Profiling of gastric cancer cell-surface markers to achieve tumour–normal discrimination

Abstract

BackgroundDifferentiating 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.MethodsWhole 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.ResultsAnalysis 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).ConclusionCEAMCAM5, 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.

Spatial profiling of gastric cancer patient-matched primary and locoregional metastases reveals principles of tumour dissemination

“… 8 9 22 Certain genes with therapeutic and clinical relevance such as CEACAM6 , CEACAM5 and EGF had the highest ITH (above the 90th centile) ( online supplemental table S12 ). 23 …”

“…8 9 22 Certain genes with therapeutic and clinical relevance such as CEACAM6, CEACAM5 and EGF had the highest ITH (above the 90th centile) (online supplemental table S12). 23 In this study, we selected tissue blocks for analysis based on highest tumour content. Subregions were annotated for DNA and RNA extraction and morphometric and phenotypic assessment by a senior GI pathologist (figure 1).…”

Oncogenic Fatty Acid Metabolism Rewires Energy Supply Chain in Gastric Carcinogenesis

“…We also observed the expression of 2 additional markers in the apical membranes of dysplastic cells in HGD: CD133, a marker of dysplastic stem cells that contribute to adenocarcinoma development, 13 and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), a dysplasia and gastric adenocarcinoma marker in human 28 , 29 ( Figure 1K and L and Supplementary Figure 1E , arrowheads). Also, the cell proliferation zone and level were expanded throughout the glands in HGD ( Figure 1M and N and Supplementary Figure 1F ) and aligned with a distribution of CEACAM5-positive dysplastic cells.…”

Current Methods for Identifying Plasma Membrane Proteins as Cancer Biomarkers

“…As a result, even though the detection is still biased for known proteins, many cancer markers with prognostic and predictive values could be identified [35]. For instance, Toh et al employed this method to profile gastric cell surface markers for discriminating between normal and tumor gastric cells [36]. Previously, several genes and proteins were shown to be differentially expressed in gastric cancer; however, a systematic study on the cell surface markers was missing.…”

“…Then, by using a tissue microarray of normal, marginal, and tumor tissues, they performed mIHC/IF and digital image analysis using antibodies to the candidate membrane proteins from the cohort. They could identify the cell adhesion molecules CEAMCAM5, CEAMCAM6, EpCAM, and a tumor-associated glycoprotein CA 72-4 as cell surface markers, which can be used to identify tumor tissue in gastric cancer [36].…”