Clinical Sample Collection
We screened 1,556 consecutive patients who underwent radical or partial nephrectomy for the treatment of renal tumors at the Department of Urology of Fudan University Shanghai Cancer Center (FUSCC, Shanghai, China) from January 2007 to March 2014. Electronic medical records were screened retrospectively. In total, 232 eligible ccRCC patients who had undergone radical nephrectomy at the FUSCC were consecutively enrolled. Median follow-up was 85 months (range, 3–138 months). At the last follow-up, 79 patients (34.1%) had progressive disease and 49 patients (21.1%) had died of ccRCC. Clinicopathological indicators, including age at surgery, sex, clinical manifestation, laterality, tumor size, chronic diseases status, TNM stage, and ISUP grading classification are summarized in Table S1. Tumor and adjacent non-tumor tissue samples were collected during surgery and are available from the FUSCC tissue bank. Samples were collected according to following criteria: 1) tumor adjacent tissues were collected >2cm from the tumor margin; 2) each tumor/adjacent sample was checked by an expert pathologist to confirm the sample quality. Hematoxylin and eosin (H&E)-stained slides of tumor and tumor adjacent tissues were uploaded to the Mendeley data (https://data.mendeley.com/datasets/pb5tbs2by5/draft?a=1b8aa955-40a7-4df0-970a-e936666ffd99).
Among the 1,324 excluded patients, 161 patients were diagnosed with benign renal tumor, 118 with urinary tract carcinoma, 326 with non-clear RCC, and 89 with other simultaneous or heterochronous malignancies. Further, 577 patients (mainly those who underwent partial nephrectomy) were excluded because of unavailable adjacent normal tissues, and 53 samples failed to pass pathological quality check, such as tumor cell rate < 90% (Figure S1A). All cases were staged according to the 2010 American Joint Committee on Cancer TNM staging system. H&E-stained sections were reviewed by an experienced genitourinary pathologist to determine the ISUP grade, and frozen sections were reviewed to determine the tumor cell rate of the ccRCC tissues. The study was compliant with the ethical standards of Helsinki Declaration II and was approved by the institutional review board of FUSCC (050432-4-1212B). Written informed consent was obtained from each patient before any study-specific investigation was conducted.
DNA Extraction and WES
WES was conducted at Life Healthcare Clinical Laboratory (China). DNA isolated from fresh or frozen tumor tissue samples was used for WES, and matched germline DNA was obtained from adjacent non-tumor tissue samples. DNA was isolated from fresh tissues using DNeasy Blood & Tissue Kit (Qiagen, 69504) according to the manufacturer’s instructions. Purified DNA was quantified using a Qubit 3.0 Fluorometer (Life Technologies). For matched germline and tumor tissues, 100 ng of DNA was sheared to 200–300-bp fragments using a Covaris M220 system. Tumor and matched germline DNA libraries were constructed using Accel-NGS 2S HYB DNA LIBRARY KIT (Swift Biosciences, 23096) and Accel-NGS 2S MID S1-S4 (Swift Biosciences, 279384). xGen Exome Research Panel v1.0 (IDT, 1056115) and xGen Lockdown reagents (IDT, 1072281) were used for exome enrichment. Dynabeads M-270 Streptavidin (Thermo, 65306) was used for library purification, P5/P7 primers (Nanodigmbio, ND10010) and HotStart ReadyMix (KAPA, KK2612) were used for library amplification. The amplified libraries were purified using SPRISELECT (Beckman, B23319). DNA quality was assessed using a Bioanalyzer High Sensitivity DNA Analysis kit (Agilent Technologies, 5067-4626). Samples underwent paired-end sequencing on a Nextseq CN500 platform (Illumina), with a 150-bp read length. The WES target region was 33 M. A mean coverage of 100×, a capture rate of 95%, and a dup rate of 40% were achieved for tumor sequencing.
Somatic Variant Detection
Read-depth statistics were calculated using the DepthOfCoverage function in the Genome Analysis Toolkit (GATK v3.8.1.0) 57. Paired-end reads in Fastq format were aligned to a reference human genome 58 (UCSC Genome Browser, hg38) using Burrows-Wheeler Aligner. Variant calling was conducted following GATK best practices. Somatic single-nucleotide variations and small insertions and deletions were detected using MuTect2 (GATK v4.1.2.0) and were annotated using ANNOVAR 59 based on UCSC known genes. Two longest genes, TTN and MUC16, were excluded as they tended to acquire numerous mutations by chance in large-scale genome/exome sequencing experiments. The Maftools R package60 was used to display mutant genes with non-synonymous mutations. MutSigCV61 was used to identify significantly mutated genes with default parameters. Genes with Benjamini–Hochberg-adjusted p < 0.01 were identified as significantly mutated genes.
Mutation Frequency Variances Across Regions
TCGA ccRCC genome data were downloaded from xenabrowser.net 62 and data for a European ccRCC cohort were obtained from 5. The top 10 most frequently mutated genes in our Chinese cohort and these two cohorts were compared using Fisher’s exact test.
Mutual Exclusivity and Mutation Co-occurrence Analysis
Mutually exclusive or co-occurring sets of genes were detected using the somaticInteractions function in the Maftools R package, using pair-wise Fisher’s exact test to detect significant gene pairs. p < 0.05 was used as a threshold for statistical significance.
Mutational Signature
SBSs are defined as a replacement of a certain nucleotide base. There are six possible substitutions: C>A, C>G, C>T, T>A, T>C, and T>G. Considering the nucleotide context, these SBS classes can be further expanded to 96 possible mutation types. The frequencies of the 96 mutation types were estimated for each sample. The non-negative matrix factorization algorithm of SigProfiler63 was used to estimate the minimal components that could explain maximum variance among samples. De novo mutation signatures were decomposed using COSMIC v314. After decomposing a matrix of the 96 substitution classes of the samples into five signatures, the contribution of each signature in each sample was estimated.
CNA Calling
CNAs were called following somatic CNA best practice, using the Calculate Target Coverage function in GATK (v4.1.2.0). We applied Genomic Identification of Significant Targets in Cancer (GISTIC2.0)18 to identify significantly amplified or deleted focal-level and arm-level events, with q < 0.05 considered significant. The following parameters were used: amplification threshold = 0.1; deletion threshold = 0.1; cap value = 1.5; broad length cutoff = 0.90; remove X-chromosome = 0; confidence level = 0.95; join segment size = 4; arm-level peel off = 1; maximum sample segments = 2,000; gene GISTIC = 1.
Each gene in each sample is assigned a threshold copy number that reflects the magnitude of its deletion or amplification. These are integer values ranging from –2 to 2, where 0 means no amplification or deletion of a magnitude greater than the threshold parameters described above. Amplifications are represented by positive numbers: 1 indicates amplification above the amplification threshold; 2 indicates amplification larger than the arm-level amplifications observed in the sample. Deletions are represented by negative numbers: –1 indicates deletion beyond the threshold; –2 indicates deletions greater than the minimum arm-level copy number observed in the sample.
Protein Extraction and Trypsin Digestion
Samples were minced and lysed in lysis buffer (8 M urea, 100 mM Tris hydrochloride, pH 8.0) containing protease and phosphatase inhibitors (Thermo Scientific) and then sonicated for 1 min (3 s on and 3 s off, amplitude 25%). The lysates were centrifuged at 14,000 ×g for 10 min and supernatants were collected as whole-tissue extracts. Protein concentrations were determined by the Bradford protein assay (TaKaRa, T9310A). Extracts (100 μg protein) were reduced with 10 mM dithiothreitol at 56°C for 30 min and alkylated with 10 mM iodoacetamide at room temperature in the dark for 30 min. The samples were digested with trypsin using a filter-aided sample preparation method64. Tryptic peptides were separated in a home-made reverse-phase C18 column. Peptides were eluted and separated into nine fractions using an acetonitrile gradient (6%, 9%, 12%, 15%, 18%, 21%, 25%, 30%, and 35%) at pH 10. The nine fractions were pooled into three fractions (6%+15%+25%; 9%+18%+30%; 12%+21%+35%), vacuum-dried (Concentrator Plus, Eppendorf), and analyzed by liquid chromatography tandem MS (LC-MS/MS).
LC-MS/MS
Samples were analyzed on a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific) coupled with a high-performance liquid chromatograph (EASY-nLC 1200 System, Thermo Fisher Scientific). Dried peptide samples were dissolved in solvent A (0.1% formic acid in water) and loaded onto a trap column (100 μm × 2 cm, home-made; particle size, 3 μm; pore size, 120 Å; SunChrom) with a maximum pressure of 280 bar using solvent A, then separated on a home-made 150 μm × 12 cm silica microcolumn (particle size, 1.9 μm; pore size, 120 Å; SunChrom) with a gradient of 5%–35% mobile phase B (acetonitrile and 0.1% formic acid) at a flow rate of 600 nL/min for 75 min. MS analysis was conducted with one full scan (300–1,400 m/z, R = 120,000 at 200 m/z) at an automatic gain control target of 3e6 ions, followed by up to 20 data-dependent MS/MS scans with higher-energy collision dissociation (target 5e4 ions, max injection time 20 ms, isolation window 1.6 m/z, normalized collision energy of 27%). Detection was done using Orbitrap (R = 7,500 at 200 m/z). Data were acquired using the Xcalibur software (Thermo Fischer Scientific).
MS Platform QC and ccRCC Proteome Quality Assessment
For QC of MS performance, tryptic digests of HEK293T cell lysates were measured as a QC standard every 2 days. The QC standard was made and run using the same method, conditions, software, and parameters as those used for ccRCC samples. Pairwise Spearman’s correlation coefficients were calculated using the R package corrplot 65 for all QC runs, and the results are shown in Figure S1B. The average correlation coefficient among standards was 0.95, with a maximum of 0.82 and minimum of 0.99. Log10-transformed fractions of total (FOTs) for each ccRCC sample (Figure S1C-D) were plotted to show consistency of data quality. The Sva R package v3.34.0 66 was used to evaluate batch effects. We found no significant batch effect in the proteome data. Moreover, PCA plots showed that the batch effects were negligible for batch number, but significant for sample types (Figure 3A).
Proteome Identification and Quantification
Raw files were processed in Firmiana17 and searched against the human National Center for Biotechnology Information (NCBI) RefSeq protein database (updated on 04-07-2013, 32,015 entries) using the Mascot 2.4 search engine (Matrix Science Inc). Mass tolerances were 20 ppm for precursor and 50 mmu for product ions. Up to two missed cleavages were allowed. Cysteine carbamidomethylation was set as a fixed modification and methionine N-acetylation and oxidation as variable modifications. Precursor ion score charges were limited to +2, +3, and +4. The data were also searched against a decoy database so that protein identifications were accepted at FDR of 1%. Label-free protein quantifications were calculated using a label-free, intensity-based absolute quantification (iBAQ) approach 16. Match between runs 67 was used to improve parallelism between tumor/adjacent samples. We built a dynamic regression function based on common peptides in tumor/adjacent samples. Based on the correlation value R2, Firmiana chooses a linear or quadratic function for regression to calculate the retention time (RT) of corresponding hidden peptides and checks the existence of the extracting ion current (XIC) based on the m/z and calculated RT. The program determines the peak area values of existing XICs. We calculated peak area values as parts of corresponding proteins. Proteins with at least 1 unique peptide with a 1% FDR at the peptide level were selected for further analysis. The FOT was used to represent the normalized abundance of a particular protein across samples. FOT was defined as a protein’s iBAQ divided by the total iBAQ of all proteins identified in each sample. FOT values were multiplied by 105 for ease of presentation and missing values were assigned 10–5 (Table S3).
Protein and Pathway Alterations in Tumor vs. Adjacent Tissues
PCA was conducted to visualize the separation of tumor and tumor-adjacent proteomes using the R package factoextra v1.0.668. In total, 6,111 proteins identified in both >25% of tumor and tumor-adjacent samples were used for subsequent analysis. Volcano plots were used to display DEPs in tumor and adjacent tissues by applying thresholds of fold change >2 and Benjamini–Hochberg-adjusted p < 0.05. Among the DEPs, 1,296 proteins were significantly upregulated and 699 proteins were significantly downregulated in ccRCC tumor tissues. The DEPs were then subjected to KEGG pathway enrichment analyses in DAVID69, with a p value cutoff of 0.05 (Table S4). Signature proteins of the nephrons (including glomerulus, proximal tubule, distal tubule and collecting duct) were obtained from the Human Protein Atlas database (https://www.proteinatlas.org/humanproteome/tissue/kidney).
Tumor Purity, Immune, Stromal, APM, Immunosuppression, CD8 cluster, and Metabolic Pathway Scores
Tumor purity, immune, and stromal scores were inferred using the R package ESTIMATE v1.0.11 41. Although the ESTIMATE algorithm was designed to analyze transcriptome data, some studies have used it for proteome analysis 3,7. The results indicate the feasibility to evaluate the engagement of each subtype of immune cells. APM, immunosuppression, and CD8 cluster signatures were obtained from previous reports 50,70 and computed using single-sample GSEA71. Metabolic pathway scores for 232 paired ccRCC samples were computed using the R package GSVA v1.34.072 (Table S4). KEGG and Reactome gene sets downloaded from the Molecular Signatures Database (MSigDB v7.1, http://software.broadinstitute.org/gsea/msigdb/index.jsp) were set as background.
GSEA
GSEA was conducted using the GSEA 4.0.3 software (http://software.broadinstitute.org/gsea/index.jsp)73. KEGG, Reactome, and HALLMARK gene sets downloaded from the MSigDB v7.1 were set as background. FDR < 0.05 was used as a cutoff. The normalized enrichment score was used to reflect the degree of pathway overrepresentation.
Associations Between Clinical Characteristics and the ccRCC Proteome
Specific clinical information is presented in Table S1. TNM stage- and ISUP grade-specific proteins were screened out based on a fold change > 1.5 and p < 0.05. Specific proteins of each TNM stage and ISUP grade were subjected to over-representation analysis using ConsensusPathDB (http://cpdb.molgen.mpg.de/)74. Clinical characteristics-associated pathways are listed in Table S6.
Proteomic Subtyping of ccRCC, and Subtype Features
Consensus clustering was conducted using the R package Consensus Cluster Plus 75 using Pearson correlation as the distance measure. The 1,000 proteins with the highest median absolute deviation in tumor samples were used for k-means clustering with up to five groups. Consensus matrices for k = 2, 3, 4, 5 clusters are shown in Figure S8E-F. The consensus matrix for k = 3 showed clear separation among clusters. The cumulative distribution function of the consensus matrix for each k-value was also measured (Figure S8F). The relative change in area under the cumulative distribution function curve increased by 33% from 2 clusters to 3 clusters, whereas others exhibited no appreciable increase. Thus, proteome clusters were defined using k-means consensus clustering with k = 3. Subtype-specific upregulated proteins are: (1) detected in ³25% tumor samples; (2) expressed higher than other subtypes (FC > 2, t test p < 0.05). Subtype-specific upregulated proteins were further analyzed in ConsensusPathDB74. DEPs of each subtype and relevant enriched pathways are listed in Table S7.
Validation of Proteomic Subtyping Performance
GSEA was conducted to identify signature proteins of each proteomic subtype using GSEA v4.0.3 73, and the 20 proteins with the highest scores in each subtype were selected. Hierarchical clustering of CPTAC ccRCC cohort 3 (available follow-up is three years at present) proteome data with signature proteins also classified the CPTAC cohort into three subgroups with a similar survival curve in our population, with GP1 showing distinctly worse survival than the other two subtypes (log-rank test, p = 0.001) (Figure S8).
Correlations Between Subtypes and Clinical Features
To evaluate correlations between proteomic subtypes and clinical features, Fisher’s exact test was conducted on categorical variables, including driver gene mutations, significant arm-level CNA events, age, sex, hypertension status, obesity status, cardiovascular and cerebrovascular disease status, family history of cancer, TNM stage, ISUP grade, and CPTAC subtype. Only variables that varied significantly among the three proteome subtypes are shown in Figure 5A. Scaled CPTAC ccRCC proteome data were used to identify signature proteins of each subtype by GSEA. The 20 proteins with the highest GSEA scores were selected as support vectors to build a support vector machine classifier. Chinese ccRCC cohort was divided into four CPTAC subtype using this classifier.
Effects of CNAs
Spearman’s correlations between CNA values (gene level) and protein abundances were calculated using 14,538 genes quantified at both CNA and proteome levels. CNAs with significant correlation with proteins were selected based on FDR < 0.01. In total, 89,992 CNA and protein pairs showed significant correlation. Correlations were visualized using the R package multiOmicsViz. Genomic alterations that affect gene expression at the same locus are said to act in cis, whereas an impact of another locus is defined as a trans effect (vertical patterns in Figure 2C), whereas the impact of other locus was defined as a trans effect (diagonal patterns in Figure 2C).
Survival Analysis
The Kaplan–Meier method was used for survival analyses, and groups were compared using the log-rank test. The R survival package 3.2-376 and survminer 0.4.8 were used for statistical tests and visualization. The HR was calculated by Cox proportional hazards regression analysis. Variates with p < 0.05 were considered to significantly impact prognosis. OS was used as a primary endpoint. Clinical and molecular variates with p < 0.05 in single variant analysis were selected for Cox regression multivariate analysis (Table S1).
Drug Target Analysis
Target proteins were selected based on three criteria: significantly upregulated in tumor vs. adjacent (Benjamini–Hochberg-adjusted p < 0.05, FC > 2), upregulated in GP1 compared to GP2&3 (Benjamini–Hochberg-adjusted p < 0.05, FC > 2), and associated with poor prognosis (HR > 1.8, p < 0.05). The proteins were mapped using the Drugbank druggable protein database (https://www.drugbank.ca/). Druggable proteins are listed in Table S7.
Cell Culture
Human HEK293T (ATCC, CRL-11268) A-498 (ATCC, HTB-44; RRID: CVCL_1056) and ACHN (ATCC, CRL-1611) cells were cultured in high-glucose Dulbecco’s modified Eagle’s medium (DMEM; HyClone) supplemented with 10% fetal bovine serum (FBS; Invitrogen), 100 units/mL penicillin (Invitrogen), and 100 μg/mL streptomycin (Invitrogen). 769-P (ATCC, CRL-1933) and 786-O cells (ATCC, CRL-1932) were maintained in RPMI 1640 medium (Invitrogen) containing 10% FBS. Cells were incubated in 5% CO2 at 37°C. Cells were transfected using polyethylenimine (linear, 25 KDa) or Lipofectamine 2000 (Invitrogen). To generate a cell model of nutrition stress, ACHN and 786-O cells were cultured in medium without serum and glucose for 12 h before the assays. To generate a cell model of genotoxic stress, cultured cells were irradiated with 4 Gy X-ray radiation using a linear accelerator (Oncor, Siemens) before the experiments.
Plasmid Construction and Transfection
Whole-length NNMT, MARS, KU70, KU80, and p53 cDNA clones were purchased from Origene. A whole-length DNA-PKcs cDNA clone was obtained from Prof. Yanhui Xu49. After confirming the sequences by Sanger sequencing, DNA-PKcs, NNMT, and p53 were amplified and subcloned into the NheI and EcoRI restriction sites of the pcDNA3.1-Flag vector, using ClonExpress MultiS One Step Cloning Kit (#C113-02, Vazyme). KU70 and KU80 were amplified and subcloned into the NheI and EcoRI restriction sites of the pcDNA3.1-Myc vector using the same kit. DNA-PKcs mutants were generated by site-directed mutagenesis using the MutanBEST kit (TaKaRa). The primers used were as follows: NNMT: forward, 5'- ggg aga ccc aag ctg gct agc ATG GAA TCA GGC TTC ACC TCC -3', and reverse, 5'- tag tcc agt gtg gtg gaa ttc CAG GGG TCT GCT CAG CTT CC -3'; p53: forward, 5'- ggg aga ccc aag ctg gct agc ATG GAG GAG CCG CAG TCA G -3', and reverse, 5'- tag tcc agt gtg gtg gaa ttc GTC TGA GTC AGG CCC TTC TGT C -3'; KU70: forward, 5'- ggg aga ccc aag ctg gct agc ATG TCA GGG TGG GAG TCA TAT TAC A -3', and reverse, 5'- tag tcc agt gtg gtg gaa ttc GTC CTG GAA GTG CTT GGT GAG G -3'; KU80: forward, 5'- ggg aga ccc aag ctg gct agc ATG GTG CGG TCG GGG AAT -3', and reverse, 5'- tag tcc agt gtg gtg gaa ttc TAT CAT GTC CAA TAA ATC GTC CAC A -3'; DNA-PKcs: forward, 5'- ggg aga ccc aag ctg gct agc ATG GCG GGC TCC GGA GCC G -3', and reverse, 5'- tag tcc agt gtg gtg gaa ttc CAT CCA GGG CTC CCA TCC T -3'; DNA-PKcs K122W: forward, 5'- GA GCT GCT tgg TGT AAA ATT CCA GCC CTG GAC C -3', and reverse, 5'- TTT ACA cca AGC AGC TCT ATC TTT TGT ATA AAC ACT G -3'; DNA-PKcs K712W: forward, 5'- AA TTT GGC tgg GAG GTG GCA GTT AAA ATG AAG CA -3', and reverse, 5'- CAC CTC cca GCC AAA TTT CAC AAA TAA AGC AAA -3'; DNA-PKcs K868W: forward, 5'- C tgg AAT CTT CTG ACA GTC ACG TCC TCA GAT G -3', and reverse, 5'- C TGT CAG AAG ATT cca GTT TAT TTG TCC TCC TAG AGA TCC AAG -3'; DNA-PKcs K902W: forward, 5'- A GAG ATG tgg CCT GTC ATT TTC CTG GAT GTG TT -3', and reverse, 5'- T GAC AGG cca CAT CTC TCT AAA GGG CAC TGC AA -3'. For transient transfection, 1 μg of each plasmid was transfected using Lipofectamine 2000 (Invitrogen) according to the manufacturer’s instructions.
RNA Interference
Synthetic oligos were used for siRNA-mediated silencing of NNMT (5¢-CCTCTCTGCTTGTGAATCCTT-3¢) and SAHH (5¢-GTCAGGAGGGCA ACATCTTTG-3¢), and scramble siRNA was used as a control. Cells were transfected with siRNAs using Lipofectamine 2000 according to the manufacturer’s protocol. Knockdown efficiency was verified by qRT-PCR or western blotting.
Gene Silencing and Overexpression
For NNMT stable shRNA knockdown or overexpression, cells were co-transfected with pCMV-VSV-G, pCMV-Gag-Pol, and plasmids using the calcium phosphate method 77. Transfected cells were cultured in DMEM containing 10% FBS for 6 h. Twenty-four hours after transfection, culture supernatant was collected and used for retrovirus preparation to infect cells at 10% confluency in 90-mm-diameter dishes. Cells were re-infected 48 h after the initial infection and selected using 5 μg/mL puromycin (Amresco). NNMT shRNA was cloned into the AgeI and EcoRI restriction sites of the pMKO vector. NNMT was subcloned into the BamHI and EcoRI restriction sites of the pBABE vector using ClonExpress MultiS One Step Cloning Kit. The sequences of primers used were as follows: shNNMT-Forward: 5'- CCG GCC TCT CTG CTT GTG AAT CCT TCT CGA GAA GGA TTC ACA AGC AGA GAG GTT TTT G -3', shNNMT-Reverse: 5'- AAT TCA AAA ACC TCT CTG CTT GTG AAT CCT TCT CGA GAA GGA TTC ACA AGC AGA GAG G -3'; NNMT (pBABE): forward, 5'- ctc tag gcg ccg gcc gga tcc ATG GAA TCA GGC TTC ACC TCC -3', and reverse, 5'- ggt ctt ctc gtc cat gaa ttc CAG GGG TCT GCT CAG CTT CC -3'.
Western Blot Analysis
Cultured cells or cells from human ccRCC and matched normal tissues were lysed with 0.5% NP-40 buffer containing 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 0.5% Nonidet P-40, and a mixture of protease inhibitors (Sigma-Aldrich). After centrifugation at 13,800 × g and 4°C for 15 min, supernatants were collected for western blotting according to standard procedures. Antibodies against DNA-PKcs (#38168), phospho-Ser2056 DNA-PKcs (#68716), H2AX (#7631), γ-H2AX (#9718), p53 (#9282), phospho-Ser15 p53 (#9284), KU70 (#4103), and KU80 (#2753) were purchased from Cell Signaling Technology. Antibody against NNMT was purchased from Abcam (#ab58743). Antibody against Actin was purchased from Genscript (#A00702). Anti-K-Hcy antibody was generated as described previously 47. Chemiluminescence was measured on a Typhoon FLA 9500 instrument (GE Healthcare).
IHC
Sections of ccRCC and adjacent tissues were obtained from formalin-fixed, paraffin-embedded tissue blocks (not enrolled in the proteogenomic cohort). Immunostaining was carried out as reported previously78,79. Sections were stained using relevant antibodies and the Envision detection kit (Dako). Immunostaining quantified based on the number of immunoreactive cells (quantity score) and the staining intensity (intensity score), as reported 78,79.
Metabolite Quantification
Human tissues were homogenized in ice-cold phosphate-buffered saline (PBS) and centrifuged, and supernatants were collected for Hcy quantification. Hcy concentrations were determined using an Axis Homocysteine Enzyme Immunoassay Kit (Axis-Shield). To assay HTL, cells were harvested by PBS washing and denatured in pre-chilled 60% methanol (in ddH2O, pre-cooled at -80°C for 1–2 h). Cell lysates were centrifuged (10,000 × g) at 4°C for 5 min. Supernatants were vacuum-dried, re-dissolved in ddH2O, and subjected to ultrafiltration on a polyvinylidene fluoride low protein binding membrane (Millex-GV4 and Millex-HV4, Millipore). Metabolites were extracted and HTL was analyzed using LC-MS. SAM and SAH levels were detected using a SAM & SAH ELISA Combo Kit (Cell Biolabs). 1-Methylnicotinamide was measured using a UHPLC-QTOF-MS System (Agilent Technologies, 1290 LC, 6550 MS) as described previously 80. NAD+ levels were determined according using an NAD/NADH assay kit (Abcam) per the manufacturer’s instructions. Each assay was repeated in triplicate, and means were used for analysis.
Lysine-homocysteinylation Site Identification in ccRCC Tissues
To identify lysine-homocysteinylation sites in tissue samples, ccRCC tumor and non-tumor tissues were ground in 0.5% NP-40 buffer, and supernatants were immunoprecipitated with anti-DNA-PKcs antibody and digested with trypsin. LC-MS/MS experiments were conducted on an EASY-nLC100 chromatograph coupled with an Orbitrap Elite (both from Thermo Fischer Scientific) equipped with an online nano-electrospray ion source. Peptides were desalted and suspended in 10 μL solvent A (A: water with 0.1% formic acid; B: acetonitrile with 0.1% formic acid). Each sample was loaded onto a self-packed C18 column (100 μm × 2 cm, 5-μm particle size), with a flow rate of 5 μL/min for 5 min and subsequently separated on the analytical column (C18, 75 μm × 20 cm) with a linear gradient from 5% B to 90% over 120 min. The column was re-equilibrated at initial conditions for 15 min. The column flow rate was maintained at 200 nL/min. The mass spectrometer was set as follows: ion-transfer capillary, 275°C; spray voltage, 2 kV; and full MS range, 400–2,000 m/z. Full mass spectra were acquired at 60,000 resolution with a target ion setting of 106. One full MS scan was followed by 15 MS/MS scans, and multistage activation was enabled. The dynamic exclusion function was set as follows: repeat count, 2; repeat duration, 30 seconds; and exclusion duration, 60 s.
DNA-PKcs In Vitro Kinase Assay
In vitro DNA-PKcs kinase assays were conducted as described previously49. In brief, 200 ng DNA-PKcs and 3 μg p53 were incubated in a buffer containing 50 mM HEPES (pH 7.4), 100 mM KCl, 10 mM MgCl2, 2 mM EGTA, 0.1 mM EDTA, and 1 mM ATP at 30°C for 30 min. Y-shape DNA and KU70/KU80 were added as indicated. Reactions were terminated by addition of sodium dodecyl sulfate (SDS) sample loading buffer and boiling for 5 min. Samples were subjected to SDS-polyacrylamide gel electrophoresis and immunoblotting using site-specific antibody against p53.
DNA-PK Kinase Assay
DNA-PKcs activity was measured using the ADP-GloTM + DNA-PK kinase system (Promega, Cat#4107). Briefly, we isolated DNA-PKcs protein from cells subjected to various treatments. To measure DNA-PKcs activity, 1 μL 5% DMSO, 2 μL of enzyme, and 2 μL of substrate/ATP mix were added to the wells of a 384-well plate. The plate was incubated at room temperature for 60 min. Then, 5 μL of ADP-GloTM reagent was added and the plate was incubated at room temperature for 40 min. Consequently, 10 μL of kinase detection reagent was added and the plate was incubated at room temperature for 30 min. Luminescence was recorded with an integration time of 0.5–1 s.
Cell Proliferation Assay
Cell proliferation was assessed using the Cell Counting Kit-8 (Dojindo Laboratories). In brief, cells were seeded in a 96-well plate at 4×103 cells/well and allowed to adhere. Cell Counting Kit-8 solution (10 μL) was added to each well, and the cells were incubated in 5% CO2 at 37°C for 2 h. Cell proliferation was determined by measuring the absorbance at 450 nm.
Comet Assay
A Comet Assay Kit (Trevigen) was used to detect single- and double-stranded DNA breaks in cultured cells and tissues. Slides were examined under a Leica DMI 4000B epifluorescence microscope (425–500-nm excitation). Comet slides were used for each condition. In normal cells, fluorescence is mostly confined to the nucleus because intact DNA cannot migrate. In DNA-damaged cells, DNA is denatured with an alkaline or neutral solution to detect single- or double-stranded breaks, respectively; negatively charged DNA fragments are released from the nucleus and migrate toward the anode.
In Vivo Xenograft studies
Four-to-six-week-old Balb/C nude mice were obtained from Shanghai SLAC Laboratory Animal Co., Ltd. Control and NNMT-overexpressing ACHN and 786-O cell lines were subcutaneously transplanted into the left and right flanks of each mouse. For the IR group, irradiated control and NNMT-overexpressing cells were transplanted into the left and right flanks of each mouse. For the IR+NAC group, irradiated control and NNMT-overexpressing cells were transplanted into the left and right flanks of each mouse, and the mice were intraperitoneally injected with NAC (500 mg/kg) every other day. At the end of the experiment, following euthanasia, tumors were excised, weighed, and imaged. All procedures were approved by the Animal Care Committee at Fudan University.
QUANTIFICATION AND STATISICAL ANALYSIS
Quantification methods and statistical analysis methods for proteomic and integrated analyses were mainly described and referenced in the respective Method Details subsections.
Additionally, standard statistical tests were used to analyze the clinical data, including but not limited to Student’s t test, Fisher’s exact test, Kruskal-Wallis test, log-rank test. All statistical tests were two-sided, and statistical significance was considered when p value < 0.05. To account for multiple-testing, the p values were adjusted using the Benjamini–Hochberg FDR correction. Kaplan–Meier plots (log-rank test) were used to describe overall survival. Variables associated with overall survival were identified using univariate Cox proportional hazards regression models. Significant factors in univariate analysis were further subjected to a multivariate Cox regression analysis. All the analyses of clinical data were performed in R and GraphPad Prism. For functional experiments, each was repeated at least three times independently, and results were expressed as mean ± standard error of the mean (SEM). Statistical analysis was performed using GraphPad Prism.
DATA AVAILABLITY
Proteome raw datasets are publicly available at the iProx data portal: https://www.iprox.org/page/PSV023.html;?url=1605014585802S8oG, with a password rEiV. WES data files can be accessed at https://www.biosino.org/node/review/detail/OEV000128?code=XZPGYZGS.