Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer (original) (raw)

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Acknowledgements

We thank A. Rudensky, A. Snyder-Charan, C. Callan, Y. Elhanati, Z. Sethna, J. Leung, J. Ruan, C. Crabtree, P. Garcia, M. Singh, A. McNeil, D. Haviland, J. Melchor and J. Tsoi for discussions, technical and editorial assistance. This work was supported by National Institutes of Health (NIH) R01DK097087-01 Pancreatic Cancer Action Network-AACR Research Acceleration Network Grant (S.D.L.), P30 CA008748-50S4 administrative supplement (S.D.L., V.P.B.), Suzanne Cohn Simon Pancreatic Cancer Research Fund (S.D.L.), National Cancer Institute K12CA184746-01A1 (V.P.B.), Damon Runyon Clinical Investigator Award (V.P.B.), Stand Up to Cancer, Lustgarden Foundation, and the National Science Foundation (J.D.W., B.D.G.), the V Foundation (V.P.B., J.A.M., J.D.W., B.D.G.), the Phil A. Sharp Innovation Award (B.D.G., J.D.W.), Swim Across America, and the Ludwig Institute for Cancer Research (J.D.W., T.M.), and the Parker Institute for Cancer Immunotherapy (D.K.W., C.I.O.C., J.D.W., T.M.). Services by the Integrated Genomics Core were funded by the National Cancer Institute Cancer Center Support Grant (P30 CA08748), Cycle for Survival, and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology.

Author information

Author notes

  1. Taha Merghoub and Steven D. Leach: These authors jointly supervised this work.

Authors and Affiliations

  1. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Vinod P. Balachandran, Julia N. Zhao, John Alec Moral, Marc Attiyeh, Benjamin Medina, Jennifer Zhang, Jennifer Loo, Peter J. Allen, Ronald P. DeMatteo & Steven D. Leach
  2. David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Vinod P. Balachandran, Julia N. Zhao, John Alec Moral, Brian Herbst, Gokce Askan, Olivera Grbovic-Huezo, Marc Attiyeh, Joseph Saglimbeni, Peter J. Allen, Christine A. Iacobuzio-Donahue, Ronald P. DeMatteo & Steven D. Leach
  3. Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Vinod P. Balachandran, Julia N. Zhao, John Alec Moral, Jedd D. Wolchok, Timothy A. Chan & Taha Merghoub
  4. The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, New Jersey, USA
    Marta Łuksza & Arnold J. Levine
  5. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Vladimir Makarov, Christine A. Iacobuzio-Donahue, Timothy A. Chan & Steven D. Leach
  6. Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Vladimir Makarov, Nadeem Riaz & Timothy A. Chan
  7. Tisch Cancer Institute, Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Romain Remark, Miriam Merad & Sacha Gnjatic
  8. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Gokce Askan, Umesh Bhanot, Olca Basturk & Christine A. Iacobuzio-Donahue
  9. Swim Across America/Ludwig Collaborative Laboratory, New York, New York, USA
    Yasin Senbabaoglu, Mohsen Abu-Akeel, Roberta Zappasodi, Jedd D. Wolchok & Taha Merghoub
  10. Parker Institute for Cancer Immunotherapy, San Francisco, California, USA
    Daniel K. Wells & Charles Ian Ormsby Cary
  11. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Nadeem Riaz & Timothy A. Chan
  12. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
    Martin Smoragiewicz
  13. Cold Spring Harbor Laboratory, New York, New York, USA
    Z. Larkin Kelley & Douglas T. Fearon
  14. Department of Microbiology and Immunology, Weill Cornell Medical School, New York, New York, USA
    Z. Larkin Kelley & Douglas T. Fearon
  15. Department of Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Mithat Gönen
  16. Department of Medicine, Melanoma and Immunotherapeutics Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Jedd D. Wolchok
  17. Weill Cornell Medical College, Cornell University, New York, New York, USA
    Jedd D. Wolchok
  18. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
    Jedd D. Wolchok & Taha Merghoub
  19. Departments of Medicine, Tisch Cancer Institute, Hematology and Medical Oncology, Oncological Sciences, and Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Benjamin D. Greenbaum
  20. Dartmouth Norris Cotton Cancer Center, Lebanon, New Hampshire, USA
    Steven D. Leach
  21. The Kinghorn Cancer Centre, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, Sydney, New South Wales 2010, Australia
    Amber L. Johns, R. Scott Mead, Anthony J. Gill, David K. Chang, Skye H. McKay, Lorraine A. Chantrill, Venessa T. Chin, Angela Chou, Jeremy L. Humphris, Marina Pajic, Angela Steinmann, Mehreen Arshi, Ali Drury, Danielle Froio, Ashleigh Morgan, Paul Timpson, David Hermann, Claire Vennin, Sean Warren, Mark Pinese, Jianmin Wu, Andreia V. Pinho, R. Scott Mead, Anthony J. Gill, David K. Chang, Angela Chou & Lorraine A. Chantrill
  22. Prince of Wales Hospital, Barker Street, Randwick, New South Wales 2031, Australia
    R. Scott Mead, R. Scott Mead, Katherine Tucker & Lesley Andrews
  23. Royal North Shore Hospital, Westbourne Street, St Leonards, New South Wales 2065, Australia
    Anthony J. Gill, Anthony J. Gill, Jaswinder S. Samra, Jennifer Arena, Nick Pavlakis, Hilda A. High & Anubhav Mittal
  24. Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Switchback Road, Bearsden, Glasgow G61 1BD, UK
    David K. Chang, David K. Chang, Andrew V. Biankin, Peter Bailey, Sancha Martin, Elizabeth A. Musgrove, Marc D. Jones, Craig Nourse & Nigel B. Jamieson
  25. St Vincent’s Hospital, 390 Victoria Street, Darlinghurst, New South Wales, 2010, Australia
    Lorraine A. Chantrill, Angela Chou, Angela Chou, Lorraine A. Chantrill, Alina Stoita, David Williams & Allan Spigelman
  26. QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Queensland 4006, Australia
    Nicola Waddell, John V. Pearson, Ann-Marie Patch, Katia Nones, Felicity Newell, Pamela Mukhopadhyay, Venkateswar Addala, Stephen Kazakoff, Oliver Holmes, Conrad Leonard, Scott Wood & Christina Xu
  27. University of Melbourne, Centre for Cancer Research, Victorian Comprehensive Cancer Centre, 305 Grattan Street, Melbourne, Victoria 3000, Australia
    Sean M. Grimmond & Oliver Hofmann
  28. Institute for Molecular Bioscience, University of Queensland, St Lucia, Queensland 4072, Australia
    Peter J. Wilson, Angelika Christ & Tim Bruxner
  29. Bankstown Hospital, Eldridge Road, Bankstown, New South Wales 2200, Australia
    Ray Asghari, Neil D. Merrett, Darren Pavey & Amitabha Das
  30. Liverpool Hospital, Elizabeth Street, Liverpool, New South Wales 2170, Australia
    Annabel Goodwin, Peter H. Cosman, Kasim Ismail, Chelsie O’Connor & Annabel Goodwin
  31. Royal Prince Alfred Hospital, Missenden Road, Camperdown, New South Wales 2050, Australia
    Annabel Goodwin, Caroline L. Cooper, Annabel Goodwin, Peter Grimison, James G. Kench & Charbel Sandroussi
  32. Westmead Hospital, Hawkesbury and Darcy Roads, Westmead, New South Wales 2145, Australia
    Vincent W. Lam, Duncan McLeod, Adnan M. Nagrial, Judy Kirk & Virginia James
  33. Fremantle Hospital, Alma Street, Fremantle, Western Australia 6959, Australia
    Michael Texler, Cindy Forest, Krishna P. Epari, Mo Ballal, David R. Fletcher & Sanjay Mukhedkar
  34. St John of God Healthcare, 12 Salvado Road, Subiaco, Western Australia 6008, Australia
    Nikolajs Zeps, Maria Beilin & Kynan Feeney
  35. Royal Adelaide Hospital, North Terrace, Adelaide, South Australia 5000, Australia
    Nan Q. Nguyen, Andrew R. Ruszkiewicz & Chris Worthley
  36. Flinders Medical Centre, Flinders Drive, Bedford Park, South Australia 5042, Australia
    John Chen, Mark E. Brooke-Smith & Virginia Papangelis
  37. Envoi Pathology, 1/49 Butterfield Street, Herston, Queensland 4006, Australia
    Andrew D. Clouston & Patrick Martin
  38. Princess Alexandria Hospital, Cornwall Street & Ipswich Road, Woolloongabba, Queensland 4102, Australia
    Andrew P. Barbour, Thomas J. O’Rourke, Jonathan W. Fawcett, Kellee Slater, Michael Hatzifotis & Peter Hodgkinson
  39. Austin Hospital, 145 Studley Road, Heidelberg, Victoria 3084, Australia
    Mehrdad Nikfarjam
  40. Johns Hopkins Medical Institute, 600 North Wolfe Street, Baltimore, Maryland 21287, USA
    James R. Eshleman, Ralph H. Hruban, Christopher L. Wolfgang & Mary Hodgin
  41. ARC-NET Center for Applied Research on Cancer, University of Verona, Via dell’Artigliere, 19 37129 Verona, Province of Verona, Italy
    Aldo Scarpa, Rita T. Lawlor, Stefania Beghelli, Vincenzo Corbo, Maria Scardoni & Claudio Bassi

Authors

  1. Vinod P. Balachandran
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  2. Marta Łuksza
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  3. Julia N. Zhao
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  4. Vladimir Makarov
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  5. John Alec Moral
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  6. Romain Remark
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  7. Brian Herbst
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  8. Gokce Askan
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  9. Umesh Bhanot
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  10. Yasin Senbabaoglu
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  11. Daniel K. Wells
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  12. Charles Ian Ormsby Cary
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  13. Olivera Grbovic-Huezo
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  14. Marc Attiyeh
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  15. Benjamin Medina
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  16. Jennifer Zhang
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  17. Jennifer Loo
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  18. Joseph Saglimbeni
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  19. Mohsen Abu-Akeel
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  20. Roberta Zappasodi
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  21. Nadeem Riaz
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  22. Martin Smoragiewicz
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  23. Z. Larkin Kelley
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  24. Olca Basturk
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  25. Mithat Gönen
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  26. Arnold J. Levine
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  27. Peter J. Allen
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  28. Douglas T. Fearon
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  29. Miriam Merad
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  30. Sacha Gnjatic
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  31. Christine A. Iacobuzio-Donahue
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  32. Jedd D. Wolchok
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  33. Ronald P. DeMatteo
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  34. Timothy A. Chan
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  35. Benjamin D. Greenbaum
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  36. Taha Merghoub
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  37. Steven D. Leach
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Consortia

Australian Pancreatic Cancer Genome Initiative

Contributions

V.P.B., M.Ł., P.J.A., D.T.F., J.D.W., R.P.D., B.D.G., T.A.C., T.M. and S.D.L. conceived the study and V.P.B., J.D.W., T.A.C., B.D.G., T.M. and S.D.L. designed all experiments. V.P.B., M.Ł., J.N.Z., V.M., J.A.M., R.R., B.H., G.A., U.B., Y.S., D.K.W., C.I.O.C., O.G.-H., M.A., B.M., J.Z., J.L., J.S., M.A.-A., R.Z., N.R., M.S., Z.L.K., O.B., A.J.L., P.J.A., D.T.F., M.M., S.G., C.A.I.-D. and members of the Australian Pancreatic Cancer Genome Initiative acquired and analysed data. G.A., U.B. and O.B. performed the histopathological analyses. M.A. and C.A.I.D. performed tissue acquisition, and mutational identification for rapid autopsy tissues. V.M., N.R., T.A.C., D.K.W. and C.I.O.C. performed the neoantigen identification. M.Ł. and B.D.G. constructed the neoantigen fitness models. V.P.B., J.N.Z., M.A.-A. and J.A.M. performed the in vitro T-cell assays. O.G.-H. performed the transfections, immunocytochemistry, and western blots. M.G. provided statistical oversight. V.P.B., M.Ł., J.N.Z., D.T.F., J.D.W., R.P.D., B.D.G., T.A.C., T.M. and S.D.L. interpreted the data. V.P.B., M.Ł., J.D.W., B.D.G., T.M. and S.D.L. drafted the manuscript.

Corresponding author

Correspondence toVinod P. Balachandran.

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Competing interests

V.P.B., V.M., N.R., J.D.W. and T.A.C. have received research funding from Bristol-Myers Squibb. N.R. has received honoraria from MedImmune. D.T.F. is a co-founder of Myosotis LLC. S.G. has received research support from Immune Design, Janssen R&D, and Agenus, and serves on advisory boards for Third Rock, Ventures/Neon Therapeutics, B4CC, and Oncomed Pharmaceuticals. M.M., S.G. and R.R. are inventors of a patent regarding ‘Tissue profiling using multiplexed immunohistochemical consecutive staining’ (patent number pending). T.A.C. is a co-founder of Gritstone Oncology and is also an advisor for Genocea, Cancer Genetics, and Illumina.

Additional information

Reviewer Information Nature thanks E. Verdegaal, R. Vonderheide and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Clinicopathological characteristics of the MSKCC cohort.

a, Overall survival and patient overlap of short- and long-term survivors in tissue microarray, whole-exome sequencing, TCR Vβ sequencing, and bulk tumour transcriptomic profiling cohorts. b–g, Clinicopathological characteristics of patients in tissue microarray, transcriptome, TCR sequencing, whole-exome sequencing, matched primary–metastatic, and very long-term survivor cohorts. In b–e, the asterisk indicates three patients with metastases noted on final pathology (one liver metastasis, one metastasis to small bowel/mesentery, one splenic metastasis). n is the number of biologically independent samples in individual patients. P values were determined using a log-rank test (a) and a two-sided Fisher’s exact test (gender, tumour location, pN, pM, margin, chemotherapy), a two-sided _χ_2 test (procedure, pathological stage, pT, adjuvant treatment) and an unpaired, two-tailed Student’s _t_-test (age) (b–g).

Extended Data Figure 2 Long-term survivors of PDAC display enhanced intratumoural T-cell immunity.

a, Left: representative sequential immunohistochemical staining of a single short-term and a single long-term core tumour section. Sections bounded by black rectangles in the 100× panels are magnified to 275× in the panels immediately to the right for each core section. Top right: representative merged images of multiplexed immunohistochemistry are shown. The red rectangular sections are enlarged to 50×. CK19 was used to stain tumour cells. The arrows indicate CD3+CD8+granzyme B+ T cells. Middle right: immunofluorescent quantification of CD8+ and CD4+ cells in tumour tissue microarrays of short- and long-term survivors. Slides used were cut from separate sections of the block as those used for sequential immunohistochemistry (in a and Fig. 1b). Bottom right: quantification of multiplexed immunophenotyping as shown in a (left and top right). All immunofluorescent and immunohistochemical staining was repeated independently in triplicate for each patient. In a, short term n = 45, long term n = 51. b, Bulk tumour transcriptomic immune profiling in short- and long-term survivors. Dendritic-cell signature genes include CCL13, CCL17, CCL22, PPFIBP2, NPR1, HSD11B1 and CD209 (also known as DC-SIGN)3. c, Flow-cytometric gating strategy to phenotype human T cells (n = 7). The first plot is pre-gated on live cells, followed by CD45+ and CD3+CD56− cells. Values indicate the percentage of cells within the red boxes, and are gated based on isotype controls. d, Top: overall survival of patients who did or did not receive adjuvant chemotherapy (adjuvant chemotherapy+/− respectively, top left), and of patients with tumours harbouring more or fewer than the median number of CD3-CD8-granzyme B triple-positive cells (CD3-CD8-granzyme Bhi/low respectively, top right). Overall survival of all four groups is shown in the bottom panels. The table shows univariate and multivariate Cox regression analysis of clinicopathological features, adjuvant chemotherapy, and CD3-CD8-granzyme B density associations with overall survival. Horizontal bars indicate median values, error bars represent the s.e.m. n is the number of biologically independent samples in individual patients. P values were determined using a two-tailed Mann–Whitney _U_-test (a, b), a one-way ANOVA (c) and a log-rank test (d).

Source data

Extended Data Figure 3 Neoantigen quantity and CD8+ T-cell infiltrate identify long-term pancreatic cancer survivors.

a, Left: number of nonsynonymous, missense and neoantigenic mutations per patient in the MSKCC cohort. The tick marks on the x axis correspond to individual tumours. Right: oncoprint demonstrating the frequency of oncogenic driver-gene mutations in the MSKCC cohort. b, Overall survival of patients with tumours harbouring more than the median number of neoantigens (neoantigenhi), and greater than the median intratumoural T-cell repertoire polyclonality (polyclonalhi), compared to all other patients (rest). Neoantigens were determined using the MSKCC (top) and the pVAC-Seq (bottom) neoantigen prediction pipelines. c, Left: number of neoantigens per tumour, as determined by the MSKCC and pVAC-Seq neoantigen calling pipelines. Tick marks on the x axis correspond to individual tumours. Right: correlation matrix of neoantigens as determined by the MSKCC and pVAC-Seq neoantigen calling pipelines. The solid red line indicates the line of best fit, dotted lines indicate 95% confidence intervals. d, Top: overall survival of patients with tumours harbouring more or fewer than the median number of neoantigens (neoantigenhi/low) and CD3-CD8 double-positive cells (CD3-CD8hi/low), compared to all other patients (rest) (top left). Patients who did or did not receive adjuvant chemotherapy (adjuvant chemotherapy+/−, respectively) (top right), and all four groups (bottom) are also shown. The table shows univariate and multivariate Cox regression analysis of the associations of clinicopathological features, adjuvant chemotherapy, and neoantigen-CD3-CD8 number with overall survival. e Distribution of tumours with high- and low-quality neoantigens in neoantigenhi CD3-CD8hi long-term pancreatic cancer survivors compared to all other patients (rest). n is the number of biologically independent samples in individual patients. P values were determined using a log-rank test (b, d) and a _χ_2 test (e).

Source data

Extended Data Figure 4 Unique genomic features alone do not identify long-term survivors of PDAC.

a, Overall survival of patients with tumours harbouring more or fewer than the median number of neoantigens (neoantigenhi/low), CD3-CD8 double-positive cells (CD3-CD8hi/low), polyclonality (polyclonalhi/low), mutations (mutationhi/low), and CD4 single positive cells (CD4hi/low). b, Oncoprint demonstrating no difference in the frequency of oncogenic driver mutations in short- and long-term tumours. c, No difference was found in the number of nonsynonymous, missense, and immunogenic mutations (neoantigens) in short- and long-term PDAC tumours. d, Overall survival stratified by mutations in ARID1A, _KRAS_Q61H, RBM10 and MLL-related genes (KMT2A, KMT2B, KMT2C and KMT2E (also known as MLL, MLL2, MLL3 and MLL5, respectively)). Horizontal bars indicate median values. n is the number of biologically independent samples in individual patients. P values were determined using a log-rank test (a, d).

Source data

Extended Data Figure 5 Neoantigen immune fitness models.

a, Comprehensive flowchart of neoantigen quality identification pipeline. Software programs used for each step are indicated in italicized text. Mathematical formulae for the calculation of individual components of neoantigen quality are defined in the Methods. All software components of the pipeline are published and/or publicly available. b, Top: schematic of neoantigen immune fitness models. Each circle represents a tumour clone in an evolutionary tree. Clones in both models are identical with respect to the number of mutations and neoantigens. The numbers represent hypothetical neoantigens gained in a successive tumour clone. Shades of red indicate the immunogenicity of each clone, as ascribed by the two models, namely neoantigen quality or neoantigen quantity. Bottom: parameters defining the quality score in the quality model (1)–(3). In (1), amino acid sequences of a hypothetical wild-type epitope, tumour neoepitope, and a homologous microbial epitope are shown. Yellow highlights the changing amino acid between the wild-type and the tumour sequence as a consequence of a tumour-specific mutation. The amino acids in red indicate homology between the tumour neoepitope and the microbial epitope.

Extended Data Figure 6 Neoantigen quality is independently prognostic of survival.

a, Top: overall survival of patients whose tumours displayed high compared to low neoantigen quality (neoantigen qualityhi/low) (left), and overall survival of patients who did or did not receive adjuvant chemotherapy (right). Bottom: overall survival of all four groups. Neoantigen quality defined by pipeline and schema as defined in Extended Data Fig. 5a, b. The table shows univariate and multivariate Cox regression analyses of the associations of clinicopathological features, adjuvant chemotherapy and neoantigen quality with overall survival. Data include all patients in the whole-exome sequencing MSKCC cohort. b, Number of nonsynonymous, missense and neoantigenic mutations per patient in the ICGC cohort (n = 166). c, Top: overall survival of patients in the ICGC cohort whose tumours displayed high compared to low neoantigen quality (neoantigen qualityhi/low) (left), and overall survival of patients in the ICGC cohort stratified by adjuvant chemotherapy administration (right). Bottom: overall survival of all four groups. Neoantigen quality defined by pipeline and schema as defined in Extended Data Fig. 5a, b. The table shows univariate and multivariate Cox regression analyses of the associations of clinicopathological features, adjuvant chemotherapy and neoantigen quality with overall survival in the ICGC cohort. Data on adjuvant chemotherapy is included for patients whose treatment status was available. n is the number of biologically independent samples in individual patients. P values were determined using a log-rank test (a, c).

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Extended Data Figure 7 Stability of neoantigen quality model parameters on subsampled cohorts and prognostic dependence of neoantigen quality on infectious disease-derived peptides.

a, b, Parameters of the neoantigen fitness quality model for the MSKCC (a) and the ICGC (b) cohorts. Left: log-rank test score landscape as a function of the model parameters (the horizontal alignment score displacement a, and the characteristic time τ); the significance of the score is denoted in the legend. Right: two-dimensional histograms showing distributions of optimal parameters obtained on subsampled datasets with 50, 70, and 90% of patients left, over 500 iterations of subsampling at each frequency. c, Overall survival of patients in the MSKCC and ICGC cohorts; whole tumours displayed high compared to low neoantigen quality (neoantigen qualityhi/low). Neoantigen quality was calculated using alignment to immunogenic infectious disease-derived IEDB peptides (microbial peptides) or using alignment to immunogenic non-infectious disease-derived allergy or autoimmune peptides in the IEDB database (non-microbial peptides). n is the number of biologically independent samples in individual patients. P values were determined using a log-rank test (c).

Extended Data Figure 8 Predicted MUC16 neoantigens are recognized by the human TCR repertoire.

a, PBMCs pulsed with no peptide, wild-type control peptide, cross-reactive peptide or high-quality neopeptide (mutant). Representative gating strategies for CD8+ T-cell expansion and degranulation are shown. b, PBMCs pulsed with no peptide, wild-type control peptide, and MUC16 neopeptides (mutant). Representative gating strategies for CD8+ T-cell expansion are shown. c, Representative gating strategy to identify CD8+ T cells in peripheral blood of healthy donors (top panel). Identification of CD8+ T cells in healthy donors reactive to unique MUC16 neoepitopes predicted to bind to the B*0801 HLA-allele, using MUC16-neoepitope–HLA multimers. Quantification of all healthy donors (neoepitope no. 1, 2, n = 5) is shown (right). Multimer staining is shown on the x axis, CD8 is shown on the y axis. Peptide information is provided in Supplementary Table 3. n is the number of biologically independent samples in individual patients. Horizontal bars indicate median values, error bars represent the s.e.m. P values were determined using a one-way ANOVA (c).

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Extended Data Figure 9 Long-term survivors do not display differences in MUC16 mutations, transcriptional regulators or downstream targets of MUC16, or differences in other mucins and tissue expression antigens.

a, The frequency of MUC16 mutations in short- and long-term PDAC tumours. Lollipop plot showing the location of MUC16 mutations and neoantigens in short- and long-term survivors of pancreatic cancer. b, Expression of bulk tumour MUC16 mRNA (left) and protein (middle) by immunohistochemistry. Immunohistochemical staining was repeated independently in triplicate for each patient. Right: MUC16 mutant allele frequency in non-hypermutated tumours with MUC16 mutations. c, Left: frequency of patients with MUC16 neoantigens in MSKCC and ICGC cohorts. Middle: frequency of patients with neoantigens in genes recurrently harbouring neoantigens in >5% of patients in both MSKCC and ICGC cohorts. Right: genes most frequently harbouring neoantigens in the MSKCC cohort as determined by pVAC-Seq. Frequency of patients (y axis) and raw numbers (above bar graphs) are indicated. d, mRNA expression of transcriptional activators of MUC16 (top left), mediators implicated in MUC16-dependent tumour progression (top right), and mRNA (bottom left) and protein (bottom right) levels of tissue expression antigens MUC1, MUC4, WT1, mesothelin and annexin A2 in short- and long-term tumours. WT1 protein was undetectable in both short- and long-term survivors. n = 15 per group in top left, top right, and bottom left; short term n = 45, long term n = 51 in bottom right. e, MUC16 mRNA and protein expression in MUC16 non-mutated (WT; n = 18 (top), n = 20 (bottom)) and mutated (mutant; n = 10 (top), n = 9 (bottom)) tumours. f, TCR Vβ sequencing of T-cell product following the pulse of peripheral blood T cells with MUC16 neopeptides as in Fig. 4e. Brown open circles indicate stable or contracted clones with mutant neopeptide; blue open circles indicate expanded clones with mutant neopeptide; red solid circles indicate expanded clones with mutant neopeptide detected in archival primary tumours. Arrows indicate clones in archival primary tumours with rank frequencies; Venn diagrams show clonal overlap in respective compartments. Horizontal bars indicate median values, error bars represent the s.e.m. n is the number of biologically independent samples in individual patients. P values were determined using two-tailed Mann–Whitney _U_- and Student’s _t_-tests (b), a _χ_2 test (c) and as described in the Methods (f).

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Extended Data Figure 10 M_UC16_ mutations do not alter tumour cell-intrinsic MUC16 protein expression.

a, Representative immunohistochemical staining (left) and quantification (right) of MUC16 expression in tissue microarrays of short- and long-term survivors of pancreatic cancer, as assessed using three independent anti-MUC16 antibodies. Ab no. 1: clone EPSISR23, purchased from Abcam; Ab no. 2: polyclonal, purchased from Abcam ab133419; Ab no. 3: clone 4H1127. Each open circle represents the median expression of independent immunohistochemical staining performed in triplicate for each patient. b, Western blot (top) and immunocytochemistry (bottom) of untransfected (−), empty vector (vector), MUC16 wild-type (MUC16 WT) and MUC16 mutant (MUC16 R15C) HEK293 cells. The left blot was probed with anti-MUC16-specific antibody (clone 4H1127) and the right blot with anti-β-actin. The red rectangle indicates the MUC16-specific band. All cells in the bottom panels were probed with anti-MUC16 antibody (clone 4H1127). The inserted mutation was identical to a neoantigenic MUC16 mutation (detected in patient 1 shown in Extended Data Fig. 9a). Data are representative of two independent experiments with similar results. c, MUC16 immunohistochemistry on samples from two long-term survivors of pancreatic cancer with MUC16 neoepitopes in primary resected tumours. Areas in rectangular low-power fields are shown magnified in the right panels. Immunohistochemical staining was performed independently in triplicate for each patient in tissue microarrays, and confirmed with immunohistochemical staining on whole tumour sections (shown). Horizontal bars indicate median values, error bars represent the s.e.m. n is the number of biologically independent samples in individual patients. P values were determined using a two-tailed Student’s _t_-test (a).

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Supplementary information

Life Sciences Reporting Summary (PDF 85 kb)

Supplementary Table 1

This file contains a comprehensive list of all neopeptide sequences and HLA alleles with predicted neopeptide binding in the MSKCC cohort. (XLSX 334 kb)

Supplementary Table 2

This file contains a comprehensive list of all human infectious derived, class-I restricted peptide sequences with positive immune assays derived from the Immune Epitope Database used in this study. (XLSX 110 kb)

Supplementary Table 3

This file contains a comprehensive list of all peptide sequences, respective HLA alleles with predicted binding, crossreacting microbial sequences, and microbial species (where applicable) used in this study. (XLSX 40 kb)

Supplementary Table 4

This file contains a comprehensive list of all human non-infectious (allergy/autoimmune) derived, class-I restricted peptide sequences with positive immune assays derived from the Immune Epitope Database used in this study. (XLSX 53 kb)

Supplementary Data 1

This folder contains the source code for the neoantigen quality assessment performed in this manuscript. A text file of Supplementary Table 1 is included to enable code execution. (ZIP 764 kb)

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Balachandran, V., Łuksza, M., Zhao, J. et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer.Nature 551, 512–516 (2017). https://doi.org/10.1038/nature24462

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