Genomic Predictors of Response to Immune Checkpoint Inhibitors

 

Immune checkpoint inhibition therapy is now a mainstay of oncology, remarkably improving survival for some patients. Predicting which patients will benefit most remains an open challenge, however. Several biomarkers have been approved by the FDA for use as companion diagnostics to various ICI agents and in various clinical settings. These include immunohistochemistry for PD-L1, microsatellite instability (MSI), and tumor mutation burden (TMB). While the PD-L1 biomarker can be assessed by a variety of laboratory methods, MSI and TMB is deployed in the setting of comprehensive genomic profiling, made possible by next generation sequencing. The relationship between these biomarkers and the predictive value of each will be explored in this lecture, which will hopefully enable the practicing pathologist to help inform test selection when called upon by oncology colleagues.

Originally published on October 10, 2022

Lecture Presenter

Joshua F. Coleman, MD

Joshua F. Coleman, MD

Assistant Professor of Pathology
University of Utah School of Medicine
Medical Director, Molecular Oncology
ARUP Laboratories

Dr. Joshua F. Coleman serves as a medical director of molecular oncology at ARUP Laboratories and an assistant professor at the University of Utah School of Medicine. Dr. Coleman received his medical degree from Case Western Reserve University School of Medicine and the completed an anatomic and clinical pathology residency at Cleveland Clinic. Following residency, Dr. Coleman completed a hematopathology fellowship at the University of New Mexico and a molecular genetic pathology fellowship at the University of Utah School of Medicine. He is board certified in anatomic and clinical pathology, hematology, and molecular genetic pathology by the American Board of Pathology. Dr. Coleman has received the Clinical Pathology Resident Education of the Year award. He specializes in cancer genomics, bioinformatics, fluorescence in situ hybridization, and his research interests include the application of bioinformatic technologies, data science, and machine learning in molecular genetic pathology.

Objectives

After this presentation, participants will be able to:

  • Describe the essential rationale of immune checkpoint inhibitory (ICI) therapy
  • Define comprehensive genomic profiling (CGP)
  • Describe the advantages and limitations of microsatellite instability (MSI) as a predictor of ICI therapy
  • Describe the advantages and limitations of tumor mutation burden (TMB) as a predictor of ICI therapy

Sponsored by:

University of Utah School of Medicine, Department of Pathology, and ARUP Laboratories