Abstract
Despite the transformative impact of cancer immunotherapy, the need for improved patient stratification remains critical due to suboptimal response rates. While neoantigens are central to anti-tumor immunity, current metrics, such as tumor mutation burden (TMB), are limited by their neglect of immunogenicity and tumor heterogeneity. Here we present NeoPrecis, a computational framework designed to improve immunotherapy response prediction by refining neoantigen characterization across MHC-I and MHC-II pathways and by integrating tumor clonality information. NeoPrecis features an interpretable T-cell-recognition model that reveals the critical influence of MHC molecules on TCR recognition beyond mere antigen presentation. Benefit HLA alleles, identified through model-driven contribution analysis, exhibit significant predictive power for patient outcomes in immune checkpoint inhibitor treatment (melanoma: p-value = 0.04; NSCLC: p-value = 0.01). NeoPrecis, via its clonality-aware neoantigen landscape feature, improves immunotherapy response prediction in tumor types with varying prevalence of neoantigens, including heterogeneous NSCLC, which retains more subclonal neoantigens due to lower immunoediting pressure. We thus propose NeoPrecis as a comprehensive evaluative framework for neoantigen assessment by incorporating both immunogenicity and tumor clonality, offering insights into the link between the collective quality of neoantigen landscapes and immunotherapy response.
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Data availability
The processed data generated in this study are provided in the Supplementary Data files, including cross-reactive peptide triplets, CEDAR immunogenicity data, processed NCI gastrointestinal cancer cohort data, and the ICI cohort metadata with analysis results. Publicly available datasets analyzed in this study were obtained from the Immune Epitope Database (IEDB, https://www.iedb.org), VDJdb (https://vdjdb.cdr3.net), and the Cancer Epitope Database and Analysis Resource (CEDAR, https://cedar.iedb.org). The mutation-centric immunogenicity data (NCI dataset) were obtained from the supplementary materials of Parkhurst et al. (https://doi.org/10.1158/2159-8290.CD-18-1494). Clinical and genomic data from the reanalyzed ICI cohorts were obtained with the following accession numbers: Hugo et al. (SRP090294 [https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP090294], SRP067938), Van Allen et al. (SRP011540 [https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP011540]), Snyder et al. (SRP072934 [https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP072934]), Riaz et al. (SRP094781 [https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP094781]), Liu et al. (SRP011540 [https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP011540]), Ravi et al. (SRP413932 [https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP413932]), Anagnostou et al. (SRP238904 [https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP238904]), Rizvi et al. (SRP064805 [https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP064805]).
Code availability
Codes for conducting immunogenicity prediction and neoantigen landscape evaluation are deposited at both GitHub (https://github.com/cartercompbio/NeoPrecis) and Zenodo (https://doi.org/10.5281/zenodo.17959604).
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Acknowledgements
This work was funded by the Mark Foundation Emerging Leader Award #18-022-ELA, NCI grant R01CA269919, and support from NCI grant U24CA248138 to H. Carter. Computational resources were supported by an infrastructure grant 2P41GM103504-11. The ICI cohorts were collected from several published studies. The melanoma cohort from Hugo and colleagues was obtained from the Sequence Read Archive (SRA) under accessions SRP090294 and SRP067938. The melanoma cohorts from Van Allen et al. and Liu et al. were obtained from dbGaP under accession phs000452, supported by the National Human Genome Research Institute (NHGRI) Large Scale Sequencing Program, Grant U54 HG003067 to the Broad Institute (PI, Lander). The melanoma cohort from Riaz and colleagues was obtained from SRA under accession SRP094781. The melanoma cohort from Snyder and colleagues was obtained from dbGaP under accession phs001041; we thank Martin Miller at Memorial Sloan Kettering Cancer Center (MSKCC) for his assistance with the NetMHC server, Agnes Viale and Kety Huberman at the MSKCC Genomics Core, Annamalai Selvakumar and Alice Yeh at the MSKCC HLA typing laboratory for their technical assistance, and John Khoury for assistance in chart review. The NSCLC cohort from Rizvi and colleagues was obtained from dbGaP under accession phs000980. We thank the members of the Thoracic Oncology Service and the Chan and Wolchok labs at MSKCC for helpful discussions. We thank the Immune Monitoring Core at MSKCC, including L. Caro, R. Ramsawak, and Z. Mu, for exceptional support with processing and banking peripheral blood lymphocytes. We thank P. Worrell and E. Brzostowski for help in identifying tumor specimens for analysis. We thank A. Viale for superb technical assistance. We thank D. Philips, M. van Buuren, and M. Toebes for help performing the combinatorial coding screens. The data presented in this paper are tabulated in the main paper and in the supplementary materials. This work was supported by the Geoffrey Beene Cancer Research Center (MDH, NAR, TAC, JDW, AS), the Society for Memorial Sloan Kettering Cancer Center (MDH), Lung Cancer Research Foundation (WL), Frederick Adler Chair Fund (TAC), The One Ball Matt Memorial Golf Tournament (EBG), Queen Wilhelmina Cancer Research Award (TNS), The STARR Foundation (TAC, JDW), the Ludwig Trust (JDW), and a Stand Up To Cancer-Cancer Research Institute Cancer Immunology Translational Cancer Research Grant (JDW, TNS, TAC). Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research. The NSCLC cohort from Anagnostou and colleagues was obtained from dbGaP under accession phs001940, supported in part by US National Institutes of Health grant CA121113. The NSCLC cohort from Ravi and colleagues was obtained from dbGaP under accession phs002822. We express our deep gratitude to the patients and families whose participation enabled this study. We further thank the respective sequencing centers at Yale University, Johns Hopkins University, and the Broad Institute of MIT and Harvard for processing the whole exome and RNA-seq data presented here. Funding for this study was provided by a Stand Up To Cancer - American Cancer Society Lung Cancer Dream Team Translational Research Grant (Grant Number: SU2C-AACR-DT17-15). Stand Up to Cancer is a program of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the scientific partner of SU2C. This work was additionally supported by The Mark Foundation for Cancer Research (Grant Number: 19-029-MIA) Expanding Therapeutic Options for Lung Cancer (EXTOL) project.
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Conceptualization: K.H.L., T.J.S., M.Z., H.C. Methodology: K.H.L. Investigation: K.H.L., T.J.S. Visualization: K.H.L. Funding acquisition: H.C. Project administration: H.C. Supervision: M.Z., H.C. Writing – original draft: K.H.L. Writing – review & editing: T.J.S., M.Z., H.C.
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Lee, KH., Sears, T.J., Zanetti, M. et al. NeoPrecis: enhancing immunotherapy response prediction through integration of qualified immunogenicity and clonality-aware neoantigen landscapes. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68651-6
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DOI: https://doi.org/10.1038/s41467-026-68651-6
