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J Immunother Cancer. 2018 Mar 6;6(1):18. doi: 10.1186/s40425-018-0328-8.

Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients.

Author information

1
Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Stanford, CA, USA.
2
Center for Immuno-oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
3
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
4
Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
5
Center for Immuno-oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA. stephen_hodi@dfci.harvard.edu.
6
Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA. stephen_hodi@dfci.harvard.edu.
7
Melanoma Disease Center, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA. stephen_hodi@dfci.harvard.edu.
8
Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA. stephen_hodi@dfci.harvard.edu.

Abstract

BACKGROUND:

While immune checkpoint blockade has greatly improved clinical outcomes in diseases such as melanoma, there remains a need for predictive biomarkers to determine who will likely benefit most from which therapy. To date, most biomarkers of response have been identified in the tumors themselves. Biomarkers that could be assessed from peripheral blood would be even more desirable, because of ease of access and reproducibility of sampling.

METHODS:

We used mass cytometry (CyTOF) to comprehensively profile peripheral blood of melanoma patients, in order to find predictive biomarkers of response to anti-CTLA-4 or anti-PD-1 therapy. Using a panel of ~ 40 surface and intracellular markers, we performed in-depth phenotypic and functional immune profiling to identify potential predictive biomarker candidates.

RESULTS:

Immune profiling of baseline peripheral blood samples using CyTOF revealed that anti-CTLA-4 and anti-PD-1 therapies have distinct sets of candidate biomarkers. The distribution of CD4+ and CD8+ memory/non-memory cells and other memory subsets was different between responders and non-responders to anti-CTLA-4 therapy. In anti-PD-1 (but not anti-CTLA-4) treated patients, we discovered differences in CD69 and MIP-1β expressing NK cells between responders and non-responders. Finally, multivariate analysis was used to develop a model for the prediction of response.

CONCLUSIONS:

Our results indicate that anti-CTLA-4 and anti-PD-1 have distinct predictive biomarker candidates. CD4+ and CD8+ memory T cell subsets play an important role in response to anti-CTLA-4, and are potential biomarker candidates. For anti-PD-1 therapy, NK cell subsets (but not memory T cell subsets) correlated with clinical response to therapy. These functionally active NK cell subsets likely play a critical role in the anti-tumor response triggered by anti-PD-1.

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