Improving gastric cancer preclinical studies using diverse in vitro and in vivo model systems

Differences in treatment outcome between translational platforms in developing therapies for gastrointestinal cancers

The variability in translational models profoundly impacts the outcomes and predictive value of preclinical studies for gastrointestinal (GI) cancer treatments. Preclinical models, including 2D cell cultures, 3D organoids, patient-derived xenografts (PDXs), and animal models, provide distinct advantages and limitations in replicating the complex tumor microenvironment (TME) of human cancers. Each model's unique biological and structural differences contribute to discrepancies in treatment responses, challenging the direct translation of experimental results to clinical settings. While 2D cell cultures are cost-effective and suitable for high-throughput screening, they lack the 3D architecture and cellular interactions of the in vivo TME. Organoids offer a more comprehensive 3D structure that better mirrors tumor heterogeneity, yet they still face limitations in fully mimicking in vivo conditions, such as vascularization and immune cell interactions. PDXs, although more representative of human cancers due to their genetic fidelity and TME preservation, are costly and resource-intensive, with human stromal and immune components gradually replaced by murine counterparts over time. This review assesses the strengths and limitations of each model, highlighting recent advancements in translational platforms that incorporate complex TME features. Understanding the influence of model selection on treatment efficacy predictions is essential for enhancing the reliability of preclinical findings and advancing personalized therapeutic strategies for GI cancers.

HIF1A protein expression is correlated with clinical features in gastric cancer: an updated systematic review and meta-analysis

To elucidate the correlation of HIF1A with clinicopathologic characteristics in patients with gastric cancer (GC), we conducted a systematic review and meta-analysis. We searched PubMed, Embase and Web of Science for studies on GC and HIF1A, covering studies published until January 31st, 2022. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for clinical characteristics based on high and low HIF1A protein levels. We used random-effects and fixed-effects meta-analysis methods to determine mean effect sizes of ORs and evaluated publication heterogeneity with τ2, I2, and Q values. Additionally, we generated funnel plots to inspect publication bias. Our meta-analysis included 20 publications with 3416 GC patients to estimate the association between high or low HIF1A expression and clinical characteristics. Positive HIF1A expression was significantly associated with T stage progression (OR: 2.46; 95% CI 1.81-3.36; P < 0.01), TNM stage progression (OR: 2.50; 95% CI 1.61-3.87; P < 0.01), lymph node metastasis (OR: 2.06; 95% CI 1.44-2.94; P < 0.01), undifferentiated status (OR: 1.83; 95% CI 1.45-2.32; P < 0.01), M stage progression (OR: 2.34; 95% CI 1.46-3.77; P < 0.01), Borrmann stage progression (OR: 1.48; 95% CI 1.02-2.15; P = 0.04), larger tumor size (OR: 1.27; 95% CI 1.06-1.52; P < 0.01), vascular invasion (OR: 1.94; 95% CI 1.38-2.72; P < 0.01), and higher vascular endothelial growth factor (VEGF) protein expression (OR: 2.61; 95% CI 1.79-3.80; P < 0.01) in our meta-analysis. GC Patients highly expressing HIF1A protein might be prone to tumor progression, poorly differentiated GC cell types, and a high VEGF expression.

RHOA protein expression correlates with clinical features in gastric cancer: a systematic review and meta-analysis

Background

Gastric cancer (GC) is one of the most fatal cancers worldwide and is generally only detected after it has progressed to an advanced stage. Since there is a lack of comprehensive data on RHOA protein expression of patients with GC, this study utilized a systematic review and meta-analysis to address the limitation. The objective of this meta-analysis was to link GC clinical features with RHOA protein high- vs. low-expressing patients with GC.

Methods

The PubMed and Web of Science were used for a systematic literature review of GC related to RHOA. The included studies were obtained from two literature databases from past to Aug 31, 2021, by searching keywords. This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The odds ratios (ORs) and 95% confidential intervals (CIs) for clinical features were estimated according to the high and low protein expression levels of RhoA. The mean effect sizes of ORs were obtained using the random-effects and fixed-effects models of meta-analysis. Heterogeneity of the studies was assesed by using statistics: τ², I²; and Q values. The symmetry of funnel plots were inspected for publication bias.

Results

Finally, 10 studies including 1,389 patients with GC (735 RHOA-positive and 654 RHOA-negative) were eligible for our meta-analysis to estimate associations between the protein expression and clinical features (e.g., Union for International Cancer Control [UICC] stage progression, differentiation, Lauren histological classification, and vascular invasion). In our meta-analysis, RHOA positive expression was determined to have a statistically significant association with UICC stage progression (P = 0.02) and poorly differentiated status (P = 0.02). The association between RHOA positivity and Lauren subtypes was not statistically significant (P = 0.07).

Conclusions

This meta-analysis suggested that RhoA protein expression in patients with GC was associated with clinical features: UICC stage progression and poorly differentiated status. Our findings are inconclusive but indicate that high RHOA protein expressing patients with GC could predict advanced UICC stages. A large prospective cohort study is required for validation in future.

Any Role of PIK3CA and PTEN Biomarkers in the Prognosis in Oral Squamous Cell Carcinoma?

Oral squamous cell carcinoma (OSCC) accounts for 95% of the lesions in the oral cavity. Despite development in OSCC management, the outcome is still unsatisfactory. Identification of new therapies in OSCC is urgently needed. One objective of such treatment may be a signaling pathway of phosphatidylinositol 3-kinase. The study group included 92 patients treated for OSCC at the University Clinical Centre in Gdańsk, Poland. Study was performed on formalin-fixed paraffin-embedded samples from primary OSCC. Phosphatidylinositol-4,5-bisphosphate 3-kinase (PIK3CA) and phosphatase and tensin homolog encoded on chromosome 10 (PTEN) protein expression was assessed by immunohistochemistry (IHC). PIK3CA gene copy number was analyzed using chromogenic and silver in situ hybridization where molecular probes are marked by chromogens and silver ions. PIK3CA IHC H-score ≥ 70 was found in 51.65% patients, and loss of PTEN protein was noticed in 31.46% cases. PIK3CA amplification was detected in 5 tumors. In the case of PTEN protein expression, there was an inverse correlation with the T stage of the primary tumor (r = -0.243) and positive correlation with a 5-year survival (r = 0.235). The number of copies of the PIK3CA gene was associated with the tumor grading (r = 0.208). The present study shows that loss of PTEN protein and the grading (p = 0.040), distant metastases (p = 0.033), smoking (p = 0.016), and alcohol abuse (p = 0.042) were prognostic factors for the survival of patients with OSCC. In contrast, the presence of amplification and OSCC on the floor of the mouth resulted in a nearly six-fold increase in the risk of shortening survival (p = 0.037). Our finding suggests a potential prognostic significance of PTEN loss and PIK3CA amplification in OSCC. Future studies are needed to confirm our results.

Rational design of small molecule RHOA inhibitors for gastric cancer

Previously, we identified Ras homologous A (RHOA) as a major signaling hub in gastric cancer (GC), the third most common cause of cancer death in the world, prompting us to rationally design an efficacious inhibitor of this oncogenic GTPase. Here, based on that previous work, we extend those computational analyses to further pharmacologically optimize anti-RHOA hydrazide derivatives for greater anti-GC potency. Two of these, JK-136 and JK-139, potently inhibited cell viability and migration/invasion of GC cell lines, and mouse xenografts, diversely expressing RHOA. Moreover, JK-136's binding affinity for RHOA was >140-fold greater than Rhosin, a nonclinical RHOA inhibitor. Network analysis of JK-136/-139 vs. Rhosin treatments indicated downregulation of the sphingosine-1-phosphate, as an emerging cancer metabolic pathway in cell migration and motility. We assert that identifying and targeting oncogenic signaling hubs, such as RHOA, represents an emerging strategy for the design, characterization, and translation of new antineoplastics, against gastric and other cancers.

A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients

Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs’ molecular “fingerprints”, along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.

Identification of different gene expressions between diffuse- and intestinal-type spheroid-forming gastric cancer cells

BACKGROUND

Three-dimensional in vitro spheroid models are unique because they are considered for enrichment of specific cell populations with self-renewal ability. In this study, we explored the different mechanisms of gastric cancer spheroid-forming cells according to the Lauren classification.

METHODS

We isolated and enriched cells with self-renewal ability using spheroid-forming methods from gastric cancer cell lines. The expression of candidate target genes was investigated using western blot and qRT-PCR analysis. Lentiviral shRNA knockdown of target gene expression was performed and the effects on spheroid, colony forming, and tumorigenic ability were analyzed.

RESULTS

The SNU-638, SNU-484, MKN-28, and NCI-N87 successfully formed spheroid from single cell and enriched for self-renewal ability from 11 gastric cancer cell lines, including diffuse and intestinal types. The expression of SOX2 and E-cadherin increased in spheroid-forming cells in a diffuse-type cell line (SNU-638 and SNU-484), but not in the intestinal type (MKN-28 and NCI-N87). In contrast, ERBB3 expression was only increased in intestinal-type spheroid cells. The depletion of each candidate target gene expression suppressed self-renewal ability to grow as spheroids and colonies in a soft agar assay. In particular, down-regulated ERBB3 in the intestinal-type cell lines inhibited tumor growth in a mouse xenograft model. We found that high ERBB3 gene expression correlates with decreased survival in the intestinal type of gastric cancer.

CONCLUSIONS

Our results suggest that diffuse- and intestinal-type spheroid-forming cells express genes differently. Our data suggest that these candidate genes from spheroid-forming cells can be used in applications in targeted therapy.

RHOA in Gastric Cancer: Functional Roles and Therapeutic Potential

The well-known signal mediator and small GTPase family member, RHOA, has now been associated with the progression of specific malignancies. In this review, we appraise the biomedical literature regarding the role of this enzyme in gastric cancer (GC) signaling, suggesting potential clinical significance. To that end, we examined RHOA activity, with regard to second-generation hallmarks of cancer, finding particular association with the hallmark "activation of invasion and metastasis." Moreover, an abundance of studies show RHOA association with Lauren classification diffuse subtype, in addition to poorly differentiated GC. With regard to therapeutic value, we found RHOA signaling to influence the activity of specific widely used chemotherapeutics, and its possible antagonism by various dietary constituents. We also review currently available targeted therapies for GC. The latter, however, showed a paucity of such agents, underscoring the urgent need for further investigation into treatments for this highly lethal malignancy.

Gene Regulation and Targeted Therapy in Gastric Cancer Peritoneal Metastasis: Radiological Findings from Dual Energy CT and PET/CT

Gastric cancer remains fourth in cancer incidence worldwide with a five-year survival of only 20%-30%. Peritoneal metastasis is the most frequent type of metastasis that accompanies unresectable gastric cancer and is a definitive determinant of prognosis. Preventing and controlling the development of peritoneal metastasis could play a role in helping to prolong the survival of gastric cancer patients. A non-invasive and efficient imaging technique will help us to identify the invasion and metastasis process of peritoneal metastasis and to monitor the changes in tumor nodules in response to treatments. This will enable us to obtain an accurate description of the development process and molecular mechanisms of gastric cancer. We have recently described experiment using dual energy CT (DECT) and positron emission tomography/computed tomography (PET/CT) platforms for the detection and monitoring of gastric tumor metastasis in nude mice models. We have shown that weekly continuous monitoring with DECT and PET/CT can identify dynamic changes in peritoneal metastasis. The sFRP1-overexpression in gastric cancer mice models showed positive radiological performance, a higher FDG uptake and increasing enhancement, and the SUVmax (standardized uptake value) of nodules demonstrated an obvious alteration trend in response to targeted therapy of TGF-β1 inhibitor. In this article, we described the detailed non-invasive imaging procedures to conduct more complex research on gastric cancer peritoneal metastasis using animal models and provided representative imaging results. The use of non-invasive imaging techniques should enable us to better understand the mechanisms of tumorigenesis, monitor tumor growth, and evaluate the effect of therapeutic interventions for gastric cancer.

MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data

Pathway based analysis of high throughput transcriptome data is a widely used approach to investigate biological mechanisms. Since a pathway consists of multiple functions, the recent approach is to determine condition specific sub-pathways or subpaths. However, there are several challenges. First, few existing methods utilize explicit gene expression information from RNA-seq. More importantly, subpath activity is usually an average of statistical scores, e.g., correlations, of edges in a candidate subpath, which fails to reflect gene expression quantity information. In addition, none of existing methods can handle multiple phenotypes. To address these technical problems, we designed and implemented an algorithm, MIDAS, that determines condition specific subpaths, each of which has different activities across multiple phenotypes. MIDAS utilizes gene expression quantity information fully and the network centrality information to determine condition specific subpaths. To test performance of our tool, we used TCGA breast cancer RNA-seq gene expression profiles with five molecular subtypes. 36 differentially activate subpaths were determined. The utility of our method, MIDAS, was demonstrated in four ways. All 36 subpaths are well supported by the literature information. Subsequently, we showed that these subpaths had a good discriminant power for five cancer subtype classification and also had a prognostic power in terms of survival analysis. Finally, in a performance comparison of MIDAS to a recent subpath prediction method, PATHOME, our method identified more subpaths and much more genes that are well supported by the literature information.

AVAILABILITY

http://biohealth.snu.ac.kr/software/MIDAS/.

Databases and tools for constructing signal transduction networks in cancer

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., “big data”), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called “systems biology”. One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets.