Revolutionary Tumor Reversion Method
Dynamic Modeling of Cellular Identity
Reimagining the Fight Against Cancer: From KAIST's Attractor Landscape to Our Dynamic Tumor Reversion Approach
Cancer, one of the most challenging diseases of our era, has traditionally been fought with strategies focused on eradicating malignant cells. However, a new frontier of research seeks not only to destroy, but to "reprogram" or "revert" cancer cells to a healthy state.
A fundamental milestone in this journey was the development of the REVERT method by the KAIST team (Korea Advanced Institute of Science and Technology), which introduced an innovative way to visualize and manipulate cellular fate through the concept of attractor landscapes.
Inspired by this pioneering vision and seeking to expand its frontiers, our method aims to deepen the understanding of the dynamics inherent to this landscape, proposing an even more comprehensive approach to tumor reversion.
The Attractor Landscape According to KAIST
Mapping Cellular Destiny and Transitions
The KAIST work with the REVERT method brilliantly demonstrated how dynamical systems theory can be applied to understand cancer. They conceptualized the state of a cell – whether normal or cancerous – as an "attractor" within a vast "landscape" of molecular possibilities, where cells can transition between different states.
The Central Concept:
Imagine a landscape with valleys and mountains. Each valley represents a stable cellular state (an attractor) toward which cells tend to move and remain. Healthy cells reside in a valley of "normality," while cancer cells are trapped in a distinct valley of "malignancy." Crucially, the model allows cells to transition between these valleys.
The KAIST team used single-cell gene expression data to build computational models (Boolean networks) representing gene interactions. They could infer the shape of this attractor landscape, identifying valleys corresponding to normal and cancerous states, as well as "critical transition states" or "inflection points" that cells traverse.
The REVERT method opened a promising path, proving that reversal of the cancerous phenotype is achievable through intelligent manipulation of cellular regulatory networks and navigation in the landscape of cellular states.
Expanding Horizons
Our Vision: "Dynamic Modeling of Cellular Identity"
While KAIST's work is fundamental in demonstrating the navigability of attractor landscapes, cancer biology shows us that the landscape itself defining cellular identity is not fixed terrain, but rather a playing field that changes. To advance robust tumor reversion capability, our approach, called "Dynamic Modeling of Cellular Identity," proposes a conceptual evolution.
Explicit Focus on Landscape Remodeling that Defines Cellular Identity
While previous methods focus on inducing transitions within a modeled landscape, our approach emphasizes that this landscape – the foundation of cellular identity – is actively and continuously remodeled by continuous somatic evolution, fluctuating microenvironment influences, and therapeutic impact on the landscape architecture itself.
An Ecology of Multiple Cellular Identities and Their Interconversions
We recognize a complex network of multiple potential attractors, representing not only "normal" and "cancerous" identities, but also various intermediate states, differentiation, senescence, epithelial-mesenchymal transition (EMT), or diverse cancer stem cell and resistance identities.
Understanding and exploring this meta-dynamics is key to tumor reversion. We seek to quantify the dynamics of barriers and transitions between cellular identities, identifying temporal windows where barriers for malignant identity reversion are naturally lower or can be therapeutically reduced.
Our Methodology
Dynamically Modeling Cellular Identity for Tumor Reversion
Based on our "Dynamic Modeling of Cellular Identity" approach, our method implements the following strategies:
Integrated and Predictive Multi-omic Modeling of Landscape and Cellular Identity Dynamics
We will use longitudinal multi-omic data (genomics, epigenomics, proteomics, metabolomics) to build computational models that not only map attractors defining existing cellular identities, but also predict how the landscape and consequently possible cellular identities will likely change in response to different conditions or over time.
Landscape Configuration-Dependent Vulnerability Identification and Adaptive Targets for Cellular Identity Modulation
Instead of seeking universal "switches," our method will identify targets and target combinations whose effectiveness in altering cellular identity depends on the current state and predicted trajectory of the dynamic landscape. This includes combinatorial and sequential strategies that adapt to landscape topography changes.
"Landscape Sculpting" Strategies to Redirect Cellular Identity and Evolutionary Trajectories
The goal is not just to "push" cells to a new identity, but to actively "sculpt" the landscape to make normal cellular identity more accessible and stable, and malignant identities less deep or easier to escape. This may involve modulating factors that control the very plasticity of the landscape governing cellular identity.
The Future of Oncological Therapy
Personalized and Proactive Cellular Identity Reversion
We believe that our "Dynamic Modeling of Cellular Identity" approach allows us to go beyond, building on foundations established by pioneers like the KAIST team. By embracing and actively modeling the mutable and adaptive character of cellular identity and the attractor landscape that governs it, our goal is to develop tumor reversion therapies that are:
More Proactive and Adaptive
Capable of anticipating and countering tumor evolution and the emergence of new resistant cellular identities.
Deeply Personalized
Adjusted not only to the current cellular identity of the tumor, but to its likely evolutionary trajectory in the dynamic landscape.
Strategically Robust
Targeting mechanisms that govern the very stability of malignant cellular identities and landscape plasticity.
We are dedicated to transforming the fight against cancer, moving from a reactive approach to a proactive cellular identity reprogramming strategy, guided by a deep understanding of its dynamic modeling and the attractor landscape that defines it.
This represents the pinnacle of what oncological therapy can achieve.
Beyond Current Limitations
Identifying the exact mechanism that pushes cells to the cancerous state and developing the reversion mechanism is just the beginning, as the REVERT method had problems with cells returning to the cancerous state or dying.
This leads us to believe that the reversion mechanism itself still needs improvement and/or the state prior to reversion could not return to its previous configuration due to some change in the landscape configuration.
This gives us a line of reasoning where we must modify certain cellular elements so that the attractor valley is on a trajectory with the lowest possible cost so that its reversion is not too drastic.
Our approach represents the evolution beyond current limitations, creating pathways for sustainable and robust cellular identity reversion.