A Multidisciplinary Biospecimen Bank of Renal Cell Carcinomas Compatible with Discovery Platforms at Mayo Clinic, Scottsdale, Arizona

To address the need to study frozen clinical specimens using next-generation RNA, DNA, chromatin immunoprecipitation (ChIP) sequencing and protein analyses, we developed a biobank work flow to prospectively collect biospecimens from patients with renal cell carcinoma (RCC). We describe our standard operating procedures and work flow to annotate pathologic results and clinical outcomes. We report quality control outcomes and nucleic acid yields of our RCC submissions (N=16) to The Cancer Genome Atlas (TCGA) project, as well as newer discovery platforms, by describing mass spectrometry analysis of albumin oxidation in plasma and 6 ChIP sequencing libraries generated from nephrectomy specimens after histone H3 lysine 36 trimethylation (H3K36me3) immunoprecipitation. From June 1, 2010, through January 1, 2013, we enrolled 328 patients with RCC. Our mean (SD) TCGA RNA integrity numbers (RINs) were 8.1 (0.8) for papillary RCC, with a 12.5% overall rate of sample disqualification for RIN <7. Banked plasma had significantly less albumin oxidation (by mass spectrometry analysis) than plasma kept at 25°C (P<.001). For ChIP sequencing, the FastQC score for average read quality was at least 30 for 91% to 95% of paired-end reads. In parallel, we analyzed frozen tissue by RNA sequencing; after genome alignment, only 0.2% to 0.4% of total reads failed the default quality check steps of Bowtie2, which was comparable to the disqualification ratio (0.1%) of the 786-O RCC cell line that was prepared under optimal RNA isolation conditions. The overall correlation coefficients for gene expression between Mayo Clinic vs TCGA tissues ranged from 0.75 to 0.82. These data support the generation of high-quality nucleic acids for genomic analyses from banked RCC. Importantly, the protocol does not interfere with routine clinical care. Collections over defined time points during disease treatment further enhance collaborative efforts to integrate genomic information with outcomes.


Introduction
To support medical research, institutional biobanking efforts have encompassed archived formalin-fixed, paraffin-embedded (FFPE) tissue blocks, frozen tissues, peripheral blood, questionnaires, and medical records [1][2][3][4]. Genitourinary diseases are heterogeneous and range from benign to malignant conditions [5,6]. The construction of a biobank encompassing both benign and malignant genitourinary diseases may yield clues regarding molecular progression of disease. In 2010, Mayo Clinic (Scottsdale, Arizona) initiated the Multidisciplinary Genitourinary Diseases Biospecimen Bank to prospectively collect biospecimens from patients with genitourinary diseases and to support health-related research. Herein, we report our experience with frozen banking protocols for renal cell carcinoma (RCC) biospecimens that are compatible with standard clinical practices.
The Cancer Genome Atlas (TCGA) Research Network has analyzed the DNA, RNA, and protein from various human tumors to generate molecular profiles, identify recurrent molecular aberrations, and create public data portals to support medical research [7][8][9][10][11]. Currently, TCGA integrates data from DNA methylation at CpG islands, microarray-based measurement of copy number, whole-exome sequencing, RNA sequencing, and reverse-phase protein arrays. The development of next-generation DNA, RNA, chromatin immunoprecipitation (ChIP) sequencing, and high-throughput protein analyses provides an opportunity for institutions to characterize genomic, transcriptomic, and proteomic alterations. However, the application of established and emerging platforms for molecular analysis depends on well-annotated and high-quality biospecimens. Furthermore, these platforms may necessitate large quantities of frozen biospecimens, requiring standard operating procedures to be in place at the time of tissue collection.
To address an unmet need for banked clinical specimens suitable for high-throughput genomic analysis, we developed a protocol to collect frozen tissue specimens, blood, and urine from patients affected by RCC through longitudinal biospecimen collection during follow-up appointments. We describe our standard operating procedures, work flow, and research coordinator efforts, as well as our quality assessment of biospecimens submitted to TCGA from a single institution. We also assessed the albumin oxidation in banked plasma, a surrogate of plasma quality. It is unknown whether banked specimens collected under routine clinical care are compatible with emerging discovery platforms such as ChIP sequencing. Herein, we demonstrate that our biobanking protocol is compatible with multiple platforms, including DNA, RNA, and ChIP sequencing, as well as mass spectrometry (MS) analysis. diseases (kidney, urothelial, prostate, testicular, and penile malignancies or benign prostatic hyperplasia or nephrolithiasis). Patients were contacted during routine clinical visits or in preoperative settings within Mayo Clinic departments and divisions, including urology, radiation oncology, pathology, and medical oncology. Patients were excluded if they declined to participate or if the banking of their biospecimens would compromise the availability of tissue for diagnosis and standard clinical care. The protocol for collecting biospecimens was approved by the Mayo Clinic Institutional Review Board. The current informed consent form (S1 File), which was updated in 2012, was approved by the Mayo Clinic Institutional Review Board (protocol no. 08-000980). Patients were enrolled from June 1, 2010, through January 1, 2013. Patients provided written consent and the signed consent form was scanned into the electronic medical record. Patients also received a copy of the signed consent form. This consent procedure was approved by the Mayo Clinic Institutional Review Board.

Blood and Urine Collection
Blood and urine specimens are collected from patients preoperatively, postoperatively, and during systemic treatments (every 3 months) for routine clinical care. The blood and urine are transferred from the phlebotomist to the laboratory processing area within 30 minutes of collection. Blood is collected in BD Vacutainer tubes (red top, Fisher Scientific 02-685-112, no anticoagulants; lavender top, Fisher Scientific 02-657-32, K 2 EDTA). Contents of red-top tubes are allowed to clot for 10 to 30 minutes, then centrifuged at 1,882 relative centrifugal force (RCF) for 10 minutes, and serum is transferred into 3×1.8-mL Nunc cryovials (Fisher Scientific 12565167N). Lavender-top tubes are centrifuged at 1,882 RCF in a refrigerated centrifuge; plasma is then divided and placed into 3×1.8-mL Nunc cryovials (Fisher Scientific 12565167N); the remaining buffy coat is separated into 3×1.8-mL Nunc cryovials (Fisher Scientific 12565167N). This protocol yields approximately 4.5 mL of plasma, serum, and buffy coat per collection visit. Samples are stored at −80°C.
For urine collections, 2 mL of urine is divided into 2 cryovials for storage; the remaining urine is centrifuged at 1,882 RCF for 5 minutes in a Hycor KOVA (Fisher Scientific VT87139) tube in a refrigerated centrifuge. The supernatant is removed and the pellet resuspended in 1 mL of urine and stored in a cryovial. Centrifuged samples are stored at −80°C.

Tissue Collection
Surgical specimens are handled according to standards of clinical care. The specimens are delivered directly from the operating room to the pathology department. After immediate macroscopic review, the pathologist decides whether adequate tissue is available for banking. The time from the start of warm ischemia to freezing can be as short as 10 minutes but as long as 110 minutes. The mean warm-ischemia time (ligation of blood flow) to freezing was 45.8 minutes. The warm-ischemia time is influenced by factors such as partial vs radical nephrectomy or an open vs minimally invasive approach. The tumor sample (and a sample of uninvolved tissue, if available) are macrodissected from the surgical specimen. Each sample is bisected, with 1 FFPE half as a mirror image representing the frozen biobank sample. The mirror-image FFPE sample is used as one of the clinical diagnostic slides. The biobank samples are immediately frozen in 7-mL polycarbonate tubes (Sarstedt 71.9923.610) in a −90°C bath of Novec engineered fluid (3M HFE-7000) cooled in a HistoChill freezing bath (SP Scientific HC80A0). No embedding medium or preservative is added to frozen tissue. Tumor cellularity of the FFPE mirror image is assessed by a pathologist and reported as part of the standard-of-care pathology synoptic report.

Biospecimen Storage
Unique identification numbers are assigned to each individual container with bar code labels. Biospecimens are stored in a Brooks BioStore −80°C Automated Sample Storage System (Brooks Automation Inc). Samples can be retrieved manually or robotically.

Biobank Research Electronic Data Capture Database
A coded and secure Research Electronic Data Capture (REDCap) database was constructed with pertinent clinical information and database identifiers. Biospecimens are tracked by unique identification numbers assigned by the Research and Laboratory Information Management System (RLIMS). Pertinent pre-, peri-, and postoperative clinical variables (procedures, complications, and surgical outcomes) are annotated and linked to biospecimen data. In oncologic cases, we also annotate progression, survival, and treatment outcomes from the patient medical record. Clinical follow-up is maintained by data coordinators for matched blood, urine, and tissue (primary nephrectomies, biopsies, and metastectomies).

TCGA Papillary RCC Sample Submission
For papillary RCC solicited by TCGA (KIRP cases), a genitourinary pathologist (M.L.S.) reviewed the diagnostic slide representing the frozen tumor. Specimens were selected only if they met the standard TCGA biospecimen criteria with a corresponding buffy coat (for germline DNA analysis). The minimum DNA, RNA, and germline DNA yield required was 6.9 μg, 5.15 μg, and 4.9 μg, respectively. For reverse-phase protein array analysis, at least 10 mg of frozen tumor was required. Standard TCGA criteria included: 1) tumor sample composed predominantly of histologically viable-appearing tumor cells with !60% tumor nuclei; 2) 20% necrosis of sample volume; and 3) tumor weight !100 mg. The Mayo Clinic Institutional Review Board protocol was subsequently modified to reflect the shipment of samples to TCGA's Biospecimen Core Resource (The Research Institute, Nationwide Children's Hospital, Columbus, Ohio). Prior to shipping, protected health information was removed from all materials (specimen containers, diagnostic slides, pathology reports). The top slide submitted for review was the diagnostic slide cut from the bisected tumor, representing both the frozen tumor and the FFPE tumor. The initial enrollment, case quality control, other malignancy, and follow-up forms were completed by data coordinators using clinicopathologic variables entered from the REDCap database. Prism (v6.02; GraphPad Software, Inc [commercial software]) was used to generate scatter plots and statistics.

Analysis of Albumin and Apolipoprotein A-I Oxidation
Albumin S-cysteinylation and apolipoprotein A-I (apoA-I) methionine oxidation were analyzed as described in detail elsewhere [12]. Briefly, samples were thawed, mixed, and then centrifuged to sediment particulates. One-half microliter was removed and diluted 1,000-fold in 0.1% (v/v) trifluoroacetic acid. Five microliters of this solution were injected without delay onto a liquid chromatography-electrospray ionization-mass spectrometry instrument (Eksigent nanoLC Ã 1D connected to a Bruker MicrOTOF-Q operating in time-of-flight only mode) equipped with a protein CapTrap (Optimize Technologies) rather than a conventional column. After flushing small molecules to waste for 3 minutes, albumin and apoA-I were eluted into the mass spectrometer by increasing the acetonitrile concentration over the CapTrap.
Mass spectra containing protein charge envelopes were averaged across approximately 1 minute and then charge-deconvoluted with DataAnalysis (v3.4; Bruker Corp [commercial software]) to a mass range of 1,000 Da on either side of any identified peak. Baseline values were subtracted from deconvoluted spectra and all peak heights were calculated. Tabulated MS peak heights were exported to a spreadsheet for further calculation. The fractional abundance of S-cysteinylated (oxidized) albumin was determined by dividing the height of the MS peak representing S-cysteinylated albumin by the sum of the peak heights for native and Scysteinylated albumin. ApoA-I methionine oxidation was calculated in an analogous fashion. No MS peaks representing apoA-I with 2 or 3 oxides were detected; however, if they had been, these peaks would have been weighted to calculate total weighted oxidation, as described elsewhere [12].
ChIP-seq libraries were then prepared from 10 ng ChIP and input DNA using the Ovation Ultralow DR Multiplex kit (NuGEN Technologies Inc). ChIP-seq libraries were sequenced to 51 base pairs (bp) from both ends on an Illumina HiSeq 2000 instrument. Sequence data were analyzed by the Mayo Clinic Center for Individualized Medicine Bioinformatics Program. The ChIP-Seq pipeline version 2 integrates open-source software packages to analyze ChIP sequencing data and identify profiles from chromatin regulators, posttranslational histone modifications, and transcription factor binding [13]. The source code is publicly available at http://bioinformaticstools.mayo.edu/research/hichipseq-pipeline/, with hyperlinks corresponding to individual software packages. The main features include 1) read-quality checking; 2) read mapping and filtering; 3) library quality assessment; 4) peak calling analysis; and 5) data visualization (S1 Fig). FastQC (http://www.bioinformatics.babraham.ac.uk/ projects/fastqc/) (publicly available software) was used to assess the 51-bp paired-end reads for the average quality score per base position. The Burrows-Wheeler Alignment tool (http://biobwa.sourceforge.net/) (publicly available software) was used to map the paired-end reads to the reference genome (hg19) [14]. SICER, a package developed for scoring broad binding events (http://home.gwu.edu/~wpeng/Software.htm) (publicly available software), was used to identify H3K36me3 peaks [15]. To plot the genome-wide enrichment, we split the human genome into nonoverlapping 500-bp windows, estimated the number of mapped fragments overlapping each window, and normalized to the number of fragments per 10 million mapped pairs. To profile the level of H3K36me3 occupancy within the gene-body, we downloaded the RefGene annotation from the UCSC Genome Browser table browser (http://genome.ucsc.edu/ cgi-bin/hgTables) and intersected that with peak coordinates using BEDTools (http://bedtools. readthedocs.org/en/latest/#) (publicly available) [16].

RNA Sequencing of Frozen Nephrectomy Specimens
Frozen nephrectomy tissue samples (25-30 mg) were pulverized using a hammer and placed in lysis reagent (Qiazol, no. 79306; Qiagen Inc). RNA from the pulverized tissue was isolated using the miRNeasy Mini Kit (no. 217004; Qiagen Inc) on the QIAcube instrument (no. 9001292; Qiagen Inc). RNA libraries were prepared according to the manufacturer's instructions for the Encore NGS Library Systems (NuGEN Technologies Inc). Briefly, first-strand cDNA was generated from total RNA using DNA/RNA chimeric primers and reverse transcriptase, creating a cDNA/RNA hybrid. The second-strand cDNA was then synthesized from the DNA/RNA duplex. The resulting double-stranded cDNA was amplified by Single Primer Isothermal Amplification (NuGEN Technologies Inc) using a chimeric primer, DNA polymerase, and RNase H. After amplification, products were modified by random priming and extension to create double-stranded products that were suitable for generating libraries for sequencing. The double-stranded products (200 ng) were fragmented in an ultrasonicator (E210; Covaris Inc) for 5 minutes to generate~150-bp fragments, which then underwent blunt-end repair. Illumina-compatible paired-end index adapters were ligated to the 5 0 and 3 0 ends of each fragment. The adapter-modified cDNA fragments were enriched with 5 cycles of PCR amplification. The concentration and size distribution of the resulting libraries were determined by using a Bioanalyzer DNA 1000 chip (Agilent Technologies, Inc). The concentration values were confirmed by Qubit fluorometry (Life Technologies Corp). Libraries were loaded onto Illumina TruSeq v3 paired-end flow cells at concentrations of 8-10 pM to generate cluster densities of 600,000-800,000/mm 2

Biobanking Work Flow and Pathologic Characteristics of the RCC Biobank Specimens
Patients undergoing evaluation for genitourinary diseases were recruited and provided consent during urology, radiation oncology, pathology, or medical oncology appointments. Pathologic characteristics of the cohort enrolled from June 1, 2010, to January 1, 2013, are summarized in Table 1. The biobank represents earlier T stages of RCC, consistent with surgical treatments for localized disease. Fig 1 illustrates the work flow for consent of patients, collection and processing of biospecimens, and data input into the centralized REDCap database. To accommodate tissues from diverse anatomical sites, we developed a generalized work flow for collection of frozen tissue, blood, and urine. For tissue, the priority is for diagnostic testing for purposes of routine clinical care, with the rest of the tissue frozen at −80°C in a manner compatible with TCGA tissue collection requirements [17,18]. The protocol is compatible with collection of tissue at the time of surgical resection or biopsy at the primary site (nephrectomy) and at distant metastatic sites.

TCGA Specimen Submission and Quality Control
Frozen papillary RCC samples were submitted for processing to TCGA. As a control, buffy coat collected concomitant with tissue was submitted as a germline control. The genitourinary pathologist reviewed the diagnostic slide, which is a mirror image of the frozen tissue and FFPE, to ensure that samples met tumor-specific pathology requirements. The mean (SD) tumor weight was 376 (130) mg. DNA and RNA yields from tumor and matched buffy coat are listed in Table 2. Representative diagnostic slides were sent to TCGA (Fig 2). All extracted DNA samples passed the TCGA requirements. With our work flow, 2 of 16 submitted tumors (12.5%) did not meet RNA integrity number (RIN) requirements (RIN<7). Our mean (SD) TCGA RINs were 8.1 (0.8) for papillary RCC. Table 3 lists the genomic platforms used to analyze TCGA samples with publicly accessible data.

Analysis of Albumin and apoA-I Oxidation as a Metric of Plasma Specimen Integrity
As previously reported [12], albumin and apoA-I in human blood plasma are susceptible to oxidative S-cysteinylation and methionine sulfoxidation, respectively, when plasma is exposed  to temperatures above its freezing point of −30°C. At both room temperature and −20°C, albumin oxidation (S-cysteinylation) increases as per the complement of an exponential decay function before reaching a plateau (for most samples) at a relative fractional abundance of about 0.4. ApoA-I methionine oxidation proceeds at a slower pace, requiring days of exposure to room temperature or months of storage at −20°C [12]. On average, the banked samples from patients with RCC described in this study had albumin oxidation (S-cysteinylation) levels consistent with several hours' exposure to room temperature and no long-term storage above −30°C (Fig 3). Consistent with this observation, apoA-I oxidation remained below quantifiable levels for tested samples.

H3K36me3 Profiling by ChIP Sequencing
We tested the utility of our banked specimens for emerging technologies such as ChIP sequencing that require larger amounts of frozen tissue (>100 mg). Altered chromatin accessibility is observed in clear cell RCC and is associated with alterations of H3K36me3 [19][20][21][22]. Compared to other genome-wide discovery platforms, ChIP sequencing often requires larger amounts of tissue (>100 mg). To determine whether our biobanking work flow for specimens was compatible with H3K36me3 ChIP sequencing, we performed ChIP quantitative polymerase chain reaction to ensure quality and yield of immunoprecipitated chromatin from 6 samples. An H3K36me3 antibody was used for immunoprecipitation of endogenous H3K36me3-bound DNA. Since H3K36me3 is enriched at exons and active genes, polymerase chain reaction primers were optimized for active genes, such as actin, and for an intergenic region (negative control) [23,24]. At a minimum, 4-fold enrichment is required before proceeding to sequencing [25,26]. We found that actin was enriched 8-to 32-fold over the intergenic controls (Fig 4), which indicated that the quality and yield of immunoprecipitated DNA/chromatin complexes were sufficient for generating ChIP-sequencing libraries. S2 Fig shows the optimal 100-400 bp DNA fragments that were compatible with ChIP sequencing.
To evaluate the properties of ChIP-sequencing libraries derived from banked frozen nephrectomy samples, we checked paired-end read quality, genome-wide enrichment level, and distribution of H3K36me3 occupancy. Both reads had good quality, with FastQC scores of 32 to 39 across all 51 bp (Fig 5A and 5B). The first 3 bp had the lowest quality score for both reads, with the first read having slightly better sequencing quality, as is typically seen for paired-end sequencing. In addition, over 95% of the first reads had an average quality score of !30 compared to 91% to 92% of the second reads. Only a small proportion of the reads had poor quality scores ( 20, on average): <0.5% for the first read and <3% for the second,  For ChIP-sequencing experiments, another quality measurement is the level of enrichment between immunoprecipitated DNA and the input DNA control. The box plots represent the top 5% of the 500-bp windows with the highest number of fragments (Fig 5C). The peak regions from each H3K36me3 immunoprecipitated library showed an average of about 4-fold enrichment over the corresponding regions in the input library. In published ChIP-sequencing studies in cell lines, H3K36me3 is predominantly associated with gene bodies [23,27]. To map H3K36me3 in gene bodies, we examined the distribution of H3K36me3 across each chromosome. The 3 RCC libraries showed a 2% to 5% reduction of H3K36me3 occupancy over the gene body, compared to matched normal samples (Fig 5D). H3K36me3 is enriched at exons, and the difference between different chromosomes in H3K36me3 occupancy over the gene body largely reflected the ratio of exon sizes over gene sizes. For example, chromosome 19 had the highest size ratio of exon/gene (13.6%) and also the highest H3K36me3 coverage of gene body (47.4%-53.5%). Similar patterns were also found for chromosomes 16 and 22.

RNA Sequencing Quality of Frozen Nephrectomy Samples
To assess the quality of RNA sequencing data from frozen nephrectomy samples, we analyzed RNA from frozen tissue profiled in parallel by ChIP sequencing (N = 6). A representative bioanalyzer profile and RIN of isolated RNA from paired samples (tumor and normal) is depicted in S3 Fig. We first examined the sequencing quality per base for forward and reverse reads. As shown in S4 Fig, the average Phred score of each base in all reads was above 30, indicating a high-quality sequencing run. Next, we compared the quality of RNA sequencing from our frozen tissues with that of an RCC cell line (786-O), which was prepared under optimal RNA isolation conditions. After genome alignments, only 0.2%-0.4% of total reads from frozen tissues failed the default quality check steps of Bowtie2 (disqualified reads are shown in Table 4), which was comparable to the disqualification ratio (0.1%) of the 786-O cell line. The mapping ratios for our frozen tissues were slightly lower (76.4%-83.8%) than those from the cell line (84.5%).
One of the key metrics of RNA sequencing quality and coverage is the number of identified genes. To identify genes expressed in both the Mayo Clinic and TCGA cohorts with high confidence, we counted genes with normalized expression values !128 that corresponded approximately to an average of 1.0 RPKM across all samples. As shown in Table 5, among~20,500 protein-coding genes, we observed a similar number of expressed genes in the Mayo Clinic and TCGA tissues. Poor-quality RNA sequencing often results in biased amplification and consequent overrepresentation of a subset of genes. To assess the RNA sequencing quality, expression levels of the most abundant genes across all samples were examined. Fig 6A shows that TPT1, EEF1A1, B2M, and GPX3 were most abundant in both the Mayo Clinic and TCGA samples. With the exception of TPT1 (highly abundant in the Mayo Clinic samples and accounted for up to 9.5% of expression values of total normalized counts), we did not identify any other overrepresented genes. Despite fundamental differences in sample source, sample processing, and sequencing library generation, the overall correlation coefficients between the Mayo Clinic vs TCGA tissues ranged from 0.75 to 0.82 (Fig 6B). Although expression levels of low-abundance genes were slightly overestimated in the Mayo Clinic samples, genes with mid to high abundance were more linearly aligned. These data support the compatibility of our biobanking protocol with RNA and ChIP-sequencing platforms for frozen RCC.

Discussion
The Multidisciplinary Genitourinary Diseases Biospecimen Bank in Arizona is a collaboration among investigators in urology, radiation oncology, pathology, and medical oncology to study the spectrum of benign and malignant genitourinary disease. Our work flow suggests that our biobanking protocol for RCC does not interfere with routine clinical care and is compatible with systematic cancer genomics projects such as TCGA and MS analysis of plasma, as well as with newer technologies (eg, ChIP sequencing) requiring larger quantities of frozen tissue. Herein, we reported the quality control of our TCGA submissions for papillary RCC (paired frozen tissue, mirror slide, and buffy coat), MS analysis of albumin S-cysteinylation as a surrogate of plasma integrity, and ChIP sequencing analysis of frozen tissues.
With matched temporal collections of frozen tissue and buffy coat, we can link molecular characterizations of DNA, RNA, and protein to treatment outcomes. The annotation of tumor cellularity of matched FFPE specimens in the pathology report expedites the identification of tumor blocks suitable for targeted exome sequencing, and immunohistochemistry [28][29][30]. Integration of the clinical information into a REDCap database facilitates collaborations to query specific disease cohorts linked to outcomes data.

Limitations
Our study has several limitations. First, our study represents biospecimens processed within 4 years of collection. The biobank started prospective collections in 2010, and our DNA and RNA yields, MS analysis, and ChIP sequencing may not be representative of samples stored longer than 4 years or of other genitourinary tumors (eg, urothelial, testicular, penile, or prostate cancers). For ChIP sequencing, we chose a covalent histone modification, H3K36me3, for enrichment of DNA; our H3K36me3 ChIP enrichment may not be comparable to ChIP enrichment using antibodies directed to transcription factors, which may not be as stable in banked frozen tissue. Our RNA yields reflect an overall mean warm-ischemia time of 45.8 minutes (range, 10-110 minutes) for the RCC specimens (N = 328), which was influenced by factors such as partial vs radical nephrectomy or an open vs a minimally invasive approach. Despite the warm-ischemia time, the overall rate of sample disqualification for RIN <7 for TCGA submissions was 12.5%. In a prior study, RNA degradation in nephrectomy specimens was observed primarily after 4 hours at 37°C, and prolonged warm ischemia was associated with changes in gene expression profiles [31]. Furthermore, we studied only nephrectomy specimens; other tissues may have different sensitivities to warm-ischemia times. Second, we examined the compatibility only of frozen nephrectomy specimens, rather than archival FFPE nephrectomy specimens, with various discovery platforms. The use of FFPE has additional challenges that include fragmentation and degradation of nucleic acids, and alterations of epitopes during the fixation and deparaffinization process. FFPE-compatible DNA, RNA, and ChIP sequencing protocols for various tissues have been published, but further study is required to determine the compatibility of these protocols with FFPE nephrectomy tissue [32][33][34].
Third, elevated concentrations of circulating free cysteine and cystine (the disulfide dimer of free cysteine) have been reported in patients with severely compromised renal function [35]. Their effects on albumin S-cysteinylation in vivo are not entirely clear because the ratio of cysteine to cystine in blood does not vary much [36], presumably because the redox potential of blood plasma is rather tightly regulated. Ex vivo, however, elevated concentrations of cysteine and cystine in the presence of oxygen would almost certainly lead to higher-than-normal maximum levels of S-cysteinylated albumin through disulfide exchange with cystine and through sulfenic acid-mediated disulfide bond formation with cysteine [37][38][39][40]. In some patients with renal failure, the fractional abundance of S-cysteinylated albumin has been reported at over 50%, although these studies lacked detailed documentation of sample-handling conditions [41][42][43][44]. Essentially, this means that elevated ex vivo levels of albumin S-cysteinylation are likely to occur faster in patients with renal failure than in patients without elevated plasma cysteine or cystine.

Conclusion
Genitourinary diseases represent a spectrum of disease with varying phenotypes and genotypes. The increase in tumor molecular profiling has led to a need for integration of genotypephenotype relationships with contemporary treatments (definitive surgical management, medical therapy, and/or radiation). The construction of a biobank that encompasses both benign and malignant genitourinary diseases may also elucidate the mechanism of the molecular progression of disease prior to overt clinical symptoms. Our multidisciplinary collaboration ensures longitudinal collection of matched tissue, blood, and urine during the course of standard care as well as during experimental treatments that will be available for projects that require access to pre-and/or postintervention time points. The prospective collection of frozen biospecimens allows future next-generation platforms to interrogate specimens linked to outcomes. A comprehensive prospective collection also facilitates banking of frozen tumors with rare subtypes and histologies, which may not be identified until completion of dedicated immunohistochemical staining (ie, not intraoperatively). Furthermore, the comparison of benign and malignant tissue may yield clues regarding risk-based algorithms for personalized treatment. The elucidation of distinct molecular phenotypes of RCC will further improve upon the prognostic and therapeutic stratification of patients.