Biomarker-Driven Therapy in Metastatic Gastric and Esophageal Cancer: Real-Life Clinical Experience

The Growth Modulation Index (GMI) as an Efficacy Outcome in Cancer Clinical Trials: A Scoping Review with Suggested Reporting Guidelines

PURPOSE OF REVIEW

The growth modulation index (GMI) is defined as the ratio between the time to progression of a new line of treatment and the previous line. This ratio can be used to determine whether the new line of treatment brings a clinical benefit. It has been proposed as an outcome in trials evaluating non-cytotoxic drugs. Its interest lies in the intra-patient comparison. The terminology employed to refer to the GMI, as well as its definitions, are highly variable in the literature. Some uses of the GMI are arbitrary and not based on any scientific rationale. Our aim is to describe how the GMI is reported in the scientific literature.

RECENT FINDINGS

We carried out a scoping review using PubMed, Scopus, Web of Science and BASE (Bielefeld Academic Search Engine). The algorithm was composed of the terms "growth modulation index", "time to progression ratio" and "progression-free survival ratio". Documents in English, with full-text available, published up to 2023, were included. Among 227 included documents, 166 of which discussed GMI specifically. On these 166 documents, 76 reported on observational studies, 62 on interventional studies and 17 on methodological or statistical developments pertaining to the GMI. All were about oncology. Our review highlights significant variability in the reporting and use of the GMI. To address this, we propose standardized reporting guidelines. Additionally, we emphasize the need for methodological and statistical developments to improve the use of the GMI and to develop novel GMI-based trial designs.

Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors

Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC = 0.62, AUC = 0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell 'avatars' capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.

Innovative lanthanide complexes: Shaping the future of cancer/ tumor chemotherapy

Developing new therapeutic and diagnostic metals and metal complexes is a stunning example of how inorganic chemistry is rapidly becoming an essential part of modern medicine. More study of bio-coordination chemistry is needed to improve the design of compounds with fewer harmful side effects. Metal-containing drugs are widely utilized in the treatment of cancer. Platinum complexes are effective against some cancers, but new coordination compounds are being created with improved pharmacological properties and a broader spectrum of anticancer action. The coordination complexes of the 15 lanthanides or rare earth elements in the periodic table are crucial for diagnosing and treating cancer. Understanding and treating cancer requires the detection of binding lanthanide (III) ions or complexes to DNA and breaking DNA by these complexes. Current advances in lanthanide-based coordination complexes as anticancer treatments over the past five years are discussed in this study.

Inhibitory Effects of Rabdosia rubescens in Esophageal Squamous Cell Carcinoma: Network Pharmacology and Experimental Validation

Esophageal squamous cell carcinoma (ESCC) is one of the most frequently occurring diseases in the world. Rabdosia rubescens (RR) has been demonstrated to be effective against ESCC; however, the mechanism is unknown. The primary gene modules related to the clinical characteristics of ESCC were initially investigated in this research using weighted gene co-expression network analysis (WCGNA) and differential expression gene (DEG) analysis. We employed network pharmacology to study the hub genes linked with RR therapy on ESCC. A molecular docking simulation was achieved to identify the binding activity of central genes to RR compounds. Lastly, a chain of experimentations was used to verify the inhibitory effect of RR water extract on the ESCC cell line in vitro. The outcomes revealed that CCNA2, TOP2A, AURKA, CCNB2, CDK2, CHEK1, and other potential central targets were therapeutic targets for RR treatment of ESCC. In addition, these targets are over-represented in several cancer-related pathways, including the cell cycle signaling pathway and the p53 signaling pathway. The predicted targets displayed good bonding activity with the RR bioactive chemical according to a molecular docking simulation. In vitro experiments revealed that RR water extracts could inhibit ESCC cells, induce cell cycle arrest, inhibit cell proliferation, increase P53 expression, and decrease CCNA2, TOP2A, AURKA, CCNB2, CDK2, and CHEK1. In conclusion, our study reveals the molecular mechanism of RR therapy for ESCC, providing great potential for identifying effective compounds and biomarkers for ESCC therapy.

Integrated Analysis of Hub Genes and Pathways In Esophageal Carcinoma Based on NCBI's Gene Expression Omnibus (GEO) Database: A Bioinformatics Analysis

BACKGROUND Esophageal carcinoma (ESCA) is a health challenge with poor prognosis and limited treatment options. Our aim is to screen for hub genes and pathways associated with ESCA pathology as diagnostic or therapeutic targets. MATERIAL AND METHODS We downloaded 2 ESCA-related datasets from the Gene Expression Omnibus (GEO) database. Subsequently, differentially expressed genes (DEGs) of ESCA were determined by statistical analysis. Both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using online analytic tools. Network analysis was employed to construct a protein-protein interaction (PPI) network and to filter hub genes. We evaluated the expression level and impact of hub genes on survival of ESCA patients using the OncoLoc webserver. RESULTS A total of 210 DEGs were identified. The GO analysis showed that the DEGs were enriched in cell division. The KEGG pathway analysis showed DEGs that were enriched in cell cycle regulation, known cancer pathways, the PI3K-Akt signaling pathway, and the cGMP-PKG signaling pathway. The top 10 hub genes were markedly upregulated in ESCA tissue compared with normal esophageal tissue. Moreover, the expression level of the hub genes was different at different pathological stages of ESCA. Further prognostic analysis identified that the top 10 hub genes were related to late survival of ESCA patients, while exhibiting few associations with early survival time. CONCLUSIONS The signaling pathways involving the DEGs probably represent the pathological mechanism underlying ESCA. The hub genes were associated with survival of ESCA patients, and as such have the potential to serve as diagnostic indicators and therapeutic targets.

Identification of crucial genes correlated with esophageal cancer by integrated high-throughput data analysis

BACKGROUND

Esophageal cancer (ESCA) is one of the most deadly malignancies in the world. Although the management and treatment of patients with ESCA have improved, the overall 5-year survival rate is still very poor.

METHODS

The study aimed to identify potential key genes associated with the pathogenesis and prognosis of ESCA. In the study, integrated bioinformatics methods were used to screen differentially expressed genes (DEGs) between ESCA and normal tissue in the data set of gene expression profiles. The hub gene in DEGs was further analyzed by protein-protein interaction (PPI) network and survival analysis to explore its relationship with the pathogenesis and poor prognosis of ESCA.

RESULTS

134 up-regulated genes and 183 down-regulated genes were obtained in ESCA compared with normal tissues. Moreover, the PPI network was established with 176 nodes and 800 interactions. Ten hub genes (AURKA, CDC20, BUB1, TOP2A, ASPM, DLGAP5, TPX2, CENPF, UBE2C, and NEK2) were filtered out based on the degree value. Functional enrichment analysis indicated that a variety of extracellular related items and ECM-receptor interaction pathway were all correlated with the ESCA.

CONCLUSIONS

The results of this study would provide some guidance for further study of diagnostic and prognostic biomarkers to promote ESCA treatment.

Precision Surgical Therapy for Adenocarcinoma of the Esophagus and Esophagogastric Junction

INTRODUCTION

To facilitate the initial clinical decision regarding whether to use esophagectomy alone or neoadjuvant therapy in surgical care for individual patients with adenocarcinoma of the esophagus and esophagogastric junction-information not available from randomized trials-a machine-learning analysis was performed using worldwide real-world data on patients undergoing different therapies for this rare adenocarcinoma.

METHODS

Using random forest technology in a sequential analysis, we (1) identified eligibility for each of four therapies among 13,365 patients: esophagectomy alone (n = 6649), neoadjuvant therapy (n = 4706), esophagectomy and adjuvant therapy (n = 998), and neoadjuvant and adjuvant therapy (n = 1022); (2) performed survival analyses incorporating interactions of patient and cancer characteristics with therapy; (3) determined optimal therapy as that predicted to maximize lifetime within 10 years (restricted mean survival time; RMST) for each patient; and (4) compared lifetime gained from optimal versus actual therapies.

RESULTS

Actual therapy was optimal in 61% of those receiving esophagectomy alone; neoadjuvant therapy was optimal for 36% receiving neoadjuvant therapy. Many patients were predicted to benefit from postoperative adjuvant therapy. Total RMST for actual therapy received was 58,825 years. Had patients received optimal therapy, total RMST was predicted to be 62,982 years, a 7% gain.

CONCLUSIONS

Average treatment effect for adenocarcinoma of the esophagus yields only crude evidence-based therapy guidelines. However, patient response to therapy is widely variable, and survival after data-driven predicted optimal therapy often differs from actual therapy received. Therapy must address an individual patient's cancer and clinical characteristics to provide precision surgical therapy for adenocarcinoma of the esophagus and esophagogastric junction.