ORIGINAL RESEARCH Open Access Gating, enhanced gating, and beyond
Systems Immune Monitoring with Mass Cytometry in Melanoma ... · Adapted from Diggins et al....
Transcript of Systems Immune Monitoring with Mass Cytometry in Melanoma ... · Adapted from Diggins et al....
Supported by : [email protected]
Allie Greenplate
Systems Immune Monitoring of anti-PD-1 therapy
http://my.vanderbilt.edu/irishlab/
unstained
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Introduction and Aims
Systems Immune Monitoring with Mass Cytometry in Melanoma Patients Treated with Pembrolizumab
1Vanderbilt University Department of Cell & Developmental Biology, Nashville, TN, USA
2Vanderbilt University Department of Pathology, Microbiology, and Immunology, Nashville, TN, USA
Caroline E. Roe2, Allison R. Greenplate1,2, and Jonathan M. Irish1,2
Conclusions
Pre-therapyblood draw
Melanoma Patient
Pembrolizumab Starts
3 weeks of treatment
Post-therapyblood draw
Systems Immune MonitoringMeasurement of Single Cell Subsets
Bendall and Nolan, Nat Biotech 2012
Time-of-flightChelated elemental
isotope (e.g. Gd-156)
Antibody, labeled w/ elemental
isotope
Helios Mass CytometHelerLabel single cells
with 34+ mass tagged antibodies
Heavy (>100 Da)Reporter ions
Light (<100 Da)Overly abundant ions
NN
NO
OO
O
OO
OO
O
OO
Gd
H H
Plasma
Nebulizer
Quadrupole
CyTOF: 34+ Dimensional Single cell Analysis
138 143 148 153 158 163 168 178173
sign
al in
tens
ity
Stable isotope (atomic mass)
No spectral overlap and no compensation
Greenplate et al., EJC 2016
Microenvironment Cell:cell interactions
Immunophenotype Signaling & Function
t-SN
E-2
t-SNE-1
viSNE
Adapted from Diggins et al. Methods 2015
Clean-up Manual Gating
1 Comparative Analysesall panel markers used to generate viSNE plots, excluding CD45
CD45
2 Identify cell subsets
3 4
Density
CD49D CD5
CD16
CD4
CD8
TIM3
CD25CD7
CD28CXCR3
CD95
CD27
CD57
CD19
CD14
CCR4
CD45RAICOS
CD44CD45RO
CCR7
CD3 CD9
PD-1
HLA-DR CD1274-1BB
Study Design
MP-C01, pre-treatment
CD161
CD4+ T cells
CD8+ T cells
CD45RA+5 CD16+4 CD7+2 CD161+2 R
CD45RA+6 CD16+4 CD7+3 CD57+3 HLADR+2 R
CD45RA+6 HLADR+3 CCR7+2 R
CD45RO+4 CD14+3 CD95+3 CD9+2 HLADR+2
CD45RO+5 CD14+4 CD9+4 CD4+3 CD95+3 HLADR+3 CD44+2
CD8a+4 CD3+3 CD57+3 CD49D+2 CD7+2 HLADR+2
CD8a+9 CD5+5 CD3+4 CD95+3 CD45RO+3 CD49D+2 CD28+2
CD8a+7 CD3+4 CD5+3 CD7+3 CD28+3 CD27+3 CD49D+2 CD95+2 CD45RO+2
CD8a+8 CD45RA+7 CD7+5 CD5+4 CCR7+3 CD27+3 CD3+3 CD28+2 CD45R
CD4+10 CD7+6 CD5+5 CD3+5 CCR7+4 CD27+4 CD45RA+4 CD28+3
CD45R
CD4+10 CD5+3 CD28+3 CD3+3 CD8a+2 CD7+2 CD27+2 CD127+2
CD4+10 CD28+4 CD3+4 CD5+3 CD8a+3 CD45RO+3 CD95+2 CCR4+2 CCR7+2 CD27+2
141 CD49d142 CD19143 CD5144 CD69145 CD4146 CD8147 CD7148 CD16149 CD25150 CD134151 CD14152 CD95153 TIM3154 CD45155 PD-1156 CXCR3158 CCR4
159 CCR7160 CD28161 CTLA-4162 Ki67164 CD161165 CD45RO166 CD44167 CD27168 ICOS169 CD45RA170 CD3171 CD9172 CD57173 4-1BB174 HLA-DR175 Lag3176 CD127
Addition of Specificities
tSNE 1
Multidimensional Immunophenotyping Systems Immune Monitoring in D1 therapy
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tSNE 1
MP-C05MP-C04MP-C03MP-C02MP-C01
CD
45R
A
CD45RO
MP-C05MP-C04MP-C03MP-C02MP-C01
Pre-Tx
Pre-Tx
Post
MP-C02 live PBMCCD4CD3Density CD8 CD45R0 CD45RA
4-1BBICOSPD-1 CD95 CD25 TIM3
Pre-Tx
Post
MP-C01 live Pre-TxCD4Density CD8
CD14CD16CD7 CD19
Pre-Tx
Post
tSN
E 2
tSNE 1
Pre-Tx
Post
CD3
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tSNE 1
Caroline E. RoeManaging Director
Mass Cytometry Center of Excellence at Vanderbilt [email protected]://my.vanderbilt.edu/mcce/
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Systems immune monitoring during cancer treatment can track therapy response and reveal biomarkers [1]. In metastatic melanoma, this approach has implicated proliferating T cell subsets as a cellular effector mecha-nism for checkpoint inhibitors [2, 3].
Aims: 1) develop a robust cancer immune monitoring panel for multi-center clinical correlative research con-ducted by a core, and 2) generate pilot data to train and test computational tools employing machine learning al-gorithms.
Methods: Viably cryopreserved PBMC samples were analyzed from five melanoma patients under-going pembroluzimab treatment. For each patient, samples were collect before,and three weeks after,starting therapy. Samples were stained with a modified version of Fluidigm’s immuno-oncology T cell focused panel (right) and run on a Helios mass-cytometer. Data were visualized in Cytobank. This work was done in collaborationwith Fluidigm Corp.
Above: Addition of CD14 and CD19 to commercially available T cell focused panel en-abled better resolution of B cell and monocyte populations.
CD45+ cells from each patient are shown above following analysis by viSNE, a dimensionality reduction tool. In these plots, cells positioned in the same part of the graph are phenotypi-cally similar for the 30+ proteins mea-sures. Individual patient immune signa-tures are apparent as a mostly stable pattern over time. However, deeper analysis at right and at the top of the panel, reveals shifts in population abun-dance and phenotype.
Use of mass cytometry and commercially available metal conjugated antibodies provided a robust method for systems immune monitoring in cancer therapy com-patible with correlative research in larger clinical stud-ies. Multidimensional analysis tools enable comprehen-sive characterization of the immune system in patients undergoing immunotherapy and the potential to dis-cover biomarkers of response to treatment.
1. Greenplate, A.R., et al., Systems immune monitoring in cancer therapy. Eur J Cancer, 2016. 61: p. 77-84.2. Huang, A.C., et al., T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature, 2017.3. Spitzer, M.H., et al., Systemic Immunity Is Required for Effective Cancer Immunotherapy. Cell, 2017. 168(3): p. 487-502 e15.
MEM (Marker Enrichment Modeling) scores for each gated population above left.
Diggins, K.E., et al., Characterizing cell subsets using marker enrichment modeling. Nat Methods, 2017. 14(3): p. 275-278.
ki67-162
conc
entra
tion
At left and above, titration of ki67-162 on MV411s, an AML/APL cell line, and normal PBMC. The highly proliferative CD45 low MV411s are easily distinguished from the mostly quiescent PBMCs. 1X is the Fluidigm recommended concentration.
CD
45
ki67