Organic photothermal agents (PTAs) with high photothermal conversion efficiency (PCE) and biocompatibility are ideal for mild photothermal therapy (PTT), which can selectively eliminate tumor cells and elicit an active immune response. However, the challenge lies in developing PTAs with high PCE, and the impact of PTT-induced temperature gradients on the cytolytic potential of natural killer (NK) cells against tumor cells has yet been investigated. Herein a novel NIR-II aggregation-induced emission (AIE) molecule named C12T-BBT is proposed by conjugating an electron donor TPA with a strong electron acceptor BBT, using a long alkyl chain (C12) substituted thiophene as π-bridge. By doing this, C12T-BBT has a relative planar structure to ensure a high extinction coefficient, while the long alkyl chain restricts the π-π interaction and provides more room for molecular motion in excited state. Together, these design strategies assure C12T-BBT with a high PCE of 84.7 %. In vivo experiments exhibit favorable NIR-II imaging and tumor elimination using water-soluble cRGD@C12T-BBT nanoparticles. The application of mild PTT results in an effective induction of NK cell response in terms of shortening its distance with tumor cells from 25.6 μm to 10.6 μm, characterized using a machine-learning based spatial analysis, thereby enhancing the efficacy of cancer therapy. Therefore, this work provides evidence for a novel combined anti-tumor strategy of aligning mild PTT and NK cell immunotherapy by illustrating crucial optimization of NK-tumor intercellular proximity in mild PTT.

The treatment paradigm for gastroesophageal cancers is evolving with immune checkpoint inhibitors (ICIs) as first-line therapy, making it crucial to understand their efficacy across patient subgroups, especially concerning PD-L1 expression. We performed a meta-analysis of Phase III randomized controlled trials targeting the effectiveness of ICIs with or without chemotherapy for advanced/metastatic HER2-negative gastroesophageal adenocarcinoma (GEA) or esophageal squamous cell carcinoma (ESCC). Kaplan-Meier (KM) curves of all-comer populations and subgroups according to reported PD-L1 cut-offs were extracted from published reports. Using KMSubtraction algorithm, unreported PD-L1 subgroup survival data were reconstructed by utilizing published KM survival curves. Thirteen first-line phase III RCTs involving 11,795 patients with GEA or ESCC were included. For GEA, ICI with or without chemotherapy showed longer OS in patients with PD-L1 combined positive score ≥1 (HR 0.77, 95 % confidence intervals [CI] 0.71-0.83 for ICI plus chemotherapy; HR 0.86, 95 %CI 0.75-1.01 for ICI alone) compared to chemotherapy alone, showing less benefits in low PD-L1 subgroups. ICI, with or without chemotherapy displayed survival benefits among PD-L1 tumor proportion score ≥1 % for ESCC (HR 0.62, 95 %CI 0.52-0.74 for ICI plus chemotherapy; HR 0.67, 95 %CI 0.54-0.84 for ICI alone) compared to chemotherapy alone. ICI combinations were similarly beneficial for Asian and global patients with GEA or ESCC. In conclusion, this meta-analysis, which includes unreported PD-L1 subgroups show benefit of ICIs with or without chemotherapy as a first-line treatment for advanced gastroesophageal cancers, particularly among patients with high PD-L1 expression.

Gastric cancer (GC) is a malignant tumor with one of the highest morbidity and death rates in the world. Neoadjuvant therapy, including neoadjuvant chemotherapy (NAC) and NAC combined with immunotherapy, can improve the resection and long-term survival rates. However, not all patients respond well to neoadjuvant therapy. It has been confirmed that immune cells in the tumor immune microenvironment, including T cells, B cells, and natural killer cells, can affect the efficacy of neoadjuvant therapy. This paper summarizes current preclinical and clinical evidence to more fully describe the effects of neoadjuvant therapy on the immune microenvironment of GC, to provide the impetus to identify biomarkers to predict the potency of neoadjuvant therapy, and to identify the mechanisms of drug resistance, which should promote the development of individualized and accurate treatments for GC patients.

Pancreatic adenosquamous cancer (PASC) is an extremely rare subtype of pancreatic cancer characterized by a poorer prognosis and higher likelihood of metastasis compared with the more prevalent pancreatic ductal adenocarcinoma (PDAC). Although genomic changes during PASC tumorigenesis have been documented, the corresponding alterations in the tumor immune microenvironment (TIME) remain inadequately elucidated. Therefore, this study aims to analyze the immune landscape of PASC by employing multiplex immunohistochemistry (mIHC) and digital image analysis.

In this study, we analyzed four independent cohorts comprising 120 patients with PASC and 386 patients with PDAC. We employed mIHC to quantify three in situ panels of immuno-oncology-related biomarkers at subcellular resolution. We then used five samples to perform laser capture microdissection, RNA sequencing, and whole-exome sequencing to explore the underlying mechanisms of the compartment-specific immune phenotypes in PASC.

Our findings revealed a more immunosuppressive TIME in PASC compared with PDAC, characterized by a decreased abundance of T cells. Immune cell types indicative of enhanced immune surveillance, including cytotoxic and memory T cells and antigen-experienced T cells, were present at significantly lower densities in PASC compared with PDAC. Conversely, some immunosuppressive macrophage phenotypes exhibited increased distribution in PASC. Immunosuppressive immune cells (ICs) were abundant, enriched within stromal regions, highly heterogeneous across tumors, and exhibited distinct distributions between squamous cell (SQC) and adenocarcinoma (ADC) compartments in PASC. Notably, the TIME of SQC compartments harbored more exhausted T cells compared with synchronous ADC compartments, indicating attenuated immune surveillance during squamous transformation. Transcriptomic profiling of microdissected SQC and ADC regions revealed immune exhaustion signatures and downregulated T-cell differentiation pathways in SQC compartments, alongside altered antigen presentation machinery and elevated tumor mutational burden, suggesting squamous-specific tumor-associated antigens with potential immunotherapeutic relevance. Beyond differences in IC density, we observed closer spatial proximity of CD45RO+ and PD-1+CD3+CD8+ T cells to tumor cells within 10, 20 and 30 µm ranges in PASC compared with PDAC, with variations by histological subregion. Furthermore, we found distinct expression patterns of the programmed cell death protein-1 (PD-1)/programmed death ligand 1 (PD-L1) and T-cell immunoreceptor with immunoglobulin and the ITIM domain (TIGIT)/CD155 axes in the PASC TIME associated with survival outcomes. Notably, TIGIT+CD8+ T cells and CD155+ CD68+macrophages, along with their proximity to tumor cells, served as independent prognostic indicators. These findings were validated in an independent cohort study.

Our study advances the understanding of PASC by providing updated insights into its immunoenvironmental features. These findings underscore the potential of targeting immune checkpoint pathways, particularly the TIGIT/CD155 and PD-1/PD-L1 axes, as a therapeutic strategy for PASC.

The high heterogeneity of tumor microenvironment (TME) is a key factor affecting immunotherapy. We integrated immunotherapy related scRNA-seq and bulk datasets to analyze the TME, mine key factors and dissect the mechanism in immunotherapy. Analyzing the cell composition of TME in cancer immunotherapy, we revealed key TME cell types, including B cells, CD8+ T cells and fibroblasts. Through cell subsets identification, pseudo-time sequence and functional status analysis of malignant cells, it was found that the functional status of malignant cell was heterogeneous under different immunotherapy conditions. Key cellular interactions in cancer immunotherapy were also revealed. Further, 16 marker genes and 6 marker cell types of cancer immunotherapy were identified, and then gene and cell model were constructed to predict the immunotherapy response of individuals, and the model were evaluated based on multiple independent validation datasets. This study can provide important guidance for improving the efficiency and understanding the mechanism of cancer immunotherapy.

Our objective was to evaluate the prognostic value of immune infiltration within the intratumoral and peritumoral tissues and to establish a novel histopathology-related immunoscore associated with postoperative colorectal cancer prognosis.

In the tissue microarrays, a total of 104 patients with colorectal cancer were enrolled and randomly assigned to the derivation cohort (n = 61) or the validation cohort (n = 43). Eighteen prognostic immune biomarkers in both intratumoral and peritumoral tissues were examined by the multiplexed immunohistochemistry method, with quantification performed through digital pathology. The histopathology-related immunoscore score was constructed using least absolute shrinkage and selection operator Cox analysis by selected immune features. On the basis of the Cox regression analysis, 3 predictive models were established. Harrell C-statistics were used to assess the performance of those models.

The area under the curve was 0.743 (confidence interval, 0.457-1.000) in the derivation cohort and 0.739 (confidence interval, 0.538-0.940) in the validation cohort. Subsequently, the groups were classified on the basis of the optimal cutoff value, with the high-risk group exhibiting a poorer prognosis. Furthermore, 3 predictive clinical models were constructed, incorporating the significant risk factors and histopathology-related immunoscore score. The first model incorporating both histopathology-related immunoscore score and statistically significant factors identified through univariate analysis demonstrated superior predictive capability for survival across all 3 models, with an area under the curve of 0.852 and C-index of 0.837.

The histopathology-related immunoscore score offers a novel means of estimating of survival in patients with colorectal cancer. These findings indicated that the immunoscore and the clinical factors might serve as complementary tools to TNM staging to improve the accuracy of patient survival prediction.

Breast cancer is the most prevalent and lethal malignancy among females, with a critical need for safer and less invasive treatments. Photodynamic therapy (PDT) can effectively eliminate tumor cells with minimal side effects. Furthermore, the combination of PDT and immunotherapy using nanoparticles has shown promise in treating both primary and distant metastatic tumor cells. Therefore, this study proposes applying the PDT-immunotherapy combination to breast cancer treatment. However, the low immunogenicity characteristic of "cold" tumors in part of breast cancer significantly diminishes therapeutic efficacy. To address this challenge, here, a nano-gel system (designated as HCSC-gel) is constructed, which co-delivers a mitochondria-targeted photosensitizer and a STING agonist, capable of robustly activating "cold" tumor immunity. This system is further enhanced by collagenase (CN) to improve therapeutic outcomes. Upon injection into the primary tumor site, HCSC-gel rapidly forms a gel matrix, releasing CN to degrade the tumor extracellular matrix and facilitate the penetration of photosensitizers, STING agonists, and oxygen into the tumor tissue. Under laser irradiation, PDT and STING-mediated immune responses are activated, reversing the low immunogenicity of breast cancer and effectively treating both primary and metastatic lesions. This HCSC-gel nano hydrogel delivery platform is anticipated to provide novel insights for the clinical management of breast cancer and other low immunogenic "cold" tumors, offering significant benefits to patients.

The tumor microenvironment (TME) plays a crucial role in the occurrence and progression of gastric cancer. Yet, we still don't understand how immune and stromal components of TMEs are modulated. In this study, we applied the ESTIMATE algorithm to calculate the number of immune and stromal components in 410 STAD cases in the Cancer Genome Atlas (TCGA) database. COX regression analysis and protein-protein interaction (PPI) network construction were used to analyze differentially expressed genes (DEGs). Then, P-selectin (SELP) was identified as a predictor by cross-analysis of univariate COX and PPI. After verifying the clinical significance of SELP for study, we performed an immune infiltration analysis and identified 54 immunomodulators associated with SELP through public data. Immunomodulation associated with gastric cancer prognosis was then confirmed by LASSO regression, and the previous results were further validated with single-cell data. Finally, we verified that SELP can promote EMT on gastric cancer cells. In conclusion, we validated that SELP may affect the biological phenotype of gastric cancer with the immune microenvironment alteration of gastric cancer.

Gastric cancer (GC) ranks as the fifth most prevalent malignant tumor and stands as the fourth leading contributor to cancer-related fatalities on a global scale. The specific link between CD2 Associated Protein (CD2AP) expression and the tumor microenvironment (TME) remains unclear, and further exploration is needed to understand its potential role in immune response and as a target for immunotherapy in GC. Utilizing RNA sequencing data acquired from The Cancer Genome Atlas (TCGA) for a pan-cancer analysis, a comprehensive evaluation was carried out to determine the expression pattern and immunological involvement of CD2AP. Systematic association of CD2AP with immunological features within the stomach adenocarcinoma (STAD) TME was subsequently performed, encompassing factors like cancer immunity cycles, immune checkpoints, immunomodulators, tumor-infiltrating immune cells (TIICs). We found that CD2AP was enhanced expression in the TME of a variety of malignancies. CD2AP contributes to forming a stromal reduced TME in GC and improve the efficacy of immunotherapy. It was observed that patients with elevated levels of CD2AP, along with high scores on their CD4, CD20, and CD57 immune markers, tended to experience the most favorable prognosis. Furthermore, an IRS was constructed to accurately assess the prognosis of STAD patients. Since CD2AP was associated with the formation of stromal reduced TME in STAD, the expression of CD2AP can improve the effect of immunotherapy of STAD. CD2AP could emerge as a novel prognostic biomarker for STAD, offering a fresh avenue for molecular targeted therapy.

Gastric cancer (GC) remains a significant clinical challenge due to its frequent late-stage diagnosis and limited treatment stratification. Telomere maintenance genes (TMGs) are crucial in GC progression, but their prognostic value has not been fully explored. This study is the first to integrate TMGs with machine learning to develop a prognostic model for GC. Using clinical and gene expression data from the TCGA database, differentially expressed genes (DEGs) were identified and intersected with TMGs. Prognostic TMGs were determined through Cox regression and machine learning techniques, including Lasso, random forest, and Xgboost algorithms. A five-gene prognostic model (CCT6A, ELOVL4, PC, PLCL1, RPS4Y1) was developed and validated using TCGA data. The model demonstrated strong predictive performance, with AUCs of 0.71, 0.71, and 0.70 at 1-, 3-, and 5-year survival, respectively. High-risk patients had significantly poorer overall survival (OS). Further analysis of the tumor microenvironment (TME) showed that high-risk patients exhibited increased immune cell infiltration, and TMG-associated pathways such as apoptosis, epithelial-mesenchymal transition (EMT), and IL6/JAK/STAT3 signaling were prominent. High EMT scores were linked to worse prognosis. In addition, the hub genes were upregulated in GC patients and cells, correlating with decreased OS. PLCL1 significantly promoted GC cell proliferation, migration, and invasion, and it also activated the inflammation-related pathways in GC. In conclusion, this study not only highlights the prognostic relevance of TMGs in GC but also underscores the clinical translation potential of the prognostic model, offering novel targets for personalized therapeutic strategies in GC.

Neoadjuvant combined immunotherapy has provided more treatment options for patients with gastric adenocarcinoma (GA). However, some GA patients, especially those with primitive enterocyte phenotype (GAPEP) show a poor response to immunotherapy, even with positive PD-L1 expression.

We enrolled multiple cohorts from our center and utilized public data to identify the predictive factors and explore the immunosuppressive features of GAPEP by multi-omics methods.

Forty-seven patients with neoadjuvant combined immunotherapy were enrolled. After testing, we found PD-L1 combined positive score (CPS) ≥ 50 in biopsy tissues was significantly associated with major pathological response (MPR) (P = 0.04). RNA testing and immunohistochemical staining highlighted the cytotoxicity-associated markers (GZMA and PRF1) as the predictors to better response. Notably, GAPEP patients demonstrated resistance to therapy and exhibited worse survival outcomes. Our own and public bulk/single-cell transcriptomic analyses identified PVR as a predictor of treatment resistance and as an important immune suppressor, especially in GAPEP. Cell interaction analyses, multiple staining, and cell experiments verified the association between GAPEP and PVR.

Cytotoxic markers, especially GZMA and PRF1, could predict the benefit of neoadjuvant combined immunotherapy in GA than PD-L1 CPS, while PVR is a negative predictor, particularly for GAPEP patients.

BACKGROUND Stomach adenocarcinoma (STAD) is a common malignant tumor, known for its poor prognosis and challenges in early detection. PANoptosis, a recently discovered form of cell death, is characterized by the integrated activation of pyroptosis, apoptosis, and/or necroptosis pathways. The connection between PANoptosis and the initiation, progression, and prognosis of gastric cancer remains inadequately investigated. MATERIAL AND METHODS Previous research has identified 19 PANoptosis-related genes (PRGs). Using these genes, we performed an in-depth analysis of gastric cancer to identify differentially expressed genes related to prognosis (PRDEGs). These differentially expressed genes were subsequently identified. We analyzed the risk scores, prognoses, and immune landscapes of the patients. Confirmed PRGs and gene clusters have been linked to cancer initiation and progression, patient survival, and immunity. Risk scores were computed, and patients were categorized into 2 groups on the basis of prognostic characteristics linked to 8 specific genes. To increase the accuracy of predicting patient survival, we developed a nomogram that integrates the risk score with various clinical characteristics. RESULTS The analysis revealed that gastric cancer patients classified into high-risk subgroups experienced reduced survival times and a diminished response to immunotherapy. We also found that risk scores demonstrated correlations with immune cell infiltration, tumor microenvironment characteristics (TME), and cancer stem cell (CSC) levels. The differential expression of GPA33 and APOD between gastric tumor and normal tissues was validated by RT-qPCR and immunohistochemical data from the Human Protein Atlas (HPA). In conclusion, our research indicates that genes linked to PANoptosis may serve as key indicators for evaluating the prognosis and survival rates of patients with gastric cancer. CONCLUSIONS This research has the potential to improve the early detection of gastric cancer and contribute to the development of more effective therapeutic approaches.

Gastric cancer (GC) represents a global health-care challenge. Recent progress in immunotherapy has elicited attracted considerable attention as a viable treatment option through modulating the host immune system and unleashing pre-existing immunity, which has profoundly revolutionized oncology, especially GC. Nonetheless, low clinical response and intrinsic and acquired resistance remain persistently challenging. The microenvironment of GC comprising multifarious stromal cell types has remarkable immunosuppressive elements that may impact the efficacy of immunotherapy. Immunosenescence is increasingly regarded as a factor that contributes to cancer development, remodels the tumor microenvironment and affects the efficacy of immunotherapy. Natural products are at the forefront of traditional medicine. Senotherapeutics is a class of drugs and natural products capable of delaying, preventing, or reversing the senescence process (i.e., senolytics) or suppressing senescence-associated secretory phenotype (i.e., senomorphics). Emerging evidence supports that natural products can improve the efficacy of existing immunotherapy and expand their indications in GC mainly based upon remodeling the immunosuppressive microenvironment and reversing immunosenescence. The review provides an integrated review of previously reported and ongoing clinical trials with immunotherapeutic regimens in GC and discusses current challenges. Next, we focus on natural compounds that exert anti-GC functions and possess immunomodulatory properties. More attention is paid to the potential of these natural compounds in modulating the immune microenvironment and immunosenescence. Lastly, we discuss the nanomedicine that can overcome the deficiencies of natural products. Altogether, our review suggests the enormous potential of natural compounds in GC immunotherapy, and provides an important direction for future research.

T cells are crucial for immunosurveillance and tumor eradication, with their dysregulation or absence in the tumor microenvironment linked to immunotherapy resistance. In lung adenocarcinoma (LUAD), this resistance is a significant barrier to effective treatment, highlighting the need for robust biomarkers and therapeutic targets to improve clinical outcomes.

T cell-related markers were identified through single-cell RNA sequencing analysis. The TCGA dataset was used for consensus clustering to define molecular subtypes associated with distinct survival outcomes and immune profiles. A T cell-related prognostic signature was developed by integrating LUAD datasets from TCGA, GSE31210, GSE50081, and GSE68465 using 10 machine learning algorithms. Further analysis linked risk scores to immune infiltration and drug sensitivity. The role of a hub gene in CD4+ T cell function and its involvement in tumor immunity was explored through in vitro experiments and molecular biology techniques.

Cluster analysis identified three LUAD subtypes, with cluster1 showing the best prognosis and immune characteristics. A Lasso + PLSRcox-based signature was a significant risk factor for predicting LUAD patient outcomes, outperforming traditional clinicopathological factors. The risk score correlated with immune microenvironment features, immune cell infiltration, and sensitivity to immunotherapy and chemotherapy. CPA3 expression was elevated in activated CD4+ T cells, particularly in Th1 cells, promoting differentiation and IFN-γ secretion. Overexpression of CPA3 enhanced tumor cell apoptosis and increased Granzyme B and IFN-γ levels, highlighting its role in immune responses.

We developed a powerful prognostic signature in LUAD that accurately predicts clinical outcomes and can guide immunotherapy and chemotherapy responses.

This review provides an in-depth exploration of the evolving role of immunotherapy in gastrointestinal (GI) cancers, with a particular focus on immune checkpoint inhibitors (ICIs) and their associated predictive biomarkers. We present a detailed analysis of established biomarkers, such as PD-L1, microsatellite instability (MSI), tumor mutational burden (TMB), and the tumor microenvironment (TME), as well as emerging biomarkers, including gut microbiota and Epstein-Barr virus (EBV). The predictive value of these biomarkers in guiding clinical decision-making and optimizing immunotherapy outcomes is thoroughly discussed. Additionally, we highlight recent advancements in biomarker evaluation technologies, including next-generation sequencing (NGS), multiplex immunohistochemistry, and artificial intelligence (AI)-driven models. These technologies are instrumental in advancing precision medicine by enhancing the accuracy and efficiency of biomarker detection and facilitating personalized treatment approaches. The integration of these predictive biomarkers with advanced detection technologies has significantly improved the clinical efficacy of immunotherapy in GI cancers by addressing challenges such as tumor heterogeneity, immune evasion, and variable patient responses. By providing a deeper understanding of tumor biology and patient-specific factors, these tools offer the potential to optimize patient selection, treatment regimens, and, ultimately, clinical outcomes. This review underscores the transformative impact of combining predictive biomarkers with cutting-edge technologies, marking a significant step forward in the field of precision oncology for GI cancer treatment.

Advancements in the management of gastric cancer (GC) and innovative therapeutic approaches highlight the significance of the role of biomarkers in GC prognosis. Machine-learning (ML)-based methods can be applied to identify the most important predictors and unravel their interactions to classify patients, which might guide prioritized treatment decisions.

A total of 140 patients with histopathological confirmed GC who underwent surgery between 2011 and 2016 were enrolled in the study. The inspired modification of the partial least squares (SIMPLS)-based model was used to identify the most significant predictors and interactions between variables. Predictive partition analysis was employed to establish the decision tree model to prioritize markers for clinical use. ML models have also been developed to predict TNM stage and different subtypes of GC. Latent class analysis (LCA) and principal component analysis (PCA) were carried out to cluster the GC patients and to find a subgroup of survivors who tended to die.

The findings revealed that the SIMPLS method was able to predict the mortality of GC patients with high predictabilities (Q2 = 0.45-0.70). The analysis identified MMP-7, P53, Ki67, and vimentin as the top predictors. Correlation analysis revealed different patterns of prognostic markers in the non-survivor and survivor cohorts and different GC subtypes. The main prediction models were verified via other ML-based analyses, with a high area under the curve (AUC) (0.84-0.99), specificity (0.82-0.99) and sensitivity (0.87-0.99). Patients were classified into three clusters of mortality risk, which highlighted the most significant mortality predictors. Partition analysis prioritizes the most significant predictors P53 ≥ 6, COX-2 > 2, vimentin > 2, Ki67 ≥ 13 in mortality of patients (AUC = 0.85-0.90).

The present study highlights the importance of considering multiple variables and their interactions to predict the prognosis of mortality and stage in GC patients through ML-based techniques. These findings suggest that the incorporation of molecular biomarkers may enhance patient prognosis compared to relying solely on clinical factors. Furthermore, they demonstrate the potential for personalized medicine in GC treatment by identifying high-risk patients for early intervention and optimizing therapeutic strategies. The partition analysis technique offers a practical tool for identifying cutoffs and prioritizing markers for clinical application. Additionally, providing Clinical Decision Support systems with predictive tools can assist clinicians and pathologists in identifying aggressive cases, thereby improving patient outcomes while minimizing unnecessary treatments. Overall, this study contributes to the ongoing efforts to improve patient outcomes by advancing our comprehension of the intricate nature of GC.

To optimize precision immunotherapy for advanced NSCLC, comprehensive tumor immune microenvironment (TIME) characterization is crucial for efficacy prediction.

Pretreatment tumor samples from 46 advanced NSCLC patients treated with PD-1/PD-L1 inhibitors were analyzed. The subregional abundance and spatial proximity scores of TIME cell subpopulations in 27 samples were assessed via multiplex immunohistochemistry (mIHC) targeting pan-CK, CD163, CD8, FoxP3, PD-1, and PD-L1. Correlations between the TIME features, clinicopathologic factors, treatment response, and prognosis were evaluated.

CD8+FoxP3+ cells were identified in NSCLC tissues, predominantly expressing PD-1/PD-L1. The PD-L1 TPS subgroups showed significant immune cell density/proximity differences, but CD8+FoxP3+PD-1+ infiltration was PD-L1 TPS-independent. Responders had higher CD8+FoxP3+PD-1high density (p = 0.0497) and proximity scores (p = 0.0099) than non-responders. The CD8+FoxP3+PD-1+ presence and tumor proximity were essential for favorable outcomes. In low-PD-L1 TPS patients, the CD8+FoxP3+PD-1+ abundance and proximity scores strongly predicted the response (AUC: 0.79 and 0.75 vs. PD-L1 TPS AUC = 0.58). A survival analysis linked the presence and proximity score of CD8+FoxP3+PD-1+ cells to prolonged overall survival (OS) and progression-free survival (PFS). Notably, a low proximity score of CD8+FoxP3+PD-1+ cells emerged as an independent risk factor for a shorter PFS (HR = 6.16, 95% CI: 2.12-17.93, p = 0.001).

The CD8+FoxP3+PD-1+ spatial proximity to tumor cells robustly predicts improved immunotherapy outcomes in advanced NSCLC.

Apatinib is a systemic therapeutic agent for advanced gastric adenocarcinoma (GAC) and gastroesophageal junction adenocarcinoma (GEJA). Its efficacy can be enhanced by applying it as a combination therapy, but the evidence supporting its combination application as a second-line treatment is not well documented. In the current study, we aimed to assess the efficacy and safety profile of apatinib, both as a monotherapy and in combination regimens, for second-line treatment of GAC and GEJA in real-world settings.

In this retrospective cohort analysis, we analyzed clinical data from 96 patients with advanced GAC or GEJA who received second-line apatinib monotherapy or combination therapy. Cox regression analysis was performed to identify prognostic factors influencing clinical outcomes of different treatment approaches (apatinib combination with other drugs).

The results indicated that the overall objective response rate (ORR) and disease control rate (DCR) for second-line apatinib therapy were 19.8% and 31.3%, respectively. The median progression-free survival (mPFS) was 4.8 months (95% CI: 4.3-6.2m), while the median overall survival (mOS) was 10.3 months (95% CI: 8.9-12.4m). Multivariable Cox regression analysis identified gender, liver metastasis, and peritoneal metastasis as independent predictors of inferior PFS and OS outcomes. In terms of safety, the primary adverse reactions included myelosuppression, elevated AST and ALT levels, hypertension, hand-foot syndrome, hyperbilirubinemia, proteinuria, fatigue, and vomiting, with a low incidence of grade 3-4 toxicities.

Apatinib-based combination therapy significantly enhances both progression-free survival and overall survival in patients with advanced gastric cancer when compared to monotherapy, while also demonstrating a safe and reliable profile.

More than 90% of advanced gastric cancers (GC) are microsatellite-stable (MSS). Compared to the high response rate of immune checkpoint inhibitors (ICI) in microsatellite-instability-high (MSI-H) GCs, only 10% of unstratified MSS GCs respond to ICIs. In this study, we apply semi-supervised learning to stratify potential ICI responders in MSS GCs, achieving high accuracy, quantified by an area under the curve of 0.924. Spatial analysis of the tumor microenvironment of ICI-sensitive GCs reveals a high level of T-bet+ CD8 + T cell infiltration in their tumor compartments. T-bet+ CD8 + T cells exhibit superior anti-tumor activity due to their increased ability to infiltrate tumors and secrete cytotoxic molecules. Adoptive transfer of T-bet+ CD8 + T cells boosts anti-tumor immunity and confers susceptibility to ICIs in immune-ignorant MSS GCs in a humanized mouse model. Spatial RNA sequencing suggests a positive-feedback loop between T-bet+ T cells and PD-L1+ tumor cells, which eventually drives T cell exhaustion and can therefore be leveraged for ICI therapy. In summary, our research provides insights into the underlying mechanism of anti-tumor immunity and deepens our understanding of varied ICI responses in MSS GCs.

Resistance to anti-PD-1 therapy remains a significant challenge in gastric cancer (GC) treatment. Here, we revealed that the USP14-IMP2-CXCL2 axis in tumor-associated macrophages (TAMs) drove resistance by recruiting myeloid-derived suppressor cells (MDSCs). Endoscopic biopsy samples were obtained from patients with inoperable GC who were candidates for anti-PD-1 therapy. Single-cell RNA sequencing (scRNA-seq) analysis showed a higher prevalence of USP14+ TAMs in therapy-resistant cases, where USP14 was linked to the immunosuppressive phenotype of TAMs. Clinically, GC samples with elevated USP14+ TAM infiltration exhibited decreased CD8+ T cell presence and increased MDSC infiltration. In vivo experiments further confirmed that USP14+ TAMs facilitated resistance to anti-PD-1 therapy in GC, reduced the infiltration of CD8+ T cells, and significantly increased the infiltration of MDSCs. In particular, USP14+ TAMs markedly enhanced the recruitment of MDSCs into the GC microenvironment through the secretion of CXCL2. Mechanistically, USP14 stabilized the m6A reader IMP2 through deubiquitination, thus enhancing CXCL2 expression and secretion. Conversely, the E3 ligase RNF40 facilitated IMP2 degradation via increasing its ubiquitination, with USP14 and RNF40 dynamically balancing IMP2's protein abundance. Furthermore, animal experiments demonstrated that targeted intervention of USP14 markedly enhanced the sensitivity of GC to anti-PD-1 therapy. This study provided a comprehensive exploration of USP14's oncogenic roles in TAMs, suggesting a novel strategy to enhance the efficacy of anti-PD-1 therapy by inhibiting the USP14/IMP2/CXCL2 signaling axis in GC.

We present the clinical results of a phase 2 trial combining neoadjuvant docetaxel, cisplatin, 5 Flourouracil, and the PD-L1 inhibitor avelumab in locally advanced gastro-esophageal adenocarcinoma (GEA). Fifty-one patients receive neoadjuvant therapy with 50 proceeding to surgery. Grade 3-4 adverse events occur in 40%; complete/major pathological response is found in 7/50 (14%) and 9/50 (18%), with 2-year disease-free survival of 67.5%. There is no correlation between tumor regression and PD-L1 or mismatch repair (MMR) status. Multiplex immunohistochemistry and longitudinal single-cell transcriptomic profiling reveal alterations in certain innate immune cell populations, particularly noting an M2-tumor-associated macrophage (M2-TAM) proliferation in non-responding tumors. These findings describe the effective nature of this treatment regimen for GEA and reveal associated features of the inflammatory milieux associated with response to chemo-immunotherapy. The specific character of the inflammatory environment in non-responders may, in the future, help personalize treatment. This study was registered at ClinicalTrials.gov (NCT03288350).

With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development of personalized treatment strategies. The application of AI in digestive oncology has demonstrated its transformative potential. AI not only alleviates the diagnostic burden on clinicians, but it improves tumor screening sensitivity, specificity, and accuracy. Additionally, AI aids the detection of biomarkers such as microsatellite instability and mismatch repair, supports intraoperative assessments of tumor invasion depth, predicts treatment responses, and facilitates the design of personalized treatment plans to potentially significantly enhance patient outcomes. Moreover, the integration of AI with multiomics analyses and imaging technologies has led to substantial advancements in foundational research on the tumor microenvironment. This review highlights the progress of AI in gastrointestinal oncology over the past 5 years with focus on early tumor screening, diagnosis, molecular marker identification, treatment planning, and prognosis predictions. We also explored the potential of AI to enhance medical imaging analyses to aid tumor detection and characterization as well as its role in automating and refining histopathological assessments.

One characteristic that makes gastric cancer (GC) against other cancers is the intricate immune system's reaction, particularly to tenacious inflammation. Consequently, the immunological function is essential to the growth of this malignancy. Tumor immunotherapy has yielded several encouraging outcomes, but despite this, different patients continue to not respond to treatment, and a far larger number become resistant to it. Also, activated CAR-T cells express a majority of immunological checkpoint factors, containing PD1, CTLA4, and LAG3, which counteracts the anti-tumor actions of CAR-T cells. Moreover, cytokine release syndrome is one of the possible adverse responses of CAR-T cell therapy. Therefore, producing universal allogeneic T lymphocytes with potent anti-tumor activity is essential. This study demonstrates current research on this cutting-edge technology, including the composition and mode of action of CAR-NK and CAR-T cells in GC. Also, in this study, we examined recent studies about various specific GC biomarkers that target CAR-T cells and CAR-NK cells.

Advances in the identification of molecular biomarkers and the development of targeted therapies have enhanced the prognosis of patients with advanced gastric cancer. Several established biomarkers have been widely integrated into routine clinical diagnostics of gastric cancer to guide personalized treatment. Human epidermal growth factor receptor 2 (HER2) was the first molecular biomarker to be used in gastric cancer with trastuzumab being the first approved targeted therapy for HER2-positive gastric cancer. Programmed death-ligand 1 positivity and microsatellite instability can guide the use of immunotherapies, such as pembrolizumab and nivolumab. More recently, zolbetuximab has been approved for patients with claudin 18.2-positive diseases in some countries. More targeted therapies, including savolitinib for MET-positive patients, are currently under clinical investigation. However, the clinical application of these diagnostic approaches could be hampered by many existing challenges, including invasive and costly sampling methods, variability in immunohistochemistry interpretation, high costs and long turnaround times for next-generation sequencing, the absence of standardized and clinically validated diagnostic cut-off values for some biomarkers, and tumor heterogeneity. Novel testing and analysis techniques, such as artificial intelligence-assisted image analysis and multiplex immunohistochemistry, and emerging therapeutic strategies, including combination therapies that integrate immune checkpoint inhibitors with targeted therapies, offer potential solutions to some of these challenges. This article reviews recent progress in gastric cancer testing, outlines current challenges, and explores future directions for biomarker testing and targeted therapy for gastric cancer.

Nowadays, gastric cancer has become a significant issue in the global cancer burden, and its impact cannot be ignored. The rapid development of artificial intelligence technology is attempting to address this situation, aiming to change the clinical management landscape of gastric cancer fundamentally. In this transformative change, machine learning and deep learning, as two core technologies, play a pivotal role, bringing unprecedented innovations and breakthroughs in the diagnosis, treatment, and prognosis evaluation of gastric cancer. This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. Although most of the current AI-based models have not been widely used in clinical practice, with the continuous deepening and expansion of precision medicine, we have reason to believe that a new era of AI-driven gastric cancer care is approaching.

Gastric cancer ranks fifth as the most common cancer and third as the leading cause of death worldwide. Risk factors include advancing age, low-fiber diets, high salt intake and Helicobacter pylori infection. Diagnosis relies on histological examination following endoscopic biopsy with staging accomplished through various imaging modalities. Early gastric cancer is primarily managed via endoscopic resection, while non-early operable cases typically undergo surgery. Advanced cases are addressed through sequential chemotherapy lines, with initial treatment usually comprising a platinum and fluoropyrimidine combination. Linitis plastica (LP) is a rare, aggressive form of gastric cancer characterized by diffuse infiltration of the gastric wall, resulting in poor outcomes even after curative resection. The absence of a standardized definition contributes to uncertainty regarding the precise incidence of these tumors. LP is often diagnosed at advanced stages, with a reported median survival rate of approximately 4%-29%, despite "curative resection". Its distinctive biological behavior includes perineural invasion, nodal metastasis, and peritoneal dissemination. The bleak prognosis for LP patients partly stems from delayed diagnosis and its aggressive biological nature, posing significant challenges for clinical management. Currently, no specialized treatment strategy exists for LP, and clinical approaches typically align with those used for general gastric cancer treatment. Surgical resection is the primary treatment, but the optimal surgical approach remains contentious. Recent studies have investigated the efficacy of neoadjuvant chemotherapy and radiotherapy in improving survival outcomes for LP patients. However, controversies persist regarding the role of adjuvant chemotherapy and postoperative radiotherapy. LP requires a multidisciplinary approach and personalized treatment strategies tailored to each patient's condition. Further research is needed to elucidate optimal therapeutic interventions and improve outcomes for LP patients.

To evaluate the efficacy and safety of KN026, a novel bispecific HER2 (ECD2 and ECD4) antibody, plus KN046, a PD-L1, and CTLA4 bispecific antibody, in patients with advanced HER2-positive solid tumors. We conducted two sequentially designed phase Ib and II studies with similar target populations and evaluation schedules. The primary endpoints included safety, maximum tolerated dose (MTD), the recommended phase II dose (RP2D) for the phase Ib study, and the objective response rate (ORR) and duration of response (DoR) for the phase II study. Hereby, we solely report the results from 113 nonbreast cancer patients. In phase Ib, MTD was not reached. Dose 3 was confirmed to be acceptable for the phase II study. An objective response has been exclusively observed in HER2-positive patients. Any grade treatment-related adverse events (TRAEs) were reported in 108 (95.6%) patients. The most common TRAEs were infusion reactions (38.9%), anemia (37.2%), elevated AST (31.0%), and diarrhea (30.1%). Among the 108 patients evaluated for efficacy, the overall ORR was 55.6% (95%CI, 45.7%, 65.1%). In the HER2-positive GC subgroup, 38 patients received this regimen as the 1st-line treatment and 30 patients achieved an objective response, with an ORR of 78.9% (95%CI, 62.7%, 90.4%). Among 27 pretreated patients, the ORR was 44.4% (95%CI, 25.5%, 64.7%). In the other HER2-positive solid tumor subgroup (n = 34), the ORR was 52.9% (95%CI 35.1%,70.2%). Thus, KN026 plus KN04 exhibits promising efficacy and acceptable safety profiles in HER2-positive nonbreast cancer, as does the 1st-line treatment for GC.

Immunotherapy has surfaced as a promising therapeutic modality for gastric cancer (GC). A comprehensive review of advancements, current status, and research trends in GC immunotherapy is essential to inform future investigative efforts.

To delineate the trends, advancements, and focal points in immunotherapy for GC.

We performed a bibliometric analysis of 2906 articles in English concerning GC immunotherapy published from 2000 to December 20, 2023, indexed in the Web of Science Core Collection. Data analysis and visualization were facilitated by CiteSpace (6.1.6R), VOSviewer v.1.6.17, and GraphPad Prism v8.0.2.

There has been an increase in the annual publication rate of GC immunotherapy research. China leads in publication volume, while the United States demonstrates the highest citation impact. Fudan University is notable for its citation frequency and publication output. Co-citation analysis and keyword frequency revealed and highlighted a focus on GC prognosis, the tumor microenvironment (TME), and integrative immunotherapy with targeted therapy. Emerging research areas include gastroesophageal junction cancer, adoptive immunotherapy, and the role of Treg cell in immunotherapy.

GC immunotherapy research is an expanding field attracting considerable scientific interest. With the clinical adoption of immunotherapy in GC, the primary goals are to enhance treatment efficacy and patient outcomes. Unlike hematological malignancies, GC's solid TME presents distinct immunological challenges that may attenuate the cytotoxic effects of immune cells on cancer cells. For instance, although CAR-T therapy is effective in hematological malignancies, it has underperformed in GC settings. Current research is centered on overcoming immunosuppression within the TME, with a focus on combinations of targeted therapy, adoptive immunotherapy, Treg cell dynamics, and precise prognosis prediction in immunotherapy. Additionally, immunotherapy's role in treating gastroesophageal junction cancer has become a novel research focus.

To systematically evaluate the efficacy and safety of combination therapy with toripalimab in the treatment of advanced gastric cancer (GC).

We conducted a thorough search for relevant studies in PubMed, Embase, Cochrane Library, and Web of Science. Effect estimates were computed utilizing Stata software (version 14.0) and either random or fixed effects models, as applicable. A subgroup analysis was undertaken to assess the effect of various combination therapies on overall response rate (ORR). Begg and Egger's tests were employed to assess publication bias.

The study consisted of 8 trials, which included 277 participants with advanced gastric cancer. The overall ORR was 41.4% (95% CI, 32.4%-50.3%), with a disease control rate (DCR) of 83.6% (95% CI, 74.6%-92.7%), a median overall survival (mOS) of 11.0 months (95% CI, 9.6-12.4), and a median progression-free survival (mPFS) of 4.2 months (95% CI, 2.5-6.0) for the combination therapy with toripalimab. Subgroup analysis revealed that the combination of toripalimab and chemotherapy achieved a greater ORR compared to the non-chemotherapy group, with ORR rates of 49.8% (95% CI, 42.2%-57.4%) and 31.9% (95% CI, 26.7%-37.1%), respectively. The combination therapy with toripalimab led to adverse events (AEs) of any grade at 94.0% of cases (95% CI, 89.5%-98.5%) and grade 3 AEs at 32.4% (95% CI, 17.8%-47.1%). The sensitivity analysis indicated that no single study affected the overall results.

Combination therapy of toripalimab can improve clinical efficacy, although with increased but manageable toxicity. Additional clinical trials are required to assess comprehensively the efficacy and safety of alternative toripalimab regimens. The review agreement has been recorded with PROSPERO (CRD42024585696).

Alpha-fetoprotein-producing gastric or gastro-esophageal junction (AFP-G/GEJ) cancer, a rare gastric cancer subtype, exhibits increased angiogenesis and more immunosuppression than non-AFP-G/GEJ cancer. The potential benefits of anti-angiogenic agents and immunotherapy for this specific subtype remain unknown. This multi-center, single-arm, phase 2 trial (ClinicalTrials.gov NCT04609176) evaluated the antitumor activity, safety, and biomarkers of camrelizumab plus apatinib and S-1 and oxaliplatin (SOX), followed by maintenance treatment with camrelizumab plus apatinib, as a first-line treatment in patients with AFP-G/GEJ adenocarcinoma. Primary endpoint was the confirmed objective response rate (ORR) per RECIST v1.1 in the full analysis set. Secondary endpoints were disease control rate (DCR), progression-free survival (PFS), overall survival (OS), duration of response, time to response, and safety. Between December 4, 2020, and August 4, 2023, 36 patients were enrolled and treated. The trial met its primary endpoint with a confirmed ORR of 66.7% (95% CI: 49.0-81.4). The DCR was 88.9% (95% CI: 73.9-96.9). With a median follow-up of 11.7 months (range: 3.2-37.9), the median PFS reached 7.8 months (95% CI: 4.9-12.3) and the median OS reached 18.0 months (95% CI: 10.5-NR). No new safety concerns were identified. In exploratory analysis, patients with durable clinical benefit exhibited higher pre-treatment (PD-1+) CD8+ T cell densities and effective scores. First-line treatment with camrelizumab plus apatinib and SOX, followed by maintenance treatment with camrelizumab plus apatinib, is effective and safe in AFP-G/GEJ adenocarcinoma. Further studies are necessary to validate these findings.

Disulfidptosis is a newly discovered form of cell death, and long non-coding RNAs (lncRNAs) play a crucial role in tumor cell growth, migration, recurrence, and drug resistance, particularly in bladder cancer (BLCA). This study aims to investigate disulfidptosis-related lncRNAs (DRLs) as potential prognostic markers for BLCA patients.

Utilizing single-cell sequencing data, RNA sequencing data, and corresponding clinical information sourced from the GEO and TCGA databases, this study conducted cell annotation and intercellular communication analyses to identify differentially expressed disulfide death-related genes (DRGs). Subsequently, Pearson correlation and Cox regression analyses were employed to discern DRLs that correlate with overall survival. A prognostic model was constructed through LASSO regression analysis based on DRLs, complemented by multivariate Cox regression analysis. The performance of this model was rigorously evaluated using Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC). Furthermore, this investigation delved into the potential signaling pathways, immune status, tumor mutation burden (TMB), and responses to anticancer therapies associated with varying prognoses in patients with BLCA.

We identified twelve differentially expressed DRGs and elucidated their corresponding intercellular communication relationships. Notably, epithelial cells function as ligands, signaling to other cell types, with the interactions between epithelial cells and both monocytes and endothelial cells exhibiting the strongest connectivity. This study identified six DRLs in BLCA-namely, C1RL-AS1, GK-AS1, AC134349.1, AC104785.1, AC011092.3, and AC009951.6, and constructed a nomogram to improve the predictive accuracy of the model. The DRL features demonstrated significant associations with various clinical variables, diverse immune landscapes, and drug sensitivity profiles in BLCA patients. Furthermore, RT-qPCR validation confirmed the aberrant expression levels of these DRLs in BLCA tissues, affirming the potential of DRL characteristics as prognostic biomarkers.

We established a DRLs model that serves as a predictive tool for the prognosis of BLCA patients, as well as for assessing tumor mutation burden, immune cell infiltration, and responses to immunotherapy and targeted therapies. Collectively, this study contributes valuable insights toward advancing precision medicine within the context of BLCA.

Gastric cancer (GC) is one of the most common types of clinically malignant tumors and a global health challenge due to its high mortality and poor prognosis. The coagulation cascade is closely related to GC and plays a key role in the tumor immune microenvironment. However, the specific mechanisms by which coagulation-related genes involved in the occurrence and development of GC remains unclear. The data of GC patients and coagulation-related genes were obtained from the TCGA and the GSEA databases, respectively. After univariate Cox regression analysis, the non-negative matrix factorization method was used to identify coagulation-related molecular subtypes. GC patients were categorized into high-risk and low-risk score groups based on median risk scores, which included six genes (PCDHAC1, HABP2, GPC3, GFRA1, F5, and DKK1). There was a significant difference in survival between the two groups, and the predictive abilities for 1-, 3-, and 5-year survival were valid. Here, we demonstrated that coagulation-related gene signatures are valuable in predicting the survival of GC patients. Besides, the high- and low-risk grouping also better reflects the status of tumor mutation burden and the characteristics of tumor immune infiltration in GC, which provides a theoretical basis for individualized chemotherapy and immunotherapy for GC patients.

Metastasis and recurrence are the primary obstacles to long-term survival in colorectal cancer (CRC) patients. In this study, we employed single-cell RNA sequencing (scRNA-seq) to comprehensively delineate the transcriptomic landscape of primary and liver metastatic CRCs, and revealed novel cellular crosstalk between cancer cell subpopulation and myofibroblastic CAFs (myCAFs) at single-cell resolution. We identified a cancer cell subpopulation termed stem/transient amplifying-like (stem/TA-like) cells, which expressed genes associated with stem cell-like characteristics and metastatic potential. MyCAFs in their microenvironment showed the potential to remodel the extracellular matrix (ECM), regulate angiogenesis, and support a pro-metastatic microenvironment through paracrine signaling involving FN1, BGN, and other ECM components. Notably, we found that they may communicate through the ligand-receptor pairs FN1-CD44 and GDF15-TGFBR2, which may be linked to the liver metastatic process. Additionally, our findings suggest that both stem/TA-like cells and myCAFs could be involved in CRC recurrence following chemotherapy. A unique gene signature generated using the gene expression characteristics of stem/TA-like cells and myCAFs (SM signature) can be used to assess recurrence risk in CRC patients. Collectively, these findings highlight the intratumor heterogeneity and the potential roles of cancer stem cells and myCAFs in CRC liver metastasis and recurrence, providing new targets and insights for the prognostic assessment of CRC patients and the improved selection of effective treatment options.

FAP-positive cancer-associated fibroblasts (CAFs), recognized as a critical subset of CAFs, have been implicated in fostering an immunosuppressive tumor microenvironment in various cancers. However, their potential mechanisms of immunosuppression, particularly in modulating T cells, remain elusive. In this study, multiple internal cohorts consisting of 328 patients as well as 5 external cohorts were integrated to delineate the association between unfavorable prognosis or therapeutic resistance and FAP+ CAFs in gastric cancer patients. Subsequently, using in vivo mice models and in vitro co-culture system, we found that elevated infiltration levels of FAP+ CAF exacerbated immunosuppression in the tumor microenvironment by facilitating CD8+ T cells dysfunction. Mechanistically, FAP impeded the degradation of STAT1 protein in CAFs, thereby sustaining PD-L1 transcription and fostering T cell exhaustion. Treatment with PD-L1 neutralizing antibodies effectively attenuated FAP-mediated immunosuppression, restoring anti-tumor immunity of T cells. Overall, our findings underscore the vital role of FAP+ CAFs in directly suppressing T cell-mediated anti-tumor immunity via PD-L1 upregulation, paving the way for the development of FAP-targeted therapies in clinical settings.