Getting to the root of the problem: the causes of relapse in multiple myeloma

Chung, Kim Chan; Tiedemann, Rodger E.

doi:10.1586/14737140.2014.868776

Cited by 9 publications

(7 citation statements)

References 35 publications

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“…The system can help further our knowledge of MM pathobiology by enabling studies on the communication between multiple cell types within the MM bone marrow microenvironment, which is not well understood, but critical to understanding disease progression. 15,34 The system could also aid in the pre-clinical evaluation of new MM therapies, or be used as a chemo-sensitivity resistance assay (CSRA) device to identify the most appropriate treatment for individual MM patients, as demonstrated recently. 19 The current study provides strong evidence that soluble factor concentration is nearly uniform across the center well, verifying the integrity of the single-cell data obtained from this system, and further validating the usefulness of the platform as a low-cost, high-throughput tool for advancing MM biology research and drug treatment selection for personalized medicine applications.…”

Section: Discussionmentioning

confidence: 99%

Single cell functional analysis of multiple myeloma cell populations correlates with diffusion profiles in static microfluidic coculture systems

Moore

Young

2016

Biomicrofluidics

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Microfluidic cell culture systems are becoming increasingly useful for studying biology questions, particularly those involving small cell populations that are cultured within microscale geometries mimicking the complex cellular microenvironment. Depending on the geometry and spatial organization of these cell populations, however, paracrine signaling between cell types can depend critically on spatial concentration profiles of soluble factors generated by diffusive transport. In scenarios where single cell data are acquired to study cell population heterogeneities in functional response, uncertainty associated with concentration profiles can lead to interpretation bias. To address this issue and provide important evidence on how diffusion develops within typical microfluidic cell culture systems, a combination of experimental and computational approaches were applied to measure and predict concentration patterns within microfluidic geometries, and characterize the functional response of culture cells based on single-cell resolution transcription factor activation. Using a model coculture system consisting of multiple myeloma cells (MMCs) and neighboring bone marrow stromal cells (BMSCs), we measured concentrations of three cytokines (IL-6, VEGF, and TNF-α) in conditioned media collected from separate culture compartments using a multiplex ELISA system. A 3D numerical model was developed to predict biomolecular diffusion and resulting concentration profiles within the tested microsystems and compared with experimental diffusion of 20 kDa FITC-Dextran. Finally, diffusion was further characterized by controlling exogenous IL-6 diffusion and the coculture spatial configuration of BMSCs to stimulate STAT3 nuclear translocation in MMCs. Results showed agreement between numerical and experimental results, provided evidence of a shallow concentration gradient across the center well of the microsystem that did not lead to a bias in results, and demonstrated that microfluidic systems can be tailored with specific geometries to avoid spatial bias when desired.

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Section: Discussionmentioning

confidence: 99%

Single cell functional analysis of multiple myeloma cell populations correlates with diffusion profiles in static microfluidic coculture systems

Moore

Young

2016

Biomicrofluidics

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“…Important findings included the frequent MYC translocation with non-immunoglobulin heavy (IgH) chain locus partner (5/11) and the overall pattern of driver alterations similar to overt MM, indicating a clear earliness of their onset during myelomagenesis (40). Analysis of a significant interaction between driver events revealed two associations, between PRDM1 deletions and t (4,14), which confers a worse OS and between PRDM1 deletions and BIRC2/3 deletions, which confers a better OS.…”

Section: Genetics and Genomics Of Multiple Myeloma And Pathways Of Stmentioning

confidence: 99%

“…More recently, a great deal of research efforts are being devoted to the immunotherapy with anti-B-cell maturation antigen (BCMA) Chimeric Antigen Receptor (CAR)-T cells or bispecific T cell engagers (2,3). Nonetheless, MM remains a difficultto-eradicate tumor because it displays a great predisposition toward biological heterogeneity and clonal evolution in time and space that ultimately confers resilience to stress and resistance to cytotoxic agents (4)(5)(6)(7)(8). Being the pathobiological features of MM as such, the identification of targets that sustain MM cell "invulnerability" seems a central research goal to pursue.…”

Section: Introductionmentioning

confidence: 99%

Actionable Strategies to Target Multiple Myeloma Plasma Cell Resistance/Resilience to Stress: Insights From “Omics” Research

et al. 2020

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While the modern therapeutic armamentarium to treat multiple myeloma (MM) patients allows a longer control of the disease, this second-most-frequent hematologic cancer is still uncurable in the vast majority of cases. Since MM plasma cells are subjected to various types of chronic cellular stress and the integrity of specific stress-coping pathways is essential to ensure MM cell survival, not surprisingly the most efficacious anti-MM therapy are those that make use of proteasome inhibitors and/or immunomodulatory drugs, which target the biochemical mechanisms of stress management. Based on this notion, the recently realized discoveries on MM pathobiology through high-throughput techniques (genomic, transcriptomic, and other "omics"), in order for them to be clinically useful, should be elaborated to identify novel vulnerabilities in this disease. This groundwork of information will likely allow the design of novel therapies against targetable molecules/pathways, in an unprecedented opportunity to change the management of MM according to the principle of "precision medicine." In this review, we will discuss some examples of therapeutically actionable molecules and pathways related to the regulation of cellular fitness and stress resistance in MM.

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“…74 Accumulating evidence suggests most hematologic cancer patients respond to initial treatments, but relapse and fail to respond to further therapies, resulting in drug resistance. 25,57,75,76 Although there are several stand-alone and combination therapeutic options available to use in the latter case, 77,78 rapidly identifying an effective therapy is critical for a patient with a short median survival rate. 79–81 Hence, to select therapies for individual hematologic cancer patients in a clinical setting, it is instructive to address the following issues: (1) reducing the time to identify effective therapies for patients, (2) identifying the sensitivity and resistivity of patients to specific therapeutic drugs, (3) identifying patients for whom a drug could induce adverse side effects, and (4) personalizing treatment selection to improve treatment decision-making and clinical outcomes.…”

Section: Challenges In Drug Development and Treatment Selectionmentioning

confidence: 99%

Biomicrofluidic Systems for Hematologic Cancer Research and Clinical Applications

et al. 2019

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A persistent challenge in developing personalized treatments for hematologic cancers is the lack of patient specific, physiologically relevant disease models to test investigational drugs in clinical trials and to select therapies in a clinical setting. Biomicrofluidic systems and organ-on-a-chip technologies have the potential to change how researchers approach the fundamental study of hematologic cancers and select clinical treatment for individual patient. Here, we review microfluidics cell-based technology with application toward studying hematologic tumor microenvironments (TMEs) for the purpose of drug discovery and clinical treatment selection. We provide an overview of state-of-the-art microfluidic systems designed to address questions related to hematologic TMEs and drug development. Given the need to develop personalized treatment platforms involving this technology, we review pharmaceutical drugs and different modes of immunotherapy for hematologic cancers, followed by key considerations for developing a physiologically relevant microfluidic companion diagnostic tool for mimicking different hematologic TMEs for testing with different drugs in clinical trials. Opportunities lie ahead for engineers to revolutionize conventional drug discovery strategies of hematologic cancers, including integrating cell-based microfluidics technology with machine learning and automation techniques, which may stimulate pharma and regulatory bodies to promote research and applications of microfluidics technology for drug development.

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Getting to the root of the problem: the causes of relapse in multiple myeloma

Cited by 9 publications

References 35 publications

Single cell functional analysis of multiple myeloma cell populations correlates with diffusion profiles in static microfluidic coculture systems

Single cell functional analysis of multiple myeloma cell populations correlates with diffusion profiles in static microfluidic coculture systems

Actionable Strategies to Target Multiple Myeloma Plasma Cell Resistance/Resilience to Stress: Insights From “Omics” Research

Biomicrofluidic Systems for Hematologic Cancer Research and Clinical Applications

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