Systems Immune Monitoring with Mass Cytometry in Melanoma ... · Adapted from Diggins et al....
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Allie Greenplate Systems Immune Monitoring of anti-PD-1 therapy unstained 0.13X 1X 0.25X 0.5X 2X tSNE 2 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. Roe 2 , Allison R. Greenplate 1,2 , and Jonathan M. Irish 1,2 Conclusions Pre-therapy blood draw Melanoma Patient Pembrolizumab Starts 3 weeks of treatment Post-therapy blood draw Systems Immune Monitoring Measurement of Single Cell Subsets Bendall and Nolan, Nat Biotech 2012 Time-of-flight Chelated elemental isotope (e.g. Gd-156) Antibody, labeled w/ elemental isotope Helios Mass CytometHeler Label single cells with 34+ mass tagged antibodies Heavy (>100 Da) Reporter ions Light (<100 Da) Overly abundant ions N N N O O O O O O O O O O O Gd H H Plasma Nebulizer Quadrupole CyTOF: 34+ Dimensional Single cell Analysis 138 143 148 153 158 163 168 178 173 signal intensity Stable isotope (atomic mass) No spectral overlap and no compensation Greenplate et al., EJC 2016 Microenvironment Cell:cell interactions Immunophenotype Signaling & Function t-SNE-2 t-SNE-1 viSNE Adapted from Diggins et al. Methods 2015 Clean-up Manual Gating 1 Comparative Analyses all panel markers used to generate viSNE plots, excluding CD45 CD45 2 Identify cell subsets 3 4 Density CD49D CD5 CD16 CD4 CD8 TIM3 CD25 CD7 CD28 CXCR3 CD95 CD27 CD57 CD19 CD14 CCR4 CD45RA ICOS CD44 CD45RO CCR7 CD3 CD9 PD-1 HLA-DR CD127 4-1BB Study Design MP-C01, pre-treatment CD161 CD4+ T cells CD8+ T cells ŸCD45RA+5 CD16+4 CD7+2 CD161+2 ź&'í &'í &'í &'R2í &'í &&5í &'í ,&26í &'í +/$'5í &'í ŸCD45RA+6 CD16+4 CD7+3 CD57+3 HLADR+2 ź&'í &'í &'í &'R2í &'í &&5í &'í ,&26í &'í ŸCD45RA+6 HLADR+3 CCR7+2 ź&'í &'í &'í &'R2í &'Dí &'í &'í &'í &'í &'í ,&26í &'í ŸCD45RO+4 CD14+3 CD95+3 CD9+2 HLADR+2 ź&'í &'í &'í &'í &'Dí &'í &'5$í &&5í &'í ,&26í &'í ŸCD45RO+5 CD14+4 CD9+4 CD4+3 CD95+3 HLADR+3 CD44+2 ź&'í &'í &'í &'Dí &'í &'5$í &'í &'í &&5í ,&26í &'í Ÿ CD8a+4 CD3+3 CD57+3 CD49D+2 CD7+2 HLADR+2 ź &'í &&5í &'í &'í &'í ŸCD8a+9 CD5+5 CD3+4 CD95+3 CD45RO+3 CD49D+2 CD28+2 ź&'í &'í &'5$í &&5í +/$'5í ŸCD8a+7 CD3+4 CD5+3 CD7+3 CD28+3 CD27+3 CD49D+2 CD95+2 CD45RO+2 ź&'í &'5$í &'í +/$'5í ŸCD8a+8 CD45RA+7 CD7+5 CD5+4 CCR7+3 CD27+3 CD3+3 CD28+2 źCD45R2í &'í &'í &'í +/$'5í Ÿ CD4+10 CD7+6 CD5+5 CD3+5 CCR7+4 CD27+4 CD45RA+4 CD28+3 źCD45R2í &'Dí &'í &&5í &'í +/$'5í ŸCD4+10 CD5+3 CD28+3 CD3+3 CD8a+2 CD7+2 CD27+2 CD127+2 ź&'5$í &'í +/$'5í ŸCD4+10 CD28+4 CD3+4 CD5+3 CD8a+3 CD45RO+3 CD95+2 CCR4+2 CCR7+2 CD27+2 ź&'5$í &'í +/$'5í 141 CD49d 142 CD19 143 CD5 144 CD69 145 CD4 146 CD8 147 CD7 148 CD16 149 CD25 150 CD134 151 CD14 152 CD95 153 TIM3 154 CD45 155 PD-1 156 CXCR3 158 CCR4 159 CCR7 160 CD28 161 CTLA-4 162 Ki67 164 CD161 165 CD45RO 166 CD44 167 CD27 168 ICOS 169 CD45RA 170 CD3 171 CD9 172 CD57 173 4-1BB 174 HLA-DR 175 Lag3 176 CD127 Addition of Specificities tSNE 1 Multidimensional Immunophenotyping Systems Immune Monitoring in Į3D1 therapy tSNE 2 tSNE 1 MP-C05 MP-C04 MP-C03 MP-C02 MP-C01 CD45RA CD45RO MP-C05 MP-C04 MP-C03 MP-C02 MP-C01 Pre-Tx Pre-Tx Post Į3' MP-C02 live PBMC CD4 CD3 Density CD8 CD45R0 CD45RA 4-1BB ICOS PD-1 CD95 CD25 TIM3 Pre-Tx Post Į3' MP-C01 live Pre-Tx CD4 Density CD8 CD14 CD16 CD7 CD19 Pre-Tx Post Į3' tSNE 2 tSNE 1 Pre-Tx Post Į3' CD3 tSNE 2 tSNE 1 Caroline E. Roe Managing Director Mass Cytometry Center of Excellence at Vanderbilt University [email protected] https://my.vanderbilt.edu/mcce/ 2 2 1 1 5 5 10 10 6 6 3 3 4 4 8 8 9 9 7 7 11 11 12 12 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 concentration 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. CD45 ki67
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
0.13X1X 0.25X0.5X2X
tSN
E 2
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
tSN
E 2
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
tSN
E 2
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
