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

被引:25
作者
Brogden, Kim A. [1 ]
Parashar, Deepak [2 ]
Hallier, Andrea R. [3 ]
Braun, Terry [3 ]
Qian, Fang [1 ,4 ]
Rizvi, Naiyer A. [5 ]
Bossler, Aaron D. [6 ]
Milhem, Mohammed M. [7 ]
Chan, Timothy A. [8 ]
Abbasi, Taher [9 ]
Vali, Shireen [9 ]
机构
[1] Univ Iowa, Coll Dent, Iowa Inst Oral Hlth Res, 801 Newton Rd, Iowa City, IA 52242 USA
[2] Cellworks Res India Ltd, Bangalore 560066, Karnataka, India
[3] Univ Iowa, Biomed Engn, 5318 SC, Iowa City, IA 52242 USA
[4] Univ Iowa, Coll Dent, Div Biostat & Res Design, 801 Newton Rd, Iowa City, IA 52242 USA
[5] Columbia Univ, Med Ctr, Div Hematol Oncol, 177 Ft Washington Ave, New York, NY 10032 USA
[6] Univ Iowa Hosp & Clin, Dept Pathol, Mol Pathol Lab, 200 Hawkins Dr,C606GH, Iowa City, IA 52242 USA
[7] Univ Iowa, Holden Comprehens Canc Ctr, Clin Serv Expt Therapeut Melanoma & Sarcoma Progr, Iowa City, IA 52242 USA
[8] Mem Sloan Kettering Canc Ctr, Dept Radiat Oncol, Human Oncol & Pathogenesis Program, Immunogen & Precis Oncol Platform, New York, NY 10065 USA
[9] Cellworks Grp Inc, 2033 Gateway Pl Suite 500, San Jose, CA 95110 USA
基金
美国国家卫生研究院;
关键词
Computational modeling; PD-1; PD-L1; NSCLC; Immunotherapy; ENDOTHELIAL GROWTH-FACTOR; DENDRITIC CELLS; LUNG-CANCER; PROGRAMMED DEATH-1; BLOCKADE; PATHWAY; IMMUNOHISTOCHEMISTRY; INHIBITION; TUMORS; TIM-3;
D O I
10.1186/s12885-018-4134-y
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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.
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页数:15
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