Sparse Whole Genome Sequencing CNV Profile Adjustment for Tumor Contamination

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Solution Overview

Problem

Current methods for determining copy number variation (CNV) profiles in tumor cells are complicated by tumor cell contamination, requiring accurate adjustment to achieve reliable results, and existing technologies lack cost-effective and efficient solutions for characterizing CNV profiles using sparse whole genome sequencing.

Innovation Solution

A system and method that utilize sparse genome data to generate an unadjusted CNV profile, normalize it, and adjust for ploidy and contamination rates to select the best fit CNV profile, generating an adjusted report that accounts for contamination without needing control samples, leveraging sparse whole genome sequencing for cost-effectiveness and sensitivity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If sparse whole genome sequencing is used to determine CNV profiles, then cost and time are reduced, but measurement precision and reliability deteriorate due to tumor cell contamination and mixed cell populations

Engineering Contradiction:

Improvetime for CNV determinationVSAvoidCNV profile accuracy

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The method extracts and separates the tumor cell CNV signal from the mixed cell population by deconvoluting the sequencing data to identify and remove contributions from non-tumor cells, stromal cells, and other contaminants, isolating the pure tumor CNV profile for accurate analysis

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system implements iterative refinement where initial CNV calls are used to estimate tumor purity and contamination levels, which then feed back into adjusted segmentation and purity correction algorithms to refine the CNV profile, repeating the process until convergence to achieve accurate results from sparse data

Inventive Principle:

Principle #23Feedback

2Measurement precision

If traditional CNV determination methods are used with control samples, then measurement precision improves, but device complexity and ease of operation worsen due to requiring matched normal samples and complex deconvolution

Engineering Contradiction:

ImproveCNV detection accuracyVSAvoidcomplexity of CNV profiling system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The method enables the tumor sample itself to serve as its own control by using the CNV profile data within the sample to estimate tumor purity and contamination levels, eliminating the need for separate matched normal samples or external control materials while maintaining accurate CNV detection

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs multiple functions using the same sparse sequencing data: it simultaneously determines CNV profiles, estimates tumor purity, corrects for contamination, and identifies actionable mutations without requiring separate assays or control samples, simplifying the overall workflow while maintaining precision

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Measurement precision

If adjusted segmentation and purity correction are applied, then CNV profile accuracy improves, but loss of information increases due to multiple transformation steps

Engineering Contradiction:

Improveadjusted CNV profile accuracyVSAvoidinformation loss during normalization and adjustment

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The method performs preliminary normalization of the sparse sequencing data to a standard reference genome before applying segmentation and purity correction, preserving the original signal characteristics and enabling reversible transformations that minimize information loss throughout the analysis pipeline

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20230011085A1Method and system for determining a CNV profile for a tumor using sparse whole genome sequencing

Publication Date: 2023.01.12 KONINKLIJKE PHILIPS NV

AI summary

A method (100) for determining a copy number variation (CNV) profile, comprising: (i) receiving (110) sparse genome sequencing data; (ii) determining (120) an unadjusted CNV profile; (iii) normalizing (130) the unadjusted CNV profile; (iv) receiving (140) a range for possible ploidy and for a possible contamination rate; (v) determining (150) adjusted segmentation values for the CNV profile; (vi) determining (160) a plurality of adjustment scores comprising a distance between an adjusted segmentation value and a closest whole integer for a CNV call; (vii) comparing (170) the determined plurality of adjustment scores to one or more predetermined factors for selecting a CNV profile best fit; (viii) selecting (180) one of the plurality of adjustment scores as a best fit for the copy number variation profile of the tumor cells of the tumor; (ix) generating (190) an adjusted CNV profile report; and (x) reporting (192) the generated adjusted CNV profile report.