• IMC allows for successful multiplex imaging of the TME in FFPE tissues.

• Images can be resolved at the single cell level with simultaneous

measurement of multiple membrane/cytoplasmic and nuclear markers

(including cell signaling and functional states), which can then be clustered

into relevant phenotypic subsets and re-analyzed in their spatial context on

the original tissue section images.

• By identifying distinct immune subsets and mapping their interactions, this

technology may potentially allow TME-based tumor subclassification and

identification of novel biomarkers.

• Additional studies on larger sample sizes of CHL and NLPHL are ongoing,

including “neighborhood” analyses to comprehensively define cell-cell

interactions in the TME.

• In classic Hodgkin lymphoma (CHL) and nodular lymphocyte predominant Hodgkin

lymphoma (NLPHL), composition of the tumor microenvironment (TME) varies

significantly across and within histologic subtypes and affects disease prognosis.

• Characterization of the TME is currently hindered by the phenotypic complexity of T cell

subsets, macrophage and other myeloid subtypes, and stromal/ vascular components.

Assessment of the spatial relationships of tumor cells and immune cell subsets is

difficult using conventional techniques that either require tissue disruption (flow

cytometry, gene expression profiling) or limit analysis to one to few markers per tissue

section (immunohistochemistry, immunofluorescence).

• Using metal-tagged antibodies on FFPE tissue sections, the Fluidigm Hyperion imaging

mass cytometry (IMC) combines a mass cytometer with a laser ablation system,

enabling simultaneous immunophenotyping by >40 markers on a single slide with

subcellular resolution (1 um).

• The complex multidimensional datasets generated by IMC can be analyzed by a

variety of data analysis tools to segment and classify individual cells, cluster similar

phenotypic subsets and determine their spatial relationships.

• In this study, we demonstrate the feasibility of this approach to analyzing the TME of

Hodgkin lymphomas.

Highly Multiplexed Analysis of the Tumor Microenvironment in

Hodgkin Lymphomas by Imaging Mass CytometryImran N. Siddiqi, Erik Gerdtsson, Monirath Hav, Mohan Singh, Parvesh Chaudhry, Wendy Cozen, James Hicks, Peter Kuhn, Akil A Merchant

University of Southern California, Los Angeles, CA-90033, USA

CD15

This work was supported by Ming Hsieh Institute, STOP Cancer, and National Institutes of

Health/ National Cancer Institute.

CD68

CD15

Antibody Clone TagBCL2 EPR17509 146NdBCL6 K112-91 147Sm

CD134/OX40 Polyclonal 151Eu

CD16 EPR16784 146Nd

CD183 (CXCR3) G025H7 142Nd

CD185/CXCR5 RF8B2 153Eu

CD194 (CCR4) 205410 149Sm

CD20 H1 161GdCD206 5C11 169Tm

CD279 (PD-1) NAT105 175Lu

CD3 Polyclonal, C-Terminal 170Er

CD30 JCM182 165HoCD31 C31.3 144NdCD34 QBEND/10 152SmCD4 EPR6855 156GdCD45RA HI100 155GdCD45RO UCHL1 173YbCD68 KP1 159TbCD8a C8/144B 162DyEphrinB2 EPR10072(B) 166ErFoxP3 236A/E7 163Dy

Granzyme B EPR20129-217 167Er

HLA-DR YE2/36 HLK 174YbICOS D1K2T 148NdKi-67 B56 168ErLAG-3 D2G40 153EupERK1/2 D13.14.4E 171Yb

pStat3 [Y705] 4PSTAT3 158Gd

T-bet D6N8B 145NdTim3 D505R 154SmVimentin RV202 143Nd

Figure 1: Malignant Reed Sternberg cell co-excessing CD30 (blue) and CD15 (cyan) surrounded

by CD4+ helper T cells (magenta), CD8+ cytotoxic T cells (green) and CD68+ macrophages

(orange) in classical Hodgkin lymphoma (A). Malignant LP cells (blue) surrounded by PD1+

(magenta) CD4+ helper T cells (orange), CD8+ Cytotoxic T cells (green) and CD68+ macrophages

(cyan) in nodular lymphocyte predominant Hodgkin lymphoma (B).

Figure 2. After segmentation

of the images single cell

feature data was extracted in

HistoCAT[1]. The

multidimensional data was

visualized using the tSNE

algorithm[2] to generate two

dimensional plots with the

default t-SNE parameters

(initial dimensions, 110;

perplexity, 30; theta, 0.5). We

used PhenoGraph version 0.2

for unsupervised clustering to

identify the multiple cell

populations.

1. Amir, A.D. et al. Nat. Biotechnol. 31: 545–552 (2013).

2. Levine J.H. et al. Cell. 162(1):184-97 (2015).

3. Schapiro D. et al. Nat. Methods. 14(9):873-6 (2017).

4. Vitozanotelli & Bernd Bodenmiller (2017). A flexible image segmentation pipeline for

heterogenous multiplexed tissue images based on pixel classification.

https://github.com/BodenmillerGroup/ImcSegmentationPipeline/blob/20170915_imc_semin

ar/documentation/201709_imctools_guide.pdf

IMC Workflow

1) Design

panels using

Antibodies conjugated

to metal tags

2) Stain

FFPE tissue

sections or Tissue

micro arrays(TMA)

3) Laser Ablate/Image

1mm2 tissue sections using the

Hyperion Imaging System

(Fluidigm). The data generated

by the instrument is converted

into 1 megapixels images.

4) Image Segmention for Single Cell data

CellProfiler is utilized to preprocess the images for iterative Ilastik

pixel classification. Using the Ilastik framework, a supervised random

forest based learning algorithm allows for dividing the pixels into

different classes e.g. nuclear, cytoplasmic/membranous and

background.

5) Data Analysis

The images along with the mask are analyzed with HistoCAT,

developed for IMC data analysis. In addition, the features of each

cell can be imported into the R environment for data visualization,

statistical analysis, and clustering of cell phenotypes.

Introduction Antibody Panel Classic Hodgkin Lymphoma

Figure 1: (A) Merged image showing manual identification of malignant Reed Sternberg (RS) cell

co-expressing CD30 (blue) and CD15 (cyan) with associated CD4+ helper T cells (magenta),

CD8+ cytotoxic T cells (green) and CD68+ macrophages (orange). (B) Close-up image showing

TME interactions with malignant RS cells.

PC 17: RS cells

PC11: Th1/ TIM3

PC14: exhausted T

PC10:?Macrophage

CD206+ Ki67+

PC1: Th cells

PC5: B cells

PC13: Macrophages

PC3: cytotox T

PC16: T regs

PC2: B cells (naïve/ memory)

PC4: B cells

Cluster

1

2

3

4

5

6

7

8

9

10 11 12 13 14

Cluster Presumptive Cell Types (?)

Th [CD3 dim, CD45 RO dim, CD4 dim]

Naïve, mem B [CD20, CD45RA, CXCR5+]

Cytotox T [CD3, CD8, CD45RO]

B cells [CD20 dim, CD45RA dim, CXCR5-]

B cells [CD20 dim, CD45RA dim]

? [CD8, CD45RO dim, CD45RA dim]

2 populations? [ICOS, CD3 dim, CD45RA dim]

Myeloid [CD16, CD206, CCR4]

B cells [CXCR5, Vim, CD20 low]

M2? [CD206, Ki67]

Th1 [Tim3, CD4 dim, CD3 dim]

TFH [CD3, CD4, CD45RO, CXCR5]

Macrophage [CCR4, CD68, CXCR3, CXCR5]

Exhausted T [CD3, CD4, Lag3, CD45RO]

? [vim low]

T regs [FOXP3, CD4]

RS cells [CD15, CD30, Ki67, Tbet, Tim3, Vim]

Cytotox T [Granzyme B]

? [CD4, Tbet, CD3]

CC

R4

CD

15

CD

16

CD

20

6C

D2

0C

D3

0C

D3

CD

45

RA

CD

45

RO

CD

4C

D6

8C

D8

CX

CR

3C

XC

R5

Ep

hrin

B2

FO

XP

3G

rz B

ICO

SK

i67

La

g3

Tb

et

Tim

3V

ime

ntin

A BC

Nodular Lymphocyte Predominant Hodgkin Lymphoma

Figure 3. (A) CHL PhenoGraph. Unsupervised algorithm identifies 19 phenotypic clusters (represented by different colors) in the assayed regions. Putative cell types are identified by their

overall marker expression patterns (arrowed). (B) Heatmap representing ranges of expression of each marker on Phenograph clusters and corresponding presumptive cell types. (C)

Comparison of manually gated RS cells compared to backgating of Phenograph cluster 17 (PC17) on the source image. PC17 gating identifies higher numbers of RS cells in the image.

1

2

3

4

5

6

78

9

10

11

13

14

15

16

12

17

1819

BCL2 BCL6 CCR4 CD20 CD31 CD3

CD45RA CD45RO

CD4 CD68

CD8 CXCR3 FOXP3 GRZ B

ICOS Ki67 Lag3 PD1

Vimentin

Tbet Tim3

1234567891011121314151617 Cluster

30 µ m

30 µ m

Figure 4. NLPHL was analyzed with a

similar panel of markers. Cell segmentation,

tSNE analysis and Phenograph clustering

(not shown) identified 17 phenotypic

clusters. (A) Heatmap showing patterns of

marker expression in the various

Phenograph clusters (PC). PC #7 (red

square) shows a pattern of reactivity most

consistent T follicular helper cells (TFH;

BCL6+, CD3+, PD1+, CD4). (B) PC7

backgated and projected back on the source

image clearly identifies TFH cell rosettes

around LP cells. (C) Merged image

identifies LP cell (CD20, red) and associated

CD4 (green) and CD8 (blue) T-cells).

LP

CD4

CD4

CD8

A

B C

Summary

Acknowledgments

References

DisclosuresThe authors do not report relevant disclosures.