell death. Similar to ATRA, BMS961, a selective RARγ agonist, could also (re)sensitize MM cells to Cfz in vitro, and both ATRA and BMS961 significantly enhanced the therapeutic effects of Cfz in established MM in vivo. In support of these findings, analyses of large datasets of patients' gene profiling showed a strong and positive correlation between RARγ and OAS1 expression and patient's response to PI treatment. Thus, this study highlights the potential for RARγ agonists to sensitize and overcome MM resistance to Cfz treatment in patients.

RNA demethylase ALKBH5 promotes tumorigenesis in multiple myeloma via TRAF1-mediated activation of NF-κB and MAPK signaling pathways

N⁶-methyladenosine (m⁶A), an internal modification in mRNA, plays a critical role in regulating gene expression. Dysregulation of m⁶A modifiers promotes oncogenesis through enzymatic functions that disrupt the balance between the deposition and removal of m⁶A modification on critical transcripts. However, the roles of mRNA m⁶A in multiple myeloma (MM) are poorly understood. The present study showed that RNA demethylase ALKBH5 was overexpressed in MM and associated with a poor prognosis in MM patients. Knocking down ALKBH5 induced apoptosis and inhibited the growth of MM cells in vitro. Xenograft models and gene set enrichment analysis with patient transcriptome datasets also supported the oncogenic role of ALKBH5 in MM. Mechanistic studies showed that ALKBH5 exerted tumorigenic effects in myeloma in an m⁶A-dependent manner, and TNF receptor-associated factor 1 (TRAF1) was a critical target of ALKBH5. Specifically, ALKBH5 regulated TRAF1 expression via decreasing m⁶A abundance in the 3'-untranslated region (3'-UTR) of TRAF1 transcripts and enhancing TRAF1 mRNA stability. As a result, ALKBH5 promoted MM cell growth and survival through TRAF1-mediated activation of NF-κB and MAPK signaling pathways. Collectively, our data demonstrated that ALKBH5 played a critical role in MM tumorigenesis and suggested that ALKBH5 could be a novel therapeutic target in MM.

Gene expression profiling as a prognostic tool in multiple myeloma

Multiple myeloma (MM) is an aggressive plasma cell malignancy with high degrees of variability in outcome, some patients experience long remissions, whilst others survive less than two years from diagnosis. Therapy refractoriness and relapse remain challenges in MM management, and there is a need for improved prognostication and targeted therapies to improve overall survival (OS). The past decade has seen a surge in gene expression profiling (GEP) studies which have elucidated the molecular landscape of MM and led to the identification of novel gene signatures that predict OS and outperform current clinical predictors. In this review, we discuss the limitations of current prognostic tools and the emerging role of GEP in diagnostics and in the development of personalised medicine approaches to combat drug resistance.

ALCAM-EGFR Interaction Regulates Myelomagenesis

The expression of ALCAM was positively correlated with patients’ survival.

ALCAM-EGFR interaction regulates myelomagenesis via the crosstalk among Mek/Erk signaling, PI3K/Akt signaling, and hedgehog pathway.

Multiple myeloma, a plasma cell malignancy in the bone marrow, remains largely incurable with currently available therapeutics. In this study, we discovered that the activated leukocyte cell adhesion molecule (ALCAM) interacted with epidermal growth factor receptor (EGFR), and regulated myelomagenesis. ALCAM was a negative regulator of myeloma clonogenicity. ALCAM expression was positively correlated with patients’ survival. ALCAM-knockdown myeloma cells displayed enhanced colony formation in the presence of bone marrow stromal cells (BMSCs). BMSCs supported myeloma colony formation by secreted epidermal growth factor (EGF), which bound with its receptor (EGFR) on myeloma cells and activated Mek/Erk cell signaling, PI3K/Akt cell signaling, and hedgehog pathway. ALCAM could also bind with EGFR, block EGF from binding to EGFR, and abolish EGFR-initiated cell signaling. Hence, our study identifies ALCAM as a novel negative regulator of myeloma pathogenesis.

Bortezomib induces anti-multiple myeloma immune response mediated by cGAS/STING pathway activation

Proteasome inhibitor bortezomib induces apoptosis in multiple myeloma (MM) cells, and has transformed patient outcome. Using in vitro as well as in vivo immunodeficient and immunocompetent murine MM models, we here show that bortezomib also triggers immunogenic cell death (ICD) characterized by exposure of calreticulin on dying MM cells, phagocytosis of tumor cells by dendritic cells, and induction of MM specific immunity. We identify a bortezomib-triggered specific ICD-gene signature associated with better outcome in two independent MM patient cohorts. Importantly, bortezomib stimulates MM cells immunogenicity via activation of cGAS/STING pathway and production of type-I interferons; and STING agonists significantly potentiate bortezomib-induced ICD. Our studies therefore delineate mechanisms whereby bortezomib exerts immunotherapeutic activity, and provide the framework for clinical trials of STING agonists with bortezomib to induce potent tumor-specific immunity and improve patient outcome in MM.

Targeting the methyltransferase SETD8 impairs tumor cell survival and overcomes drug resistance independently of p53 status in multiple myeloma

Background

Multiple myeloma (MM) is a malignancy of plasma cells that largely remains incurable. The search for new therapeutic targets is therefore essential. In addition to a wide panel of genetic mutations, epigenetic alterations also appear as important players in the development of this cancer, thereby offering the possibility to reveal novel approaches and targets for effective therapeutic intervention.

Results

Here, we show that a higher expression of the lysine methyltransferase SETD8, which is responsible for the mono-methylation of histone H4 at lysine 20, is an adverse prognosis factor associated with a poor outcome in two cohorts of newly diagnosed patients. Primary malignant plasma cells are particularly addicted to the activity of this epigenetic enzyme. Indeed, the inhibition of SETD8 by the chemical compound UNC-0379 and the subsequent decrease in histone H4 methylation at lysine 20 are highly toxic in MM cells compared to normal cells from the bone marrow microenvironment. At the molecular level, RNA sequencing and functional studies revealed that SETD8 inhibition induces a mature non-proliferating plasma cell signature and, as observed in other cancers, triggers an activation of the tumor suppressor p53, which together cause an impairment of myeloma cell proliferation and survival. However, a deadly level of replicative stress was also observed in p53-deficient myeloma cells treated with UNC-0379, indicating that the cytotoxicity associated with SETD8 inhibition is not necessarily dependent on p53 activation. Consistent with this, UNC-0379 triggers a p53-independent nucleolar stress characterized by nucleolin delocalization and reduction of nucleolar RNA synthesis. Finally, we showed that SETD8 inhibition is strongly synergistic with melphalan and may overcome resistance to this alkylating agent widely used in MM treatment.

Conclusions

Altogether, our data indicate that the up-regulation of the epigenetic enzyme SETD8 is associated with a poor outcome and the deregulation of major signaling pathways in MM. Moreover, we provide evidences that myeloma cells are dependent on SETD8 activity and its pharmacological inhibition synergizes with melphalan, which could be beneficial to improve MM treatment in high-risk patients whatever their status for p53.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01160-z.

IDH2 contributes to tumorigenesis and poor prognosis by regulating m6A RNA methylation in multiple myeloma

Epigenetic alterations have been previously shown to contribute to multiple myeloma (MM) pathogenesis via DNA methylations and histone modifications. RNA methylation, a novel epigenetic modification, is required for cancer cell survival, and targeting this pathway has been proposed as a new therapeutic strategy. The extent to the N6-methyladenosine (m6A)-regulatory pathway functions in MM remains unknown. Here, we show that an imbalance of RNA methylation may underlies the tumorigenesis of MM. Mechanistically, isocitrate dehydrogenase 2 (IDH2) is highly expressed in CD138⁺ cells from MM and its levels appear a progressive increase in the progression of plasma cell dyscrasias. Downregulation of IDH2 increases global m6A RNA levels and reduces myeloma cell growth in vitro, decreases the burden of disease and prolongs overall survival in vivo. IDH2 regulates RNA methylation by activating the RNA demethylase FTO, which is an α-KG-dependent dioxygenase. Furthermore, IDH2-mediated FTO activation decreases the m6A level on WNT7B transcripts, then increases WNT7B expression and thus activated Wnt signaling pathway. Moreover, survival analysis indicates that the elevated expression of IDH2 predicts a poor prognosis. Higher expression of FTO is related to higher International Staging System (ISS) stage and higher Revised-ISS (R-ISS) stage of MM. Collectively, our studies reveal that IDH2 regulates global m6A RNA modification in MM via targeting RNA demethylases FTO. The imbalance of m6A methylation activates the Wnt signaling pathway by enhancing the WNT7B expression, and thus promoting tumorigenesis and progression of MM. IDH2 might be used as a therapeutic target and a possible prognostic factor for MM.

Targeting coenzyme Q10 synthesis overcomes bortezomib resistance in multiple myeloma

During the development of drug resistance, multiple myeloma (MM) cells undergo changes to their metabolism. However, how these metabolic changes can be exploited to improve treatment efficacy is not known. Here we demonstrate that targeting coenzyme Q10 (CoQ) biosynthesis through the mevalonate pathway works in synergy with the proteasome inhibitor bortezomib (BTZ) in MM. We show that gene expression signatures relating to the mitochondrial tricarboxylic acid (TCA) cycle and electron transport chain (ETC) predispose to clinical BTZ resistance and poor prognosis in MM patients. Mechanistically, BTZ-resistant cells show increased activity of glutamine-driven TCA cycle and oxidative phosphorylation, together with an increased vulnerability towards ETC inhibition. Moreover, BTZ resistance is accompanied by high levels of the mitochondrial electron carrier CoQ, while the mevalonate pathway inhibitor simvastatin increases cell death and decreases CoQ levels, specifically in BTZ-resistant cells. Both in vitro and in vivo, simvastatin enhances the effect of bortezomib treatment. Our study links CoQ synthesis to drug resistance in MM and provides a novel avenue for improving BTZ responses through statin-induced inhibition of mitochondrial metabolism.

Machine Learning in Drug Discovery: A Review

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

A Comprehensive Review of the Genomics of Multiple Myeloma: Evolutionary Trajectories, Gene Expression Profiling, and Emerging Therapeutics

Multiple myeloma (MM) is a blood cancer characterized by the accumulation of malignant monoclonal plasma cells in the bone marrow. It develops through a series of premalignant plasma cell dyscrasia stages, most notable of which is the Monoclonal Gammopathy of Undetermined Significance (MGUS). Significant advances have been achieved in uncovering the genomic aberrancies underlying the pathogenesis of MGUS-MM. In this review, we discuss in-depth the genomic evolution of MM and focus on the prognostic implications of the accompanied molecular and cytogenetic aberrations. We also dive into the latest investigatory techniques used for the diagnoses and risk stratification of MM patients.

Intratumor Heterogeneity of MIF Expression Correlates With Extramedullary Involvement of Multiple Myeloma

Macrophage migration inhibitory factor (MIF) has been shown to promote disease progression in many malignancies, including multiple myeloma (MM). We previously reported that MIF regulates MM bone marrow homing and knockdown of MIF favors the extramedullary myeloma formation in mice. Here, based on MIF immunostaining of myeloma cells in paired intramedullary and extramedullary biopsies from 17 patients, we found lower MIF intensity in extramedullary MM (EMM) versus intramedullary MM (IMM). Flow cytometry and histology analysis in xenograft models showed a portion of inoculated human MM cells lost their MIF expression (MIF^Low) in vivo. Of note, IMM had dominantly MIF^High cells, while EMM showed a significantly increased ratio of MIF^Low cells. Furthermore, we harvested the extramedullary human MM cells from a mouse and generated single-cell transcriptomic data. The developmental trajectories of MM cells from the MIF^High to MIF^Low state were indicated. The MIF^High cells featured higher proliferation. The MIF^Low ones were more quiescent and harbored abundant ribosomal protein genes. Our findings identified in vivo differential regulation of MIF expression in MM and suggested a potential pathogenic role of MIF in the extramedullary spread of disease.

Transient Receptor Potential C 1/4/5 Is a Determinant of MTI-101 Induced Calcium Influx and Cell Death in Multiple Myeloma

Multiple myeloma (MM) is a currently incurable hematologic cancer. Patients that initially respond to therapeutic intervention eventually relapse with drug resistant disease. Thus, novel treatment strategies are critically needed to improve patient outcomes. Our group has developed a novel cyclic peptide referred to as MTI-101 for the treatment of MM. We previously reported that acquired resistance to HYD-1, the linear form of MTI-101, correlated with the repression of genes involved in store operated Ca²⁺ entry (SOCE): PLCβ, SERCA, ITPR3, and TRPC1 expression. In this study, we sought to determine the role of TRPC1 heteromers in mediating MTI-101 induced cationic flux. Our data indicate that, consistent with the activation of TRPC heteromers, MTI-101 treatment induced Ca²⁺ and Na⁺ influx. However, replacing extracellular Na⁺ with NMDG did not reduce MTI-101-induced cell death. In contrast, decreasing extracellular Ca²⁺ reduced both MTI-101-induced Ca²⁺ influx as well as cell death. The causative role of TRPC heteromers was established by suppressing STIM1, TRPC1, TRPC4, or TRPC5 function both pharmacologically and by siRNA, resulting in a reduction in MTI-101-induced Ca²⁺ influx. Mechanistically, MTI-101 treatment induces trafficking of TRPC1 to the membrane and co-immunoprecipitation studies indicate that MTI-101 treatment induces a TRPC1-STIM1 complex. Moreover, treatment with calpeptin inhibited MTI-101-induced Ca²⁺ influx and cell death, indicating a role of calpain in the mechanism of MTI-101-induced cytotoxicity. Finally, components of the SOCE pathway were found to be poor prognostic indicators among MM patients, suggesting that this pathway is attractive for the treatment of MM.

CHEK1 and circCHEK1_246aa evoke chromosomal instability and induce bone lesion formation in multiple myeloma

BACKGROUND

Multiple myeloma (MM) is still incurable and characterized by clonal expansion of plasma cells in the bone marrow (BM). Therefore, effective therapeutic interventions must target both myeloma cells and the BM niche.

METHODS

Cell proliferation, drug resistance, and chromosomal instability (CIN) induced by CHEK1 were confirmed by Giemsa staining, exon sequencing, immunofluorescence and xenograft model in vivo. Bone lesion was evaluated by Tartrate-resistant acid phosphatase (TRAP) staining. The existence of circCHEK1_246aa was evaluated by qPCR, Sanger sequencing and Mass Spectrometer.

RESULTS

We demonstrated that CHEK1 expression was significantly increased in human MM samples relative to normal plasma cells, and that in MM patients, high CHEK1 expression was associated with poor outcomes. Increased CHEK1 expression induced MM cellular proliferation and evoked drug-resistance in vitro and in vivo. CHEK1-mediated increases in cell proliferation and drug resistance were due in part to CHEK1-induced CIN. CHEK1 activated CIN, partly by phosphorylating CEP170. Interestingly, CHEK1 promoted osteoclast differentiation by upregulating NFATc1 expression. Intriguingly, we discovered that MM cells expressed circCHEK1_246aa, a circular CHEK1 RNA, which encoded and was translated to the CHEK1 kinase catalytic center. Transfection of circCHEK1_246aa increased MM CIN and osteoclast differentiation similarly to CHEK1 overexpression, suggesting that MM cells could secrete circCHEK1_246aa in the BM niche to increase the invasive potential of MM cells and promote osteoclast differentiation.

CONCLUSIONS

Our findings suggest that targeting the enzymatic catalytic center encoded by CHEK1 mRNA and circCHEK1_246aa is a promising therapeutic modality to target both MM cells and BM niche.