Multiomic Gene Regulatory Network Modeling for Multiple Myeloma Therapy Resistance
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Solution Overview
Problem
In multiple myeloma, the evolution of therapy resistance leads to poor patient survival and drug toxicity, with no biomarkers for choosing effective therapies, necessitating new methods for assessing patient responsiveness to treatments.
Innovation Solution
A method involving obtaining multiple myeloma cells, culturing them, and using image analysis to determine drug sensitivity through contact with anti-cancer agents, coupled with RNA sequencing to identify gene signatures associated with therapy resistance, and applying these to a gene regulatory network model to develop novel therapeutic strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple anti-cancer agents are administered in combination, then therapeutic efficacy is improved, but therapy resistance evolves leading to poor patient survival
Solution Approach 1:
The patent performs ex vivo drug sensitivity testing on patient-derived multiple myeloma cells before administering therapy. This preliminary assessment identifies which drugs the patient's tumor is sensitive to, allowing clinicians to select effective combinations upfront and avoid treatments that will fail, thereby preventing the evolution of resistance and improving survival outcomes.
Solution Approach 2:
The patent implements a feedback loop where ex vivo drug sensitivity results directly inform in vivo treatment decisions. The automated image analysis system provides quantitative viability data that feeds into drug selection algorithms, creating a closed-loop system that continuously optimizes therapy based on actual patient tumor response characteristics.
2Ease of operation
If traditional clinical guidelines are used for therapy selection, then treatment simplicity is maintained, but no biomarkers are available for choosing effective therapies
Solution Approach 1:
The patent introduces an intermediary ex vivo testing system that bridges the gap between complex molecular profiling and clinical decision-making. Instead of directly interpreting complex genomic or transcriptomic data, the system uses drug sensitivity testing as an intermediary assay that translates tumor characteristics into actionable viability readouts that directly guide therapy selection.
Solution Approach 2:
The patent replaces traditional manual clinical acumen-based therapy selection with an automated image analysis system. The automated viability assessment algorithm objectively quantifies drug effects on patient-derived cells, eliminating subjectivity and providing consistent, reproducible results that inform treatment decisions without requiring extensive expert interpretation.
3Measurement precision
If ex vivo drug sensitivity testing is implemented, then patient response prediction is improved, but device complexity and testing infrastructure requirements increase
Solution Approach 1:
The patent employs patient-derived multiple myeloma cells cultured in patient plasma as the testing system. The patient's own biological materials serve as the test substrate, eliminating the need for complex external reagents or standardized cell lines. The system essentially tests itself using the patient's own tumor cells and biological environment.
Solution Approach 2:
The patent uses a universal automated bright-field imaging system that can assess drug sensitivity across multiple drugs and patient samples simultaneously. The same imaging platform and analysis algorithm handle all samples, eliminating the need for drug-specific or patient-specific specialized equipment. The system is adaptable to testing any anti-cancer agent against any patient-derived cells using the same methodology.
Data Source
AI summary
Disclosed are methods for identifying a gene regulatory network and treatment regimens combined with MM standard of care drugs to either delay, or reverse resistance to the standard of care therapy. Also disclosed is a synergy between Selinexor (SELI) and dexamethasone (DEX), pomalidomide (POM), elotuzumab (ELO), and daratumumab (DARA), and expression signatures and mutations associated with response to these agents.