The T cell receptor β chain repertoire of tumor infiltrating lymphocytes improves neoantigen prediction and prioritization

• Published in: eLife Vol. 13
• Main Authors: Pham, Thi Mong Quynh, Nguyen, Thanh Nhan, Tran Nguyen, Bui Que, Diem Tran, Thi Phuong, Diem Pham, Nguyen My, Phuc Nguyen, Hoang Thien, Cuong Ho, Thi Kim, Linh Nguyen, Dinh Viet, Nguyen, Huu Thinh, Tran, Duc Huy, Tran, Thanh Sang, Pham, Truong Vinh Ngoc, Le, Minh Triet, Vy Nguyen, Thi Tuong, Phan, Minh-Duy, Giang, Hoa, Nguyen, Hoai-Nghia, Tran, Le Son
• Format: Journal Article
• Language: English
• Published: England eLife Sciences Publications Ltd 28.10.2024; eLife Sciences Publications, Ltd
• Subjects: Algorithms; Antigen (tumor-associated); Antigens; Antigens, Neoplasm - genetics; Antigens, Neoplasm - immunology; Cancer Biology; Cancer immunotherapy; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - genetics; Colorectal Neoplasms - immunology; CRC; Enzyme-linked immunosorbent assay; Gene expression; Genomics; Humans; Immune system; Immunotherapy; Information processing; Leukocytes; Lymphocytes; Lymphocytes T; Lymphocytes, Tumor-Infiltrating - immunology; Machine Learning; Metastasis; Mutation; neoantigen; Neoantigens; Patients; Peptides; Performance evaluation; Predictions; Receptors, Antigen, T-Cell, alpha-beta - genetics; Receptors, Antigen, T-Cell, alpha-beta - immunology; Receptors, Antigen, T-Cell, alpha-beta - metabolism; T cell receptors; TCR sequencing; TCRseq; Tumor cells; tumor variant calling; Tumor-infiltrating lymphocytes; Tumors; neoantigen; TCRseq; colorectal cancer; TCR sequencing; neoantigen prioritization; CRC; cancer immunotherapy; cancer biology; human; tumor variant calling
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.94658

In the realm of cancer immunotherapy, the meticulous selection of neoantigens plays a fundamental role in enhancing personalized treatments. Traditionally, this selection process has heavily relied on predicting the binding of peptides to human leukocyte antigens (pHLA). Nevertheless, this approach often overlooks the dynamic interaction between tumor cells and the immune system. In response to this limitation, we have developed an innovative prediction algorithm rooted in machine learning, integrating T cell receptor β chain (TCRβ) profiling data from colorectal cancer (CRC) patients for a more precise neoantigen prioritization. TCRβ sequencing was conducted to profile the TCR repertoire of tumor-infiltrating lymphocytes (TILs) from 28 CRC patients. The data unveiled both intra-tumor and inter-patient heterogeneity in the TCRβ repertoires of CRC patients, likely resulting from the stochastic utilization of V and J segments in response to neoantigens. Our novel combined model integrates pHLA binding information with pHLA-TCR binding to prioritize neoantigens, resulting in heightened specificity and sensitivity compared to models using individual features alone. The efficacy of our proposed model was corroborated through ELISpot assays on long peptides, performed on four CRC patients. These assays demonstrated that neoantigen candidates prioritized by our combined model outperformed predictions made by the established tool NetMHCpan. This comprehensive assessment underscores the significance of integrating pHLA binding with pHLA-TCR binding analysis for more effective immunotherapeutic strategies.