Phylogenetic Tumour Mapping for Subclonal Cancer Risk Assessment
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Solution Overview
Problem
The molecular origins and clinical consequences of tumour subclonality in cancer, particularly prostate cancer, remain unclear due to limited spatio-genomic studies, leading to uncertainties in prognostic biomarkers and treatment responses.
Innovation Solution
Reconstructing the subclonal architectures of cancer samples using phylogenetic mapping of genetic aberrations from cancer DNA sequencing data to determine clonal and subclonal populations, assigning risk levels based on the number and evolution of these populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatio-genomic studies are conducted on multiple regions of a single tumour, then the understanding of tumour subclonality and evolutionary architecture is improved, but the complexity of the study and the number of samples required increases significantly
Solution Approach 1:
The patent segments the tumour into multiple distinct regions and sequences each region separately to identify different clonal populations. This segmentation approach allows the complex tumour heterogeneity to be broken down into manageable sub-clones, each with its own mutational profile, thereby improving understanding of tumour evolution while making the study feasible through systematic regional analysis
Solution Approach 2:
The patent adds the spatial dimension to genomic analysis by mapping genetic aberrations to specific tumour regions. This dimensional approach transforms conventional single-sample sequencing into a multi-dimensional study that captures both genetic variation and spatial distribution, enabling reconstruction of tumour evolutionary architecture without requiring excessively large sample sizes
2Measurement precision
If multiple regions of a single tumour are sequenced to identify subclonal populations, then the accuracy of prognostic assessment is improved, but the cost and resource requirements increase
Solution Approach 1:
The patent extracts specific genetic aberrations and mutational signatures from each tumour region that are most informative for prognostic assessment. By focusing on key diagnostic markers rather than analyzing all genetic variations, the method achieves high prognostic accuracy while reducing the effective number of samples and resources needed for comprehensive analysis
Solution Approach 2:
The patent applies partial sequencing coverage to multiple regions rather than full sequencing of fewer samples. This approach sequences selected genomic regions across multiple tumour areas, providing sufficient information for accurate prognostic assessment of subclonal populations without the excessive resource requirements of complete genome sequencing of all regions
3Reliability
If clonal and subclonal populations are distinguished through genetic aberration analysis, then the ability to predict treatment response and patient outcome is improved, but the complexity of data analysis and interpretation increases
Solution Approach 1:
The patent performs preliminary classification of genetic aberrations into clonal and subclonal categories based on their frequency and distribution patterns across tumour regions. This preliminary action establishes a framework for interpreting subsequent data, reducing the complexity of final analysis by pre-organizing genetic variations according to their evolutionary significance and potential treatment implications
Solution Approach 2:
The patent uses phylogenetic trees as an intermediary structure to visualize and interpret the relationships between different clonal populations. This intermediary representation transforms complex genetic data into an intuitive evolutionary map, making it easier to understand tumour heterogeneity and predict treatment responses without directly analyzing the full complexity of raw genetic aberration data
Data Source
AI summary
In an aspect, there is provided a method for diagnosing or prognosing a subject with cancer, the method comprising: providing cancer DNA sequencing data from a cancer sample comprising cancer DNA from the subject; comparing the cancer DNA sequencing data with control DNA sequencing data to determine genetic aberrations; determining, from the genetic aberrations, the clonal and subclonal populations present in the sample; constructing a phylogenetic map of the clonal and subclonal populations; assigning to the subject a risk level associated with a better or worse patient outcome or response to therapy; wherein a relatively higher risk level is associated with a higher level of evolution and number of subclonal populations and a relatively lower risk level is associated with a lower level of evolution and number of subclonal populations.