The Role of DNA Repair in Genomic Instability of Multiple Myeloma

Multiple myeloma: signaling pathways and targeted therapy

Multiple myeloma (MM) is the second most common hematological malignancy of plasma cells, characterized by osteolytic bone lesions, anemia, hypercalcemia, renal failure, and the accumulation of malignant plasma cells. The pathogenesis of MM involves the interaction between MM cells and the bone marrow microenvironment through soluble cytokines and cell adhesion molecules, which activate various signaling pathways such as PI3K/AKT/mTOR, RAS/MAPK, JAK/STAT, Wnt/β-catenin, and NF-κB pathways. Aberrant activation of these pathways contributes to the proliferation, survival, migration, and drug resistance of myeloma cells, making them attractive targets for therapeutic intervention. Currently, approved drugs targeting these signaling pathways in MM are limited, with many inhibitors and inducers still in preclinical or clinical research stages. Therapeutic options for MM include non-targeted drugs like alkylating agents, corticosteroids, immunomodulatory drugs, proteasome inhibitors, and histone deacetylase inhibitors. Additionally, targeted drugs such as monoclonal antibodies, chimeric antigen receptor T cells, bispecific T-cell engagers, and bispecific antibodies are being used in MM treatment. Despite significant advancements in MM treatment, the disease remains incurable, emphasizing the need for the development of novel or combined targeted therapies based on emerging theoretical knowledge, technologies, and platforms. In this review, we highlight the key role of signaling pathways in the malignant progression and treatment of MM, exploring advances in targeted therapy and potential treatments to offer further insights for improving MM management and outcomes.

Activated interferon response from DNA damage in multiple myeloma cells contributes to the chemotherapeutic effects of anthracyclines

Introduction

Multiple myeloma (MM) is a malignant plasma cell disease caused by abnormal proliferation of clonal plasma cells in bone marrow. Upfront identification of tumor subgroups with specific biological markers has the potential to improve biologically-driven therapy. Previously, we established a molecular classification by stratifying multiple myeloma into two subtypes with a different prognosis based on a gene module co-expressed with MCL-1 (MCL1-M).

Methods

Gene Ontology (GO) analysis with differentially expressed genes was performed to identify signal pathway. Drug sensitivity was analyzed using the OncoPredict algorithm. Drug sensitivity of different myeloma cell lines was detected by CCK8 and flow cytometry. RNA-seq was performed on drug-sensitive cell lines before and after adriamycin treatment. RT-qPCR was used to further verify the sequencing results. The expression of γ-H2AX and dsDNA in sensitive and resistant cell lines was detected by immunofluorescence method.

Results

In our study, we demonstrated that MCL1-M low MM were more sensitive to anthracyclines. We treated different myeloma cell lines with doxorubicin in vitro and discovered the association of drug sensitivity with IFN signaling. Herein, we demonstrate that the doxorubicin-sensitive myeloma cell line showed significant DNA damage and up-regulated expression of genes related to the IFN response, which was not observed in drug-insensitive cell lines.

Discussion

Our results suggest that the active IFN signaling pathway may serve as a marker for predicting chemotherapy sensitivity in patients with myeloma. With our MCL1-M molecular classification system, we can screen patients with a potentially good response to the interferon signaling pathway and provide individualized treatment for MM. We propose IFN-a as adjuvant therapy for patients with myeloma sensitive to anthracyclines to further improve the therapeutic effect and prolong the survival of patients.

Gene interaction network analysis in multiple myeloma detects complex immune dysregulation associated with shorter survival

The plasma cell cancer multiple myeloma (MM) varies significantly in genomic characteristics, response to therapy, and long-term prognosis. To investigate global interactions in MM, we combined a known protein interaction network with a large clinically annotated MM dataset. We hypothesized that an unbiased network analysis method based on large-scale similarities in gene expression, copy number aberration, and protein interactions may provide novel biological insights. Applying a novel measure of network robustness, Ollivier-Ricci Curvature, we examined patterns in the RNA-Seq gene expression and CNA data and how they relate to clinical outcomes. Hierarchical clustering using ORC differentiated high-risk subtypes with low progression free survival. Differential gene expression analysis defined 118 genes with significantly aberrant expression. These genes, while not previously associated with MM, were associated with DNA repair, apoptosis, and the immune system. Univariate analysis identified 8/118 to be prognostic genes; all associated with the immune system. A network topology analysis identified both hub and bridge genes which connect known genes of biological significance of MM. Taken together, gene interaction network analysis in MM uses a novel method of global assessment to demonstrate complex immune dysregulation associated with shorter survival.

Patient-Derived Multiple Myeloma 3D Models for Personalized Medicine-Are We There Yet?

Despite the wide variety of existing therapies, multiple myeloma (MM) remains a disease with dismal prognosis. Choosing the right treatment for each patient remains one of the major challenges. A new approach being explored is the use of ex vivo models for personalized medicine. Two-dimensional culture or animal models often fail to predict clinical outcomes. Three-dimensional ex vivo models using patients' bone marrow (BM) cells may better reproduce the complexity and heterogeneity of the BM microenvironment. Here, we review the strengths and limitations of currently existing patient-derived ex vivo three-dimensional MM models. We analyze their biochemical and biophysical properties, molecular and cellular characteristics, as well as their potential for drug testing and identification of disease biomarkers. Furthermore, we discuss the remaining challenges and give some insight on how to achieve a more biomimetic and accurate MM BM model. Overall, there is still a need for standardized culture methods and refined readout techniques. Including both myeloma and other cells of the BM microenvironment in a simple and reproducible three-dimensional scaffold is the key to faithfully mapping and examining the relationship between these players in MM. This will allow a patient-personalized profile, providing a powerful tool for clinical and research applications.

Lethal and Non-Lethal Functions of Caspases in the DNA Damage Response

Members of the caspase family are well known for their roles in the initiation and execution of cell death. Due to their function in the removal of damaged cells that could otherwise become malignant, caspases are important players in the DNA damage response (DDR), a network of pathways that prevent genomic instability. However, emerging evidence of caspases positively or negatively impacting the accumulation of DNA damage in the absence of cell death demonstrates that caspases play a role in the DDR that is independent of their role in apoptosis. This review highlights the apoptotic and non-apoptotic roles of caspases in the DDR and how they can impact genomic stability and cancer treatment.