Predictive test for chemotherapy response in resectable gastric cancer: a multi-cohort, retrospective analysis

Deep Learning Histology for Prediction of Lymph Node Metastases and Tumor Regression after Neoadjuvant FLOT Therapy of Gastroesophageal Adenocarcinoma

BACKGROUND

The aim of this study was to establish a deep learning prediction model for neoadjuvant FLOT chemotherapy response. The neural network utilized clinical data and visual information from whole-slide images (WSIs) of therapy-naïve gastroesophageal cancer biopsies.

METHODS

This study included 78 patients from the University Hospital of Cologne and 59 patients from the University Hospital of Heidelberg used as external validation.

RESULTS

After surgical resection, 33 patients from Cologne (42.3%) were ypN0 and 45 patients (57.7%) were ypN+, while 23 patients from Heidelberg (39.0%) were ypN0 and 36 patients (61.0%) were ypN+ (p = 0.695). The neural network had an accuracy of 92.1% to predict lymph node metastasis and the area under the curve (AUC) was 0.726. A total of 43 patients from Cologne (55.1%) had less than 50% residual vital tumor (RVT) compared to 34 patients from Heidelberg (57.6%, p = 0.955). The model was able to predict tumor regression with an error of ±14.1% and an AUC of 0.648.

CONCLUSIONS

This study demonstrates that visual features extracted by deep learning from therapy-naïve biopsies of gastroesophageal adenocarcinomas correlate with positive lymph nodes and tumor regression. The results will be confirmed in prospective studies to achieve early allocation of patients to the most promising treatment.

Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients

PURPOSE

Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients.

METHODS

This multi-center study retrospectively included 322 patients diagnosed with gastric cancer from January 2013 to June 2023 at two hospitals. Handcrafted radiomics technique and the EfficientNet V2 neural network were applied to arterial, portal venous, and delayed phase CT images to extract two-dimensional handcrafted and deep learning features. A nomogram model was built by integrating the handcrafted signature, the deep learning signature, with clinical features. Discriminative ability was assessed using the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve. Model fitting was evaluated using calibration curves, and clinical utility was assessed through decision curve analysis (DCA).

RESULTS

The nomogram exhibited excellent performance. The area under the ROC curve (AUC) was 0.848 [95% confidence interval (CI), 0.793-0.893)], 0.802 (95% CI 0.688-0.889), and 0.751 (95% CI 0.652-0.833) for the training, internal validation, and external validation sets, respectively. The AUCs of the P-R curves were 0.838 (95% CI 0.756-0.895), 0.541 (95% CI 0.329-0.740), and 0.556 (95% CI 0.376-0.722) for the corresponding sets. The nomogram outperformed the clinical model and handcrafted signature across all sets (all P < 0.05). The nomogram model demonstrated good calibration and provided greater net benefit within the relevant threshold range compared to other models.

CONCLUSION

This study created a deep learning nomogram using CECT images and clinical data to predict NAC response in LAGC patients undergoing surgical resection, offering personalized treatment insights.

RNA expression of 6 genes from metastatic mucosal gastric cancer serves as the global prognostic marker for gastric cancer with functional validation

BACKGROUND

Molecular analysis of advanced tumors can increase tumor heterogeneity and selection bias. We developed a robust prognostic signature for gastric cancer by comparing RNA expression between very rare early gastric cancers invading only mucosal layer (mEGCs) with lymph node metastasis (Npos) and those without metastasis (Nneg).

METHODS

Out of 1003 mEGCs, all Npos were matched to Nneg using propensity scores. Machine learning approach comparing Npos and Nneg was used to develop prognostic signature. The function and robustness of prognostic signature was validated using cell lines and external datasets.

RESULTS

Extensive machine learning with cross-validation identified the prognostic classifier consisting of four overexpressed genes (HDAC5, NPM1, DTX3, and PPP3R1) and two downregulated genes (MED12 and TP53), and enabled us to develop the risk score predicting poor prognosis. Cell lines engineered to high-risk score showed increased invasion, migration, and resistance to 5-FU and Oxaliplatin but maintained sensitivity to an HDAC inhibitor. Mouse models after tail vein injection of cell lines with high-risk score revealed increased metastasis. In three external cohorts, our risk score was identified as the independent prognostic factor for overall and recurrence-free survival.

CONCLUSION

The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer.

Exploring the current landscape of single-cell RNA sequencing applications in gastric cancer research

Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer-related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA-seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA-seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA-seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice.

New clinical trial design in precision medicine: discovery, development and direction

In the era of precision medicine, it has been increasingly recognized that individuals with a certain disease are complex and different from each other. Due to the underestimation of the significant heterogeneity across participants in traditional "one-size-fits-all" trials, patient-centered trials that could provide optimal therapy customization to individuals with specific biomarkers were developed including the basket, umbrella, and platform trial designs under the master protocol framework. In recent years, the successive FDA approval of indications based on biomarker-guided master protocol designs has demonstrated that these new clinical trials are ushering in tremendous opportunities. Despite the rapid increase in the number of basket, umbrella, and platform trials, the current clinical and research understanding of these new trial designs, as compared with traditional trial designs, remains limited. The majority of the research focuses on methodologies, and there is a lack of in-depth insight concerning the underlying biological logic of these new clinical trial designs. Therefore, we provide this comprehensive review of the discovery and development of basket, umbrella, and platform trials and their underlying logic from the perspective of precision medicine. Meanwhile, we discuss future directions on the potential development of these new clinical design in view of the "Precision Pro", "Dynamic Precision", and "Intelligent Precision". This review would assist trial-related researchers to enhance the innovation and feasibility of clinical trial designs by expounding the underlying logic, which be essential to accelerate the progression of precision medicine.

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

Identification of a lipid homeostasis-related gene signature for predicting prognosis, immunity, and chemotherapeutic effect in patients with gastric cancer

Gastric cancer (GC) is one of the most common and deadliest cancers worldwide. Lipid homeostasis is essential for tumour development because lipid metabolism is one of the most important metabolic reprogramming pathways within tumours. Elucidating the mechanism of lipid homeostasis in GC might significantly improve treatment strategies and patient prognosis. GSE62254 was applied to construct a lipid homeostasis-related gene signature score (HGSscore) by multiple bioinformatic algorithms including weighted gene coexpression network analysis (WGCNA) and LASSO-Cox regression. A nomogram based on HGSscore and relevant clinical characteristics was constructed to predict the survival of patients with GC. TIMER and xCell were used to evaluate immune and stromal cell infiltration in the tumour microenvironment. Correlations between lipid homeostasis-related genes and chemotherapeutic efficacy were analysed in GSCAlite. RT‒qPCR and cell viability assays were applied to verify the findings in this study. HGSscore was constructed based on eighteen lipid homeostasis-related genes that were selected by WGCNA and LASSO-Cox regression. HGSscore was strongly associated with advanced TNM stage and showed satisfactory value in predicting GC prognosis in three independent cohorts. Furthermore, we found that HGSscore was associated with the tumour mutation burden (TMB) and immune/stromal cell infiltration, which are related to GC prognosis, indicating that lipid homeostasis impacts the formation of the tumour microenvironment (TME). With respect to the GSCAlite platform, PLOD2 and TGFB2 were shown to be positively related to chemotherapeutic resistance, while SLC10A7 was a favourable factor for chemotherapy efficacy. Cell viability assays showed that disrupted lipid homeostasis could attenuate GC cell viability. Moreover, RT‒qPCR revealed that lipid homeostasis could influence expression of specific genes. We identified a lipid homeostasis-related gene signature that correlated with survival, clinical characteristics, the TME, and chemotherapeutic efficacy in GC patients. This research provides a new perspective for improving prognosis and guiding individualized chemotherapy for patients with GC.

DDX24 promotes tumor progression by mediating hexokinase-1 induced glycolysis in gastric cancer

Metabolic reprogramming allows tumor cells to meet high demand of biogenesis and increased energy for rapid proliferation. Gastric cancer (GC) ranks among the most prevalent malignancies globally. Exploring the underlying mechanisms of glycolytic reprogramming in GC could provide new therapeutic target for GC treatment. Here, we showed that DEAD-box helicase 24 (DDX24) played a critical role in hexokinase-1 (HK1) induced glycolysis. DDX24 expression was significantly elevated in GC tissues and was closely associated with worse survival in GC patients. In addition, DDX24 promoted glucose uptake and lactate production in GC cells. Mechanistically, DDX24 could bind the HK1 mRNA and positively regulated HK1 level at the transcriptional level. Moreover, DDX24 promoted the proliferation, migration, and invasion ability of GC cells by upregulating HK1. Collectively, these results suggested that DDX24 was a critical player in the regulation of glycolytic reprogramming and also implicated DDX24 as a valuable therapeutic target for GC.

Construction and testing of a risk prediction classifier for cardia carcinoma

OBJECTIVES

This research aimed to construct a prediction model for stages II and III cardia carcinoma (CC), and provide an effective preoperative evaluation tool for clinicians.

METHODS

CC mRNA expression matrix was obtained from Gene Expression Omnibus and The Cancer Genome Atlas databases. Non-negative matrix factorization was used to cluster data to obtain subgroup information, and weighted gene co-expression network analysis was used to uncover key modules linked to different subgroups. Gene-set enrichment analysis analyzed biological pathways of different subgroups. The related pathways of multiple modules were scrutinized with Kyoto Encyclopedia of Genes and Genomes. Key modules were manually annotated to screen CC-related genes. Subsequently, quantitative real-time polymerase chain reaction assessed CC-related gene expression in fresh tissues and paraffin samples, and Pearson correlation analysis was performed. A classification model was constructed and the predictive ability was evaluated by the receiver operating characteristic curve.

RESULTS

CC patients had four subgroups that were associated with brown, turquoise, red, and black modules, respectively. The CC-related modules were mainly associated with abnormal cell metabolism and inflammatory immune pathways. Then, 76 CC-elated genes were identified. Pearson correlation analysis presented that THBS4, COL14A1, DPYSL3, FGF7, and SVIL levels were relatively stable in fresh and paraffin tissues. The area under the curve of 5-gene combined prediction for staging was 0.8571, indicating good prediction ability.

CONCLUSIONS

The staging classifier for CC based on THBS4, COL14A1, DPYSL3, FGF7, and SVIL has a good predictive effect, which may provide effective guidance for whether CC patients need emergency surgery.

Caveolin-1 mediates the utilization of extracellular proteins for survival in refractory gastric cancer

Despite advances in cancer therapy, the clinical outcome of patients with gastric cancer remains poor, largely due to tumor heterogeneity. Thus, finding a hidden vulnerability of clinically refractory subtypes of gastric cancer is crucial. Here, we report that chemoresistant gastric cancer cells rely heavily on endocytosis, facilitated by caveolin-1, for survival. caveolin-1 was highly upregulated in the most malignant stem-like/EMT/mesenchymal (SEM)-type gastric cancer cells, allowing caveolin-1-mediated endocytosis and utilization of extracellular proteins via lysosomal degradation. Downregulation of caveolin-1 alone was sufficient to induce cell death in SEM-type gastric cancer cells, emphasizing its importance as a survival mechanism. Consistently, chloroquine, a lysosomal inhibitor, successfully blocked caveolin-1-mediated endocytosis, leading to the marked suppression of tumor growth in chemorefractory gastric cancer cells in vitro, including patient-derived organoids, and in vivo. Together, our findings suggest that caveolin-1-mediated endocytosis is a key metabolic pathway for gastric cancer survival and a potential therapeutic target.

Long-term prognostic benefit of adjuvant chemotherapy for patients with hepatoid adenocarcinoma of the stomach after radical resection: A national multicenter study

BACKGROUND

There is no consensus on whether adjuvant chemotherapy (AC) is effective for hepatoid adenocarcinoma of the stomach (HAS). The aim of this study was to investigate the relationship between AC and the long-term prognosis of patients with HAS.

METHODS

The clinicopathological data of 239 patients with primary HAS who underwent radical surgery from April 1, 2004 to December 31, 2019 in 14 centers in China were retrospectively analyzed. Patients were divided into the AC group (127 patients) and the nonadjuvant chemotherapy (NAC) group (112 patients).

RESULTS

Kaplan‒Meier (KM) analysis showed that there were no significant differences in the 1-year3-year overall survival rate (OS) and 1-year, 3-year recurrence-free survival rate (RFS) between the AC group and the NAC group (1-year OS: 85.6% vs. 79.8%, 3-year OS: 59.8% vs. 62.4%, 1-year RFS: 69.8% vs. 74.4%, 3-year RFS: 57.2% vs. 55.9%, all P > 0.05). The subpopulation treatment effect pattern plots (STEPP) did not show treatment heterogeneity of AC in patients with HAS. The proportions of local recurrence and metastasis sites in the two groups were similar. Although the smoothed hazard curves of the NAC and AC groups crossed, the peak hazard time was later in the AC group (5.9 and 4.7 months), and the peak hazard rate was lower (0.032 and 0.038, P = 0.987).

CONCLUSION

The current AC regimen may not significantly improve the survival of patients with HAS after radical surgery.

Comprehensive evaluation of cell death-related genes as novel diagnostic biomarkers for breast cancer

Background

Breast cancer (BRCA) ranks first among cancers in terms of incidence and mortality rates in women, primarily owing to metastasis, chemo-resistance, and heterogeneity. To predict long-term prognosis and design novel therapies for BRCA, more sensitive markers need to be explored.

Methods

Data from 1089 BRCA patients were downloaded from TCGA database. Pearson's correlation analysis and univariate and multivariate Cox regression analyses were performed to assess the role of cell death-related genes (CDGs) in predicting BRCA prognosis. Kaplan-Meier survival curves were generated to compare the overall survival in the two subgroups. A nomogram was constructed using risk scores based on the five CDGs and other clinicopathological features. CCK-8, EdU incorporation, and colony formation assays were performed to verify the inhibitory effect of NFKBIA on BRCA cell proliferation. Transwell assay, flow cytometry, and immunohistochemistry analyses were performed to ascertain the biological function of NFKBIA.

Results

Five differentially expressed CDGs were detected among 156 CDGs. The risk score for each patient was then calculated based on the expression levels of the five CDGs. Distinct differences in immune infiltration, expression of immune-oncological targets, mutation status, and half-maximal inhibitory concentration values of some targeted drugs were observed between the high- and low-risk groups. Finally, in vitro cell experiments verified that NFKBIA overexpression suppresses the proliferation and migration of BRCA cells.

Conclusions

Our study revealed that some CDGs, especially NFKBIA, could serve as sensitive markers for predicting the prognosis of patients with BRCA and designing more personalized clinical therapies.

Evaluation of dynamic recurrence risk for locally advanced gastric cancer in the clinical setting of adjuvant chemotherapy: a real-world study with IPTW-based conditional recurrence analysis

BACKGROUND

The long-term dynamic recurrence hazard of locally advanced gastric cancer (LAGC) in the clinical setting of adjuvant chemotherapy (ACT) remains unclear.

PURPOSE

This study aimed to investigate the dynamic recurrence risk of LAGC in patients who received ACT or not.

METHODS

The study assessed data from patients with LAGC who underwent radical gastrectomy between January, 2010 and October, 2015. Inverse probability of treatment weighting (IPTW) was performed to reduce selection bias between the ACT and observational (OBS) groups. Conditional recurrence-free survival (cRFS) and restricted mean survival time (RMST) were used to assess the survival differences.

RESULTS

In total, 1,661 LAGC patients were included (ACT group, n = 1,236 and OBS group, n = 425). The recurrence hazard gradually declined; in contrast, cRFS increased with RFS already accrued. Following IPTW adjustment, the cRFS rates were higher in the ACT group than those in the OBS group for patients at baseline or with accrued RFS of 1 and 2 years (p˂0.05). However, the cRFS rates of the ACT group were comparable with those of the OBS group for patients with accrued RFS of 3 or more years (p > 0.05). Likewise, the 5-year △RMST between the ACT and OBS groups demonstrated a similar trend. Moreover, the hematological metastasis rate of the ACT group was significantly lower than that of the OBS group for patients at baseline or with accrued RFS of 1 and 2 years, respectively (p˂0.05).

CONCLUSIONS

Although ACT could provide substantial benefits for patients with LAGC, the differences in recurrence hazard between the ACT and OBS groups may attenuate over time, which could help guide surveillance and alleviate patients' anxiety. Further prospective large-scale studies are warranted.

Establishment of a 7-gene expression panel to improve the prognosis classification of gastric cancer patients

Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox β regression coefficients. Cox regression (p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91, DYNC1I1, FAM83D, LBH, SLITRK5, WTIP, and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-β pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC.

Clinically conserved genomic subtypes of gastric adenocarcinoma

Gastric adenocarcinoma (GAC) is a lethal disease characterized by genomic and clinical heterogeneity. By integrating 8 previously established genomic signatures for GAC subtypes, we identified 6 clinically and molecularly distinct genomic consensus subtypes (CGSs). CGS1 have the poorest prognosis, very high stem cell characteristics, and high IGF1 expression, but low genomic alterations. CGS2 is enriched with canonical epithelial gene expression. CGS3 and CGS4 have high copy number alterations and low immune reactivity. However, CGS3 and CGS4 differ in that CGS3 has high HER2 activation, while CGS4 has high SALL4 and KRAS activation. CGS5 has the high mutation burden and moderately high immune reactivity that are characteristic of microsatellite instable tumors. Most CGS6 tumors are positive for Epstein Barr virus and show extremely high levels of methylation and high immune reactivity. In a systematic analysis of genomic and proteomic data, we estimated the potential response rate of each consensus subtype to standard and experimental treatments such as radiation therapy, targeted therapy, and immunotherapy. Interestingly, CGS3 was significantly associated with a benefit from chemoradiation therapy owing to its high basal level of ferroptosis. In addition, we also identified potential therapeutic targets for each consensus subtype. Thus, the consensus subtypes produced a robust classification and provide for additional characterizations for subtype-based customized interventions.

Stem Cell Properties of Gastric Cancer Stem-Like Cells under Stress Conditions Are Regulated via the c-Fos/UCH-L3/β-Catenin Axis

Gastric cancer stem-like cells (GCSCs) possess stem cell properties, such as self-renewal and tumorigenicity, which are known to induce high chemoresistance and metastasis. These characteristics of GCSCs are further enhanced by autophagy, worsening the prognosis of patients. Currently, the mechanisms involved in the induction of stemness in GCSCs during autophagy remain unclear. In this study, we compared the cellular responses of GCSCs with those of gastric cancer intestinal cells (GCICs) whose stemness is not induced by autophagy. In response to glucose starvation, the levels of β-catenin and stemness-related genes were upregulated in GCSCs, while the levels of β-catenin declined in GCICs. The pattern of deubiquitinase ubiquitin C-terminal hydrolase-L3 (UCH-L3) expression in GCSCs and GCICs was similar to that of β-catenin expression depending on glucose deprivation. We also observed that inhibition of UCH-L3 activity reduced β-catenin protein levels. The interaction between UCH-L3 and β-catenin proteins was confirmed, and it reduced the ubiquitination of β-catenin. Our results suggest that UCH-L3 induces the stabilization of β-catenin, which is required to promote stemness during autophagy activation. Also, UCH-L3 expression was regulated by c-Fos, and the levels of c-Fos increased in response to autophagy activation. In summary, our findings suggest that the inhibition of UCH-L3 during nutrient deprivation could suppress stress resistance of GCSCs and increase the survival rates of gastric cancer patients.

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.

Development and validation of nomogram models to predict radiotherapy or chemotherapy benefit in stage III/IV gastric adenocarcinoma with surgery

Objectives

The advanced gastric adenocarcinoma (GAC) patients (stage III/IV) with surgery may have inconsistent prognoses due to different demographic and clinicopathological factors. In this retrospective study, we developed clinical prediction models for estimating the overall survival (OS) and cancer-specific survival (CSS) in advanced GAC patients with surgery.

Methods

A retrospective analysis was conducted using the Surveillance, Epidemiology, and End Results (SEER) database. The total population from 2004 to 2015 was divided into four levels according to age, of which 179 were younger than 45 years old, 695 were 45-59 years old, 1064 were 60-74 years old, and 708 were older than 75 years old. There were 1,712 men and 934 women. Univariate and multivariate Cox regression analyses were performed to identify prognostic factors for OS and CSS. Nomograms were constructed to predict the 1-, 3-, and 5-year OS and CSS. The models' calibration and discrimination efficiency were validated. Discrimination and accuracy were evaluated using the consistency index, area under the receiver operating characteristic curve, and calibration plots; and clinical usefulness was assessed using decision curve analysis. Cross-validation was also conducted to evaluate the accuracy and stability of the models. Prognostic factors identified by Cox regression were analyzed using Kaplan-Meier survival analysis.

Results

A total of 2,646 patients were included in our OS study. Age, primary site, differentiation grade, AJCC 6th_TNM stage, chemotherapy, radiotherapy, and number of regional nodes examined were identified as prognostic factors for OS in advanced GAC patients with surgery (P < 0.05). A total of 2,369 patients were included in our CSS study. Age, primary site, differentiation grade, AJCC 6th_TNM stage, chemotherapy, radiotherapy, and number of regional nodes examined were identified as risk factors for CSS in these patients (P < 0.05). These factors were used to construct the nomogram to predict the 1-, 3-, and 5-year OS and CSS of advanced GAC patients with surgery. The consistency index and area under the receiver operating characteristic curve demonstrated that the models effectively differentiated between events and nonevents. The calibration plots for 1-, 3-, and 5-year OS and CSS probability showed good consistence between the predicted and the actual events. The decision curve analysis indicated that the nomogram had higher clinical predictive value and more significant net gain than AJCC 6th_TNM stage in predicting OS and CSS of advanced GAC patients with surgery. Cross-validation also revealed good accuracy and stability of the models.

Conclusion

The developed predictive models provided available prognostic estimates for advanced GAC patients with surgery. Our findings suggested that both OS and CSS can benefit from chemotherapy or radiotherapy in these patients.

A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC

Background

Most patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for cancer development, and its transcriptomic profile may be associated with treatment response and differential outcomes. The aim of this study was to develop a new predictive signature for chemotherapy in patients with HGSOC.

Methods

Two HGSOC single-cell RNA sequencing datasets from patients receiving chemotherapy were reinvestigated. The subtypes of endoplasmic reticulum stress-related XBP1+ B cells, invasive metastasis-related ACTB+ Tregs, and proinflammatory-related macrophage subtypes with good predictive power and associated with chemotherapy response were identified. These results were verified in an independent HGSOC bulk RNA-seq dataset for chemotherapy. Further validation in clinical cohorts used quantitative real-time PCR (qRT-PCR).

Results

By combining cluster-specific genes for the aforementioned cell subtypes, we constructed a chemotherapy response prediction model containing 43 signature genes that achieved an area under the receiver operator curve (AUC) of 0.97 (p = 2.1e-07) for the GSE156699 cohort (88 samples). A huge improvement was achieved compared to existing prediction models with a maximum AUC of 0.74. In addition, its predictive capability was validated in multiple independent bulk RNA-seq datasets. The qRT-PCR results demonstrate that the expression of the six genes has the highest diagnostic value, consistent with the trend observed in the analysis of public data.

Conclusions

The developed chemotherapy response prediction model can be used as a valuable clinical decision tool to guide chemotherapy in HGSOC patients.

Early stage gastric adenocarcinoma: clinical and molecular landscapes

Gastric adenocarcinoma, even when diagnosed at an early (localized) disease stage, poses a major health-care burden with cure rates that remain unsatisfactorily low, particularly in Western countries. This lack of progress reflects, among other aspects, the impracticality of early diagnosis, considerable variations in therapeutic approaches that is partly based on regional preferences, and the ingrained heterogeneity of gastric adenocarcinoma cells and their associated tumour microenvironment (TME). Clinical trials have long applied empirical interventions with the assumption that all early stage gastric adenocarcinomas are alike. Despite certain successes, the shortcomings of these approaches can potentially be overcome by targeting the specific molecular subsets of gastric adenocarcinomas identified by genomic and/or multi-omics analyses, including microsatellite instability-high, Epstein-Barr virus-induced, DNA damage repair-deficient, HER2-positive and PD-L1-high subtypes. Future approaches, including the availability of sophisticated vaccines, novel antibody technologies, agents targeting TME components (including fibroblasts, macrophages, cytokines or chemokines, and T cells) and novel immune checkpoint inhibitors, supported by improved tissue-based and blood-based diagnostic assays, seem promising. In this Review, we highlight current knowledge of the molecular and cellular biology of gastric adenocarcinomas, summarize the current approaches to clinical management of the disease, and consider the role of novel management and/or treatment strategies.

Tumour infiltrating lymphocytes and survival after adjuvant chemotherapy in patients with gastric cancer: post-hoc analysis of the CLASSIC trial

BACKGROUND

Only a subset of gastric cancer (GC) patients with stage II-III benefits from chemotherapy after surgery. Tumour infiltrating lymphocytes per area (TIL density) has been suggested as a potential predictive biomarker of chemotherapy benefit.

METHODS

We quantified TIL density in digital images of haematoxylin-eosin (HE) stained tissue using deep learning in 307 GC patients of the Yonsei Cancer Center (YCC) (193 surgery+adjuvant chemotherapy [S + C], 114 surgery alone [S]) and 629 CLASSIC trial GC patients (325 S + C and 304 S). The relationship between TIL density, disease-free survival (DFS) and clinicopathological variables was analysed.

RESULTS

YCC S patients and CLASSIC S patients with high TIL density had longer DFS than S patients with low TIL density (P = 0.007 and P = 0.013, respectively). Furthermore, CLASSIC patients with low TIL density had longer DFS if treated with S + C compared to S (P = 0.003). No significant relationship of TIL density with other clinicopathological variables was found.

CONCLUSION

This is the first study to suggest TIL density automatically quantified in routine HE stained tissue sections as a novel, clinically useful biomarker to identify stage II-III GC patients deriving benefit from adjuvant chemotherapy. Validation of our results in a prospective study is warranted.

PHGDH preserves one-carbon cycle to confer metabolic plasticity in chemoresistant gastric cancer during nutrient stress

Molecular classification of gastric cancer (GC) identified a subgroup of patients showing chemoresistance and poor prognosis, termed SEM (Stem-like/Epithelial-to-mesenchymal transition/Mesenchymal) type in this study. Here, we show that SEM-type GC exhibits a distinct metabolic profile characterized by high glutaminase (GLS) levels. Unexpectedly, SEM-type GC cells are resistant to glutaminolysis inhibition. We show that under glutamine starvation, SEM-type GC cells up-regulate the 3 phosphoglycerate dehydrogenase (PHGDH)-mediated mitochondrial folate cycle pathway to produce NADPH as a reactive oxygen species scavenger for survival. This metabolic plasticity is associated with globally open chromatin structure in SEM-type GC cells, with ATF4/CEBPB identified as transcriptional drivers of the PHGDH-driven salvage pathway. Single-nucleus transcriptome analysis of patient-derived SEM-type GC organoids revealed intratumoral heterogeneity, with stemness-high subpopulations displaying high GLS expression, a resistance to GLS inhibition, and ATF4/CEBPB activation. Notably, coinhibition of GLS and PHGDH successfully eliminated stemness-high cancer cells. Together, these results provide insight into the metabolic plasticity of aggressive GC cells and suggest a treatment strategy for chemoresistant GC patients.

Clinical molecular subtyping reveals intrinsic mesenchymal reprogramming in gastric cancer cells

The mesenchymal cancer phenotype is known to be clinically related to treatment resistance and a poor prognosis. We identified gene signature-based molecular subtypes of gastric cancer (GC, n = 547) based on transcriptome data and validated their prognostic and predictive utility in multiple external cohorts. We subsequently examined their associations with tumor microenvironment (TME) features by employing cellular deconvolution methods and sequencing isolated GC populations. We further performed spatial transcriptomics analysis and immunohistochemistry, demonstrating the presence of GC cells in a partial epithelial-mesenchymal transition state. We performed network and pharmacogenomic database analyses to identify TGF-β signaling as a driver pathway and, thus, a therapeutic target. We further validated its expression in tumor cells in preclinical models and a single-cell dataset. Finally, we demonstrated that inhibition of TGF-β signaling negated mesenchymal/stem-like behavior and therapy resistance in GC cell lines and mouse xenograft models. In summary, we show that the mesenchymal GC phenotype could be driven by epithelial cancer cell-intrinsic TGF-β signaling and propose therapeutic strategies based on targeting the tumor-intrinsic mesenchymal reprogramming of medically intractable GC.

Selective cytotoxicity of a novel mitochondrial complex I inhibitor, YK-135, against EMT-subtype gastric cancer cell lines due to impaired glycolytic capacity

Epithelial-to-mesenchymal transition (EMT)-subtype gastric cancers have the worst prognosis due to their higher recurrence rate, higher probability of developing metastases and higher chemoresistance compared to those of other molecular subtypes. Pharmacologically actionable somatic mutations are rarely found in EMT-subtype gastric cancers, limiting the utility of targeted therapies. Here, we conducted a high-throughput chemical screen using 37 gastric cancer cell lines and 48,467 synthetic smallmolecule compounds. We identified YK-135, a small-molecule compound that showed higher cytotoxicity toward EMT-subtype gastric cancer cell lines than toward non-EMT-subtype gastric cancer cell lines. YK-135 exerts its cytotoxic effects by inhibiting mitochondrial complex I activity and inducing AMP-activated protein kinase (AMPK)-mediated apoptosis. We found that the lower glycolytic capacity of the EMT-subtype gastric cancer cells confers synthetic lethality to the inhibition of mitochondrial complex I, possibly by failing to maintain energy homeostasis. Other well-known mitochondrial complex I inhibitors (e.g., rotenone and phenformin) mimic the efficacy of YK-135, supporting our results. These findings highlight mitochondrial complex I inhibitors as promising therapeutic agents for EMT-subtype gastric cancers and YK-135 as a novel chemical scaffold for further drug development. [BMB Reports 2022; 55(12): 645-650].

Prognostic and predictive value of a pathomics signature in gastric cancer

The current tumour-node-metastasis (TNM) staging system alone cannot provide adequate information for prognosis and adjuvant chemotherapy benefits in patients with gastric cancer (GC). Pathomics, which is based on the development of digital pathology, is an emerging field that might improve clinical management. Herein, we propose a pathomics signature (PS_GC) that is derived from multiple pathomics features of haematoxylin and eosin-stained slides. We find that the PS_GC is an independent predictor of prognosis. A nomogram incorporating the PS_GC and TNM staging system shows significantly improved accuracy in predicting the prognosis compared to the TNM staging system alone. Moreover, in stage II and III GC patients with a low PS_GC (but not in those with a high PS_GC), satisfactory chemotherapy benefits are observed. Therefore, the PS_GC could serve as a prognostic predictor in patients with GC and might be a potential predictive indicator for decision-making regarding adjuvant chemotherapy.

Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study

BACKGROUND

Accurate pre-treatment prediction of neoadjuvant chemotherapy (NACT) resistance in patients with locally advanced gastric cancer (LAGC) is essential for timely surgeries and optimized treatments. We aim to evaluate the effectiveness of deep learning (DL) on computed tomography (CT) images in predicting NACT resistance in LAGC patients.

METHODS

A total of 633 LAGC patients receiving NACT from three hospitals were included in this retrospective study. The training and internal validation cohorts were randomly selected from center 1, comprising 242 and 104 patients, respectively. The external validation cohort 1 comprised 128 patients from center 2, and the external validation cohort 2 comprised 159 patients from center 3. First, a DL model was developed using ResNet-50 to predict NACT resistance in LAGC patients, and the gradient-weighted class activation mapping (Grad-CAM) was assessed for visualization. Then, an integrated model was constructed by combing the DL signature and clinical characteristics. Finally, the performance was tested in internal and external validation cohorts using area under the receiver operating characteristic (ROC) curves (AUC).

RESULTS

The DL model achieved AUCs of 0.808 (95% CI 0.724-0.893), 0.755 (95% CI 0.660-0.850), and 0.752 (95% CI 0.678-0.825) in validation cohorts, respectively, which were higher than those of the clinical model. Furthermore, the integrated model performed significantly better than the clinical model (P < 0.05).

CONCLUSIONS

A CT-based model using DL showed promising performance for predicting NACT resistance in LAGC patients, which could provide valuable information in terms of individualized treatment.

DKK1 as a robust predictor for adjuvant platinum chemotherapy benefit in resectable pStage II-III gastric cancer

BACKGROUND

Adjuvant chemotherapy (ACT) with 5-FU alone or 5-FU plus platinum after curative surgery improves the prognosis of pStage II-III gastric cancer (GC). However, only a subset of patients benefits from adjuvant platinum. To avoid the side effects of platinum, it is significant to accurately screen the patients who would benefit maximally with this treatment. The present study aimed to assess the value of DKK1 in predicting the benefit of adjuvant platinum chemotherapy in patients with pStage II -III GC.

METHODS

Platinum sensitivity-related genes were screened by bioinformatics. DKK1 expression in 380 GC specimens was detected by immunohistochemistry (IHC) staining, and the correlation with adjuvant platinum-specific benefits were analyzed.

RESULTS

DKK1 was screened as the most significant platinum sensitivity-related gene. In patients with DKK1^high GC, the estimated absolute 5-year overall survival (OS) benefits from adjuvant platinum for pStage II-III, II, IIIA, IIIB, and IIIC were 25.5%, 17.3%, 36.4%, 29.2% and 31.1%, respectively, and the estimated absolute 5-year disease-free survival (DFS) benefits in the corresponding stages were 27.4%, 17.5%, 36.7%, 29.7% and 31.5%, respectively. These benefits were significantly higher than those in the same TNM stage without adjusting for DKK1 status. The performance of DKK1 was independent of the TNM stage and other clinicopathological variables. Similar results were obtained in the TCGA and ACRG cohorts. Furthermore, nomograms were constructed to predict the survival benefits in DKK1 subgroups.

CONCLUSIONS

The stratification strategy based on DKK1 status is more precise than the TNM staging system for the selection of pStage II-III GC patients suitable for platinum-containing ACT.

Single Cell Analysis of Gastric Cancer Reveals Non-Defined Telomere Maintenance Mechanism

Telomere maintenance mechanisms (TMMs) are important for cell survival and homeostasis. However, most related cancer research studies have used heterogenous bulk tumor tissue, which consists of various single cells, and the cell type properties cannot be precisely recognized. In particular, cells exhibiting non-defined TMM (NDTMM) indicate a poorer prognosis than those exhibiting alternative lengthening of telomere (ALT)-like mechanisms. In this study, we used bioinformatics to classify TMMs by cell type in gastric cancer (GC) in single cells and compared the biological processes of each TMM. We elucidated the pharmacological vulnerabilities of NDTMM type cells, which are associated with poor prognosis, based on molecular mechanisms. We analyzed differentially expressed genes in cells exhibiting different TMMs in two single-cell GC cohorts and the pathways enriched in single cells. NDTMM type cells showed high stemness, epithelial-mesenchymal transition, cancer hallmark activity, and metabolic reprogramming with mitochondrial abnormalities. Nuclear receptor subfamily 4 group A member 1 (NR4A1) activated parkin-dependent mitophagy in association with tumor necrosis factor-alpha (TNFA) to maintain cellular homeostasis without TMM. NR4A1 overexpression affected TNFA-induced GC cell apoptosis by inhibiting Jun N-terminal kinase/parkin-dependent mitophagy. Our findings also revealed that NR4A1 is involved in cell cycle mediation, inflammation, and apoptosis to maintain cell homeostasis, and is a novel potential therapeutic target in recalcitrant GC.

Development and Multicenter Validation of a Novel Immune-Inflammation-Based Nomogram to Predict Survival in Western Resectable Gastric and Gastroesophageal Junction Adenocarcinoma (GEA): The NOMOGAST

Background. More than 50% of operable GEA relapse after curative-intent resection. We aimed at externally validating a nomogram to enable a more accurate estimate of individualized risk in resected GEA. Methods. Medical records of a training cohort (TC) and a validation cohort (VC) of patients undergoing radical surgery for c/uT2-T4 and/or node-positive GEA were retrieved, and potentially interesting variables were collected. Cox proportional hazards in univariate and multivariate regressions were used to assess the effects of the prognostic factors on OS. A graphical nomogram was constructed using R software’s package Regression Modeling Strategies (ver. 5.0-1). The performance of the prognostic model was evaluated and validated. Results. The TC and VC consisted of 185 and 151 patients. ECOG:PS > 0 (p < 0.001), angioinvasion (p < 0.001), log (Neutrophil/Lymphocyte ratio) (p < 0.001), and nodal status (p = 0.016) were independent prognostic values in the TC. They were used for the construction of a nomogram estimating 3- and 5-year OS. The discriminatory ability of the model was evaluated with the c-Harrell index. A 3-tier scoring system was developed through a linear predictor grouped by 25 and 75 percentiles, strengthening the model’s good discrimination (p < 0.001). A calibration plot demonstrated a concordance between the predicted and actual survival in the TC and VC. A decision curve analysis was plotted that depicted the nomogram’s clinical utility. Conclusions. We externally validated a prognostic nomogram to predict OS in a joint independent cohort of resectable GEA; the NOMOGAST could represent a valuable tool in assisting decision-making. This tool incorporates readily available and inexpensive patient and disease characteristics as well as immune-inflammatory determinants. It is accurate, generalizable, and clinically effectivex.

Identification of a Biomarker Combination for Survival Stratification in pStage II/III Gastric Cancer after Curative Resection

Simple Summary

Gastric cancer (GC) is the fifth most common cancer worldwide and the fourth most common cause of cancer-related deaths, with a high frequency of recurrence and metastasis, and a poor prognosis. This study presents a novel combination of four proteins (PDGFRB, INHBA, MMP11, and galectin-10) in GC tissues that have been identified as useful survival stratification markers in patients with pStage II/III GC after curative resection by quantitative polymerase chain reaction (qPCR), proteomic analysis, and immunohistochemistry (IHC).

Abstract

Background: We sought to identify an optimal combination of survival risk stratification markers in patients with pathological (p) stage II/III gastric cancer (GC) after curative resection. Methods: We measured the expression levels of 127 genes in pStage II/III GC tissues of two patient cohorts by quantitative polymerase chain reaction (qPCR) and the expression of 1756 proteins between two prognosis (good and poor) groups by proteomic analysis to identify candidate survival stratification markers. Further, immunohistochemistry (IHC) using tumor microarrays (TMAs) in another cohort of patients was performed to identify an optimal biomarker combination for survival stratification in GC patients. Results: secreted protein acidic and rich in cysteine (SPARC), erb-b2 receptor tyrosine kinase 2 (ERBB2), inhibin subunit beta A (INHBA), matrix metallopeptidase-11 (MMP11), tumor protein p53 (TP53), and platelet-derived growth factor receptor-beta (PDGFRB) were identified as candidate biomarkers from qPCR analysis, and SPARC and galectin-10 were obtained from the proteomic analysis. The combination of PDGFRB, INHBA, MMP11, and galectin-10 was identified as the optimal combination of survival risk stratification markers. Conclusions: A combination of four proteins in GC tissues may serve as useful survival risk stratification markers in patients with pStage II/III GC following curative resection. Our results may facilitate future multicenter prospective clinical trials.

An artificial intelligence model to predict survival and chemotherapy benefits for gastric cancer patients after gastrectomy development and validation in international multicenter cohorts

BACKGROUND

Gastric cancer (GC) is a major health problem worldwide, with high prevalence and mortality. The present GC staging system provides inadequate prognostic information and does not reflect the chemotherapy benefit of GC.

METHODS

Two hundred fifty-five patients who underwent surgical resection were enrolled in our study (training cohort = 212, internal validation cohort = 43). Nine clinicopathologic features were obtained to construct an support vector machine (SVM) model. The cohorts from 4 domestic centres and The Cancer Genome Atlas (TCGA) were used for external validation.

RESULTS

In the training cohort, the AUCs were 0.773 (95% CI 0.708-0.838) for 5-year overall survival (OS) and 0.751 (95% CI 0.683-0.820) for 5-year disease-free survival (DFS); in the domestic validation cohort, the AUCs were 0.852 (95% CI 0.810-0.894) and 0.837 (95% CI 0.792-0.882), respectively. The model performed better than the TNM staging system according to the receiver operator characteristic(ROC) curve. GC patients were significantly divided into low, moderate and high risk based on the SVM. High-risk TNM stage Ⅱ and Ⅲ patients were more likely to benefit from adjuvant chemotherapy than low-risk patients.

CONCLUSIONS

The SVM-based model may be used to predict OS and DFS in GC patients and the benefit of adjuvant chemotherapy in TNM stage Ⅱ and Ⅲ GC patients.

Neoadjuvant and Adjuvant Therapy Approaches to Gastric Cancer

OPINION STATEMENT

Gastric cancer is an aggressive malignancy, requiring a multimodality approach to achieve optimal curative rates even when the disease is amenable to surgical resection. Neoadjuvant and adjuvant approaches differ across the globe-a preference for peri-operative chemotherapy exists in Europe, in contrast to the adoption of adjuvant chemotherapy in Asia and adjuvant chemoradiotherapy in North America. There are nuances and limitations associated with each therapeutic strategy and an understanding of these distinct approaches is integral to judicious clinical application of the available data. Although a multimodal approach provides a clear survival benefit above a surgical-only approach, data report low completion rates of adjuvant therapy components and strongly suggest a need to refine patient selection particularly for ongoing treatment in the post-operative period. This may be achieved using a risk-stratified strategy. Hence, there is a need to transition from a generalised approach to a multimodality treatment towards one guided by individual patient clinical features and biomarker profiles in order to improve tolerability and patient outcomes irrespective of geographical variation in clinical practice. While the evidences supporting molecular features such as microsatellite instability and predictive gene signatures are provocative, prospective validation is required before these can be confidently used to direct clinical decision-making.

Efficacy of S-1 or Capecitabine plus Oxaliplatin Adjuvant Chemotherapy for Stage II or III Gastric Cancer after Curative Gastrectomy: A Systematic Review and Meta-Analysis

Simple Summary

Adjuvant chemotherapy regimens tegafur/gimeracil/oteracil (S-1) and capecitabine plus oxaliplatin (CAPOX) have predominated, owing to evidence of their remarkable oncologic outcomes, however, there has been a lack of studies on the difference in efficacy between the two regimens. We conducted pairwise meta-analyses comparing the efficacy of S-1 and CAPOX regimens for overall survival (OS) and disease-free survival (DFS) in stage II or III gastric cancer patients. In all stages (stages II and III), the five-year OS was not different between the two regimens (hazard ratio [HR] 0.96, 95% confidence interval [CI] 0.78–1.17; p = 0.56). Additionally, the five-year DFS was not different at any stage (HR 1.00, 95% CI 0.85–1.18; p = 0.21). The present meta-analysis showed that five-year OS and DFS for stage II or III gastric cancer patients were comparable between the S-1 and CAPOX adjuvant chemotherapy regimens.

Abstract

Background: Adjuvant chemotherapy (AC) regimens tegafur/gimeracil/oteracil (S-1) and capecitabine plus oxaliplatin (CAPOX) have predominated, however, there has been a lack of studies on their differences in efficacy. Methods: We conducted pairwise meta-analyses comparing the efficacy of S-1 and CAPOX regimens for overall survival (OS) and disease-free survival (DFS) in stage II or III GC patients. Results: Three studies were enrolled and analyzed using a forest plot for meta-analysis. Two of them were propensity score matching studies, and the remaining one was a retrospective observational study. In all stages, the five-year OS was not different between the two regimens (HR 0.96, 95% CI 0.78–1.17; p = 0.56). Additionally, the 5-year DFS was not different at any stage (HR 1.00, 95% CI 0.85–1.18; p = 0.21). After omitting the retrospective observational study, the five-year OS (HR 1.40, 95% CI 0.53–3.73) and DFS (HR 1.41, 95% CI 0.57–3.44) of S-1 tended to be better in stage II, and the five-year OS (HR 0.81, 95% CI 0.56–1.16) and DFS (HR 0.85, 95% CI 0.63–1.13) of CAPOX tended to be better in stage III, without statistical significance. Conclusions: In the present meta-analysis, the five-year OS and DFS for stage II or III GC patients were comparable between S-1 and CAPOX regimens as AC.

The pathophyiological role of aminoacyl-tRNA synthetases in digestive system diseases

Aminoacyl-tRNA synthetases (ARSs) catalyze the ligation of amino acids to their cognate transfer RNAs and are indispensable enzymes for protein biosynthesis in all the cells. Previously, ARSs were considered simply as housekeeping enzymes, however, they are now known to be involved in a variety of physiological and pathological processes, such as tumorigenesis, angiogenesis, and immune response. In this review, we summarize the role of ARSs in the digestive system, including the esophagus, stomach, small intestine, colon, as well as the auxiliary organs such as the pancreas, liver, and the gallbladder. Furthermore, we specifically focus on the diagnostic and prognostic value of ARSs in cancers, aiming to provide new insights into the pathophysiological implications of ARSs in tumorigenesis.

Single Cell Analysis Reveals Reciprocal Tumor-Macrophage Intercellular Communications Related with Metabolic Reprogramming in Stem-like Gastric Cancer

Metabolic alterations and direct cell–cell interactions in the tumor microenvironment (TME) affect the prognostic molecular landscape of tumors; thus, it is imperative to investigate metabolic activity at the single-cell level rather than in bulk samples to understand the high-resolution mechanistic influences of cell-type specific metabolic pathway alterations on tumor cells. To investigate tumor metabolic reprogramming and intercellular communication at the single-cell level, we analyzed eighty-four metabolic pathways, seven metabolic signatures, and tumor-stroma cell interaction using 21,084 cells comprising gastric cancer and paired normal tissue. High EMT-score cells and stem-like subtype tumors showed elevated glycosaminoglycan metabolism, which was associated with poor patient outcome. Adenocarcinoma and macrophage cells had higher reactive oxidative species levels than the normal controls; they largely constituted the highest stemness cluster. They were found to reciprocally communicate through the common ligand RPS19. Consequently, ligand-target regulated transcriptional reprogramming resulted in HS6ST2 expression in adenocarcinoma cells and SERPINE1 expression in macrophages. Gastric cancer patients with increased SERPINE1 and HS6ST2 expression had unfavorable prognoses, suggesting these as potential drug targets. Our findings indicate that malignant stem-like/EMT cancer cell state might be regulated through reciprocal cancer cell-macrophage intercellular communication and metabolic reprogramming in the heterogeneous TME of gastric cancer at the single-cell level.

Clinicopathologic and genomic characteristics of mucinous gastric adenocarcinoma

BACKGROUND

Mucinous gastric adenocarcinoma (MGC) is a rare but distinctive histologic subtype of gastric cancer (GC). The clinico-pathologic and genomic characteristics of MGC have not been well evaluated.

METHODS

We collected individual data from five cohorts targeting the microsatellite instability (MSI) of GC (n = 5089) to evaluate the clinico-pathologic characteristics of MGC. In addition, public genomic databases were used for genomic analysis. The characteristics of MGC were compared with those of non-mucinous GC (NMGC).

RESULTS

MGC (n = 158, 3.1%) showed distinctive characteristics in terms of age, sex, and TNM stage compared to NMGC (n = 4931). MGC was frequently associated with MSI-high (OR: 2.24, 95% confidence interval [CI] 1.44-3.40, p < 0.001), while mutually exclusive to the Epstein-Barr virus type. The prognosis of MGC was better than that of NMGC (adj.HR: 0.731, 95% CI 0.556-0.962, p = 0.025). There was no clear benefit from postoperative chemotherapy in MGC. TP53 was the main driver mutation in the MGC without recurrent variants. MGC was related to high expression of GPR120 and B3GNT6 and moderate regulation of epithelial-mesenchymal transition (EMT)-up signature with a high EMT-down signature, and those characteristics was related to favorable prognosis of GC (log-rank p = 0.044, p < 0.001, p < 0.001, respectively). MSI-H of MGC was associated with low cancer-associate fibroblasts but high CD274 (PD-L1) expression compared to microsatellite stable MGC, suggesting that immune checkpoint inhibitors may be useful for the MSI-H of MGC.

CONCLUSION

MGC could be a surrogate for performing MSI but not the EBV test in GC. Further, its genetic characteristics lead to a favorable prognosis for MGC.

Machine Learning Predictor of Immune Checkpoint Blockade Response in Gastric Cancer

Predicting responses to immune checkpoint blockade (ICB) lacks official standards despite the discovery of several markers. Expensive drugs and different reactivities for each patient are the main disadvantages of immunotherapy. Gastric cancer is refractory and stem-like in nature and does not respond to immunotherapy. In this study, we aimed to identify a characteristic gene that predicts ICB response in gastric cancer and discover a drug target for non-responders. We built and evaluated a model using four machine learning algorithms for two cohorts of bulk and single-cell RNA seq to predict ICB response in gastric cancer patients. Through the LASSO feature selection, we discovered a marker gene signature that distinguishes responders from non-responders. VCAN, a candidate characteristic gene selected by all four machine learning algorithms, had a significantly high prevalence in non-responders (p = 0.0019) and showed a poor prognosis (p = 0.0014) at high expression values. This is the first study to discover a signature gene for predicting ICB response in gastric cancer by molecular subtype and provides broad insights into the treatment of stem-like immuno-oncology through precision medicine.

Synaptotagmin 11 scaffolds MKK7-JNK signaling process to promote stem-like molecular subtype gastric cancer oncogenesis

Background

Identifying biomarkers related to the diagnosis and treatment of gastric cancer (GC) has not made significant progress due to the heterogeneity of tumors. Genes involved in histological classification and genetic correlation studies are essential to develop an appropriate treatment for GC.

Methods

In vitro and in vivo lentiviral shRNA library screening was performed. The expression of Synaptotagmin (SYT11) in the tumor tissues of patients with GC was confirmed by performing Immunohistochemistry, and the correlation between the expression level and the patient’s survival rate was analyzed. Phospho-kinase array was performed to detect Jun N-terminal kinase (JNK) phosphorylation. SYT11, JNK, and MKK7 complex formation was confirmed by western blot and immunoprecipitation assays. We studied the effects of SYT11 on GC proliferation and metastasis, real-time cell image analysis, adhesion assay, invasion assay, spheroid formation, mouse xenograft assay, and liver metastasis.

Results

SYT11 is highly expressed in the stem-like molecular subtype of GC in transcriptome analysis of 527 patients with GC. Moreover, SYT11 is a potential prognostic biomarker for histologically classified diffuse-type GC. SYT11 functions as a scaffold protein, binding both MKK7 and JNK1 signaling molecules that play a role in JNK1 phosphorylation. In turn, JNK activation leads to a signaling cascade resulting in cJun activation and expression of downstream genes angiopoietin-like 2 (ANGPTL2), thrombospondin 4 (THBS4), Vimentin, and junctional adhesion molecule 3 (JAM3), which play a role in epithelial-mesenchymal transition (EMT). SNU484 cells infected with SYT11 shRNA (shSYT11) exhibited reduced spheroid formation, mouse tumor formation, and liver metastasis, suggesting a pro-oncogenic role of SYT11. Furthermore, SYT11-antisense oligonucleotide (ASO) displayed antitumor activity in our mouse xenograft model and was conferred an anti-proliferative effect in SNU484 and MKN1 cells.

Conclusion

SYT11 could be a potential therapeutic target as well as a prognostic biomarker in patients with diffuse-type GC, and SYT11-ASO could be used in therapeutic agent development for stem-like molecular subtype diffuse GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02420-3.

Running title: EEF1AKMT3/MAP2K7/TP53 axis in gastric cancerThe EEF1AKMT3/MAP2K7/TP53 axis suppresses tumor invasiveness and metastasis in gastric cancer

The importance of methylation in the tumorigenic responses of nonhistone proteins, such as TP53, PTEN, RB1, AKT, and STAT3, has been emphasized in numerous studies. In parallel, the corresponding nonhistone protein methyltransferases have been acknowledged in the pathophysiology of cancer. Thus, this study aimed to explore the pathological role of a nonhistone methyltransferase in gastric cancer (GC), identify nonhistone substrate protein, and understand the underlying mechanism. Interestingly, among the 24 methyltransferases and methyltransferase family 16 (MTF16) proteins, EEF1AKMT3 (METTL21B) expression was prominently lower in GC tissues than in normal adjacent tissues and was associated with a worse prognosis. In addition, EEF1AKMT3-knockdown induced gastric tumor invasiveness and migration. Through gain and loss-of-function studies, mass spectrometry analysis, RNA-seq, and phospho-antibody array, we identified EEF1AKMT3 as a novel tumor-suppressive methyltransferase that catalyzes the monomethylation of MAP2K7 (MKK7) at K296, thereby decreasing the phosphorylation, ubiquitination, and degradation of TP53. Furthermore, EEF1AKMT3, p-MAP2K7, and TP53 protein levels were positively correlated in GC tissues. Collectively, our results delineate the tumor-suppressive function of the EEF1AKMT3/MAP2K7/TP53 signaling axis and suggest the dysregulation of the signaling axis as potential targeted therapy in GC.

SFRP4 and CDX1 Are Predictive Genes for Extragastric Recurrence of Early Gastric Cancer after Curative Resection

Extragastric recurrence of early gastric cancer (EGC) after curative resection is rare, but prognosis has been poor in previous reports. Recently, single patient classifier (SPC) genes, such as secreted frizzled-related protein 4 (SFRP4) and caudal-type homeobox 1 (CDX1), were associated with prognosis and chemotherapy response in stage II–III gastric cancer. The aim of our study is, therefore, to elucidate predictive factors for extragastric recurrence of EGC after curative resection, including with the expression of SPC genes. We retrospectively reviewed electronic medical records of 1974 patients who underwent endoscopic or surgical curative resection for EGC. We analyzed clinicopathological characteristics to determine predictive factors for extragastric recurrence. Total RNA was extracted from formalin-fixed, paraffin-embedded (FFPE) tumor tissue and amplified by real-time reverse transcription polymerase chain reaction to evaluate expression of SPC genes. Overall incidences of extragastric recurrence were 0.9%. In multivariate analysis, submucosal invasion (odds ratio [OR] = 6.351, p = 0.032) and N3 staging (OR = 171.512, p = 0.012) were independent predictive factors for extragastric recurrence. Mean expression of SFRP4 in extragastric recurrence (−2.8 ± 1.3) was significantly higher than in the control group (−4.3 ± 1.6) (p = 0.047). Moreover, mean expression of CDX1 in extragastric recurrence (−4.6 ± 2.0) was significantly lower than in the control group (−2.4 ± 1.8) (p = 0.025). Submucosal invasion and metastasis of more than seven lymph nodes were independent predictive factors for extragastric recurrence. In addition, SFRP4 and CDX1 may be novel predictive markers for extragastric recurrence of EGC after curative resection.

Gastric cancer biomarker analysis in patients treated with different adjuvant chemotherapy regimens within SAMIT, a phase III randomized controlled trial

Biomarkers for selecting gastric cancer (GC) patients likely to benefit from sequential paclitaxel treatment followed by fluorinated-pyrimidine-based adjuvant chemotherapy (sequential paclitaxel) were investigated using tissue samples of patients recruited into SAMIT, a phase III randomized controlled trial. Total RNA was extracted from 556 GC resection samples. The expression of 105 genes was quantified using real-time PCR. Genes predicting the benefit of sequential paclitaxel on overall survival, disease-free survival, and cumulative incidence of relapse were identified based on the ranking of p-values associated with the interaction between the biomarker and sequential paclitaxel or monotherapy groups. Low VSNL1 and CD44 expression predicted the benefit of sequential paclitaxel treatment for all three endpoints. Patients with combined low expression of both genes benefitted most from sequential paclitaxel therapy (hazard ratio = 0.48 [95% confidence interval, 0.30-0.78]; p < 0.01; interaction p-value < 0.01). This is the first study to identify VSNL1 and CD44 RNA expression levels as biomarkers for selecting GC patients that are likely to benefit from sequential paclitaxel treatment followed by fluorinated-pyrimidine-based adjuvant chemotherapy. Our findings may facilitate clinical trials on biomarker-oriented postoperative adjuvant chemotherapy for patients with locally advanced GC.

Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.

Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial

OBJECTIVE

To date, there are no predictive biomarkers to guide selection of patients with gastric cancer (GC) who benefit from paclitaxel. Stomach cancer Adjuvant Multi-Institutional group Trial (SAMIT) was a 2×2 factorial randomised phase III study in which patients with GC were randomised to Pac-S-1 (paclitaxel +S-1), Pac-UFT (paclitaxel +UFT), S-1 alone or UFT alone after curative surgery.

DESIGN

The primary objective of this study was to identify a gene signature that predicts survival benefit from paclitaxel chemotherapy in GC patients. SAMIT GC samples were profiled using a customised 476 gene NanoString panel. A random forest machine-learning model was applied on the NanoString profiles to develop a gene signature. An independent cohort of metastatic patients with GC treated with paclitaxel and ramucirumab (Pac-Ram) served as an external validation cohort.

RESULTS

From the SAMIT trial 499 samples were analysed in this study. From the Pac-S-1 training cohort, the random forest model generated a 19-gene signature assigning patients to two groups: Pac-Sensitive and Pac-Resistant. In the Pac-UFT validation cohort, Pac-Sensitive patients exhibited a significant improvement in disease free survival (DFS): 3-year DFS 66% vs 40% (HR 0.44, p=0.0029). There was no survival difference between Pac-Sensitive and Pac-Resistant in the UFT or S-1 alone arms, test of interaction p<0.001. In the external Pac-Ram validation cohort, the signature predicted benefit for Pac-Sensitive (median PFS 147 days vs 112 days, HR 0.48, p=0.022).

CONCLUSION

Using machine-learning techniques on one of the largest GC trials (SAMIT), we identify a gene signature representing the first predictive biomarker for paclitaxel benefit.

TRIAL REGISTRATION NUMBER

UMIN Clinical Trials Registry: C000000082 (SAMIT); ClinicalTrials.gov identifier, 02628951 (South Korean trial).

OSgc: A Web Portal to Assess the Performance of Prognostic Biomarkers in Gastric Cancer

Evaluating the prognostic value of genes of interest in different populations of gastric cancer (GC) is difficult and time-consuming for basic and translational researchers even though many datasets are available in public dataset depositories. In the current study, we developed a robust web-based portal called OSgc (Online consensus Survival analysis of gastric cancer) that enables easy and swift verification of known and novel biomarker candidates in GC. OSgc is composed of gene expression profiling data and clinical follow-up information of 1,824 clinical GC cases, which are collected from 7 public independent datasets derived from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). By OSgc, users input the official gene symbol and will promptly retrieve the Kaplan-Meier survival plot with hazard ratio (HR) and log rank p value on the output webpage, by which users could assess the prognostic value of interesting genes for GC patients. Five survival end points containing overall survival, progression-free survival, progression-free interval, relapse-free survival, and disease-free survival could be measured in OSgc. OSgc can greatly help cancer biologists and clinicians to explore the effect of gene expression on patient survival. OSgc is freely available without restrictions at http://bioinfo.henu.edu.cn/GC/GCList.jsp.

The Matrisome Is Associated with Metabolic Reprograming in Stem-like Phenotypes of Gastric Cancer

Simple Summary

Our results suggested a correlation between the metabolic reprogramming associated with the high-matrisome group and stem-like phenotype in gastric cancer. Carbohydrate sulfotransferase 7 was found to be associated with the signaling transduction of overexpressed oncogenes and tumor suppressor genes in the high-matrisome group. The high expression of glycosaminoglycan biosynthesis-chondroitin sulfate metabolic pathway genes was associated with poor prognosis.

Abstract

The extracellular matrix (ECM) is an important regulator of all cellular functions, and the matrisome represents a major component of the tumor microenvironment. The matrisome is an essential component comprising genes encoding ECM glycoproteins, collagens, and proteoglycans; however, its role in cancer progression and the development of stem-like molecular subtypes in gastric cancer is unknown. We analyzed gastric cancer data from five molecular subtypes (n = 497) and found that metabolic reprograming differs based on the state of the matrisome. Approximately 95% of stem-like cancer type samples of gastric cancer were in the high-matrisome category, and energy metabolism was considerably increased in the high-matrisome group. Particularly, high glycosaminoglycan biosynthesis-chondroitin sulfate metabolic reprograming was associated with an unfavorable prognosis. Glycosaminoglycan biosynthesis-chondroitin sulfate metabolic reprograming may occur according to the matrisome status and contribute to the development of stem-like phenotypes. Our analysis suggests the possibility of precision medicine for anticancer therapies.