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.

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.

Circulating succinate-modifying metabolites accurately classify and reflect the status of fumarate hydratase-deficient renal cell carcinoma.

Germline or somatic loss-of-function mutations of fumarate hydratase (FH) predispose patients to an aggressive form of renal cell carcinoma (RCC). Since other than tumor resection there is no effective therapy for metastatic FH-deficient RCC, an accurate method for early diagnosis is needed. Although MRI or CT scans are offered, they cannot differentiate FH-deficient tumors from other RCCs. Therefore, finding noninvasive plasma biomarkers suitable for rapid diagnosis, screening, and surveillance would improve clinical outcomes. Taking advantage of the robust metabolic rewiring that occurs in FH-deficient cells, we performed plasma metabolomics analysis and identified 2 tumor-derived metabolites, succinyl-adenosine and succinic-cysteine, as excellent plasma biomarkers for early diagnosis. These 2 molecules reliably reflected the FH mutation status and tumor mass. We further identified the enzymatic cooperativity by which these biomarkers are produced within the tumor microenvironment. Longitudinal monitoring of patients demonstrated that these circulating biomarkers can be used for reporting on treatment efficacy and identifying recurrent or metastatic tumors.

Conventional chemotherapy and emerging targeted therapy for advanced adrenocortical carcinoma.

Adrenocortical carcinoma (ACC) is a rare but typically aggressive malignancy. Radical surgery remains the potentially curative option. However, about one third of patients initially present with distant metastases. Regarding to chemotherapy, mitotane alone or in combination with cytotoxic drugs should be the first selection. Meanwhile, a phase lll clinical trial of etoposide, doxorubicin, cisplatin plus mitotane or streptozotocin plus mitotane is currently undergoing worldwide. The study on molecular pathogenesis of ACC is progressing. A lot of targeted therapies are also enrolled in preclinical investigations and clinical trials, including small-molecule tyrosine kinase inhibitors, antiangiogenic compounds. This article introduced the conventional chemotherapy, newly developed targeted therapy for advanced ACC.

Myxoid adrenal cortical tumor: report of four cases.

Myxoid adrenocortical neoplasms are rare. Surgical resection of the mass is the first-line therapy. Here we reported a total of four patients, aged 44­66 years, diagnosed with myxoid adrenocortical tumor. The clinical characteristics and immunohistochemical features of the tumor are discussed in the current literature.

Significance of heparanase-1 and vascular endothelial growth factor in adrenocortical carcinoma angiogenesis: potential for therapy.

The purpose of this study was to determine the correlation between human adrenocortical carcinoma (ACC) and the proteins involved in tumor angiogenesis, and to evaluate the angiogenic status of ACC. The expression of heparanase-1 (HPA-1), vascular endothelial growth factor (VEGF), and vascular endothelial growth factor receptor-2 (VEGFR-2) as well as microvessel density (MVD) were measured in a series of tissue samples from 44 human sporadic adrenocortical tumors by immunohistochemistry. These specimens were classified as adenomas (n = 20) and carcinomas (n = 24) according to the histological criteria defined by Weiss. A total of 22 of 24 (91.67%) malignant cases showed positive staining for HPA-1 and 3 of 20 (15%) benign cases showed positive, the difference of HPA-1 expression between ACA and ACC was statistically significant (P < 0.001). Similarly, VEGF staining was seen in 70.83% (17/24) of the malignant cases versus 25% (5/20) of the benign, the difference of VEGF expression among two groups was statistically significant (P = 0.002). VEGFR-2 expressed highly in the ACC group (79.17%, 19/24) and lowly in the benign group (25%, 5/20), the two groups had extremely significant difference (P < 0.001). Malignant cases showed higher MVD compared to benign tumors (84.70 ± 12.44 vs. 21.05 ± 8.07, P < 0.001). HPA-1 and VEGF expression were positively correlated with MVD in all specimens (r_s = 0.812, P = 0.001; r_s = 0.834, P < 0.001). In conclusion, these results suggest that angiogenesis of human ACC maybe mediated by these proteins and they could represent selective targets for the molecularly targeted treatments of ACC.