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BMC Cancer. 2018 Feb 27;18(1):225. doi: 10.1186/s12885-018-4134-y.

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

Author information

1
Iowa Institute for Oral Health Research, College of Dentistry, The University of Iowa, 801 Newton Road, Iowa City, IA, 52242, USA. kim-brogden@uiowa.edu.
2
Cellworks Research India Ltd., Whitefield, Bangalore, 560066, India.
3
Biomedical Engineering, The University of Iowa, 5318 SC, Iowa City, IA, 52242, USA.
4
Iowa Institute for Oral Health Research, College of Dentistry, The University of Iowa, 801 Newton Road, Iowa City, IA, 52242, USA.
5
Division of Biostatistics and Research Design, College of Dentistry, The University of Iowa, 801 Newton Road, Iowa City, IA, 52242, USA.
6
Division of Hematology/Oncology, Columbia University Medical Center, 177 Fort Washington Avenue, New York, NY, 10032, USA.
7
Molecular Pathology Laboratory, Department of Pathology, University of Iowa Hospitals and Clinics, 200 Hawkins Dr., C606GH, Iowa City, IA, 52242, USA.
8
Clinical Services, Experimental Therapeutics, Melanoma and Sarcoma Program, Holden Comprehensive Cancer Center, The University of Iowa, Iowa City, IA, 52242, USA.
9
Department of Radiation Oncology, Human Oncology and Pathogenesis Program, Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
10
Cellworks Group, Inc., 2033 Gateway Place Suite 500, San Jose, CA, 95110, USA.

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.

TRIAL REGISTRATION:

ClinicalTrials.gov NCT01295827.

KEYWORDS:

Computational modeling; Immunotherapy; NSCLC; PD-1; PD-L1

PMID:
29486723
PMCID:
PMC5897943
DOI:
10.1186/s12885-018-4134-y
[Indexed for MEDLINE]
Free PMC Article

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