Whole-exome sequencing identified mutational profiles of urothelial carcinoma post kidney transplantation

The sample collection and study protocol were conducted under the Institutional Review Board of Kaohsiung Medical University Hospital (KMUHIRB-G(I)-20150030). Written informed consent was obtained from all the patients, and all clinical investigations were conducted in accordance with the Declaration of Helsinki.

A total of 12 tumor samples, comprising 7 formalin-fixed archival UC samples from 6 recipients of a kidney transplant and 5 UC samples from patients on hemodialysis, were collected. Recipients of a kidney transplant who had a diagnosis of UC before their respective transplants were excluded. Because chronic kidney disease (CKD) and ESKD are risk factors for UC, five patients on hemodialysis who received a diagnosis of UC after the commencement of dialysis were selected to form the control group. Each patient’s medical history was confirmed by chart review. None of the patients had previously received chemotherapy or radiotherapy.

After a pathologist reviewed all the samples, the tumor samples from the paraffin blocks were macrodissected. All the specimens were placed in Eppendorf containers to prevent tissue cross-contamination. Genomic DNA was extracted using a FavorPrep formalin-fixed paraffin-embedded (FFPE) Tissue DNA Extraction Micro Kit (Favorgen Biotech, Pingtung, Taiwan) from FFPE tumor tissue. The DNA was quantified and qualified using Qubit 4 Fluorometer (Thermo Fisher Scientific, Waltham, USA), 4150 TapeStation System (Agilent, Santa Clara, USA), and Nanodrop spectrophotometer (ASP-2680, Celltagen, Seoul, Republic of Korea) according to the manufacturer’s protocol. One microgram DNA from each tumor sample was subjected to run the WES analysis.

Library enrichment for WES was conducted using a SureSelect^XT V6_r2 reagent kit (Agilent Technologies, Santa Clara, CA, USA). The enriched samples were sequenced using an Illumina HiSeq2500 system (San Diego, CA, USA) with a 2 × 76–base pair (bp) paired-end sequencing approach. The mean coverage of the exomes was 79.76 × , and more than 96% of the exomes had a coverage higher than10 × . The sequencing data were annotated according to the GRCh37/hg19 reference genome.

The data were examined using a data analysis pipeline developed at the Cologne Center for Genomics (CCG) [19]. Initially, raw sequencing reads were mapped to human genome reference (hg19) using Burrows-Wheeler Alignment tool (BWA) [20] followed by duplication marking using Picard [21].

Subsequently, base quality score recalibration and local indel realignment were performed according to Genomic Analysis Toolkit (GATK) practice guideline [22, 23]. After the post-alignment improvements, variant calling was performed using GATK’s unified-genotyper and SAMtools’ mpileup [24]. The variant lists from both callers were merged and annotated using various public databases: dbSNP [25], 1000 Genomes Project [26], Exome Variant Server [27], HGDM professional database [28], dbVAR and DGVa [29], GERP, and Ensembl [30]. The functional effect of variants were predicted using PolyPhen [31], SIFT [32], and RVIS [33]. All of these downstream analysis has been assembled together with a few scripts developed in-house. Splice site analysis based on Yeo [34] et al. was performed. Finally, the annotated variant list is uploaded to CCG’s web interface and VARBANK database [35]. Scripts developed in-house at the Cologne Center for Genomics were applied to detect protein changes, affected donor and acceptor splice sites, and overlaps with known variants. Our analysis focused on single-nucleotide variants/polymorphisms (SNVs or SNPs) and insertions or deletions (InDels) that may result in alterations in primary protein structure or in strong splice site effects [34].

The processed results of all 12 samples were then downloaded from VARBANK analysis platform. A series of steps were performed to identify cancer driver genes that contain novel mutations and are not reported previously in UCKT patients. First, mutations with dbSNPs annotations and with low allele frequencies (≤ 5%) were excluded from the data sets. Mutations at the same genomic location that were annotated as different transcripts were counted as one occurrence.

To ensure that these genetic alterations identified in our study were associated with tumor development, we identified genes with these mutations and using these gene names to search against the Catalogue of Somatic Mutations in Cancer (COSMIC) [36]. If in our list, a gene is also a reported cancer driver gene in COSMIC, the gene and its mutations was retained for further analysis. In other words, these newly identified mutations in our samples were considered potential driver mutations.

Subsequently, we utilized previous studies or databases that had analyzed bladder cancer including the COSMIC [36], the Integrative Onco Genomic (IntOGen) cancer mutations browser [37], the TCGA bladder tumor database [15], and the Chinese bladder cancer genome [38] to compare with our data. Cancer driver genes that were retained in the previous step but had not been reported in these bladder cancer studies were of great interest.

Then we selected the above driver genes which were identified in UCKT but not in UCHD groups. Finally, we reported genes that were found in two or more UCKT patients. Figure 1 illustrates the data processing steps and corresponding mutations and gene numbers for UCKT and UCHD groups. COSMIC UC single-base mutation data was retrieved from the Cancer Browser in COSMIC [39] by using the selection of Primary site (Urinary tract), Sub site (Bladder), Histology (Carcinoma) and Sub histology (Transitional cell carcinoma). The mutational signatures were conducted using the SigProfiler tools (MatrixGenerator, Extractor and Plotting) [40]. Data from UCKT, UCHD and COSMIC UC were applied separately.

Genomic DNA from patients’ peripheral blood mononuclear cells (PBMC) was used as reference for detecting somatic mutations. We used a FavorPrep Blood / Cultured Cell Genomic DNA Extraction Mini Kit (Favorgen Biotech, Pingtung, Taiwan) to extract the genomic DNA from PBMC. All the somatic mutations of the selected genes were independently verified through Sanger sequencing of the PBMC genomic DNA. Primer pairs were designed using the University of California, Santa Cruz Genome Browser [41] and Primer-BLAST Browser of the National Institutes of Health [42].

The target regions were investigated using a touchdown polymerase chain reaction (PCR) protocol. The PCR mixture contained primers at 5 μmol L⁻¹ each, Taq DNA polymerase (Vazyme Biotech, China), and 50 ng/uL genomic DNA. The following touchdown PCR protocol was executed in a T100 thermal cycler (Bio-Rad Laboratories, CA, USA): initial denaturation at 94 °C for 5 min; annealing temperature decreased by 0.5 °C every other cycle: 94 °C for 30 s, annealing from 72 to 55 °C for 30 s, 72 °C for 30 s; followed by a cycle of 94 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s; and extension at 72 °C for 10 min. The PCR products were resolved in 1.5% (w/v) agarose gel for 20 min at 100 V in 1 × TAE buffer and stained with Gel Red biosafety dye (Biotium, CA, USA). The gel was documented on a UV transilluminator (Major Science, Taiwan). The DNA sequencing was performed at the core facility of the National Yang Ming Chiao Tung University (NYCU) Genome Center.

The Ingenuity Pathway Analysis (IPA version 68752261; QIAGEN) tool was used to establish protein networks. The main molecules detected from WES analysis and Sanger sequencing verified from the PBMC genomic DNA PCR products were set as the focusing molecules and were analyzed using the build tool to determine how their relationships were affected by UC. The molecules from the QIAGEN knowledge base were then added to the network according to their specific ontological associations.

CNV analysis was performed using ExomeDepth (ver 1.1.15) R software package, which uses read depth data to call CNVs from exome sequencing experiments. We generated read count data from WES BAM files, and compare the count results with an aggregate reference made up of samples from the same sequencing run to determine copy number at exon level resolution. Data were analyzed according to the ExomeDepth standard procedure [43].

The baseline characteristics of all the patients and their clinical and pathological features are listed in Table 1. All patients were female and sporadic, with no family history of UC nor any history of smoking. The majority (10 out of 12) had high-grade tumors. The KT80 and KT721 samples were from the same patient, a woman who underwent surgical removal of different parts of her genitourinary tract system over 5 years (Table 1). In the KT recipients, all the tumors occurred in their native genitourinary systems. The patients underwent induction treatments with steroids and IL-2 receptor antagonists. Tacrolimus, mycophenolate mofetil, and steroids were selected as maintenance agents. The average age at UC diagnosis was younger in the UCKT group than in the UCHD group (55 vs. 61.6 years, respectively; Table 2).

After WES was performed, the results were aligned to the human reference genome (hg19) and further used in a bioinformatics analysis. At least 100 million reads were collected from each WES sample. In the UCKT and UCHD groups, the mean coverages were 81 and 77 reads for UCKT and UCHD, respectively, and 96.18% and 95.78% of the coding exons, respectively, were covered by at least 10 reads (Table 2). A total of 18,733 and 11,093 variants were identified from the UCKT and UCHD cohorts (Fig. 1).

The majority of the mutations were unique to individual patients. Mutations were slightly more common in the UCKT group than in the UCHD group. An analysis of the WES data revealed an average of 564 known SNPs per individual. To identify new UC-related alterations, the mutations with dbSNP annotations were excluded.

We used the COSMIC cancer driver genes database to identify new SNPs in our cohorts. During this step, 278 and 235 genes in the UCKT and UCHD groups, respectively, were retained (Fig. 1). To identify oncodriver genes specific to our cohorts (i.e., previously unknown to be related to UC), we first collected UC-related studies from the COSMIC [36], IntOGen cancer mutations browser [37], TCGA bladder tumor database [15], and Chinese bladder cancer genome [38] and we excluded genes that had already been reported in these studies. Ultimately, we identified 150 and 121 genes in the UCKT and UCHD groups, respectively, that had never previously been known to be associated with UC. Of these cancer driver genes, 79 were uniquely identified in the UCKT cohort, and 17 of them (BTK, CARD11, ELL, FNBP1, GNAQ, HOXD13, IKZF1, MAX, MLLT10, NTRK3, PAX5, SEPTIN6, SEPTIN9, SH3GL1, SLC34A2, TAL1, and TRAF7) exhibited mutations that occurred in two or more patients (Table 3).

Table 4 showed the related genes with non-synonymous mutations. After deleting the genes without amino acid change, only 14 genes (CARD11, FNBP1, GNAQ, HOXD13, IKZF1, MAX, MLLT10, NTRK3, SEPTIN6, SEPTIN9, SH3GL1, SLC34A2, TAL1, and TRAF7) were left.

To verify the tumorigenic nature of the 14 genes in the UCKT cohort (CARD11, FNBP1, GNAQ, HOXD13, IKZF1, MAX, MLLT10, NTRK3, SEPTIN6, SEPTIN9, SH3GL1, SLC34A2, TAL1, and TRAF7), we developed a reliable PCR system to validate the mutations in the patients’ PBMC genomic DNA and subjected to Sanger sequencing analysis. The results revealed that mutations in GNAQ, SEPTIN 6, NTRK3, and IKZF1 occur only in tumor samples. The possible interactions among these genes deserved further pathway analysis.

The mutations were classified into two categories: single-base substitutions (SBSs) and InDels (insertions/deletions) (Table 2). The single-base substitution category was further divided into six subcategories: synonymous, missense, nonsense, frameshift, splice acceptor or donor, and nonstop (Table 2). The frameshift subcategory comprising one-base-pair differences between the alleles and reference sites, whereas the InDels category comprises mutations involving longer regions. Missense mutations were the most common type in both the UCKT and UCHD groups (74.75% and 79.40%, respectively; Table 2). These mutations are associated with amino acids changes in protein sequences, thereby affecting the structures and functions of the proteins that their respective genes encode. Interestingly, synonymous substitutions were much more common among the patients with UCKT than among those with UCHD (10.57% vs. 0.59%), which might indicate that the overall mutation rate increases after kidney transplantation, resulting in a higher risk of cancer. Among the nucleotide changes, T > A was the most common one in both the UCKT and UCHD groups (Table 2). In addition, in a TCGA study, 51% of all the mutations in bladder cancers were TpC > T or G mutations and 3.8% were TpC > A mutations [15]. Figure 2 illustrates the mutational features of the patients. Although missense mutations were the most common somatic mutations, followed by nonsense mutations, splice site mutations were also common. The effects of splice site mutations are difficult to determine; however, they may affect protein function by causing alternative splicing, intron retention, or exon skipping.

To further analyze the mutation signatures and compare to other cohort, total of 95,657 unique mutations were obtained from the Cancer browser in COSMIC for UC. The distribution of six classes of SBS showed distinct patterns among UCKT, UCHD and COMIC UC data (Fig. 3). The UCKT and UCHD showed relative high percentage in T > A (40.03% and 44.63%) while COSMIC showed high C > T (42.12%) and C > G (35.17%) mutations.

The results from SigProfiler tool [44] showed that both SBSs in UCKT and UCHD presented highly similar to the SBS22 signature (83.6% and 81.12%) with the proposed aetiology “Aristolochic acid exposure”, SBS5 (10.44% and 11.18%) with “unknown aetiology mutational burden is increased in bladder cancer samples with ERCC2 mutations and in many cancer types due to tobacco smoking” and SBS1 (5.96% and 7.7%) with “an endogenous mutational process initiated by spontaneous or enzymatic deamination” while COSMIC UC is similar to SBS5 (27.12%) SBS1 (4.48%), SBS13 (40.36%) and SBS2 (28.04%), respectively (Additional file 1: Fig. S1). These findings might indicate the possible unique aetiology for the UC development in Taiwan.

In addition, CNV data was retrieved from the WES results and divided into two groups. We observed 1495 and 1033 events in UCKT and UCHD groups. Using the start site as a reference, events occur in two or more patients in either group were selected. We removed records that were found in both groups and only reported those that were specific in UCKT. We identified 208 events with 92 unique CNVs in UCKT group (Additional file 2: Table S1). None of 14 genes of interest in Table 4 were found in this CNV analysis.

Through IPA, we explored the interactions and pathways among GNAQ, SEPTIN6, NTRK3, and IKZF1 (Fig. 4). SEPTIN6 exhibited no connections in the protein network. GNAQ, NTRK3, and IKZF1 were analyzed using the build tool to determine how their relationships are affected by UC. The protein network associated with the three main molecules is involved in the canonical signaling pathways of bladder cancer, PI3K/AKT, and mTOR. Each molecule was treated as a seed, and the network was constructed by connecting these seeds, thereby enabling us to explore the relationships among them. As indicated in Fig. 4, GNAQ, NTRK3, and IKZF1 may regulate UC through AKT1, MTOR, PI3KR1, HRAS, SRC, TP53, CCDN1, and RUNX3. The relationships among molecules involve activation, causation, correlation, expression, phosphorylation, protein–protein binding, were indicated in the network.

As indicated in Fig. 5, the network analysis revealed that GNAQ, NTRK3, and IKZF1 are related to UC because they are involved in PI3K/AKT and bladder cancer signaling. The 25 interacting proteins in the network (AKT1, CCND1, CDKN1A, CDKN1B, CDKN2A, CXCL8, EGFR, ERBB2, FOXO1, HDAC5, HRAS, HSP90AA1, HSP90AB1, MAP2K1, MAPK1, MMP2, MTOR, PIK3CA, PIK3R1, RB1, SIN3A, TP53, TSC2, VEGFA, and RUNX3) warranted further investigation. Thus, the result from IPA analysis explored the target proteins and networks that potentially contribute to UC and bladder cancer signaling pathways, which may inform future studies of the underlying mechanisms of UCKT.