Clinicopathologic and genomic characteristics of mucinous gastric adenocarcinoma

Comparison of Clinicopathological Features and Prognosis of Mucinous Gastric Carcinoma and other Gastric Cancers: A Retrospective Study of 4,417 Patients

BACKGROUND

Mucinous gastric carcinoma (MGC) is a distinct histologic subtype of gastric cancer (GC) that is often diagnosed at an advanced stage. The clinicopathological characteristics and prognosis of MGC, when compared to adenocarcinoma and signet-ring cell carcinoma (SRCC), are currently subjects of debate and require further investigation.

METHODS

In this study, we conducted an investigation on 4,417 patients who were hospitalized with GC at Zhejiang Cancer Hospital between April 2008 and December 2019. The objective was to compare the prognosis and clinicopathological characteristics of MGC with other types of GC.

RESULTS

In comparison to adenocarcinoma, MGC patients exhibited more advanced tumor infiltration (p < 0.001), lower tumor differentiation (p < 0.001), and higher rates of preoperative tumor marker positivity (except for AFP and CA125) (all p < 0.05). However, after propensity score matching (PSM) to eliminate confounding factors, MGC patients surprisingly exhibited a better prognosis than adenocarcinoma patients (p = 0.008), and the results in multifactorial COX regression were similar (HR = 0.792, 95% CI 0.629-0.997, p = 0.047). Among patients with MGC, age, pN stage, as well as preoperative levels of CA125 and CA724 (all p < 0.05), emerged as independent prognostic markers. While overall survival did not significantly differ between MGC and SRCC (p = 0.196), significant survival disparities emerged in advanced-stage patients (p = 0.009), with MGC showing better survival rates. Furthermore, a nomogram was developed to predict 1-, 3-, and 5-year survival in gastric cancer patients based on various factors, achieving a C-index of 0.772 (95% CI: 0.745-0.799).

CONCLUSIONS

While the poorer prognosis associated with MGC may be linked to its advanced stage and lower degree of differentiation, its biological behavior could contribute to improved survival.

Dual-layer spectral-detector CT for predicting microsatellite instability status and prognosis in locally advanced gastric cancer

OBJECTIVE

To construct and validate a prediction model based on dual-layer detector spectral CT (DLCT) and clinico-radiologic features to predict the microsatellite instability (MSI) status of gastric cancer (GC) and to explore the relationship between the prediction results and patient prognosis.

METHODS

A total of 264 GC patients who underwent preoperative DLCT examination were randomly allocated into the training set (n = 187) and validation set (n = 80). Clinico-radiologic features and DLCT parameters were used to build the clinical and DLCT model through multivariate logistic regression analysis. A combined DLCT parameter (CDLCT) was constructed to predict MSI. A combined prediction model was constructed using multivariate logistic regression analysis by integrating the significant clinico-radiologic features and CDLCT. The Kaplan-Meier survival analysis was used to explore the prognostic significant of the prediction results of the combined model.

RESULTS

In this study, there were 70 (26.52%) MSI-high (MSI-H) GC patients. Tumor location and CT_N staging were independent risk factors for MSI-H. In the validation set, the area under the curve (AUC) of the clinical model and DLCT model for predicting MSI status was 0.721 and 0.837, respectively. The combined model achieved a high prediction efficacy in the validation set, with AUC, sensitivity, and specificity of 0.879, 78.95%, and 75.4%, respectively. Survival analysis demonstrated that the combined model could stratify GC patients according to recurrence-free survival (p = 0.010).

CONCLUSION

The combined model provides an efficient tool for predicting the MSI status of GC noninvasively and tumor recurrence risk stratification after surgery.

CRITICAL RELEVANCE STATEMENT

MSI is an important molecular subtype in gastric cancer (GC). But MSI can only be evaluated using biopsy or postoperative tumor tissues. Our study developed a combined model based on DLCT which could effectively predict MSI preoperatively. Our result also showed that the combined model could stratify patients according to recurrence-free survival. It may be valuable for clinicians in choosing appropriate treatment strategies to avoid tumor recurrence and predicting clinical prognosis in GC.

KEY POINTS

• Tumor location and CT_N staging were independent predictors for MSI-H in GC. • Quantitative DLCT parameters showed potential in predicting MSI status in GC. • The combined model integrating clinico-radiologic features and CDLCT could improve the predictive performance. • The prediction results could stratify the risk of tumor recurrence after surgery.

Role of Endoscopic Biopsies and Morphologic Features in Predicting Microsatellite Instability Status in Gastric Cancer: A Multicenter Comparative Study of Endoscopic Biopsies and Surgical Specimens

Evaluation of mismatch repair (MMR) protein and microsatellite instability (MSI) status plays a pivotal role in the management of gastric cancer (GC) patients. In this study, we aimed to evaluate the accuracy of gastric endoscopic biopsies (EBs) in predicting MMR/MSI status and to uncover histopathologic features associated with MSI. A multicentric series of 140 GCs was collected retrospectively, in which EB and matched surgical specimens (SSs) were available. Laurén and WHO classifications were applied and detailed morphologic characterization was performed. EB/SS were analyzed by immunohistochemistry (IHC) for MMR status and by multiplex polymerase chain reaction (mPCR) for MSI status. IHC allowed accurate evaluation of MMR status in EB (sensitivity: 97.3%; specificity: 98.0%) and high concordance rates between EB and SS (Cohen κ=94.5%). By contrast, mPCR (Idylla MSI Test) showed lower sensitivity in evaluating MSI status (91.3% vs. 97.3%), while maintaining maximal specificity (100.0%). These results suggest a role of IHC as a screening method for MMR status in EB and the use of mPCR as a confirmatory test. Although Laurén/WHO classifications were not able to discriminate GC cases with MSI, we identified specific histopathologic features that are significantly associated with MMR/MSI status in GC, despite the morphologic heterogeneity of GC cases harboring this molecular phenotype. In SS, these features included the presence of mucinous and/or solid components ( P =0.034 and <0.001) and the presence of neutrophil-rich stroma, distant from tumor ulceration/perforation ( P <0.001). In EB, both solid areas and extracellular mucin lakes were also discriminating features for the identification of MSI-high cases ( P =0.002 and 0.045).

Primary renal mucinous adenocarcinoma masquerading as a giant renal cyst: a case report

Mucinous adenocarcinoma of the kidney is rarely reported in the literature. We present a previously unreported mucinous adenocarcinoma arising from the renal parenchyma. A 55-year-old male patient with no complaints showed a large cystic hypodense lesion in the upper left kidney on contrast-enhanced computed tomography (CT) scan. A left renal cyst was initially considered, and a partial nephrectomy (PN) was performed. During the operation, a large amount of jelly-like mucus and bean-curd-like necrotic tissue was found in the focus. The pathological diagnosis was mucinous adenocarcinoma, and further systemic examination revealed no clinical evidence of primary disease elsewhere. Then the patient underwent left radical nephrectomy (RN), and the cystic lesion was found in the renal parenchyma, while neither the collecting system nor the ureters were involved. Postoperative sequential chemotherapy and radiotherapy were administered, and no signs of disease recurrence were observed over 30 months of follow-up. Based on a literature review, we summarize the lesion with rarity and the associated dilemma in preoperative diagnosis and treatment. Given the high degree of malignancy, a careful history analysis accompanied by dynamic observation of imaging and tumor markers is recommended for the diagnosis of the disease. Comprehensive treatment based on surgery may improve its clinical outcomes.

Relationship between the deep features of the full-scan pathological map of mucinous gastric carcinoma and related genes based on deep learning

Background

Long-term differential expression of disease-associated genes is a crucial driver of pathological changes in mucinous gastric carcinoma. Therefore, there should be a correlation between depth features extracted from pathology-based full-scan images using deep learning and disease-associated gene expression. This study tried to provides preliminary evidence that long-term differentially expressed (disease-associated) genes lead to subtle changes in disease pathology by exploring their correlation, and offer a new ideas for precise analysis of pathomics and combined analysis of pathomics and genomics.

Methods

Full pathological scans, gene sequencing data, and clinical data of patients with mucinous gastric carcinoma were downloaded from TCGA data. The VGG-16 network architecture was used to construct a binary classification model to explore the potential of VGG-16 applications and extract the deep features of the pathology-based full-scan map. Differential gene expression analysis was performed and a protein-protein interaction network was constructed to screen disease-related core genes. Differential, Lasso regression, and extensive correlation analyses were used to screen for valuable deep features. Finally, a correlation analysis was used to determine whether there was a correlation between valuable deep features and disease-related core genes.

Result

The accuracy of the binary classification model was 0.775 ± 0.129. A total of 24 disease-related core genes were screened, including ASPM, AURKA, AURKB, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CDCA8, CDK1, CENPF, DLGAP5, KIF11, KIF20A, KIF2C, KIF4A, MELK, PBK, RRM2, TOP2A, TPX2, TTK, UBE2C, and ZWINT. In addition, differential, Lasso regression, and extensive correlation analyses were used to screen eight valuable deep features, including features 51, 106, 109, 118, 257, 282, 326, and 487. Finally, the results of the correlation analysis suggested that valuable deep features were either positively or negatively correlated with core gene expression.

Conclusion

The preliminary results of this study support our hypotheses. Deep learning may be an important bridge for the joint analysis of pathomics and genomics and provides preliminary evidence for long-term abnormal expression of genes leading to subtle changes in pathology.

The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers

Gastrointestinal cancers account for 22.5% of cancer related deaths worldwide and represent circa 20% of all cancers. In the last decades, we have witnessed a shift from histology-based to molecular-based classifications using genomic, epigenomic, and transcriptomic data. The molecular based classification revealed new prognostic markers and may aid the therapy selection. Because of the high-costs to perform a molecular classification, in recent years immunohistochemistry-based surrogate classification were developed which permit the stratification of patients, and in parallel multiple groups developed hematoxylin and eosin whole slide image analysis for sub-classifying these entities. Hence, we are witnessing a return to an image-based classification with the purpose to infer hidden information from routine histology images that would permit to detect the patients that respond to specific therapies and would be able to predict their outcome. In this review paper, we will discuss the current histological, molecular, and immunohistochemical classifications of the most common gastrointestinal cancers, gastric adenocarcinoma, and colorectal adenocarcinoma, and will present key aspects for developing a new artificial intelligence aided image-based classification of these malignancies.