Development and validation of a serum microRNA biomarker panel for detecting gastric cancer in a high-risk population

Abstract

Objective: An unmet need exists for a non-invasive biomarker assay to aid gastric cancer diagnosis. We aimed to develop a serum microRNA (miRNA) panel for identifying patients with all stages of gastric cancer from a high-risk population.

Design: We conducted a three-phase, multicentre study comprising 5248 subjects from Singapore and Korea. Biomarker discovery and verification phases were done through comprehensive serum miRNA profiling and multivariant analysis of 578 miRNA candidates in retrospective cohorts of 682 subjects. A clinical assay was developed and validated in a prospective cohort of 4566 symptomatic subjects who underwent endoscopy. Assay performance was confirmed with histological diagnosis and compared with Helicobacter pylori (HP) serology, serum pepsinogens (PGs), 'ABC' method, carcinoembryonic antigen (CEA) and cancer antigen 19-9 (CA19-9). Cost-effectiveness was analysed using a Markov decision model.

Results: We developed a clinical assay for detection of gastric cancer based on a 12-miRNA biomarker panel. The 12-miRNA panel had area under the curve (AUC)=0.93 (95% CI 0.90 to 0.95) and AUC=0.92 (95% CI 0.88 to 0.96) in the discovery and verification cohorts, respectively. In the prospective study, overall sensitivity was 87.0% (95% CI 79.4% to 92.5%) at specificity of 68.4% (95% CI 67.0% to 69.8%). AUC was 0.848 (95% CI 0.81 to 0.88), higher than HP serology (0.635), PG 1/2 ratio (0.641), PG index (0.576), ABC method (0.647), CEA (0.576) and CA19-9 (0.595). The number needed to screen is 489 annually. It is cost-effective for mass screening relative to current practice (incremental cost-effectiveness ratio=US$44 531/quality-of-life year).

Conclusion: We developed and validated a serum 12-miRNA biomarker assay, which may be a cost-effective risk assessment for gastric cancer.

Trial registration number: This study is registered with ClinicalTrials.gov (Registration number: NCT04329299).

Keywords: gastric cancer; screening.

Conflict of interest statement

Competing interests: KGY, JBYS, WPY, HPT, LZ, RZ and FZ were coinventors in the patent application 'Serum MicroRNA Biomarker for the Diagnosis of Gastric Cancer'. HPT, LZ and RZ are founders and shareholders of MiRXES. LZ, RZ and YCT are employees of MiRXES. HCC received grants from Lilly, GSK, MSD. Merck-Serono, BMS-Ono, Taiho outside the submitted work. The rest of authors declare no competing interests.

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1

Identification of candidate miRNA biomarkers and multi-miRNA biomarker panels for gastric cancer detection. (A) Heat-map showing expression levels of serum miRNAs that were differentially regulated in gastric cancer. The full list can be found in in online supplemental table S2; absolute miRNA expression levels (copy/mL) of miRNAs were presented in log2 scale and standardised to zero mean. Hierarchical clustering was carried out for both dimensions (miRNAs and samples) based on Euclidean distance. (B) Correlation in expression levels between differentially regulated miRNAs. Pearson’s linear correlation coefficients were calculated between all 75 miRNAs that were identified to be differentially regulated in gastric cancer (online supplemental table S2). (C) Gastric cancer detection accuracy of multi-miRNA biomarker panels with 3–10 miRNAs as determined by mean area under ROC curve (AUC). Biomarker panels were tested in the discovery cohort. Two hundred iterations of a cross-validation process were carried out by dividing the Discovery cohort into two data sets: training and testing. Error bars indicate SD. Statistical significance of difference in AUC was determined using Student’s t-test (one sided, **p

Figure 2

Verification of gastric cancer miRNA biomarkers and multi-miRNA biomarker panel detection accuracy in independent cohorts. (A) Correlation in expression level fold changes (cancer over control) of verified miRNA biomarkers between the discovery cohort and verification cohorts. (B) Receiver operating characteristics (ROC) curves for the 12-miRNA biomarker panel in detecting all gastric cancers (A) and early stage (stage 1–2) cancers (B). Area under the ROC curve (AUC) used to determine gastric cancer detection accuracy. Maximum classification accuracy is determined to occur at the point indicated by the red box. The sensitivity and specificity at this point is shown. miRNA, micro-RNA.

Figure 3

Prospective validation of 12-miR biomarker assay for detection of gastric cancer. Flow chart of prospective validation study design prepared in accordance with Standards for Reporting of Diagnostic Accuracy Studies guidelines. miR, micro RNA, NC, negative control; QR, quantitative reference.

Figure 4

Gastric cancer detection accuracy of 5-miR biomarker assay compared with other serum-based biomarker tests. (A) ROC curves for 12-miR assay, PG 1/2 ratio, HP serology, CEA, and CA19-9 for detection of gastric cancer. (B) AUC for 12-miR biomarker assay compared with HP serology, PG 1/2 ratio, PG index, ABC method, CEA, and CA19-9 tests. Bars show 95% CI (C) Overall sensitivity and associated specificity of GC detection using the 12-miR assay (both high sensitivity and high specificity cut-offs), HP serology, PG 1/2 ratio, PG index, ABC method, CEA, and CA19-9 tests. (D) Combinations of biomarker tests with optimal AUC for detecting gastric cancer. AUC, area under the curve; CA19, cancer antigen 19; CEA, carcinoembryonic antigen; HP, Helicobactor pylori; miR, micro-RNA; PG, pepsinogen.

Figure 5

Detection sensitivity of 12-miR assay by gastric cancer stage and clinicopathological characteristics. Detection sensitivity at 68.4% specificity according to (A) gastric cancer stage, (B) age range, (C) tumour size, (D) histological subtype (Lauren classification), (E) gender and (F) ethnicity. miR, microRNA.