Tumor Genomic Profiling for Predicting Cancer Resistance Evolution
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Solution Overview
Problem
Current methods are inadequate for predicting patient response and emerging resistance to cancer treatment, particularly due to the development of resistance mechanisms in tumors, which complicates the clinical management of advanced cancers.
Innovation Solution
A computer-implemented method using genetic profiling of tumors from cell-free bodily fluids to generate predictive algorithms that determine the probability of treatment outcomes over time, incorporating decision trees to optimize treatment choices based on genetic variants and patient profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed studies of tumor tissue are conducted before treatment and after relapse to characterize tumor drug resistance, then measurement precision of resistance mechanisms is improved, but loss of time for treatment decision-making increases
Solution Approach 1:
The patent performs genomic sequencing and resistance mechanism characterization on tumor tissue samples obtained before treatment begins. This preliminary action identifies potential resistance mechanisms in advance, allowing clinicians to select treatments that are less likely to be resisted, thereby reducing the time needed for treatment decision-making while maintaining high measurement precision.
Solution Approach 2:
The patent segments the analysis into distinct genomic regions and resistance mechanisms, examining specific genes and pathways separately. This segmentation allows for more efficient processing and interpretation of genomic data, improving measurement precision while reducing the overall time required for comprehensive resistance characterization.
2Reliability
If comprehensive genomic profiling is performed to predict patient response and resistance, then prediction accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent employs a universal genomic profiling platform that can analyze multiple genes, pathways, and resistance mechanisms simultaneously from a single tumor sample. This multi-functional approach improves prediction accuracy by comprehensively assessing various resistance mechanisms while reducing the need for multiple separate tests, thereby managing system complexity.
Solution Approach 2:
The patent adjusts the scope and depth of genomic profiling based on patient-specific factors such as cancer type, treatment history, and clinical presentation. By dynamically changing the parameters of the genomic analysis (which genes to sequence, depth of sequencing), the system maintains high prediction accuracy while optimizing resource utilization and managing complexity.
3Measurement precision
If tumor tissue samples are obtained through biopsy to analyze resistance mechanisms, then measurement precision is improved, but patient discomfort and procedural risk increase
Solution Approach 1:
The patent extracts the necessary genomic information from small, targeted biopsies rather than requiring large tissue samples. By extracting only the specific genomic regions and resistance-related genes that are most relevant to treatment prediction, the system achieves high measurement precision while minimizing the invasiveness of the biopsy procedure and associated patient discomfort.
Data Source
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AI summary
The present disclosure provides methods for determining a probability that after any of a number of therapeutic interventions, an initial state of a subject, such as somatic cell mutational status of a subject with cancer, will develop a subsequent state. Such probabilities can be used to inform a health care provider as to particular courses of treatment to maximize probability of a desired outcome for the subject.