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Circulating succinate-modifying metabolites accurately classify and reflect the status of fumarate hydratase–deficient renal cell carcinoma
Liang Zheng, … , Jin Zhang, Eyal Gottlieb
Liang Zheng, … , Jin Zhang, Eyal Gottlieb
Published April 13, 2023
Citation Information: J Clin Invest. 2023;133(11):e165028. https://doi.org/10.1172/JCI165028.
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Research Article Metabolism Oncology

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

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Abstract

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.

Authors

Liang Zheng, Zi-Ran Zhu, Tal Sneh, Wei-Tuo Zhang, Zao-Yu Wang, Guang-Yu Wu, Wei He, Hong-Gang Qi, Hang Wang, Xiao-Yu Wu, Jonatan Fernández-García, Ifat Abramovich, Yun-Ze Xu, Jin Zhang, Eyal Gottlieb

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Figure 2

Identification of potential liquid biopsy biomarkers in RCC.

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Identification of potential liquid biopsy biomarkers in RCC.
(A) PCA of ...
(A) PCA of plasma samples (1,637 features) from NC individuals and patients with RCC categorized according to FH mutation status, tumor mass, and disease stage (represented by the size of each dot). (B) Venn diagram of altered plasma metabolites of FH-MT versus NC samples (blue) and FH-MT versus FH-WT samples (yellow) and tumor size correlation analysis (red). (C) The top 20 ROCAUC-ranked plasma metabolites discriminating FH-MT from FH-WT and NC samples. (D) Heatmap classification of FH-MT, FH-WT, and NC samples based on the metabolites in C. Scale bar: log2(normalized abundance). (E) Regularized partial correlation network of significantly altered metabolites in B. Each node represents a metabolite, and each edge represents the strength of the partial correlation coefficient between 2 compounds that were mapped into biochemical pathways. The size of each circle represents the strength of the correlation with tumor burden.

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