AI-Guided Immune Programming & Personalized Cancer mRNA Vaccines

Cancer cells carry somatic mutations that produce peptides not present in healthy tissue. This research concept explores two complementary AI-guided strategies: (1) Personalized cancer mRNA vaccines — tumor sequencing identifies patient-specific mutations, AI models rank immunogenic neoantigen candidates, and a designed mRNA encodes the selected neoantigens to provoke a tumor-specific T-cell response. (2) AI-guided immune programming — selected synthetic small molecules are computationally designed and modeled for their ability to prime and train patient-derived immune cells toward durable, targeted immunity. The molecular target is the patient-specific neoantigen repertoire and the adaptive immune response itself.

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   Tumor sequencing / mutation analysis — identify somatic mutations and the patient-specific tumor profile.

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   Neoantigen prediction with AI — rank and select immunogenic neoantigens specific to the patient.

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   Personalized mRNA vaccine design — encode the selected neoantigens in a patient-specific mRNA construct.

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   Synthetic drug molecule design — computationally design small molecules and predict immune-relevant features for optimal immunogenicity.

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   Ex vivo immune-cell training concept — model how patient-derived immune cells can be primed and trained with designed antigens.

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   Integrated evaluation — combine the vaccine and immune-programming tracks into a patient-specific neutralizing immune response concept.

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   Tumor sequencing data reveal patient-specific somatic mutations.

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   AI models predict which mutated peptides are most likely to be presented and recognized immunologically.

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   A designed mRNA construct encodes the selected neoantigens.

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   Antigen-presenting cells take up the mRNA, translate it and present the neoantigen-derived epitopes.

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   Activated CD8+ T cells recognize and target neoantigen-expressing tumor cells (modeled cytotoxic response).

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   CD4+ helper responses support a durable, targeted immune memory (proposed, modeled response — not an experimentally demonstrated effect).

All steps describe intended, modeled or proposed behavior — no effect above is experimentally proven or clinically demonstrated.

The manuscript is a conceptual framework and contains no numerical results. Technical details are intentionally withheld by the author because of provisional patent protection.

AI-Guided Immune Programming & Personalized Cancer mRNA Vaccines — Conceptual / computational

These projects are scientific research designs and computational proof-of-concept studies. They are not approved medical products, experimentally validated cures, or guarantees of therapeutic effectiveness.