Skip to main content
  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

AACR logo

  • Register
  • Log in
  • My Cart
Advertisement

Main menu

  • Home
  • About
    • The Journal
    • AACR Journals
    • Journal Sections
    • Subscriptions
    • Reviewing
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Collections
      • COVID-19 & Cancer Resource Center
      • Clinical Trials
      • Immuno-oncology
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
    • Journal Press Releases
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

User menu

  • Register
  • Log in
  • My Cart

Search

  • Advanced search
Cancer Discovery
Cancer Discovery
  • Home
  • About
    • The Journal
    • AACR Journals
    • Journal Sections
    • Subscriptions
    • Reviewing
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Collections
      • COVID-19 & Cancer Resource Center
      • Clinical Trials
      • Immuno-oncology
      • Editors' Picks
      • "Best of" Collection
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
    • Journal Press Releases
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

In the Spotlight

Debugging the Black Box

James C. Yang
James C. Yang
Surgery Branch, National Cancer Institute, Bethesda, Maryland.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: JamesYang@mail.nih.gov
DOI: 10.1158/2159-8290.CD-17-0070 Published March 2017
  • Article
  • Info & Metrics
  • PDF
Loading

Abstract

Summary: A better understanding of the mechanisms by which tumor rejection succeeds and fails is needed to improve immunotherapies. Here, Anagnostou and colleagues find that mutations predicted to be the most immunogenic are preferentially lost when cancer progresses through checkpoint blockade. Cancer Discov; 7(3); 250–1. ©2017 AACR.

See related article by Anagnostou et al., p. 264.

Tumor immunologists have spent decades pursuing the goal of inducing the intact immune system to reject a malignant autologous tissue as it does a transplanted allogeneic organ. Historically, the rare anecdotes supporting the former stood in stark contrast to the inevitability of the latter. Enormous progress has been made in the past two decades. A molecular understanding of tumor-associated antigens, MHC, and antigen processing has led to the ability to identify, induce, or indeed construct meaningful immune responses against human cancers. Most recently, a better understanding of the inhibitory tumor microenvironment and the advent of checkpoint inhibitors to release the T-cell response have produced clinical benefit against a number of cancers.

Currently, our understanding of tumor rejection requires that an adequate antitumor T-cell repertoire be present, the tumor exhibit sufficient tumor-associated antigens, which are properly processed and presented, and that immunosuppressive factors in the tumor microenvironment be neutralized. It has recently become clear that mutated “neoantigens” resulting from tumor-specific mutations are also an important (perhaps the important) target of the endogenous antitumor T-cell response (1–3). This is both good and bad news. It is good because all tumors have mutations, but it is bad because some do not have enough and mutations are typically patient specific, stymieing the development of shared therapeutic reagents.

The checkpoint inhibitors, such as anti-CTLA4 and anti–PD-1/PD-L1, have shown consistent clinical activity against only a limited array of tumors characterized by high mutational frequencies. In addition, they have a low rate of complete responses, even against susceptible cancers. In this issue of Cancer Discovery, Anagnostou and colleagues (4) sought to understand the reasons why tumors escape checkpoint antibody therapy. They serially sampled tumors from 4 patients with non–small cell lung cancer who initially responded to anti–PD-1 antibody with or without anti-CTLA4 antibody, but then relapsed, using whole-exome sequencing (WES) of pre- and post-relapse specimens. After WES, potential MHC class I–restricted mutation-associated neoantigens (MANA) were identified in silico using MHC-binding prediction algorithms. In 3 of these patients, candidate MANA peptides were synthesized and used to stimulate peripheral blood, using quantitative T-cell receptor (TCR) clonotype analysis as the readout of a proliferative immune response. In summary, the findings were that mutations were both lost and gained in the relapsed tumors. Of particular note was the loss of 6 to 18 tumor-associated (predicted) mutated antigens per patient and the fact that these lost MANAs had higher predicted affinities for an autologous MHC allele than MANAs either retained or gained in the relapsed tumor. This finding was reinforced by limited data showing that the lost MANAs were also associated with more proliferative TCR responses on peptide stimulation of peripheral blood lymphocytes, suggesting they could represent bona fide antigens being “immunoedited” (5). A better understanding of the homogeneity of neoantigen expression and the functional significance of each tumor-associated mutation might be needed to assess the contribution from an immune attack on a specific target. Other shortcomings of this study were that the specificity of the responsive TCR clonotypes responding to candidate MANA peptides was not validated, and there were no data on class II–restricted T-cell epitopes. Isolated data make a compelling story that such class II–restricted T cells can also be sufficient to achieve tumor regression in some patients (6). Yet overall, the findings of this study are not surprising, but contrast with another small study (7) showing that relapsing tumors were characterized by the loss of β2-microglobulin (with consequent loss of all MHC class I expression) and biallelic defects in JAK1/JAK2 that disrupt IFNγ receptor signaling. Similar losses were not documented in this study, nor was loss of PD-L1 found.

It is clear that such small studies cannot yield a comprehensive picture of the true “landscape” of immunotherapy resistance, and, in fact, these studies were limited to therapy with checkpoint blockade. Evidence from adoptive T-cell therapy using tumor-infiltrating lymphocytes (TIL) in patients with melanoma show that these TILs contain extensive reactivity against mutated neoantigens (1) and can achieve high rates of durable complete clinical response (8). The additional observation that TIL adoptive transfer can achieve responses in patients who have progressed through checkpoint blockade (8) also points to an insufficient T-cell repertoire as one cause of unresponsiveness to checkpoint blockade.

So as we look at what was once a “black box” concealing the process of tumor rejection, we now have a much better understanding of what is necessary. The importance of the PD-1–inhibitory pathway in the tumor microenvironment is now clear, but even for susceptible tumor types, this is most beneficial when there is tumor expression of the PD-L1 ligand. Also central to the process is the need for a robust antitumor T-cell repertoire, likely driven by reactivity to properly processed and presented tumor-associated mutated neoantigens. Such a repertoire appears to be consistently present in only a limited number of tumor types, notably melanoma (induced by UV irradiation), lung cancer (from tobacco carcinogens), and tumors with microsatellite instability (9). Patients with other common tumors that account for most deaths from cancer in the United States (colorectal, breast, prostate, and pancreatic cancers) tend to have fewer mutations and thus limited T-cell repertoires. One promising approach for these patients is to apply a much more aggressive selection process to TIL adoptive therapy, driven by a personalized analysis of neoantigen recognition (10).

The other area for potential progress is to better understand and perhaps augment the T-cell effector functions that cause tumor rejection. Here, we encounter one of the last dark corners of the tumor regression black box. The “end game” by which T cells eliminate tumors remains obscure. The relative contributions to tumor destruction of cytolysis, cytokines, and stromal/vascular collapse are murky. There are conflicting data on the efficacy of nonlytic tumor-reactive T cells and the impact of knocking out specific host stromal elements or tumor cytokine receptors. Many studies point to a critical role for IFNγ, but what that role is remains unclear. Studies such as this one, looking at bugs in the program leading to tumor rejection, may reveal factors we are already aware of, but they also hold the promise of shedding new light on the final common pathway(s) of tumor destruction that may be critical to improving all forms of immunotherapy.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

  • ©2017 American Association for Cancer Research.

References

  1. 1.↵
    1. Robbins PF,
    2. Lu YC,
    3. El-Gamil M,
    4. Li YF,
    5. Gross C,
    6. Gartner J,
    7. et al.
    Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat Med 2013;19:747–52.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Snyder A,
    2. Makarov V,
    3. Merghoub T,
    4. Yuan J,
    5. Zaretsky JM,
    6. Desrichard A,
    7. et al.
    Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 2014;371:2189–99.
    OpenUrlCrossRefPubMed
  3. 3.↵
    1. Rizvi NA,
    2. Hellmann MD,
    3. Snyder A,
    4. Kvistborg P,
    5. Makarov V,
    6. Havel JJ,
    7. et al.
    Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 2015;348:124–8.
    OpenUrlAbstract/FREE Full Text
  4. 4.↵
    1. Anagnostou V,
    2. Smith KN,
    3. Forde PM,
    4. Niknafs N,
    5. Bhattacharya R,
    6. White J,
    7. et al.
    Evolution of neoantigen landscape during immune checkpoint blockade in non–small cell lung cancer. Cancer Discov 2017;7:264–76.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    1. Schreiber RD,
    2. Old LJ,
    3. Smyth MJ
    . Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science 2011;331:1565–70.
    OpenUrlAbstract/FREE Full Text
  6. 6.↵
    1. Tran E,
    2. Turcotte S,
    3. Gros A,
    4. Robbins PF,
    5. Lu Y,
    6. Dudley ME,
    7. et al.
    Cancer immunotherapy based on mutation-specific CD4+ T cells in a patient with epithelial cancer. Science 2014;344:641–5.
    OpenUrlAbstract/FREE Full Text
  7. 7.↵
    1. Zaretsky JM,
    2. Garcia-Diaz A,
    3. Shin DS,
    4. Escuin-Ordinas H,
    5. Hugo W,
    6. Hu-Lieskovan S,
    7. et al.
    Mutations associated with acquired resistance to PD-1 blockade in melanoma. N Engl J Med 2016;375:819–29.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Goff SL,
    2. Dudley ME,
    3. Citrin DE,
    4. Somerville RP,
    5. Wunderlich JR,
    6. Danforth DN,
    7. et al.
    Randomized, prospective evaluation comparing intensity of lymphodepletion before adoptive transfer of tumor-infiltrating lymphocytes for patients with metastatic melanoma. J Clin Oncol 2016;34:2389–97.
    OpenUrlAbstract/FREE Full Text
  9. 9.↵
    1. Le D T,
    2. Uram JN,
    3. Wang H,
    4. Bartlett BR,
    5. Kemberling H,
    6. Eyring AD,
    7. et al.
    PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 2015;372:2509–20.
    OpenUrlCrossRefPubMed
  10. 10.↵
    1. Tran E,
    2. Robbins PF,
    3. Lu YC,
    4. Prickett TD,
    5. Gartner JJ,
    6. Jia L,
    7. et al.
    T-Cell transfer therapy targeting mutant KRAS in cancer. N Engl J Med 2016;375:2255–62.
    OpenUrl
View Abstract
PreviousNext
Back to top
Cancer Discovery: 7 (3)
March 2017
Volume 7, Issue 3
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Editorial Board (PDF)

Sign up for alerts

View this article with LENS

Open full page PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Cancer Discovery article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Debugging the Black Box
(Your Name) has forwarded a page to you from Cancer Discovery
(Your Name) thought you would be interested in this article in Cancer Discovery.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Debugging the Black Box
James C. Yang
Cancer Discov March 1 2017 (7) (3) 250-251; DOI: 10.1158/2159-8290.CD-17-0070

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Debugging the Black Box
James C. Yang
Cancer Discov March 1 2017 (7) (3) 250-251; DOI: 10.1158/2159-8290.CD-17-0070
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Disclosure of Potential Conflicts of Interest
    • References
  • Info & Metrics
  • PDF
Advertisement

Related Articles

Cited By...

More in this TOC Section

  • ATR Inhibition as an Attractive Therapeutic Resource against Cancer
  • Conditional Cancer Immunotherapy as a Safer Way to Step on the Gas
  • Suppressing Nucleotide Exchange to Inhibit KRAS-Mutant Tumors
Show more In the Spotlight
  • Home
  • Alerts
  • Feedback
  • Privacy Policy
Facebook   Twitter   LinkedIn   YouTube   RSS

Articles

  • OnlineFirst
  • Current Issue
  • Past Issues

Info For

  • Authors
  • Subscribers
  • Advertisers
  • Librarians

About Cancer Discovery

  • About the Journal
  • Editors
  • Journal Sections
  • Permissions
  • Submit a Manuscript
AACR logo

Copyright © 2021 by the American Association for Cancer Research.

Cancer Discovery
eISSN: 2159-8290
ISSN: 2159-8274

Advertisement