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
    • Subscriptions
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Cancer Immunology Essentials
    • Collections
      • COVID-19 & Cancer Resource Center
      • Toolbox: Coding and Computation
      • Toolbox: Signatures and Cells
      • "Best of" Collection
      • Editors' Picks
  • 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
  • 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 Immunology Research
Cancer Immunology Research
  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Cancer Immunology Essentials
    • Collections
      • COVID-19 & Cancer Resource Center
      • Toolbox: Coding and Computation
      • Toolbox: Signatures and Cells
      • "Best of" Collection
      • Editors' Picks
  • 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
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

Research Articles

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;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael A. Postow
3Memorial Sloan-Kettering Cancer Center;
4Weill-Cornell Medical and Graduate Schools; and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carly G.K. Ziegler
3Memorial Sloan-Kettering Cancer Center;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Deborah Kuk
3Memorial Sloan-Kettering Cancer Center;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Katherine S. Panageas
3Memorial Sloan-Kettering Cancer Center;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Czrina Cortez
3Memorial Sloan-Kettering Cancer Center;
5Ludwig Collaborative and Swim Across America Lab, New York, New York
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Teresa Rasalan
2Ludwig Center for Cancer Immunotherapy;
3Memorial Sloan-Kettering Cancer Center;
5Ludwig Collaborative and Swim Across America Lab, New York, New York
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mathew Adamow
2Ludwig Center for Cancer Immunotherapy;
3Memorial Sloan-Kettering Cancer Center;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jianda Yuan
2Ludwig Center for Cancer Immunotherapy;
3Memorial Sloan-Kettering Cancer Center;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Philip Wong
2Ludwig Center for Cancer Immunotherapy;
3Memorial Sloan-Kettering Cancer Center;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gregoire Altan-Bonnet
3Memorial Sloan-Kettering Cancer Center;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
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
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
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
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: lesokhia@mskcc.org
DOI: 10.1158/2326-6066.CIR-14-0013 Published August 2014
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Article Figures & Data

Figures

  • Tables
  • Additional Files
  • Figure 1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 1.

    Analysis of MDSC frequency. PBMCs from patients with advanced melanoma and from healthy donors were stained with surface antibody and analyzed by multicolor flow cytometry. We defined monocytic myeloid cells based on the presence of CD14, CD11b in a CD3, CD16, CD19, CD20, and CD56 in a Lin− population. Within this monocytic cell population, m-MDSCs were isolated on the basis of their low levels of HLA-DR expression. A, gating strategy to isolate myeloid-derived cells as CD14+CD11b+Lin− cells. On the basis of the 99th percentile of healthy donor values, a cutoff for low expression of HLA-DR was set to isolate the population of m-MDSC (shaded in gray). B, m-MDSC composition by HLA-DR GMFI is subject to fluctuations in staining acquisition and sample handling. CVHLA-DR represents a self-normalizing measurement and is stable among replicate measurements. C, comparison of CV for HLA expression within the myeloid compartment reveals a larger spread for patients pretreatment, compared with healthy donors and large differences in CV between patients (healthy donors vs. patients; P < 0.05). D, normogram plotting relationship between CV values and m-MDSC frequency. E, evaluation of whole blood collected in standard heparin or Cyto-Chex tubes (n = 9) for m-MDSC frequency and stored at room temperature for the specified interval between analysis and acquisition. Data are expressed as a percentage of total m-MDSCs present at baseline. *, P = 0.002. F, correlation between m-MDSC analysis of samples (n = 8) cryopreserved using BD Vacutainer CPT tubes, standard heparin tubes, and collected in Cyto-Chex tubes.

  • Figure 2.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 2.

    Functionally suppressive m-MDSCs are increased in patients with metastatic melanoma who are less likely to achieve prolonged OS following ipilimumab. A, PBMCs from patients with advanced melanoma and from healthy donors analyzed for %m-MDSC based on CVHLA-DR. The frequency of m-MDSC in healthy donors (n = 20) and patients with melanoma analyzed at pretreatment baseline and week 6 (healthy donors vs. pretreatment, P = 0.05; healthy donors vs. week 6, P = 0.03). B, pretreatment values for subsets of patients treated with ipilimumab 10 mg/kg (n = 28) or 3 mg/kg (n = 40). C, OS based on m-MDSC quantity at pretreatment baseline. D, OS from 6 weeks after start of ipilimumab treatment. E, correlation between percentage change in CD8 T cells and week 6 m-MDSC frequency (r = −0.541; P = 0.02). Percentage change in CD8 T cells = [(wk 6 absolute CD8 − baseline absolute CD8)/(baseline absolute CD8)]. F, average SI graphed for 2 patients with melanoma with clinical benefit and 2 patients with melanoma with nonclinical benefit assessed at week 24. SI = (% proliferated CD3+ T cells in CD14-depleted PBMCs)/(% proliferated CD3+ T cells in CD14-PBMCs with CD14+ cells added back).

Tables

  • Figures
  • Additional Files
  • Table 1.

    Patient and healthy donor characteristics

    CharacteristicsIpilimumab 10 mg/kgIpilimumab 3 mg/kgHealthy donorsa
    Number of patients284020
    Median age (range)62 (34–83)60 (34–80)38 (26–58)
    Sex (%)
     Male17 (61)29 (73)10 (50)
     Female11 (39)11 (27)7 (35)
    Stage of disease (%)
     III (unresectable)01—
     M1a30—
     M1b45—
     M1c2134—
    Median number of prior therapies (range)1 (0–3)1 (0–5)—
     Median LDH (range)209 (113–968)211 (117–816)—
     ≥Upper limit of normal (% of available LDH)13 (46)28 (70)—
     <Upper limit of normal (% of available LDH)15 (54)12 (30)—
    MDSC frequency
     %HLA-DRlow/− in Lin−CD14+CD11b+ (range)b11.4 (3.9–24.5)11.2 (5.8–20.9)10.3 (6.4–14.3)
     ≥14.9 (%)7 (25)7 (18)0 (0)
     <14.9 (%)21 (75)33 (82)20 (100)
    Median baseline ALC (range)1,250 (500–5,100)1,100 (600–8,100)—
     ≥1,000/μL (%)19 (68)25 (63)—
     <1,000/μL (%)9 (32)15 (37)—
    • ↵aData for anonymously donated blood bank samples are unavailable.

    • ↵bBaseline values.

  • Table 2.

    Univariate analysis of relationship between m-MDSC and OS at pretreatment baseline and week 6 after ipilimumab

    Ipilimumab treated
    PretreatmentWeek 6
    nHR (95% CI)PnHR (95% CI)P
    MDSC < 14.9%680.35 (0.18–0.70)0.002640.38 (0.19–0.75)0.004
    ALC ≥ 1,000 cells/μL680.73 (0.41–1.33)0.303640.22 (0.11–0.45)<0.001
    LDH < 250680.33 (0.18–0.59)<0.001650.37 (0.20–0.68)0.001
    Monocytes < 300 cells/μL680.70 (0.25–1.95)0.495641.77 (0.69–4.51)0.233
  • Table 3.

    Multivariate analysis of relationship between m-MDSC and OS at pretreatment baseline and week 6 after ipilimumab treatment

    Ipilimumab
    PretreatmentWeek 6
    nHR (95% CI)PnHR (95% CI)P
    MDSC ≤ 14.9%680.47 (0.23–0.94)0.033630.38 (0.18–0.81)0.012
    ALC ≥ 1,000 cells/μL———630.21 (0.10–0.46)<0.001
    LDH < 250680.38 (0.21–0.69)0.002630.29 (0.15–0.56)<0.001

Additional Files

  • Figures
  • Tables
  • Supplementary Data

    Files in this Data Supplement:

    • Supplementary Figure Legends - PDF file - 81K
    • Supplementary Figure 1 - PDF file - 142K, Supplemental Figure 1. m-MDSC gating strategy using various HLA-DR cutoffs based on expression of HLA-DR in lineage positive cells yields differing m-MDSC frequencies and is susceptible to inter-user variability.
    • Supplementary Figure 2 - PDF file - 58K, Supplemental Figure 2. PBMCs available in multiple aliquots were serially analyzed over 3 weeks to evaluate day-to-day reproducibility of CVHLA-DR measures. A standard error of measurement of 1.4% and 0.7%, respectively for QC sample 1 and 2 was detected.
    • Supplementary Figure 3 - PDF file - 88K, Supplemental Figure 3. PBMCs from metastatic melanoma patients treated with ipilimumab depleted of CD14-expressing cells were stimulated to proliferate with OKT-3 and IL-2. CFSE dilution of CD3+ T cells in the culture is measured in the absence of CD14+ cells or with CD14+ cells added back.
    • Supplementary Figure 4 - PDF file - 101K, Supplemental Figure 4. Different summary statistics can be applied to measure HLA-DR expression on lineage negative, CD14+CD11b+ cells and assess relationship to overall survival (using maximum logrank statistics as described in Methods). CV was used as a self-normalizing measurement that was preferred to eliminate non-biological variation in clinical measurements (e.g. day-to-day variation, differences in sample handling and FACS acquisition).
    • Supplementary Table 1 - PDF file - 103K, Supplemental table 1. Univariate analysis of relationship between m-MDSC and overall survival at pre-treatment baseline and week 6 after ipilimumab treatment at 10mg/kg and 3mg/kg.
PreviousNext
Back to top
Cancer Immunology Research: 2 (8)
August 2014
Volume 2, Issue 8
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover

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 Immunology Research 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.
Computational Algorithm-Driven Evaluation of Monocytic Myeloid-Derived Suppressor Cell Frequency for Prediction of Clinical Outcomes
(Your Name) has forwarded a page to you from Cancer Immunology Research
(Your Name) thought you would be interested in this article in Cancer Immunology Research.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
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

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
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
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
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Disclosure of Potential Conflicts of Interest
    • Authors' Contributions
    • Grant Support
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF
Advertisement

Related Articles

Cited By...

More in this TOC Section

  • Machine Learning–Based Prognostic Marker of HCC
  • Lenalidomide Turns Myeloma-Associated Macrophage Tumoricidal
  • Inflammasome-Independent IL1β Inhibits Antitumor Immunity
Show more Research Articles
  • Home
  • Alerts
  • Feedback
  • Privacy Policy
Facebook   Twitter   LinkedIn   YouTube   RSS

Articles

  • Online First
  • Current Issue
  • Past Issues
  • Cancer Immunology Essentials

Info for

  • Authors
  • Subscribers
  • Advertisers
  • Librarians

About Cancer Immunology Research

  • About the Journal
  • Editorial Board
  • Permissions
  • Submit a Manuscript
AACR logo

Copyright © 2021 by the American Association for Cancer Research.

Cancer Immunology Research
eISSN: 2326-6074
ISSN: 2326-6066

Advertisement