However, a major challenge arises from the need to distinguish true mutated sites from numerous false positives, particularly in samples ofliquid biopsy, where detection rates may be relatively low. To address this issue, we havedeveloped a robust pipeline to enhance the precision and efficiency of detecting target genemutations, which is crucial during the neoantigen design process. Methods: In this study, an extensive analysis was conducted using tissue and blood samples from a cohort of 859 CRCpatients. We identified mutations in the top third most frequently mutated genes (APC, TP53,and KRAS). To design antigenic sequences, the mutations at these gene sites were concatenated with 14 amino acids from both up and down flanking regions. Subsequently, we utilized a similarity scoring algorithm (pairwise2.align from Biopython) to assess the mutation corre-lation between oncogenes (APC and KRAS) and the suppressor gene TP53. Results: This study observed that mutations within a specific gene tend to generate distinct patterns of newantigens. The majority of APC mutations (90.04%) in the vicinity of the mutation sites ledto premature stop codons. Regarding KRAS, the mutation sites with the highest frequency werein the vicinity of the c.35 and c.182 positions, sharing identical neoantigens with NRAS. Incontrast, TP53 mutations appeared to be more influenced by other mutations. Neoantigens derived from TP53 were identified, and it was observed that 82.27% of the sequences exhibited more than 40% similarity with other mutations in the same sample.This may be indicative of aco-occurrence mechanism. Conclusions: The classification of neoantigens appears to be sim- ilar to the classification of oncogenes and tumor suppressor genes, which may contribute toadjust the minimum detection threshold of liquid biopsy from the perspective of neoantigens.Research Sponsor: None. GeneMutation Number Classification Mutation NeoantigenMutation Number within Gene Percentage APC 954 premature stop codonc.4348C .T c.4666dup c.646C .T c.2626C .T c.637C .T c.694C .T ...SKTPPPPPQTAQTK* PKESNENQEKEAEKNY* EQLGTCQDMEKRAQ* NYHPATENPGTSSK* AMEEQLGTCQDMEK* IARIQQIEKDILRI* ...859 90.04% other 95 9.96% KRAS 484 cluster and shared with NRASc.35G.A c.35G.T c.38G.A c.34G.T c.34G.A c.183A .C c.182A .G c.182A .T ...MTEYKLVVVGADGVGKSALTIQLIQN MTEYKLVVVGAVGVGKSALTIQLIQN MTEYKLVVVGAGDVGKSALTIQLIQNH DGETCLLDILDTAGHEEYSAMRDQYMRTG DGETCLLDILDTAGREEYSAMRDQYMRTG ...419 86.57% other 65 13.43% GeneTotal sample (tissue and blood)Sample with TP53 mutationSimilarity scores above 0.4Percentage TP53 90 79 65 82.27%GASTROINTESTINAL CANCER —COLORECTAL AND ANAL Downloaded from ascopubs.org by 190.236.76.167 on December 10, 2024 from 190.236.076.167 Copyright © 2024 American Society of Clinical Oncology. All rights reserved.