MVP Score for Lung Cancer Clonality Differentiation
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Solution Overview
Problem
Current methods for distinguishing between multiple primary lung cancers (MPLCs) and intrapulmonary metastases are inadequate, leading to inaccurate staging and inappropriate treatment plans due to low detection sensitivity and stringent sample input requirements.
Innovation Solution
The development of methods and systems that quantify common clonality by analyzing somatic copy number variation (SCNV) in tissue samples using low pass whole genome sequencing (LPWGS) and bioinformatics pipelines, enabling differentiation between MPLCs and metastases with high accuracy and lower costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current clinical guidelines and market available methodologies are used to distinguish MPLCs from intrapulmonary metastases, then the diagnostic process is simple and accessible, but the detection sensitivity is low and accuracy is insufficient
Solution Approach 1:
The patent introduces somatic copy number variation (SCNV) analysis as an intermediary method to bridge the gap between simple clinical guidelines and accurate tumor classification. By analyzing SCNV patterns in tumor DNA, the method provides a quantitative measure of tumor relatedness that serves as a mediator to distinguish MPLCs from intrapulmonary metastases with high sensitivity, resolving the contradiction between detection accuracy and method complexity
Solution Approach 2:
The patent replaces traditional mechanical/pathological examination methods with molecular genetics-based SCNV analysis. This substitution enables detection of tumor clonality relationships at the DNA level, achieving high detection sensitivity without requiring complex surgical or pathological interventions, thus resolving the contradiction between measurement precision and device complexity
2Measurement precision
If advanced platforms such as next-generation sequencing are used to improve MPLC detection accuracy, then detection sensitivity improves, but the requirement on sample input becomes stringent and cost increases
Solution Approach 1:
The patent extracts and focuses specifically on somatic copy number variation (SCNV) features from tumor DNA, rather than requiring comprehensive genomic sequencing. By isolating and analyzing only the relevant SCNV patterns, the method achieves high detection sensitivity while reducing the total DNA input requirement and lowering costs compared to full next-generation sequencing approaches
Solution Approach 2:
The patent applies partial action by performing targeted SCNV analysis on specific genomic regions and features rather than comprehensive whole-genome sequencing. This partial analysis approach maintains high detection sensitivity for tumor classification while significantly reducing sample input requirements and computational resources, resolving the contradiction between measurement precision and quantity of substance
3Reliability
If accurate discrimination between MPLCs and intrapulmonary metastases is achieved, then treatment planning accuracy improves, but the current methodologies are insufficient and require improvement
Solution Approach 1:
The patent implements feedback by using SCNV analysis results to provide quantitative information about tumor clonality relationships. This feedback mechanism enables clinicians to make informed decisions about treatment planning based on objective molecular data, improving treatment plan accuracy and reliability while maintaining a manageable level of methodological complexity through standardized SCNV scoring systems
Data Source
AI summary
The subject invention pertains to methods and systems for the quantification of clonality between tissue samples obtained from a subject having or suspected to have cancer and treatment of the subject tailored to the origin of tumors in the tissue samples as primary cancer or metastases based on the clonality quantification.