A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy (original) (raw)

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Acknowledgements

We thank N. Bhardwaj, C. Callan, S. Cocco, Y. Elhanati, J. Finnegan, D. Krotov, M. Lässig, S. Leach, S. Leibler, A. Libchaber, R. Monasson, A. Nourmohammad, V. Roudko, Z. Sethna, A. Snyder-Charen, P. Sulc, D. T. Ting and the members of the Chan, Greenbaum and Wolchok laboratories for discussions; M. Lässig for suggestions regarding the biophysical model and comments on the manuscript; A. Snyder-Charen and D. T. Ting for their reading of the manuscript. Research was supported by a Stand Up To Cancer–American Cancer Society Lung Cancer Dream Team Translational Research Grant (SU2C-AACR-DT17-15) (M.D.H., T.M., T.A.C. and J.D.W.), a Stand Up To Cancer–National Science Foundation–Lustgarten Foundation Convergence Dream Team Grant (V.P.B., A.S., J.D.W., and B.D.G.), a Phillip A. Sharp Innovation in Collaboration Award from Stand Up To Cancer (B.D.G. and J.D.W.), the Janssen Research & Development LLC (M.Ł.), the STARR Cancer Consortium (T.A.C.), the Pershing Square Sohn Cancer Research Alliance (T.A.C.), the NIH R01 CA205426 (N.A.R. and T.A.C.), the V Foundation (V.P.B., A.S., J.D.W. and B.D.G), the Lustgarten Foundation (V.P.B., A.S., J.D.W. and B.D.G.), the National Science Foundation (NSF) 1545935 (B.D.G. and J.D.W.), the Swim Across America (V.P.B., T.M. and J.D.W.), Ludwig Institute for Cancer Research, the Parker Institute for Cancer Immunotherapy, the NCI K12 Paul Calabresi Career Development Award for Clinical Oncology K12CA184746-01A1 (V.P.B.). The work was also supported in part by the MSKCC Core Grant (P30 CA008748).

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Authors and Affiliations

  1. The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, New Jersey, USA
    Marta Łuksza & Arnold J. Levine
  2. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Nadeem Riaz & Timothy A. Chan
  3. Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Nadeem Riaz, Vladimir Makarov & Timothy A. Chan
  4. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Vladimir Makarov & Timothy A. Chan
  5. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Vinod P. Balachandran
  6. David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Vinod P. Balachandran
  7. Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Vinod P. Balachandran, Matthew D. Hellmann, Taha Merghoub, Timothy A. Chan & Jedd D. Wolchok
  8. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Matthew D. Hellmann & Jedd D. Wolchok
  9. Department of Medicine, Weill Cornell Medical College, Cornell University, New York, New York, USA
    Matthew D. Hellmann
  10. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Alexander Solovyov & Benjamin D. Greenbaum
  11. Department of Medicine, Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Alexander Solovyov & Benjamin D. Greenbaum
  12. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Alexander Solovyov & Benjamin D. Greenbaum
  13. Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Alexander Solovyov & Benjamin D. Greenbaum
  14. Department of Medicine, Columbia University Medical Center, New York, New York, USA
    Naiyer A. Rizvi
  15. Ludwig Collaborative and Swim Across America Laboratory, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Taha Merghoub & Jedd D. Wolchok
  16. Department of Medicine, Melanoma and Immunotherapeutics Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Taha Merghoub & Jedd D. Wolchok

Authors

  1. Marta Łuksza
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  2. Nadeem Riaz
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  3. Vladimir Makarov
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  4. Vinod P. Balachandran
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  5. Matthew D. Hellmann
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  6. Alexander Solovyov
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  7. Naiyer A. Rizvi
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  8. Taha Merghoub
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  9. Arnold J. Levine
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  10. Timothy A. Chan
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  11. Jedd D. Wolchok
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  12. Benjamin D. Greenbaum
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Contributions

M.Ł. and B.D.G. designed the mathematical model, analysed data and wrote the manuscript with critical comments from all the authors. N.R., V.M., V.P.B., M.D.H., A.S., N.A.R., T.M., A.J.L., T.A.C. and J.D.W. contributed to data acquisition and analysis. M.Ł., T.A.C., J.D.W. and B.D.G. contributed to study conception and design. M.Ł., N.R., V.M., V.P.B., M.D.H., A.S., N.A.R., T.M., A.J.L., T.A.C., J.D.W. and B.D.G. interpreted the data and provided a critical reading of the manuscript.

Corresponding authors

Correspondence toMarta Łuksza or Benjamin D. Greenbaum.

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Competing interests

M.Ł. has consulted for Merck. V.P.B. has received research funding from Bristol-Myers Squibb. A.J.L. is on the board of directors for Adaptive Biotechnologies and has consulted for Jansen Pharmaceuticals and Merck. T.A.C. is a co-founder of Gritstone Oncology and holds equity. T.A.C. receives grant funding from Bristol Myers Squibb. N.A.R is co-founder and shareholder of Gritstone Oncology. M.D.H. has consulted for Genentech, BMS, Merck, AstraZeneca, Janssen and Novartis. B.D.G. has consulted for Merck.

Additional information

Reviewer Information Nature thanks R. Simon, S. van den Burg and the other anonymous reviewer(s) 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 figures and tables

Extended Data Figure 1 Inferred MHC binding affinities of mutant versus wild-type peptides.

Neoantigens used in this study are nine-residue-long peptides with affinities predicted to be less than 500 nM by NetMHC3.4 (ref. 19) (Supplementary Information). We plot predicted affinities of mutant peptides compared to the predicted affinities of their wild-type peptides. A single point mutation can lead to a predicted affinity difference of up to four orders of magnitude.

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Extended Data Figure 2 Positions 2 and 9 have a subset of neoantigens with a lower predictive value.

The violin plots represent the data density at a given value on the vertical axis. a, Neoantigens with mutations at position 2 or 9 tend to have wild-type peptides with larger predicted affinities. In particular, this is magnified if the corresponding wild-type residue is non-hydrophobic. b, Those biases are reflected by a wider distribution of amplitudes (Methods, equation (7)) for wild-type peptides with non-hydrophobic residues at positions 2 and 9. c, Shannon entropy of amino acid diversity by position in neoantigens, shown for all distinct HLA types and computed based on neoantigens across all datasets. Positions 2 and 9 have a lower entropy than other residues. Other sites have the same entropy as the overall proteome23 and are therefore unconstrained. Five HLA profiles with non-canonical entropies are highlighted in the plot. These HLA types contributed only five informative neoantigens across all datasets and are therefore not treated differentially in our model.

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Extended Data Figure 3 Survival analysis score landscape as a function of model parameters.

a–c, The landscape of log-rank test scores as the function of the parameters of the TCR-binding model (a and 1 / k), shown for the consistent choice of τ = 0.09; colours represent the significance level of the long-rank test. The regions of high scores are similar across all three datasets. The point corresponding to consistent parameters (a = 26 and k = 4.87) is marked by a black dot in each plot. d–f, log-rank score for the fitness model at consistent binding-function parameters, plotted as a function of τ. Dashed vertical lines are at τ = 0.09, thin solid lines mark the score values corresponding to significance of P = 0.05, P = 0.01 and P = 0.005 (n = 103 (a, d), n = 64 (b, e), n = 34 (c, f)).

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Extended Data Figure 4 Alignments to IEDB epitopes.

The TCR recognition probability for a neoantigen is a sigmoidal function of the alignment scores of a given neoantigen to the IEDB epitopes, evaluated for the set of neoantigens from the cohort of patients from ref. 5, using the consistent set of parameters.

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Extended Data Figure 5 Effect of IEDB sequence content on predictive power of the neoantigen fitness model.

Predictions were performed using subsampled IEDB epitope sequences, with subsampling rate varying between 0.1 and 0.9. For each rate, 10,000 iterations were performed to obtain a distribution of log-rank test scores. The violin plots represent the data density at a given value on the vertical axis (n = 10,000). Solid black lines mark the log-rank test score of the prediction for the full set of epitope sequences and grey thick lines mark the median scores of the subsampled data. a–c, Subsampling of the original set of IEDB sequences, supported by positive T-cell assays, shows that the quality of predictions decreases with subsampling rate. Prediction quality is more robust in the datasets from refs 4, 6. d–f, The analogous subsampling procedure was repeated for IEDB sequences that were not supported by positive T-cell assays. For the datasets from refs 4, 5, model performance is substantially decreased.

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Extended Data Figure 6 Reshuffling patient HLA types reduces the predictive power of the neoantigen fitness model.

For each cohort, we performed 10 iterations for the analysis of reshuffled patient HLA types, followed by computational neoantigen prediction, fitness model calculation and survival analysis. We report the distribution of log-rank test scores over these iterations: boxes mark 75% confidence intervals and whiskers mark the range of scores (n = 10). The score values for the model of the original (unshuffled) data are marked with blue squares.

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Extended Data Figure 7 Cytolytic score improves prediction quality.

a, Kaplan–Meier curves of overall survival shown for our model applied to the dataset from ref. 4 for a subset of n = 40 patients with transcriptional data. Samples were split by the median value of their tumour’s relative population size n(τ) (equation (1)). Error bars represent the standard error due to sample size. b, A model optimized for cytolytic score significantly separates patients (Methods). c, Inclusion of the cytolytic score in our model improves prediction for the subset of 40 patients. The P values from log-rank tests comparing the two Kaplan–Meier curves are shown above each plot. For a and c, we used consistent parameters trained on the three cohorts (Extended Data Fig. 2); for b, the parameter τ is optimized.

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Extended Data Table 1 Ranking of fitness models when accounting for tumour subclonal composition

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Extended Data Table 2 Ranking of fitness models without accounting for the subclonal composition of tumours

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Extended Data Table 3 Multivariate analysis with a Cox proportional hazards model

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Łuksza, M., Riaz, N., Makarov, V. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy.Nature 551, 517–520 (2017). https://doi.org/10.1038/nature24473

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