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PD-L1 and Immunotherapy of GI cancers: What do you need to know Rondell P. Graham September 3, 2017 2017 MFMER slide-2
Disclosure No conflicts of interest to disclose 2017 MFMER slide-3
Objectives Define immunotherapy Describe the biology underlying the PD-1/ PD-L1 axis Discuss the available biomarkers for prediction of response to PD-1 axis inhibition Outline the challenges with PD-L1 immunohistochemistry Summarize the role of the diagnostic pathologist in patient selection for immunotherapy 2017 MFMER slide-4
Immunotherapy Immunotherapy is a treatment that uses parts of the host immune system to attack a disease 2017 MFMER slide-5
Advances in the understanding of immunology Cellular constituents of the immune system How they functioned T-cells TCR and CD28 CTLA4 2017 MFMER slide-7
1. T-cell regulation is complex 2. T-cell regulation is dynamic 2017 MFMER slide-9
PD-L1 (programmed cell death ligand-1) Recognized as a T-cell inhibitory protein Binds to PD-1 Promotes effector T-cell exhaustion Promotes persistence of immunosuppressive T-reg Downregulates active tumor immunity in vivo Dong et al, 2002 2017 MFMER slide-10
1. T-cell regulation is complex 2. T-cell regulation is dynamic 3. PD-1/PD-L1 provides a mechanism of immune evasion 2017 MFMER slide-11
Under physiologic conditions The immune system detects foreign antigen Cytotoxic T-cells are generated and traffic to the site Cytotoxic T-cells attack the foreign cells and kill them PD-1/PD-L1 provide brakes to ensure appropriate intensity and scope to cytotoxic activity 2017 MFMER slide-12
PD-1 T-cell APC TCR MHC-1 PD-L1 Cancer 2017 MFMER slide-13
Targeting the PD-1/PD-L1 axis Requires activated T-cells in the tumor microenvironment A prerequisite for activated T-cells is an immunogenic tumor A tumor with neo-antigens A tumor with a permissive genetic and epigenetic makeup 2017 MFMER slide-14
This disrupted the existing order of anti-cancer therapy PD-1/PD-L1 axis therapies do not target the tumor cells Do not focus activation on a particular target on the tumor cells Anti PD-1/PD-L1 therapy is somewhat tumor-type agnostic 2017 MFMER slide-15
Biomarkers to predict efficacy of PD-1/PD-L1 blockade PD-L1 expression by immunohistochemistry Mutation associated neoantigens Others 2017 MFMER slide-16
Does PD-L1 expression predict clinical response PD-L1 expressed by colorectal, gastric, esophageal, hepatobiliary and pancreatic carcinomas 2017 MFMER slide-17
Gastric Pancreatic 2017 MFMER slide-18
Does PD-L1 expression predict clinical response Reasonable from the foregoing to believe that PD-L1 expressing tumors should respond to PD-1 axis inhibition Study ORR in PD-L1 positive (%) ORR in PD-L1 negative (%) Topalian et al (2012) 36 0 Grosso et al (2013) 44 17 Powles et al (2014) 43 11 Herbst et al (2014) 46 13 2017 MFMER slide-19
Does PD-L1 expression predict clinical response PD-L1 expression correlates with response but does not predict response And there are other issues 2017 MFMER slide-20
Issues with PD-L1 expression Antibody variability Observer variability Threshold variability Tumor heterogeneity Differences across tumor types 2017 MFMER slide-21
PD-L1 antibody variability Therapy Therapy target Antibody clone Antibody vendor Atezolizumab PD-L1 SP142 Ventana Nivolumab PD-1 28-8 Dako/Agilent Pembrolizumab PD-1 22c3 Dako/Agilent Durvalumab PD-L1 SP263 Ventana 2017 MFMER slide-22
PD-L1 antibody variability 22C3, 28-8, SP263 closely aligned in tumor cells stained SP142 stained fewer tumor cells Immune staining showed greater variability Hirsch et al, 2017 2017 MFMER slide-23
PD-L1 antibody variability SP142 labels fewer tumor and inflammatory cells Rimm et al, 2017 2017 MFMER slide-24
Observer variability Pathologists concordant in assessment of tumor cells Not concordant in assessment of immune cells 2017 MFMER slide-25
PD-L1 and Tumor Heterogeneity Rehman et al, Mod Pathol. 2017 2017 MFMER slide-27
PD-L1 and Differences across Tumor types PD-L1 expression varies across tumor types May not predict degree of response May not predict survival Kluger et al, 2017 2017 MFMER slide-28
PD-L1 IHC is limited as a predictive biomarker 2017 MFMER slide-29
Neoantigens Mutation associated neoantigens Mutations alter the coding sequence In the appropriate context this leads to an immunogenic tumor 2017 MFMER slide-30
Neoantigens Mutation associated neoantigens Microsatellite instability leads to the development of neoantigens Tumor Normal Stable Tumor Unstable Normal 2017 MFMER slide-31
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Microsatellite instability is a good predictive marker of response to PD-1/PD-L1 axis inhibition 2017 MFMER slide-36
Histology teaches us that there must be other mechanisms of immunogenic tumors Gastric carcinoma with lymphoid stroma Lymphocyte rich HCC 2017 MFMER slide-37
Role of Diagnostic Pathologist Accurate diagnosis Identify adequate and appropriate specimens for microsatellite instability and other ancillary testing Ensure efficient use of tissue samples 2017 MFMER slide-38
PD-L1 and Immunotherapy in GI malignancy An important subset of malignancies have an immunogenic environment Inhibition of the PD-1/PD-L1 axis is a disruptive new modality in GI cancer therapy In GI malignancies, MSI-H is an excellent predictor of response to PD-1 inhibition Other predictive biomarkers are under study Pathologists will be key participants in the new wave of immuno-oncology 2017 MFMER slide-39