Cancer Genome Visualization System for Clonal Evolution Tracking
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Solution Overview
Problem
Current technologies lack the ability to effectively model cancer evolution under selective pressure from treatments, leading to inadequate visualization of genomic and clonal changes, which hampers informed treatment decisions.
Innovation Solution
A platform-agnostic system that identifies and tracks cancer clones through genomic and molecular analysis, using algorithms like PyClone-VI and Pairtree, to generate dynamic visualizations correlating phylogenetic data with clinical information, enabling clinicians to make data-driven treatment choices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional cancer treatments are applied, then tumor burden is reduced in the short term, but cancer relapses occur due to clonal evolution and selection pressure
Solution Approach 1:
The system performs preliminary identification and tracking of resistant subclones through serial sampling and phylogenetic analysis before they become dominant. By detecting early signs of clonal evolution and resistance mechanisms, the system enables proactive treatment adjustments to prevent relapse rather than reacting after cancer returns
Solution Approach 2:
The system continuously monitors tumor evolution through serial sampling and uses phylogenetic trees to track clonal changes over time. This feedback loop allows the treatment strategy to be dynamically adjusted based on real-time detection of resistant subclones, transforming static treatment protocols into adaptive, evolution-responsive therapy
2Loss of information
If serial sampling and genomic sequencing are performed, then clonal evolution can be tracked, but the complexity of data analysis and visualization increases
Solution Approach 1:
The system segments the complex genomic data into manageable components by identifying specific mutations, tracking their allele frequencies across samples, and organizing them into phylogenetic trees. This segmentation transforms raw sequencing data into structured evolutionary narratives that are easier to interpret clinically
Solution Approach 2:
The system uses phylogenetic trees as an intermediary representation that bridges raw genomic data and clinical decision-making. These visualizations translate complex molecular evolution patterns into intuitive graphics that show lineage relationships and resistance development, making the data accessible to clinicians without requiring expert bioinformatics knowledge
3Productivity
If treatment decisions are based on current tumor composition, then immediate response can be optimized, but future relapse risk is not accounted for
Solution Approach 1:
The system identifies and tracks resistant subclones in advance before they take over the tumor population. By performing preliminary phylogenetic analysis on serial samples, the system detects early evolutionary changes that predict future relapse, allowing treatment adjustments to be made proactively to ensure long-term durability
Solution Approach 2:
The system transitions from static treatment decisions based on current tumor composition to dynamic, evolution-responsive therapy. By continuously updating phylogenetic trees with new sampling data, the system adapts treatment strategies in real-time to account for ongoing clonal evolution, optimizing both immediate response and long-term durability
Data Source
AI summary
A system generates a visualization that represents progression of a cancer genome. To generate the visualization, the system obtains a genetic dataset derived from cancer cell samples of a patient and clinical data associated with the patient. A genomic data structure is generated to represent the genetic dataset, and clinical event data structures are generated to represent each of a plurality of clinical events based on the clinical data. The visualization includes interactive elements that are populated based on the genomic data structure and the clinical event data structures. When a user input associated with a first interactive element is detected, the system displays information associated with the first interactive element based on the genomic data structure or the clinical event data structures.