Personalized Neoantigen-Based Cancer Treatment System

A comprehensive system for personalized neoantigen-based cancer treatment, integrating bioinformatics, immunotherapy, and machine learning to provide tailored vaccine compositions and predict treatment outcomes.

The original patent disclosed a vaccine composition for treating bladder cancer using antigen presenting cells and IL-15 based superagonists. However, this approach has limitations in terms of personalized treatment and predicting treatment outcomes. The new inventive concept addresses these limitations by introducing a system that leverages bioinformatics, machine learning, and advanced immunotherapy to provide personalized neoantigen-based cancer treatment.

The system consists of a bioinformatics platform for identifying patient-specific tumor mutations, a module for generating antigen presenting cells from a subject's own immune cells, and a component for formulating a vaccine composition with the generated antigen presenting cells and a superagonist. The vaccine composition is tailored to the subject's specific tumor mutation profile. Additionally, the system incorporates a machine learning model trained on a dataset of gene expression profiles from BCG-treated patients to predict treatment outcomes. The system can also be integrated with a nanoparticle-based delivery system for targeted delivery of antigen presenting cells and a superagonist to the tumor site, and a module for real-time monitoring of the immune response and adjusting the composition's formulation accordingly.

The new claims introduce a paradigm shift in cancer treatment by integrating bioinformatics, machine learning, and advanced immunotherapy to provide personalized neoantigen-based cancer treatment. The inventive concept's novelty lies in its ability to tailor vaccine compositions to individual patients' tumor mutation profiles and predict treatment outcomes using machine learning.

Alternative embodiments of the inventive concept could include using different types of immune cells or antigen presentation methods, integrating with other omics technologies such as proteomics or metabolomics, or incorporating additional machine learning models to predict treatment outcomes. Variations of the system could be designed for treating different types of cancer or for use in combination with other therapies.

The inventive concept has significant commercial potential in the cancer treatment market, particularly in the areas of personalized medicine and immunotherapy. The system could be marketed as a comprehensive solution for cancer treatment, providing a competitive advantage over existing treatments. Target industries could include pharmaceutical companies, biotechnology firms, and healthcare providers.