Functional binding of PD1 ligands predicts response to anti-PD1 treatment in patients with cancer

Author:

Kaufman Bar12ORCID,Abramov Orli12,Ievko Anna3,Apple Daria4,Shlapobersky Mark25,Allon Irit25,Greenshpan Yariv12,Bhattachrya Baisali12ORCID,Cohen Ofir12ORCID,Charkovsky Tatiana6,Gayster Alexandra3,Shaco-Levy Ruthy24,Rouvinov Keren23ORCID,Livoff Alejandro25,Elkabets Moshe12ORCID,Porgador Angel12ORCID

Affiliation:

1. The Shraga Segal Department of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

2. Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

3. Department of Oncology, Soroka University Medical Center, Beer-Sheva, Israel.

4. Department of Pathology, Soroka University Medical Center, Beer-Sheva, Israel.

5. Department of Pathology, Barzilai Medical Center, Ashkelon, Israel.

6. Department of Oncology, Barzilai Medical Center, Ashkelon, Israel.

Abstract

Accurate predictive biomarkers of response to immune checkpoint inhibitors (ICIs) are required for better stratifying patients with cancer to ICI treatments. Here, we present a new concept for a bioassay to predict the response to anti-PD1 therapies, which is based on measuring the binding functionality of PDL1 and PDL2 to their receptor, PD1. In detail, we developed a cell-based reporting system, called the immuno-checkpoint artificial reporter with overexpression of PD1 (IcAR-PD1) and evaluated the functionality of PDL1 and PDL2 binding in tumor cell lines, patient-derived xenografts, and fixed-tissue tumor samples obtained from patients with cancer. In a retrospective clinical study, we found that the functionality of PDL1 and PDL2 predicts response to anti-PD1 and that the functionality of PDL1 binding is a more effective predictor than PDL1 protein expression alone. Our findings suggest that assessing the functionality of ligand binding is superior to staining of protein expression for predicting response to ICIs.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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