Applicability of Spatial Technology in Cancer Research.

This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

Ensemble Deep Learning Model to Predict Lymphovascular Invasion in Gastric Cancer.

Lymphovascular invasion (LVI) is one of the most important prognostic factors in gastric cancer as it indicates a higher likelihood of lymph node metastasis and poorer overall outcome for the patient. Despite its importance, the detection of LVI(+) in histopathology specimens of gastric cancer can be a challenging task for pathologists as invasion can be subtle and difficult to discern. Herein, we propose a deep learning-based LVI(+) detection method using H&E-stained whole-slide images. The ConViT model showed the best performance in terms of both AUROC and AURPC among the classification models (AUROC: 0.9796; AUPRC: 0.9648). The AUROC and AUPRC of YOLOX computed based on the augmented patch-level confidence score were slightly lower (AUROC: -0.0094; AUPRC: -0.0225) than those of the ConViT classification model. With weighted averaging of the patch-level confidence scores, the ensemble model exhibited the best AUROC, AUPRC, and F1 scores of 0.9880, 0.9769, and 0.9280, respectively. The proposed model is expected to contribute to precision medicine by potentially saving examination-related time and labor and reducing disagreements among pathologists.

A robust model training strategy using hard negative mining in a weakly labeled dataset for lymphatic invasion in gastric cancer.

Gastric cancer is a significant public health concern, emphasizing the need for accurate evaluation of lymphatic invasion (LI) for determining prognosis and treatment options. However, this task is time-consuming, labor-intensive, and prone to intra- and interobserver variability. Furthermore, the scarcity of annotated data presents a challenge, particularly in the field of digital pathology. Therefore, there is a demand for an accurate and objective method to detect LI using a small dataset, benefiting pathologists. In this study, we trained convolutional neural networks to classify LI using a four-step training process: (1) weak model training, (2) identification of false positives, (3) hard negative mining in a weakly labeled dataset, and (4) strong model training. To overcome the lack of annotated datasets, we applied a hard negative mining approach in a weakly labeled dataset, which contained only final diagnostic information, resembling the typical data found in hospital databases, and improved classification performance. Ablation studies were performed to simulate the lack of datasets and severely unbalanced datasets, further confirming the effectiveness of our proposed approach. Notably, our results demonstrated that, despite the small number of annotated datasets, efficient training was achievable, with the potential to extend to other image classification approaches used in medicine.

Efficient diagnosis of IDH-mutant gliomas: 1p/19qNET assesses 1p/19q codeletion status using weakly-supervised learning.

Accurate identification of molecular alterations in gliomas is crucial for their diagnosis and treatment. Although, fluorescence in situ hybridization (FISH) allows for the observation of diverse and heterogeneous alterations, it is inherently time-consuming and challenging due to the limitations of the molecular method. Here, we report the development of 1p/19qNET, an advanced deep-learning network designed to predict fold change values of 1p and 19q chromosomes and classify isocitrate dehydrogenase (IDH)-mutant gliomas from whole-slide images. We trained 1p/19qNET on next-generation sequencing data from a discovery set (DS) of 288 patients and utilized a weakly-supervised approach with slide-level labels to reduce bias and workload. We then performed validation on an independent validation set (IVS) comprising 385 samples from The Cancer Genome Atlas, a comprehensive cancer genomics resource. 1p/19qNET outperformed traditional FISH, achieving R2 values of 0.589 and 0.547 for the 1p and 19q arms, respectively. As an IDH-mutant glioma classifier, 1p/19qNET attained AUCs of 0.930 and 0.837 in the DS and IVS, respectively. The weakly-supervised nature of 1p/19qNET provides explainable heatmaps for the results. This study demonstrates the successful use of deep learning for precise determination of 1p/19q codeletion status and classification of IDH-mutant gliomas as astrocytoma or oligodendroglioma. 1p/19qNET offers comparable results to FISH and provides informative spatial information. This approach has broader applications in tumor classification.

Epithelial-mesenchymal Transition Factor and Its Association With Mammographic Density and Breast Cancer Prognosis.

BACKGROUND/AIM: The tumor microenvironment influences tumor progression, invasion, and metastasis. This study determined the expression levels of epithelial-mesenchymal transition (EMT) factors according to zone and their correlation with mammographic breast density and investigated the prognostic value of EMT factors. PATIENTS AND METHODS: The clinical and pathological data of invasive carcinoma and ductal carcinoma in situ were reviewed. Primary breast tissue samples were evaluated using immunohistochemistry (IHC) staining of the EMT-associated markers, including α-SMA, vimentin, MMP-9, and CD34. The expression levels were analyzed in three areas: the tumor center, interface, and distal zones. EMT factors were correlated with mammographic breast density and oncologic outcomes. RESULTS: An overall EMT phenotype conversion from positive to negative was seen in 55.7% of α-SMA- and 34.4% of MMP-9-positive cells between the tumor center and interface zones, which was significantly different (p<0.05). Most changes in EMT expression from the center to the distal zone were from positive to negative, but 23.0% of CD34-expressing cells showed negative to positive conversion. The proportion of α-SMA, vimentin, and MMP-9 expression was higher in the non-dense breast group compared to the dense breast group in the interface and distal zones (p<0.05). CD34 expression in the distal zone was an independent favorable prognostic factor for disease-free survival (p=0.039). CONCLUSION: The differential expression of EMT markers in each zone suggests heterogeneous cancer cell populations within each zone of breast cancer. EMT factor expression can also interplay between breast density stroma and geographical tumor zone.

Deep learning model to predict Epstein-Barr virus associated gastric cancer in histology.

The detection of Epstein-Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal EBV testing. Herein, we propose a deep learning-based EBV prediction method from H&E-stained whole-slide images (WSI). Our model was developed using 319 H&E stained WSI (26 EBV positive; TCGA dataset) from the Cancer Genome Atlas, and 108 WSI (8 EBV positive; ISH dataset) from an independent institution. Our deep learning model, EBVNet consists of two sequential components: a tumor classifier and an EBV classifier. We visualized the learned representation by the classifiers using UMAP. We externally validated the model using 60 additional WSI (7 being EBV positive; HGH dataset). We compared the model's performance with those of four pathologists. EBVNet achieved an AUPRC of 0.65, whereas the four pathologists yielded a mean AUPRC of 0.41. Moreover, EBVNet achieved an negative predictive value, sensitivity, specificity, precision, and F1-score of 0.98, 0.86, 0.92, 0.60, and 0.71, respectively. Our proposed model is expected to contribute to prescreen patients for confirmatory testing, potentially to save test-related cost and labor.

Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality.

BACKGROUND: Several prediction models have been proposed for preoperative risk stratification for mortality. However, few studies have investigated postoperative risk factors, which have a significant influence on survival after surgery. This study aimed to develop prediction models using routine immediate postoperative laboratory values for predicting postoperative mortality. METHODS: Two tertiary hospital databases were used in this research: one for model development and another for external validation of the resulting models. The following algorithms were utilized for model development: LASSO logistic regression, random forest, deep neural network, and XGBoost. We built the models on the lab values from immediate postoperative blood tests and compared them with the SASA scoring system to demonstrate their efficacy. RESULTS: There were 3817 patients who had immediate postoperative blood test values. All models trained on immediate postoperative lab values outperformed the SASA model. Furthermore, the developed random forest model had the best AUROC of 0.82 and AUPRC of 0.13, and the phosphorus level contributed the most to the random forest model. CONCLUSIONS: Machine learning models trained on routine immediate postoperative laboratory values outperformed previously published approaches in predicting 30-day postoperative mortality, indicating that they may be beneficial in identifying patients at increased risk of postoperative death.

Landscape of epigenetically regulated lncRNAs and DNA methylation in smokers with lung adenocarcinoma.

In this study, we identified long non-coding RNAs (lncRNAs) associated with DNA methylation in lung adenocarcinoma (LUAD) using clinical and methylation/expression data from 184 qualified LUAD tissue samples and 21 normal lung-tissue samples from The Cancer Genome Atlas (TCGA). We identified 1865 differentially expressed genes that correlated negatively with the methylation profiles of normal lung tissues, never-smoker LUAD tissues and smoker LUAD tissues, while 1079 differentially expressed lncRNAs were identified using the same criteria. These transcripts were integrated using ingenuity pathway analysis to determine significant pathways directly related to cancer, suggesting that lncRNAs play a crucial role in carcinogenesis. When comparing normal lung tissues and smoker LUAD tissues, 86 candidate genes were identified, including six lncRNAs. Of the 43 candidate genes revealed by comparing never-smoker LUAD tissues and smoker LUAD tissues, 13 were also different when compared to normal lung tissues. We then investigated the expression of these genes using the Gene Expression of Normal and Tumor Tissues (GENT) and Methylation and Expression Database of Normal and Tumor Tissues (MENT) databases. We observed an inverse correlation between the expression of 13 genes in normal lung tissues and smoker LUAD tissues, and the expression of five genes between the never-smoker and smoker LUAD tissues. These findings were further validated in clinical specimens using bisulfite sequencing, revealing that AGR2, AURKB, FOXP3, and HMGA1 displayed borderline differences in methylation. Finally, we explored the functional connections between DNA methylation, lncRNAs, and gene expression to identify possible targets that may contribute toward the pathogenesis of cigarette smoking-associated LUAD. Together, our findings suggested that differentially expressed lncRNAs and their target transcripts could serve as potential biomarkers for LUAD.

High-sensitivity C-reactive Protein and Regression of Low-grade Squamous Intraepithelial Lesion: The Role of Low-grade Inflammation in Cervical Carcinogenesis.

BACKGROUND: Inflammation is emerging as a potential mechanism of cervical carcinogenesis. However, few studies have investigated the association between host inflammatory status and the natural course of cervical precursor lesion. The aim of this study was to assess the probability of LSIL regression, associated with an inflammatory biomarker, high-sensitivity C-reactive protein (hs-CRP). METHODS: In a longitudinal cohort study, female participants were examined annually or biannually using cervical cytology between 2006 and 2015. Incident LSIL cases were included in the analysis, with regression defined as at least one consecutive normal cytologic result. A total of 520 women aged 22-64 years were followed up for LSIL regression. The multivariable-adjusted hazard ratios (HRs) for LSIL regression were estimated using a parametric proportional hazards model. RESULTS: During 827.5 person-years of follow-up, 486 out of 520 subjects (93.5%) showed LSIL regression. After adjusting several important potential confounders, a higher quartile of hs-CRP levels was significantly associated with a lower rate of regression (for quartile 4 vs quartile 1, inverse HR 1.33; 95% CI, 1.04-1.69; P for trend = 0.028). CONCLUSIONS: The low rate of spontaneous regression recorded in women with higher hs-CRP lends support to the role of the perturbated host inflammatory status in cervical carcinogenesis, and suggests that hs-CRP level could help monitor LSIL.

Total length of positive resection margins can predict remnant gastric cancer following endoscopic submucosal dissection.

BACKGROUND: Prediction of remnant tumor is important in determining subsequent treatment options for gastric cancer patients with positive resection margin (RM) after endoscopic submucosal dissection (ESD). METHODS: Based on the assumption that pathologic factors, including the length and type of involved RM, could potentially predict residual tumor, we evaluated 451 ESD specimens in patients with early gastric cancer. RESULTS: Of 408 cases, 37 (9.1 %) showed positive RMs. RM involvement in gastric cancer ESD specimens was associated with extended or beyond ESD criteria, greater tumor size, poor differentiation, submucosal invasion, lymphovascular invasion, and upper third location. Among the 37 positive RM cases, residual tumor was present in seven (18.9 %). The presence of residual tumor was not significantly associated with any clinicopathologic parameters except for tumor size and RM status. The total length of the involved RM was the most significant factor associated with the presence of residual tumor (P < 0.008). A total length cut-off value of 6 mm yielded a sensitivity of 85.7 % and negative predictive value of 94.7 % for predicting remnant tumor at gastrectomy following ESD. CONCLUSIONS: In conclusion, when the ESD specimen exhibits positive RM, a quantitative assessment of the involved RM should be included in the pathology report, as this can help the clinician predict remnant tumor and determine appropriate future treatment.

Pancreatic High-Grade Neuroendocrine Neoplasms in the Korean Population: A Multicenter Study.

PURPOSE: The most recent 2017 World Health Organization (WHO) classification of pancreatic neuroendocrine neoplasms (PanNENs) has refined the three-tiered 2010 scheme by separating grade 3 pancreatic neuroendocrine tumors (G3 PanNETs) from poorly differentiated pancreatic neuroendocrine carcinomas (PanNECs). However, differentiating between G3 Pan- NETs and PanNECs is difficult in clinical practice. MATERIALS AND METHODS: Eighty-two surgically resected PanNENs were collected from 16 institutions and reclassified according to the 2017 WHO classification based on the histological features and proliferation index (mitosis and Ki-67). Immunohistochemical stains for ATRX, DAXX, retinoblastoma, p53, Smad4, p16, and MUC1 were performed for 15 high-grade PanNENs. RESULTS: Re-classification resulted in 20 G1 PanNETs (24%), 47 G2 PanNETs (57%), eight G3 well-differentiated PanNETs (10%), and seven poorly differentiated PanNECs (9%). PanNECs showed more frequent diffuse nuclear atypia, solid growth patterns and apoptosis, less frequent organoid growth and regular vascular patterns, and absence of low-grade PanNET components than PanNETs. The Ki-67 index was significantly higher in PanNEC (58.2%± 15.1%) compared to G3 PanNET (22.6%±6.1%, p < 0.001). Abnormal expression of any two of p53, p16, MUC1, and Smad4 could discriminate PanNECs from G3 PanNETs with 100% specificity and 87.5% sensitivity. CONCLUSION: Histological features supporting the diagnosis of PanNECs over G3 PanNETs were the absence of a low-grade PanNET component in the tumor, the presence of diffuse marked nuclear atypia, solid growth pattern, frequent apoptosis and markedly increased proliferative activity with homogeneous Ki-67 labeling. Immunohistochemical stains for p53, p16, MUC1, and Smad4 may be helpful in distinguishing PanNECs from G3 PanNETs in histologically ambiguous cases, especially in diagnostic practice when only small biopsied tissues are available.

The Suggestion of Revised Criteria for Endoscopic Resection of Differentiated-Type Submucosal Gastric Cancer.

BACKGROUND: Early gastric cancer that meets the expanded criteria for endoscopic resection (ER) is expected to be associated with a negligible risk for lymph node metastasis (LNM); however, recent studies have reported LNM in submucosal gastric cancer patients who met the existing criteria. In this study, we develop the revised criteria for ER of submucosal gastric cancer with the aim of minimizing LNM. METHODS: We analyzed the clinicopathological data of 2461 patients diagnosed with differentiated, submucosal gastric cancer who underwent surgery at three tertiary hospitals between March 2001 and December 2012, and re-analyzed the pathological slides of all patients. The depth of submucosal invasion was measured histopathologically in two different ways (the classic and alternative methods) to obtain accurate data. RESULTS: Of the enrolled subjects, 306 (17.0%) had LNM. The width of submucosal invasion correlated well with the LNM. We defined the depth and width of submucosal infiltration associated with the lowest incidence of LNM. None of the 254 subjects developed LNM when the following criteria were met: tumor diameter ≤ 3 cm, submucosal invasion depth < 1000 µm (as measured using the alternative method), submucosal invasion width < 4 mm, no lymphovascular invasion, and no perineural invasion; however, LNM was observed in 2.7% of subjects (6/218) who met the existing criteria. CONCLUSIONS: We revised the criteria for ER by adopting the alternative method to measure the depth of submucosal invasion and adding the width of such invasion. Our criteria better predicted LNM than the current criteria used to select ER to treat submucosal gastric cancer.

Predictive value of WHO classification for PD-L1 and Her2/Neu expression and distinct associations with protein expression based classification in gastric carcinoma.

The Cancer Genome Atlas data on gastric carcinoma has identified four biologic pathways as potential drivers of gastric carcinogenesis and suggested targeted therapies based on the genomic alterations underscoring each subset. The correlation between morphology, biologic groups and their corresponding biomarkers has been eluded in several previous studies; however, a comprehensive analysis in consideration of the recent advancements has not been performed. In this study we explored the predictive value of morphology for biomarker expression and its association with protein expression based classification of gastric carcinoma. Four hundred eighty six gastric carcinomas which had been classified into protein expression-based groups formed the case cohort. Upon analysis, we found a low positive predictive value of an individual morphologic pattern for biomarker-expression, indicating that an individual morphologic pattern alone cannot predict PD-L1, Her2/neu expression and EBV- or MSI-gastric cancer. A combination approach targeting the test in certain WHO patterns can be employed for maximizing the positive predictive values. These include, PD-L1 testing in tubular, carcinoma with lymphoid stroma, undifferentiated and poorly cohesive patterns and Her2/neu testing in tubular, mixed, papillary, mucinous and solid patterns. The predictive values and morphologic associations presented here have the potential to select patients for personalized therapy.

Clinicopathologic characteristics of early gastric cancer according to specific intragastric location.

BACKGROUND: Although the incidence of early gastric cancer (EGC) continues to rise, there have been few studies on the intra-gastric distribution and locational characteristics of EGCs. In addition, there has been no attempt to visualize the intra-gastric distribution of EGCs using a merged tumor map. METHODS: We investigated the anatomic distribution of 644 cases of EGCs and analyzed the correlation between clinicopathologic findings and location by dividing areas of the stomach vertically and transversely. Merged tumor maps were generated using 310 surgically resected cases. RESULTS: Early gastric cancer was most commonly located in the antrum (57.5%) along the lesser curvature (37.8%). The intra-gastric distributions were similar in the merged tumor maps. Vertically, cancers of the middle third were associated with younger patient age, larger tumor size, and more frequent poorly differentiated (PD) or signet ring cell histology than cancers in other sites. Submucosal invasion was most frequently observed in the upper third. When divided transversely, tumors in the anterior or posterior wall showed more frequent PD or signet ring cell histology than those along the lesser or greater curvatures. CONCLUSIONS: EGC is the most prevalent in the antrum along the lesser curvature and has characteristic locational features, including histologic type, invasion depth, patient age, and tumor size. These results will improve the endoscopic detection rate of EGC and help to determine endoscopic resectability.

Predictive factors for lymph node metastasis in early gastric cancer with lymphatic invasion after endoscopic resection.

BACKGROUND: Lymph node (LN) metastasis is found in only about 5-10% of the patients who undergo additional surgery after non-curative endoscopic resection. Lymphatic invasion after endoscopic submucosal dissection (ESD) is regarded as non-curative resection due to risk of reginal LN metastasis. This study was aimed to identify clinicopathologic predictive factors for LN metastasis in early gastric cancer (EGC) with lymphatic invasion after endoscopic resection. METHODS: Among a total of 2036 patients who underwent endoscopic resection for EGC at Samsung Medical Center from April 2000 to May 2011, 146 patients were diagnosed with lymphatic invasion. And 123 patients who had gastrectomy with LN dissection due to presence of lymphatic invasion as one of the non-curative factors were included in this study. Demographics, endoscopic tumor findings, histological findings, surgical findings with pathologic reports, and follow-up data were collected from the patient's medical records. Pathological re-evaluation of resected specimens was performed. RESULTS: Among a total of 123 patients, LN metastases were found in seven patients (5.7%). The univariate analysis revealed that the LN metastasis was significantly more frequent in patients with certain morphology of lymphatic invasion that shows adhesion to endothelium of lymphatic tumor emboli (p = 0.016), higher number of lymphatic tumor emboli in whole section (p < 0.001) and papillary adenocarcinoma component (p = 0.024). In multivariate analysis, the number of lymphatic tumor emboli [OR 93.5, 95% CI (2.62-3330.81)] and the presence of papillary adenocarcinoma component [OR 552.5, 95% CI (1.20-254871.81)] were identified as independent predictors of LN metastasis in patients with lymphatic invasion after endoscopic resection. CONCLUSIONS: The number of lymphatic tumor emboli and the presence of papillary adenocarcinoma component were significant predictors for LN metastasis in patients with lymphatic invasion after endoscopic resection.

Expression of Myxovirus Resistance A (MxA) Is Associated with Tumor-Infiltrating Lymphocytes in Human Epidermal Growth Factor Receptor 2 (HER2)-Positive Breast Cancers.

PURPOSE: The prognostic significance of tumor-infiltrating lymphocytes (TILs) has been determined in breast cancers. Interferons can affect T-cell activity through direct and indirect mechanisms. Myxovirus resistance A (MxA) is an excellent marker of interferon activity. Here,we evaluated TILs and MxA expression in human epidermal growth factor receptor 2 (HER2)-positive breast cancers. MATERIALS AND METHODS: Ninety cases of hormone receptor (HR)+/HER2+ tumors and 78 cases of HR-/HER2+ tumors were included. The TILs level was assessed using hematoxylin and eosin-stained full face sections, and MxA expressionwas evaluated by immunohistochemistrywith a tissue microarray. RESULTS: MxA protein expression was significantly higher in tumors with high histologic grade (p=0.023) and high levels of TILs (p=0.002). High levels of TILs were correlated with high histological grade (p=0.001), negative lymphovascular invasion (p=0.007), negative lymph node metastasis (p=0.007), absence of HR expression (p < 0.001), abundant tertiary lymphoid structures (TLSs) around ductal carcinoma in situ (p=0.018), and abundant TLSs around the invasive component (p < 0.001). High levels of TILs were also associated with improved disease-free survival, particularly in HR-/HER2+ breast cancers. However, MxA was not a prognostic factor. CONCLUSION: High expression of MxA in tumor cells was associated with high levels of TILs in HER2-positive breast cancers. Additionally, a high level of TILs was a prognostic factor for breast cancer, whereas the level of MxA expression had no prognostic value.

High-throughput Protein and mRNA Expression-based Classification of Gastric Cancers Can Identify Clinically Distinct Subtypes, Concordant With Recent Molecular Classifications.

Gastric cancers have recently been classified into several types on the basis of molecular characterization, and the new taxonomy has shown to have clinical relevance. However, the technology required for thorough molecular classification is complicated and expensive, currently preventing widespread use. We aimed to reproduce the results of molecular classification using only simple techniques, that is, immunohistochemical analysis and in situ hybridization. We classified a cohort of 349 successive gastric adenocarcinomas into 5 subtypes, on the basis of protein or mRNA expression of MLH1, E-cadherin, p53, and Epstein-Barr virus. We observed that the subtypes presented distinct clinicopathologic characteristics and corresponded to the molecular classifications previously reported. Epstein-Barr virus -positive tumors were more common in male individuals and in the body of the stomach. Microsatellite-unstable (MSI) tumors, which showed aberrant MLH1 expression, were correlated with increased age and intestinal histology. Both types showed better overall survival than the other types. Gastric cancers with reduced expression of E-cadherin, corresponding to the epithelial to mesenchymal transition or genome stable subtypes, showed the poorest overall survival, with a high prevalence of poorly cohesive carcinoma (ie, diffuse type, of the Lauren classification system). In conclusion, we were able to reproduce a previously reported molecular classification of gastric cancers using immunohistochemical analysis and in situ hybridization. We verified the effectiveness and applicability of this method, which shows promise for use in a clinical setting in the foreseeable future.

Endoscopic resection for duodenal subepithelial tumors: a single-center experience.

BACKGROUND: Subepithelial tumors (SETs) in the gastrointestinal tract are often incidentally found during endoscopic examinations. Although the utility and safety of endoscopic resection (ER) of SETs in the esophagus and stomach have been described, data about the ER of duodenal SETs remain scant. Therefore, we aimed to investigate the clinical outcomes associated with the ER of duodenal SETs and to assess possible predictive factors for incomplete resection. METHODS: We conducted a retrospective observational study of 62 patients (64 lesions) that underwent ER of duodenal SETs between June 2005 and December 2015 at the Pusan National University Hospital. The therapeutic outcomes from ER and procedure-related complications were analyzed. RESULTS: Endoscopic mucosal resection (EMR) was performed in 38 tumors, EMR with a ligation device (EMR-L) in 18 and endoscopic submucosal dissection (ESD) in 8. The overall en bloc resection and complete ER rates were 96.9 % (62/64) and 100 % (64/64), respectively. The complete pathologic resection rate was 76.6 % (49/64). Multivariate logistic regression analyses determined that the macroscopic type (Yamada type I or II; odds ratio [OR] 6.460, 95 % confidence interval [CI] 1.569-37.458, p = 0.027) and the treatment method (ESD; OR 7.178, 95 % CI 1.291-39.323, p = 0.024) were independently associated with incomplete pathologic resection. The procedure-related bleeding and perforation rates were 6.3 % and 4.7 %, respectively. No recurrences were observed in patients who had undergone complete ER at a median follow-up period of 20 months (range 6-112 months). CONCLUSION: ER is an effective, safe, and feasible treatment for duodenal SETs, especially when the SET is located in the deep mucosal layer and/or the submucosal layer.

Dysregulated Wnt signalling and recurrent mutations of the tumour suppressor RNF43 in early gastric carcinogenesis.

Several recurrent mutations and epigenetic changes have been identified in advanced gastric cancer, but the genetic alterations associated with early gastric carcinogenesis and malignant transformation remain unclear. We investigated the genomic and transcriptomic landscape of adenomas with low-grade dysplasia (LGD) and high-grade dysplasia (HGD), and intestinal-type early gastric cancer (EGC). The results were validated in an independent cohort that included EGCs directly adjacent to adenoma (EGC-adenomas) that were in the process of malignant transformation, and de novo EGCs that do not seem to have been derived from adenoma. The expression patterns clearly divided into normal, LGD, and EGC, whereas those of HGD overlapped with LGD or EGC. These results suggest that HGD is the critical stage determining malignant transformation. We found that genes related to focal adhesion and extracellular matrix receptor interaction pathways were upregulated as LGD progressed to EGC, whereas canonical Wnt signalling and peroxisome proliferator-activated receptor (PPAR) signalling pathway genes were downregulated in EGC. Genomic alterations such as somatic mutation, gene fusion and copy number variation increased gradually from LGD to EGC. APC mutations were present in 67% of LGDs, 58% of HGDs, and 18% of EGCs. RNF43 mutations were present only in HGD and EGC, and TP53 mutations were present only in EGC. In a validation cohort, RNF43 mutations were present in 35.2% of EGC-adenomas, but in only 8.6% of de novo EGCs. This is the first study to investigate the genomic and transcriptomic landscape of multistep gastric carcinogenesis. We investigated important alterations and their related pathways in each step as tumours progressed from LGD to HGD and eventually to EGC. We suggest that mutations and downregulation of RNF43 may play a critical role in the transition from adenoma to carcinoma. Given these findings and Wnt dependency in tumours with RNF43 mutation, intestinal-type gastric cancer or adenoma with RNF43 mutation might represent a promising indication for Wnt-targeted agents. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.