Clear cell renal cell carcinoma: gene expression analyses identify a potential signature for tumor aggressiveness

SAA1 identified as a potential prediction biomarker for metastasis of hepatocellular carcinoma via multi-omics approaches

Background

Metastasis is the major cause of high recurrence and mortality of hepatocellular carcinoma (HCC). Unfortunately, there are few reports on effective biomarkers of HCC metastasis. Previous studies have reported that SAA1 may be a predictor and prognostic biomarker for multiple malignant tumors. However, the role of SAA1 in HCC has not yet been investigated.

Methods

We applied RNA sequencing and proteomics analysis to investigate the expression landscape of HCC cell lines and patient serum, respectively. SAA1 is a common key gene and listed as a candidate biomarker of HCC metastasis. It was validated in two cell lines, 107 participants serum, and 63 matched HCC and adjacent non-tumorous liver tissues. Human Protein Atlas (HPA), Genotype-Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) datasets were integrated to explore SAA1 expression among various cell types and organs. The diagnostic and prognostic value of SAA1 in HCC were determined through receiver operating characteristic (ROC) and Kaplan-Meier curves. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network were constructed for SAA1, as well as for its co-expressed genes. We further analyzed the correlation between SAA1 and co-expression genes.

Results

We found 7 differentially expressed genes (DEGs) and 14 differentially expressed proteins (DEPs) were related to HCC metastasis. SAA1, a key candidate biomarker, was highly enriched in hepatocytes and liver organ, and it was also highly expressed in HCC cells and the serum and tissues of HCC patients. The results of ROC curve analysis indicated that SAA1 had better predictive values for distinguishing HCC metastasis from non-metastasis. Kaplan-Meier curve analysis revealed that HCC patients with higher SAA1 expression had worse overall survival.

Conclusions

Our findings provide new insights into HCC metastasis by identifying candidate gene prediction biomarkers for HCC metastasis.

Biomarkers in Urological Cancers

Urological tumours have become one of the most common cancers in the last decade. It is important to apply an approach that evaluates many factors related to the patient and the disease carefully to minimize cancer-associated morbidity and mortality. The clinical use of cancer biomarkers is a valuable part of the clinical management of urological cancers. These biomarkers may lead to optimized detection, treatment, and follow-up of urological cancers. With the development of molecular research, newly developed biomarkers and next-generation sequencing have also contributed to patient management. In this chapter, we will present biomarkers in the most common urological cancers under subheadings of bladder cancer, prostate cancer, kidney cancer, and testicular cancer. Additionally, due to the development that occurred in the next-generation sequencing (NGS), all the above-mentioned malignancies are evaluated with regard to NGS.

Identification and Verification of Immune Subtype-Related lncRNAs in Clear Cell Renal Cell Carcinoma

Background

According to clinical study results, immune checkpoint blockade (ICB) treatment enhances the survival outcome of patients with clear cell renal cell carcinoma (ccRCC). Previous research has divided ccRCC patients into immune subtypes with distinct ICB response rates. However, the study on the association between lncRNAs and ccRCC immune subtypes is lacking.

Methods

Differentially expressed lncRNAs/mRNAs between two major immune subgroups were calculated. A weighted gene co-expression network analysis (WGCNA) was conducted to establish the lncRNA-mRNA co-expression network and select the key lncRNAs. Then, prognostic lncRNAs were selected from the network by the bioinformatics method. Next, the risk-score was estimated by lncRNA expression and their coefficients. Finally, a nomogram based on lncRNAs and clinical parameters was created to predict the prognosis of ccRCC.

Results

LncRNAs and mRNAs associated with ccRCC immune subtypes were identified. The lncRNAs and mRNAs from a gene module closely linked to the immune subtype were used to construct a network. The KEGG pathways enriched in the network were related to immune system activation processes. These 8 lncRNAs (AL365361.1, LINC01934, AC090152.1, PCED1B-AS1, LINC00426, AC007728.2, AC243829.4, and LINC00158) were found to be positively correlated with immune cells of the tumor microenvironment. The C-index of the nomogram was 0.777, and the calibration curve data suggests that the nomogram has a high degree of discriminating capacity.

Conclusion

In summary, we discovered core lncRNAs linked with immune subtypes and created corresponding lncRNA–mRNA networks. These lncRNAs are anticipated to have predictive significance for ccRCC and may provide insight into novel biomarkers for the disease.

Single-Cell Sequencing to Identify Six Heat Shock Protein (HSP) Genes-Mediated Progression Subtypes of Clear Cell Renal Cell Carcinoma

Background

Heat shock proteins (HSPs) are widely involved in tumor occurrence and development and are prognostic markers for multiple tumors. However, the role of HSPs in clear cell renal cell carcinoma (ccRCC) remains unclear.

Methods

We used Cytoscape to identify hub genes in the ccRCC single-cell sequencing data set from the Gene Expression Omnibus (GEO) data repository. We identified subtypes, C1 and C2, of The Cancer Genome Atlas (TCGA) patients based on the expression of hub genes using unsupervised consensus clustering. Principal component analysis (PCA) was used to verify the clustering differences, and Kaplan-Meier (K-M) estimate was used to verify the survival differences between C1 and C2 patients. We used TIMER 2.0 and CIBERSORT to evaluate the immune cell infiltration of HSP genes and C1 and C2 patients. The R package "pRRophetic" was used to evaluate the sensitivity in C1 and C2 patients to the four first-line treatment drugs.

Results

We identified six hub genes (HSP90AA1, HSPH1, HSPA1B, HSPA8, and HSPA1A) encoding HSP, five of which were significantly downregulated in TCGA group, and four had a protective effect on prognosis (p <0.05). Survival analysis showed that C1 patients had a better overall survival (p <0.001). TIMER 2.0 analysis showed that three HSP genes were significantly correlated with the infiltration of CD4+ T cells and CD4+ Th1 cells (|cor|>0.5, p<0.001). CIBERSORT showed significant differences in multiple infiltrating immune cells between C1 and C2 patients. Meanwhile, the expression of PD1 was significantly lower in C1 patients than in C2 patients, and the expression of PDL1 is the another way around. Drug sensitivity analysis showed that C1 patients were more sensitive to sorafenib, pazopanib, and axitinib (p <0.001).

Conclusion

Our research revealed two molecular subtypes of ccRCC based on 6 HSP genes, and revealed significant differences between the two subtypes in terms of clinical prognosis, immune infiltration, and drug sensitivity.

Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning

Clear cell renal cell carcinoma (ccRCC) is the most common histological type of kidney cancer and has high heterogeneity. Stratification of ccRCC is important since distinct subtypes differ in prognosis and treatment. Here, we applied a systems biology approach to stratify ccRCC into three molecular subtypes with different mRNA expression patterns and prognosis of patients. Further, we developed a set of biomarkers that could robustly classify the patients into each of the three subtypes and predict the prognosis of patients. Then, we reconstructed subtype-specific metabolic models and performed essential gene analysis to identify the potential drug targets. We identified four drug targets, including SOAT1, CRLS1, and ACACB, essential in all the three subtypes and GPD2, exclusively essential to subtype 1. Finally, we repositioned mitotane, an FDA-approved SOAT1 inhibitor, to treat ccRCC and showed that it decreased tumor cell viability and inhibited tumor cell growth based on in vitro experiments.

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Three consistent molecular ccRCC subtypes were found to guide patients' prognoses

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REOs-based biomarker was developed to robustly classify patients at individual level

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SOAT1 is identified as a common drug target for all ccRCC subtypes

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Mitotane was repositioned treatment of ccRCC via inhibiting SOAT1

Review of Prognostic Expression Markers for Clear Cell Renal Cell Carcinoma

Context: The number of prognostic markers for clear cell renal cell carcinoma (ccRCC) has been increasing regularly over the last 15 years, without being integrated and compared.

Objective: Our goal was to perform a review of prognostic markers for ccRCC to lay the ground for their use in the clinics.

Evidence Acquisition: PubMed database was searched to identify RNA and protein markers whose expression level was reported as associated with survival of ccRCC patients. Relevant studies were selected through cross-reading by two readers.

Evidence Synthesis: We selected 249 studies reporting an association with prognostic of either single markers or multiple-marker models. Altogether, these studies were based on a total of 341 distinct markers and 13 multiple-marker models. Twenty percent of these markers were involved in four biological pathways altered in ccRCC: cell cycle, angiogenesis, hypoxia, and immune response. The main genes (VHL, PBRM1, BAP1, and SETD2) involved in ccRCC carcinogenesis are not the most relevant for assessing survival.

Conclusion: Among single markers, the most validated markers were KI67, BIRC5, TP53, CXCR4, and CA9. Of the multiple-marker models, the most famous model, ClearCode34, has been highly validated on several independent datasets, but its clinical utility has not yet been investigated.

Patient Summary: Over the years, the prognosis studies have evolved from single markers to multiple-marker models. Our review highlights the highly validated prognostic markers and multiple-marker models and discusses their clinical utility for better therapeutic care.

Development and validation of the prognostic value of the immune-related genes in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous tumor, resulting a challenge of developing target therapeutics. Not long ago, immune checkpoint blockade regimens combine with tyrosin kinase inhibitors have evolved frontline options in metastatic RCC, which implies arrival of the era of tumor immunotherapy. Studies have demonstrated immune-related genes (IRGs) could characterize tumor milieu and related to patient survival. Nevertheless, the clinical significance of classifier depending on IRGs in ccRCC has not been well established.

Methods

The R package limma, univariate and LASSO cox regression analysis were used to screen the prognostic related IRGs from TCGA database. Multivariate cox regression was utilized to establish a risk prediction model for candidate genes. Quantitative real-time PCR was used to confirm the expression of candidates in clinical samples from our institution. CIBERSORT algorithm and correlation analysis were applied to explore tumor-infiltrating immune cells signature between different risk groups. A clinical nomogram was also developed to predict OS by using the rms R package based on the risk prediction model and other independent risk factors. The ICGC data was used for external validation of either gene risk model or nomogram.

Results

We identified 382 differentially expressed immune related genes. Four unique prognostic IRGs (CRABP2, LTB4R, PTGER1 and TEK) were finally affirmed to associate with tumor survival independently and utilized to establish the risk score model. All candidates’ expression was successfully laboratory confirmed by q-PCR. CIBERSORT analysis implied patients in unfavorable-risk group with high CD8 T cell, regulatory T cell and NK cell infiltration, as well as high expression of PD-1, CTLA4, TNFRSF9, TIGIT and LAG3. A nomogram combined IRGs risk score with age, gender, TNM stage, Fuhrman grade, necrosis was further generated to predict of 3- and 5-year OS, which exhibited superior discriminative power (AUCs were 0.811 and 0.795).

Conclusions

Our study established and validated a survival prognostic model system based on 4 unique immune related genes in ccRCC, which expands knowledge in tumor immune status and provide a potent prediction tool in future.

SAA1 knockdown promotes the apoptosis of glioblastoma cells via downregulation of AKT signaling

Serum amyloid A1 (SAA1) is an inflammatory associated high-density lipoprotein. And It is also considered as a predictor and prognostic marker of cancer risk. However, its role and mechanisms in glioblastoma (GBM) still unclear. In this study, we validate that SAA1 is up-regulated in GBM, and its high expression predicts poor prognosis. SAA1 knockdown promotes the apoptosis of GBM cell. Mechanistically, SAA1 knockdown can inhibit serine/threonine protein kinase B (AKT) phosphorylation, thereby regulating the expression of apoptosis-related proteins such as Bcl2 and Bax, leading to GBM cell death. Moreover, Gliomas with low SAA1 expression have increased sensitivity to Temozolomide (TMZ). Low SAA1 expression segregated glioma patients who were treated with Temozolomide (TMZ) or with high MGMT promoter methylation into survival groups in TCGA and CGGA dataset. Our study strongly suggested that SAA1 was a regulator of cells apoptosis and acted not only as a prognostic marker but also a novel biomarker of sensitivity of glioma to TMZ.

Prognostic role of PHYH for overall survival (OS) in clear cell renal cell carcinoma (ccRCC)

This study attempts to evaluate the prognostic role of PHYH for overall survival (OS) in clear cell renal cell carcinoma (ccRCC) by means of publicly available data from The Cancer Genome Atlas (TCGA). Clinical pathologic features and PHYH expression were downloaded from the TCGA database and relationships between them were analyzed by univariate and multivariate Cox regression analyses. Gene Set Enrichment Analysis (GSEA) and gene–gene interactions were also performed between tissues with different PHYH expression levels. PHYH expression levels were significantly lower in patient with ccRCC compared with normal tissues (p = 1.156e−19). Kaplan–Meier survival analysis showed that high expression of PHYH had a better prognosis than low expression (p = 9e−05). Moreover, PHYH expression was also significantly associated with high grade (G2-4, p = 0.025), high stage (StageIII & IV, p = 5.604e−05), and high level of stage_T (T3-4, p = 4.373e−05). Univariate and multivariate Cox regression analyses indicated that PHYH could be acted as an independent prognostic factor (p < 0.05). Nomogram including clinical pathologic features and PHYH expression were also provided. GSEA revealed that butanoate metabolism, histidine metabolism, propanoate metabolism, pyruvate metabolism, tryptophan metabolism, PPAR signalling pathway, and renin–angiotensin system were differentially enriched in PHYH high-expression phenotype. ICGC database was utilized to verify the expression level and survival benefit of PHYH (both p < 0.05). We suspect that elevated PHYH expression may be served as a potential prognostic molecular marker of better survival in ccRCC. Besides, alpha-oxidation was closely regulated by PHYH, and PPAR signalling, pyruvate metabolism, butanoate metabolism, and RAS might be the key pathways regulated by PHYH in CCRC.

RNA-binding protein SORBS2 suppresses clear cell renal cell carcinoma metastasis by enhancing MTUS1 mRNA stability

RNA-binding proteins (RBPs) predominantly contribute to abnormal posttranscriptional gene modulation and disease progression in cancer. Sorbin and SH3 domain-containing 2 (SORBS2), an RBP, has been reported to be a potent tumor suppressor in several cancer types. Through integrative analysis of clinical specimens, we disclosed that the expression level of SORBS2 was saliently decreased in metastatic tissues and positively correlated with overall survival. We observed that overexpression of SORBS2 brought about decreased metastatic capacity in ccRCC cell lines. Transcriptome-wide analysis revealed that SORBS2 notably increased microtubule-associated tumor-suppressor 1 gene (MTUS1) expression. In-depth mechanistic exploring discovered that the Cys2-His2 zinc finger (C2H2-ZnF) domain of SORBS2 directly bound to the 3′ untranslated region (3′UTR) of MTUS1 mRNA, which increased MTUS1 mRNA stability. In addition, we identified that MTUS1 regulated microtubule dynamics via promoting KIF2C^S192 phosphorylation by Aurora B. Together, our research identified SORBS2 as a suppressor of ccRCC metastasis by enhancing MTUS1 mRNA stability, providing a novel understanding of RBPs during ccRCC progression.

DNA Methylation-Based Panel Predicts Survival of Patients With Clear Cell Renal Cell Carcinoma and Its Correlations With Genomic Metrics and Tumor Immune Cell Infiltration

DNA methylation based prognostic factor for patients with clear cell renal cell carcinoma (ccRCC) remains unclear. In the present study, we identified survival-related DNA methylation sites based on the differentially methylated DNA CpG sites between normal renal tissue and ccRCC. Then, these survival-related DNA methylation sites were included into an elastic net regularized Cox proportional hazards regression (CoxPH) model to build a DNA methylation-based panel, which could stratify patients into different survival groups with excellent accuracies in the training set and test set. External validation suggested that the DNA methylation-based panel could effectively distinguish normal controls from tumor samples and classify patients into metastasis group and non-metastasis group. The nomogram containing DNA methylation-based panel was reliable in clinical settings. Higher total mutation number, SCNA level, and MATH score were associated with higher methylation risk. The innate immune, ratio between CD8⁺T cell versus Treg cell as well as Th17 cell versus Th2 cell were significantly decreased in high methylation risk group. In inclusion, we developed a DNA methylation-based panel which might be independent prognostic factor in ccRCC. Patients with higher methylation risk were associated genomic alteration and poor immune microenvironment.

Influence of gene expression on survival of clear cell renal cell carcinoma

Approximately 10%‐20% of patients with clinically localized clear cell renal cell carcinoma (ccRCC) at time of surgery will subsequently experience metastatic progression. Although considerable progression was seen in the systemic treatment of metastatic ccRCC in last 20 years, once ccRCC spreads beyond the confines of the kidney, 5‐year survival is less than 10%. Therefore, significant clinical advances are urgently needed to improve overall survival and patient care to manage the growing number of patients with localized ccRCC. We comprehensively evaluated expression of 388 candidate genes related with survival of ccRCC by using TCGA RNAseq (n = 515), Total Cancer Care (TCC) expression array data (n = 298), and a well characterized Moffitt RCC cohort (n = 248). We initially evaluated all 388 genes for association with overall survival using TCGA and TCC data. Eighty‐one genes were selected for further analysis and tested on Moffitt RCC cohort using NanoString expression analysis. Expression of nine genes (AURKA, AURKB, BIRC5, CCNE1, MK167, MMP9, PLOD2, SAA1, and TOP2A) was validated as being associated with poor survival. Survival prognostic models showed that expression of the nine genes and clinical factors predicted the survival in ccRCC patients with AUC value: 0.776, 0.821 and 0.873 for TCGA, TCC and Moffitt data set, respectively. Some of these genes have not been previously implicated in ccRCC survival and thus potentially offer insight into novel therapeutic targets. Future studies are warranted to validate these identified genes, determine their biological mechanisms and evaluate their therapeutic potential in preclinical studies.

Cell cycle progression score has potential prognostic value for stage T1 renal cell carcinomas

BACKGROUND

There is an ongoing effort to identify a biomarker which predicts metastatic progression of renal cell carcinoma (RCC).

OBJECTIVE

To evaluate the utility of the cell cycle progression (CCP) score biomarker in predicting metastasis in RCC after local resection of pathologic T1 disease.

DESIGN, SETTING, AND PARTICIPANTS

Pathologic T1 tumors at the University of Iowa were reviewed in patients who had a radical or partial nephrectomy between 1995 and 2010. Patients with known or suspected metastasis, who had received chemotherapy, or who developed metastasis within 60 days of surgery were excluded. Final analysis included 163 patients with RCC who developed metastasis or a new primary within 5 years after surgery or had been followed for 5 years without developing metastasis.

INTERVENTION(S)

Expression levels of 31 cell cycle genes and 15 control genes from the tumor were measured and reported as a CCP score.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The sensitivity, specificity, positive predictive value, and negative predictive value for the development of a metastasis or new primary within 5 years of resection was calculated for varying CCP score cutoffs.

RESULTS AND LIMITATIONS

A total of 4 (2.5%) patients developed metastasis and 7 (4.3%) developed a new primary renal tumor. A CCP score of >-0.25 had a 100% sensitivity and 43% specificity for predicting metastatic progression. A CCP score of >-0.7 had a 100% sensitivity and 20% specificity for predicting the development of a new renal primary.

CONCLUSIONS

The CCP score has potential prognostic value in predicting metastatic progression and might be a useful tool for the management of patients with RCC.

PATIENT SUMMARY

In this study we looked at the utility of a particular gene expression profile from kidney tumors. We found that this gene expression test has the potential to identify tumors at risk of metastasis and thus could be a useful tool in the management of patients with kidney tumors.

Classification of clear cell renal cell carcinoma based on PKM alternative splicing

Clear cell renal cell carcinoma (ccRCC) accounts for 70-80% of kidney cancer diagnoses and displays high molecular and histologic heterogeneity. Hence, it is necessary to reveal the underlying molecular mechanisms involved in progression of ccRCC to better stratify the patients and design effective treatment strategies. Here, we analyzed the survival outcome of ccRCC patients as a consequence of the differential expression of four transcript isoforms of the pyruvate kinase muscle type (PKM). We first extracted a classification biomarker consisting of eight gene pairs whose within-sample relative expression orderings (REOs) could be used to robustly classify the patients into two groups with distinct molecular characteristics and survival outcomes. Next, we validated our findings in a validation cohort and an independent Japanese ccRCC cohort. We finally performed drug repositioning analysis based on transcriptomic expression profiles of drug-perturbed cancer cell lines and proposed that paracetamol, nizatidine, dimethadione and conessine can be repurposed to treat the patients in one of the subtype of ccRCC whereas chenodeoxycholic acid, fenoterol and hexylcaine can be repurposed to treat the patients in the other subtype.

High ubiquitin conjugating enzyme E2 T mRNA expression and its prognostic significance in lung adenocarcinoma: A study based on the TCGA database

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with a high mortality disease which has been positioned the first and second cancer morbidity of men and women in China, separately. Our study was to assess the prognostic meaningful of ubiquitin conjugating enzyme E2 T (UBE2T) expression in LUAD dependent on data acquired from The Cancer Genome Atlas (TCGA) and so as to increase further knowledge into the biological pathways involved in LUAD pathogenesis related to UBE2T.Information on gene expression and comparing clinical data were recognized and downloaded from TCGA. Gene set enrichment analysis (GSEA) created an arranged list of all genes s indicated by their connection with UBE2T expression.Our study cohort included 265 (54.5%) female and 221 (36.0%) male patients. The scatter plot and paired plot showed the difference of UBE2T expression between normal and tumor samples (P < .01). Overall survival (OS) analysis demonstrated that LUAD with UBE2T-high had a more terrible prognosis than that with UBE2T-low (P < .01). Multivariate analysis with the cox proportional hazards model indicated that the expression of UBE2T (hazard ratio [HR]: 1.28; 95% Confidence Interval (CI): 1.06-1.56; P = .011) and stage (HR: 2.02; 95% CI: 1.27-3.21; P = .003) were independent prognostic factors for patients with LUAD. The GSEA results showed that cell cycle, DNA replication, RNA degradation, oxidative phosphorylation, pathogenic Escherichia coli infection, citrate cycle tricarboxylic acid cycle, Alzheimer's disease, P53 signaling pathway, and purine metabolism are differentially enriched in UBE2T high expression phenotype.Our study found that the expression of UBE2T was significantly increased in LUAD patients and associated with several clinical features. UBE2T may be a potentially useful prognostic molecular biomarker of bad survival in LUAD, while further experimental ought to be performed to demonstrate the biologic effect of UBE2T.

Development and validation of a robust multigene signature as an aid to predict early relapse in stage I-III clear cell and papillary renal cell cancer

Background and objectives: Multi-gene signature can be used as prognostic indicator in many types of cancer, but the association with early-relapse in patients with stage I-III clear cell and papillary renal cell cancer (RCC) is unknown. We aim to establish a mRNAs signature for improving prediction of early-relapse in patients with stage I-III clear cell and papillary RCC.

Methods: The data of 610 patients with stage I-III RCC from The Cancer Genome Atlas (TCGA) and 270 patients from Fudan University Shanghai Cancer Center (FUSCC) were extracted. Propensity score matching analysis, linear models for microarray data VOOM method, least absolute shrinkage and selection operation Cox regression modeling analysis was conducted in turn for selecting multi-mRNA signature. Survival differences were assessed by Kaplan-Meier estimate and compared using log-rank test. Multivariable Cox regression and time-dependent receiver operating characteristic curves were used to evaluate the association of mRNAs signature with relapse-free survival (RFS).

Results: Seventeen mRNAs were identified to constitute the early-relapse signature. Among patients with stage I-III RCC, those with high-risk score calculated from 17 mRNAs signature showed shorter RFS than those with low-risk score, both in TCGA discovery and internal validation sets, and in FUSCC discovery and internal validation sets (all p < 0.05). In multivariable Cox regression analysis, the 17 mRNAs signature remained an independent prognostic factor both in TCGA discovery (HR 2.43, 95%CI 1.98-2.96) and internal validation sets (HR 1.66, 95%CI 1.19-2.30), and FUSCC discovery (HR 1.28, 95%CI 1.13-1.43) and internal validation sets (HR 1.65, 95%CI 1.11-2.48). Additionally, the 17 mRNAs signature achieved a higher accuracy for RFS estimation beyond clinical indicator.

Conclusion: The 17 mRNAs signature could classify stage I-III RCC patients into low- or high-risk of early-relapse, and will help to guide interventions to optimize survival outcomes.

Stereotactic Body Radiation Therapy for Extracranial Oligometastatic Non-small-cell Lung Cancer: A Systematic Review

Stereotactic body radiation therapy (SBRT) has emerged as a treatment modality for selected patients with oligometastatic non-small-cell lung cancer (NSCLC). The objectives of this systematic review were to explore the benefits and risks of SBRT for extracranial oligometastatic NSCLC. The MEDLINE, Embase, PubMed, and CENTRAL databases were searched for relevant articles from January 1, 2000 to July 23, 2019. Fully published phase III or phase II trials of any sample size were included. Retrospective series published in manuscript form with at least 50 patients were also included. Four prospective phase II randomized trials (total, 188 participants), 4 prospective non-randomized studies (total, 140 participants), and eleven retrospective studies (total, 1288 participants) were included in this systematic review. A variety of dose fractionation schemes were used. The median overall survival (OS) ranged from 13.5 to 55 months. Progression-free survival (PFS) ranged from 4.4 to 14.7 months. Quality of life outcomes were reported in 2 studies. None of the studies reported symptom control outcomes. There are no fully completed phase III randomized trials that clarify the risks and benefits of SBRT for oligometastatic NSCLC. Higher PFS and OS with SBRT were reported in 4 phase II randomized studies. The results from mature phase III randomized data regarding whether SBRT for oligometastatic NSCLC benefits patients in terms of OS, PFS, quality of life, and symptom control are needed.

[Oligometastases and oligoprogressions: Concepts and natural history]

The oligometastatic paradigm refers to an intermediate biologic state of cancer with restricted metastatic capacity. Its phenotype is characterized by a limited number of metastases and a slow tumor growth. Various clinical and pre-clinical studies associated this state to alterations of the biological mechanisms involved in metastatic diffusion. Eventually, this transitional state leads to a wide metastatic dissemination. However, there is a period during which the patient could benefit from local ablative treatment. Depending on several prognostic factors and the treatment provided, long survival or even healing can sometimes be achieved. The selection of patients eligible for such a curative strategy may be adapted following clinical, radiological or biological markers. Recent improvement of therapeutic and imaging are changing the clinical definition of oligometastatic cancer, which should be adapted to evidence from recent clinical and preclinical data.

Identification of differentially expressed key genes between glioblastoma and low-grade glioma by bioinformatics analysis

Gliomas are a very diverse group of brain tumors that are most commonly primary tumor and difficult to cure in central nervous system. It’s necessary to distinguish low-grade tumors from high-grade tumors by understanding the molecular basis of different grades of glioma, which is an important step in defining new biomarkers and therapeutic strategies. We have chosen the gene expression profile GSE52009 from gene expression omnibus (GEO) database to detect important differential genes. GSE52009 contains 120 samples, including 60 WHO II samples and 24 WHO IV samples that were selected in our analysis. We used the GEO2R tool to pick out differently expressed genes (DEGs) between low-grade glioma and high-grade glioma, and then we used the database for annotation, visualization and integrated discovery to perform gene ontology analysis and Kyoto encyclopedia of gene and genome pathway analysis. Furthermore, we used the Cytoscape search tool for the retrieval of interacting genes with molecular complex detection plug-in applied to achieve the visualization of protein–protein interaction (PPI). We selected 15 hub genes with higher degrees of connectivity, including tissue inhibitors metalloproteinases-1 and serum amyloid A1; additionally, we used GSE53733 containing 70 glioblastoma samples to conduct Gene Set Enrichment Analysis. In conclusion, our bioinformatics analysis showed that DEGs and hub genes may be defined as new biomarkers for diagnosis and for guiding the therapeutic strategies of glioblastoma.

Whole-transcriptome sequencing identified gene expression signatures associated with aggressive clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of kidney cancer, yet molecular biomarkers have not been used for the prognosis of ccRCC to aide clinical decision making. This study aimed to identify genes associated with ccRCC aggressiveness and overall survival (OS). Samples of ccRCC tumor tissue were obtained from 33 patients who underwent nephrectomy. Gene expression was determined using whole-transcriptome sequencing. The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) RNA-seq data was used to test association with OS. 290 genes were differentially expressed between tumors with high and low stage, size, grade, and necrosis (SSIGN) score (≥7 vs. ≤3) with P_ADJ<0.05. Four genes, G6PD, APLP1, GCNT3, and PLPP2, were also over-expressed in advanced stage (III and IV) and high grade (3 and 4) ccRCC and tumor with necrosis (P_ADJ<0.05). Investigation stratifying by stage found that APLP1 and PLPP2 overexpression were significantly associated with poorer OS in the early stage (Quartile 1 vs. Quartile 4, HR = 3.87, 95% CI:1.25-11.97, P = 0.02 and HR = 4.77, 95% CI:1.37-16.57, P = 0.04 respectively). These genes are potential biomarkers of ccRCC aggressiveness and prognosis that direct clinical and surgical management.

Comprehensive assessment gene signatures for clear cell renal cell carcinoma prognosis

There are many prognostic gene signature models in clear cell renal cell carcinoma (ccRCC). However, different results from various methods and samples are hard to contribute to clinical practice. It is necessary to develop a robust gene signature for improving clinical practice in ccRCC.A method was proposed to integrate least absolute shrinkage and selection operator and multiple Cox regression to obtain mRNA and microRNA signature from the cancer genomic atlas database for predicting prognosis of ccRCC. The gene signature model consisted by 5 mRNAs and 1 microRNA was identified. Prognosis index (PI) model was constructed from RNA expression and median value of PI is used to classified patients into high- and low-risk groups.The results showed that high-risk patients showed significantly decrease survival comparison with low-risk groups [hazard ratio (HR) =7.13, 95% confidence interval = 3.71-13.70, P < .001]. As the gene signature was mainly consisted by mRNA, the validation data can use transcriptomic data to verify. For comparison of the performance with previous works, other gene signature models and 4 datasets of ccRCC were retrieved from publications and public database. For estimating PI in each model, 3 indicators including HR, concordance index , and the area under the curve of receiver operating characteristic for 3 years were calculated across 4 independent datasets.The comparison results showed that the integrative model from our study was more robust than other models via comprehensive analysis. These findings provide some genes for further study their functions and mechanisms in ccRCC tumorigenesis and malignance, and may be useful for effective clinical decision making of ccRCC patients.

Prognostic Value of a Long Non-coding RNA Signature in Localized Clear Cell Renal Cell Carcinoma

BACKGROUND

Long non-coding RNAs (lncRNAs) can be used as prognostic biomarkers in many types of cancer.

OBJECTIVE

We sought to establish an lncRNA signature to improve postoperative risk stratification for patients with localized clear cell renal cell carcinoma (ccRCC).

DESIGN, SETTING, AND PARTICIPANTS

Based on the RNA-seq data of 444 stage I-III ccRCC tumours from The Cancer Genome Atlas project, we built a four-lncRNA-based classifier using the least absolute shrinkage and selection operation (LASSO) Cox regression model in 222 randomly selected samples (training set) and validated the classifier in the remaining 222 samples (internal validation set). We confirmed this classifier in an external validation set of 88 patients with stage I-III ccRCC from a Japan cohort and using quantitative reverse transcription polymerase chain reaction (RT-PCR) in another three independent sets that included 1869 patients from China with stage I-III ccRCC.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Univariable and multivariable Cox regression, Harrell's concordance index (c-index), and time-dependent receiver operating characteristic curves were used to evaluate the association of the classifier with overall survival, disease-specific survival, and disease-free survival.

RESULTS AND LIMITATIONS

Using the LASSO Cox regression model, we built a classifier named RCClnc4 based on four lncRNAs: ENSG00000255774, ENSG00000248323, ENSG00000260911, and ENSG00000231666. In the RNA-seq and RT-PCR data sets, the RCClnc4 signature significantly stratified patients into high-risk versus low-risk groups in terms of clinical outcome across and within subpopulations and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 1.34 [95% confidence interval {CI}: 1.03-1.75; p=0.028] to 1.89 [95% CI, 1.55-2.31; p<0.001]) after adjusting for clinical and pathologic factors. The RCClnc4 signature achieved a higher accuracy (mean c-index, 0.72) than clinical staging systems such as TNM (mean c-index, 0.62) and the stage, size, grade, and necrosis (SSIGN) score (mean c-index, 0.64), currently reported prognostic signatures and biomarkers for the estimation of survival. When integrated with clinical characteristics, the composite clinical and lncRNA signature showed improved prognostic accuracy in all data sets (TNM + RCClnc4 mean c-index, 0.75; SSIGN + RCClnc4 score mean c-index, 0.75). The RCClnc4 classifier was able to identify a clinically significant number of both high-risk stage I and low-risk stage II-III patients.

CONCLUSIONS

The RCClnc4 classifier is a promising and potential prognostic tool in predicting the survival of patients with stage I-III ccRCC. Combining the lncRNA classifier with clinical and pathological parameters allows for accurate risk assessment in guiding clinical management.

PATIENT SUMMARY

The RCClnc4 classifier could facilitate patient management and treatment decisions.

Identifying prognostic biomarkers based on aberrant DNA methylation in kidney renal clear cell carcinoma

The outcome of kidney renal clear cell carcinoma (KIRC) differs even among individuals with similar clinical characteristics. DNA methylation is regarded as a regulator of gene expression in cancers, which may be a molecular marker of prognosis. In this study, we aimed to mine novel methylation markers of the prognosis of KIRC. We revealed a total of 2793 genes differentially methylated in their promoter regions (DMGs) and 2979 differentially expressed genes (DEGs) in KIRC tissues compared with normal tissues using The Cancer Genome Atlas datasets. Then, we detected 57 and 34 subpathways enriched among the DMGs and DEGs, respectively, using the R package iSubpathwayMiner. We retained 56 subpathways related to both aberrant methylation and expression based on a hypergeometric test for further analysis. An integrated gene regulatory network was constructed using the regulatory relationships between genes in the subpathways. Using the top 15% of the nodes from the network ranked by degree, survival analysis was performed. We validated four DNA methylation signatures (RAC2, PLCB2, VAV1, and PARVG) as being highly correlated with prognosis in KIRC. These findings suggest that DNA methylation might become a prognostic predictor in KIRC and could supplement histological prognostic prediction.

Clinical and Molecular Characteristics and Burden of Kidney Cancer Among Hispanics and Native Americans: Steps Toward Precision Medicine

Cancer disparities in Native Americans (NAs) and Hispanic Americans (HAs) vary significantly in terms of cancer incidence and mortality rates across geographic regions. This review reports that kidney and renal pelvis cancers are unevenly affecting HAs and NAs compared to European Americans of non-Hispanic origin, and that currently there is significant need for improved data and reporting to be able to advance toward genomic-based precision medicine for the assessment of such cancers in these medically underserved populations. More specifically, in states along the US-Mexico border, HAs and NAs have higher kidney cancer incidence rates as well as a higher prevalence of kidney cancer risk factors, including obesity and chronic kidney disease. They are also more likely to receive suboptimal care compared to European Americans. Furthermore, they are underrepresented in epidemiologic, clinical, and molecular genomic studies of kidney cancer. Therefore, we maintain that progress in precision medicine for kidney cancer care requires an understanding of various factors among HAs and NAs, including the real kidney cancer burden, variations in clinical care, issues related to access to care, and specific clinical and molecular characteristics.

Differential gene expression profiling of matched primary renal cell carcinoma and metastases reveals upregulation of extracellular matrix genes

Background

The majority of renal cell carcinoma (RCC) studies analyze primary tumors, and the corresponding results are extrapolated to metastatic RCC tumors. However, it is unknown if gene expression profiles from primary RCC tumors differs from patient-matched metastatic tumors. Thus, we sought to identify differentially expressed genes between patient-matched primary and metastatic RCC tumors in order to understand the molecular mechanisms underlying the development of RCC metastases.

Patients and methods

We compared gene expression profiles between patient-matched primary and metastatic RCC tumors using a two-stage design. First, we used Affymetrix microarrays on 15 pairs of primary RCC [14 clear cell RCC (ccRCC), 1 papillary] tumors and patient-matched pulmonary metastases. Second, we used a custom NanoString panel to validate seven candidate genes in an independent cohort of 114 ccRCC patients. Differential gene expression was evaluated using a mixed effect linear model; a random effect denoting patient was included to account for the paired data. Third, The Cancer Genome Atlas (TCGA) data were used to evaluate associations with metastasis-free and overall survival in primary ccRCC tumors.

Results

We identified and validated up regulation of seven genes functionally involved in the formation of the extracellular matrix (ECM): DCN, SLIT2, LUM, LAMA2, ADAMTS12, CEACAM6 and LMO3. In primary ccRCC, CEACAM6 and LUM were significantly associated with metastasis-free and overall survival (P < 0.01).

Conclusions

We evaluated gene expression profiles using the largest set to date, to our knowledge, of patient-matched primary and metastatic ccRCC tumors and identified up regulation of ECM genes in metastases. Our study implicates up regulation of ECM genes as a critical molecular event leading to visceral, bone and soft tissue metastases in ccRCC.

Serum amyloid A1 is upregulated in human glioblastoma

Serum amyloid A1 (SAA1) is a sensitive acute phase reactant primarily produced by the liver in response to acute inflammation. We have recently shown that SAA affects proliferation, migration, and invasion of glioblastoma cell lines, which suggest its participation in the malignant process. Consistently, levels of SAA have been used as a non-invasive biomarker for the prognosis of many cancers. In this study, we aimed to investigate SAA serum levels and expression of SAA genes in human astrocytomas tissues. Serum and tissue samples were obtained from patients with astrocytoma grades I to III and glioblastoma (GBM or grade IV). Levels of circulating SAA were significantly higher in the serum of patients with AGII-IV when compared to non-neoplastic samples derived from non-neoplastic patients (NN) (p > 0.0001). Quantitative real time PCR (qRT-PCR) of 148 astrocytomas samples (grades I-IV) showed that SAA1 mRNA was significantly higher in GBM when compared to AGI-III and NN samples (p < 0.0001). Immunohistochemistry analysis revealed cytoplasmic positivity for SAA in GBM. There was no correlation of SAA1 with clinical end-point of overall survival among GBM patients. However, it was found a positive correlation between SAA1 and genes involved in tumor progression, such as: HIF1A (r = 0.50; p < 0.00001), CD163 (r = 0.52; p < 0.00001), CXCR4 (r = 0.42; p < 0.00001) and CXCR7 (r = 0.33; p = 0.002). In conclusions, we show that astrocytoma patients have increased levels of serum SAA and SAA1 is expressed and secreted in GBM, and its co-expression with tumor-related genes supports its involvement in GBM angiogenesis and progression.

Structure and Expression of Different Serum Amyloid A (SAA) Variants and their Concentration-Dependent Functions During Host Insults

Serum amyloid A (SAA) is, like C-reactive protein (CRP), an acute phase protein and can be used as a diagnostic, prognostic or therapy follow-up marker for many diseases. Increases in serum levels of SAA are triggered by physical insults to the host, including infection, trauma, inflammatory reactions and cancer. The order of magnitude of increase in SAA levels varies considerably, from a 10- to 100-fold during limited inflammatory events to a 1000-fold increase during severe bacterial infections and acute exacerbations of chronic inflammatory diseases. This broad response range is reflected by SAA gene duplications resulting in a cluster encoding several SAA variants and by multiple biological functions of SAA. SAA variants are single-domain proteins with simple structures and few post-translational modifications. SAA1 and SAA2 are inducible by inflammatory cytokines, whereas SAA4 is constitutively produced. We review here the regulated expression of SAA in normal and transformed cells and compare its serum levels in various disease states. At low concentrations (10-100 ng/ml), early in an inflammatory response, SAA induces chemokines or matrix degrading enzymes via Toll-like receptors and functions as an activator and chemoattractant through a G protein-coupled receptor. When an infectious or inflammatory stimulus persists, the liver continues to produce more SAA (> 1000 ng/ml) to become an antimicrobial agent by functioning as a direct opsonin of bacteria or by interference with virus infection of host cells. Thus, SAA regulates innate and adaptive immunity and this information may help to design better drugs to treat specific diseases.

Spectra-first feature analysis in clinical proteomics — A case study in renal cancer

In proteomics, useful signal may be unobserved or lost due to the lack of confident peptide-spectral matches. Selection of differential spectra, followed by associative peptide/protein mapping may be a complementary strategy for improving sensitivity and comprehensiveness of analysis (spectra-first paradigm). This approach is complementary to the standard approach where functional analysis is performed only on the finalized protein list assembled from identified peptides from the spectra (protein-first paradigm). Based on a case study of renal cancer, we introduce a simple spectra-binning approach, MZ-bin. We demonstrate that differential spectra feature selection using MZ-bin is class-discriminative and can trace relevant proteins via spectra associative mapping. Moreover, proteins identified in this manner are more biologically coherent than those selected directly from the finalized protein list. Analysis of constituent peptides per protein reveals high expression inconsistency, suggesting that the measured protein expressions are in fact, poor approximations of true protein levels. Moreover, analysis at the level of constituent peptides may provide higher resolution insight into the underlying biology: Via MZ-bin, we identified for the first time differential splice forms for the known renal cancer marker MAPT. We conclude that the spectra-first analysis paradigm is a complementary strategy to the traditional protein-first paradigm and can provide deeper level insight.

Prognostic role of survivin in renal cell carcinoma: A system review and meta-analysis

BACKGROUND

Survivin plays an important role in cancer pathogenesis, which has been suggested to be related to poor prognosis in renal cell carcinoma (RCC). We performed a meta-analysis to quantify the prognostic value of survivin in renal cell carcinoma (RCC) patients.

PATIENTS AND METHODS

Relevant articles exploring the relationship between survivin expression and survival of RCC patients published up to 10 November 2015 were retrieved in PubMed, Web of science and Cochrane databases. The summary hazard ratio (HR) and 95% confidence interval (CI) were calculated by a random-effects model.

RESULTS

A total of 1583 patients from 10 studies were included. The high expression of survivin was associated with poor Overall Survival (OS) in two studies (Pooled HRs=2.77, 95%CI: 1.46-5.26, I(2)=0.0%, P=0.329). Further, meta-analysis of seven studies showed that elevated survivin expression predicted shorter cancer-specific survival (CSS) of RCC (HR=2.85, 95% CI: 2.25-3.61, I(2)=17.6%, P=0.295). Additionally, subgroup analysis showed that survivin over-expression was associated significantly with worse CSS regardless of different source of survivin, sample size, follow-up duration, race and detection method.

CONCLUSIONS

A higher survivin expression seems to represent an unfavorable and robust prognostic factor for clinical outcomes in RCC patients.

Prognostic and Clinicopathological Significance of Survivin Expression in Renal Cell Carcinoma: A Systematic Review and Meta-Analysis

Previous studies have elevated the prognostic value of survivin in renal cell carcinoma (RCC). To increase statistical power and improve translation, we systematically searched PubMed, Web of Science, and Embase to identify relevant studies until December 2015 and conducted a standard meta-analysis. Based on the inclusion and exclusion criteria, a total of 12 studies, including 2051 patients, were eligible for further analysis. Results showed that high survivin expression in RCC was associated with poor OS (HR = 2.84, 95% CI 1.68-4.79), CSS (HR = 2.36, 95% CI 1.41-3.95), and PFS (HR = 2.20, 95% CI 1.58-3.08). Survivin expression was also correlated with TNM stage (RR = 2.75, 95% CI 2.21-3.44), pathological T stage (RR = 2.19, 95% CI 1.75-2.75), lymph node metastasis (RR = 2.28, 95% CI 1.61-3.25), distant metastasis (RR = 1.56, 95% CI 1.16-2.08), Fuhrman grade (RR = 2.81, 95% CI 2.29-3.45), tumor size (RR = 1.49, 95% CI 1.24-1.78). Our study suggested that survivin was a prognostic marker in RCC. High survivin expression was correlated with poor prognosis and more advanced clinicopathological features, and it could serve as a biomarker for disease management.

Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma

BACKGROUND

Intratumoral heterogeneity hampers the success of marker-based anticancer treatment because the targeted therapy may eliminate a specific subpopulation of tumor cells while leaving others unharmed. Accordingly, a rational strategy minimizing survival of the drug-resistant subpopulation is essential to achieve long-term therapeutic efficacy.

RESULTS

Using single-cell RNA sequencing (RNA-seq), we examine the intratumoral heterogeneity of a pair of primary renal cell carcinoma and its lung metastasis. Activation of drug target pathways demonstrates considerable variability between the primary and metastatic sites, as well as among individual cancer cells within each site. Based on the prediction of multiple drug target pathway activation, we derive a combinatorial regimen co-targeting two mutually exclusive pathways for the metastatic cancer cells. This combinatorial strategy shows significant increase in the treatment efficacy over monotherapy in the experimental validation using patient-derived xenograft platforms in vitro and in vivo.

CONCLUSIONS

Our findings demonstrate the investigational application of single-cell RNA-seq in the design of an anticancer regimen. The approach may overcome intratumoral heterogeneity which hampers the success of precision medicine.

A CpG-methylation-based assay to predict survival in clear cell renal cell carcinoma

Clear cell renal cell carcinomas (ccRCCs) display divergent clinical behaviours. Molecular markers might improve risk stratification of ccRCC. Here we use, based on genome-wide CpG methylation profiling, a LASSO model to develop a five-CpG-based assay for ccRCC prognosis that can be used with formalin-fixed paraffin-embedded specimens. The five-CpG-based classifier was validated in three independent sets from China, United States and the Cancer Genome Atlas data set. The classifier predicts the overall survival of ccRCC patients (hazard ratio=2.96−4.82; P=3.9 × 10⁻⁶−2.2 × 10⁻⁹), independent of standard clinical prognostic factors. The five-CpG-based classifier successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome in respective clinical stages and individual ‘stage, size, grade and necrosis' scores. Moreover, methylation at the five CpGs correlates with expression of five genes: PITX1, FOXE3, TWF2, EHBP1L1 and RIN1. Our five-CpG-based classifier is a practical and reliable prognostic tool for ccRCC that can add prognostic value to the staging system.

Using molecular markers is a useful way to predict the prognosis of cancer patients. Here, Wei et al. describe a five gene methylation signature that can predict the prognosis of renal clear cell cancer and validate its use in multiple patient cohorts.

A Molecular Model for Predicting Overall Survival in Patients with Metastatic Clear Cell Renal Carcinoma: Results from CALGB 90206 (Alliance)

BACKGROUND

Prognosis associated with metastatic renal cell carcinoma (mRCC) can vary widely.

METHODS

This study used pretreatment nephrectomy specimens from a randomized phase III trial. Expression levels of candidate genes were determined from archival tumors using the OpenArray® platform for TaqMan® RT-qPCR. The dataset was randomly divided at 2:1 ratio into training (n = 221) and testing (n = 103) sets to develop a multigene prognostic signature.

FINDINGS

Gene expressions were measured in 324 patients. In the training set, multiple models testing 424 candidate genes identified a prognostic signature containing 8 genes plus MSKCC clinical risk factors. In the testing set, the time dependent (td) AUC for a prognostic model containing the 8 genes with and without MSKCC risk factors were 0.72 and 0.69, respectively. The tdAUC for the clinical risk factors alone was 0.61. Additional primary mRCCs from patients with mRCC (n = 12) were sampled in multiple sites and standard deviations of gene expressions within a tumor were used as a measure of heterogeneity. All 8 genes in the final prognostic model met our criteria for minimal heterogeneity.

CONCLUSIONS

A molecular prognostic signature based on 8 genes was developed and is ready for external validation in this patient population and other related settings such as nonmetastatic RCC.

A 16-gene assay to predict recurrence after surgery in localised renal cell carcinoma: development and validation studies

BACKGROUND

The likelihood of tumour recurrence after nephrectomy in localised clear cell renal cell carcinoma is well characterised by clinical and pathological parameters. However, these assessments can be improved and personalised by the addition of molecular characteristics of each patient's tumour. We aimed to develop and validate a prognostic multigene signature to improve prediction of recurrence risk in clear cell renal cell carcinoma.

METHODS

In the development stage, we investigated the association between expression of 732 genes, measured by reverse-transcription PCR, and clinical outcome in 942 patients with stage I-III clear cell renal cell carcinoma who had undergone a nephrectomy at the Cleveland Clinic (OH, USA). 516 genes were associated with recurrence-free interval. 11 of these genes were selected by further statistical analyses, and were combined with five reference genes (ie, 16 genes in total), from which a recurrence score algorithm was developed. The recurrence score was then validated in an independent cohort of 626 patients from France with stage I-III clear cell renal cell carcinoma who had also undergone nephrectomy. The association between the recurrence score and the risk of recurrence and cancer-specific survival in the first 5 years after surgery was assessed using Cox proportional hazard regression, stratified by tumour stage (stage I vs stage II vs III).

FINDINGS

In our primary univariate analysis, the continuous recurrence score (median 37, IQR 31-45) was significantly associated with recurrence-free interval (hazard ratio 3·91 [95% CI 2·63-5·79] for a 25-unit increase in score, p<0·0001). In multivariable analyses, the recurrence score was significantly associated with the risk of tumour recurrence (hazard ratio per 25-unit increase in the score 3·37 [95% CI 2·23-5·08], p<0·0001) after stratification by stage and adjustment for tumour size, grade, or Leibovich score. The recurrence score was able to identify a clinically significant number of both high-risk stage I and low-risk stage II-III patients. A heterogeneity study on separate samples showed little to no intratumoural variability among the 16 genes.

INTERPRETATION

Our findings validate the recurrence score as a predictor of clinical outcome in patients with stage I-III clear cell renal cell carcinoma, providing a more accurate and individualised risk assessment beyond existing clinical and pathological parameters.

FUNDING

Genomic Health Inc and Pfizer Inc.

The biology and treatment of oligometastatic cancer

Clinical reports of limited and treatable cancer metastases, a disease state that exists in a transitional zone between localized and widespread systemic disease, were noted on occasion historically and are now termed oligometastasis. The ramification of a diagnosis of oligometastasis is a change in treatment paradigm, i.e. if the primary cancer site (if still present) is controlled, or resected, and the metastatic sites are ablated (surgically or with radiation), a prolonged disease-free interval, and perhaps even cure, may be achieved. Contemporary molecular diagnostics are edging closer to being able to determine where an individual metastatic deposit is within the continuum of malignancy. Preclinical models are on the outset of laying the groundwork for understanding the oligometastatic state. Meanwhile, in the clinic, patients are increasingly being designated as having oligometastatic disease and being treated owing to improved diagnostic imaging, novel treatment options with the potential to provide either direct or bridging therapy, and progressively broad definitions of oligometastasis.

Predictive molecular biomarkers to guide clinical decision making in kidney cancer: current progress and future challenges

Although the past decade has seen a surfeit of new targeted therapies for renal cell carcinoma (RCC), no predictive molecular biomarker is currently used in routine clinical practice to guide personalized therapy as a companion diagnostic. Many putative biomarkers have been suggested, but none have undergone rigorous validation. There have been considerable advances in the biological understanding of RCC in recent years, with the development of accompanying molecular diagnostics that with additional validation, may be helpful for routine clinical decision making. In this review, we summarize the current understanding of predictive biomarkers in RCC management and also highlight upcoming developments of interest in biomarker research for personalizing RCC diagnostics and therapeutics.

Extracranial oligometastatic renal cell carcinoma: current management and future directions

The term 'oligometastases' was formulated to describe an intermediate state between widespread metastases and locally confined disease. The standard of care in metastatic renal cell carcinoma is systemic therapy; however, in patients with solitary or limited metastases, aggressive local therapies may potentially prolong survival. The literature suggests a survival benefit with surgical metastasectomy, with a reported 5-year survival as high as 45% in those who achieve complete resection. More recently, an expanding body of evidence supports the role of stereotactic ablative body radiation therapy for the treatment of oligometastatic renal cell carcinoma and early results demonstrate comparable local control rates with surgery. There is also increasing interest in the abscopal and immunologic effects of localized radiation. With the proliferation of newer targeted agents and immunomodulatory agents, current work is addressing the optimization of patient selection and avenues towards sequencing and combining the various treatment options.

Systematic evaluation of the prognostic impact and intratumour heterogeneity of clear cell renal cell carcinoma biomarkers

BACKGROUND

Candidate biomarkers have been identified for clear cell renal cell carcinoma (ccRCC) patients, but most have not been validated.

OBJECTIVE

To validate published ccRCC prognostic biomarkers in an independent patient cohort and to assess intratumour heterogeneity (ITH) of the most promising markers to guide biomarker optimisation.

DESIGN, SETTING, AND PARTICIPANTS

Cancer-specific survival (CSS) for each of 28 identified genetic or transcriptomic biomarkers was assessed in 350 ccRCC patients. ITH was interrogated in a multiregion biopsy data set of 10 ccRCCs.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Biomarker association with CSS was analysed by univariate and multivariate analyses.

RESULTS AND LIMITATIONS

A total of 17 of 28 biomarkers (TP53 mutations; amplifications of chromosomes 8q, 12, 20q11.21q13.32, and 20 and deletions of 4p, 9p, 9p21.3p24.1, and 22q; low EDNRB and TSPAN7 expression and six gene expression signatures) were validated as predictors of poor CSS in univariate analysis. Tumour stage and the ccB expression signature were the only independent predictors in multivariate analysis. ITH of the ccB signature was identified in 8 of 10 tumours. Several genetic alterations that were significant in univariate analysis were enriched, and chromosomal instability indices were increased in samples expressing the ccB signature. The study may be underpowered to validate low-prevalence biomarkers.

CONCLUSIONS

The ccB signature was the only independent prognostic biomarker. Enrichment of multiple poor prognosis genetic alterations in ccB samples indicated that several events may be required to establish this aggressive phenotype, catalysed in some tumours by chromosomal instability. Multiregion assessment may improve the precision of this biomarker.

PATIENT SUMMARY

We evaluated the ability of published biomarkers to predict the survival of patients with clear cell kidney cancer in an independent patient cohort. Only one molecular test adds prognostic information to routine clinical assessments. This marker showed good and poor prognosis results within most individual cancers. Future biomarkers need to consider variation within tumours to improve accuracy.

Genomic Analysis as the First Step toward Personalized Treatment in Renal Cell Carcinoma

Drug resistance mechanisms in renal cell carcinoma (RCC) still remain elusive. Although most patients initially respond to targeted therapy, acquired resistance can still develop eventually. Most of the patients suffer from intrinsic (genetic) resistance as well, suggesting that there is substantial need to broaden our knowledge in the field of RCC genetics. As molecular abnormalities occur for various reasons, ranging from single nucleotide polymorphisms to large chromosomal defects, conducting whole-genome association studies using high-throughput techniques seems inevitable. In principle, data obtained via genome-wide research should be continued and performed on a large scale for the purposes of drug development and identification of biological pathways underlying cancerogenesis. Genetic alterations are mostly unique for each histological RCC subtype. According to recently published data, RCC is a highly heterogeneous tumor. In this paper, the authors discuss the following: (1) current state-of-the-art knowledge on the potential biomarkers of RCC subtypes; (2) significant obstacles encountered in the translational research on RCC; and (3) recent molecular findings that may have a crucial impact on future therapeutic approaches.

Loss of CDC14B expression in clear cell renal cell carcinoma: meta-analysis of microarray data sets

OBJECTIVES

To discover significant differentially expressed genes (DEGs) in clear cell renal cell carcinoma (ccRCC) that might be unidentified by single microarray analysis.

METHODS

The effect sizes of five ccRCC microarray data sets were combined using a random-effects model. The most downregulated gene was validated in paired 80 ccRCC tissues by immunohistochemistry.

RESULTS

CDC14B was the most downregulated gene among 1,761 DEGs. CDC14B was strongly expressed in the apical proximal tubules in the nonneoplastic tissues, while it was completely absent in 10 (12.5%) of 80 or downregulated in 70 (87.5%) of 80 ccRCC cases. The complete loss of CDC14B correlated with high T stage (P = .038), advanced TNM stage (P = .027), tumor recurrence (P = .038), and shorter recurrence-free survival (P = .046) compared with the partial loss of CDC14B.

CONCLUSIONS

Microarray meta-analysis is a useful tool for pathologists. CDC14B expression is downregulated in ccRCC, suggesting its role in renal carcinogenesis.

The current status of tailor-made medicine with molecular biomarkers for patients with clear cell renal cell carcinoma

Appropriate use of multiple reliable molecular biomarkers in the right context will play a role in tailormade medicine of clear cell renal cell carcinoma (RCC) patients in the future. A total of 11,056 patients from 53 studies were included in this review. The article numbers of the each evidence levels, using the grading system defined by the Oxford Centre for Evidence-based Medicine, in 1b, 2a, 2b, and 3b were 5 (9%), 18 (34%), 29 (55%), and 1 (2%), respectively. The main goal of using biomarkers is to refine predictions of tumor progression, pharmacotherapy responsiveness, and cancer-specific and/or overall survival. Currently, carbonic anhydrase (CA9) and vascular endothelial growth factor (VEGF) in peripheral blood and p53 in tumor tissues are measured to predict metastasis, while VEGF-related proteins in peripheral blood are used to assess pharmacotherapy responsiveness with sunitinib. Furthermore, interleukin 8, osteopontin, hepatocyte growth factor, and tissue inhibitors of metalloproteinases-1 in peripheral blood enable assessment of responsiveness to pazopanib treatment. Other reliable molecular biomarkers include von Hippel–Lindau gene alteration, hypoxia-inducible factor-1a, CA9, and survivin in tumor tissues and VEGF in peripheral blood for predicting cancer-specific survival. In the future, studies should undergo external validation for developing tailored management of clear cell RCC with molecular biomarkers, since individual institutional studies lack the generalization and consistency required to maintain accuracy among different patient series.

Identification of predictive biomarkers for early diagnosis of larynx carcinoma based on microRNA expression data

The abnormal expression of microRNAs (miRNAs) plays a key role in tumorigenesis. In order to identify potential miRNA biomarkers for early diagnosis of larynx carcinoma, we employed a miRNA microarray technique and applied bioinformatic algorithms to characterize miRNA classifiers in early larynx carcinoma and normal esophageal mucosa tissue samples from 69 patients who were selected retrospectively for this study. We identified 47 miRNAs that were significantly differentially expressed in primary larynx tumor tissues compared to normal tissues using a SAM algorithm. Of these, 30 were up-regulated and 17 down-regulated in early larynx cancer, including hsa-miR-657, which was overexpressed, and hsa-miR-1287, which was underexpressed. These two candidate miRNA biomarkers were combined as a single classifier to recognize the biological characteristics in early larynx carcinoma. Real-time quantitative reverse-transcription PCR validated the microarray results in both trial and test samples. The hsa-miR-657-hsa-miR-1287 classifier displayed high sensitivity and specificity for discriminating between early larynx carcinoma and normal mucosa tissues, suggesting they may be suitable as potential predictive biomarkers for the early diagnosis of larynx carcinoma.

Immunohistochemical analysis of prognostic protein markers for primary localized clear cell renal cell carcinoma

Recurrence is not reliably predictable in localized clear cell renal cell carcinoma. Proteinmarkers could improve predictive accuracy. Tissue-microarrays from 132 patients with primary localized ccRCC were immunohistochemically analyzed for VHL, Ki67, p53, p21, survivin, and, for microvessel-density, UEA-1. Nuclear stainings of Ki67, p21, and survivin were significantly associated with disease-specific survival and increased predictive ability from 74% to 76%, 77%, and 78%, respectively in a multivariate model including T-stage and Fuhrman grade. A score-variable, combining Ki67-, p21-, and nS-staining identified a subset of patients with high risk of disease recurrence and increased predictive ability in the multivariate model to 84%.

Expression profiling of metastatic renal cell carcinoma using gene set enrichment analysis

OBJECTIVE

To identify complex changes in cell biology occurring during metastatic progression of renal cell carcinoma using a novel gene expression analysis algorithm.

METHODS

Whole genome expression profiling was carried out on 32 snap-frozen samples of clear-cell renal cell carcinoma metastases, 29 primary tumors (14 low grade, 15 high grade) and 14 samples of normal kidney tissue using oligonucleotide microarrays. These data were analyzed with the gene set enrichment analysis method, which is able to detect even small, but significant, expression changes in functionally connected genes that cannot be shown by gene-by-gene comparisons.

RESULTS

There were 95 gene sets (pathways) with significant upregulation in metastases compared with normal kidney tissue (P < 0.01), and 77 gene sets with significant downregulation, respectively. Low-grade and high-grade tumors showed deregulation of various pathways that have previously not been described in renal cell carcinoma. There were significant changes of genes involved in cell cycle control, apoptosis, cell motility, metabolism, cell adhesion and cytoskeleton. Some promising new potential therapy targets were identified in renal cell carcinoma metastases; for example, aurora-kinase A and flap structure-specific endonuclease 1.

CONCLUSION

Expression profiling of metastatic renal cell carcinoma using the gene set enrichment analysis pathway analysis method provides new and detailed insights in alterations occurring in renal cell carcinoma during malignant transformation and progression. These data can help to develop new and specifically targeted renal cell carcinoma therapies.

Clinical applications of recent molecular advances in urologic malignancies: no longer chasing a "mirage"?

As our understanding of the molecular events leading to the development and progression of genitourologic malignancies, new markers of detection, prognostication, and therapy prediction can be exploited in the management of these prevalent tumors. The current review discusses the recent advances in prostate, bladder, renal, and testicular neoplasms that are pertinent to the anatomic pathologist.

A genetic variant in pre-miR-27a is associated with a reduced renal cell cancer risk in a Chinese population

BACKGROUND

MicroRNAs (miRNAs) are a class of small non-coding RNAs to regulate cell differentiation, proliferation, development, and apoptosis. The single nucleotide polymorphism (SNP) rs895819 is located at the terminal loop of pre-miR-27a. Here, we aimed to investigate whether SNP rs895819 was associated with the development of renal cell cancer (RCC) in a Chinese population.

METHODS

In this case-control study, we recruited 594 RCC patients and 600 cancer-free controls with frequency matched by age and sex. We genotyped this polymorphism using the TaqMan assay and assessed the effect of this polymorphism on RCC survival. Logistic regression model was used to assess the genetic effects on the development of RCC and interactions between rs895819 polymorphism and risk factors.

RESULTS

Compared with AA homozygote, individuals carrying AG/GG genotypes had a statistically significant reduced susceptibility to RCC (adjusted OR = 0.71, 95% CI = 0.56-0.90). Furthermore, AG/GG genotypes were associated with reduced RCC susceptibility in localized clinical stage (adjusted OR = 0.71, 95% CI = 0.55-0.91), and similar effects were observed in well differentiated and poorly differentiated RCC (adjusted OR = 0.71, 95% CI = 0.55-0.93 for well differentiated, adjusted OR = 0.51, 95% CI = 0.28-0.93 for poorly differentiated). We also observed that rs895819 had multiplicative interactions with age and hypertension. However, the polymorphism did not influence the survival of RCC.

CONCLUSION

Our results suggest that the pre-miR-27a rs895819 polymorphism can predict RCC risk in a Chinese population. Larger population-based prospective studies should be used to validate our findings.