Glomerular transcriptomics predicts long term outcome and identifies therapeutic strategies for patients with assumed benign IgA nephropathy.

Some patients diagnosed with benign IgA nephropathy (IgAN) develop a progressive clinical course, not predictable by known clinical or histopathological parameters. To assess if gene expression can differentiate between progressors and non-progressors with assumed benign IgAN, we tested microdissected glomeruli from archival kidney biopsy sections from adult patients with stable clinical remission (21 non-progressors) or from 15 patients that had undergone clinical progression within a 25-year time frame. Based on 1 240 differentially expressed genes from patients with suitable sequencing results, we identified eight IgAN progressor and nine non-progressor genes using a two-component classifier. These genes, including APOL5 and ZXDC, predicted disease progression with 88% accuracy, 75% sensitivity and 100% specificity on average 21.6 years before progressive disease was clinically documented. APOL lipoproteins are associated with inflammation, autophagy and kidney disease while ZXDC is a zinc-finger transcription factor modulating adaptive immunity. Ten genes from our transcriptomics data overlapped with an external genome wide association study dataset, although the gene set enrichment test was not statistically significant. We also identified 45 drug targets in the DrugBank database, including angiotensinogen, a target of sparsentan (dual antagonist of the endothelin type A receptor and the angiotensin II type 1 receptor) currently investigated for IgAN treatment. Two validation cohorts were used for substantiating key results, one by immunohistochemistry and the other by nCounter technology. Thus, glomerular mRNA sequencing from diagnostic kidney biopsies from patients with assumed benign IgAN can differentiate between future progressors and non-progressors at the time of diagnosis.

Transcriptomic analysis reveals partial epithelial-mesenchymal transition and inflammation as common pathogenic mechanisms in hypertensive nephrosclerosis and Type 2 diabetic nephropathy.

Hypertensive nephrosclerosis (HN) and Type 2 diabetic nephropathy (T2DN) are the leading causes of chronic kidney disease (CKD). To explore shared pathogenetic mechanisms, we analyzed transcriptomes of kidney biopsies from patients with HN or T2DN. Total RNA was extracted from 10 µm whole kidney sections from patients with HN, T2DN, and normal controls (Ctrl) (n = 6 for each group) and processed for RNA sequencing. Differentially expressed (log2 fold change >1, adjusted p < 0.05) genes (DEG) and molecular pathways were analyzed, and selected results were validated by immunohistochemistry (IHC). ELISA on serum samples was performed on a related cohort consisting of patients with biopsy-proven HN (n = 13) and DN (n = 9), and a normal control group (n = 14). Cluster analysis on RNA sequencing data separated diseased and normal tissues. RNA sequencing revealed that 88% (341 out of 384) of DEG in HN were also altered in T2DN, while gene set enrichment analysis (GSEA) showed that over 90% of affected molecular pathways, including those related to inflammation, immune response, and cell-cycle regulation, were similarly impacted in both HN and T2DN samples. The increased expression of genes tied to interleukin signaling and lymphocyte activation was more pronounced in HN, while genes associated with extracellular matrix organization were more evident in T2DN. Both HN and T2DN tissues exhibited significant upregulation of genes connected with inflammatory responses, T-cell activity, and partial epithelial to mesenchymal transition (p-EMT). Immunohistochemistry (IHC) further confirmed T-cell (CD4+ and CD8+ ) infiltration in the diseased tissues. Additionally, IHC revealed heightened AXL protein expression, a key regulator of inflammation and p-EMT, in both HN and T2DN, while serum analysis indicated elevated soluble AXL levels in patients with both conditions. These findings underline the shared molecular mechanisms between HN and T2DN, hinting at the potential for common therapeutic strategies targeting both diseases.

Glomerular proteomic profiling of kidney biopsies with hypertensive nephropathy reveals a signature of disease progression.

Hypertensive nephropathy (HN) requires a kidney biopsy as diagnostic gold-standard but histological findings are unspecific and specific prognostic markers are missing. We aimed at identifying candidate prognostic markers based on glomerular protein signatures. We studied adult patients (n = 17) with eGFR >30 ml/min/1.73m2 and proteinuria <3 g/d from the Norwegian Kidney Biopsy Registry, including subjects non progressing (NP, n = 9), or progressing (P, n = 8) to end-stage renal disease (ESRD) within an average follow-up of 22 years. Glomerular cross-sections from archival kidney biopsy sections were microdissected and processed for protein extraction. Proteomic analyses were performed using Q-exactive HF mass spectrometer and relative glomerular protein abundances were compared between P and NP patients. Immunohistochemistry (IHC) was used to validate selected data. Amongst 1870 quality filtered proteins, 58 were differentially expressed in P and NP patients' glomeruli, with absolute fold changes (FC) ≥1.5, p ≤ 0.05. Supervised classifier analysis (K nearest neighbor) identified a set of five proteins, including Gamma-butyrobetaine dioxygenase (BBOX1, O75936) and Cadherin 16 (CDH16, O75309), overexpressed in P, and Eosinophil peroxidase (EPX, P11678), DnaJ homolog subfamily B member 1 (DNAJB1, P25685) and Alpha-1-syntrophin (SNTA1, Q13424), overexpressed in NP glomeruli, correctly classifying 16/17 kidney biopsy samples. Geneset Enrichment Analysis (GSEA), showed that metabolic pathways were generally enriched in P, and structural cell pathways in NP. Pathway analysis identified Epithelial Adherens Junction Signaling as most affected canonical pathway. IHC analysis confirmed overexpression of BBOX1 and Cadherin 16 in glomeruli from P patients. In conclusion, glomerular proteomic profiling can be used to discriminate P from NP HN patients.

A multiomics disease progression signature of low-risk ccRCC.

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Identification of ccRCC likely to progress, despite an apparent low risk at the time of surgery, represents a key clinical issue. From a cohort of adult ccRCC patients (n = 443), we selected low-risk tumors progressing within a 5-years average follow-up (progressors: P, n = 8) and non-progressing (NP) tumors (n = 16). Transcriptome sequencing, miRNA sequencing and proteomics were performed on tissues obtained at surgery. We identified 151 proteins, 1167 mRNAs and 63 miRNAs differentially expressed in P compared to NP low-risk tumors. Pathway analysis demonstrated overrepresentation of proteins related to "LXR/RXR and FXR/RXR Activation", "Acute Phase Response Signaling" in NP compared to P samples. Integrating mRNA, miRNA and proteomic data, we developed a 10-component classifier including two proteins, three genes and five miRNAs, effectively differentiating P and NP ccRCC and capturing underlying biological differences, potentially useful to identify "low-risk" patients requiring closer surveillance and treatment adjustments. Key results were validated by immunohistochemistry, qPCR and data from publicly available databases. Our work suggests that LXR, FXR and macrophage activation pathways could be critically involved in the inhibition of the progression of low-risk ccRCC. Furthermore, a 10-component classifier could support an early identification of apparently low-risk ccRCC patients.

AGAP2-AS1 as a prognostic biomarker in low-risk clear cell renal cell carcinoma patients with progressing disease.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer and one of the most common cancers. While survival for localized ccRCC is good, the survival of metastatic disease is not, and thirty percent of patients with ccRCC develop metastases during follow-up. Although current scoring methods accurately identify patients at low progression risk, a small subgroup of those patients still experience metastasis. We therefore aimed to identify ccRCC progression biomarkers in "low-risk" patients who were potentially eligible for adjuvant treatments or more intensive follow-up. METHODS: We assembled a cohort of ccRCC patients (n = 443) and identified all "low-risk" patients who later developed progressing tumors (n = 8). Subsequently, we performed genome-wide expression profiling from formalin-fixed samples obtained at initial surgery from these "low-risk" patients and 16 matched patients not progressing to recurrence with metastasis. The patients were matched for Leibovich sore, creatinine, age, sex, tumor size and tumor stage. Key results were confirmed with qPCR and external data from The Cancer Genome Atlas. RESULTS: Principal component analysis indicated that systematic transcriptomic differences were already detectable at the time of initial surgery. One thousand one hundred sixty-seven genes, mainly associated with cancer and immune-related pathways, were differentially expressed between progressors and nonprogressors. A search for a classifier revealed that overexpression of AGAP2-AS1, an antisense long noncoding RNA, correctly classified 23 of 24 samples, years (4.5 years average) in advance of the discovery of metastasis and without requiring larger gene panels. Subsequently, we confirmed AGAP2-AS1 gene overexpression by qPCR in the same samples (p < 0.05). Additionally, in external data from The Cancer Genome Atlas, overexpression of AGAP2-AS1 is correlated with overall unfavorable survival outcome in ccRCC, irrespective of other prognostic predictors (p = 2.44E-7). CONCLUSION: AGAP2-AS1 may represent a novel biomarker identifying high-risk ccRCC patients currently classified as "low risk" at the time of surgery.

The SEQC2 epigenomics quality control (EpiQC) study.

BACKGROUND: Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA's Epigenomics Quality Control Group. RESULTS: Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms. CONCLUSIONS: The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research.

Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study.

With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing.

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing.

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.

AXL targeting reduces fibrosis development in experimental unilateral ureteral obstruction.

The AXL receptor tyrosine kinase (RTK) is involved in partial epithelial-to-mesenchymal transition (EMT) and inflammation - both main promoters of renal fibrosis development. The study aim was to investigate the role of AXL inhibition in kidney fibrosis due to unilateral ureteral obstruction (UUO). Eight weeks old male C57BL/6 mice underwent UUO and were treated with oral AXL inhibitor bemcentinib (n = 22), Angiotensin-converting enzyme inhibitor (ACEI, n = 10), ACEI and bemcentinib (n = 10) or vehicle alone (n = 22). Mice were sacrificed after 7 or 15 days and kidney tissues were analyzed by immunohistochemistry (IHC), western blot, ELISA, Sirius Red (SR) staining, and hydroxyproline (Hyp) quantification. RNA was extracted from frozen kidney tissues and sequenced on an Illumina HiSeq4000 platform. After 15 days the ligated bemcentinib-treated kidneys showed less fibrosis compared to the ligated vehicle-treated kidneys in SR analyses and Hyp quantification. Reduced IHC staining for Vimentin (VIM) and alpha smooth muscle actin (αSMA), as well as reduced mRNA abundance of key regulators of fibrosis such as transforming growth factor (Tgfß), matrix metalloproteinase 2 (Mmp2), Smad2, Smad4, myofibroblast activation (Aldh1a2, Crlf1), and EMT (Snai1,2, Twist), in ligated bemcentinib-treated kidneys was compatible with reduced (partial) EMT induction. Furthermore, less F4/80 positive cells, less activity of pathways related to the immune system and lower abundance of MCP1, MCP3, MCP5, and TARC in ligated bemcentinib-treated kidneys was compatible with reduction in inflammatory infiltrates by bemcentinib treatment. The AXL RTK pathway represents a promising target for pharmacologic therapy of kidney fibrosis.

Transcriptome-proteome integration of archival human renal cell carcinoma biopsies enables identification of molecular mechanisms.

Renal cell cancer is among the most common forms of cancer in humans, with around 35,000 deaths attributed to kidney carcinoma in the European Union in 2012 alone. Clear cell renal cell carcinoma (ccRCC) represents the most common form of kidney cancer and the most lethal of all genitourinary cancers. Here, we apply omics technologies to archival core biopsies to investigate the biology underlying ccRCC. Knowledge of these underlying processes should be useful for the discovery and/or confirmation of novel therapeutic approaches and ccRCC biomarker development. From partial or full nephrectomies of 11 patients, paired core biopsies of ccRCC-affected tissue and adjacent ("peritumorous") nontumor tissue were both sampled and subjected to proteomics analyses. We combined proteomics results with our published mRNA sequencing data from the same patients and with published miRNA sequencing data from an overlapping patient cohort from our institution. Statistical analysis and pathway analysis were performed with JMP Genomics and Ingenuity Pathway Analysis (IPA), respectively. Proteomics analysis confirmed the involvement of metabolism and oxidative stress-related pathways in ccRCC, whereas the most affected pathways in the mRNA sequencing data were related to the immune system. Unlike proteomics or mRNA sequencing alone, a combinatorial cross-omics pathway analysis approach captured a broad spectrum of biological processes underlying ccRCC, such as mitochondrial damage, repression of apoptosis, and immune system pathways. Sirtuins, immunoproteasome genes, and CD74 are proposed as potential targets for the treatment of ccRCC.

Detection of CNVs in NGS Data Using VS-CNV.

Copy number variations have been linked to numerous genetic diseases including cancer, Parkinson's disease, pancreatitis, and lupus. While current best practices for CNV detection often require using microarrays for detecting large CNVs or multiplex ligation-dependent probe amplification (MLPA) for gene-sized CNVs, new methods have been developed with the goal of replacing both of these specialized assays with bioinformatic analysis applied to next-generation sequencing (NGS) data. Because NGS is already used by clinical labs to detect small coding variants, this approach reduces associated costs, resources, and analysis time. This chapter provides an overview of the various approaches to CNV detection via NGS data, and examines VS-CNV, a commercial tool developed by Golden Helix, which provides robust CNV calling capabilities for both gene panel and exome data.

Expanding the Utilization of Formalin-Fixed, Paraffin-Embedded Archives: Feasibility of miR-Seq for Disease Exploration and Biomarker Development from Biopsies with Clear Cell Renal Cell Carcinoma.

Novel predictive tools for clear cell renal cell carcinoma (ccRCC) are urgently needed. MicroRNAs (miRNAs) have been increasingly investigated for their predictive value, and formalin-fixed paraffin-embedded biopsy archives may potentially be a valuable source of miRNA sequencing material, as they remain an underused resource. Core biopsies of both cancerous and adjacent normal tissues were obtained from patients (n = 12) undergoing nephrectomy. After small RNA-seq, several analyses were performed, including classifier evaluation, obesity-related inquiries, survival analysis using publicly available datasets, comparisons to the current literature and ingenuity pathway analyses. In a comparison of tumour vs. normal, 182 miRNAs were found with significant differential expression; miR-155 was of particular interest as it classified all ccRCC samples correctly and correlated well with tumour size (R² = 0.83); miR-155 also predicted poor survival with hazard ratios of 2.58 and 1.81 in two different TCGA (The Cancer Genome Atlas) datasets in a univariate model. However, in a multivariate Cox regression analysis including age, sex, cancer stage and histological grade, miR-155 was not a statistically significant survival predictor. In conclusion, formalin-fixed paraffin-embedded biopsy tissues are a viable source of miRNA-sequencing material. Our results further support a role for miR-155 as a promising cancer classifier and potentially as a therapeutic target in ccRCC that merits further investigation.

Renal carcinoma/kidney progenitor cell chimera organoid as a novel tumorigenesis gene discovery model.

Three-dimensional (3D) organoids provide a new way to model various diseases, including cancer. We made use of recently developed kidney-organ-primordia tissue-engineering technologies to create novel renal organoids for cancer gene discovery. We then tested whether our novel assays can be used to examine kidney cancer development. First, we identified the transcriptomic profiles of quiescent embryonic mouse metanephric mesenchyme (MM) and of MM in which the nephrogenesis program had been induced ex vivo The transcriptome profiles were then compared to the profiles of tumor biopsies from renal cell carcinoma (RCC) patients, and control samples from the same kidneys. Certain signature genes were identified that correlated in the developmentally induced MM and RCC, including components of the caveolar-mediated endocytosis signaling pathway. An efficient siRNA-mediated knockdown (KD) of Bnip3, Gsn, Lgals3, Pax8, Cav1, Egfr or Itgb2 gene expression was achieved in mouse RCC (Renca) cells. The live-cell imaging analysis revealed inhibition of cell migration and cell viability in the gene-KD Renca cells in comparison to Renca controls. Upon siRNA treatment, the transwell invasion capacity of Renca cells was also inhibited. Finally, we mixed E11.5 MM with yellow fluorescent protein (YFP)-expressing Renca cells to establish chimera organoids. Strikingly, we found that the Bnip3-, Cav1- and Gsn-KD Renca-YFP+ cells as a chimera with the MM in 3D organoid rescued, in part, the RCC-mediated inhibition of the nephrogenesis program during epithelial tubules formation. Altogether, our research indicates that comparing renal ontogenesis control genes to the genes involved in kidney cancer may provide new growth-associated gene screens and that 3D RCC-MM chimera organoids can serve as a novel model with which to investigate the behavioral roles of cancer cells within the context of emergent complex tissue structures.

Clear Cell Renal Cell Carcinoma is linked to Epithelial-to-Mesenchymal Transition and to Fibrosis.

Clear cell renal cell carcinoma (ccRCC) represents the most common type of kidney cancer with high mortality in its advanced stages. Our study aim was to explore the correlation between tumor epithelial-to-mesenchymal transition (EMT) and patient survival. Renal biopsies of tumorous and adjacent nontumorous tissue were taken with a 16 g needle from our patients (n = 26) undergoing partial or radical nephrectomy due to ccRCC RNA sequencing libraries were generated using Illumina TruSeq® Access library preparation protocol and TruSeq Small RNA library preparation kit. Next generation sequencing (NGS) was performed on Illumina HiSeq2500. Comparative analysis of matched sample pairs was done using the Bioconductor Limma/voom R-package. Liquid chromatography-tandem mass spectrometry and immunohistochemistry were applied to measure and visualize protein abundance. We detected an increased generic EMT transcript score in ccRCC Gene expression analysis showed augmented abundance of AXL and MMP14, as well as down-regulated expression of KL (klotho). Moreover, microRNA analyses demonstrated a positive expression correlation of miR-34a and its targets MMP14 and AXL Survival analysis based on a subset of genes from our list EMT-related genes in a publicly available dataset showed that the EMT genes correlated with ccRCC patient survival. Several of these genes also play a known role in fibrosis. Accordingly, recently published classifiers of solid organ fibrosis correctly identified EMT-affected tumor samples and were correlated with patient survival. EMT in ccRCC linked to fibrosis is associated with worse survival and may represent a target for novel therapeutic interventions.

Renal Fibrosis mRNA Classifier: Validation in Experimental Lithium-Induced Interstitial Fibrosis in the Rat Kidney.

Accurate diagnosis of fibrosis is of paramount clinical importance. A human fibrosis classifier based on metzincins and related genes (MARGS) was described previously. In this investigation, expression changes of MARGS genes were explored and evaluated to examine whether the MARGS-based algorithm has any diagnostic value in a rat model of lithium nephropathy. Male Wistar rats (n = 12) were divided into 2 groups (n = 6). One group was given a diet containing lithium (40 mmol/kg food for 7 days, followed by 60mmol/kg food for the rest of the experimental period), while a control group (n = 6) was fed a normal diet. After six months, animals were sacrificed and the renal cortex and medulla of both kidneys removed for analysis. Gene expression changes were analysed using 24 GeneChip® Affymetrix Rat Exon 1.0 ST arrays. Statistically relevant genes (p-value<0.05, fold change>1.5, t-test) were further examined. Matrix metalloproteinase-2 (MMP2), CD44, and nephroblastoma overexpressed gene (NOV) were overexpressed in the medulla and cortex of lithium-fed rats compared to the control group. TGFß2 was overrepresented in the cortex of lithium-fed animals 1.5-fold, and 1.3-fold in the medulla of the same animals. In Gene Set Enrichment Analysis (GSEA), both the medulla and cortex of lithium-fed animals showed an enrichment of the MARGS, TGFß network, and extracellular matrix (ECM) gene sets, while the cortex expression signature was enriched in additional fibrosis-related-genes and the medulla was also enriched in immune response pathways. Importantly, the MARGS-based fibrosis classifier was able to classify all samples correctly. Immunohistochemistry and qPCR confirmed the up-regulation of NOV, CD44, and TGFß2. The MARGS classifier represents a cross-organ and cross-species classifier of fibrotic conditions and may help to design a test to diagnose and to monitor fibrosis. The results also provide evidence for a common pathway in the pathogenesis of fibrosis.

Development and confirmation of potential gene classifiers of human clear cell renal cell carcinoma using next-generation RNA sequencing.

OBJECTIVE: A previous study by this group demonstrated the feasibility of RNA sequencing (RNAseq) technology for capturing disease biology of clear cell renal cell carcinoma (ccRCC), and presented initial results for carbonic anhydrase-9 (CA9) and tumor necrosis factor-α-induced protein-6 (TNFAIP6) as possible biomarkers of ccRCC (discovery set) [Eikrem et al. PLoS One 2016;11:e0149743]. To confirm these results, the previous study is expanded, and RNAseq data from additional matched ccRCC and normal renal biopsies are analyzed (confirmation set). MATERIALS AND METHODS: Two core biopsies from patients (n = 12) undergoing partial or full nephrectomy were obtained with a 16 g needle. RNA sequencing libraries were generated with the Illumina TruSeq® Access library preparation protocol. Comparative analysis was done using linear modeling (voom/Limma; R Bioconductor). RESULTS: The formalin-fixed and paraffin-embedded discovery and confirmation data yielded 8957 and 11,047 detected transcripts, respectively. The two data sets shared 1193 of differentially expressed genes with each other. The average expression and the log2-fold changes of differentially expressed transcripts in both data sets correlated, with R² = .95 and R² = .94, respectively. Among transcripts with the highest fold changes were CA9, neuronal pentraxin-2 and uromodulin. Epithelial-mesenchymal transition was highlighted by differential expression of, for example, transforming growth factor-ß1 and delta-like ligand-4. The diagnostic accuracy of CA9 was 100% and 93.9% when using the discovery set as the training set and the confirmation data as the test set, and vice versa, respectively. These data further support TNFAIP6 as a novel biomarker of ccRCC. TNFAIP6 had combined accuracy of 98.5% in the two data sets. CONCLUSIONS: This study provides confirmatory data on the potential use of CA9 and TNFAIP6 as biomarkers of ccRCC. Thus, next-generation sequencing expands the clinical application of tissue analyses.

Transcriptome Sequencing (RNAseq) Enables Utilization of Formalin-Fixed, Paraffin-Embedded Biopsies with Clear Cell Renal Cell Carcinoma for Exploration of Disease Biology and Biomarker Development.

Formalin-fixed, paraffin-embedded (FFPE) tissues are an underused resource for molecular analyses. This proof of concept study aimed to compare RNAseq results from FFPE biopsies with the corresponding RNAlater® (Qiagen, Germany) stored samples from clear cell renal cell carcinoma (ccRCC) patients to investigate feasibility of RNAseq in archival tissue. From each of 16 patients undergoing partial or full nephrectomy, four core biopsies, such as two specimens with ccRCC and two specimens of adjacent normal tissue, were obtained with a 16g needle. One normal and one ccRCC tissue specimen per patient was stored either in FFPE or RNAlater®. RNA sequencing libraries were generated applying the new Illumina TruSeq® Access library preparation protocol. Comparative analysis was done using voom/Limma R-package. The analysis of the FFPE and RNAlater® datasets yielded similar numbers of detected genes, differentially expressed transcripts and affected pathways. The FFPE and RNAlater datasets shared 80% (n = 1106) differentially expressed genes. The average expression and the log2 fold changes of these transcripts correlated with R2 = 0.97, and R2 = 0.96, respectively. Among transcripts with the highest fold changes in both datasets were carbonic anhydrase 9 (CA9), neuronal pentraxin-2 (NPTX2) and uromodulin (UMOD) that were confirmed by immunohistochemistry. IPA revealed the presence of gene signatures of cancer and nephrotoxicity, renal damage and immune response. To simulate the feasibility of clinical biomarker studies with FFPE samples, a classifier model was developed for the FFPE dataset: expression data for CA9 alone had an accuracy, specificity and sensitivity of 94%, respectively, and achieved similar performance in the RNAlater dataset. Transforming growth factor-ß1 (TGFB1)-regulated genes, epithelial to mesenchymal transition (EMT) and NOTCH signaling cascade may support novel therapeutic strategies. In conclusion, in this proof of concept study, RNAseq data obtained from FFPE kidney biopsies are comparable to data obtained from fresh stored material, thereby expanding the utility of archival tissue specimens.

Robust and tissue-independent gender-specific transcript biomarkers.

CONTEXT: Correct gender assignment in humans at the molecular level is crucial in many scientific disciplines and applied areas. MATERIALS AND METHODS: Candidate gender markers were identified through supervised statistical analysis of genome wide microarray expression data from human blood samples (N = 123, 58 female, 65 male) as a training set. The potential of the markers to predict undisclosed tissue donor gender was tested on microarray data from 13 healthy and 11 cancerous human tissue collections (internal) and external datasets from samples of varying tissue origin. The abundance of some genes in the marker panel was quantified by RT-PCR as alternative analytical technology. RESULTS: We identified and qualified predictive, gender-specific transcript markers based on a set of five genes (RPS4Y1, EIF1AY, DDX3Y, KDM5D and XIST). CONCLUSION: Gene expression marker panels can be used as a robust tissue- and platform-independent predictive approach for gender determination.

Alteration of human blood cell transcriptome in uremia.

BACKGROUND: End-stage renal failure is associated with profound changes in physiology and health, but the molecular causation of these pleomorphic effects termed "uremia" is poorly understood. The genomic changes of uremia were explored in a whole genome microarray case-control comparison of 95 subjects with end-stage renal failure (n = 75) or healthy controls (n = 20). METHODS: RNA was separated from blood drawn in PAXgene tubes and gene expression analyzed using Affymetrix Human Genome U133 Plus 2.0 arrays. Quality control and normalization was performed, and statistical significance determined with multiple test corrections (qFDR). Biological interpretation was aided by knowledge mining using NIH DAVID, MetaCore and PubGene RESULTS: Over 9,000 genes were differentially expressed in uremic subjects compared to normal controls (fold change: -5.3 to +6.8), and more than 65% were lower in uremia. Changes appeared to be regulated through key gene networks involving cMYC, SP1, P53, AP1, NFkB, HNF4 alpha, HIF1A, c-Jun, STAT1, STAT3 and CREB1. Gene set enrichment analysis showed that mRNA processing and transport, protein transport, chaperone functions, the unfolded protein response and genes involved in tumor genesis were prominently lower in uremia, while insulin-like growth factor activity, neuroactive receptor interaction, the complement system, lipoprotein metabolism and lipid transport were higher in uremia. Pathways involving cytoskeletal remodeling, the clathrin-coated endosomal pathway, T-cell receptor signaling and CD28 pathways, and many immune and biological mechanisms were significantly down-regulated, while the ubiquitin pathway and certain others were up-regulated. CONCLUSIONS: End-stage renal failure is associated with profound changes in human gene expression which appears to be mediated through key transcription factors. Dialysis and primary kidney disease had minor effects on gene regulation, but uremia was the dominant influence in the changes observed. This data provides important insight into the changes in cellular biology and function, opportunities for biomarkers of disease progression and therapy, and potential targets for intervention in uremia.