Untangling the tumor microenvironment
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- Shauna Lyons
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1 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
2 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:
3 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
4 Disclosures Research Support: Ventana/ Roche Bristol-Myers Squibb Scientific Advisory Board: Astra-Zeneca
5 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
6 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
7 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
8 Histomorphology Diagnostic Information 1. Cellular composition 2. Cellular morphology 3. Tissue architecture
9 Histomorphology Diagnostic Information 1. Cellular composition 2. Cellular morphology 3. Tissue architecture Histomorphology remains the most information-rich method of analysis in diagnostic pathology
10 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)
11 Histomorphology and IHC Excellent to identify cancer and its cell of origin
12 Histomorphology and IHC Excellent to identify cancer and its cell of origin Tumor Cell Tumor Cell Tumor Cell Tumor Cell
13 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
14 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
15 But Cancer is More Complicated! Tissue carries untapped but vital information in era of targeted therapy Hanahan and Weinberg, Cell, 2011
16 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
17 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
18 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
19 Quantitative Immunofluorescence (QIF) Quantitative Immunofluorescence (QIF) and Image Analysis Co-localization studies Visualize signaling networks Quantify protein expression Quantify cellular and architectural features
20 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
21 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
22 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: Wilson WH et al., ASH abstract #686, 2012
23 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
24 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
25 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
26 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
27 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
28 Visualization of B-cell Receptor (BCR) Signaling using QIF Bogusz A et al, Clinical Cancer Res, 2012
29 Visualization of B-cell Receptor (BCR) Signaling using QIF Bogusz A et al, Clinical Cancer Res, 2012
30 Visualization of B-cell Receptor (BCR) Signaling using QIF Fixed, primary DLBCL biopsy samples: DLBCLs stained for: Nuclei (DAPI; blue) Cell lineage (CD20; red) Phospho-biomarker (3 slides; green) % 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
31 Visualization of B-cell Receptor (BCR) Signaling using QIF Fixed DLBCL biopsy samples: 1. Independent cohort of 94 DLBCLs % 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
32 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)
33 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
34 Quantifying Anti-Tumor Immunity T-cell B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma B-cell Lymphoma T-cell Macrophage
35 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
36 9p24.1 Amplification and PD-1 Ligand Cell Surface Expression in Classical Hodgkin Lymphoma CHL Cell Lines Green M et al, Blood, 2010
37 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
38 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
39 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
40 PD-1/PD-1 Ligand Interaction Triggers T-cell Exhaustion Freeman G et al., Proc. Natl. Acad. Sci., 2008
41 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
42 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
43 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
44 Majority of Patients Relapsed/ Refractory CHL Respond to PD-1 Blockade Nivolumab: Ansell S et al, NEJM, in press
45 PD-L1 Expression in Primary CHL Chen BJ et al., Clin Cancer Res., 2013
46 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
47 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?
48 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
49 PD-L1 Expression in CHL 1. CD30 = Reed-Sternberg cells
50 PD-L1 Expression in CHL 1. CD30 = Reed-Sternberg cells PD-L1
51 PD-L1 Expression in CHL 1. CD30 = Reed-Sternberg cells PD-L1/ pstat3
52 PD-L1 Expression in CHL 1. CD30 = Reed-Sternberg cells PD-L1/ pstat3/ CD68
53 PD-L1 Expression in CHL 1. CD30 = Reed-Sternberg cells PD-L1/ pstat3/ CD68/ CD163
54 T-cell Subsets in CHL 1. CD30 = Reed-Sternberg cells
55 T-cell Subsets in CHL 1. CD4/ CD8
56 T-cell Subsets in CHL 1. CD30 = Reed-Sternberg cells CD4/ CD8/ FOXP3
57 PD-L1 Expression in CHL 2. Segmenting cells by cell shape CD30 = Reed-Sternberg cells PD-L1/ CD68
58 PD-L1 Expression in CHL 2. Segmenting cells by cell shape CD30 = Reed-Sternberg cells Tumor Cells: PD-L1/ CD68 CD30+ PDL1+ = 91.1%
59 PD-L1 Expression in CHL 2. Segmenting cells by cell shape CD30 = Reed-Sternberg cells PD-L1/ CD68
60 PD-L1 Expression in CHL 2. Segmenting cells by cell shape CD30 PD-L1/ CD68 = Reed-Sternberg cells Macrophages CD68+ PDL1+ = 39.4%
61 3. PD-L1 Expression in CHL Percentage of lineage-specified cells
62 3. PD-L1 Expression in CHL Percentage of lineage-specified cells
63 3. PD-L1 Expression in CHL Percentage of lineage-specified cells
64 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
65 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
66 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
67 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
68 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
69 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:
70 Untangling the tumor microenvironment: Leveraging Quantitative Pathology to Interrogate Protein Expression in Cancer Edward C. Stack, Ph.D PerkinElmer
71 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
72 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
73 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 73
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) 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
75 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 75
76 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 76
77 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 77
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 78
79 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 79
80 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 80
81 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 81
82 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 82
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 83
84 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 84
85 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 85
86 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 86
87 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 87
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) 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
89 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
90 Result of Opal Multiplexing in Breast Cancer Breast tissue CD4, CD8, CD20, cytokeratin, DAPI 90 Opal with TSA Plus, and imaged with Vectra
91 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 91
92 Problems of autofluorescence in FFPE Monochrome bandpass image 6.9 Signal (Cts) Detecting EGFR in breast tissue Conventional membrane 69.1 nuclear 99.9 Off sample 6.9 S/B: 0.7 to 1 Multispectral Unmixed EGFR signal 0.4 Signal (Cts) 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
93 Spatial Phenotypical Characterization of Cancer Associated Infiltrating Lymphocytes
94 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
95 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
96 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).
97 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 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 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 Distance in microns Distance in microns
98 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.
99 Phenotyping Follow up
100 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
101 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, inform tumor stroma and cell phenotype maps
102 Phenotyping Follow up 33% of tumor cells have a killer T cell within 25 microns distance 102 Spatial point pattern analysis in R
103 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
104 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
105 Phenotyping Follow up 3% of tumor cells have a killer T cell within 25 microns distance 105 Spatial point pattern analysis in R
106 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.
107 With Thanks PerkinElmer Cliff Hoyt Kent Johnson Kristin Roman Chi Wang UPenn Mike Feldman MDACC Beth Mittendorf
108 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:
109 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:
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