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


Authors: Brogden, K. A.; Parashar, D.; Hallier, A. R.; Braun, T.; Qian, F.; Rizvi, N. A.; Bossler, A. D.; Milhem, M. M.; Chan, T. A.; Abbasi, T.; Vali, S.
Article Title: Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy
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. © 2018 The Author(s).
Keywords: immunotherapy; pd-1; nsclc; pd-l1; computational modeling
Journal Title: BMC Cancer
Volume: 18
ISSN: 1471-2407
Publisher: Biomed Central Ltd  
Date Published: 2018-02-27
Start Page: 225
Language: English
DOI: 10.1186/s12885-018-4134-y
PROVIDER: scopus
PUBMED: 29486723
PMCID: PMC5897943
DOI/URL:
Notes: Correction issued, see DOI: 10.1186/s12885-018-4200-5 -- Article -- Export Date: 2 April 2018 -- Source: Scopus
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  1. Timothy Chan
    262 Chan