Targeting the biophysical properties of the myeloma initiating cell niches: a pharmaceutical synergism analysis using multi-scale agent-based modeling

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.

The bone ecosystem facilitates multiple myeloma relapse and the evolution of heterogeneous drug resistant disease

Multiple myeloma (MM) is an osteolytic malignancy that is incurable due to the emergence of treatment resistant disease. Defining how, when and where myeloma cell intrinsic and extrinsic bone microenvironmental mechanisms cause relapse is challenging with current biological approaches. Here, we report a biology-driven spatiotemporal hybrid agent-based model of the MM-bone microenvironment. Results indicate MM intrinsic mechanisms drive the evolution of treatment resistant disease but that the protective effects of bone microenvironment mediated drug resistance (EMDR) significantly enhances the probability and heterogeneity of resistant clones arising under treatment. Further, the model predicts that targeting of EMDR deepens therapy response by eliminating sensitive clones proximal to stroma and bone, a finding supported by in vivo studies. Altogether, our model allows for the study of MM clonal evolution over time in the bone microenvironment and will be beneficial for optimizing treatment efficacy so as to significantly delay disease relapse.

Scaffold-based 3D cell culture models in cancer research

Three-dimensional (3D) cell cultures have emerged as valuable tools in cancer research, offering significant advantages over traditional two-dimensional (2D) cell culture systems. In 3D cell cultures, cancer cells are grown in an environment that more closely mimics the 3D architecture and complexity of in vivo tumors. This approach has revolutionized cancer research by providing a more accurate representation of the tumor microenvironment (TME) and enabling the study of tumor behavior and response to therapies in a more physiologically relevant context. One of the key benefits of 3D cell culture in cancer research is the ability to recapitulate the complex interactions between cancer cells and their surrounding stroma. Tumors consist not only of cancer cells but also various other cell types, including stromal cells, immune cells, and blood vessels. These models bridge traditional 2D cell cultures and animal models, offering a cost-effective, scalable, and ethical alternative for preclinical research. As the field advances, 3D cell cultures are poised to play a pivotal role in understanding cancer biology and accelerating the development of effective anticancer therapies. This review article highlights the key advantages of 3D cell cultures, progress in the most common scaffold-based culturing techniques, pertinent literature on their applications in cancer research, and the ongoing challenges.

ALCAM regulates multiple myeloma chemoresistant side population

Drug-resistance is a major problem preventing a cure in patients with multiple myeloma (MM). Previously, we demonstrated that activated-leukocyte-cell-adhesion-molecule (ALCAM) is a prognostic factor in MM and inhibits EGF/EGFR-initiated MM clonogenicity. In this study, we further showed that the ALCAM-EGF/EGFR axis regulated the MM side population (SP)-mediated drug-resistance. ALCAM-knockdown MM cells displayed an enhanced ratio of SP cells in the presence of bone marrow stromal cells (BMSCs) or with the supplement of recombinant EGF. SP MM cells were resistant to chemotherapeutics melphalan or bortezomib. Drug treatment stimulated SP-genesis. Mechanistically, EGFR, primed with EGF, activated the hedgehog pathway and promoted the SP ratio; meanwhile, ALCAM inhibited EGFR downstream pro-MM cell signaling. Further, SP MM cells exhibited an increased number of mitochondria compared to the main population. Interference of the mitochondria function strongly inhibited SP-genesis. Animal studies showed that combination therapy with both an anti-MM agent and EGFR inhibitor gefitinib achieved prolonged MM-bearing mice survival. Hence, our work identifies ALCAM as a novel negative regulator of MM drug-resistance, and EGFR inhibitors may be used to improve MM therapeutic efficacy.

Antitumor effect of poly lactic acid nanoparticles loaded with cisplatin and chloroquine on the oral squamous cell carcinoma

PURPOSE

Poly lactic acid (PLA) combined with cisplatin-chloroquine nanoparticles (CDDP/CQ-PLA NPs) and PLA combined with cisplatin nanoparticles (CDDP-PLA NPs) were prepared to investigate their inhibitory effects on the proliferation of oral squamous cell carcinoma (OSCC) Cal-27cell line.

PATIENTS AND METHODS

We prepared CDDP/CQ-PLA NPs and CDDP-PLA NPs. Transmission electron microscopy (TEM) and dynamic light scattering (DLS) were used to detect the physiological characteristics and particle size parameters of drug-loaded nanoparticles. The drug concentration and cumulative release were measured by UV and visible spectrophotometer. MTT assay was used to detect viability of Cal-27 cells. Annexin/PI staining was used to detect cell apoptosis. Biological kits were used to detect malondialdehyde (MDA) content, catalase (CAT) activity, antioxidant enzyme superoxide dismutase (SOD) activity and glutathione peroxidase (GSH PX) activity in Cal-27 cells. Western blot was used to detect apoptosis and autophagy of Cal-27 cells.

RESULTS

CDDP/CQ-PLA NPs and CDDP -PLA NPs had good drug loaded nanoparticles and drug release. CDDP/CQ-PLA NPs showed higher ROS and apoptosis rate, and lower autophagy than CDDP-PLA NPs.

CONCLUSION

CDDP/CQ-PLA NPs reduced autophagy and enhanced ROS and apoptosis of Cal-27 cells, which shows a potential in the clinical treatment of OSCC.

Systematically understanding the immunity leading to CRPC progression

Prostate cancer (PCa) is the most commonly diagnosed malignancy and the second leading cause of cancer-related death in American men. Androgen deprivation therapy (ADT) has become a standard treatment strategy for advanced PCa. Although a majority of patients initially respond to ADT well, most of them will eventually develop castration-resistant PCa (CRPC). Previous studies suggest that ADT-induced changes in the immune microenvironment (mE) in PCa might be responsible for the failures of various therapies. However, the role of the immune system in CRPC development remains unclear. To systematically understand the immunity leading to CRPC progression and predict the optimal treatment strategy in silico, we developed a 3D Hybrid Multi-scale Model (HMSM), consisting of an ODE system and an agent-based model (ABM), to manipulate the tumor growth in a defined immune system. Based on our analysis, we revealed that the key factors (e.g. WNT5A, TRAIL, CSF1, etc.) mediated the activation of PC-Treg and PC-TAM interaction pathways, which induced the immunosuppression during CRPC progression. Our HMSM model also provided an optimal therapeutic strategy for improving the outcomes of PCa treatment.

The Quiescent Metabolic Phenotype of Glioma Stem Cells

Introduction

Glioblastoma (GBM) is the most common primary malignant brain tumor in humans and, even with aggressive treatment that includes surgical resection, radiation (IR), and chemotherapy administration, prognosis is poor due to tumor recurrence. There is evidence that within GBMs a small number of glioma stem-like cells (GSLCs) exist, which are thought to be therapy resistant and are thus capable of repopulating a tumor after treatment. Like most cancers, GBMs largely employ aerobic glycolysis to create ATP, a phenomenon known as the Warburg Effect. There is no consensus on the metabolic characteristics of cancer stem cells. GSLCs have been shown to rely more heavily on oxidative phosphorylation, but there is also evidence that cancer stem cells can adapt their metabolism by fluctuating between energy pathways or acquiring intermediate metabolic phenotypes. We hypothesized that the metabolism of GSLCs differs from that of differentiated GBM tumor cell lines, and that the steady state metabolism would be differentially altered following radiation treatment.

Materials and Methods

We evaluated the oxygen consumption rate, extracellular acidification rate, and metabolic enzyme levels of GBM cell lines and GSLCs before and after irradiation using extracellular flux assays. We also measured absolute metabolite levels in these cells via mass spectroscopy with and without radiation treatment.

Results

GSLCs were found to be significantly more quiescent in comparison to adherent GBM cell lines, highlighted by lower glycolytic and maximal respiratory capacities as well as lower oxygen consumption and extracellular acidification rates. Analysis of individual metabolite concentrations revealed lower total metabolite concentrations overall but also elevated levels of metabolites in different energy pathways for GSLCs compared to GBM cell lines. Additionally, the metabolism of both GSLCs and GBM cell lines were found to be altered by IR.

Conclusions

While there is not one metabolic alteration that distinguishes irradiated GSLC metabolism from that of GBM cell lines, therapies targeting more metabolically quiescent tumor cells and thus the resistant GSLC population may increase a cancer's sensitivity to radiotherapy.

MicroRNA-221 promotes cisplatin resistance in osteosarcoma cells by targeting PPP2R2A

Osteosarcoma (OS), the most common malignant bone tumor, is the main cause of cancer-related deaths in children and young adults. Despite the combination of surgery and multi-agent chemotherapy, patients with OS who develop resistance to chemotherapy or experience recurrence have a dismal prognosis. MicroRNAs (miRNAs) are a class of small noncoding RNAs that repress their targets by binding to the 3′-UTR and/or coding sequences, leading to the inhibition of gene expression. miR-221 is found to be up-regulated in tumors when compared with their matched normal osteoblast tissues. We also observed significant miR-221 up-regulation in the OS cell lines, MG-63, SaoS-2, and U2OS, when compared with the normal osteoblast cell line, HOb. Overexpression of miR-221 promoted OS cell invasion, migration, proliferation, and cisplatin resistance. MG-63 and SaoS-2 cells transfected with miR-221 mimics were more resistant to cisplatin. The IC₅₀ of MG-63 cells transfected with control mimics was 1.24 μM. However, the IC₅₀ of MG-63 cells overexpressing miR-221 increased to 7.65 μM. Similar results were found in SaoS-2 cells, where the IC₅₀ for cisplatin increased from 3.65 to 8.73 μM. Thus, we report that miR-221 directly targets PP2A subunit B (PPP2R2A) in OS by binding to the 3′-UTR of the PPP2R2A mRNA. Restoration of PPP2R2A in miR-221-overexpressing OS cells recovers the cisplatin sensitivity of OS cells. Therefore, the present study suggests a new therapeutic approach by inhibiting miR-221 for anti-chemoresistance in OS.

TGF-β prevents the denervation-induced reduction of bone formation and promotes the bone regeneration through inhibiting ubiquitin-proteasome pathway

Background: Transforming growth factor beta (TGF-β) can stimulate osteogenesis as a multifunctional protein. The present study was to explore if TGF-β can prevent denervation-induced reduction of bone formation.Materials & methods: The 6-week-old male mice were treated with recombinant human TGF-β1 (rhTGF-β1). Bone formation, endochondral bone growth rates, and gene expression of osteoblast markers were measured in the skeletal tissue by real-time PCR.Results: RhTGF-β1 treatment prevented the denervation-induced decrease in bone formation rates, endochondral growth, and expression of Cbfa1/Runx2 (runt-related transcription factor 2), Ostecalcin (OC), and ColIA1. TGF-β1 partially inhibited the denervation-induced ubiquitination of Cbfa1/Runx2 in mouse cancellous bones via ubiquitin-proteasome pathway.Conclusion: TGF-β prevents denervation-induced reduction of bone formation and promotes the bone regeneration through inhibiting ubiquitin-proteasome pathway at least partially.

LOXL1-AS1 predicts poor prognosis and promotes cell proliferation, migration, and invasion in osteosarcoma

lncRNA LOXL1 antisense RNA 1 (lncRNA LOXL1-AS1) was recently found to function as oncogenic lncRNA in glioblastoma, prostate cancer, and medulloblastoma. The role of LOXL1-AS1 in osteosarcoma was still unknown. In our study, we found LOXL1-AS1 expression levels were higher in osteosarcoma tissues and cell lines than normal bone tissues and normal osteoblast cell line, respectively. Moreover, high-expression of LOXL1-AS1 was correlated with Enneking stage, tumor size, distant metastasis, histological grade, and overall survival time in osteosarcoma patients. Furthermore, LOXL1-AS1 overexpression acted as an independent poor predictor for overall survival in osteosarcoma patients. The loss-of-function studies showed knockdown of LOXL1-AS1 dramatically inhibited osteosarcoma cell proliferation, migration, and invasion through suppressing PI3K-AKT pathway. In conclusion, LOXL1-AS1 predicts clinical progression and poor prognosis in osteosarcoma patients and functions as oncogenic lncRNA to regulate cell proliferation, cell cycle, migration, and invasion.

Establishment of a Gentamicin Cochlear Poisoning Model in Guinea Pigs and Cochlear Nerve Endings Recognition of Ultrasound Signals

Background

Aminoglycosides, a type of gram-negative antibacterial, are broad-spectrum antibiotics that are highly potent and have satisfactory therapeutic efficacy in the treatment of life-threatening infections. Our study aimed to establish a gentamicin-induced cochlear injury model and to investigate the cochlear nerve endings’ recognition of ultrasound signals.

Material/Methods

A guinea pig cochlear injury model was established by intraperitoneal injection of gentamycin. Auditory brainstem response (ABR) and fMRI an affected cerebral cortex region of interest (ROI) of the cerebral cortex blood oxygenation level dependent (BOLD) effect was induced by bone-conducted ultrasound. Immunofluorescence was used to detect expression of Prestin in outer hair cells, Otoferlin in inner hair cells, and cochlear hair cell microfilament protein (F-Actin).

Results

For 30–35 KHz bone-conducted ultrasound, the induction rate of ABR threshold or ROI in the control group and the cochlear injury group was 40% and 0%, respectively, and for 80–90 KHz the induction rate was 20% and 20%, respectively. Gentamicin poisoning induced downregulation of expression of Prestin in cochlear outer cochlea, and Otoferlin and F-Actin in cochlear hair cells in different regions.

Conclusions

Gentamicin poisoning can cause different degrees of damage to cochlea hair cells in different regions. Guinea pigs with gentamicin poisoning can recognize high-frequency ultrasonic signals.

Predicting the impact of combined therapies on myeloma cell growth using a hybrid multi-scale agent-based model

Multiple myeloma is a malignant still incurable plasma cell disorder. This is due to refractory disease relapse, immune impairment, and development of multi-drug resistance. The growth of malignant plasma cells is dependent on the bone marrow (BM) microenvironment and evasion of the host's anti-tumor immune response. Hence, we hypothesized that targeting tumor-stromal cell interaction and endogenous immune system in BM will potentially improve the response of multiple myeloma (MM). Therefore, we proposed a computational simulation of the myeloma development in the complicated microenvironment which includes immune cell components and bone marrow stromal cells and predicted the effects of combined treatment with multi-drugs on myeloma cell growth. We constructed a hybrid multi-scale agent-based model (HABM) that combines an ODE system and Agent-based model (ABM). The ODEs was used for modeling the dynamic changes of intracellular signal transductions and ABM for modeling the cell-cell interactions between stromal cells, tumor, and immune components in the BM. This model simulated myeloma growth in the bone marrow microenvironment and revealed the important role of immune system in this process. The predicted outcomes were consistent with the experimental observations from previous studies. Moreover, we applied this model to predict the treatment effects of three key therapeutic drugs used for MM, and found that the combination of these three drugs potentially suppress the growth of myeloma cells and reactivate the immune response. In summary, the proposed model may serve as a novel computational platform for simulating the formation of MM and evaluating the treatment response of MM to multiple drugs.

Three-dimensional cell culture: A powerful tool in tumor research and drug discovery

In previous years, three-dimensional (3D) cell culture technology has become a focus of research in tumor cell biology, using a variety of methods and materials to mimic the in vivo microenvironment of cultured tumor cells ex vivo. These 3D tumor cells have demonstrated numerous different characteristics compared with traditional two-dimensional (2D) culture. 3D cell culture provides a useful platform for further identifying the biological characteristics of tumor cells, particularly in the drug sensitivity area of the key points of translational medicine. It promises to be a bridge between traditional 2D culture and animal experiments, and is of great importance for further research in the field of tumor biology. In the present review, previous 3D cell culture applications, focusing on anti-tumor drug susceptibility testing, are summarized.

Mathematical and Computational Modeling in Complex Biological Systems

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

Isolation of Human CD138(+) Microparticles from the Plasma of Patients with Multiple Myeloma

The confinement of multiple myeloma (MM) to the bone marrow microenvironment requires an invasive bone marrow biopsy to monitor the malignant compartment. The existing clinical tools used to determine treatment response and tumor relapse are limited in sensitivity mainly because they indirectly measure tumor burden inside the bone marrow and fail to capture the patchy, multisite tumor infiltrates associated with MM. Microparticles (MPs) are 0.1- to 1.0-μm membrane vesicles, which contain the cellular content of their originating cell. MPs are functional mediators and convey prothrombotic, promalignant, proresistance, and proinflammatory messages, establishing intercellular cross talk and bypassing the need for direct cell-cell contact in many pathologies. In this study, we analyzed plasma cell–derived MPs (CD138⁺) from deidentified MM patients (n = 64) and normal subjects (n = 18) using flow cytometry. The morphology and size of the MPs were further analyzed using scanning electron microscopy. Our study shows the proof of a systemic signature of MPs in MM patients. We observed that the levels of MPs were significantly elevated in MM corresponding to the tumor burden. We provide the first evidence for the presence of MPs in the peripheral blood of MM patients with potential applications in personalized MM clinical monitoring.

Multiscale Modeling in the Clinic: Drug Design and Development

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success

Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.

Expression Profiles of the Individual Genes Corresponding to the Genes Generated by Cytotoxicity Experiments with Bortezomib in Multiple Myeloma

OBJECTIVE

Multiple myeloma (MM) is currently incurable due to refractory disease relapse even under novel anti-myeloma treatment. In silico studies are effective for key decision making during clinicopathological battles against the chronic course of MM. The aim of this present in silico study was to identify individual genes whose expression profiles match that of the one generated by cytotoxicity experiments for bortezomib.

MATERIALS AND METHODS

We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of metadata derived from relevant information. The E-MTAB-783 dataset containing expression data from 789 cancer cell lines including 8 myeloma cell lines with drug screening data from the Wellcome Trust Sanger Institute database obtained from ArrayExpress was "Robust Multi-array analysis" normalized using GeneSpring v.12.5. Drug toxicity data were obtained from the Genomics of Drug Sensitivity in Cancer project. In order to identify individual genes whose expression profiles matched that of the one generated by cytotoxicity experiments for bortezomib, we used a linear regression-based approach, where we searched for statistically significant correlations between gene expression values and IC50 data. The intersections of the genes were identified in 8 cell lines and used for further analysis.

RESULTS

Our linear regression model identified 73 genes and some genes expression levels were found to very closely correlated with bortezomib IC50 values. When all 73 genes were used in a hierarchical cluster analysis, two major clusters of cells representing relatively sensitive and resistant cells could be identified. Pathway and molecular function analysis of all the significant genes was also investigated, as well as the genes involved in pathways.

CONCLUSION

The findings of our present in silico study could be important not only for the understanding of the genomics of MM but also for the better arrangement of the targeted anti-myeloma therapies, such as bortezomib.

Dosage and Dose Schedule Screening of Drug Combinations in Agent-Based Models Reveals Hidden Synergies

The fungus Candida albicans is the most common causative agent of human fungal infections and better drugs or drug combination strategies are urgently needed. Here, we present an agent-based model of the interplay of C. albicans with the host immune system and with the microflora of the host. We took into account the morphological change of C. albicans from the yeast to hyphae form and its dynamics during infection. The model allowed us to follow the dynamics of fungal growth and morphology, of the immune cells and of microflora in different perturbing situations. We specifically focused on the consequences of microflora reduction following antibiotic treatment. Using the agent-based model, different drug types have been tested for their effectiveness, namely drugs that inhibit cell division and drugs that constrain the yeast-to-hyphae transition. Applied individually, the division drug turned out to successfully decrease hyphae while the transition drug leads to a burst in hyphae after the end of the treatment. To evaluate the effect of different drug combinations, doses, and schedules, we introduced a measure for the return to a healthy state, the infection score. Using this measure, we found that the addition of a transition drug to a division drug treatment can improve the treatment reliability while minimizing treatment duration and drug dosage. In this work we present a theoretical study. Although our model has not been calibrated to quantitative experimental data, the technique of computationally identifying synergistic treatment combinations in an agent based model exemplifies the importance of computational techniques in translational research.

SDF-1α stiffens myeloma bone marrow mesenchymal stromal cells through the activation of RhoA-ROCK-Myosin II

Multiple myeloma (MM) is a B lymphocyte malignancy that remains incurable despite extensive research efforts. This is due, in part, to frequent disease recurrences associated with the persistence of myeloma cancer stem cells (mCSCs). Bone marrow mesenchymal stromal cells (BMSCs) play critical roles in supporting mCSCs through genetic or biochemical alterations. Previously, we identified mechanical distinctions between BMSCs isolated from MM patients (mBMSCs) and those present in the BM of healthy individuals (nBMSCs). These properties of mBMSC contributed to their ability to preferentially support mCSCs. To further illustrate mechanisms underlying the differences between mBMSCs and nBMSCs, here we report that (i) mBMSCs express an abnormal, constitutively high level of phosphorylated Myosin II, which leads to stiffer membrane mechanics, (ii) mBMSCs are more sensitive to SDF-1α-induced activation of MYL2 through the G(i./o)-PI3K-RhoA-ROCK-Myosin II signaling pathway, affecting Young's modulus in BMSCs and (iii) activated Myosin II confers increased cell contractile potential, leading to enhanced collagen matrix remodeling and promoting the cell-cell interaction between mCSCs and mBMSCs. Together, our findings suggest that interfering with SDF-1α signaling may serve as a new therapeutic approach for eliminating mCSCs by disrupting their interaction with mBMSCs.

Immunological dysregulation in multiple myeloma microenvironment

Multiple Myeloma (MM) is a systemic hematologic disease due to uncontrolled proliferation of monoclonal plasma cells (PC) in bone marrow (BM). Emerging in other solid and liquid cancers, the host immune system and the microenvironment have a pivotal role for PC growth, proliferation, survival, migration, and resistance to drugs and are responsible for some clinical manifestations of MM. In MM, microenvironment is represented by the cellular component of a normal bone marrow together with extracellular matrix proteins, adhesion molecules, cytokines, and growth factors produced by both stromal cells and PC themselves. All these components are able to protect PC from cytotoxic effect of chemo- and radiotherapy. This review is focused on the role of immunome to sustain MM progression, the emerging role of myeloid derived suppressor cells, and their potential clinical implications as novel therapeutic target.