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Review ArticleReview
Open Access

Advances and challenges in gastric cancer testing: the role of biomarkers

Yu Sun, Pavitratha Puspanathan, Tony Lim and Dongmei Lin
Cancer Biology & Medicine March 2025, 22 (3) 212-230; DOI: https://doi.org/10.20892/j.issn.2095-3941.2024.0386
Yu Sun
1State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Department of Pathology, Peking University Cancer Hospital & Institute, Beijing 100142, China
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Pavitratha Puspanathan
2Department of Pathology, Hospital Pulau Pinang, Georgetown 10450, Malaysia
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Tony Lim
3Division of Pathology, Singapore General Hospital, Singapore 169608, Singapore
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Dongmei Lin
4Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing 100142, China
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  • Figure 1
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    Figure 1

    Timeline of selected milestones in gastric cancer testing7–12. Gastric cancer testing and diagnosis have traditionally relied on histopathologic classifications, such as the Lauren classification and conventional tumor markers (e.g., CEA and CA19-9). Since the 2010s, advancements in molecular biomarkers and targeted therapies have greatly transformed gastric cancer testing. HER2, PD-L1, and MSI/MMR are now integral to routine clinical diagnostics for gastric cancer, allowing personalized treatment strategies. Anti-CLDN 18.2 therapy has been approved in Japan. More investigations of novel biomarkers, such as MET, and diagnostic techniques are currently underway. AI, artificial intelligence; ACRG, Asian Cancer Research Group; CA19-9, carbohydrate antigen 19-9; CA72-4, carbohydrate antigen 72-4; CCD, charge coupled device; CEA, carcinoembryonic antigen; CLDN18.2, claudin 18.2; FDA, Food and Drug Administration; FGFR2, fibroblast growth factor receptor 2; HER2, human epidermal growth factor receptor 2; MSI/MMR, microsatellite instability/mismatch repair; PD-1, programmed death-1; PD-L1, programmed death-ligand 1; TCGA, The Cancer Genome Atlas; TNM, tumor-node-metastasis; WHO, World Health Organization.

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    Figure 2

    Key molecular alterations and signaling pathways involved in the pathogenesis and progression of gastric cancer, along with selected targeted therapies (adapted from Lei et al.8). Many biomarkers have been associated with gastric cancer, such as molecules in growth factors pathways and immune checkpoint control modulators. Growth factor receptors (e.g., HER2, MET, and FGFR2) typically activate essential downstream pathways through dimerization and tyrosine kinase signaling. The downstream pathways, including PI3K/AKT/mTOR, RAS/RAF/MEK/ERK, and JAK/STAT/IRF, mediate essential cellular processes, including growth, proliferation, differentiation and survival, and participate in the tumorigenesis and progression of many cancer types. DKK1 regulates the Wnt/β-catenin signaling pathway, which is also an essential pathway involved in cell proliferation, migration, and death. PD-L1 binds to PD-1 and suppresses T-cell receptor signaling, and this mechanism is commonly hijacked by cancer cells to escape immune recognition. On a related note, dysregulated expression of the MMR genes can impair cellular repair function during DNA replication, resulting in the MSI-H/dMMR phenotype. This phenotype contributes to gastric cancer through different mechanisms, including an upregulated PD-L1 expression. Likewise, EBV is associated with different oncogenic effects and it is known to increase PD-L1 expression through the JAK/STAT/IRF pathway. B7-H3 and VISTA are also immune checkpoint proteins and have been associated with immune invasion in gastric cancer. Another notable biomarker is CLDN18.2, a tight junction protein commonly expressed in differentiated gastric mucosa cells. CLDN18.2 may become more exposed when tight junctions are disrupted upon malignant transformation of gastric epithelial cells. In terms of biomarker-guided treatments for gastric cancer, trastuzumab and trastuzumab deruxtecan are recommended for patients with HER2-positivity. PD-L1-positivity and MSI-H/dMMR can guide the use of immunotherapies, such as pembrolizumab. Zolbetuximab has been approved for patients with CLDN18.2-positive disease in some countries. More targeted therapies, including savolitinib for MET-positive patients, are currently under clinical investigation. AKT, protein kinase B; APC, adenomatous polyposis coli; CLDN18.2, claudin 18.2; CKIα, casein kinase Iα; DKN-01, Dikkopf-1 monoclonal antibody 1; DKK1, Dikkopf-1; DVL, disheveled; EBV, Epstein-Bar virus; EBNA, Epstein-Bar nuclear antigen; ERK1/2, extracellular signal-regulated kinase 1/2; FGFR2, fibroblast growth factor receptor 2; HER2, human epidermal growth factor receptor 2; GSK-3β, glycogen synthase kinase 3β; IFH, interferon; IRF, interferon regulatory factor; JAK, Janus kinase; LRP5/6, low-density lipoprotein receptor-related protein 5/6; MEK1/2, mitogen-activated protein kinase 1/2; MSI-H/dMMR, microsatellite instability high/defective mismatch repair; mTOR, mammalian target of rapamycin; PD-1, programmed death-1; PD-L1, programmed death-ligand 1; PDK1, phosphoinositide-dependent protein kinase 1; PI3K, phosphoinositide 3-kinase; PIP2, phosphatidylinositol diphosphate; PIP3, phosphatidylinositol 3-phosphate; PTEN, phosphatase and tensin homolog; RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma; STAT, signaling transducer and activator of transcription; TCF/LEF T-cell factor/lymphoid enhancer factor; TSC1/2, tuberous sclerosis complex 1/2; VISTA, V-domain immunoglobulin-containing suppressor of T-cell activation.

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    Figure 3

    Challenges and future directions of biomarker testing and biomarker-guided treatment. Molecular testing and targeted therapies for gastric cancer face several important challenges: 1) Traditional tissue biopsy is invasive and costly. 2) Essential testing methods may be limited by inter-observer variability (such as for IHC) or high cost and long turnaround time (such as for NGS). 3) Lack of unified, clinically validated cut-off values may hinder the effective application of some biomarkers. 4) Tumor heterogeneity, both spatial and temporal, may affect the efficacy of biomarker-guided treatment. Novel testing techniques and treatment strategies may help overcome some of these challenges: 1) Lipid biopsy offers a non-invasive alternative to tissue biopsy. 2) AI may be employed to improve image analysis and minimize subjectivity. 3) Multiplex IHC allows the simultaneous detection of multiple biomarkers, providing a more comprehensive tumor profile. 4) Ongoing clinical trials are also exploring combination treatment to address heterogeneity and treatment resistance. AI, artificial intelligence; IHC, immunohistochemistry; NGS, next-generation sequencing.

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    Table 1

    Important clinical trials for key molecular biomarkers

    BiomarkerTrialnPatientsTesting methodBiomarker cut-off valueTreatmentEfficacy outcomes
    Median OSMedian PFSORR
    Established biomarkers
     HER2ToGA31
    Phase III
    594Gastric or GEJ cancerIHC & FISHIHC3+ or HER2:CEP17 ratio ≥ 2Trastuzumab + chemo vs. PL + chemo13.8 m vs. 11.1 m (HR = 0.74, 95% CI: 0.60–0.91, P = 0.0046)6.7 m vs. 5.5 m (HR = 0.71, 95% CI: 0.59–0.85, P = 0.0002)47% vs. 35% (P = 0.0017)
    DESTINY-Gastric 0129
    Phase II
    187Gastric or GEJ cancerIHC & ISHIHC3+ or IHC2+/ISH+Trastuzumab deruxtecan vs. chemo12.5 m vs. 8.4 m (HR = 0.59, 95% CI: 0.39–0.88, P = 0.01)5.6 m vs. 3.5 m (HR = 0.47, 95% CI: 0.31–0.71, P: NR)51% vs. 14% (P < 0.001)
    DESTINY-Gastric 0232
    Phase II
    79Gastric or GEJ cancerIHC & ISHIHC3+ or IHC2+/ISH+Trastuzumab deruxtecan12.1 m (95% CI: 9.4 m–15.4 m)5.6 m (95% CI: 4.2 m–8.3 m)42%
     PD-L1CheckMate 64933
    Phase III
    1,581Gastric or GEJ or oesophageal cancerIHCCPS ≥ 5Nivolumab + chemo vs. chemo14.4 m vs. 11.1 m (HR = 0.71, 98.4% CI: 0.59–0.86, P < 0.0001)7.7 m vs. 6.05 m (HR = 0.68, 98% CI: 0.56–0.81, P < 0.0001)60% vs. 45% (P: NR)
    KEYNOTE-85934
    Phase III
    1,579Gastric or GEJ cancerIHCCPS ≥ 1 and CPS ≥ 10Pembrolizumab + chemo vs. PL + chemoCPS ≥ 1: 13.0 m vs. 11.4 m (HR = 0.74, 95% CI: 0.65–0.84, P < 0.0001)
    CPS ≥ 10: 15.7 m vs. 11.8 m (HR = 0.65, 95% CI: 0.53–0.79, P < 0.0001)
    CPS ≥ 1: 6.9 m vs. 5.6 m (HR = 0.72, 95% CI: 0.63–0.82, P < 0.0001)
    CPS ≥ 10: 8.1 m vs. 5.6 m (HR = 0.62, 95% CI: 0.51–0.76, P < 0.0001)
    CPS ≥ 1: 52% vs. 43% (P = 0.0004)
    CPS ≥ 10: 61% vs. 43% (P < 0.0001)
     CLDN18.2SPOTLIGHT35
    Phase III
    565Gastric of GEJ cancerIHC≥ 75% of cells with moderate-to-strong membrane stainingZolbetuximab + mFOLFOX6 vs. PL + mFOLFOX618.23 m vs. 15.54 m (HR = 0.75 m, 95% CI: 0.60–0.94, P = 0.0135)10.61 m vs. 8.67 m (HR = 0.75, 95% CI: 0.60–0.94, P = 0.0066)48% vs. 48% (P: NR)
    GLOW36
    Phase III
    507Gastric or GEJ cancerIHCSame as SPOTLIGHTZolbetuximab + CAPOX vs. PL + CAPOX14.39 m vs. 12.16 m (HR = 0.771, 95% CI: 0.615–0.965, P = 0.0118)8.21 m vs. 6.80 m (HR = 0.687, 95% CI: 0.544–0.866, P = 0.0007)42.5% vs. 40.3% (P: NR)
    Exploratory biomarkers
     METVIKTORY37
    Phase II
    20†Gastric cancerNGS or FISHMET amplificationSavolitinibNRNR50%
     FGFR2FIGHT38
    Phase II
    155Gastric or GEJ cancerIHC or NGSIHC2+/3+ or amplification by ctDNA NGSBemarituzumab + mFOLFOX6 vs. PL + mFOLFOX6Not reached vs. 12.9 m (HR = 0.58, 95% CI: 0.35–0.95, P = 0.027)9.5 m vs. 7.4 m (HR = 0.68, 95% CI: 0.44–1.04, P = 0.073)47% vs. 33% (P = 0.11)
    • ↵†The VIKTORY umbrella trial assigned a total of 105 patients to different biomarker-specific trials. We only described the 20 MET-amplified patients assigned to savolitinib treatment.

    • CAPOX, capecitabine and oxaliplatin; chemo, chemotherapy; CI, confidence interval; CLDN18.2, claudin 18.2; CPS, combined positive score; FGFR2, fibroblast growth factor receptor 2; FISH, fluorescence in situ hybridisation; GEJ, gastro-oesophageal junction; HR, hazard ratio; HER2, human epidermal growth factor receptor 2; IHC, immunohistochemistry; ISH, in situ hybridization; m, month; mFOLFOX6, modified 5-fluorouracil, leucovorin and oxaliplatin; NGS, next-generation sequencing; NR, not reported; ORR, objective response rate; OS, overall survival; PD-L1, programmed death-ligand 1; PFS, progression-free survival; PL, placebo.

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    Table 2

    Summary of selected novel biomarkers for gastric cancer

    BiomarkersKey preclinical evidenceTargeted therapy in clinical trials
    DKK1
    • Promotes epithelial-to-mesenchymal transition and contributes to cisplatin resistance93

    • Promotes tumor immune invasion and impede anti-PD-1 treatment94

    • DKK1 blockade reduced the growth of human gastric cancer tumors with high DKK1 expression in a xenograft model94

    DKN-01
    • Well-tolerated in phase Ib and IIa trials95,96

    • Phase II trial ongoing (NCT04363801)96

    VISTA
    • Expression associated with PD-L1 expression97

    • Predominantly expressed on tumor-associated macrophages in gastric cancer98

    • VISTA blockade promoted T cell-medicated antitumor immunity and enhanced the efficacy of PD-1 inhibitor in ex vivo tumor inhibition assay98

    SNS-101
    • Phase I/II study ongoing (NCT05864144)99

    B7-H3
    • High level associated with low intra-tumoral CD8+ T cell density100

    • Promotes stemness characteristics to gastric cancer cells by promoting glutathione metabolism101

    • B7-H3-directed CAR-T cells showed anti-tumor effect in xenograft model102

    None identified
    Aquaporin-5
    • Specifically highly expressed by gastric cancer stem cells103

    • Coordinates with LGR5 to determine the fates of gastric cancer stem cells103

    • Overexpression associated with lymph node metastasis104

    None identified

    CAR-T cells, chimeric antigen receptor T cells; DKK1, dickkopf-1; LGR5, leucine-rich repeat-containing G protein-coupled receptor 5; PD-1, programmed death-1; PD-L1, programmed death-ligand 1; VISTA, V-domain immunoglobulin-containing suppressor of T-cell activation.

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    Advances and challenges in gastric cancer testing: the role of biomarkers
    Yu Sun, Pavitratha Puspanathan, Tony Lim, Dongmei Lin
    Cancer Biology & Medicine Mar 2025, 22 (3) 212-230; DOI: 10.20892/j.issn.2095-3941.2024.0386

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    Advances and challenges in gastric cancer testing: the role of biomarkers
    Yu Sun, Pavitratha Puspanathan, Tony Lim, Dongmei Lin
    Cancer Biology & Medicine Mar 2025, 22 (3) 212-230; DOI: 10.20892/j.issn.2095-3941.2024.0386
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    • Article
      • Abstract
      • Introduction
      • Established biomarkers in gastric cancer
      • Exploratory biomarkers in gastric cancer
      • Existing challenges and future directions of biomarker testing and biomarker-guided treatment
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