Spatial Imaging as a Tool for Discovery of New Candidates to Predict Response to Immunotherapies

Learn more about Solutions for Oncology Therapeutics Discovery: https://cst-science.com/ep1pk9

Featured Speakers: Dr. David Rimm MD, PhD (Yale University School of Medicine), and Christopher Grange (Cell Signaling Technology)

While tremendous progress has been made using immunotherapies to treat a variety of cancers, it remains a challenge to accurately predict patient response. Part of this challenge is due to the inherent complexity of the tumor microenvironment (TME), including the ability to differentiate various components and accurately identify their interactions, phenotype, and function. Multiplexing spatial imaging technologies can be used to maximize the amount of data collected in the TME, thereby answering more questions from fewer samples. Dr. David Rimm of Yale School of Medicine discusses these approaches and his recent work to improve response prediction to immunotherapy.

Solutions for Oncology Drug Discovery
Scientists at Cell Signaling Technology are biology, application, and therapeutic area domain experts and are here to help you streamline your discovery. Our scientific expertise in oncology therapeutic areas allows us to offer a broad antibody portfolio against key targets and readouts that could lead to new, more efficacious therapies.

Contents:
0:00 Introduction
0:21 Why Spatial biology?
1:23 What is Required for Spatial Biology Studies?
2:42 Hallmarks of Antibody Validation™
3:05 Binary Strategy: Positive & Negative Tissues
3:49 Binary Strategy: Knockout Models
4:03 Knockout Validation is a Single Data Point
5:07 Ranged Expression: High and Low Expressing Tissues
5:47 Orthogonal Strategy: Mass Spec vs IHC
6:25 Multiple Antibody Strategy: Two Antigen Support
7:24 Recombinant Strategy: Family Member Cross-Reactivity
8:09 Complementary Strategy: Verification of Modification Specificity
10:14 Species Reactivity Testing for PDx and Humanized Mouse Models
12:23 Dr. David Rimm
13:04 Spatial Imaging as a Tool for Discovery
14:17 There are NO continuous quantitative assays in Anatomic Pathology
14:58 How assays work; terminology of analytics
15:37 Molecular Compartmentalization vs Segmentation
16:51 Immunofluorescent Image & Molecular
18:16 AstroPath from Janis Taube and Alex Szalay Improving cell segmentation and phenotyping
20:46 Comparison of Cell-based Segmentation vs Molecular Compartmentalization
22:57 Chromogenic One-Plex – The PD-L1 IHC assay for Immunotherapy
23:25 Moving beyond Chromogenic 1-plex – IF to the Clinic?
24:58 ImmunoProfile (IP): Scope and Highlights
26:13 Serially Collected Breast Cancer Cases (2011-2014)
26:35 QDAP Low HER2 Assay (aka QHER2-low) Tentative Workflow
27:47 High-plex Methods for Protein Biomarker Discovery
29:01 MultiOmyx (from GE to NeoGenomics to ????)
29:44 In-depth tissue profiling
30:35 CyCIF – Peter Sorger Lab - Harvard
31:02 Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging
31:47 Miltenyi Biotec MACSima and Lunaphore Cormet
32:23 MIBI-Multiplex Ion Beam Imaging
33:26 Imaging Mass Cytometry (IMC)
34:07 histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data
35:39 Summary of Protein High-Plex Methods
36:19 When will High-Plex be in a CLIA Lab?
38:54 High expression of CD95 (APO1/Fas) in macrophages melanoma associated with increase recurrence on ICI therapy
39:50 Summary

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