A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types

Jared J Gartner, Maria R Parkhurst, Alena Gros, Eric Tran, Mohammad S Jafferji, 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, Jared J Gartner, Maria R Parkhurst, Alena Gros, Eric Tran, Mohammad S Jafferji, 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

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.

Conflict of interest statement

Competing interests The authors declare no competing interests.

Figures

Extended Data Fig. 1 |. Percentile Rank…
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…
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…
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…
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…
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…
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.
Fig. 1 |. Evaluating criteria for their…
Fig. 1 |. Evaluating criteria for their effects on nmer discovery.
a, Known CD8+ minimal epitopes predicted MHCflurry1.6 percentile rank for each HLA; HLA-A, n = 60; HLA-B, n = 47 and HLA-C, n = 12. The red line indicates the rank two cutoff. b, Histogram plotting the percentage of positive nmers found within the screened nmers from each expression decile. N = 1,240, 1,342, 1,371, 1,317, 1,081, 870, 539, 449, 317 and 1,015 nmers for deciles 10, 9, 8, 7, 6, 5, 4, 3, 2 and 1, respectively. c, Analysis of variant presence in corresponding RNA-seq data. The frequencies of reactive class I variants seen in nmers encoded by mutations that were detected in RNA-seq data, comprising 5,686 tested nmers and 124 class I–reactive nmers were compared with those that were not seen in the RNA-seq data, comprising a total of 3,855 tested nmers and 15 class I–reactive nmers, are shown.. Significance was calculated by a two-sided Fisher’s exact test. d, Box plot comparing the VAFs of reactive (n = 139) and non-reactive (n = 9,402) nmers. The VAFs were normalized and expressed as deciles to facilitate comparisons between samples. For box plots, the box indicates quartiles two and three and interquartile range (IQR), and the median is indicated by the line in the box plot. Whiskers represent quartiles 1 and 4 ± 1.5× IQR or minimum/maximum value if within the whisker values. Significance was calculated using the Mann–Whitney U-test.
Fig. 2 |. Analysis of unique minimal…
Fig. 2 |. Analysis of unique minimal epitopes.
Comparison of reactive (positive mmps, n = 119) and non-reactive (negative mmps, n = 2,681,162) peptide scores for MHCflurry1.6 percentile rank binding predictions (a), MHCflurry1.6 processing score (b) and MHCflurry1.6 presentation score (c). d, Contour plot showing CD8+ frequency for minimal epitopes at different thresholds of MHCflurry1.6 percentile rank binding and expression decile. e, NetMHCstabpan-1.0 stability score for peptide–HLA class I binding complexes. Proteasomal processing score for C-terminal residues using either the NetChop-3.1 C-term (f) or 20S (g) models. h, TAP transport score. i, Box plot of MHCflurry1.6 percentile binding rank for the wild-type peptide in relation to the snv mmp (wild-type:mutant) for reactive (positive mmps, n = 54) and non-reactive (negative mmps, n = 1,083,564) mmps with mutant anchors defined as positions two, three or the C terminus and reactive (positive mmps, n = 64) and non-reactive (negative mmps, n = 1,513,799) snv mmps without mutant anchors. For box plots, the box indicates quartiles two and three and IQR, and the median is indicated by the line. Whiskers represent quartiles 1 and 4 ± 1.5× IQR or minimum/maximum value if within the whisker values. Significance was calculated by a Mann–Whitney U-test. j, Scatter plot of MHCflurry1.6 percentile binding rank for mutant mmps compared to matched wild-type mmps for snv changes. Negative mmps (n = 2,582,291) are shown in blue, positive mmps with mutations outside of anchor residue (n = 64) are shown in red and positive mmps with mutant anchors (n = 54) are shown in orange.
Fig. 3 |. Evaluation of mmp input…
Fig. 3 |. Evaluation of mmp input features.
a, Spearman correlation matrix representing the associations between input features tested for the MMP model. b, The relative importance of features in the final MMP model trained with n = 120 positive mmps and n = 1,412,494 negative mmps. The relative importance represents the overall ability of a feature to purify nodes within the model.
Fig. 4 |. Evaluation of models in…
Fig. 4 |. Evaluation of models in a test set.
a, MMP model evaluation. Receiver operating characteristic (ROC) curve of the MMP model in a test set of n = 8,771 nmers, with n = 46 class I–reactive nmers ranked by their best-scoring mmp from five different prediction binding models. All nmers from the 26 test samples were scored and included. b, Individual rank of positive nmers (n = 46) found within an individual’s repertoire when sorted by the NMER model score or by the top NetMHCpan4.0 EL percentile binding-ranked mmp derived from each nmer. c, Percentile rank of positive nmers (n = 46) found within an individual’s repertoire when sorted by the NMER model score or by the top NetMHCpan4.0 EL percentile binding-ranked mmp derived from each nmer; P values were calculated with a two-sided Wilcoxon signed-rank test.

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