Untangling the tumor microenvironment

Webinar Series Untangling the tumor microenvironment Illuminating the complex interactions & functions of immune cells December 10, 2014 Instructions for Viewers To share webinar via social media: To share webinar via e mail: To see speaker biographies, click: View Bio under speaker name Sponsored by: To ask a question, click the Ask A Question button under the slide window

Untangling the tumor microenvironment Illuminating the complex interactions & functions of immune cells December 10, 2014 Webinar Series Brought to you by the Science/AAAS Custom Publishing Office Participating Experts Scott Rodig, M.D., Ph.D. DFCI/Brigham & Women s Hospital Boston, MA Ed Stack, Ph.D PerkinElmer Hopkinton, MA Sponsored by:

Unlocking the Tumor Microenvironment with Multiplex Immunofluorescent Analysis of Fixed Tissue Biopsy Specimens Scott Rodig, M.D., Ph.D. Associate Professor of Pathology Brigham and Women s Hospital Dana-Farber Cancer Institute Harvard Medical School

Disclosures Research Support: Ventana/ Roche Bristol-Myers Squibb Scientific Advisory Board: Astra-Zeneca

Analysis of Tissue Biopsy Specimens to Diagnose Disease 1. Histomorphology 2. Immunohistochemistry 3. Electron microscopy 4. Flow cytometry 5. Cell culture/ cytogenetics 6. Molecular diagnostics

Virtually All Patient Biopsies are Prepared in a Standard Manner to Preserve Cellular Structure 1. Tissue fixation using 10% formaldehyde (formalin) 2. Automated tissue dehydration > embedding in paraffin wax 3. Thin sectioning using microtome > hematoxylin & eosin (H&E) staining 4. Visualization with standard white light transmission microscopy

Histomorphology Diagnostic Information 1. Cellular composition 2. Cellular morphology 3. Tissue architecture

Histomorphology Diagnostic Information 1. Cellular composition 2. Cellular morphology 3. Tissue architecture Histomorphology remains the most information-rich method of analysis in diagnostic pathology

Immunohistochemistry Allows for the Detection of Proteins in situ 1. Expose antigens in fixed tissues by epitope retrieval 2. Apply antibodies directed against specified human proteins 3. Detect bound antibody using secondary detection and chromogenic visualization reagents 4. Visualization with standard white light transmission microscopy IHC for PAX5 (B-cell lineage marker)

Histomorphology and IHC Excellent to identify cancer and its cell of origin

Histomorphology and IHC Excellent to identify cancer and its cell of origin Tumor Cell Tumor Cell Tumor Cell Tumor Cell

Histomorphology and IHC Excellent to identify cancer and its cell of origin Diffuse Large B-cell Lymphoma Diffuse Large B-cell Lymphoma Diffuse Large B-cell Lymphoma Diffuse Large B-cell Lymphoma

Histomorphology and IHC Excellent to identify cancer and its cell of origin Diffuse Large B-cell Lymphoma Diffuse Large B-cell Lymphoma Diffuse Large B-cell Lymphoma Diffuse Large B-cell Lymphoma Tumor-specific chemotherapy

But Cancer is More Complicated! Tissue carries untapped but vital information in era of targeted therapy Hanahan and Weinberg, Cell, 2011

Cancer is More Complicated Tissue carries vital information in era of targeted therapy B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma

Cancer is More Complicated Tissue carries vital information in era of targeted therapy T-cell B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma T-cell Macrophage

Cancer is More Complicated Tissue carries vital information in era of targeted therapy DRUG T-cell B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma T-cell Macrophage DRUG Tumor-specific targeted therapies

Quantitative Immunofluorescence (QIF) Quantitative Immunofluorescence (QIF) and Image Analysis Co-localization studies Visualize signaling networks Quantify protein expression Quantify cellular and architectural features

Visualization of B-cell Receptor Signaling using QIF Example: Diffuse Large B-cell Lymphoma (DLBCL): The most common, aggressive non- Hodgkin lymphoma Chemotherapy- rituximab, cyclophosphamide, doxorubicin, vincristine; prednisone (R- CHOP) Cure rate 60-80% Cure rate for relapsed/refractory disease is low

Visualization of B-cell Receptor (BCR) Signaling using QIF Diffuse Large B-cell Lymphoma (DLBCL): Many DLBCL cell lines show constitutive/ tonic BCR signaling BCR signaling is necessary for cell growth and survival Malignant B-cell FOXO1 NFAT Proliferation Survival Chen L et al Blood 2008;111:2230

Visualization of B-cell Receptor (BCR) Signaling using QIF Diffuse Large B-cell Lymphoma (DLBCL): Clinical trial of Fostamatinib/R406 (SYK inhibitor) in patients with relapsed/refractory DLBCL showed 24% overall response rate DRUG Clinical trial of Ibrutinib (BTK inhibitor) in patients with DLBCL showed 22% overall response rate Malignant B-cell FOXO1 NFAT Proliferation Survival Chen L et al Blood 2008;111:2230 Friedberg, J. W. et al. Blood 2010;115:2578-2585 Wilson WH et al., ASH abstract #686, 2012

Visualization of B-cell Receptor (BCR) Signaling using QIF Diffuse Large B-cell Lymphoma (DLBCL): Clinical trial of Fostamatinib/R406 (SYK inhibitor) in patients with relapsed/refractory DLBCL showed 25% overall response rate DRUG Clinical trial of Ibrutinib (BTK inhibitor) in patients with DLBCL showed 22% overall response rate Can we detect BCR signaling in tissue samples? Malignant B-cell FOXO1 NFAT Proliferation Survival

Visualization of B-cell Receptor (BCR) Signaling using QIF Diffuse Large B-cell Lymphoma (DLBCL): Clinical trial of Fostamatinib/R406 (SYK inhibitor) in patients with relapsed/refractory DLBCL showed 25% overall response rate DRUG Clinical trial of Ibrutinib (BTK inhibitor) in patients with DLBCL showed 22% overall response rate Can we detect BCR signaling in tissue samples? How common is BCR activation in DLBCL? Malignant B-cell FOXO1 NFAT Proliferation Survival

Visualization of B-cell Receptor (BCR) Signaling using QIF Diffuse Large B-cell Lymphoma (DLBCL): Clinical trial of Fostamatinib/R406 (SYK inhibitor) in patients with relapsed/refractory DLBCL showed 25% overall response rate DRUG Clinical trial of Ibrutinib (BTK inhibitor) in patients with DLBCL showed 22% overall response rate Can we detect BCR signaling in tissue samples? How common is BCR activation in DLBCL? Does BCR activation correlate with DLBCL subtypes (ABC, GCB, T3) by Cell-of-origin? Malignant B-cell FOXO1 NFAT Proliferation Survival

Visualization of B-cell Receptor (BCR) Signaling using QIF = anti- plyn (Y396) = anti- psyk (Y323) DRUG = anti- pbtk (Y551) Malignant B-cell Bogusz A et al, Clinical Cancer Res, 2012

Visualization of B-cell Receptor (BCR) Signaling using QIF 1. IF Staining (DAPI, lineage marker; p-ab) QIF imaging Image Storage 2. Image processing Cell Identification Biomarker protein localization and quantification

Visualization of B-cell Receptor (BCR) Signaling using QIF Bogusz A et al, Clinical Cancer Res, 2012

Visualization of B-cell Receptor (BCR) Signaling using QIF Fixed, primary DLBCL biopsy samples: 1. 60 DLBCLs stained for: Nuclei (DAPI; blue) Cell lineage (CD20; red) Phospho-biomarker (3 slides; green). 1. 45% of DLBCLs classified as BCR+ with high confidence. 2. No correlation with DLBCL subtype as defined by COO. Bogusz A et al, Clinical Cancer Res, 2012

Visualization of B-cell Receptor (BCR) Signaling using QIF Fixed DLBCL biopsy samples: 1. Independent cohort of 94 DLBCLs. 2. 47% of DLBCLs classified as BCR+ with high confidence (plyn+, psyk+, pbtk+). 3. No correlation with germinal center cell type or activated B-cell type of DLBCL. Bogusz A et al, Clinical Cancer Res, 2012

Visualization of B-cell Receptor (BCR) Signaling using QIF 1. One-half of primary DLBCLs show evidence of active BCR signaling 2. Intensity of BCR signaling in-situ is often comparable to anti-igm crosslinked cell lines B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma 3. No correlation with DLBCL subtype (COO)

Visualization of B-cell Receptor (BCR) Signaling using QIF 1. One-half of primary DLBCLs show evidence of active BCR signaling 2. BCR signaling in-situ is often comparable to anti- IgM crosslinked cell lines B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma 3. No correlation with DLBCL subtype (COO) Possible selection criteria for BCR inhibitor trials DRUG

Quantifying Anti-Tumor Immunity T-cell B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma T-cell Macrophage

Quantifying Anti-Tumor Immunity Example: Classical Hodgkin Lymphoma (CHL) Aggressive lymphoma of B-cell lineage with unique morphology, microenvironment, and phenotype 20-30% associated with EBV Chemotherapy- adriamycin, bleomycin, vinblastine, dacarbazine (ABVD) Cure rate 80-85% Reed-Sternberg cell in an extensive inflammatory background Cure rate for relapsed/refractory disease is very low

9p24.1 Amplification and PD-1 Ligand Cell Surface Expression in Classical Hodgkin Lymphoma CHL Cell Lines Green M et al, Blood, 2010

9p24.1 Amplification and PD-1 Ligand Cell Surface Expression in Classical Hodgkin Lymphoma 9p24.1 copy number Anti-PD-L1/ PD-L2 Isotype control CHL Cell Lines Green M et al, Blood, 2010

9p24.1 Amplification and PD-1 Ligand Cell Surface Expression in Classical Hodgkin Lymphoma CHL Cell Lines 1. Micro-dissected Reed-Sternberg cells from approximately 40% of primary CHL have 9p24.1 copy gain Green M et al, Blood, 2010

9p24.1 Amplification and PD-1 Ligand Cell Surface Expression in Classical Hodgkin Lymphoma 1. Micro-dissected Reed-Sternberg cells from approximately 40% of primary CHL have 9p24.1 copy gain CHL Cell Lines 2. EBV infection can induce PD-L1 expression 3. Cytokines (JAK-STAT signaling) promotes PD-Ligand expression Green M et al, Blood, 2010

PD-1/PD-1 Ligand Interaction Triggers T-cell Exhaustion Freeman G et al., Proc. Natl. Acad. Sci., 2008

PD-1/PD-1 Ligand Interaction Triggers T-cell Exhaustion T cell Exhaustion is reversible with PD 1 blockade Freeman G et al., Proc. Natl. Acad. Sci., 2008

PD-L1 Expression in Primary CHL 80% of CHL shows extensive PD-L1 expression by the malignant Reed-Sternberg cells Chen BJ et al., Clin Cancer Res., 2013

Majority of Patients Relapsed/ Refractory CHL Respond to PD-1 Blockade Nivolumab (anti-pd1, BMS): ORS= 87% Pembrolizumab (anti-pd1, Merck): ORS= 53% Armand P et al, ASH abstract #289, 2014 Moskowitz ch et al, ASH abstract #290, 2014 Ansell S et al, NEJM, in press

Majority of Patients Relapsed/ Refractory CHL Respond to PD-1 Blockade Nivolumab: Ansell S et al, NEJM, in press

PD-L1 Expression in Primary CHL Chen BJ et al., Clin Cancer Res., 2013

PD-L1 Expression in Primary CHL PD-L1/ CD68 1. Approximately 80% of CHL shows extensive PD-L1 expression by the malignant Reed-Sternberg cells and by non-malignant inflammatory cells 2. Non-malignant cells appear to contribute the majority of PD-L1 in the microenvironment Chen BJ et al., Clin Cancer Res., 2013

Majority of Patients Relapsed/ Refractory CHL Respond to PD-1 Blockade Nivolumab (anti-pd1, BMS): ORS= 87% Pembrolizumab (anti-pd1, Merck): ORS= 53% Does PD-Ligand expression on the malignant cells, the inflammatory cells, or both predict response to PD-1 blockade in CHL?

Qualitative and Quantitative Assessment of the Tumor Microenvironment 1. IF Staining (4-5 antibodies per slide) Multispectral imaging Image Storage 2. Spectral Unmixing Cell Identification Biomarker protein localization and quantification 3. Architectural Analysis

PD-L1 Expression in CHL 1. CD30 = Reed-Sternberg cells

PD-L1 Expression in CHL 1. CD30 = Reed-Sternberg cells PD-L1

PD-L1 Expression in CHL 1. CD30 = Reed-Sternberg cells PD-L1/ pstat3

PD-L1 Expression in CHL 1. CD30 = Reed-Sternberg cells PD-L1/ pstat3/ CD68

PD-L1 Expression in CHL 1. CD30 = Reed-Sternberg cells PD-L1/ pstat3/ CD68/ CD163

T-cell Subsets in CHL 1. CD30 = Reed-Sternberg cells

T-cell Subsets in CHL 1. CD4/ CD8

T-cell Subsets in CHL 1. CD30 = Reed-Sternberg cells CD4/ CD8/ FOXP3

PD-L1 Expression in CHL 2. Segmenting cells by cell shape CD30 = Reed-Sternberg cells PD-L1/ CD68

PD-L1 Expression in CHL 2. Segmenting cells by cell shape CD30 = Reed-Sternberg cells Tumor Cells: PD-L1/ CD68 CD30+ PDL1+ = 91.1%

PD-L1 Expression in CHL 2. Segmenting cells by cell shape CD30 = Reed-Sternberg cells PD-L1/ CD68

PD-L1 Expression in CHL 2. Segmenting cells by cell shape CD30 PD-L1/ CD68 = Reed-Sternberg cells Macrophages CD68+ PDL1+ = 39.4%

3. PD-L1 Expression in CHL Percentage of lineage-specified cells

Quantifying Anti-Tumor Immunity 2 1 RS cell 1 Treg RS cell 4 3 RS cell RS cell 1 CD8+ T-cell 1 M2 Macrophage

Quantifying Anti-Tumor Immunity 2 1 RS cell 1 Treg RS cell 4 3 RS cell RS cell 2 CD8+ T-cell 1 M2 Macrophage = PD-L1

Quantifying Anti-Tumor Immunity 2 1 RS cell 1 Treg RS cell 4 3 RS cell RS cell 2 CD8+ T-cell DRUG 1 M2 Macrophage DRUG = PD-L1

Acknowledgements Brigham and Women s Hospital Benjamin Chen Min Shi Christopher Carey Heather Sun Xiaoyun Liao Courtney Connelly Agata Bogusz Jeff Kutok Dana-Farber Cancer Institute Margaret Shipp Bjoern Chapuy Marit Roemer Michael Green Gordon Freeman Steve Hodi Geraldine Pinkus

Untangling the tumor microenvironment: Leveraging Quantitative Pathology to Interrogate Protein Expression in Cancer Edward C. Stack, Ph.D. 2009 2014 PerkinElmer

Multiple ways to regulate the immune response With great complexity of the immune system and the local microenvironment, new methods are required to guide clinical assessments of cancer immunology From: Ott, et al, 2013 A complicated tumor microenvironment landscape

Challenges of multiplexed staining Antibody species interference conventional indirect labeling requires different species Weak signals low abundance, long exposures find tumor Imbalanced signals reduces effectiveness of unmixing, especially when imbalances get to > 10x Autofluorescence from FFPE masks marker signals These issues make multiplexed analysis of FFPE hard 72

A Multiplex Solution Opal and TSA Plus Opal workflow application of sequential stains TSA enables us to use multiple antibodies raised in the same species in the same tissue section (or even the same cell compartment) find tumor Tissue section Antigen A Antigen B Antigen C Antibody A (Rabbit) Antibody B (Rabbit) Antibody C (Rabbit) HRP conjugated Anti Rabbit IgG or Superpicture Fluorochrome tyramide 74

A Multiplex Solution Opal and TSA Plus Opal workflow application of sequential stains TSA enables us to use multiple antibodies raised in the same species in the same tissue section (or even the same cell compartment) Heat find tumor Tissue section Antigen A Antigen B Antigen C Antibody A (Rabbit) Antibody B (Rabbit) Antibody C (Rabbit) HRP conjugated Anti Rabbit IgG or Superpicture Fluorochrome tyramide 78

A Multiplex Solution Opal and TSA Plus Opal workflow application of sequential stains TSA enables us to use multiple antibodies raised in the same species in the same tissue section (or even the same cell compartment) Heat find tumor Tissue section Antigen A Antigen B Antigen C Antibody A (Rabbit) Antibody B (Rabbit) Antibody C (Rabbit) HRP conjugated Anti Rabbit IgG or Superpicture Fluorochrome tyramide 83

A Multiplex Solution Opal and TSA Plus Opal workflow application of sequential stains TSA enables us to use multiple antibodies raised in the same species in the same tissue section (or even the same cell compartment) Heat find tumor Tissue section Antigen A Antigen B Antigen C Antibody A (Rabbit) Antibody B (Rabbit) Antibody C (Rabbit) HRP conjugated Anti Rabbit IgG or Superpicture Fluorochrome tyramide 88

A Multiplex Solution Opal and TSA Plus Opal workflow application of sequential stains TSA enables us to use multiple antibodies raised in the same species in the same tissue section (or even the same cell compartment) find tumor Tissue section Antigen A Antigen B Antigen C Antibody A (Rabbit) Antibody B (Rabbit) Antibody C (Rabbit) HRP-conjugated Anti-Rabbit IgG or Superpicture Fluorochrome tyramide 89

Result of Opal Multiplexing in Breast Cancer Breast tissue CD4, CD8, CD20, cytokeratin, DAPI 90 Opal with TSA Plus, and imaged with Vectra

Problems of autofluorescence in FFPE Monochrome bandpass image 6.9 Signal (Cts) Detecting EGFR in breast tissue Conventional 69.1 99.9 membrane 69.1 nuclear 99.9 Off sample 6.9 S/B: 0.7 to 1 Multispectral Unmixed EGFR signal 0.4 Signal (Cts) 23.4 1.3 membrane 23.4 nuclear 1.3 Off sample 0.4 S/B: 18 to 1 Multispectral unmixed quantitation results are accurate, offering greater quantitative potential 92

Spatial Phenotypical Characterization of Cancer Associated Infiltrating Lymphocytes

Case Study TIL s in Breast Cancer Immunological correlates in Breast Cancer Assay initially optimized in human tonsil proof of concept (A) Unmixed composite of a multispectral image. (B) Image with inform pattern recognition of B cells (CD20+) in red, Killer T cells (CD8+) in purple, Helper T cells (CD4+) in green, Epithelial cells (CK) in yellow, and other cells in blue. (C) A cell phenotype map was created from the spatial information obtained in the segmentation data. 94 A B C

Case Study TIL s in Breast Cancer Multispectral Workflow 4x whole slide scans pathologist selects HPFs in 4x contexts Vectra collects HPFs Data is consolidated Image Acquisition (Vectra 4x whole slide plus 20x/40x HPFs) using Vectra s semiautomated workflow A. A composite image from unmixed CD4, CD8, CD20, CK, and DAPI signals (autofluorescence removed). B. With inform pattern recognition of tumor (red) and stroma (green). C. Tumor cells segmented, scored CD8+ only (red), CD4+ only (green), CD8+ and CD4+ (yellow), double negative (blue). D. Stroma segmented, scored CD8+ only (red), CD4+ only (green), CD8+ and CD4+ (yellow), double negative (blue) A C B D 95

Case Study TIL s in Breast Cancer Quantitative image analysis Results The relative distances of Lymphocytes from the tumor stroma interface was analyzed Green = ER+ cases, Blue = Her2+ cases, Red = triple negative cases ANOVA indicates CD8+ tumor density of each subtype approaches significance (F 3.376, p = 0.08), as opposed to CD8+ stroma density (F 2.743, p = 0.18).

Case Study TIL s in Breast Cancer Quantitative image analysis Results The relative distances of Lymphocytes from the tumor stroma interface were analyzed Histograms of distances of CD8+ lymphocytes to tumor boundary Frequency 45 40 35 30 25 20 15 10 5 Intraepithelial CD8+ Cells BRER-01-6H BRER-02-3G BRER-03-2P BRER-04-1N BRHR-04-2J BRHR-09-2B BRTN-21-2J BRTN-22-2L BRTN-23-3L ave ER ave Her2 ave TN Frequency 350 300 250 200 150 100 50 Stromal CD8+ Cells BRER-01-6H BRER-02-3G BRER-03-2P BRER-04-1N BRHR-03-2C BRHR-04-2J BRHR-09-2B BRHR-10-2C BRTN-21-2J BRTN-22-2L BRTN-23-3L BRTN-24-2H ave ER ave Her2 ave TN 0 5 10 15 20 25 30 35 40 45 50 55 60 Distance in microns 0 12.5 25 37.5 50 62.5 75 87.5 100 112.5 125 137.5 150 162.5 Distance in microns

Case Study TIL s in Breast Cancer Study Summary and Conclusions This study demonstrates a new capability for elucidating the intricacies of cancer immune response, for research and potentially for clinical use. Although a small data set, ER+ tumors appear to have lower immune response, in terms of intraepithelial and stromal TILs. Her2+ tumors showed a range in immune response, possibly dichotomous. Triple negative (ER, PR, Her2 ) had generally much higher immune response. Next steps will be repeating on a larger cohort and assessing PD L1 and Foxp3 to assess the contribution of checkpoint blockades to tumor survival.

Phenotyping Follow up

Phenotyping Follow up cyan = CK purple = killer T cell green = helper T cell red = B cell orange = PD L1 yellow = Foxp3 100 Case #1 pseudo composite

Phenotyping Follow up Phenotypes tumor killer T helper T T reg B cell other Phenotype Counts Tumor 4,380 Killer T 146 Helper T 640 Regulator T 156 B cell 208 Other 2,500 Total 9,260 101 inform tumor stroma and cell phenotype maps

Phenotyping Follow up 33% of tumor cells have a killer T cell within 25 microns distance 102 Spatial point pattern analysis in R

Phenotyping Follow up cyan = CK purple = killer T cell green = helper T cell red = B cell orange = PD L1 yellow = Foxp3 103 Case #4 pseudo composite

Phenotyping Follow up 104 Phenotypes tumor killer T helper T T reg B cell other Phenotype Counts Tumor 3,620 Killer T 203 Helper T 285 Regulator T 127 B cell 218 Other 1,850 Total 6,307 inform tumor stroma and cell phenotype maps

Phenotyping Follow up 3% of tumor cells have a killer T cell within 25 microns distance 105 Spatial point pattern analysis in R

Cell Phenotyping in Breast Cancer Summary and Conclusions The ability to accurately phenotype individual cell, based on specific marker expression, allows for highly contextual spatial data analysis Based on phenotype, nearest neighbor analysis demonstrates a valuable approach to determining phenotype interactions, shedding a new light on the tumor microenvironment. Next steps will be linking clinical data to spatial phenotype analyses to assess the interactions between the microenvironment and disease progression.. This study demonstrates the potential to use nearest neighbor analyses to assess local microenvironment interactions between immune effector cells and tumor cells. This may represent a valuable approach to patient stratification for immune checkpoint blockage therapy, and enable better clinical management of cancer.

With Thanks PerkinElmer Cliff Hoyt Kent Johnson Kristin Roman Chi Wang UPenn Mike Feldman MDACC Beth Mittendorf

Untangling the tumor microenvironment Illuminating the complex interactions & functions of immune cells December 10, 2014 Webinar Series Brought to you by the Science/AAAS Custom Publishing Office Participating Experts Scott Rodig, M.D., Ph.D. DFCI/Brigham & Women s Hospital Boston, MA To submit your questions, click the Ask a Question button Ed Stack, Ph.D PerkinElmer Hopkinton, MA Sponsored by:

Untangling the tumor microenvironment Illuminating the complex interactions & functions of immune cells December 10, 2014 Webinar Series Brought to you by the Science/AAAS Custom Publishing Office Look out for more webinars in the series at: webinar.sciencemag.org To provide feedback on this webinar, please e mail your comments to webinar@aaas.org Sponsored by: For related information on this webinar topic, go to: www.perkinelmer.com/labchipsystems