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NanoString expression profiling identifies candidate biomarkers of RAD001 response in metastatic gastric cancer
  1. Kakoli Das1,
  2. Xiu Bin Chan2,
  3. David Epstein1,
  4. Bin Tean Teh1,3,4,
  5. Kyoung-Mee Kim5,
  6. Seung Tae Kim6,
  7. Se Hoon Park6,
  8. Won Ki Kang6,
  9. Steve Rozen1,
  10. Jeeyun Lee6,
  11. Patrick Tan1,2,4
  1. 1Cancer and Stem Cell Biology Program, Duke-NUS Medical School
  2. 2Genome Institute of Singapore, Biopolis, Singapore
  3. 3Laboratory of Cancer Epigenome, Division of Medical Sciences, National Cancer Centre Singapore, Singapore
  4. 4Cancer Science Institute of Singapore, National University of Singapore, Singapore
  5. 5Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
  6. 6Department of Medicine, Division of Hematology-Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
  1. Correspondence to Dr Kakoli Das; kaks23{at}


Background Gene expression profiling has contributed greatly to cancer research. However, expression-driven biomarker discovery in metastatic gastric cancer (mGC) remains unclear. A gene expression profile predicting RAD001 response in refractory GC was explored in this study.

Methods Total RNA isolated from 54 tumour specimens from patients with mGC, prior to RAD001 treatment, was analysed via the NanoString nCounter gene expression assay. This assay targeted 477 genes representing 10 different GC-related oncogenic signalling and molecular subtype-specific expression signatures. Gene expression profiles were correlated with patient clinicopathological variables.

Results NanoString data confirmed similar gene expression profiles previously identified by microarray analysis. Signature I with 3 GC subtypes (mesenchymal, metabolic and proliferative) showed approximately 90% concordance where the mesenchymal and proliferative subtypes were significantly associated with signet ring cell carcinoma and the WHO classified tubular adenocarcinoma GC, respectively (p=0.042). Single-gene-level correlations with patient clinicopathological variables showed strong associations between FHL1 expression (mesenchymal subtype) and signet ring cell carcinoma, and NEK2, OIP5, PRC1, TPX2 expression (proliferative subtype) with tubular adenocarcinoma (adjusted p<0.05). Increased BRCA2 (p=0.040) and MMP9 (p=0.045) expression was significantly associated with RAD001 good response and longer progression-free survival outcome (BRCA2, p=0.012, HR 0.370 95% CI (0.171 to 0.800); MMP9, p=0.010, HR 0.359 95% CI (0.166 to 0.779)). In contrast, increased BTC (p=0.035) expression was significantly associated with RAD001 poor response and poor progression-free survival (p=0.031, HR 2.336 95% CI (1.079 to 5.059) by univariate Cox regression analysis.

Conclusions Microarray results are highly reproducible with NanoString nCounter gene expression profiling. Additionally, BRCA2 and MMP9 expression are potential predictive biomarkers for good response in RAD001-treated mGC.

  • gastric cancer
  • NanoString
  • RAD001
  • metastasis
  • gene expression

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