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.

  • Research Highlight
  • Published:

Predictive immune biomarkers: an unattainable chimera?

Cellular & Molecular Immunology volume 15, pages 740–742 (2018)Cite this article

How informative are these phenotypes in monitoring and predicting an immunotherapy efficacy? Only a coordinated, perspective clinical trial will provide the answer. Moreover, do we really need to scan and fish in these complex seas represented by each individual immune system in an attempt to extrapolate some common biomarkers of response? On 23 May 2017, the FDA approved the PD-1 inhibitor pembrolizumab for the treatment of adult and pediatric patients with unresectable or metastatic, microsatellite instability-high (MSI-H) or mismatch-repair-deficient (dMMR) solid tumors regardless of tumor site or histology. This approval was based on common characteristics, including lymphocytic infiltration, somatic hypermutation and increased neoantigen formation shared by MSI-H and dMMR. Of note, the incidence of MSI-H or dMMR is ~30% for endometrial cancer, 20% for colon or gastric cancer, and <5% for most other tumor types, with even lower numbers for MSI-H or dMMR status in secondary lesions.12

In contrast, in 2017, the FDA approved two PARP inhibitors, olaparib and niraparib, as maintenance treatments for women with ovarian cancers who respond to induction platinum-based chemotherapy regardless of their BRCA-mutation status. The phase III ARIEL3 trial of rucaparib confirmed the genotype agnostic benefit of PARP inhibition with improved disease progression across subgroups in which a benefit was expected (women with somatic BRCA alterations or BRCA wild-type, LOH-high disease) vs groups in which a benefit was not expected (patients with LOH-low tumors).13 Although it is evident that toxicity and costs represent crucial issues, the definition of biomarkers in this field is still difficult, and ‘listening to the shell sea’ derived from treated patients’ biopsies represents the only method to include all the notable parameters. In conclusion, the work from Jacquelot et al. helps in designing an elegant and exhaustive, although complex, algorithm toward the identification of powerful, predictive biomarkers to optimize ICB therapy.

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

Figure 1

References

  1. Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 2017; 168: 707–723.

    Article  CAS  Google Scholar 

  2. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 2015; 372: 2509–2520.

    Article  CAS  Google Scholar 

  3. Bates SE. Refining immunotherapy approvals. Clin Cancer Res. 2017; 23: 4948–4949.

    Article  Google Scholar 

  4. Mehnert JM, Monjazeb AM, Beerthuijzen JMT, Collyar D, Rubinstein L, Harris LN. The challenge for development of valuable immuno-oncology biomarkers. Clin Cancer Res 2017; 23: 4970–4979.

    Article  CAS  Google Scholar 

  5. Hamid O, Schmidt H, Nissan A, Ridolfi L, Aamdal S, Hansson J et al. A prospective phase II trial exploring the association between tumor microenvironment biomarkers and clinical activity of ipilimumab in advanced melanoma. J Transl Med 2011; 9: 204.

    Article  CAS  Google Scholar 

  6. Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 2014; 515: 568–571.

    Article  CAS  Google Scholar 

  7. Zhang Y, Chen L. Classification of advanced human cancers based on tumor immunity in the microenvironment (TIME) for cancer immunotherapy. JAMA Oncol 2016; 2: 1403–1404.

    Article  Google Scholar 

  8. Jacquelot N, Roberti MP, Enot DP, Rusakiewicz S, Ternès N, Jegou S et al. Predictors of responses to immune checkpoint blockade in advanced melanoma. Nat Commun 2017; 8: 592.

    Article  CAS  Google Scholar 

  9. Jacquelot N, Roberti MP, Enot DP, Rusakiewicz S, Semeraro M, Jégou S et al. Immunophenotyping of stage III melanoma reveals parameters associated with patient prognosis. J Invest Dermatol 2016; 136: 994–1001.

    Article  CAS  Google Scholar 

  10. Spitzer MH, Carmi Y, Reticker-Flynn NE, Kwek SS, Madhireddy D, Martins MM et al. Systemic immunity is required for effective cancer immunotherapy. Cell 2017; 168: 487–502.

    Article  CAS  Google Scholar 

  11. Wei SC, Levine JH, Cogdill AP, Zhao Y, Anang NAS, Andrews MC et al. Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 2017; 170: 1120–1133.

    Article  CAS  Google Scholar 

  12. Lemery S, Keegan P, Pazdur R, First FDA. Approval agnostic of cancer site—when a biomarker defines the indication. N Engl J Med 2017; 377: 1409–1412.

    Article  Google Scholar 

  13. Killock D. Targeted therapy: ARIEL3—broad benefit of PARP inhibitors in ovarian cancer. Nat Rev Clin Oncol 2017; 14: 713.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Functional Genomics, Istituto Nazionale per lo Studio e la Cura dei Tumori, Fondazione ‘G. Pascale’ Via Semmola, Naples, Italy

    Anna Maria Trotta & Stefania Scala

  2. Dipartimento di Scienze Biomediche Avanzate., Università Federico II. Via Pansini, Naples, Italy

    Roberto Pacelli

Authors
  1. Anna Maria Trotta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roberto Pacelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefania Scala
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefania Scala.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Trotta, A., Pacelli, R. & Scala, S. Predictive immune biomarkers: an unattainable chimera?. Cell Mol Immunol 15, 740–742 (2018). https://doi.org/10.1038/cmi.2017.162

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/cmi.2017.162

Keywords

Search

Advanced search

Quick links