Nicotinamide-N-methyltransferase is a promising metabolic drug target for primary and metastatic clear cell renal cell carcinoma.

BACKGROUND: The metabolic enzyme nicotinamide-N-methyltransferase (NNMT) is highly expressed in various cancer entities, suggesting tumour-promoting functions. We systematically investigated NNMT expression and its metabolic interactions in clear cell renal cell carcinoma (ccRCC), a prominent RCC subtype with metabolic alterations, to elucidate its role as a drug target. METHODS: NNMT expression was assessed in primary ccRCC (n = 134), non-tumour tissue and ccRCC-derived metastases (n = 145) by microarray analysis and/or immunohistochemistry. Findings were validated in The Cancer Genome Atlas (kidney renal clear cell carcinoma [KIRC], n = 452) and by single-cell analysis. Expression was correlated with clinicopathological data and survival. Metabolic alterations in NNMT-depleted cells were assessed by nontargeted/targeted metabolomics and extracellular flux analysis. The NNMT inhibitor (NNMTi) alone and in combination with the inhibitor 2-deoxy-D-glucose for glycolysis and BPTES (bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl-sulfide) for glutamine metabolism was investigated in RCC cell lines (786-O, A498) and in two 2D ccRCC-derived primary cultures and three 3D ccRCC air-liquid interface models. RESULTS: NNMT protein was overexpressed in primary ccRCC (p = 1.32 × 10-16 ) and ccRCC-derived metastases (p = 3.92 × 10-20 ), irrespective of metastatic location, versus non-tumour tissue. Single-cell data showed predominant NNMT expression in ccRCC and not in the tumour microenvironment. High NNMT expression in primary ccRCC correlated with worse survival in independent cohorts (primary RCC-hazard ratio [HR] = 4.3, 95% confidence interval [CI]: 1.5-12.4; KIRC-HR = 3.3, 95% CI: 2.0-5.4). NNMT depletion leads to intracellular glutamine accumulation, with negative effects on mitochondrial function and cell survival, while not affecting glycolysis or glutathione metabolism. At the gene level, NNMT-depleted cells upregulate glycolysis, oxidative phosphorylation and apoptosis pathways. NNMTi alone or in combination with 2-deoxy-D-glucose and BPTES resulted in inhibition of cell viability in ccRCC cell lines and primary tumour and metastasis-derived models. In two out of three patient-derived ccRCC air-liquid interface models, NNMTi treatment induced cytotoxicity. CONCLUSIONS: Since efficient glutamine utilisation, which is essential for ccRCC tumours, depends on NNMT, small-molecule NNMT inhibitors provide a novel therapeutic strategy for ccRCC and act as sensitizers for combination therapies.

Impact of NUDT15 genetics on severe thiopurine-related hematotoxicity in patients with European ancestry.

PURPOSE: Severe hematotoxicity in patients with thiopurine therapy has been associated with genetic polymorphisms in the thiopurine S-methyltransferase (TPMT). While TPMT genetic testing is clinically implemented for dose individualization, alterations in the nudix hydrolase 15 (NUDT15) emerged as independent determinant of thiopurine-related hematotoxicity. Because data for European patients are limited, we investigated the relevance of NUDT15 in Europeans. METHODS: Additionally to TPMT phenotyping/genotyping, we performed in-depth Sanger sequencing analyses of NUDT15 coding region in 107 European patients who developed severe thiopurine-related hematotoxicity as extreme phenotype. Moreover, genotyping for NUDT15 variants in 689 acute lymphoblastic leukemia (ALL) patients was performed. RESULTS: As expected TPMT was the main cause of severe hematotoxicity in 31% of patients, who were either TPMT deficient (10%) or heterozygous carriers of TPMT variants (21%). By comparison, NUDT15 genetic polymorphism was identified in 14 (13%) patients including one novel variant (p.Met1Ile). Six percent of patients with severe toxicity carried variants in both TPMT and NUDT15. Among patients who developed toxicity within 3 months of treatment, 13% were found to be carriers of NUDT15 variants. CONCLUSION: Taken together, NUDT15 and TPMT genetics explain ~50% of severe thiopurine-related hematotoxicity, providing a compelling rationale for additional preemptive testing of NUDT15 genetics not only in Asians, but also in Europeans.

Metabolic and Lipidomic Reprogramming in Renal Cell Carcinoma Subtypes Reflects Regions of Tumor Origin.

BACKGROUND: Renal cell carcinoma (RCC) consists of prognostic distinct subtypes derived from different cells of origin (eg, clear cell RCC [ccRCC], papillary RCC [papRCC], and chromophobe RCC [chRCC]). ccRCC is characterized by lipid accumulation and metabolic alterations, whereas data on metabolic alterations in non-ccRCC are limited. OBJECTIVE: We assessed metabolic alterations and the lipid composition of RCC subtypes and ccRCC-derived metastases. Moreover, we elucidated the potential of metabolites/lipids for subtype classification and identification of therapeutic targets. DESIGN, SETTING, AND PARTICIPANTS: Metabolomic/lipidomic profiles were quantified in ccRCC (n=58), chRCC (n=19), papRCC (n=14), corresponding nontumor tissues, and metastases (n=9) through a targeted metabolomic approach. Transcriptome profiling was performed in corresponding samples and compared with expression data of The Cancer Genome Atlas cohorts (patients with ccRCC, n=452; patients with papRCC, n=260; and patients with chRCC, n=59). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: In addition to cluster analyses, metabolomic/transcriptomic data were analyzed to evaluate metabolic differences of ccRCC and chRCC using Welch's t test or paired t test as appropriate. Where indicated, p values were adjusted for multiple testing using Bonferroni or Benjamini-Hochberg correction. RESULTS AND LIMITATIONS: Based on their metabolic profiles, RCC subtypes clustered into two groups separating ccRCC and papRCC from chRCC, which mainly reflected the different cells of origin. ccRCC-derived metastases clustered with primary ccRCCs. In addition to differences in certain lipids (lysophosphatidylcholines and sphingomyelins), the coregulation network of lipids differed between ccRCC and chRCC. Consideration of metabolic gene expression indicated, for example, alterations of the polyamine pathway at metabolite and transcript levels. In vitro treatment of RCC cells with the ornithine-decarboxylase inhibitor difluoromethylornithine resulted in reduced cell viability and mitochondrial activity. Further evaluation of clinical utility was limited by the retrospective study design and cohort size. CONCLUSIONS: In summary, we provide novel insight into the metabolic profiles of ccRCC and non-ccRCC, thereby confirming the different ontogeny of RCC subtypes. Quantification of differentially regulated metabolites/lipids improves classification of RCC with an impact on the identification of novel therapeutic targets. PATIENT SUMMARY: Several subtypes of renal cell carcinoma (RCC) with different metastatic potentials and prognoses exist. In the present study, we provide novel insight into the metabolism of these different subtypes, which improves classification of subtypes and helps identify novel targets for RCC therapy.