Urea Cycle Dysregulation Generates Clinically Relevant Genomic and Biochemical Signatures

Abstract

The urea cycle (UC) is the main pathway by which mammals dispose of waste nitrogen. We find that specific alterations in the expression of most UC enzymes occur in many tumors, leading to a general metabolic hallmark termed "UC dysregulation" (UCD). UCD elicits nitrogen diversion toward carbamoyl-phosphate synthetase2, aspartate transcarbamylase, and dihydrooratase (CAD) activation and enhances pyrimidine synthesis, resulting in detectable changes in nitrogen metabolites in both patient tumors and their bio-fluids. The accompanying excess of pyrimidine versus purine nucleotides results in a genomic signature consisting of transversion mutations at the DNA, RNA, and protein levels. This mutational bias is associated with increased numbers of hydrophobic tumor antigens and a better response to immune checkpoint inhibitors independent of mutational load. Taken together, our findings demonstrate that UCD is a common feature of tumors that profoundly affects carcinogenesis, mutagenesis, and immunotherapy response.

Keywords: CAD; cancer metabolism; immunotherapy; mutagenesis; pyrimidines; urea cycle.

Figure 1.. Expression of UC Enzymes and Transporters Is Commonly Dysregulated in Cancer

(A) An illustration of the substrates channeling between the urea cycle enzymes and transporters and the pyrimidine synthesis pathway. Abbreviations: ASS1, argininosuccinate synthase; ASL, argininosuccinate lyase; OTC, ornithine carbamoyltransferase; CAD, carbamoyl-phosphate synthetase 2 (CPS2); ATC, aspartate transcarbamylase; DHO, dihydroorotase; DHODH, dihydroorotate dehydrogenase; and UMP synthase, uridine monophosphate synthase. (B) Most tumor types in the TCGA database have aberrant expression of at least two components of the UC, which facilitates the supply of CAD substrates (left panel), as compared to their expression in the corresponding normal tissues in GTEx (right panel). The differences remain significant versus random choice of sets of six metabolic genes (empirical p < 0.001). Tumor type abbreviations: UCEC, uterine corpus endometrial carcinoma; THCA, thyroid carcinoma; TGCT, testicular germ cell tumors; STAD, stomach adenocarcinoma; SKCM, skin cutaneous melanoma; SARC, sarcoma; PRAD, prostate adenocarcinoma; PAAD, pancreatic adenocarcinoma; OV, ovarian serous cystadenocarcinoma; LUSC, lung squamous cell carcinoma; LUAD, lung adenocarcinoma; LIHC, liver hepatocellular carcinoma; LGG, brain lower-grade glioma; LAML, acute myeloid leukemia; KIRP, kidney renal papillary cell carcinoma; KIRC, kidney renal clear cell carcinoma; KICH, kidney chromophobe; HNSC, head-neck squamous cell carcinoma; GBM, glioblastoma multiforme; ESCA, esophageal carcinoma; DLBC, lymphoid neoplasm diffuse large B cell lymphoma; COAD, colon adenocarcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; BRCA, breast invasive carcinoma; and BLCA, bladder carcinoma. See also Figure S1 and Table S1.

Figure 2.. Cancer Proliferation Correlates with Changes in Expression of UC Enzymes and Transporters

(A) Tissue arrays comprise at least 10 tumors of each cancer type, and four or more of the corresponding normal tissues were immunostained for several UC components (scale bar represents 200 μm). Each staining was calibrated and repeated in three technical repetitions per patient sample (OD levels were compared via a Student’s t test). The quantification analyses of these plots are presented in Figure S3A. (B) UCD scores (x axis, equally divided into five bins, N~1,565 each) are positively associated with CAD expression (rank normalized in each cancer type) in the TCGA dataset (each pair of consecutive bins was compared using a Wilcoxon rank-sum test). (C) The expression of the proliferation marker Ki-67 in the TCGA data is significantly higher in UCD samples (UCD score > top 45%, n = 1,990) compared to UC WT (bottom < 45%, n = 1,990) (Wilcoxon rank-sum test, p < 2.2 × 10⁻¹⁶). (D) UCD scores in patients’ derived cells increase at more progressed cancerous states, from healthy skin (n = 8) to nevi (n = 9), primary melanoma (n = 31), and metastatic melanoma (n = 73) cells (Wilcoxon rank-sum test) (Kabbarah et al., 2010). (E) Pan-cancer Kaplan-Meier (KM) survival curves (computed across all TCGA samples) show that UCD is associated with worse survival. To confirm the robustness of the signal, we applied varying thresholds to define UCD and UC WT samples (solid line: top and bottom 30%; single-dotted line: 45%; and double-dotted line: 15%). The statistics of the log rank test with 30% threshold are reported in the figure. See also Figures S1, S2, and S3.

Figure 3.. Changes in Nitrogenous Compounds Are Detectable in Bio-fluids of Cancer Patients

(A) Increased pyrimidines (uracil and thymidine) in urine of patients with prostate cancer (n = 49) compared to controls (n = 10) (Wilcoxon rank-sum test, p < 0.05 and 0.01, respectively). (B) The distribution of pyrimidine to purine metabolites ratio for samples with low versus high UCD scores (top and bottom 15%). Upper panel: results for hepatocellular carcinoma (HCC) (Wilcoxon rank-sum test, p < 0.05, n = 9 for each group). Lower panel: results for breast cancer (BC) tumors (Wilcoxon rank-sum test, p < 0.046, n = 4 for each group). (C) Upper panel: increased excretion of pyrimidine pathway metabolites in urine of mice with colon tumors (n = 10 in each group; p < 0.05). Error bars denote SD. Lower panel: western blot demonstrating spontaneously emerging UCD in mice colon tumors compared to normal intestines (see quantification in Figure S4B). (D) Mice bearing colon cancer have decreased plasma urea levels. The error bars denote SD. (E) Children with different cancers (red, n = 100) have lower than normal plasma levels of urea on the day of admittance to the hospital (p ≤ 0.05, using one-sided Student’s t test), as compared to plasma urea levels of age and sex-matched Israeli children (gray, n = 1,363,691). See also Figure S4 and Table S2.

Figure 4.. UCD Promotes Genomic Transversion Mutations in Cancer Cell Lines

(A) Knockdown of the UC enzymes in different cancer cells increases the pyrimidine to purine ratio of synthesis favoring pyrimidines, as measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The figure is a representative of the mean of more than two biological repeats (p ≤ 0.05, one-sided paired t test). The error bars denote SEM. The values of metabolites from which the ratio was calculated are presented in Table S2 and shown as graphs in Figure S4E. (B) Inducing perturbations in individual UC components in the directions expected to increase UCD (including OTC, ASS1, ORNT1, and citrin) significantly increases R → Y mutations (Fisher’s exact test). (C) PTMB levels after UC perturbation in LOX and U2OS cell lines. PTMB levels after 2 weeks (green) are significantly higher than those after 1 week (yellow; bootstrap empirical p < 0.001 and p < 0.03 for LOX and U2OS, respectively). The error bars denote SD from bootstrapping. See also Figure S5 and Table S3.

Figure 5.. UCD Associates with Genomic Transversion Mutations in Tumor Samples

(A) Tumors with UCD have significantly higher PTMB levels versus tumors with intact UC (termed “wild-type” [WT], Wilcoxon rank-sum test, p < 0.008, n = 1,990 each), while such a significant difference is not observed for transition mutations. (B) UCD is associated with a higher level of PTMB across different cancer types (each circle denotes the median UCD and PTMB levels in each cancer type (Spearman’s R = 0.58, p < 0.01). Evidently, lung cancer (LUAD, LUSC) samples are notable outliers, probably because they are confounded with a smoking mutational signature (Alexandrov et al., 2016) enriched with transversion mutations. The exclusion of these lung cancer subtypes leads to a higher correlation, R = 0.78 (p < 0.007) (see the STAR Methods for details). (C) PTMB correlates with gene expression (Spearman’s R = 0.8, p < 0.02) in UC-perturbed cell lines but less so in the control cell lines. (D) Tumors with UCD have significantly higher levels of PTMB at the mRNA level, based on the analysis of 18 TCGA breast cancer samples (Wilcoxon rank-sum test, p < 0.05, n = 8 in each group). (E) Genome-wide proteomic analysis of 40 breast cancers shows that PTMB propagates to the proteome (Wilcoxon rank-sum test, p < 0.05, n = 18 in each group). (F) Pan-cancer KM survival curves showing significantly worse prognosis for patients with tumors bearing higher PTMB levels at varying thresholds for UCD and UC WT samples (solid line, 30%; single-dotted line, 45%; and double-dotted line, 15%). The outcome of the log rank test using a 30% threshold cutoff is denoted in the figure. See also Figure S5 and Table S4.

Figure 6.. Higher UCD and PTMB Levels Are Associated with Increased Response to Immune Checkpoint Therapy in Mouse Models and Patients

(A) UCD scores are significantly higher in responders (orange, n = 13) than non-responders (gray, n = 8) to anti-PD1 therapy (Wilcoxon rank-sum test, p < 0.05). (B) ROC curves showing higher predictive power of PTMB (AUC = 0.77, blue) compared to that obtained via mutational load (AUC = 0.34, red) in predicting the response to anti-PD1 therapy (Roh et al., 2017). In this dataset, UCD scores could not be calculated because the gene expression of UC enzymes was not measured (due to limited coverage of nanostring measurements). (C) Hydrophobicity (Janin, 1979) of 15 R → Y candidate neopeptides. The triangles denote the changes in the hydrophobicity of the candidate neopeptides in the UC dysregulated versus control cell lines. Among the predicted neopeptides, those that are either predicted to bind to MHC class I (Andreatta and Nielsen, 2016; Jurtz et al., 2017; Karosiene et al., 2012; Zhang et al., 2009; Rasmussen et al., 2016; O’Donnell et al., 2017), or whose AA sequence is found in immune epitope database (IEDB) (Vita et al., 2015), are marked in red (see the STAR Methods). The four R → Y neopeptides predicted to bind to MHC class I show significantly higher hydrophobicity following R → Y mutation (paired one-sided t test p < 0.04). (D) MC-38 mouse colon cancer cells without UCD (left) and with UCD (right) were injected into C57Bl6 mice. The mice were treated intraperitoneally with anti-PD1 immunotherapy on days 7, 12, 16, and 19. Tumor volume was palpated twice a week. Cancer cells with UCD (induced via the knockdown of ASS1 with shASS1; blue) showed a significantly higher response to anti-PD1 treatment compared to the controls (yellow), as reflected by a significant decrease in tumor volume (n = 20 mice, 5 mice in each group, Wilcoxon rank-sum test, p < 0.007). (E) Following sacrifice on day 21, flow cytometry analysis shows a significantly increased number of CD8 cytotoxic T cells infiltrating into the excised tumors (n = 20 mice, 5 mice in each group, Wilcoxon rank-sum test, p = 0.01 and 0.3, respectively for shASS1 and EV). (F) UCD mice treated with anti-PD1 (blue) show significantly attenuated tumor growth compared to the untreated mice (yellow) (p < 0.01, ANOVA with Dunnett’s correction). See also Figure S6 and Tables S5, S6, S7, and S8.

Figure 7.. Summary Slide for the Hypothesized “UCD Effect”

In normal tissue, excess nitrogen is disposed of as urea, but, in cancer cells, most nitrogen is utilized for synthesis of macromolecules, with pyrimidine synthesis playing a major role in carcinogenesis and affecting patients’ prognosis and response to ICT.