Systems Immune Monitoring with Mass Cytometry in Melanoma ... · Adapted from Diggins et al....

Post on 11-Aug-2020

1 views 0 download

Transcript of Systems Immune Monitoring with Mass Cytometry in Melanoma ... · Adapted from Diggins et al....

Supported by : allison.r.greenplate@vanderbilt.edu

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 Universitycaroline.e.roe@vanderbilt.eduhttps://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

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