Precis Future Med.  2019 Dec;3(4):165-175. 10.23838/pfm.2019.00079.

Combined biomarker for prediction of response to an immune checkpoint inhibitor in metastatic gastric cancer

Affiliations
  • 1The Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • 2Department of Pathology & Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. kkmkys@skku.edu
  • 3Division of Hematology-Oncology, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. jyunlee@skku.edu

Abstract

PURPOSE
Immune checkpoint blockades (ICB) have been successful in gastric cancer (GC). However, the majority of unselected patients with GC fail to respond to ICB. It is crucial to identify precise biomarkers to predict response to ICB.
METHODS
Gene expression profiling of formalin-fixed and paraffin-embedded GC tissues from 25 patients treated with ICB (pembrolizumab) targeting programmed cell death protein 1 (PD-1) was performed using NanoString (NanoString Technologies). For development of a gene signature to predict response to ICB, differential gene expression analysis with linear regression modeling was performed with area under the curve packages in R.
RESULTS
From the analysis, 10 genes were differentially expressed between patients with response and no response to ICB (P<0.01). To identify a biomarker predicting response to ICB, four genes were selected based on |log₂ (foldchange)|≥1. After calculating the IMmunotherapy Against GastrIc Cancer (IMAGiC) score, patients were divided into two groups: to be responder and to be non-responder, according to Response Evaluation Criteria in Solid Tumors (RECIST) guidelines. The IMAGiC score was significantly associated with RECIST groups (P=0.0057), Epstein-Barr virus (P=0.048), and tumor mutational load (P=0.023); however, was not significantly correlated with microsatellite instability status (P=0.14) and programmed death ligand 1 (PD-L1) expression (P=0.095). To reproduce IMAGiC with different technology, we retested the results with a quantitative real-time polymerase chain reaction (qRT-PCR) method, and the precision of reproduction of 87.5%. In validation cohort with 17 samples from the ongoing trial with nivolumab, the precision of IMAGiC qRT-PCR was 100%.
CONCLUSION
Our identified gene signatures and proposed IMAGiC model for predicting response to pembrolizumab in patients with GC showed validity.

Keyword

Biomarkers; Pembrolizumab; Prediction; Response; Stomach neoplasms

MeSH Terms

Biomarkers
Cell Death
Cohort Studies
Gene Expression
Gene Expression Profiling
Genes, vif
Herpesvirus 4, Human
Humans
Immunotherapy
Linear Models
Methods
Microsatellite Instability
Real-Time Polymerase Chain Reaction
Reproduction
Response Evaluation Criteria in Solid Tumors
Stomach Neoplasms*
Biomarkers
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