RNA sequencing enables neoantigen discovery and vaccine validation in breast and lung cancer

Neoantigens have emerged as promising targets for personalized cancer immunotherapy due to their tumor-specific immunogenicity. However, current neoantigen prediction methods relying on combined DNA/RNA sequencing are costly and time-consuming, limiting clinical applicability. This study aimed to establish a streamlined neoantigen identification pipeline using RNA sequencing alone, evaluating its efficacy in breast and lung cancer models.

We conducted neoantigen profiling of human and mice cancers using an in silico prediction pipeline based only on RNA sequencing. We also performed neoantigen-specific T responses experiments using autologous BMDCs and PBMCs with the predicted neoantigen peptides, and ultimately demonstrating significant antitumor efficacy in murine models through in vivo therapeutic evaluation.

We identified neoantigens in mice breast cancer cell 4T1, lung cancer cell LLC and one breast cancer patient based only on RNA sequencing. In vitro experiments demonstrated that these neoantigens triggered specific T-cell responses in BALB/c mice and the patient. Mechanistic studies revealed an increased proportion of CD3+/CD137+ T cells in the RNA-derived neoantigen peptide group, with significant infiltration of CD3+/CD137+ T cells into tumor tissues.

RNA sequencing alone enables efficient neoantigen prediction and vaccine design, and the neoantigen vaccine can elicit an antitumor reaction against mouse breast cancer and lung cancer. The study showed that neoantigen prediction using RNA sequencing alone holds promise as a novel immunotherapeutic approach for cancer patients.