Precision Medicine In Gastric Cancer Research Articles

Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.

A potential therapeutic approach for gastric cancer: inhibition of LACTB transcript 1.

This study sought to investigate the role of LACTB transcript 1 in regulating adaptive immune resistance and stemness in gastric cancer and its potential as a therapeutic target for precision medicine. Bioinformatics analysis and RT-qPCR were used to analyze the expression level of LACTB and its transcripts in gastric cancer cells. The effects of LACTB transcript 1 on adaptive immune resistance and stemness were evaluated using in vitro cell experiments and western blotting experiments. Our study findings revealed that LACTB transcript 1 modulated adaptive immune resistance and inhibited the stemness of gastric cancer cells. Knocking down the expression level of LACTB transcript 1 activated autophagy and inhibited EMT. As expected, overexpression of LACTB transcript 1 yielded the opposite findings. The expression level of LACTB transcript 1 in the peripheral blood of gastric cancer patients was consistent with the bioinformatics analysis, suggesting its potential as a biomarker of gastric cancer. LACTB transcript 1 is a promising therapeutic target for precision medicine in gastric cancer by modulating immune evasion mechanisms and stemness. These findings provide insights into leveraging long non-coding RNAs (lncRNAs) in immunotherapy, radiotherapy, and chemotherapy to enhance cancer therapy efficacy, particularly in the context of targeting tumor heterogeneity and stemness.

Precision Medicine for Gastric Cancer: Applicability from the Perspective of Helicobacter pylori Infection

Precision Medicine for Gastric Cancer: Applicability from the Perspective of Helicobacter pylori Infection

Targeted Therapies and Developing Precision Medicine in Gastric Cancer.

Gastric cancer is an aggressive disease with survival remaining poor in the advanced setting. More than a decade after the first targeted treatment was approved, still only HER2, MSI and PDL-1 status have reached everyday practice in terms of guiding treatment options for these patients. However, various new targets and novel treatments have recently been investigated and have shown promise in improving survival outcomes. In this review, we will summarise previous and currently ongoing studies on predictive biomarkers, possible new targeted treatments, potential reasons for conflicting trial results and hope for the future of precision medicine in gastric cancer.

Radiomics in precision medicine for gastric cancer: opportunities and challenges.

Radiomic features derived from routine medical images show great potential for personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and quality of radiomic research as well as its potential for identifying biomarkers to predict therapy response and prognosis in patients with GC. We performed a systematic search of the PubMed and Embase databases for articles published from inception through July 10, 2021. The phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool were applied to evaluate scientific and reporting quality. Twenty-five studies consisting of 10,432 patients were included. 96% of studies extracted radiomic features from CT images. Association between radiomic signature and therapy response was evaluated in seven (28%) studies; association with survival was evaluated in 17 (68%) studies; one (4%) study analyzed both. All results of the included studies showed significant associations. Based on the phase classification criteria for image mining studies, 18 (72%) studies were classified as phase II, with two, four, and one studies as discovery science, phase 0 and phase I, respectively. The median RQS score for the radiomic studies was 44.4% (range, 0 to 55.6%). There was extensive heterogeneity in the study population, tumor stage, treatment protocol, and radiomic workflow amongst the studies. Although radiomic research in GC is highly heterogeneous and of relatively low quality, it holds promise for predicting therapy response and prognosis. Efforts towards standardization and collaboration are needed to utilize radiomics for clinical application. • Radiomics application of gastric cancer is increasingly being reported, particularly in predicting therapy response and survival. • Although radiomics research in gastric cancer is highly heterogeneous and relatively low quality, it holds promise for predicting clinical outcomes. • Standardized imaging protocols and radiomic workflow are needed to facilitate radiomics into clinical use.

Application of artificial intelligence in microbiome study promotes precision medicine for gastric cancer

Application of artificial intelligence in microbiome study promotes precision medicine for gastric cancer

The beginning of the era of precision medicine for gastric cancer with fibroblast growth factor receptor 2 aberration.

Despite recent advances in the systemic treatment of metastatic gastric cancer (GC), prognostic outcomes remain poor. Considerable research effort has been invested in characterizing the genomic landscape of GC and identifying potential therapeutic targets. FGFR2 is one of the most attractive targets because aberrations in this gene are frequently associated with GC, particularly the diffuse type in Lauren's classification, which confers an unfavorable prognosis. Based on the preclinical data, the FGFR2 signaling pathway plays a key role in the development and progression of GC, and several FGFR inhibitors have been clinically assessed. However, the lack of robust treatment efficacy has hampered precision medicine for patients with FGFR2-aberrant GC. Recently, the clinical benefits of the FGFR2-IIIb-selective monoclonal antibody bemarituzumab for FGFR2b-positive GC patients were shown in a randomized phase II FIGHT trial of bemarituzumab combined with the first-line chemotherapy. This trial demonstrates proof of concept, suggesting that FGFR2 is a relevant therapeutic target for patients with FGFR2b-positive GC and that bemarituzumab brings new hope for diffuse-type GC patients. In this review, we summarize the oncogenic roles of FGFR2 signaling and highlight the most recent advances in FGFR inhibitors based on the findings of pivotal clinical trials for patients with FGFR2-aberrant GC. Thus, the era of precision medicine for patients with FGFR2-aberrant GC will be opened.

PSY8-2 Search of biomarkers and creation of innovative methods required for precision medicine for gastric cancer

PSY8-2 Search of biomarkers and creation of innovative methods required for precision medicine for gastric cancer

High Expression of Interleukin-2 Receptor Subunit Gamma Reveals Poor Prognosis in Human Gastric Cancer.

Precision medicine for gastric cancer (GC) is still an unsolved issue, because most available target drugs are not specifically designed for GC. Exploring novel signaling molecules with target value for GC is in urgent need. This study aimed to reveal that interleukin-2 receptor subunit gamma (IL2RG) is such a key molecule in human GC progression. GC tissues and paracancerous gastric tissues were collected from 7 patients (5 males and 2 females) during tumor radical excision surgery. These tissues were used to identify the differentially expressed genes (DEGs) with RNA-seq and serial bioinformatics analyses including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, gene expression profiling interactive analysis (GEPIA), and survival analysis. RT-qPCR and western blotting were performed to compare the mRNA and protein expression levels of IL2RG between GC tissues and adjacent normal gastric tissues as well as between GC cell line SGC-7901 and normal gastric epithelial cell line GES-1. Results showed striking elevations of IL2RG both in the mRNA and protein levels in GC tissues and human gastric cancer SGC-7901 cell line compared, respectively, with the adjacent normal gastric tissues and normal GES-1 cells, and higher IL2RG expression was associated with lower survival. Analyses on the GSE29272 and GSE15459 datasets from Gene Expression Omnibus verified that IL2RG was highly expressed in GC patients and was associated with poor overall survival. In addition, molecular docking showed that a small molecule, resatorvid (TAK 242), might be an inhibitor of IL2RG. We conclude that IL2RG is overexpressed in advanced GC and is associated with low survival. IL2RG may serve as a biomarker of GC progression and poor prognosis.

Logistic big data prediction of gastric cancer

In the era of big data, precision medicine for gastric cancer refers to the high throughput analysis of clinical data of patients with gastric cancer. The proposal of personalized diagnosis and treatment for each patient’s molecular biology and pathological characteristics, so as to achieve personalized precision medicine. In this paper, the prediction method is significance analysis and the statistical method of prediction model is logical analysis and discriminant analysis, and the significance test standard is p < 0.05. Logistic regression was used to identify and predict the prevalence of cancer patients, and the data used for prediction analysis were all from MS SQL database. The results showed that blood routine, blood biochemical and urine routine data can be used to distinguish cancer patients from healthy people. The cancer risk prediction model based on blood routine, blood biochemical and urine routine data can accurately target high-risk cancer people with an accuracy of 95.5%. In the era of big data, the accurate diagnosis and treatment strategy of gastric cancer can promote the aggregation, integration and sharing of gastric cancer related data.

A Comprehensive PDX Gastric Cancer Collection Captures Cancer Cell-Intrinsic Transcriptional MSI Traits.

Gastric cancer is the world's third leading cause of cancer mortality. In spite of significant therapeutic improvements, the clinical outcome for patients with advanced gastric cancer is poor; thus, the identification and validation of novel targets is extremely important from a clinical point of view. We generated a wide, multilevel platform of gastric cancer models, comprising 100 patient-derived xenografts (PDX), primary cell lines, and organoids. Samples were classified according to their histology, microsatellite stability, Epstein-Barr virus status, and molecular profile. This PDX platform is the widest in an academic institution, and it includes all the gastric cancer histologic and molecular types identified by The Cancer Genome Atlas. PDX histopathologic features were consistent with those of patients' primary tumors and were maintained throughout passages in mice. Factors modulating grafting rate were histology, TNM stage, copy number gain of tyrosine kinases/KRAS genes, and microsatellite stability status. PDX and PDX-derived cells/organoids demonstrated potential usefulness to study targeted therapy response. Finally, PDX transcriptomic analysis identified a cancer cell-intrinsic microsatellite instability (MSI) signature, which was efficiently exported to gastric cancer, allowing the identification, among microsatellite stable (MSS) patients, of a subset of MSI-like tumors with common molecular aspects and significant better prognosis. In conclusion, we generated a wide gastric cancer PDX platform, whose exploitation will help identify and validate novel "druggable" targets and optimize therapeutic strategies. Moreover, transcriptomic analysis of gastric cancer PDXs allowed the identification of a cancer cell-intrinsic MSI signature, recognizing a subset of MSS patients with MSI transcriptional traits, endowed with better prognosis. SIGNIFICANCE: This study reports a multilevel platform of gastric cancer PDXs and identifies a MSI gastric signature that could contribute to the advancement of precision medicine in gastric cancer.

Precision medicine in gastric cancer.

Gastric cancer (GC) is a complex disease linked to a series of environmental factors and unhealthy lifestyle habits, and especially to genetic alterations. GC represents the second leading cause of cancer-related deaths worldwide. Its onset is subtle, and the majority of patients are diagnosed once the cancer is already advanced. In recent years, there have been innovations in the management of advanced GC including the introduction of new classifications based on its molecular characteristics. Thanks to new technologies such as next-generation sequencing and microarray, the Cancer Genome Atlas and Asian Cancer Research Group classifications have also paved the way for precision medicine in GC, making it possible to integrate diagnostic and therapeutic methods. Among the objectives of the subdivision of GC into subtypes is to select patients in whom molecular targeted drugs can achieve the best results; many lines of research have been initiated to this end. After phase III clinical trials, trastuzumab, anti-Erb-B2 receptor tyrosine kinase 2 (commonly known as ERBB2) and ramucirumab, anti-vascular endothelial growth factor receptor 2 (commonly known as VEGFR2) monoclonal antibodies, were approved and introduced into first- and second-line therapies for patients with advanced/metastatic GC. However, the heterogeneity of this neoplasia makes the practical application of such approaches difficult. Unfortunately, scientific progress has not been matched by progress in clinical practice in terms of significant improvements in prognosis. Survival continues to be low in contrast to the reduction in deaths from many common cancers such as colorectal, lung, breast, and prostate cancers. Although several target molecules have been identified on which targeted drugs can act and novel products have been introduced into experimental therapeutic protocols, the overall approach to treating advanced stage GC has not substantially changed. Currently, surgical resection with adjuvant or neoadjuvant radiotherapy and chemotherapy are the most effective treatments for this disease. Future research should not underestimate the heterogeneity of GC when developing diagnostic and therapeutic strategies aimed toward improving patient survival.

Next-generation sequencing and biomarkers for gastric cancer: what is the future?

Recent years have witnessed an improved understanding of tumour biology and the molecular features of gastric cancer. Remarkable advances in next-generation sequencing technologies have defined the genomic landscape of gastric cancer. In fact, several molecular classifications have been proposed, and distinct molecular subtypes have been identified, which could serve as a roadmap for patient stratification and trials of targeted therapies. At present, clinical trials of new agents, such as receptor tyrosine kinases inhibitors, antibody–drug conjugates and IMAB362 (anti-Claudin 18.2), are ongoing. Furthermore, biomarkers of immune checkpoint inhibitors or combination therapy have been ardently investigated. These developments could facilitate precision medicine for gastric cancer in the near future.

Clinical Implementation of Precision Medicine in Gastric Cancer

Gastric cancer (GC) is one of the deadliest malignancies in the world. Currently, clinical treatment decisions are mostly made based on the extent of the tumor and its anatomy, such as tumor-node-metastasis staging. Recent advances in genome-wide molecular technology have enabled delineation of the molecular characteristics of GC. Based on this, efforts have been made to classify GC into molecular subtypes with distinct prognosis and therapeutic response. Simplified algorithms based on protein and RNA expressions have been proposed to reproduce the GC classification in the clinical field. Furthermore, a recent study established a single patient classifier (SPC) predicting the prognosis and chemotherapy response of resectable GC patients based on a 4-gene real-time polymerase chain reaction assay. GC patient stratification according to SPC will enable personalized therapeutic strategies in adjuvant settings. At the same time, patient-derived xenografts and patient-derived organoids are now emerging as novel preclinical models for the treatment of GC. These models recapitulate the complex features of the primary tumor, which is expected to facilitate both drug development and clinical therapeutic decision making. An integrated approach applying molecular patient stratification and patient-derived models in the clinical realm is considered a turning point in precision medicine in GC.

Actionable gene-based classification toward precision medicine in gastric cancer

BackgroundIntertumoral heterogeneity represents a significant hurdle to identifying optimized targeted therapies in gastric cancer (GC). To realize precision medicine for GC patients, an actionable gene alteration-based molecular classification that directly associates GCs with targeted therapies is needed.MethodsA total of 207 Japanese patients with GC were included in this study. Formalin-fixed, paraffin-embedded (FFPE) tumor tissues were obtained from surgical or biopsy specimens and were subjected to DNA extraction. We generated comprehensive genomic profiling data using a 435-gene panel including 69 actionable genes paired with US Food and Drug Administration-approved targeted therapies, and the evaluation of Epstein-Barr virus (EBV) infection and microsatellite instability (MSI) status.ResultsComprehensive genomic sequencing detected at least one alteration of 435 cancer-related genes in 194 GCs (93.7%) and of 69 actionable genes in 141 GCs (68.1%). We classified the 207 GCs into four The Cancer Genome Atlas (TCGA) subtypes using the genomic profiling data; EBV (N = 9), MSI (N = 17), chromosomal instability (N = 119), and genomically stable subtype (N = 62). Actionable gene alterations were not specific and were widely observed throughout all TCGA subtypes. To discover a novel classification which more precisely selects candidates for targeted therapies, 207 GCs were classified using hypermutated phenotype and the mutation profile of 69 actionable genes. We identified a hypermutated group (N = 32), while the others (N = 175) were sub-divided into six clusters including five with actionable gene alterations: ERBB2 (N = 25), CDKN2A, and CDKN2B (N = 10), KRAS (N = 10), BRCA2 (N = 9), and ATM cluster (N = 12). The clinical utility of this classification was demonstrated by a case of unresectable GC with a remarkable response to anti-HER2 therapy in the ERBB2 cluster.ConclusionsThis actionable gene-based classification creates a framework for further studies for realizing precision medicine in GC.

Genomic profiling using a 435-gene panel provides a vision for precision medicine in Japanese gastric cancer.

e15592 Background: Molecular heterogeneity represents a significant hurdle in realizing precision medicine for gastric cancer (GC). Large-scale whole exome sequencing projects have identified distinct molecular subtypes to help define the heterogeneity of GC. However, it remains unclear whether the targeted gene panel-based sequencing can provide optimum targeted therapies and clinical utility in GC. The aim of this study is to generate comprehensive genomic profiling data and classify Japanese GC into actionable clusters associated with targeted therapies. Methods: FFPE tumor tissues were obtained from surgical or biopsy specimens of 207 Japanese patients with GC. Extracted DNA was subjected to genomic sequencing for 435 cancer related genes including 69 druggable genes with FDA approved targeted therapies. Somatic mutations, copy number alterations (SCNA), microsatellite instability (MSI) and Epstein-Barr virus (EBV) infection were evaluated using sequencing data. Results: Genomic sequencing identified at least one alteration of 435 genes in 194 pts (94%), and that of 69 druggable genes in 141 pts (68%). The most frequently altered druggable gene was ERBB2 (14%), following BRCA2 (11%) and ATM (10%). We successfully classified 207 tumors into four molecular subtypes, similar to the previously report; EBV (4%), MSI (8%), chromosomal instability (58%) and genomically stable subtype (30%). Frequent alterations of druggable genes ( &gt; 5%) were widely observed through these subtypes. To discover the novel classifications associated with targeted therapies, we classified 207 tumors using mutation rate and hierarchical clustering. We identified a hypermutated group (n = 32), and a remaining non-hypermutated group (n = 175) which were sub-divided into six clusters including five actionable ones; ERBB2 (n = 25), CDKN2A and CDKN2B (n = 10), KRAS (n = 10), BRCA2 (n = 9) and ATM cluster (n = 12). Interestingly, we experienced a case of unresectable GC with a remarkable response for anti-HER2 therapy in the ERBB2 cluster. Conclusions: Genomic sequencing using a 435-gene panel has the potential to provide the information of optimum targeted therapies for upcoming precision medicine in Japanese GC.