Tumor Self-Antigen Discovery for Scalable Adoptive T-Cell Therapy
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Solution Overview
Problem
Current methods for identifying tumor antigens in cancers with low mutation rates, such as neuroblastoma, are labor-intensive and limited to a few labs, making them unsuitable for widespread application in adoptive immunotherapy.
Innovation Solution
A scalable method for identifying patient-specific tumor antigens using a combination of RNA sequencing, MHC ligand characterization, and machine learning to prioritize tumor antigens, followed by validation with peptide/MHC dextramers and single-cell sequencing to develop TCRs for engineered T cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for identifying tumor antigens are used, then tumor antigens can be identified, but the process is labor-intensive and limited to a few labs
Solution Approach 1:
The patent segments the tumor antigen identification process into distinct modular components: RNA sequencing to identify candidate antigens, MHC ligand characterization to validate presentation, machine learning prioritization to rank candidates, and single-cell sequencing for TCR development. This segmentation enables each module to be optimized independently and performed by different labs, increasing overall productivity while maintaining accuracy.
Solution Approach 2:
The patent replaces manual, labor-intensive methods with automated computational approaches. Machine learning algorithms prioritize tumor antigens based on multiple parameters, and high-throughput sequencing technologies automate antigen identification and validation, eliminating the need for manual screening and enabling widespread application across many laboratories.
2Measurement precision
If comprehensive tumor antigen identification methods are applied, then accurate tumor antigens are identified, but the complexity and resource requirements increase
Solution Approach 1:
The patent performs preliminary actions by using RNA sequencing to identify and prioritize candidate tumor antigens before committing to more complex validation steps. Machine learning algorithms pre-screen candidates based on multiple parameters including expression levels and mutation status, filtering out low-probability candidates early in the process. This preliminary prioritization reduces the number of candidates requiring resource-intensive validation while maintaining high accuracy.
Solution Approach 2:
The patent introduces machine learning algorithms as intermediaries between raw sequencing data and final antigen selection. These algorithms process and prioritize candidate antigens based on multiple parameters, serving as a computational mediator that translates complex data into a manageable shortlist of high-priority candidates for experimental validation, thereby simplifying the overall workflow.
3Reliability
If traditional antigen identification approaches are used, then some tumors can be targeted, but they are unsuitable for widespread application in adoptive immunotherapy
Solution Approach 1:
The patent creates a universal platform that can be applied to identify tumor antigens across different cancer types. The same workflow—RNA sequencing, MHC ligand characterization, machine learning prioritization, and single-cell sequencing—can be used for neuroblastoma, melanoma, lung cancer, and other tumor types, enabling widespread application in adoptive immunotherapy while maintaining reliability through consistent methodology.
Solution Approach 2:
The patent changes key parameters of the antigen identification process by using high-throughput sequencing technologies and machine learning prioritization instead of traditional manual methods. These parameter changes—automated data processing, computational prioritization based on multiple parameters, and standardized workflows—make the process scalable and suitable for widespread application while maintaining the reliability needed for effective immunotherapy.
Data Source
AI summary
The present disclosure provides methods and compositions for immunotherapy employing a modified T cell or NK cell comprising a receptor that binds to newly identified tumor antigens that can be administered to patients for disease (e.g., cancer) treatment. Also described are polynucleotides and vectors encoding the same.