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ABSTRACT Tumor neoepitopes presented by major histocompatibility complex (MHC) class I are recognized by tumor-infiltrating lymphocytes (TIL) and are targeted by adoptive T-cell therapies.

Identifying which mutant neoepitopes from tumor cells are capable of recognition by T cells can assist in the development of tumor-specific, cell-based therapies and can shed light on

antitumor responses. Here, we generate a ranking algorithm for class I candidate neoepitopes by using next-generation sequencing data and a dataset of 185 neoepitopes that are recognized by

HLA class I–restricted TIL from individuals with metastatic cancer. Random forest model analysis showed that the inclusion of multiple factors impacting epitope presentation and recognition

increased output sensitivity and specificity compared to the use of predicted HLA binding alone. The ranking score output provides a set of class I candidate neoantigens that may serve as

therapeutic targets and provides a tool to facilitate in vitro and in vivo studies aimed at the development of more effective immunotherapies. Access through your institution Buy or

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OF NEOANTIGENS’ HLA- AND TCR-INTERFACES IMPROVES PREDICTION OF SURVIVAL Article Open access 11 May 2021 IDENTIFICATION OF NEOANTIGENS FOR INDIVIDUALIZED THERAPEUTIC CANCER VACCINES Article

01 February 2022 A COMPREHENSIVE PROTEOGENOMIC PIPELINE FOR NEOANTIGEN DISCOVERY TO ADVANCE PERSONALIZED CANCER IMMUNOTHERAPY Article Open access 11 October 2024 DATA AVAILABILITY All

next-generation sequencing data are available on dbGap under accession number phs001003.v1.p1. Source data are available from the NIH figshare repository at

https://doi.org/10.35092/yhjc.c.4792338.v2 (ref. 56). CODE AVAILABILITY The models developed and presented in this paper are available at

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for ranking candidate HLA class I neoantigens based upon datasets of known neoepitopes’. figshare https://doi.org/10.35092/yhjc.c.4792338.v2 (2020). Download references ACKNOWLEDGEMENTS We

thank members of the NIH High Performance Computing (HPC) group for all of their support, assistance and technical advice. This work utilized the computational resources of the NIH HPC

Biowulf cluster (http://hpc.nih.gov). We also thank all members of the tissue procurement team for all of their efforts in acquiring and maintaining the specimens used in this study. AUTHOR

INFORMATION AUTHORS AND AFFILIATIONS * Surgery Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA Jared J. Gartner, Maria R. Parkhurst, Amy Copeland, 

Ken-Ichi Hanada, Nikolaos Zacharakis, Almin Lalani, Sri Krishna, Abraham Sachs, Todd D. Prickett, Yong F. Li, Maria Florentin, Scott Kivitz, Samuel C. Chatmon, Steven A. Rosenberg & Paul

F. Robbins * Vall d’Hebron Institute of Oncology (VHIO), Cellex Center, Barcelona, Spain Alena Gros * Earle A. Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA Eric

Tran * Department of Surgery, Dartmouth-Hancock Medical Center, Lebanon, NH, USA Mohammad S. Jafferji Authors * Jared J. Gartner View author publications You can also search for this author

inPubMed Google Scholar * Maria R. Parkhurst View author publications You can also search for this author inPubMed Google Scholar * Alena Gros View author publications You can also search

for this author inPubMed Google Scholar * Eric Tran View author publications You can also search for this author inPubMed Google Scholar * Mohammad S. Jafferji View author publications You

can also search for this author inPubMed Google Scholar * Amy Copeland View author publications You can also search for this author inPubMed Google Scholar * Ken-Ichi Hanada View author

publications You can also search for this author inPubMed Google Scholar * Nikolaos Zacharakis View author publications You can also search for this author inPubMed Google Scholar * Almin

Lalani View author publications You can also search for this author inPubMed Google Scholar * Sri Krishna View author publications You can also search for this author inPubMed Google Scholar

* Abraham Sachs View author publications You can also search for this author inPubMed Google Scholar * Todd D. Prickett View author publications You can also search for this author inPubMed

 Google Scholar * Yong F. Li View author publications You can also search for this author inPubMed Google Scholar * Maria Florentin View author publications You can also search for this

author inPubMed Google Scholar * Scott Kivitz View author publications You can also search for this author inPubMed Google Scholar * Samuel C. Chatmon View author publications You can also

search for this author inPubMed Google Scholar * Steven A. Rosenberg View author publications You can also search for this author inPubMed Google Scholar * Paul F. Robbins View author

publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS J.J.G., P.F.R. and S.A.R. designed the study and drafted the manuscript. J.J.G. trained models and

evaluated all nmers and mmps. T.D.P. and S.C.C. generated exomes and RNA-seq libraries. N.Z., K.H., Y.F.L. and P.F.R. designed minigene constructs encoding candidate neoantigens and

generated in vitro-transcribed RNA used to perform screening assays. M.R.P., M.F. and S. Kivitz synthesized peptides used for T-cell screening assays. M.R.P., A.G., E.T., M.S.J., A.C., K.H.,

N.Z., A.L., S. Krishna and A.S. evaluated T cells for their ability to recognize nmers/mmps in the context of the appropriate HLA class I restriction elements. CORRESPONDING AUTHOR

Correspondence to Paul F. Robbins. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PEER REVIEW INFORMATION _Nature Cancer_ thanks

the anonymous reviewers for their contribution to the peer review of this work. PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and

institutional affiliations. EXTENDED DATA EXTENDED DATA FIG. 1 PERCENTILE RANK COMPARISONS BETWEEN NETMHCPAN4.0 EL AND MHCFLURRY1.6 PERCENTILE RANK. Percentile rank of positive mmps were

mapped by their MHCflurry1.6 rank on the x-axis and the NetMHCpan4.0 EL model rank on the y-axis. Red Triangles correspond to mmps containing cysteine residues at positions 2,3 or C-terminus

(n=12) while orange dots correspond to peptides containing cysteine residues at position 1 or between positions 3 and the C-terminus (n=107). EXTENDED DATA FIG. 2 NMER LOCALIZATION

PREDICTIONS. WoLF Psort algorithm was used on all nmer proteins (n=9541) to predicted for localization. Blue bars are CD8 + Positive nmers, Orange bars are negative nmers. Y-axis represents

frequency of each group predicted to localize. X axis are the WoLF Psort prediction abbreviations. chlo = chloroplast, cyto = cytosol, cysk = cytoskeleton, E.R. = endoplasmic reticulum, extr

= extracellular, golg = Golgi apparatus, lyso = lysosome, mito = mitochondria, nucl = nuclear, pero = peroxisome, plas = plasma membrane, vacu = vacuolar membrane . Individual totals for

each groups positive and negative can be found in Supplementary Table 12. Hyphenated values denote compound prediction. P-values comparing positive to negative nmers displayed over each

prediction. P-values calculated using a two-sided Fisher’s exact test and corrected using Bonferroni correction for multiple comparisons. EXTENDED DATA FIG. 3 GENE EXPRESSION DECILE OF MMPS.

Gene expression deciles of positive (n=119) and negative mmps (n=2681162). Box indicates quartiles 2 & 3 and inter quartile range, median indicated by line in box plot, whiskers

represent quartile 1 and 4 ± 1.5X IQR or minimum/maximum value if within the whisker values. Significance calculated with Mann-Whitney U test. EXTENDED DATA FIG. 4 IEDB IMMUNOGENICITY SCORES

OF MMPS. IEDB Immunogenicity scores were generated for each mmp using the IEDB immunogenicity tool. The panels are split into all mmps (positive n=119, negative n=2681162), comparison of

just those with a mutation anchor in position 2,3 or C-terminus (positive n=55, negative n= 1167363) and those without mutations in position 2,3, or C-terminus (positive n= 64, negative n=

1513799). Box indicates quartiles 2 & 3 and inter quartile range, median indicated by line in box plot, whiskers represent quartile 1 and 4 ± 1.5X IQR or minimum/maximum value if within

the whisker values. Significance was calculated using the Mann-Whitney U test. EXTENDED DATA FIG. 5 HYDROPHOBICITY SCORES OF T-CELL CONTACT REGIONS. Hydrophobicity scores were calculated

summing the Kyte-Doolittle hydrophobicity score of positions 4 through n-1. The panels are split into all mmps (positive n=119, negative n=2681162), comparison of just those with a anchor in

position 2,3 or C-terminus (positive n=55, negative n= 1167363) and those without mutations in position 2,3, or C-terminus (positive n= 64, negative n= 1513799). Box indicates quartiles 2

& 3 and inter quartile range, median indicated by line in box plot, whiskers represent quartile 1 and 4 ± 1.5X IQR or minimum/maximum value if within the whisker values. Significance

calculated with Mann-Whitney U test. EXTENDED DATA FIG. 6 TOP NMER MODELS USING EITHER MMP SCORE OF MHCFLURRY SCORE AS INPUT. ROC curve showing the mean performance of the top models using

either MMP model scores or MHCflurry scores as input. Solid line represents mean for each model across n=5 folds, shaded area is the standard deviation at each point along the x-axis.

SUPPLEMENTARY INFORMATION REPORTING SUMMARY SUPPLEMENTARY TABLES 1–23 RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Gartner, J.J., Parkhurst, M.R.,

Gros, A. _et al._ A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types. _Nat Cancer_ 2, 563–574 (2021).

https://doi.org/10.1038/s43018-021-00197-6 Download citation * Received: 06 December 2019 * Accepted: 11 March 2021 * Published: 03 May 2021 * Issue Date: May 2021 * DOI:

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