Profiling of gastric cancer cell-surface markers to achieve tumour–normal discrimination. Issue 1 (18th August 2020)

• Record Type: Journal Article
• Authors: Toh, Judith; Hoppe, Michal Marek; Thakur, Teena; Yang, Henry; Tan, Kar Tong; Pang, Brendan; Ho, Sharmaine; Roy, Rony; Ho, Khek Yu; Yeoh, Khay Guan; Tan, Patrick; Sundar, Raghav; Jeyasekharan, Anand
• Is Part Of: BMJ open gastroenterology. Volume 7:Issue 1(2020)
• Publication Date: 2020-08-18
• Subjects: gastric adenocarcinoma -- gastric carcinoma -- diagnostic and therapeutic endoscopy -- tumour markers -- cancer genetics; Gastroenterology -- Periodicals; 616.33005
• DOI: 10.1136/bmjgast-2020-000452
• Languages: English
• ISSNs: 2054-4774

Abstract

Abstract : Background: Differentiating between malignant and normal cells within tissue samples is vital for molecular profiling of cancer using advances in genomics and transcriptomics. Cell-surface markers of tumour–normal discrimination have additional value in terms of translatability to diagnostic and therapeutic strategies. In gastric cancer (GC), previous studies have identified individual genes or proteins that are upregulated in cancer. However, a systematic analysis of cell-surface markers and development of a composite panel involving multiple candidates to differentiate tumour from normal has not been previously reported. Methods: Whole transcriptome sequencing (WTS) of GC and matched normal samples from the Singapore Gastric Cancer Consortium (SGCC) was used as a discovery cohort to identify upregulated putative cell-surface proteins. Matched WTS data from the The Cancer Genome Atlas (TCGA) was used as a validation cohort. Promising candidates from this analysis were validated orthogonally using multispectral immunohistochemistry (mIHC) with automated quantitative analysis using the Vectra platform. mIHC was performed on a tissue microarray containing matched normal, marginal and tumour tissues. The receiver-operating characteristic (ROC) curves were analysed to identify markers with the highest diagnostic validity independently and in combination. Results: Analysis of putative membrane protein transcripts from the SGCC discovery cohort WTS data (n=15 matched tumour and normal pairs) identified several differentially and highly expressed candidates in tumour compared with normal tissues. After validation with TCGA data (n=29 matched tumour and normal pairs), the following proteins were selected for mIHC analysis: CEACAM5, CEACAM6, CLDN4, CLDN7, and EpCAM. These were compared with established glycoprotein markers in GC, namely CA19-9 and CA72-4. Individual ROC curves yielded the best performance for CEACAM5 (area under the ROC curve (AUC)=0.80), CEACAM6 (AUC=0.82), EpCAM (AUC=0.83), and CA72-4 (AUC=0.76). Combined multiplexed imaging of these four markers revealed improved specificity and sensitivity for detection of tumour from normal tissue (AUC of 4-plex=0.91). Conclusion: CEAMCAM5, CEACAM6, EpCAM, and CA72-4 form a versatile set of markers for robust discrimination of GC from adjacent normal tissue. As cell-surface markers, they are compatible with both IHC and live imaging approaches. These candidates may be exploited to improve automated identification of tumour tissue in GC.