Antibodies play a crucial role in defending the human body against diseases, including life-threatening conditions like cancer. They mediate immune responses against foreign antigens and, in some cases, self-antigens. Over time, antibody-based technologies have evolved from monoclonal antibodies (mAbs) to chimeric antigen receptor T cells (CAR-T cells), significantly impacting biotechnology, diagnostics, and therapeutics. Although these advancements have enhanced therapeutic interventions, the integration of artificial intelligence (AI) is revolutionizing antibody design and optimization. This review explores recent AI advancements, including large language models (LLMs), diffusion models, and generative AI-based applications, which have transformed antibody discovery by accelerating de novo generation, enhancing immune response precision, and optimizing therapeutic efficacy. Through advanced data analysis, AI enables the prediction and design of antibody sequences, 3D structures, complementarity-determining regions (CDRs), paratopes, epitopes, and antigen–antibody interactions. These AI-powered innovations address longstanding challenges in antibody development, significantly improving speed, specificity, and accuracy in therapeutic design. By integrating computational advancements with biomedical applications, AI is driving next-generation cancer therapies, transforming precision medicine, and enhancing patient outcomes. Graphical Abstract

Gastric cancer (GC) is one of the most frequent malignant diseases in the elderly. Systemic chemotherapy showed an improvement of quality of life and survival benefit compared to supportive care alone in patients with advanced GC. Because comorbidities or age-related changes in pharmacokinetics and pharmacodynamics may lead to higher toxicity, however, many oncologists hesitate to recommend elderly patients to receive chemotherapy.

Eun Suk Jung, Sang Pyo Lee, Sea Hyub Kae, Jung Han Kim, Hyeong Su Kim, and Hyun Joo Jang. Gut and Liver 2021;15:732-41. https://doi.org/10.5009/gnl20249

The von Hippel-Lindau (VHL) gene is inactivated frequently in sporadic clear-cell renal cell carcinomas (ccRCCs) by genetic alteration (mutation, loss of heterozygosity, or promoter hypermethylation). However, the pathological or prognostic significance of VHL gene alteration has not been well defined. We conducted this meta-analysis to evaluate the association between VHL alteration and clinopathologic findings in ccRCCs. We performed a systematic computerized search of online databases, including PubMed, EMBASE, Web of Science, and Google Scholar (up to July 2018). From ten studies, 1,082 patients were included in the pooled analyses of odds ratios (ORs) with 95% confidence intervals (CIs) for pathological features (nuclear grade and disease stage) or hazard ratios (HRs) with 95% CIs for overall survival (OS). VHL alteration was not significantly associated with nuclear grade (OR = 0.79, 95% CI: 0.59–1.06, p = 0.12) or disease stage (OR = 1.07, 95% CI: 0.79–1.46, p = 0.65). There was also no significant correlation between VHL alteration and OS (HR = 0.75, 95% CI: 0.43–1.29, p = 0.30). When we pooled HRs for OS according to the VHL alteration types, the combined HRs were 0.72 (95% CI: 0.47–1.11, p = 0.14) for VHL mutations and 1.32 (95% CI: 0.70–2.47, p = 0.39) for methylation. In conclusion, this meta-analysis indicates that VHL gene alteration is not significantly associated with the pathological features and survival in patients with ccRCC.

The addition of bevacizumab in the first-line treatment for metastatic colorectal cancer: an updated meta-analysis of randomized trials The addition of bevacizumab in the first-line treatment for metastatic colorectal cancer: an updated meta-analysis of randomized trials

Purpose: CD44 isoforms are highly expressed in cancer stem cells, initiating tumor growth and sustaining tumor self-renewal. Among these isoforms, CD44 variant 9 (CD44v9) is overexpressed in chronic inflammation-induced cancer. CD44 and the mesenchymal-to-epithelial transition (MET) receptor tyrosine kinase are coactivated in some gastric cancers (GCs). In this study, we characterized MET and CD44 expression and signaling in human GC cell lines and analyzed differences in the susceptibility of these lines to foretinib. Patients and Methods: We analyzed cell viability and the rate of apoptotic cells using MTS assays and flow cytometry, respectively. Gene and protein expression were assessed by quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and immunoblotting, respectively. Results: Foretinib treatment resulted in dose-dependent inhibition of growth in c-MET-amplified MKN45 and SNU620 cells with concomitant induction of apoptosis, but not in c-MET-reduced MKN28 and AGS cells. Foretinib treatment also significantly reduced phosphor-c-MET, phosphor-AKT, beta-catenin, and COX-2 protein expression in MKN45 and SNU620 cells. Interestingly, foretinib significantly reduced CD44, CD44v9, COX-2, OCT3/4, CCND1, c-MYC, VEGFA, and HIF-1a gene expression in CD44 and MET coactivated MKN45 cells and increased CD44s gene expression; in contrast, these drugs were only slightly active against SNU620 cells. Conclusion: The results of this study indicate that foretinib could be a therapeutic agent for the prevention or treatment of GCs positive for CD44v9 and c-MET.