Proteasome Subunits Differentially Control Myeloma Cell Viability and Proteasome Inhibitor Sensitivity

Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma

OBJECTIVE

The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise in advancing cancer diagnosis and treatment strategies.

METHODS

GNNs models were developed to predict the response to immunotherapy and to pinpoint key pathways. Utilizing the genes from these key pathways, multi-omics bioinformatics methods were employed to refine the construction of a gene signature, termed responseScore, aimed at enhancing the precision of immunotherapy response predictions. Subsequently, responseScore was explored from the perspectives of prognosis, genetic variation, pathway enrichment, and the tumor microenvironment. Concurrently, the association among 13 genes contributing to responseScore and factors such as immunotherapy response, prognosis, and the tumor microenvironment was investigated. Among these genes, PSMB6 was subjected to an in-depth analysis of its biological effect through experimental approaches like transfection and co-culture.

RESULTS

In the finalized model utilizing GNNs, it has revealed an AUC of 0.854 within the training dataset and 0.824 within the testing set, pinpointing key pathways such as R-HSA-70,268. The indicator named as responseScore excelled in its predictive accuracy regarding immunotherapy response and patient prognosis. Investigations into genetic variation, pathway enrichment, tumor microenvironment disclosed a profound association between responseScore and the enhancement of immune cell infiltration and anti-tumor immunity. A negative correlation was observed between the expression of PSMB6 and immune genes, with elevated PSMB6 expression correlating with poor prognosis. ELISA detection after co-cultivation experiments revealed significant reductions in the levels of cytokines IL-6 and IL-1β in specimens from the PCDH-PSMB6 group.

CONCLUSION

The GNNs prediction model and the responseScore developed in this research effectively indicate the immunotherapy response and prognosis for patients with skin melanoma. Additionally, responseScore provides insights into the tumor microenvironment and the characteristics of tumor immunity of melanoma. Thirteen genes identified in this study show promise as potential tumor markers or therapeutic targets. Notably, PSMB6 emerges as a potential therapeutic target for skin melanoma, where its elevated expression exhibits an inhibitory effect on the tumor immunity.

Influence of PSMB6 and PSMB9 Genetic Polymorphisms on Bortezomib-Based Therapy Response in Newly Diagnosed Multiple Myeloma

BACKGROUND

Bortezomib (BTZ)-based regimens play a crucial role in the treatment of multiple myeloma (MM), significantly improving patient outcomes. BTZ, a proteasome inhibitor, interferes with cellular processes essential for cancer cell growth and survival, resulting in high response rates. Its use in initial and recurrent treatment strategies has been associated with extended disease control and improved long-term outcomes, contributing to overall survival. This study aimed to delineate the clinical characteristics of newly diagnosed MM patients and investigate the influence of single nucleotide polymorphisms (SNPs) in PSMB6 and PSMB9 genes on the risk and response to BTZ-based chemotherapy, in comparison with the control group.

METHODS

This prospective study was conducted at Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Pondicherry, India, comprising 92 newly diagnosed MM cases and 92 controls. Patient selection was based on receiving first-line BTZ therapy with available outcome data. DNA was extracted from peripheral blood samples, followed by SNP analysis using real-time polymerase chain reaction (PCR) with appropriate validation and quality control measures. The patients were stratified into good responders and poor responders. The association of genetic polymorphism with the response to BTZ treatment was carried out using chi-square. Survival analysis was conducted using Kaplan-Meier to estimate the overall survival (OS) and progression-free survival (PFS).

RESULTS

In our study, the median age of participants was 55 years, with a predominant 71.7% being males. Backache (66.3%) emerged as the most commonly reported symptom in patients followed by myalgia (53.3%) and bony swelling (12%). The GA+AA genotype in PSMB6 (rs3169950) was significantly more prevalent in poor responders compared to good responders (odds ratio (OR)=3.20, p=0.011), indicating a potential genetic marker for treatment response. The presence of the GA+AA genotype in PSMB6 gene rs3169950 was significantly higher among poor responders compared to good responders (OR=3.200, p=0.011). Likewise, the GA+AA genotype of the PSMB9 gene (rs17587) was predominantly observed among poor responders (OR=3.08, p=0.011). However, no differences were noted in survival analysis.

CONCLUSION

The findings of this study represent a potential breakthrough, indicating that common genetic variants may significantly influence the development of MM and impact treatment response, providing valuable insights for predicting patient outcomes with BTZ-based therapies.

Loss-of-function mutations in the dystonia gene THAP1 impair proteasome function by inhibiting PSMB5 expression

The 26S proteasome is a multi-catalytic protease that serves as the endpoint for protein degradation via the ubiquitin-proteasome system. Proteasome function requires the concerted activity of 33 distinct gene products, but how the expression of proteasome subunits is regulated in mammalian cells remains poorly understood. Leveraging coessentiality data from the DepMap project, here we characterize an essential role for the dystonia gene THAP1 in maintaining the basal expression of PSMB5. PSMB5 insufficiency resulting from loss of THAP1 leads to defects in proteasome assembly, impaired proteostasis and cell death. Exploiting the fact that the toxicity associated with loss of THAP1 can be rescued upon exogenous expression of PSMB5, we define the transcriptional targets of THAP1 through RNA-seq analysis and perform a deep mutational scan to systematically assess the function of thousands of single amino acid THAP1 variants. Altogether, these data identify THAP1 as a critical regulator of proteasome function and suggest that aberrant proteostasis may contribute to the pathogenesis of THAP1 dystonia.

Prognostic Value of PSMB5 and Correlations with LC3II and Reactive Oxygen Species Levels in the Bone Marrow Mononuclear Cells of Bortezomib-Resistant Multiple Myeloma Patients

Proteasome inhibitors (PIs) constitute the most common type of induction treatment for multiple myeloma. Interactions between the proteasome, autophagy, and reactive oxygen species (ROS) have been shown in the past, thus emphasizing the need for a better understanding of the underlying pathophysiology. For this study, bone marrow mononuclear cells from 110 myeloma patients were collected at different disease stages. PSMB5 and LC3I/II protein levels were determined using Western blot, proteasome proteolytic activity (PPA) with spectrofluorometry, and ROS with flow cytometry. PSMB5 accumulation was found to diminish after PI treatment (p-value = 0.014), and the same pattern was observed in PPA (p-value < 0.001). Conversely, LC3II protein levels were elevated at both remission and relapse compared to baseline levels (p-value = 0.041). Patients with a baseline PSMB5 accumulation lower than 1.06 units had longer disease-free survival compared to those with values above 1.06 units (12.0 ± 6.7 vs. 36 ± 12.1 months; p-value < 0.001). Median ROS levels in plasma cells were significantly higher at relapse compared to both baseline and remission levels (p-value < 0.001), implying poor prognosis. Overall, post-treatment PSMB5 reduction could indicate a shift from proteasomal to autophagic degradation as a main proteostatic mechanism, thus explaining resistance. The elevated oxidative stress in PI-treated patients could possibly serve as an additional compensatory mechanism.

Prognostic significance of a 3-gene ferroptosis-related signature in lung cancer via LASSO analysis and cellular functions of UBE2Z

Ferroptosis is a newly identified form of non-apoptotic programmed cell death resulting from iron-dependent lipid peroxidation. It is controlled by integrated oxidation and antioxidant systems. Ferroptosis exerts a crucial effect on the carcinogenesis of several cancers, including pulmonary cancer. Herein, a ferroptosis-associated gene signature for lung cancer prognosis and diagnosis was identified using integrative bioinformatics analyses. From the FerrDB database, 256 ferroptotic regulators and markers were identified. Of these, 25 exhibited differential expression between lung cancer and non-cancerous samples, as evidenced by the GSE19804 and GSE7670 datasets from the GEO database. Utilizing LASSO Cox regression analysis on TCGA-LUAD data, a potent 3-gene risk signature comprising CAV1, RRM2, and EGFR was established. This signature adeptly differentiates various survival outcomes in lung cancer patients, including overall survival and disease-specific intervals. Based on the 3-gene risk signature, lung cancer patients were categorized into high-risk and low-risk groups. Comparative analysis revealed 69 differentially expressed genes between these groups, with UBE2Z significantly associated with overall survival in TCGA-LUAD. UBE2Z was found to be upregulated in LUAD tissues and cells compared to normal controls. Functionally, the knockdown of UBE2Z curtailed aggressive behaviors in LUAD cells, including viability, migration, and invasion. Moreover, this knockdown led to a decrease in the mesenchymal marker vimentin while elevating the epithelial marker E-cadherin within LUAD cell lines. In conclusion, the ferroptosis-associated 3-gene risk signature effectively differentiates prognosis and clinical features in patients with lung cancer. UBE2Z was identified through this model, and it is upregulated in LUAD samples. Its knockdown inhibits aggressive cellular behaviors, suggesting UBE2Z's potential as a therapeutic target for lung cancer treatment.

Multi-omics with dynamic network biomarker algorithm prefigures organ-specific metastasis of lung adenocarcinoma

Efficacious strategies for early detection of lung cancer metastasis are of significance for improving the survival of lung cancer patients. Here we show the marker genes and serum secretome foreshadowing the lung cancer site-specific metastasis through dynamic network biomarker (DNB) algorithm, utilizing two clinical cohorts of four major types of lung cancer distant metastases, with single-cell RNA sequencing (scRNA-seq) of primary lesions and liquid chromatography-mass spectrometry data of sera. Also, we locate the intermediate status of cancer cells, along with its gene signatures, in each metastatic state trajectory that cancer cells at this stage still have no specific organotropism. Furthermore, an integrated neural network model based on the filtered scRNA-seq data is successfully constructed and validated to predict the metastatic state trajectory of cancer cells. Overall, our study provides an insight to locate the pre-metastasis status of lung cancer and primarily examines its clinical application value, contributing to the early detection of lung cancer metastasis in a more feasible and efficacious way.

Cross talk between tumor stemness and microenvironment for prognosis and immunotherapy of uveal melanoma

PURPOSE

Tumor stem cells have emerged as a crucial focus of investigation and a therapeutic target in the context of cancer metastasis and drug resistance. They represent a promising novel approach to address the treatment of uveal melanoma (UVM).

METHODS

According to the one-class logistic regression (OCLR) approach, we first estimated two stemness indices (mDNAsi and mRNAsi) in a cohort of UVM (n = 80). The prognostic value of stemness indices among four subtypes of UVM (subtype A-D) was investigated. Moreover, univariate Cox regression and Lasso-penalized algorithms were conducted to identify a stemness-associated signature and verify in several independent cohorts. Besides, UVM patients classified into subgroups based on the stemness-associated signature. The differences in clinical outcomes, tumor microenvironment, and probability of immunotherapeutic response were investigated further.

RESULTS

We observed that mDNAsi was significantly linked with overall survival (OS) time of UVM, but no association was discovered between mRNAsi and OS. Stratification analysis indicated that the prognostic value of mDNAsi was only limited in subtype D of UVM. Besides, we established and verified a prognostic stemness-associated gene signature which can classify UVM patients into subgroups with distinct clinical outcomes, tumor mutation, immune microenvironment, and molecular pathways. The high risk of UVM is more sensitive to immunotherapy. Finally, a well-performed nomogram was constructed to predict the mortality of UVM patients.

CONCLUSIONS

This study offers a comprehensive examination of UVM stemness characteristics. We discovered mDNAsi-associated signatures improved the prediction capacity of individualized UVM prognosis and indicated prospective targets for stemness-regulated immunotherapy. Analysis of the interaction between stemness and tumor microenvironment may shed light on combinational treatment that targets both stem cell and the tumor microenvironment.

The prognostic value of 19S ATPase proteasome subunits in acute myeloid leukemia and other forms of cancer

Introduction

The ubiquitin-proteasome system (UPS) is an intracellular organelle responsible for targeted protein degradation, which represents a standard therapeutic target for many different human malignancies. Bortezomib, a reversible inhibitor of chymotrypsin-like proteasome activity, was first approved by the FDA in 2003 to treat multiple myeloma and is now used to treat a number of different cancers, including relapsed mantle cell lymphoma, diffuse large B-cell lymphoma, colorectal cancer, and thyroid carcinoma. Despite the success, bortezomib and other proteasome inhibitors are subject to severe side effects, and ultimately, drug resistance. We recently reported an oncogenic role for non-ATPase members of the 19S proteasome in chronic myeloid leukemia (CML), acute myeloid leukemia (AML), and several different solid tumors. In the present study, we hypothesized that ATPase members of the 19S proteasome would also serve as biomarkers and putative therapeutic targets in AML and multiple other cancers.

Methods

We used data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) available at UALCAN and/or GEPIA2 to assess the expression and prognostic value of proteasome 26S subunit, ATPases 1-6 (PSMC1-6) of the 19S proteasome in cancer. UALCAN was also used to associate PSMC1-6 mRNA expression with distinct clinicopathological features. Finally, cBioPortal was employed to assess genomic alterations of PSMC genes across different cancer types.

Results

The mRNA and protein expression of PSMC1-6 of the 19S proteasome were elevated in several cancers compared with normal controls, which often correlated with worse overall survival. In contrast, AML patients demonstrated reduced expression of these proteasome subunits compared with normal mononuclear cells. However, AML patients with high expression of PSMC2-5 had worse outcomes.

Discussion

Altogether, our data suggest that components of the 19S proteasome could serve as prognostic biomarkers and novel therapeutic targets in AML and several other human malignancies.

Identification and Verification of Potential Biomarkers in Renal Ischemia-Reperfusion Injury by Integrated Bioinformatic Analysis

Background

Renal ischemia-reperfusion injury (RIRI) plays an important role in the poor prognosis of patients with renal transplants. However, the potential targets and mechanism of IRI are still unclear.

Method

Differential gene expression (DEG) analysis and weighted correlation network analysis (WGCNA) were performed on the GSE27274 dataset. Pathway enrichment analysis on the DEGs was performed. To identify the hub DEGs, we constructed a protein-protein interaction (PPI) network. Finally, the hub genes were verified, and candidate drugs were screened from the DsigDB database.

Results

A hundred DEGs and four hub genes (Atf3, Psmb6, Psmb8, and Psmb10) were screened out. Pathway enrichment analysis revealed that 100 DEGs were mainly enriched in apoptosis and the TNF signaling pathway. The four hub genes were verified in animal models and another dataset (GSE148420). Thereafter, a PPI network was used to identify the four hub genes (Atf3, Psmb6, Psmb8, and Psmb10). Finally, eight candidate drugs were identified as potential drugs.

Conclusion

Three hub genes (Psmb6, Psmb8, and Psmb10) were associated with RIRI and could be potential novel biomarkers for RIRI.

Single-Nucleotide Variants and Epimutations Induce Proteasome Inhibitor Resistance in Multiple Myeloma

PURPOSE

Proteasome inhibitors (PI) are the backbone of various treatment regimens in multiple myeloma. We recently described the first in-patient point mutations affecting the 20S subunit PSMB5 underlying PI resistance. Notably, in vivo, the incidence of mutations in PSMB5 and other proteasome encoding genes is too low to explain the development of resistance in most of the affected patients. Thus, additional genetic and epigenetic alterations need to be explored.

EXPERIMENTAL DESIGN

We performed DNA methylation profiling by Deep Bisulfite Sequencing in PSMB5, PSMC2, PSMC5, PSMC6, PSMD1, and PSMD5, a subset of proteasome subunits that have hitherto been associated with PI resistance, recruited from our own previous research, the literature, or a meta-analysis on the frequency of somatic mutations. Methylation was followed up on gene expression level and by dual-luciferase reporter assay. The KMS11 cell line served as a model to functionally test the impact of demethylating agents.

RESULTS

We identified PSMD5 promoter hypermethylation and subsequent epigenetic gene silencing in 24% of PI refractory patients. Hypermethylation correlated with decreased expression and the regulatory impact of this region was functionally confirmed. In contrast, patients with newly diagnosed multiple myeloma, along with peripheral blood mononuclear cells and CD138+ plasma cells from healthy donors, generally show unmethylated profiles.

CONCLUSIONS

Under the selective pressure of PI treatment, multiple myeloma cells acquire methylation of the PSMD5 promoter silencing the PSMD5 gene expression. PSMD5 acts as a key orchestrator of proteasome assembly and its downregulation was described to increase the cell's proteolytic capacity. PSMD5 hypermethylation, therefore, represents a novel mechanism of PI tolerance in multiple myeloma.

Looking into Endoplasmic Reticulum Stress: The Key to Drug-Resistance of Multiple Myeloma?

Multiple myeloma (MM) is the second most common hematologic malignancy, resulting from the clonal proliferation of malignant plasma cells within the bone marrow. Despite significant advances that have been made with novel drugs over the past two decades, MM patients often develop therapy resistance, especially to bortezomib, the first-in-class proteasome inhibitor that was approved for treatment of MM. As highly secretory monoclonal protein-producing cells, MM cells are characterized by uploaded endoplasmic reticulum stress (ERS), and rely heavily on the ERS response for survival. Great efforts have been made to illustrate how MM cells adapt to therapeutic stresses through modulating the ERS response. In this review, we summarize current knowledge on the mechanisms by which ERS response pathways influence MM cell fate and response to treatment. Moreover, based on promising results obtained in preclinical studies, we discuss the prospect of applying ERS modulators to overcome drug resistance in MM.

Evaluating the Prognostic and Therapeutic Potentials of the Proteasome 26S Subunit, ATPase (PSMC) Family of Genes in Lung Adenocarcinoma: A Database Mining Approach

This study explored the prognostic and therapeutic potentials of multiple Proteasome 26S Subunit, ATPase (PSMC) family of genes (PSMC1-5) in lung adenocarcinoma (LUAD) diagnosis and treatment. All the PSMCs were found to be differentially expressed (upregulated) at the mRNA and protein levels in LUAD tissues. The promoter and multiple coding regions of PSMCs were reported to be differentially and distinctly methylated, which may serve in the methylation-sensitive diagnosis of LUAD patients. Multiple somatic mutations (alteration frequency: 0.6-2%) were observed along the PSMC coding regions in LUAD tissues that could assist in the high-throughput screening of LUAD patients. A significant association between the PSMC overexpression and LUAD patients' poor overall and relapse-free survival (p < 0.05; HR: >1.3) and individual cancer stages (p < 0.001) was discovered, which justifies PSMCs as the ideal targets for LUAD diagnosis. Multiple immune cells and modulators (i.e., CD274 and IDO1) were found to be associated with the expression levels of PSMCs in LUAD tissues that could aid in formulating PSMC-based diagnostic measures and therapeutic interventions for LUAD. Functional enrichment analysis of neighbor genes of PSMCs in LUAD tissues revealed different genes (i.e., SLIRP, PSMA2, and NUDSF3) previously known to be involved in oncogenic processes and metastasis are co-expressed with PSMCs, which could also be investigated further. Overall, this study recommends that PSMCs and their transcriptional and translational products are potential candidates for LUAD diagnostic and therapeutic measure discovery.

New Clues to Prognostic Biomarkers of Four Hematological Malignancies

Globally, one out of every two reported cases of hematologic malignancies (HMs) results in death. Each year approximately 1.24 million cases of HMs are recorded, of which 58% become fatal. Early detection remains critical in the management and treatment of HMs. However, this is thwarted by the inadequate number of reliable biomarkers. In this study, we mined public databases for RNA-seq data on four common HMs intending to identify novel biomarkers that could serve as HM management and treatment targets. A standard RNA-seq analysis pipeline was strictly adhered to in identifying differentially expressed genes (DEGs) with DESeq2, limma+voom and edgeR. We further performed gene enrichment analysis, protein-protein interaction (PPI) network analysis, survival analysis and tumor immune infiltration level detection on the genes using G:Profiler, Cytoscape and STRING, GEPIA tool and TIMER, respectively. A total of 2,136 highly-ranked DEGs were identified in HM vs. non-HM samples. Gene ontology and pathway enrichment analyses revealed the DEGs to be mainly enriched in steroid biosynthesis (5.075×10-4), cholesterol biosynthesis (2.525×10-8), protein binding (3.308×10-18), catalytic activity (2.158×10-10) and biogenesis (5.929×10-8). The PPI network resulted in 60 hub genes which were verified with data from TCGA, MET500, CPTAC and GTEx projects. Survival analyses with clinical data from TCGA showed that high expression of SRSF1, SRSF6, UBE2Z and PCF11, and low expression of HECW2 were correlated with poor prognosis in HMs. In summary, our study unraveled essential genes that could serve as potential biomarkers for prognosis and may serve as drug targets for HM management.

Single-cell transcriptomics of neuroblastoma identifies chemoresistance-associated genes and pathways

High-Risk neuroblastoma (NB) survival rate is still <50%, despite treatments being more and more aggressive. The biggest hurdle liable to cancer therapy failure is the drug resistance by tumor cells that is likely due to the intra-tumor heterogeneity (ITH). To investigate the link between ITH and therapy resistance in NB, we performed a single cell RNA sequencing (scRNAseq) of etoposide and cisplatin resistant NB and their parental cells. Our analysis showed a clear separation of resistant and parental cells for both conditions by identifying 8 distinct tumor clusters in etoposide-resistant/parental and 7 in cisplatin-resistant/parental cells. We discovered that drug resistance can affect NB cell identities; highlighting the bi-directional ability of adrenergic-to-mesenchymal transition of NB cells. The biological processes driving the identified resistant cell subpopulations revealed genes such as (BARD1, BRCA1, PARP1, HISTH1 axis, members of RPL family), suggesting a potential drug resistance due to the acquisition of DNA repair mechanisms and to the modification of the drug targets. Deconvolution analysis of bulk RNAseq data from 498 tumors with cell subpopulation signatures showed that the transcriptional heterogeneity of our cellular models reflected the ITH of NB tumors and allowed the identification of clusters associated with worse/better survival.

Our study demonstrates the distinct cell populations characterized by genes involved in different biological processes can have a role in NB drug treatment failure. These findings evidence the importance of ITH in NB drug resistance studies and the chance that scRNA-seq analysis offers in the identification of genes and pathways liable for drug resistance.

Prognostic Value of Resistance Proteins in Plasma Cells from Multiple Myeloma Patients Treated with Bortezomib-Based Regimens

While multiple myeloma (MM) treatment with proteasome inhibitors and other agents yields encouraging results, primary and secondary resistance remains an emerging problem. An important factor in such treatment resistance is the overexpression of several proteins. The present study comprehensively evaluates the expression of POMP, PSMB5, NRF2, XBP1, cMAF and MAFb proteins in plasma cells isolated from the bone marrow of 39 MM patients treated with bortezomib-based regimens using an enzyme-linked immunosorbent assay (ELISA). The proteins were selected on the basis of previous laboratory and clinical studies in bortezomib-treated MM patients. It was found that the expression of the investigated proteins did not significantly differ between bortezomib-sensitive and bortezomib-refractory patients. However, the expression of some proteins correlated with overall survival (OS); this was significantly shorter in patients with higher POMP expression (HR 2.8, 95% CI: 1.1-7.0, p = 0.0277) and longer in those with higher MAFB expression (HR 0.32, 95% CI: 0.13-0.80, p = 0.0147). Our results indicate that a high expression of POMP and MAFB in MM plasma cells may serve as predictors of OS in MM patients treated with bortezomib-based regimens. However, further studies are needed to determine the role of these factors in effective strategies for improving anti-myeloma therapy.

Identification and Validation of a Proliferation-Associated Score Model Predicting Survival in Lung Adenocarcinomas

AIM

This study is aimed at building a risk model based on the genes that significantly altered the proliferation of lung adenocarcinoma cells and exploring the underlying mechanisms.

METHODS

The data of 60 lung adenocarcinoma cell lines in the Cancer Dependency Map (Depmap) were used to identify the genes whose knockout led to dramatical acceleration or deacceleration of cell proliferation. Then, univariate Cox regression was performed using the survival data of 497 patients with lung adenocarcinoma in The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) model was used to construct a risk prediction score model. Patients with lung adenocarcinoma from TCGA were classified into high- or low-risk groups based on the scores. The differences in clinicopathologic, genomic, and immune characteristics between the two groups were analyzed. The prognosis of the genes in the model was verified with immunohistochemical staining in 100 samples from the Department of Thoracic Surgery, Zhongshan Hospital, and the alteration in the proliferation rate was checked after these genes were knocked down in lung adenocarcinoma cells (A549 and H358).

RESULTS

A total of 55 genes were found to be significantly related to survival by combined methods, which were crucial to tumor progression in functional enrichment analysis. A six-gene-based risk prediction score, including the proteasome subunit beta type-6 (PSMB6), the heat shock protein family A member 9 (HSPA9), the deoxyuridine triphosphatase (DUT), the cyclin-dependent kinase 7 (CDK7), the polo-like kinases 1 (PLK1), and the folate receptor beta 2 (FOLR2), was built using the LASSO method. The high-risk group classified with the score model was characterized by poor overall survival (OS), immune infiltration, and relatively higher mutation load. A total of 9864 differentially expressed genes and 138 differentially expressed miRNAs were found between the two groups. Also, a nomogram comparing score model, age, and the stage was built to predict OS for patients with lung adenocarcinoma. Using immunohistochemistry, the expression levels of PSMB6, HSPA9, DUT, CDK7, and PLK1 were found to be higher in lung adenocarcinoma tissues of patients, while the expression of FOLR2 was low, which was consistent with survival prediction. The knockdown of PSMB6 and HSPA9 by siRNA significantly downregulated the proliferation of A549 and H358 cells.

CONCLUSION

The proposed score model may function as a promising risk prediction tool for patients with lung adenocarcinoma and provide insights into the molecular regulation mechanism of lung adenocarcinoma.

From Bench to Bedside: The Evolution of Genomics and Its Implications for the Current and Future Management of Multiple Myeloma

ABSTRACT

The summation of 20 years of biological studies and the comprehensive analysis of more than 1000 multiple myeloma genomes with data linked to clinical outcome has enabled an increased understanding of the pathogenesis of multiple myeloma in the context of normal plasma cell biology. This novel data have facilitated the identification of prognostic markers and targets suitable for therapeutic manipulation. The challenge moving forward is to translate this genetic and biological information into the clinic to improve patient care. This review discusses the key data required to achieve this and provides a framework within which to explore the use of response-adapted, biologically targeted, molecularly targeted, and risk-stratified therapeutic approaches to improve the management of patients with multiple myeloma.

M3258 Is a Selective Inhibitor of the Immunoproteasome Subunit LMP7 (β5i) Delivering Efficacy in Multiple Myeloma Models

Large multifunctional peptidase 7 (LMP7/β5i/PSMB8) is a proteolytic subunit of the immunoproteasome, which is predominantly expressed in normal and malignant hematolymphoid cells, including multiple myeloma, and contributes to the degradation of ubiquitinated proteins. Described herein for the first time is the preclinical profile of M3258; an orally bioavailable, potent, reversible and highly selective LMP7 inhibitor. M3258 demonstrated strong antitumor efficacy in multiple myeloma xenograft models, including a novel model of the human bone niche of multiple myeloma. M3258 treatment led to a significant and prolonged suppression of tumor LMP7 activity and ubiquitinated protein turnover and the induction of apoptosis in multiple myeloma cells both in vitro and in vivo Furthermore, M3258 showed superior antitumor efficacy in selected multiple myeloma and mantle cell lymphoma xenograft models compared with the approved nonselective proteasome inhibitors bortezomib and ixazomib. The differentiated preclinical profile of M3258 supported the initiation of a phase I study in patients with multiple myeloma (NCT04075721).

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

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

Proteasomal adaptations to FDA-approved proteasome inhibitors: a potential mechanism for drug resistance?

With proteasome inhibitors (PIs) becoming clinically available since 2003, outcomes for patients with multiple myeloma (MM) have dramatically changed, improving quality of life and survival. Despite the impressive treatment success, however, almost all MM patients who initially respond to these PIs eventually develop resistance. Furthermore, a portion of MM patients is inherently unresponsive to the PIs. Extensive mechanistic investigations identified several non-proteasomal signaling pathways suspected to be linked to the PI resistance, for which several excellent reviews are currently available. On the other hand, it is still unclear how cancer cells under high PI environments adapt to spare proteasome activity essential for survival and proliferation regardless of cancer evolution stages. This review outlines current progress towards understanding the proteasomal adaptations of cells in response to PI treatment to maintain necessary proteasome activity. A better understanding of cellular proteasomal changes in response to the PIs could provide a rationale to develop new therapeutics that could be used to overcome resistance to existing PI drugs.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

The Prognostic Value of Whole-Blood PSMB5, CXCR4, POMP, and RPL5 mRNA Expression in Patients with Multiple Myeloma Treated with Bortezomib

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