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A subset of these variants produce neoantigens that, when presented on tumor cells by MHC molecules, have the potential to elicit a robust and specific immune response. To date, there are over one hundred interventional studies listed on\n                  <jats:italic>clinicaltrials.gov<\/jats:italic>\n                  that explore the use of PCVs. We have supported a number of these trials through the creation of bioinformatic pipelines, tools, and procedures for the identification of patient-specific neoantigen candidates. While many of these steps have been automated, the final selection of neoantigen candidates often relies on expert manual review, creating a bottleneck that limits scalability and full automation of PCV workflows. Addressing this challenge, we introduce NEAT (Neoantigen Evaluation &amp; Automated Triage), a machine learning-based approach that enables automated neoantigen candidate prioritization and supports the transition toward more scalable and reproducible PCV design. We implemented a prediction model trained and tested on existing vaccine design results from 33 patients and 1,943 peptides, across 3 clinical trials, including 439 peptides prioritized for PCV inclusion. This model uses features such as tumor variant allele frequency, RNA expression, driver gene status, binding\/presentation scores, and transcript support level to automatically predict whether a peptide will be accepted, rejected, or require further human review before inclusion in a vaccine. The model achieved a sensitivity of 0.847 and specificity of 0.924, with an area under the curve of 0.955. The model predictions have been incorporated in pVACtools v7.0.0. By integrating this model into the vaccine development pipeline, we foresee a significant reduction in the time required to transition from patient sample collection to vaccine manufacturing, thereby enhancing the efficiency and scalability of PCV production.\n                <\/jats:p>","DOI":"10.64898\/2026.06.24.26356293","type":"posted-content","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T02:25:11Z","timestamp":1782959111000},"source":"Crossref","is-referenced-by-count":0,"title":["Automating neoantigen selection for personalized cancer vaccine design"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7311-012X","authenticated-orcid":false,"given":"Jennie X.","family":"Yao","sequence":"first","affiliation":[{"name":"Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3855-7950","authenticated-orcid":false,"given":"Kartik","family":"Singhal","sequence":"additional","affiliation":[{"name":"Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4378-7328","authenticated-orcid":false,"given":"Susanna","family":"Kiwala","sequence":"additional","affiliation":[{"name":"Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7737-2200","authenticated-orcid":false,"given":"Evelyn","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7084-3378","authenticated-orcid":false,"given":"S. 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