Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Low neoantigen expression and poor T-cell priming underlie early immune escape in colorectal cancer

Nature Cancer volume 2, pages 1071–1085 (2021)Cite this article

Subjects

Abstract

Immune evasion is a hallmark of cancer and therapies that restore immune surveillance have proven highly effective in cancers with high tumor mutation burden (TMB) (for example, those with microsatellite instability). Whether low TMB cancers, which are largely refractory to immunotherapy, harbor potentially immunogenic neoantigens remains unclear. Here, we show that tumors from all patients with microsatellite stable colorectal cancer express clonal predicted neoantigens despite low TMB. Unexpectedly, these neoantigens are broadly expressed at lower levels compared to those in colorectal cancer with microsatellite instability. Using a versatile platform for modulating neoantigen expression in colorectal cancer organoids and transplantation into the distal colon of mice, we show that low expression precludes productive cross-priming and drives immediate T-cell dysfunction. Notably, experimental or therapeutic rescue of priming rendered T cells capable of controlling tumors with low neoantigen expression. These findings underscore a critical role of neoantigen expression level in immune evasion and therapy response.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: MSS CRC harbors both lower burden and expression of predicted neoantigens. Analysis of predicted neoantigens in human CRC (TCGA COADREAD) with high MSI (MSI-H), low MSI (MSI-L) and MSS.
Fig. 2: Development of an organoid system to interrogate neoantigen expression level in CRC.
Fig. 3: Low neoantigen expression drives impaired T-cell effector commitment and dysfunction.
Fig. 4: T cells in tumors with low neoantigen expression become progressively dysfunctional.
Fig. 5: Low neoantigen expression limits T-cell cross-priming.
Fig. 6: Therapeutic vaccination and agonistic anti-CD40 are efficacious in low neoantigen-expressing tumors.
Fig. 7: Immunotherapy refractory low neoantigen-expressing tumors remain vulnerable to antigen-specific T-cell killing.

Similar content being viewed by others

Data availability

MS data generated in this study have been deposited on MassIVE under accession code MSV000087648. TCR-β sequencing data generated in this study have been deposited on immuneACCESS (https://clients.adaptivebiotech.com/pub/westcott-2021-nc). TCGA COADREAD data analyzed in this study are available for download on the National Cancer Institute Genomics Data Commons. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

Analyses were performed using open-source software and in-house scripts in R v.4.0.2 and Python v.2.7.13, which are available from the corresponding author on reasonable request. Quantification of CD4, CD8 and FOXP3 staining by IHC was performed using a custom CNN developed with Aiforia’s cloud-based image analysis platform. This is a commercial platform with proprietary technology and therefore did not generate any code. An interactive example of algorithm functionality can be provided free of charge upon request at https://www.aiforia.com.

References

  1. Muzny, D. M. et al. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

    Article  CAS  Google Scholar 

  2. Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Germano, G. et al. Inactivation of DNA repair triggers neoantigen generation and impairs tumour growth. Nature 552, 116–120 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. Mandal, R. et al. Genetic diversity of tumors with mismatch repair deficiency influences anti-PD-1 immunotherapy response. Science 364, 485–491 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Newey, A. et al. Immunopeptidomics of colorectal cancer organoids reveals a sparse HLA class i neoantigen landscape and no increase in neoantigens with interferon or MEK-inhibitor treatment. J. Immunother. Cancer 7, 309 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Steimle, A. & Frick, J.-S. Molecular mechanisms of induction of tolerant and tolerogenic intestinal dendritic cells in mice. J. Immunol. Res. 2016, 1958650 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Kim, M. et al. Critical role for the microbiota in CX3CR1+ intestinal mononuclear phagocyte regulation of intestinal T cell responses. Immunity 49, 151–163 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Spranger, S., Bao, R. & Gajewski, T. F. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature 523, 231–235 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Galarreta, M. R. de et al. β-Catenin activation promotes immune escape and resistance to anti–PD-1 therapy in hepatocellular carcinoma. Cancer Discov. 9, 1124–1141 (2019).

    Article  Google Scholar 

  11. Roper, J. et al. In vivo genome editing and organoid transplantation models of colorectal cancer and metastasis. Nat. Biotechnol. 35, 569–576 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Roper, J. et al. Colonoscopy-based colorectal cancer modeling in mice with CRISPR-Cas9 genome editing and organoid transplantation. Nat. Protoc. 13, 217–234 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Supek, F. & Lehner, B. Differential DNA mismatch repair underlies mutation rate variation across the human genome. Nature 521, 81–84 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wherry, E. J., Puorro, K. A., Porgador, A. & Eisenlohr, L. C. The induction of virus-specific CTL as a function of increasing epitope expression: responses rise steadily until excessively high levels of epitope are attained. J. Immunol. 163, 3735–3745 (1999).

    Article  CAS  PubMed  Google Scholar 

  16. Spiotto, M. T. et al. Increasing tumor antigen expression overcomes ‘ignorance’ to solid tumors via crosspresentation by bone marrow-derived stromal cells. Immunity 17, 737–747 (2002).

    Article  CAS  PubMed  Google Scholar 

  17. Engels, B. et al. Relapse or eradication of cancer is predicted by peptide-major histocompatibility complex affinity. Cancer Cell 23, 516–526 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Jhunjhunwala, S., Hammer, C. & Delamarre, L. Antigen presentation in cancer: insights into tumour immunogenicity and immune evasion. Nat. Rev. Cancer 21, 298–312 (2021).

    Article  CAS  PubMed  Google Scholar 

  19. Richard, A. C. et al. T cell cytolytic capacity is independent of initial stimulation strength. Nat. Immunol. 19, 849–858 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Szolek, A. et al. OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30, 3310–3316 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Jurtz, V. et al. NetMHCpan-4.0: improved peptide–MHC class i interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199, 3360–3368 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Andreatta, M. & Nielsen, M. Gapped sequence alignment using artificial neural networks: application to the MHC class i system. Bioinformatics 32, 511–517 (2016).

    Article  CAS  PubMed  Google Scholar 

  23. Peters, B. & Sette, A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinforma. 6, 1–9 (2005).

    Article  CAS  Google Scholar 

  24. Kim, Y., Sidney, J., Pinilla, C., Sette, A. & Peters, B. Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior. BMC Bioinf. 10, 394 (2009).

    Article  CAS  Google Scholar 

  25. Hundal, J. et al. PVACtools: a computational toolkit to identify and visualize cancer neoantigens. Cancer Immunol. Res. 8, 409–420 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wolf, Y. et al. UVB-induced tumor heterogeneity diminishes immune response in melanoma. Cell 179, 219–235.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Karandikar, S. H. et al. Identification of epitopes in ovalbumin that provide insights for cancer neoepitopes. JCI Insight 4 (2019).

  30. Ghosh, M. et al. Guidance document: validation of a high-performance liquid chromatography-tandem mass spectrometry immunopeptidomics assay for the identification of HLA class I ligands suitable for pharmaceutical therapies. Mol. Cell. Proteom. 19, 432–443 (2020).

    Article  CAS  Google Scholar 

  31. Ebrahimi-Nik, H. et al. Mass spectrometry-driven exploration reveals nuances of neoepitope-driven tumor rejection. JCI Insight 4 (2019).

  32. DuPage, M. et al. Endogenous T cell responses to antigens expressed in lung adenocarcinomas delay malignant tumor progression. Cancer Cell 19, 72–85 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. DuPage, M., Mazumdar, C., Schmidt, L. M., Cheung, A. F. & Jacks, T. Expression of tumour-specific antigens underlies cancer immunoediting. Nature 482, 405–409 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Dow, L. E. et al. Apc restoration promotes cellular differentiation and reestablishes crypt homeostasis in colorectal cancer. Cell 161, 1539–1552 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Drost, J. et al. Sequential cancer mutations in cultured human intestinal stem cells. Nature 521, 43–47 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. de Sousa e Melo, F. et al. A distinct role for Lgr5+ stem cells in primary and metastatic colon cancer. Nature 543, 676–680 (2017).

    Article  PubMed  CAS  Google Scholar 

  38. Schell, M. J. et al. A multigene mutation classification of 468 colorectal cancers reveals a prognostic role for APC. Nat. Commun. 7, 11743 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Mlecnik, B. et al. Integrative analyses of colorectal cancer show immunoscore is a stronger predictor of patient survival than microsatellite instability. Immunity 44, 698–711 (2016).

    Article  CAS  PubMed  Google Scholar 

  41. Parish, I. A. et al. The molecular signature of CD8+ T cells undergoing deletional tolerance. Blood 113, 4575–4585 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lin, W.-H. W. et al. CD8+ T lymphocyte self-renewal during effector cell determination. Cell Rep. 17, 1773–1782 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Danilo, M., Chennupati, V., Silva, J. G., Siegert, S. & Held, W. Suppression of Tcf1 by inflammatory cytokines facilitates effector CD8 T cell differentiation. Cell Rep. 22, 2107–2117 (2018).

    Article  CAS  PubMed  Google Scholar 

  44. Zhou, X. et al. Differentiation and persistence of memory CD8(+) T cells depend on T cell factor 1. Immunity 33, 229–240 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Durward, M., Harms, J. & Splitter, G. Antigen specific in vivo killing assay using CFSE labeled target cells. J. Vis. Exp. 45 (2010).

  46. Wherry, E. J. T cell exhaustion. Nat. Immunol. 12, 492–499 (2011).

    Article  CAS  PubMed  Google Scholar 

  47. McLane, L. M., Abdel-Hakeem, M. S. & Wherry, E. J. CD8 T cell exhaustion during chronic viral infection and cancer. Annu. Rev. Immunol. 37, 457–495 (2019).

    Article  CAS  PubMed  Google Scholar 

  48. Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Siddiqui, I. et al. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211(2019).

    Article  CAS  PubMed  Google Scholar 

  50. Kurtulus, S. et al. Checkpoint blockade immunotherapy induces dynamic changes in PD-1−CD8+ tumor-infiltrating T cells. Immunity 50, 181–194 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Provine, N. M. et al. Immediate dysfunction of vaccine-elicited CD8+ T cells primed in the absence of CD4+ T cells. J. Immunol. 197, 1809–1822 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Van Stipdonk, M. J. B. et al. Design of agonistic altered peptides for the robust induction of CTL directed towards H-2Db in complex with the melanoma-associated epitope gp100. Cancer Res. 69, 7784–7792 (2009).

    Article  PubMed  CAS  Google Scholar 

  53. Liu, H. et al. Structure-based programming of lymph-node targeting in molecular vaccines. Nature 507, 519–522 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Cella, M. et al. Ligation of CD40 on dendritic cells triggers production of high levels of interleukin-12 and enhances T cell stimulatory capacity: T-T help via APC activation. J. Exp. Med. 184, 747–752 (1996).

    Article  CAS  PubMed  Google Scholar 

  55. Byrne, K. T. & Vonderheide, R. H. CD40 stimulation obviates innate sensors and drives T cell immunity in cancer. Cell Rep. 15, 2719–2732 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kabacaoglu, D., Ciecielski, K. J., Ruess, D. A. & Algül, H. Immune checkpoint inhibition for pancreatic ductal adenocarcinoma: current limitations and future options. Front. Immunol. 9, 1878 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Wells, D. K. et al. Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction. Cell 183, 818–834 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Ott, P. A. et al. A phase Ib trial of personalized neoantigen therapy plus anti-PD-1 in patients with advanced melanoma, non-small cell lung cancer, or bladder cancer. Cell 183, 347–362 (2020).

    Article  CAS  PubMed  Google Scholar 

  59. Simoni, Y. et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).

    Article  CAS  PubMed  Google Scholar 

  60. Krogsgaard, M. & Davis, M. M. How T cells ‘see’ antigen. Nat. Immunol. 63, 239–245 (2005).

    Article  CAS  Google Scholar 

  61. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Weese, D., Holtgrewe, M. & Reinert, K. RazerS 3: faster, fully sensitive read mapping. Bioinformatics 28, 2592–2599 (2012).

    Article  CAS  PubMed  Google Scholar 

  63. Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).

    Article  CAS  PubMed  Google Scholar 

  64. Narzisi, G. et al. Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nat. Methods 11, 1033–1036 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6, 1–12 (2015).

    Article  CAS  Google Scholar 

  67. Kuraguchi, M. et al. Adenomatous polyposis coli (APC) is required for normal development of skin and thymus. PLoS Genet. 2, e146 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Johnson, L. et al. Somatic activation of the K-ras oncogene causes early onset lung cancer in mice. Nature 410, 1111–1116 (2001).

    Article  CAS  PubMed  Google Scholar 

  69. Marino, S., Vooijs, M., van Der Gulden, H., Jonkers, J. & Berns, A. Induction of medulloblastomas in p53-null mutant mice by somatic inactivation of Rb in the external granular layer cells of the cerebellum. Genes Dev. 14, 994–1004 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Hao, Z. & Rajewsky, K. Homeostasis of peripheral B cells in the absence of B cell influx from the bone marrow. J. Exp. Med. 194, 1151–1164 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Hogquist, K. A. et al. T cell receptor antagonist peptides induce positive selection. Cell 76, 17–27 (1994).

    Article  CAS  PubMed  Google Scholar 

  72. Chu, V. T. et al. Efficient generation of Rosa26 knock-in mice using CRISPR/Cas9 in C57BL/6 zygotes. BMC Biotechnol. 16, 4 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Hildner, K. et al. Batf3 deficiency reveals a critical role for CD8α+ dendritic cells in cytotoxic T cell immunity. Science 322, 1097–1100 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Miyoshi, H. & Stappenbeck, T. S. In vitro expansion and genetic modification of gastrointestinal stem cells in spheroid culture. Nat. Protoc. 8, 2471–2482 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. DuPage, M., Dooley, A. L. & Jacks, T. Conditional mouse lung cancer models using adenoviral or lentiviral delivery of Cre recombinase. Nat. Protoc. 4, 1064–1072 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Sánchez-Rivera, F. J. et al. Rapid modelling of cooperating genetic events in cancer through somatic genome editing. Nature 516, 428–431 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Certo, M. T. et al. Tracking genome engineering outcome at individual DNA breakpoints. Nat. Methods 8, 671–676 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Jaeger, A. M. et al. Rebalancing protein homeostasis enhances tumor antigen presentation. Clin. Cancer Res. 25, 6392–6405 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Steinert, E. M. et al. Quantifying memory CD8 T cells reveals regionalization of immunosurveillance. Cell 161, 737–749 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Cancer Institute Cancer Center support grant P30-CA14051, R01 CA233983 and the Howard Hughes Medical Institute. P.M.K.W. was supported by a Damon Runyon Fellowship Award. We thank K. Yee, J. Teixeira, K. Anderson and M. Magendantz for administrative support and our colleagues in the Jacks laboratory and the broader community at the Koch Institute of MIT for thoughtful discussions and technical advice, with special thanks to S. Spranger and members of her laboratory. We thank the Koch Institute Swanson Biotechnology Center for core support from the Flow Cytometry, Proteomics, Histology and Microscopy facilities, with particular thanks to A. Koller for help with design and analysis of MS experiments; M. Griffin, M. Jennings and G. Paradis for flow cytometry support; and K. Cormier and C. Condon for histology support. We are also grateful for a fruitful collaboration with T. Westerling and Aiforia in developing an automated CNN for IHC quantification. Finally, we thank T. Tammela and J. Roper for early inspiration and mentorship in the colonoscopy-guided injection technique.

Author information

Authors and Affiliations

  1. David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Peter M. K. Westcott, Nathan J. Sacks, Jason M. Schenkel, Zackery A. Ely, Olivia Smith, Haley Hauck, Alex M. Jaeger, Daniel Zhang, Coralie M. Backlund, Mary C. Beytagh, J. J. Patten, Ryan Elbashir, George Eng, Darrell J. Irvine, Omer H. Yilmaz & Tyler Jacks

  2. Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA

    Jason M. Schenkel

  3. Harvard Medical School, Boston, MA, USA

    Jason M. Schenkel

  4. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

    George Eng & Omer H. Yilmaz

  5. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Darrell J. Irvine

  6. Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA

    Darrell J. Irvine

  7. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Darrell J. Irvine

  8. Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

    Darrell J. Irvine

  9. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Omer H. Yilmaz & Tyler Jacks

Authors
  1. Peter M. K. Westcott
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nathan J. Sacks
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jason M. Schenkel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zackery A. Ely
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Olivia Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Haley Hauck
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alex M. Jaeger
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Daniel Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Coralie M. Backlund
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Mary C. Beytagh
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. J. J. Patten
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ryan Elbashir
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. George Eng
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Darrell J. Irvine
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Omer H. Yilmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Tyler Jacks
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.M.K.W. and T.J. conceived and directed the study. P.M.K.W., N.S., O.S., H.H. and A.J. carried out all aspects of the research, animal care and experimentation. J.M.S. provided essential conceptual and technical guidance in the design and execution of flow cytometry-based experiments. Z.E. designed and executed the pipeline to generate a list of predicted neoantigens from TCGA COADREAD. A.J. designed and carried out MHC-I pull-down and elution for MS. N.S. developed the triple IHC and an automated CNN for quantification in collaboration with Aiforia. N.S., O.S., D.Z., J.J.P., M.C.B. and R.E. generated lentiviral constructs and primary organoid lines used in the study. C.M.B. and D.J.I. provided guidance, reagents and technical assistance with therapeutic vaccinations. G.E. and O.Y. provided important guidance and reagents for organoid culture and colonoscopy-guided injections. All data analysis was carried out by P.M.K.W. The manuscript was written by P.M.K.W. and T.J. with feedback from all authors.

Corresponding author

Correspondence to Tyler Jacks.

Ethics declarations

Competing interests

T.J. is a member of the Board of Directors of Amgen and Thermo Fisher Scientific and a co-founder of Dragonfly Therapeutics and T2 Biosystems. T.J. serves on the Scientific Advisory Board of Dragonfly Therapeutics, SQZ Biotech and Skyhawk Therapeutics. None of these affiliations represents a conflict of interest with respect to any of the studies described in this manuscript. The Jacks laboratory also currently receives funding from Johnson & Johnson, but this did not support the research described in this manuscript. This work was supported by the Howard Hughes Medical Institute. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks Robert Samstein, Daniel Speiser 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

Extended Data Fig. 1 Lower burden and expression of predicted neoantigens in MSS versus MSI-H CRC.

(a) Total expressed neoantigens by patient including hypermutant MSS cases (in purple). N = 62 MSI-H, 68 MSI-L, and 275 (including 9 hypermutant) MSS patients. All other plots exclude hypermutant MSS cases. (b) Mean expression of all neoantigens, regardless of clonality, by patient. N = 62 MSI-H, 68 MSI-L, and 266 MSS patients. (c-d) Analysis of patients with available ABSOLUTE purity for estimation of clonality (adjVAF). N = 50 MSI-H, 58 MSI-L, and 236 non-hypermutant MSS patients. (c) Empirical cumulative distribution function of mean neoantigen clonality (adjVAF) by patient. Significance was assessed by two-sided Kolmogorov-Smirnov test. (d) Total expressed clonal neoantigens with predicted HLA-I binding IC50 ≤ 500 nM by patient. (e) Mean allele-specific expression of clonal SNV-derived neoantigens by patient, excluding neoantigens with zero gene level expression but including those with zero allele-specific expression. N = 41 MSI-H, 53 MSI-L, and 219 MSS patients. (f-g) Abundance distributions of HLA-I ligandomes by MS in PDOs from MSS CRC patients CRC_01 (f) and CRC_04 (g) with epitope abundance above the median in gray, below the median in light blue, and neoantigens in red. Data from Newey, A, et al., 2019. Significance in (b), (d), and (e) was assessed by two-tailed Wilcoxon Rank Sum test with Holm’s correction for multiple comparisons. Source data for panels (a-e) can be found in Source Data Fig. 1a-e.

Source data

Extended Data Fig. 2 Development of in vivo lentiviral and organoid models of CRC with neoantigen expression.

(a) Lentiviruses used to initiate colon tumors in Apcflox/flox and Apcflox/flox; Rag2-/- mice. (b) Efficiency of tumor formation 16 weeks post-injection. N = independent animals. Significance assessed by two-tailed Wilcoxon Rank Sum with Holm’s correction for multiple comparisons. (c) Antigen expression in LucOS-induced tumors in Rag2-/- (left) and wild-type (right) mice at 12 weeks (colonoscopy above, bioluminescence below). (d) Efficiency of tumor induction with LucOS lentivirus at 20,000 and 100,000 transduction units (TU)/μl. N = 26 independent animals. (e) Antigen expression (bioluminescence). N = 26 independent animals. Significance assessed by two-tailed Wilcoxon Rank Sum. (f) Antigen expression in LucOS-induced tumors with continuous T-cell depletion at 5 weeks (left) and 7 weeks after T-cell depletion (right), and colonoscopy (above). (g) Antigen expression versus relative tumor size (percent of colon occluded) following withdrawal of depleting antibodies. N = 4 independent animals. (h) Correlation of antigen expression and tumor burden in Rag2-/- (dark pink) and αCD4/8 (light pink)-treated mice 12 weeks post-injection with LucOS. N = 17 independent animals. Significance measured by Spearman’s rank-order correlation. (i) noSIIN, hiSIIN, and loSIIN organoids grown in the absence of WNT. Scale bars = 1 mm. Representative of N = 3 independent cultures. (j) Top 10 mutated genes in MSK-IMPACT colon adenocarcinoma (cBioPortal). (k) Lentiviral constructs used to generate organoids expressing only EGFP (noSIIN-GFP) and SIINFEKL expression variants. (l) Linear regression with Pearson correlation of SIINFEKL abundance (TMT-MS) versus mScarletSIIN MFI (flow cytometry). TMT-MS was performed on three independent preparations of each line. (m) H&E and IHC of noSIIN primary colon tumor 42 days post-transplant. Representative of N = 9 independent animals. Scale bar = 100 μM. (n-o) Images of dimSIIN (n) and midSIIN (o) tumors that formed in N = 2/9 and 1/9 transplanted animals, respectively. (p) Lentiviral constructs used to generate organoids expressing SIYRYYGL, ITYTWTRL, and VGFNFRTL at high and low levels. (q) ITYTWTRL and VGFNFRTL tetramer-specific CD8+ T cells infiltrating 42-day loITY and loVGF tumors by flow cytometry. Representative of N = 10 loITY and 9 loVGF transplanted animals.

Source data

Extended Data Fig. 3 Low neoantigen expression drives reduced T cell function and diversity.

(a-m) Flow cytometry of CD44+/CD8+ antigen-specific T cells from lesions and DLNs post-transplant of hiSIIN (red) and loSIIN (blue) organoids. (a) Total antigen-specific T cells and (b) percent Ki67 positive in DLNs at 8 days. N = 10 hiSIIN and 9 loSIIN-transplanted animals. (c-d) TCF1 and GZMB expression in antigen-specific T cells in lesions at 8 (c) and 14 (d) days. Representative of N = 5-9 animals per line and timepoint. (e) Percent of antigen-specific T cells double-positive for TNFα and IFNγ, and (f) and representative expression of TCF1 and GZMB in this subset within DLNs at 8 days. N = 10 hiSIIN and 9 loSIIN-transplanted animals. (g) Inhibitory receptor expression on TCF1−/GZMB− antigen-specific T cells from tumors at 14 days. Representative of N = 6–7 animals per line. (h) Median percent of TCF1−/GZMB− antigen-specific T cells from tumors expressing 0 through 4 inhibitory receptors (PD-1, TIM3, LAG3, and 2B4) at 8 days. N = 9 hiSIIN and 9 loSIIN-transplanted animals. Bars = standard deviation. (i-j) Percent of SIINFEKL-loaded “target” splenocytes killed in DLNs and spleens from killing assay at 8 (i) and 14 (j) days post-transplant of hiSIIN and loSIIN organoids. N = 6–7 animals per line and timepoint. (k-l) Frequency of most common clonotypes (k) and Simpson diversity score (l) from TCRβ chain sequencing of antigen-specific T cells from hiSIIN (down-sampled) and loSIIN lesions at 8 days. N = 4 independent animals per line. (m-n) Total antigen-specific T cells isolated from lesions at 14 days across all lines (m) and versus mScarletSIIN MFI (n). (o-p) Percent of antigen-specific T cells from lesions at 8 days double-negative for TCF1 and GZMB across lines (o) and versus mScarletSIIN MFI (p). Dashed lines connect medians. Significance assessed by Spearman’s rank correlation. N = 5-9 independent animals per line in (m-p). Significance in (a-b), (e), (i-j), (l-m) and (o) assessed by two-tailed Wilcoxon Rank Sum. Holm’s correction applied in (m) and (o). Source data for panels (a, h-j, m-p) can be found in Source Data Fig. 3a, i, n-r.

Source data

Extended Data Fig. 4 T cells in tumors with low neoantigen expression lose effector function over time.

(a-b) Percent of antigen-specific T cells from DLNs and tumors at 42 days negative for TCF1 and positive for TIM3 (a), and positive for TCF1 and negative for TIM3 (b) by flow cytometry. N = 4-5 independent animals per line. (c-d) Percent of antigen-specific T cells from DLNs and tumors double-positive for TNFα and IFNγ at 42 days (N = 4-5 independent animals per line) (c) and both 8 and 42 days (d) (N = 4-9 independent animals per line). Red = hiSIIN, blue = loSIIN. Significance in (a-d) was assessed by Wilcoxon Rank Sum. Source data for panels (a-d) can be found in Source Data Fig. 4d-e.

Extended Data Fig. 5 Neoantigen expression is limiting for cross priming by canonical and non-canonical antigen-presenting cells.

(a) Flow cytometry gating strategy to determine percentage of CD11c+/CD103+ DC1s in BM-DC culture. (b) Schematic of BM-DC isolation, activation, and co-culture with naïve OT-1s. (c) Histograms of CD44, Ki67, GZMB, TNFα, and IFNγ expression on OT-1s representative of N = 4 co-cultures in the 400,000 lysed organoid cells condition. (d-f) Flow cytometric analysis of antigen-specific T cells from DLNs and lesions 8 days post-co-transplant of hiSIIN (red) and loSIIN (blue) organoids at separate sites. Percent TCF1+/GZMB− (d), TCF1−/GZMB+ (e), and TCF1−/GZMB− (f). N = 12 animals. Significance assessed by two-tailed Wilcoxon Rank Sum. (g) Brightfield and fluorescent images of colons and tumors 6 weeks post co-transplant of loSIIN and hiVGF or hiITY, representative of N = 9 animals each. (h-j) Flow cytometric analysis of antigen-specific (CD44+/SIINFEKL+) CD8s in colon and DLNs 6 weeks post-transplant of hiSIIN in Batf3-/- mice. (h) Total SIINFEKL+ CD8s, with progressive tumors in gray (N = 4 animals) and rejected lesions in red (N = 4 animals). (i) Flow plot of SIINFEKL+ CD8s infiltrating rejected lesion, and (j) PD-1 and GZMB expression on CD44+/SIINFEKL+ CD8s (red) versus CD44− CD8s (gray) from rejected lesion representative of N = 4 animals. (k) Flow plots of H-2Kb/H-2Db expression on hiSIIN organoids post electroporation with Cas9 complexes targeting H2-k1 and B2m, or untargeted control (pre-sorting). Organoids were pre-treated with IFNγ. N = 1 experiment. Source data for panels (d-f) can be found in Source Data Fig. 5j.

Source data

Extended Data Fig. 6 Design of preclinical trials to test therapies that rescue priming in low neoantigen expressing tumors.

(a) Schematic of vaccination and immunotherapy preclinical trial design and dosing schedule. (b-c) Flow plots of peripheral blood antigen-specific (CD44+/SIINFEKL tetramer+) CD8+ T cells from non-specific peptide-based vaccination (b) and no vaccination control (c) mice, representative of N = 7 and 8 independent animals, respectively. (d-e) Change in tumor size after 14 days of treatment, as determined by colonoscopy. ACT = adoptive cell transfer of OT-1s. N = 17 (d) and 10 (e) independent animals. Significance assessed by Wilcoxon Rank Sum of percent change in tumor size of treatment group versus no treatment, with Holm’s correction. (f) Fraction of mice with any metastases (liver, lung, or omentum), including only mice with progressive primary disease. N = independent animals. Significance assessed by 2×2 Fisher’s exact test of number of mice with metastases across all αCD40 treatment arms (with and without ICB) versus all other arms (no treatment and ICB single agent arms). Source data for panels (d-e) can be found in Source Data Fig. 6e-j, m.

Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Westcott, P.M.K., Sacks, N.J., Schenkel, J.M. et al. Low neoantigen expression and poor T-cell priming underlie early immune escape in colorectal cancer. Nat Cancer 2, 1071–1085 (2021). https://doi.org/10.1038/s43018-021-00247-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-021-00247-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer