Overcoming immunotherapy resistance in gastric cancer: insights into mechanisms and emerging strategies

Cell Death and Disease, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 7, 2025

Abstract Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, with limited treatment options in advanced stages. Immunotherapy, particularly immune checkpoint inhibitors (ICIs) targeting PD1/PD-L1, has emerged as promising therapeutic approach. However, significant proportion patients exhibit primary or acquired resistance, limiting the overall efficacy immunotherapy. This review provides comprehensive analysis mechanisms underlying immunotherapy resistance GC, including role tumor microenvironment, dynamic PD-L1 expression, compensatory activation other checkpoints, and genomic instability. Furthermore, explores GC-specific factors such molecular subtypes, unique evasion mechanisms, impact Helicobacter pylori infection. We also discuss emerging strategies to overcome combination therapies, novel immunotherapeutic approaches, personalized based on genomics microenvironment. By highlighting these key areas, this aims inform future research directions clinical practice, ultimately improving outcomes for GC undergoing

Language: Английский

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Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 30, 2025

Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and lack of highly precise treatment options. We downloaded the multiomics data STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microRNA, long non-coding RNA, somatic mutation, DNA methylation data, from sxdyc website. synthesized using 10 clustering methods, construct consensus machine learning-driven signature (CMLS)-related prognostic models by combining learning evaluated prognosis C-index. relationship between CMLS was assessed Kaplan-Meier curves, independent value determined univariate multivariate regression analyses. we also immune characteristics, immunotherapy response, drug sensitivity different groups. results analysis classified into three subtypes, CS1 resulting best survival outcome. In total, hub genes (CES3, AHCYL2, APOD, EFEMP1, CYP1B1, ASPN, CPE, CLIP3, MAP1B, DKK1) were screened constructed significantly correlated an factor for STAD. Using risk score, all divided group low group. Patients low-CMLS had better survival, more enriched cells, higher tumor mutation load scores, suggesting responsiveness possible "hot tumor" phenotype. high-CMLS poorer less sensitive to but likely benefit chemotherapy targeted therapy. this study, methods combined analyze STAD, classify CMLS-related model features, are important accurate management effective

Language: Английский

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Establishment of panapoptotic gene signatures of platinum-chemotherapy to predict the response of chemotherapeutic drug resistance in gastric cancer

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Purpose Gastric cancer (GC) remains a daunting problem because of its inherent resistance to chemotherapy, particularly platinum-based medicines. This work was undertaken discover the molecular foundations involvement PANoptosis-related genes (PANRGs) in platinum-chemotherapy for GC. Methods A comprehensive bioinformatics analysis GC conducted dataset GSE66229 from Tumor Cancer Genome Atlas (TCGA). The RNA sequencing data were normalized, and differential expression performed identify PANRGs that distinguish platinum-sensitive from-resistant Subsequent GO functional KEGG pathway analyses elucidate biological relevance these genes. Furthermore, prognostic model constructed predict survival outcomes patients utilizing identified PANRGs. Chemotherapeutic drug sensitivity using Drug Sensitivity Genomics (GDSC) database. Results yielded 18 significantly differentially expressed platinum-resistant comparing GC, which includes upregulated genes, CASP9, CHMP6, BAG3, EYA2, HSPB1, SHH, SLC9A3R1, SMAD3, FTH1, downregulated TP53, ADORA1, CAAP1, CHEK2, DAP3, INHBA, URI1, YWHAH, XIAP. These enriched processes pathways associated with cell cycle, apoptosis, platinum resistance. Based on expressions DAP3 XIAP single factor analysis, accurately stratified into high- low-risk groups, distinct differences identified. verified an independent GEO dataset, demonstrating resilience generalizability. AZD6738, Dihydrorotenone, Paclitaxel, MK-1775, Osimertinib, Ulixertinib, AZD2014, Cytarabine, PD0325901, Wee1 inhibitors top ten chemotherapeutic medicines (comparison IC50 between P < 0.05). Conclusion finding underscores pivotal role PANoptosis modulating evaluated as models.

Language: Английский

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Comparison of immune checkpoint inhibitors in combination with chemotherapy versus chemotherapy alone in the first-line treatment of advanced gastric cancer patients with low PD-L1 expression: a systematic review and meta-analysis

Therapeutic Advances in Medical Oncology, Journal Year: 2025, Volume and Issue: 17

Published: May 1, 2025

Background: Immune checkpoint inhibitors (ICIs) + chemotherapy became standard her2-GC first line treatment. Objectives: The aim of this study is to investigate whether ICIs chemo provides benefit for patients with low programmed death-ligand 1 (PD-L1) expression. Design: This a systematic review and meta-analysis. Data sources: We searched PubMed, Embase, Web Science, Cochrane Library as well the 2019 2024 Annual Meetings European Society Medical Oncology, American Association Cancer Research, Clinical Oncology (ASCO), ASCO Symposium on Gastrointestinal (ASCO-GI) ClinicalTrials.gov database. Methods: included phase III randomized controlled trials comparing first-line immunotherapy combined versus alone in advanced gastric cancer. KMSubtraction was used estimate survival data those that did not report PD-L1 low-expression population. Results: total nine clinical trials. In positive score (CPS) < CPS 5, monoclonal antibody show an improvement overall (OS) or progression-free (PFS) (CPS OS: hazard ratio (HR) = 0.91, 95% CI: 0.77–1.08; PFS: HR 0.88, 0.73–1.07. 5 0.92, 0.79–1.08; 0.78, 0.53–1.14). However, using dual antibodies, achieved improvements PFS (HR 0.64, 0.52–0.80). tumor area positivity (TAP) scoring, subgroup TAP 5% achieve benefits OS from plus (OS: 0.75–1.13; 0.74–1.13). Conclusion: Our results indicate treatment cancer, does provide compared it noteworthy COMPASSION-15 trial, significant PFS, which may be related bispecific antibodies needs validated by further studies. Trial registration: registered PROSPERO (CRD42024568972).

Language: Английский

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AI-Powered Insights into Drug Resistance in Gastric Cancer: A Path Toward Precision Therapy

Deleted Journal, Journal Year: 2025, Volume and Issue: 24(1)

Published: May 25, 2025

Context: Gastric cancer (GC) is a major global health burden, with drug resistance representing critical barrier to effective treatment. Understanding the mechanisms underlying and leveraging advanced technologies, such as artificial intelligence (AI), are essential for developing innovative therapeutic strategies. Evidence Acquisition: This review systematically examines primary of in GC, organized into eight categories: Reduced uptake, enhanced efflux, impaired pro-drug activation or increased inactivation, molecular target alterations, DNA damage repair, imbalance apoptotic regulation, tumor microenvironment modifications, phenotypic changes. Additionally, role AI addressing these challenges explored, focus on omics-driven insights, pathway analysis, biomarker discovery, modeling drug-response relationships. Results: The highlights transformative potential advancing precision therapy GC. Key applications include stratification, optimization combinations, adaptive design, integration clinical workflows. Challenges data quality, model interpretability, need interdisciplinary collaboration identified, along strategies address barriers. Future directions emphasize development explainable models, multi-omics real-time patient data, AI-driven discovery targeting pathways. Conclusions: By bridging research practice, offers promising path more effective, personalized, Overcoming existing AI's can significantly improve treatment outcomes pressing issue

Language: Английский

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Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy

Functional & Integrative Genomics, Journal Year: 2024, Volume and Issue: 24(5)

Published: Oct. 1, 2024

Language: Английский

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