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Addressing metabolic heterogeneity in clear cell renal cell carcinoma with quantitative Dixon MRI.

ABSTRACT: Dysregulated lipid and glucose metabolism in clear cell renal cell carcinoma (ccRCC) has been implicated in disease progression, and whole tumor tissue-based assessment of these changes is challenged by the tumor heterogeneity. We studied a noninvasive quantitative MRI method that predicts metabolic alterations in the whole tumor. We applied Dixon-based MRI for in vivo quantification of lipid accumulation (fat fraction [FF]) in targeted regions of interest of 45 primary ccRCCs and correlated these MRI measures to mass spectrometry-based lipidomics and metabolomics of anatomically colocalized tissue samples isolated from the same tumor after surgery. In vivo tumor FF showed statistically significant (P < 0.0001) positive correlation with histologic fat content (Spearman correlation coefficient, ρ = 0.79), spectrometric triglycerides (ρ = 0.56) and cholesterol (ρ = 0.47); it showed negative correlation with free fatty acids (ρ = -0.44) and phospholipids (ρ = -0.65). We observed both inter- and intratumoral heterogeneity in lipid accumulation within the same tumor grade, whereas most aggressive tumors (International Society of Urological Pathology [ISUP] grade 4) exhibited reduced lipid accumulation. Cellular metabolites in tumors were altered compared with adjacent renal parenchyma. Our results support the use of noninvasive quantitative Dixon-based MRI as a biomarker of reprogrammed lipid metabolism in ccRCC, which may serve as a predictor of tumor aggressiveness before surgical intervention. NIH R01CA154475 (YZ, MF, PK, IP), NIH P50CA196516 (IP, JB, RJD, JAC, PK), Welch Foundation I-1832 (JY), and NIH P01HL020948 (JGM).

SUBMITTER: Zhang Y

PROVIDER: S-EPMC5543910 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Addressing metabolic heterogeneity in clear cell renal cell carcinoma with quantitative Dixon MRI.

Zhang Yue Y Udayakumar Durga D Cai Ling L Hu Zeping Z Kapur Payal P Kho Eun-Young EY Pavía-Jiménez Andrea A Fulkerson Michael M de Leon Alberto Diaz AD Yuan Qing Q Dimitrov Ivan E IE Yokoo Takeshi T Ye Jin J Mitsche Matthew A MA Kim Hyeonwoo H McDonald Jeffrey G JG Xi Yin Y Madhuranthakam Ananth J AJ Dwivedi Durgesh K DK Lenkinski Robert E RE Cadeddu Jeffrey A JA Margulis Vitaly V Brugarolas James J DeBerardinis Ralph J RJ Pedrosa Ivan I

JCI insight 20170803 15

<h4>Background</h4>Dysregulated lipid and glucose metabolism in clear cell renal cell carcinoma (ccRCC) has been implicated in disease progression, and whole tumor tissue-based assessment of these changes is challenged by the tumor heterogeneity. We studied a noninvasive quantitative MRI method that predicts metabolic alterations in the whole tumor.<h4>Methods</h4>We applied Dixon-based MRI for in vivo quantification of lipid accumulation (fat fraction [FF]) in targeted regions of interest of 45 ...[more]

PMID: 28768909

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Project description:BackgroundClear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment.MethodsGathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis.ResultsDiverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial-mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation.ConclusionsThe tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.

Project description:Renal cell carcinoma (RCC) is one of the leading causes of cancer-related death worldwide. Tumour metastasis and heterogeneity lead to poor survival outcomes and drug resistance in patients with metastatic RCC (mRCC). In this study, we aimed to assess intratumoural heterogeneity (ITH) in mRCC cells by performing a combined analysis of bulk data and single-cell RNA-sequencing data, and develop novel biomarkers for prognosis prediction on the basis of the potential molecular mechanisms underlying tumorigenesis. Eligible single-cell cohorts related to mRCC were acquired using the Gene Expression Omnibus (GEO) dataset to identify potential mRCC subpopulations. We then performed gene set variation analysis to understand the differential function in primary RCC and mRCC samples. Subsequently, we applied weighted correlation network analysis to identify coexpressing gene modules that were related to the external trait of metastasis. Protein-protein interactions were used to screen hub subpopulation-difference (sub-dif) markers (ACTG1, IL6, CASP3, ACTB and RAP1B) that might be involved in the regulation of RCC metastasis and progression. Cox regression analysis revealed that ACTG1 was a protective factor (HR < 1), whereas the other four genes (IL6, CASP3, ACTB and RAP1B) were risk factors (HR > 1). Kaplan-Meier survival analysis suggested the potential prognostic value of these sub-dif markers. The expression of sub-dif markers in mRCC was further evaluated in clinical samples by immunohistochemistry (IHC). Additionally, the genetic features of sub-dif marker expression patterns, such as genetic variation profiles, correlations with tumour-infiltrating lymphocytes (TILs), and targeted signalling pathway activities, were assessed in bulk RNA-seq datasets. In conclusion, we established novel subpopulation markers as key prognostic factors affecting EMT-related signalling pathway activation in mRCC, which could facilitate the implementation of a treatment for mRCC patients.

Project description:BackgroundIntratumor molecular heterogeneity has been reported for primary clear cell renal cell carcinoma (ccRCC) tumors; however, heterogeneity in metastatic ccRCC tumors has not been explored.ObjectiveTo evaluate intra- and intertumor molecular heterogeneity in resected metastatic ccRCC tumors.Design, setting, and participantsWe identified 111 patients who had tissue available from their primary tumor and at least one metastasis. ClearCode34 genes were analyzed for all tumors.Outcome measurements and statistical analysisPrimary and metastatic tumors were classified as clear cell type A (ccA) or B (ccB) subtypes. Logistic and Cox regression were used to evaluate associations with pathologic features and survival.Results and limitationsIntratumor heterogeneity of ccA/ccB subtypes was observed in 22% (95% confidence interval [CI] 3-60%) of metastatic tumors. Subtype differed across longitudinal metastatic tumors from the same patient in 23% (95% CI 10-42%) of patients and across patient-matched primary and metastatic tumors in 43% (95% CI 32-55%) of patients. Association of subtype with survival was validated in primary ccRCC tumors. The ccA/ccB subtype in metastatic tumors was significantly associated with metastatic tumor location, metastatic tumor grade, and presence of tumor necrosis. A limitation of this study is that we only analyzed patients who had both a nephrectomy and metastasectomy.ConclusionsApproximately one quarter of metastatic tumors displayed intratumor heterogeneity; a similar rate of heterogeneity was observed across longitudinal metastatic tumors. Thus, for biomarker studies it is likely adequate to analyze a single sample per metastatic tumor provided that pathologic review is incorporated into the study design. Subtypes across patient-matched primary and metastatic tumors differed 43% of the time, suggesting that the primary tumor is not a good surrogate for the metastatic tumor.Patient summaryPrimary and secondary/metastatic cancers of the kidney differed in nearly one half of ccRCC patients. The pattern of this relationship may affect tumor growth and the most suitable treatment.

Project description:Purpose: In this study, we have undertaken the whole proteomic analysis and got a better understanding of biological processes involved in the development and progression of ccRCC. We hope promising biomarkers can be uncovered to facilitate early diagnosis, predict the prognosis and progression, more importantly, to be applied as potential therapeutic targets. Experimental design: Fresh frozen tissue samples were surgically resected from patients with local or locally advanced ccRCC. Trypsin digested proteins were analyzed using TMT-based LC-MS/MS proteomic approach, followed by bioinformatic analysis. A potential prognostic marker FMNL1 was chosen to be validated in TCGA_KIRC datasets (n=525 and 72), further validation sets (n=10 and 10) and expanded validation sets (n=81 and 16). The effects of FMNL1 on proliferation, migration and invasion were determined by colony formation, wound healing, and transwell assays in 786-O and Caki-1 cells in vitro study. Results: A total of 657 differentially expressed proteins were identified and quantified between ccRCC and adjacent normal tissues (p-value<0.05, FC>2 or<1/2), of which 186 proteins were up-regulated and 471 proteins were down-regulated. Bioinformatic analysis showed enriched metabolic biological processes and pathways. Univariate and multivariate analysis defined FMNL1 as an independent negative prognostic marker in the TCGA datasets. High expression of FMNL1 correlated significantly with tumor stage and distant metastasis (P<0.05) both in the TCGA-KIRC datasets and expanded validation sets. Kaplan-Meier survival curve illustrated that the patients with high FMNL1 protein level had shorter OS time in the expanded validation sets (p=0.0273). In vitro experiments presented the functional effects of FMNL1 knockdown on the inhibition of proliferation, migration and invasion in cancer cell lines. Conclusion and clinical relevance: The proteomic results uncovered sophisticated metabolic reprogramming of ccRCC and indicated that the upregulation of rate-limiting enzymes in glycolysis and mitochondrial impairment may be the cause of metabolic reprogramming in ccRCC. Moreover, FMNL1 has been identified as a promising prognostic marker, and knockdown of FMNL1 could inhibit ccRCC cell proliferation, migration and invasion, which might be used as a new effective therapeutic strategy to inhibit the progression of ccRCC.

Dataset Information

Addressing metabolic heterogeneity in clear cell renal cell carcinoma with quantitative Dixon MRI.

Publications

Addressing metabolic heterogeneity in clear cell renal cell carcinoma with quantitative Dixon MRI.

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