The Challenge
Cancer is not a static disease but rather a dynamic process of genetic and epigenetic evolution. Under the selective pressure of treatments, resistant subclones can emerge, leading to treatment failure and disease progression. Understanding and predicting this evolutionary process is one of the most significant challenges in modern oncology.
Traditional approaches to cancer treatment often fail to account for this evolutionary capacity, resulting in short-lived responses followed by resistance. Current methods for tracking cancer evolution, such as repeated biopsies, are invasive and provide only a snapshot of a highly dynamic process, making it difficult to anticipate resistance mechanisms before they become clinically apparent.
Our Approach
Our research focuses on developing advanced AI systems that could model and predict cancer evolutionary trajectories. By utilizing evolutionary algorithms, machine learning, and mathematical modeling, we aim to:
- Reconstruct the evolutionary history of tumors from multi-region or single-cell sequencing data
- Predict likely resistance mechanisms based on a tumor's genomic profile and treatment history
- Model clonal dynamics and competitive interactions within tumors over time
- Design optimal treatment sequences that account for evolutionary constraints and resistance vulnerabilities
Evolutionary Trajectory Modeling
Using phylogenetic algorithms and machine learning to map the evolutionary history of tumors and predict future evolutionary paths under different treatment pressures.
Adaptive Therapy Algorithms
Developing AI systems that can dynamically adjust treatment regimens based on real-time monitoring of tumor responses, maintaining control while preventing the emergence of resistance.
Resistance Mechanism Prediction
Building predictive models that identify the most likely resistance mechanisms a tumor will develop, enabling proactive treatment strategies that target these vulnerabilities.
Sequential Therapy Optimization
Creating algorithms that determine optimal sequences of treatments based on evolutionary principles to maximize the duration of disease control and overall survival.
Potential Research Projects
Evolutionary Mapping of Metastatic Progression
Using AI to integrate multi-region sequencing data from primary and metastatic sites to reconstruct the evolutionary history of metastatic spread. This could identify common patterns of metastatic evolution and potentially reveal vulnerabilities specific to metastatic cells that could be targeted therapeutically.
AI-Guided Adaptive Therapy
Developing machine learning algorithms that can suggest optimal dosing schedules for chemotherapy or targeted agents based on evolutionary principles. By maintaining a stable tumor burden rather than pursuing maximum tumor kill, this approach could potentially extend the time to resistance development.
Liquid Biopsy Evolutionary Tracking
Creating AI systems that analyze circulating tumor DNA from sequential liquid biopsies to track clonal evolution in real-time. This non-invasive approach could detect emerging resistant clones before clinical progression, allowing for early intervention with alternative therapies.
Future Directions
Our long-term research goals in cancer evolution and resistance include:
- Development of AI systems that can integrate spatial, temporal, and multi-omic data to create comprehensive evolutionary models of individual tumors
- Creation of digital twin models that simulate tumor evolution in silico, allowing rapid testing of multiple treatment strategies
- Design of evolutionary-informed combination therapies that simultaneously target multiple resistance pathways
- Integration of ecological principles to model tumor-microenvironment interactions and their influence on evolutionary trajectories
Collaborations and Partnerships
We would seek partnerships with:
Cancer Evolution Laboratories
To access experimental data and validate our evolutionary models
Computational Biology Centers
To collaborate on algorithm development and evolutionary modeling
Clinical Trial Networks
To test evolutionary-informed treatment strategies in patient populations
Research Background
This research area contributes to the growing body of knowledge in AI-powered cancer research. We're currently developing foundational work in this space.
Related Research Areas
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