Driver Mutation Identification via Tumor Developmental History Reconstruction
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Solution Overview
Problem
Current methods for identifying driver mutations in cancer are not personalized or humanized, failing to consider the genetic context and tumor microenvironment, leading to ineffective targeted therapies and high cancer mortality.
Innovation Solution
A method involving the collection of multiple micro-samples from a tumor, conversion into an aggregate sample, DNA sequencing, and calculation of cancer cell fraction values to reconstruct tumor developmental history and identify driver mutations directly influencing tumor growth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current driver mutation identification methods (based on frequency and in vitro evidence) are used, then driver mutations can be identified using existing test kits, but the identified mutations do not reflect their actual role in individual patient tumor progression leading to ineffective targeted therapies
Solution Approach 1:
The tumor sample is divided into multiple micro-samples (M-samples), each containing a small number of cells (1-100 cells). This segmentation allows individual tracking of cell lineages and mutations through unique molecular barcodes, enabling precise determination of which mutations are present in specific tumor cell subpopulations and their evolutionary relationships.
Solution Approach 2:
The patent uses molecular barcoding to create unique genetic copies for each cell or small group of cells in the M-samples. These barcoded copies serve as tracers that allow reconstruction of tumor evolutionary history by tracking which mutations co-occur in the same clonal lineages, providing direct evidence of driver mutations rather than relying on indirect frequency-based predictions.
2Ease of manufacture
If aggregate tumor samples are analyzed without micro-sampling, then the analysis is simpler and less costly, but intra-tumor heterogeneity cannot be resolved and driver mutations cannot be accurately identified
Solution Approach 1:
The tumor is physically segmented into multiple M-samples, each containing limited cells (1-100 cells). This segmentation enables resolution of intra-tumor heterogeneity by analyzing mutation patterns in discrete cell populations, while the barcoding system maintains traceability to reconstruct the overall tumor evolutionary picture.
Solution Approach 2:
The patent implements a nested sampling structure where M-samples (small cell groups) are nested within the larger tumor context. Each M-sample contains barcoded cells that are nested within clonal lineages, which are themselves nested within the overall tumor evolutionary tree, allowing analysis at multiple resolution levels simultaneously.
3Measurement precision
If multiple micro-samples are collected and analyzed individually, then tumor developmental history can be reconstructed to identify true driver mutations, but the process becomes more complex and time-consuming
Solution Approach 1:
Molecular barcodes are introduced into cells during the initial M-sample collection phase, before any analysis occurs. This preliminary action of barcoding enables all subsequent analyses to be performed more efficiently, as the tracking information is already embedded in the samples, eliminating the need for complex retrospective lineage tracking.
Solution Approach 2:
The patent uses molecular barcodes as an intermediary that bridges the gap between simple sample collection and complex evolutionary reconstruction. The barcodes serve as mediators that carry clonal lineage information through all processing steps, allowing computational algorithms to reconstruct tumor history without requiring direct observation of cell division events.
4Productivity
If pre-determined driver mutations from aggregated data are used, then treatment decisions can be made quickly, but the genetic context and tumor microenvironment of individual patients are not considered
Solution Approach 1:
The patent applies local quality analysis by examining mutation patterns within specific local contexts - individual M-samples and their clonal lineages. Instead of applying uniform driver mutation definitions from aggregated data, the method determines which mutations are locally relevant to each patient's specific tumor evolutionary history and genetic context, enabling personalized treatment decisions.
Solution Approach 2:
The patent transitions from static, pre-determined driver mutation lists to a dynamic assessment of driver mutations based on reconstructed tumor evolutionary trajectories. The identification of driver mutations becomes a dynamic process that adapts to each patient's specific tumor history, considering the temporal sequence of mutation acquisition and selection pressures in their unique tumor microenvironment.
Data Source
AI summary
In certain embodiments, the present invention provides a method to identify driver mutations in a tumor and its cancer cell subpopulations. The method of the present invention is used to design treatment strategies for cancer patients.