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Cancer Immunology Research
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Computational Algorithm-Driven Evaluation of Monocytic Myeloid-Derived Suppressor Cell Frequency for Prediction of Clinical Outcomes

Shigehisa Kitano, Michael A. Postow, Carly G.K. Ziegler, Deborah Kuk, Katherine S. Panageas, Czrina Cortez, Teresa Rasalan, Mathew Adamow, Jianda Yuan, Philip Wong, Gregoire Altan-Bonnet, Jedd D. Wolchok and Alexander M. Lesokhin
Shigehisa Kitano
1Department of Experimental Therapeutics, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Tsukiji, Tokyo, Japan;
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Michael A. Postow
3Memorial Sloan-Kettering Cancer Center;
4Weill-Cornell Medical and Graduate Schools; and
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Carly G.K. Ziegler
3Memorial Sloan-Kettering Cancer Center;
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Deborah Kuk
3Memorial Sloan-Kettering Cancer Center;
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Katherine S. Panageas
3Memorial Sloan-Kettering Cancer Center;
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Czrina Cortez
3Memorial Sloan-Kettering Cancer Center;
5Ludwig Collaborative and Swim Across America Lab, New York, New York
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Teresa Rasalan
2Ludwig Center for Cancer Immunotherapy;
3Memorial Sloan-Kettering Cancer Center;
5Ludwig Collaborative and Swim Across America Lab, New York, New York
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Mathew Adamow
2Ludwig Center for Cancer Immunotherapy;
3Memorial Sloan-Kettering Cancer Center;
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Jianda Yuan
2Ludwig Center for Cancer Immunotherapy;
3Memorial Sloan-Kettering Cancer Center;
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Philip Wong
2Ludwig Center for Cancer Immunotherapy;
3Memorial Sloan-Kettering Cancer Center;
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Gregoire Altan-Bonnet
3Memorial Sloan-Kettering Cancer Center;
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Jedd D. Wolchok
2Ludwig Center for Cancer Immunotherapy;
3Memorial Sloan-Kettering Cancer Center;
4Weill-Cornell Medical and Graduate Schools; and
5Ludwig Collaborative and Swim Across America Lab, New York, New York
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Alexander M. Lesokhin
3Memorial Sloan-Kettering Cancer Center;
4Weill-Cornell Medical and Graduate Schools; and
5Ludwig Collaborative and Swim Across America Lab, New York, New York
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  • For correspondence: lesokhia@mskcc.org
DOI: 10.1158/2326-6066.CIR-14-0013 Published August 2014
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Abstract

Evaluation of myeloid-derived suppressor cells (MDSC), a cell type implicated in T-cell suppression, may inform immune status. However, a uniform methodology is necessary for prospective testing as a biomarker. We report the use of a computational algorithm-driven analysis of whole blood and cryopreserved samples for monocytic MDSC (m-MDSC) quantity that removes variables related to blood processing and user definitions. Applying these methods to samples from patients with melanoma identifies differing frequency distribution of m-MDSC relative to that in healthy donors. Patients with a pretreatment m-MDSC frequency outside a preliminary definition of healthy donor range (<14.9%) were significantly more likely to achieve prolonged overall survival following treatment with ipilimumab, an antibody that promotes T-cell activation and proliferation. m-MDSC frequencies were inversely correlated with peripheral CD8+ T-cell expansion following ipilimumab. Algorithm-driven analysis may enable not only development of a novel pretreatment biomarker for ipilimumab therapy, but also prospective validation of peripheral blood m-MDSCs as a biomarker in multiple disease settings. Cancer Immunol Res; 2(8); 812–21. ©2014 AACR.

Footnotes

  • Note: Supplementary data for this article are available at Cancer Immunology Research Online (http://cancerimmunolres.aacrjournals.org/).

  • Received January 20, 2014.
  • Revision received April 22, 2014.
  • Accepted May 12, 2014.
  • ©2014 American Association for Cancer Research.
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Cancer Immunology Research: 2 (8)
August 2014
Volume 2, Issue 8
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Computational Algorithm-Driven Evaluation of Monocytic Myeloid-Derived Suppressor Cell Frequency for Prediction of Clinical Outcomes
Shigehisa Kitano, Michael A. Postow, Carly G.K. Ziegler, Deborah Kuk, Katherine S. Panageas, Czrina Cortez, Teresa Rasalan, Mathew Adamow, Jianda Yuan, Philip Wong, Gregoire Altan-Bonnet, Jedd D. Wolchok and Alexander M. Lesokhin
Cancer Immunol Res August 1 2014 (2) (8) 812-821; DOI: 10.1158/2326-6066.CIR-14-0013

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Computational Algorithm-Driven Evaluation of Monocytic Myeloid-Derived Suppressor Cell Frequency for Prediction of Clinical Outcomes
Shigehisa Kitano, Michael A. Postow, Carly G.K. Ziegler, Deborah Kuk, Katherine S. Panageas, Czrina Cortez, Teresa Rasalan, Mathew Adamow, Jianda Yuan, Philip Wong, Gregoire Altan-Bonnet, Jedd D. Wolchok and Alexander M. Lesokhin
Cancer Immunol Res August 1 2014 (2) (8) 812-821; DOI: 10.1158/2326-6066.CIR-14-0013
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