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Published in: BMC Cancer 1/2018

Open Access 01-12-2018 | Research article

Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy

Authors: Kim A. Brogden, Deepak Parashar, Andrea R. Hallier, Terry Braun, Fang Qian, Naiyer A. Rizvi, Aaron D. Bossler, Mohammed M. Milhem, Timothy A. Chan, Taher Abbasi, Shireen Vali

Published in: BMC Cancer | Issue 1/2018

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Abstract

Background

Programmed Death Ligand 1 (PD-L1) is a co-stimulatory and immune checkpoint protein. PD-L1 expression in non-small cell lung cancers (NSCLC) is a hallmark of adaptive resistance and its expression is often used to predict the outcome of Programmed Death 1 (PD-1) and PD-L1 immunotherapy treatments. However, clinical benefits do not occur in all patients and new approaches are needed to assist in selecting patients for PD-1 or PD-L1 immunotherapies. Here, we hypothesized that patient tumor cell genomics influenced cell signaling and expression of PD-L1, chemokines, and immunosuppressive molecules and these profiles could be used to predict patient clinical responses.

Methods

We used a recent dataset from NSCLC patients treated with pembrolizumab. Deleterious gene mutational profiles in patient exomes were identified and annotated into a cancer network to create NSCLC patient-specific predictive computational simulation models. Validation checks were performed on the cancer network, simulation model predictions, and PD-1 match rates between patient-specific predicted and clinical responses.

Results

Expression profiles of these 24 chemokines and immunosuppressive molecules were used to identify patients who would or would not respond to PD-1 immunotherapy. PD-L1 expression alone was not sufficient to predict which patients would or would not respond to PD-1 immunotherapy. Adding chemokine and immunosuppressive molecule expression profiles allowed patient models to achieve a greater than 85.0% predictive correlation among predicted and reported patient clinical responses.

Conclusions

Our results suggested that chemokine and immunosuppressive molecule expression profiles can be used to accurately predict clinical responses thus differentiating among patients who would and would not benefit from PD-1 or PD-L1 immunotherapies.
Appendix
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Metadata
Title
Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy
Authors
Kim A. Brogden
Deepak Parashar
Andrea R. Hallier
Terry Braun
Fang Qian
Naiyer A. Rizvi
Aaron D. Bossler
Mohammed M. Milhem
Timothy A. Chan
Taher Abbasi
Shireen Vali
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2018
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-018-4134-y

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