Cancer Cell Fraction Estimation in Low-Purity ctDNA
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Solution Overview
Problem
Current methods for determining the clonal architecture of tumors using circulating tumor DNA (ctDNA) are ineffective in ultra-low purity samples, which are common in localized or minimal residual disease settings, due to the inability to accurately call copy number events, leading to inaccurate estimation of cancer cell fraction (CCF) and poor therapeutic targeting.
Innovation Solution
A computer-implemented method that estimates CCF by using sequence data from both tumor tissue and ctDNA samples, incorporating variant allele fraction, multiplicity, and copy number information, along with background sequencing noise correction, to deconvolve clonal architecture and minimize sampling bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If liquid biopsies using ctDNA are used for tumour monitoring, then representative tumour sampling at regular intervals is achieved, but current clonal deconvolution methods are ineffective in low tumour content samples
Solution Approach 1:
The method merges data from multiple sources: ctDNA sequencing data, tumour tissue sequencing data, and background noise estimates. By combining these diverse data sources and integrating them through a unified computational framework, the method achieves accurate CCF estimation even in low-purity samples where individual methods would fail.
Solution Approach 2:
The method transforms the problem by changing the parameters used for analysis. Instead of relying solely on raw VAF measurements which are unreliable in low-purity samples, the method incorporates copy number state parameters and background noise parameters to reconfigure the deconvolution equations, enabling accurate analysis of low-tumour-content samples.
2Measurement precision
If tissue sampling is used to determine clonal architecture, then comprehensive tumour tissue sampling is attempted, but significant sampling bias is present because spatially restricted clones may be over-sampled or under-sampled
Solution Approach 1:
The method uses ctDNA as an intermediary to bridge the gap between tissue sampling and clonal architecture determination. ctDNA serves as a mediator that reflects the true clonal composition of the tumour without being subject to spatial sampling bias, as it circulates systemically and represents the entire tumour burden.
Solution Approach 2:
Instead of directly sampling tissue to determine clonal architecture (which introduces bias), the method inverts the approach by using ctDNA sequencing data combined with tissue sequencing data to infer clonal architecture. This indirect approach through ctDNA avoids the sampling bias inherent in direct tissue sampling.
3Productivity
If current clonal deconvolution methods are applied to ctDNA samples, then analysis is performed, but inaccurate CCF estimation results due to ultra-low purity of samples
Solution Approach 1:
The method performs preliminary actions by first obtaining and analyzing tumour tissue sequencing data to establish baseline copy number states and clonal group assignments before analyzing ctDNA samples. This preparatory step provides reference information that enables accurate interpretation of the low-signal ctDNA data.
Solution Approach 2:
The method creates a composite analytical approach by combining multiple data types (ctDNA VAF data, tissue sequencing data, copy number data, background noise estimates) into a unified analysis framework. This composite methodology leverages the strengths of each data source to overcome the limitations of individual approaches in low-purity samples.
Data Source
AI summary
The present invention provides a computer-implemented method for estimating the cancer cell fraction (CCF) of at least one tumour-specific mutation in a subject. Also provided are related methods for monitoring the clonal dynamics of a tumour, monitoring a treatment of the tumour and methods for treating a subject having a cancer, as well as systems for implementing the methods of the invention.