Tumor Genetic Profiling for Predicting Cancer Treatment Resistance
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Solution Overview
Problem
Current methods for predicting patient response and resistance to cancer treatment are inadequate, particularly in managing advanced cancers with metastasis, due to the development of resistance mechanisms such as energy-dependent drug ejection and mutational or epigenetic changes.
Innovation Solution
A computer-implemented method using genetic profiling of tumors from cell-free bodily fluids to generate predictive algorithms that forecast treatment outcomes by analyzing genetic profiles and treatment histories, enabling the creation of decision trees to optimize treatment strategies and predict patient survival.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed studies of tumor tissue are conducted before and after treatment to characterize drug resistance, then measurement precision of resistance mechanisms is improved, but loss of time and complexity of the diagnostic process worsen
Solution Approach 1:
The patent performs genetic profiling of tumor tissue before treatment initiation to predict resistance mechanisms in advance. By analyzing genetic markers pre-treatment, the system identifies likely resistance pathways (such as drug efflux transporters or DNA repair mechanisms) before the patient receives therapy, enabling proactive treatment selection rather than reactive adjustment after resistance develops.
Solution Approach 2:
The patent divides the complex task of resistance characterization into separate genetic marker analyses. Instead of conducting comprehensive post-treatment tissue studies to identify resistance mechanisms, the system segments the problem into specific genetic profiles (such as analyzing individual genes or mutation types) that can be independently assessed and combined to predict overall resistance patterns.
2Reliability
If comprehensive genetic profiling and predictive algorithms are implemented, then reliability of treatment outcome prediction is improved, but device complexity and cost worsen
Solution Approach 1:
The patent creates simplified representations of complex biological systems through genetic profiling. Instead of directly observing and analyzing entire tumor tissue samples or resistance mechanisms, the system uses genetic markers as copies or proxies that represent the underlying biological state. These genetic profiles serve as manageable data representations that can be processed by computational algorithms to predict treatment outcomes without requiring direct analysis of complex tissue biology.
Solution Approach 2:
The patent transforms complex biological resistance mechanisms into quantifiable genetic parameters. By converting qualitative resistance phenomena (such as drug efflux or apoptosis resistance) into measurable genetic markers and molecular signatures, the system enables computational analysis and algorithmic prediction. This parameter transformation allows complex biological processes to be handled through standardized data processing and statistical modeling.
3Measurement precision
If genetic variants and epigenetic mechanisms are monitored to detect resistance, then measurement precision of resistance emergence is improved, but difficulty of detecting and measuring worsens
Solution Approach 1:
The patent uses genetic markers and molecular signatures as intermediary indicators of resistance mechanisms. Instead of directly measuring complex epigenetic modifications or resistance protein expression, the system identifies specific genetic variants and molecular patterns that serve as proxies or intermediaries for these harder-to-measure phenomena. These intermediary markers are more readily detectable through standard genetic profiling techniques while still providing information about underlying resistance mechanisms.
Data Source
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.