Prognosis values of modified Lauren classification in gastric cancer: a validation from SEER database

Background: It remains controversial as to which pathological classification is most valuable in predicting overall survival (OS) in patients with gastric cancer (GC). We assessed the prognostic performances of three pathological classifications in GC and developed a novel prognostic nomogram individually predicting OS. Methods: Patients were identified from the Surveillance, Epidemiology and End Results program. Univariate and multivariate analyses were performed to identify the independent prognostic factors. Model discrimination and model-fitting were evaluated by receiver operating characteristic curves and Akaike information criteria. Decision curve analysis was performed to assess clinical usefulness. The independent prognostic factors identified by multivariate analysis were further applied to develop a novel prognostic nomogram. Results: A total of 2,718 eligible GC patients were identified. The modified Lauren classification was identified as one of the independent prognostic factors of OS. It showed superior model discriminative ability and model-fitting performance over the other pathological classifications, and similar results were obtained in various patient settings. In addition, it showed superior net benefits over the Lauren classification and tumor differentiation grade in predicting 3- and 5-year OS. A novel prognostic nomogram incorporating the modified Lauren classification showed superior model discriminative ability, model-fitting performance, and net benefits over the American Joint Committee on Cancer (AJCC) 8th Edition TNM classification. Conclusion: The modified Lauren classification showed superior net benefits over the Lauren classification and tumor differentiation grade in predicting OS. A novel prognostic nomogram incorporating the modified Lauren classification showed good model discriminative ability, model-fitting performance, and net benefits.

Centromere Protein I (CENP-I) Is Upregulated in Gastric Cancer, Predicts Poor Prognosis, and Promotes Tumor Cell Proliferation and Migration

This study aimed to investigate the expression and cellular function of the centromeric family of proteins (CENPs), especially centromere protein I (CENP-I), in gastric cancer (GC) and identified its clinical significance and cellular functions. CENP-I expression in GC was studied by cDNA microarray, quantitative real-time PCR (qRT-PCR), and immunohistochemistry (IHC), and using datasets from The Cancer Genome Atlas (TCGA), UALCAN, and Gene Expression Omnibus (GEO) databases. Microarray and bioinformatic analyses identified upregulated CENP-A/E/F/H/I/K/P/W and HJURP in stomach adenocarcinoma (STAD), but not in signet ring cell carcinoma (SRCC). Significantly higher CENP-I mRNA expression was also confirmed in 40 pairs of GC tissues than in paired normal gastric tissues by qRT-PCR ( P<.001). IHC showed that elevated CENP-I expression was associated with higher tumor stage, lymph node invasion, increased HER2-positive rate (36.7% vs 10.0%), and intestinal Lauren classification in 69 GC samples compared to paired paracancerous normal tissues. The survival of the high-CENP-I group members was poor compared with that of the low-CENP-I group ( P = .0011). Cox univariate regression analysis identified tumor size ( P = .008), HER2 status ( P = .027), and CENP-I expression ( P = .049) were independent prognostic factors of GC. The cellular function of CENP-I was studied in MKN45 and MKN28 GC cell lines in vitro. Cell proliferation, migration, and apoptosis were determined using CCK-8, transwell assay, TUNEL assay, and flow cytometry. Our results showed that CENP-I promoted GC cell proliferation, inhibited apoptosis, facilitated cell migration, and induced epithelial–mesenchymal transition (EMT), possibly by activating the AKT pathway. CENP-I expression was correlated with genetic signatures of the proliferative subtype of GC, characterized by intestinal Lauren classification, HER2 amplification, and TP53 mutation. In conclusion, this study revealed an elevated CENP-I expression in GC, which was associated with malignant features and poor prognosis of GC patients, and identified its function in modulating cell proliferation, apoptosis, and migration.

Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

Background Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC. Methods A total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness. Results The combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663–0.767) in the training cohort and 0.714 (95% CI, 0.636–0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696–0.795) and a validation AUC of 0.758 (95% CI, 0.685–0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness. Conclusions The radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy.

The clinicopathological characteristics and genetic alterations of gastric cancer patients according to the Lauren classification

ObjectiveThe Lauren classification is an important histological classification of gastric cancer (GC) with different biological behaviors between histological types.BackgroundTo date, there are few reports on the genetic alterations and survival differences between different histological types according to the Lauren classification.MethodsIn total, 433 GC patients undergoing surgery were enrolled. The clinicopathological features, prognoses, and genetic alterations of the different Lauren types were compared.ResultsDiffuse-type GC was associated with a younger age, female predominance, more Borrmann type 3 and 4 tumors, more advanced pathological tumor (T) and node (N) categories, more tumor recurrences (especially peritoneal recurrence), and worse 5-year overall survival and disease-free survival rates than intestinal-type GC and mixed-type GC. Regarding genetic alterations, mixed-type GC was associated with more TP53 mutations than intestinal-type GC and diffuse-type GC. Multivariate analysis demonstrated the following independent prognostic factors: age, Lauren classification, and pathological T and N categories. Regarding mixed-type GC, diffuse-type major tumors were associated with more lymphovascular invasion, a more advanced N category and TNM stage, and fewer PI3K/AKT pathway mutations than intestinal-type major tumors.ConclusionsDiffuse-type GC had unfavorable clinicopathological features and a worse prognosis than intestinal-type GC. For mixed-type GC, the clinicopathological features and genetic alterations were different between intestinal-type major tumors and diffuse-type major tumors.

HER2 protein expression correlates with Lauren classification and P53 in gastric cancer patients

Background and objective: Human epidermal growth factor receptor 2 (HER2) is a key pathological characteristic in gastric cancer patients. However, the clinical significance of HER2 protein expression in gastric carcinoma remains controversial. The purpose of the study is to analyze the clinicopathological characteristics of HER2 protein expression, Lauren classification and P53 expression and evaluate the clinical significance of the HER2 protein expression. Methods: A total of 176 consecutive patients were recruited prospectively between January 2014 and December 2016 in The Second Affiliated hospital of Zhejiang University School of Medicine. Histological analysis was performed on resected tissue for HER2 protein expression by immunohistochemistry (IHC). The patients with IHC grade 2+ were analyzed by fluorescence in situ hybridization (FISH) to assess the expression status of HER2 protein. Moreover, standardized criteria of HER2 protein expression in gastric cancer was used in this study. Additionally, the expression status of HER2 protein and clinicopathological features were analyzed by Chi-square (c2) test. All statistical analyses were conducted using the SPSS 22.0 statistical software program (IBM Corp., SPSS statistics, Chicago, IL).Results: A total of 176 gastric cancer patients were enrolled in this study. Intratumorally heterogeneity of HER2 protein overexpression was 42 of 176 cases with IHC grade 2+ accompanied with FISH positivity and IHC grade 3+. HER2 protein expression correlated with tumor differentiation (p < 0.001), Lauren classification (p = 0.001), Borrmann type (p = 0.003) and P53 expression (p < 0.001). Overall survival (OS) was not analyzed because the follow-up duration was too short and the high rate of missed interview.Conclusions: The overexpression of HER2 protein was determined in 23.9% of the cases and significantly related to Lauren intestinal subtype and P53 expression.

Deep learning-based unsupervised morphological subtyping in histopathology images of gastric cancer.

e16580 Background: Gastric cancer subtyping system such as Lauren classification is widely used in clinical practice since it is an independent prognostic factor, and also provides the basis for individualized treatment. However, Lauren classification showed low reproducibility because it is based on qualitative method. Thus, a more reproducible system has been required. In this study, we explore the concordance between a cancer detection model (CDM)-based unsupervised subtyping and a current classification system. Methods: We focused on Lauren classification since it has relatively simple major subtypes: intestinal and diffuse types. 88 resection cases of gastric cancer are collected. Two experienced gastrointestinal pathologists evaluated each case independently by Lauren’s criteria. 200 cancer region image patches per case and its features extracted by the CDM were clustered into five groups via k-means clustering. We then defined the ratio of the number of patches in each cluster group as the case-level feature. Cases are clustered into two groups via k-means clustering using the features. We named this case-level clustering result as a deep learning-based subtyping (DLS), hereafter. Results: DLS showed a high correlation with Lauren classification: intestinal and diffuse subtype. The kappa scores between the pathologists and algorithm (0.765 and 0.796) were higher than between pathologists (0.700). The cluster centers corresponding to intestinal and diffuse types were [44.6, 21.5, 47.5, 63.7, 22.7] and [35.5, 148.2, 4.0, 9.0, 2.3], respectively. Further review of the patches contributing to the second feature (21.5 and 148.2) confirmed that they demonstrate typical morphological characteristics of diffuse type. Conclusions: Our analysis showed that the morphological characteristics of Lauren classification were inherent in gastric cancer detection task and WSIs can be subtyped by the data-driven, and unsupervised manner. Furthermore, DLS was more reliable than human from the concordance results. This suggests that the DLS can supplement current classification systems or be a new subtyping system. [Table: see text]