NanoString expression profiling identifies candidate biomarkers of RAD001 response in metastatic gastric cancer.

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 expression (mesenchymal subtype) and signet ring cell carcinoma, and , , , expression (proliferative subtype) with tubular adenocarcinoma (adjusted p<0.05). Increased (p=0.040) and (p=0.045) expression was significantly associated with RAD001 good response and longer progression-free survival outcome (, p=0.012, HR 0.370 95% CI (0.171 to 0.800); , p=0.010, HR 0.359 95% CI (0.166 to 0.779)). In contrast, increased (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, expression are potential predictive biomarkers for good response in RAD001-treated mGC.