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Edwards Et Al. DPYD Is Driver and Switch of Pyrimidine Metabolism in Human Cancer Page 1 of 20 Author Manuscript Published OnlineFirst on November 25, 2015; DOI: 10.1158/1541-7786.MCR-15-0403 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 Hypermutation of DPYD Deregulates Pyrimidine Metabolism and Promotes Malignant Progression 2 Lauren Edwards, Rohit Gupta, Fabian V. Filipp 3 Systems Biology and Cancer Metabolism, Program for Quantitative Systems Biology, University of 4 California Merced, Merced, CA 95343, USA 5 6 Running title 7 DPYD is driver and switch of pyrimidine metabolism in human cancer 8 Keywords 9 TCGA, melanoma, DPYD, pyrimidine, systems biology 10 Financial support 11 F.V.F. is grateful for the support of grant CA154887 and CA176114 from the National Institutes of 12 Health, National Cancer Institute. 13 Corresponding author 14 Fabian V. Filipp*, e-mail: [email protected]; * Systems Biology and Cancer Metabolism, Program for 15 Quantitative Systems Biology, University of California Merced, Merced, CA 95343, USA 16 Phone: +1-858-349-0349; Address: 2500 North Lake Road, Merced, CA 95343, USA 17 Conflicts of interest 18 The authors declare that there is no competing financial interest as part of the submission process of 19 this manuscript. 20 Manuscript notes 21 Word count (11 pages, 4070 words, 24713 characters), title (10 words, 81 characters), running title (11 22 words, 56 characters), abstract (174 words, 1240 characters), 6 figures, 2 tables, 1 supplementary file 23 with tables. 24 _____________________________________________________________________________________ Edwards et al. DPYD is driver and switch of pyrimidine metabolism in human cancer Page 1 of 20 Downloaded from mcr.aacrjournals.org on September 26, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on November 25, 2015; DOI: 10.1158/1541-7786.MCR-15-0403 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 Abstract 2 New strategies are needed to diagnose and target human melanoma. To this end, genomic analyses was 3 performed to assess somatic mutations and gene expression signatures using a large cohort of human 4 skin cutaneous melanoma (SKCM) patients from The Cancer Genome Atlas (TCGA) project to identify 5 critical differences between primary and metastatic tumors. Interestingly, pyrimidine metabolism is one 6 of the major pathways to be significantly enriched and deregulated at the transcriptional level in 7 melanoma progression. In addition, dihydropyrimidine dehydrogenase (DPYD) and other important 8 pyrimidine-related genes: DPYS, AK9, CAD, CANT1, ENTPD1, NME6, NT5C1A, POLE, POLQ, POLR3B, 9 PRIM2, REV3L, and UPP2 are significantly enriched in somatic mutations relative to the background 10 mutation rate. Structural analysis of the DPYD protein dimer reveals a potential hotspot of recurring 11 somatic mutations in the ligand binding sites as well as the interfaces of protein domains that mediated 12 electron transfer. Somatic mutations of DPYD are associated with upregulation of pyrimidine 13 degradation, nucleotide synthesis, and nucleic acid processing while salvage and nucleotide conversion 14 is downregulated in TCGA SKCM. 15 Implications 16 At a systems biology level, somatic mutations of DPYD cause a switch in pyrimidine metabolism and 17 promote gene expression of pyrimidine enzymes toward malignant progression. 18 _____________________________________________________________________________________ Edwards et al. DPYD is driver and switch of pyrimidine metabolism in human cancer Page 2 of 20 Downloaded from mcr.aacrjournals.org on September 26, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on November 25, 2015; DOI: 10.1158/1541-7786.MCR-15-0403 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 Introduction 2 Cancer cells take advantage of distinct metabolic pathways promoting cellular proliferation or oncogenic 3 progression. Emerging evidence highlights central metabolic pathways including glucose- and glutamine- 4 dependent biomass production to support tumor growth (1). However, complex metabolic 5 requirements of dividing, migrating, or nutrient and oxygen limited cancer cells suggest that tumor cells 6 have much more complex metabolic requirements than previously appreciated (2). The Cancer Genome 7 Atlas (TCGA) puts an even-handed view on tissue-specific genomic determinants, revolutionizing our 8 perspective on malignancies by next-generation sequencing (3). Here we describe cross-talk between 9 signatures of somatic mutations and gene expression of skin cutaneous melanoma (SKCM) based on 10 RNASeq data from 471 TCGA melanoma samples. By connecting pattern of somatic mutations with 11 responses of gene expression at a pathway level, new features of melanoma metabolism and 12 progression are elucidated. 13 Pyrimidine synthesis is a key metabolic bottleneck important for DNA replication in tumor cells and, 14 therefore, represents a valuable diagnostic and therapeutic target. Early success in cancer metabolism 15 took advantage of this characteristic by making cancer cells vulnerable to inhibition of this pathway. 16 Heidelberger and colleagues designed fluorinated uracil-based pyrimidine analogues, which disrupted 17 tumor DNA biosynthesis and which are to this day used to treat colorectal and breast cancer (4, 5). 18 To analyze TCGA SKCM dataset we have employed a bottom-up strategy involving pathway enrichment 19 analysis of RNASeq data and structural analysis of somatic mutations. The approach identifies DPYD 20 (dihydropyrimidine dehydrogenase, Gene ID: 1806) as a pivotal factor of pyrimidine metabolism and 21 offers a comprehensive view on how a hypermutated metabolic gene deregulates pyrimidine and 22 nucleic acid synthesis and promotes malignant progression of melanoma. 23 Methods 24 Patient cohort. The TCGA SKCM cohort includes RNASeq data for 471 samples allowing us to extract 25 statistical significant pattern of differential expression between solid primary tumors (TP; 103 patients) 26 and metastatic tumors (TM; 367 patients), while there is only one dataset for blood derived normal 27 tissue (NB; 1 patient) (Supplementary table 1). In addition, we utilized files from whole-exome datasets 28 of 339 patients (61 TP; 278 TM) (Supplementary table 2) (6). Clinical data including a history of drug 29 treatment was available for 447 patients (Supplementary table 3). The study was carried out as part of 30 IRB approved study dbGap ID 5094 “Somatic mutations in melanoma” and conducted in accordance _____________________________________________________________________________________ Edwards et al. DPYD is driver and switch of pyrimidine metabolism in human cancer Page 3 of 20 Downloaded from mcr.aacrjournals.org on September 26, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on November 25, 2015; DOI: 10.1158/1541-7786.MCR-15-0403 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. 1 with the Helsinki Declaration of 1975. The results shown are based upon next generation sequencing 2 data generated by the TCGA Research Network http://cancergenome.nih.gov. Restricted access clinical, 3 RNASeq, and whole-exome sequences were obtained from the TCGA genome data access center and 4 the data portal. 5 Identification of somatic mutations. Identification of somatic mutations took advantage of components 6 of the modular multi-step filter as described (6). TCGA data portal was used for cohort selection and 7 CGHub for access of raw data. Whole-exome sequencing data for 339 patients with primary tumor or 8 metastatic tumor were matched with blood-derived normal reference. For the MuTect 1.1.4 analysis (7) 9 GrCh37 (Broad Institute variant of HG19), dbSNP build 132.vcf, and COSMIC_54.vcf library were 10 referenced. Somatic incidences file was queried in bash prompt to retain all the statically significant 11 KEEP mutations. The coverage.wig files served as input to model and account for Intron vs Exon 12 functional mutation burden in InVEx 1.0.1 (8). In addition, MutSig 2.0 assessed the clustering of 13 mutations in hotspots as well as conservation of the sites (9). It is noted that the SKCM cohort contains 14 an interesting case, patient TCGA-FW-A3R5, who has more than 20,000 mutations and an APOBEC 15 signature (10). This patient shows multiple missense mutations in DPYD with nucleotide transitions 16 according to canonical UVB signature, C>T and G>A. Including or excluding this patient had no 17 implications on the outcome of this study. 18 Structural model and molecular dynamics simulation. The structural model of human DPYD was based 19 on Sus scrofa X-ray structure (PDB entry 1gth) using swiss-model. Mutations were plotted on the 20 modeled human structure and ligand proximity was evaluated by a 5A cut-off. The solvent accessible 21 surface of each residue of DPYD was determined based on a molecular dynamics simulation over a 5 ns 22 trajectory using GROMACS 5.0.2 (11). 23 Gene expression analysis and statistical analysis. Level 3 RNASeq Log2 transformed expression levels for 24 18,086 genes were collected for each sample. Differential expression was determined by DESeq in the R 25 package and Students T-test was used to determine significant differences in expression between TP 26 and TM samples and onto metabolic pathways (12). The probability of the test statistics
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