Description

Project Description

Problem

Multiple Myeloma (MM) is an aggressive blood cancer of plasma cells in the bone marrow [1].
MM constitutes approximately 10% of all hematologic cancers, underscoring its significance within the spectrum of hematopoietic cancers [2]. Despite advances in therapy, MM remains incurable as most patients eventually relapse and develop resistance to treatment [3]. In particular, mutations that activate the RAS/MAPK signaling pathway (such as mutations in the NRAS gene) occur in approximately 30-40% of MM cases and are associated with a more aggressive disease and shorter patient survival [4]

Figure 1
Figure 1 (RAS/MAPK pathway - Growth-factor binding to a receptor tyrosine kinase activates RAS at the plasma membrane, which in turn triggers the RAF → MEK → ERK kinase chain. Phosphorylated ERK translocates into the nucleus and modulates transcription programs that drive cell proliferation, survival, differentiation, and migration, while built-in GAPs, phosphatases, and feedback loops maintain the precision of the signal.)

Our project focuses on one such mutation, NRAS-G12D, a single amino acid change (Gly12Asp) that locks NRAS in a permanent “on switch.” This oncogenic mutation drives endless growth and survival signaling, enabling MM cells to proliferate more rapidly and evade cell death [5].

Figure 2
Figure 2 (RAS/MAPK pathway affected by NRAS-G12D mutation)

While some therapies have improved outcomes for certain patients, MM remains incurable, especially for those harboring aggressive genetic mutations such as NRAS-G12D. This mutation is associated with a poor prognosis and resistance to treatment; however, there is a lack of physiologically relevant models to study its functional impact. Bridging this gap is essential for advancing precision medicine in hematologic malignancies.

In summary, the NRAS-G12D mutation exemplifies how a single genetic alteration can drive the progression of MM. Our team aims to overcome current research limitations by developing a more predictive experimental system.

Challenges in understanding and treating Multiple Myeloma

<Influence of Bone Marrow Microenvironment on MM>

MM’s behavior is heavily influenced by its surrounding environment in the bone marrow. The tumor microenvironment (TME) comprises supportive stromal cells, immune cells, blood vessels, and the extracellular matrix, all of which interact with the cancer cells [6]. This nurturing niche secretes growth factors like IL-6 and VEGF that promote myeloma cell survival [7], and it provides a physical barrier (via cell adhesion and extracellular matrix) that helps myeloma cells survive and even evade immune attack [6]. The protective bone marrow environment, combined with mutations such as NRAS-G12D that enhance cell survival signaling, enables myeloma cells to develop resistance to chemotherapy [6][8].

<Gaps in Preclinical Models and Research Challenges>

However, studying these TME interactions in a lab setting is challenging [9]. Traditional 2D cell culture models (plastic dishes) fail to capture the 3D architecture and cell-to-cell interactions of real bone marrow, often leading to oversimplified or misleading results [10]. Animal models can partially mimic human myeloma, but they are expensive, time-consuming, and ethically controversial [10].

Figure 3
Figure 3 (Animal testing is expensive, time-consuming, and often considered unethical, yet it is not always accurate)

Notably, current preclinical models often fail to predict clinical outcomes accurately. Over 90% of drug candidates that show promise in laboratory or animal tests ultimately fail in human trials, usually because these models do not adequately represent human biology [11]. There is clearly a need for more predictive, human-relevant cancer models that can bridge the gap between cell culture and patient outcomes. In the context of MM, this means developing experimental systems that closely reflect the 3D bone marrow microenvironment and account for the impact of specific genetic mutations (such as the NRAS-G12D mutation) on tumor behavior [9].

Figure 4
Figure 4(Comparison of traditional 2D culture vs. a 3D bone marrow environment. In 2D (left), myeloma cells grow on a flat surface with unnaturally uniform conditions. In a 3D bone marrow-like setting (right), cells interact with a matrix, neighboring cells, and nutrients or drug factors that can alter proliferation and drug response.)
Table 1
Table 1 (Comparison of traditional 2D culture vs. a 3D BM environment)

Our approach:

To address the limitations of current models for studying MM, our team developed a lab-on-a-chip (LOC) platform to model the functional effects of NRAS mutation in an MM-like context under controlled, yet physiologically relevant conditions. A LOC is essentially a small, chip-sized system of tiny channels and chambers that can replicate key laboratory functions (cell culture, mixing, analysis, etc) at a microscopic level. Compared to traditional models, LOC platforms offer real-time observation, controlled microenvironmental conditions, and reduced use of animal models, making them ideal for translational cancer research. In this miniature three-dimensional environment, the engineered cells reside in a matrix and experience conditions designed to mimic the human bone marrow microenvironment. Such tumor-on-a-chip systems have been shown to culture cancer cells in conjunction with supporting cells, enabling more accurate predictions, such as drug response and invasion. Bone marrow-on-a-chip devices have already been used in research to study diseases such as leukemia, showing the promise of this approach [12].

Figure 5
Figure 5 (Structure of LOC)

The NRAS gene encodes a signaling protein that regulates cell growth. In MM, mutations such as NRAS lock this protein in a constantly active state, leading to uncontrolled proliferation and potentially increased migration and drug resistance. In clinical settings, NRAS mutations are detected in approximately 18% of MM patients and are associated with poor prognosis. While our cell model is not derived from MM, it serves as a controlled system to investigate how this mutation may influence cellular behavior.

To investigate the functional impact of this mutation, plasmids encoding NRAS^WT and NRAS^G12D with an mCherry reporter were introduced into NIH/3T3 cells. Stable cell pools were established to ensure consistent reporter expression and reduce heterogeneity, allowing direct comparison between mutant, wild-type, and unmodified NIH/3T3 cells.

This setup enabled direct comparison of three groups:

  1. 1. NRAS-mutant NIH/3T3 cells
  2. 2. Non-mutant (wild-type) NIH/3T3 cells
  3. 3. unmodified NIH/3T3 (mouse fibroblast)

By isolating mutations as the sole variable, we can examine how the NRAS gene mutation alone affects the behavior of NIH/3T3 cells as a model system.

Our lab-on-a-chip system includes two biomimetic barriers to replicate the bone marrow microenvironment more realistically:

  1. 1. A thin layer of 3D hydrogel matrix on top of the membrane, simulating the extracellular matrix (ECM) structure and stiffness of bone marrow.
  2. 2. A porous membrane beneath the matrix serves as a secondary physical barrier, similar to vascular or endosteal interfaces in bone.
Figure 6
Figure 6 (Created in https://BioRender.com)

After seeding the NIH/3T3 cells on top, we observe how they migrate through:

  • First, the hydrogel matrix that mimics the 3D resistance of the ECM.
  • Then, the porous membrane that mimics structural constraints like endothelial layers.

This setup enables us to study not only cell motility but also invasiveness through a layered, physiologically relevant environment, making it a powerful model for metastasis and drug response studies.

Although the chip doesn’t fully replicate the complexity of the bone marrow, it provides a structured and resistive microenvironment that is much closer to physiological conditions than standard culture methods [13].

By combining synthetic gene modulation with a microfluidic platform, we can ask targeted biological questions, such as Does NRAS mutation increase cell migration? and get quantitative, real-time answers. This approach not only improves our understanding of MM but also provides a scalable foundation for future personalized medicine research.

Figure 7
Figure 7 (Created in https://BioRender.com)

References

  1. [1] Mayo Clinic. (2017). Multiple myeloma - symptoms and causes. Mayo Clinic; Mayo Clinic.
    https://www.mayoclinic.org/diseases-conditions/multiple-myeloma/symptoms-causes/syc-20353378
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    https://doi.org/10.1002/ajh.26590
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    https://doi.org/10.3389/fonc.2024.1413494
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    https://doi.org/10.3390/jcm14020327
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    https://doi.org/10.1186/s13578-022-00887-3
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