A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma.

Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I-III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy.

Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study.

BACKGROUND: Improved markers for predicting recurrence are needed to stratify patients with localised (stage I-III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities-clinical, genomic, and histopathological-to improve the predictive accuracy for localised renal cell carcinoma recurrence. METHODS: In this retrospective analysis and validation study, we developed a histopathological whole-slide image (WSI)-based score using deep learning allied to digital scanning of conventional haematoxylin and eosin-stained tumour tissue sections, to predict tumour recurrence in a development dataset of 651 patients with distinctly good or poor disease outcome. The six single nucleotide polymorphism-based score, which was detected in paraffin-embedded tumour tissue samples, and the Leibovich score, which was established using clinicopathological risk factors, were combined with the WSI-based score to construct a multimodal recurrence score in the training dataset of 1125 patients. The multimodal recurrence score was validated in 1625 patients from the independent validation dataset and 418 patients from The Cancer Genome Atlas set. The primary outcome measured was the recurrence-free interval (RFI). FINDINGS: The multimodal recurrence score had significantly higher predictive accuracy than the three single-modal scores and clinicopathological risk factors, and it precisely predicted the RFI of patients in the training and two validation datasets (areas under the curve at 5 years: 0·825-0·876 vs 0·608-0·793; p<0·05). The RFI of patients with low stage or grade is usually better than that of patients with high stage or grade; however, the RFI in the multimodal recurrence score-defined high-risk stage I and II group was shorter than in the low-risk stage III group (hazard ratio [HR] 4·57, 95% CI 2·49-8·40; p<0·0001), and the RFI of the high-risk grade 1 and 2 group was shorter than in the low-risk grade 3 and 4 group (HR 4·58, 3·19-6·59; p<0·0001). INTERPRETATION: Our multimodal recurrence score is a practical and reliable predictor that can add value to the current staging system for predicting localised renal cell carcinoma recurrence after surgery, and this combined approach more precisely informs treatment decisions about adjuvant therapy. FUNDING: National Natural Science Foundation of China, and National Key Research and Development Program of China.

CypB promotes cell proliferation and metastasis in endometrial carcinoma.

BACKGROUND: The molecular pathogenesis of endometrial cancer is not completely understood. CypB upregulated in many cancers, however, its role in endometrial carcinoma has not been studied. Here, we determine the effect of CypB on the growth of endometrial cancer. METHODS: In this study, we examined the expression of CypB in endometrial cancer tissues using immunohistochemistry. CypB silenced in HEC-1-B cell line by shRNA. CCK-8, colony formation assays, wound healing assays, and transwell analysis were performed to assess its effect on tumor cell proliferation and metastasis. Furthermore, microarray analysis was carried out to compare the global mRNA expression profile between the HEC-1-B and CypB-silenced HEC-1-B cells. Gene ontology and KEGG pathway enrichment analysis were performed to determine the potential function of differentially expressed genes related to CypB. RESULTS: We found that CypB was upregulated in endometrial cancer, inhibit CypB expression could significantly suppress cell proliferation, metastasis, and migration. We identified 1536 differentially expressed genes related to CypB (onefold change, p < 0.05), among which 652 genes were upregulated and 884 genes were downregulated. The genes with significant difference in top were mainly enriched in the cell cycle, glycosphingolipid biosynthesis, adherens junctions, and metabolism pathways. CONCLUSION: The results of our study suggest that CypB may serve as a novel regulator of endometrial cell proliferation and metastasis, thus representing a novel target for gene-targeted endometrial therapy. TRIAL REGISTRATION: YLYLLS [2018] 008. Registered 27 November 2017.

Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma: a retrospective analysis and multicentre validation study.

BACKGROUND: Identification of high-risk localised renal cell carcinoma is key for the selection of patients for adjuvant treatment who are at truly higher risk of reccurrence. We developed a classifier based on single-nucleotide polymorphisms (SNPs) to improve the predictive accuracy for renal cell carcinoma recurrence and investigated whether intratumour heterogeneity affected the precision of the classifier. METHODS: In this retrospective analysis and multicentre validation study, we used paraffin-embedded specimens from the training set of 227 patients from Sun Yat-sen University (Guangzhou, Guangdong, China) with localised clear cell renal cell carcinoma to examine 44 potential recurrence-associated SNPs, which were identified by exploratory bioinformatics analyses of a genome-wide association study from The Cancer Genome Atlas (TCGA) Kidney Renal Clear Cell Carcinoma (KIRC) dataset (n=114, 906 600 SNPs). We developed a six-SNP-based classifier by use of LASSO Cox regression, based on the association between SNP status and patients' recurrence-free survival. Intratumour heterogeneity was investigated from two other regions within the same tumours in the training set. The six-SNP-based classifier was validated in the internal testing set (n=226), the independent validation set (Chinese multicentre study; 428 patients treated between Jan 1, 2004 and Dec 31, 2012, at three hospitals in China), and TCGA set (441 retrospectively identified patients who underwent resection between 1998 and 2010 for localised clear cell renal cell carcinoma in the USA). The main outcome was recurrence-free survival; the secondary outcome was overall survival. FINDINGS: Although intratumour heterogeneity was found in 48 (23%) of 206 cases in the internal testing set with complete SNP information, the predictive accuracy of the six-SNP-based classifier was similar in the three different regions of the training set (areas under the curve [AUC] at 5 years: 0·749 [95% CI 0·660-0·826] in region 1, 0·734 [0·651-0·814] in region 2, and 0·736 [0·649-0·824] in region 3). The six-SNP-based classifier precisely predicted recurrence-free survival of patients in three validation sets (hazard ratio [HR] 5·32 [95% CI 2·81-10·07] in the internal testing set, 5·39 [3·38-8·59] in the independent validation set, and 4·62 [2·48-8·61] in the TCGA set; all p<0·0001), independently of patient age or sex and tumour stage, grade, or necrosis. The classifier and the clinicopathological risk factors (tumour stage, grade, and necrosis) were combined to construct a nomogram, which had a predictive accuracy significantly higher than that of each variable alone (AUC at 5 years 0·811 [95% CI 0·756-0·861]). INTERPRETATION: Our six-SNP-based classifier could be a practical and reliable predictor that can complement the existing staging system for prediction of localised renal cell carcinoma recurrence after surgery, which might enable physicians to make more informed treatment decisions about adjuvant therapy. Intratumour heterogeneity does not seem to hamper the accuracy of the six-SNP-based classifier as a reliable predictor of recurrence. The classifier has the potential to guide treatment decisions for patients at differing risks of recurrence. FUNDING: National Key Research and Development Program of China, National Natural Science Foundation of China, Guangdong Provincial Science and Technology Foundation of China, and Guangzhou Science and Technology Foundation of China.

Analysis of Clinicopathological Features and Prognostic Factors in 39 Cases of Bladder Neuroendocrine Carcinoma.

AIM: Through analysis and summarization of clinicopathological features, immunohistochemical expression, pathological diagnostic criteria, prognostic and other factors in patients suffering from bladder neuroendocrine carcinoma (BNEC), a better understanding of BNEC could be achieved to provide solid evidence for clinicopathology and prognosis. MATERIALS AND METHODS: The clinicopathological data of 39 cases of BNEC with up to 5-year follow-up data (median follow-up=650 days) were analyzed retrospectively based on immunohistochemical staining. Survival analyses were carried out using the Kaplan-Meier method and tested with the log-rank method. Multivariate Cox regression analysis was adopted to screen independent risk factors affecting patients' survival. In these 39 cases of BNEC, there were 26 cases of male patients, 13 female, with the proportion of male to female being 2:1. The ages of onset ranged from 44 to 86, with the median age being 62 and the average age 61.97 years, respectively. Histologically, referring to the WHO standard of neuroendocrine lung tumor classification, there were 7 cases of typical carcinoid tumors, 8 atypical carcinoid, 12 small-cell carcinomas and 12 large-cell carcinomas. In these cases there were 11 cases of featured urothelium carcinomas and 9 cases of adenocarcinomas. RESULTS: Immunohistochemical staining showed that, in these 39 cases of BNEC, the positive expression for the neuroendocrinic markers, including neural cell adhesion molecule 56 (CD56), synaptophysin (Syn), chromogranin A (CgA), neuron-specific enolase (NSE), thyroid transcription factor-1 (TTF-1), cytokeratin (CK) and cytokeratin 7 (CK7), accounted for 39/39, 27/39, 18/39, 39/39, 19/39, 10/39 and 8/39, respectively. In contrast, cytokeratin 20 (CK20), protein 63 (P63), human melanoma black 45 (HMB45), S-lfln protein 100 (S-100) and leukocyte common antigen (LCA) were all negatively expressed. During the follow-up period, 12 patients died. The 1-, 3- and 5-year overall survival (OS) rates were 76.92%, 74.36% and 69.23%. CONCLUSION: BNEC is one of the most malignant tumors with severe invasiveness and poor prognosis. Immunohistochemistry revealed that CD56, Syn, CgA, NSE, TTF-1, CK, CK7, CK20, P63, HMB45, S-100 protein and LCA immune markers play important roles in diagnosis and differentiation. Many factors, including the patient's age, size and shape of the tumor, operative method, perineuronal invasion, vascular invasion, distant organ metastasis and pathological type, show great difference in influencing OS time of patients, among which the size of the tumor, no invasion, vascular invasion and distant organ metastasis are independent risk factors affecting prognosis (survival time). Radical cystectomy is the prior alternative to treat this tumor.