Recent Advances in the Systemic Treatment of Localized Gastroesophageal Cancer

CT-Derived Quantitative Image Features Predict Neoadjuvant Treatment Response in Adenocarcinoma of the Gastroesophageal Junction with High Accuracy

BACKGROUND

The purpose of this retrospective study was to evaluate the value of contrast-enhanced computed tomography (CE-CT) image features at baseline and after neoadjuvant chemotherapy in predicting histopathological response in patients with adenocarcinoma of the gastroesophageal junction (GEJ).

METHODS

A total of 105 patients with a diagnosis of adenocarcinoma of the GEJ were examined by CE-CT at baseline and preoperatively after neoadjuvant chemotherapy. All patients underwent surgical resection. Histopathological parameters and tumor regression grading according to Becker et al. were collected in 93 patients. Line profiles of the primary tumor area in baseline and preoperative CE-CT were generated using ImageJ. Maximum tumor density and tumor-to-wall density delta were calculated and correlated with the histopathological tumor response. In addition, tumor response was assessed according to standard RECIST measurements in all patients and by endoscopy in 72 patients.

RESULTS

Baseline and change in baseline to preoperative CE-CT parameters showed no significant differences between responders (Becker grade 1a, 1b) and non-responders (Becker grade 2, 3). After neoadjuvant therapy, responders and non-responders showed significant differences in maximum density and tumor-to-wall density delta values. Line profile measurements showed excellent inter-rater agreement. In comparison, neither RECIST nor endoscopy showed significant differences between these groups.

CONCLUSIONS

Posttreatment CE-CT can predict histopathological therapy response to neoadjuvant treatment in adenocarcinoma of GEJ patients with high accuracy and thus may improve patient management.

Predicting human epidermal growth factor receptor 2 status of patients with gastric cancer by computed tomography and clinical features

Background

There have been no studies on predicting human epidermal growth factor receptor 2 (HER2) status in patients with resectable gastric cancer (GC) in the neoadjuvant and perioperative settings. We aimed to investigate the use of preoperative contrast-enhanced computed tomography (CECT) imaging features combined with clinical characteristics for predicting HER2 expression in GC.

Methods

We retrospectively enrolled 301 patients with GC who underwent curative resection and preoperative CECT. HER2 status was confirmed by postoperative immunohistochemical analysis with or without fluorescence in situ hybridization. A prediction model was developed using CECT imaging features and clinical characteristics that were independently associated with HER2 status using multivariate logistic regression analysis. Receiver operating characteristic curves were constructed and the performance of the prediction model was evaluated. The bootstrap method was used for internal validation.

Results

Three CECT imaging features and one serum tumor marker were independently associated with HER2 status in GC: enhancement ratio in the arterial phase (odds ratio [OR] = 4.535; 95% confidence interval [CI], 2.220-9.264), intratumoral necrosis (OR = 2.64; 95% CI, 1.180-5.258), tumor margin (OR = 3.773; 95% CI, 1.968-7.235), and cancer antigen 125 (CA125) level (OR = 5.551; 95% CI, 1.361-22.651). A prediction model derived from these variables showed an area under the receiver operating characteristic curve of 0.802 (95% CI, 0.740-0.864) for predicting HER2 status in GC. The established model was stable, and the parameters were accurately estimated.

Conclusions

Enhancement ratio in the arterial phase, intratumoral necrosis, tumor margin, and CA125 levels were independently associated with HER2 status in GC. The prediction model derived from these factors may be used preoperatively to estimate HER2 status in GC and guide clinical treatment.

Role of circular RNAs and gut microbiome in gastrointestinal cancers and therapeutic targets

Gastrointestinal cancers are a huge worldwide health concern, which includes a wide variety of digestive tract cancers. Circular RNAs (circRNAs), a kind of non-coding RNA (ncRNAs), are a family of single-stranded, covalently closed RNAs that have become recognized as crucial gene expression regulators, having an impact on several cellular functions in cancer biology. The gut microbiome, which consists of several different bacteria, actively contributes to the regulation of host immunity, inflammation, and metabolism. CircRNAs and the gut microbiome interact significantly to greatly affect the growth of GI cancer. Several studies focus on the complex functions of circRNAs and the gut microbiota in GI cancers, including esophageal cancer, colorectal cancer, gastric cancer, hepatocellular cancer, and pancreatic cancer. It also emphasizes how changed circRNA expression profiles and gut microbiota affect pathways connected to malignancy as well as how circRNAs affect hallmarks of gastrointestinal cancers. Furthermore, circRNAs and gut microbiota have been recommended as biological markers for therapeutic targets as well as diagnostic and prognostic purposes. Targeting circRNAs and the gut microbiota for the treatment of gastrointestinal cancers is also being continued to study. Despite significant initiatives, the connection between circRNAs and the gut microbiota and the emergence of gastrointestinal cancers remains poorly understood. In this study, we will go over the most recent studies to emphasize the key roles of circRNAs and gut microbiota in gastrointestinal cancer progression and therapeutic options. In order to create effective therapies and plan for the future gastrointestinal therapy, it is important to comprehend the functions and mechanisms of circRNAs and the gut microbiota.

Detection of a Novel MSI2-C17orf64 Transcript in a Patient with Aggressive Adenocarcinoma of the Gastroesophageal Junction: A Case Report

Adenocarcinoma of the esophagus (EAC) and gastroesophageal junction (GEJ-AC) is associated with poor prognosis, treatment resistance and limited systemic therapeutic options. To deeply understand the genomic landscape of this cancer type, and potentially identify a therapeutic target in a neoadjuvant chemotherapy non-responder 48-year-old man, we adopted a multi-omic approach. We simultaneously evaluated gene rearrangements, mutations, copy number status, microsatellite instability and tumor mutation burden. The patient displayed pathogenic mutations of the TP53 and ATM genes and variants of uncertain significance of three kinases genes (ERBB3, CSNK1A1 and RPS6KB2), along with FGFR2 and KRAS high copy number amplification. Interestingly, transcriptomic analysis revealed the Musashi-2 (MSI2)-C17orf64 fusion that has never been reported before. Rearrangements of the RNA-binding protein MSI2 with a number of partner genes have been described across solid and hematological tumors. MSI2 regulates several biological processes involved in cancer initiation, development and resistance to treatment, and deserves further investigation as a potential therapeutic target. In conclusion, our extensive genomic characterization of a gastroesophageal tumor refractory to all therapeutic approaches led to the discovery of the MSI2-C17orf64 fusion. The results underlie the importance of deep molecular analyses enabling the identification of novel patient-specific markers to be monitored during therapy or even targeted at disease evolution.