Predicting survival in patients with metastatic kidney cancer by gene-expression profiling in the primary tumor

EMLI-ICC: an ensemble machine learning-based integration algorithm for metastasis prediction and risk stratification in intrahepatic cholangiocarcinoma

Robust strategies to identify patients at high risk for tumor metastasis, such as those frequently observed in intrahepatic cholangiocarcinoma (ICC), remain limited. While gene/protein expression profiling holds great potential as an approach to cancer diagnosis and prognosis, previously developed protocols using multiple diagnostic signatures for expression-based metastasis prediction have not been widely applied successfully because batch effects and different data types greatly decreased the predictive performance of gene/protein expression profile-based signatures in interlaboratory and data type dependent validation. To address this problem and assist in more precise diagnosis, we performed a genome-wide integrative proteome and transcriptome analysis and developed an ensemble machine learning-based integration algorithm for metastasis prediction (EMLI-Metastasis) and risk stratification (EMLI-Prognosis) in ICC. Based on massive proteome (216) and transcriptome (244) data sets, 132 feature (biomarker) genes were selected and used to train the EMLI-Metastasis algorithm. To accurately detect the metastasis of ICC patients, we developed a weighted ensemble machine learning method based on k-Top Scoring Pairs (k-TSP) method. This approach generates a metastasis classifier for each bootstrap aggregating training data set. Ten binary expression rank-based classifiers were generated for detection of metastasis separately. To further improve the accuracy of the method, the 10 binary metastasis classifiers were combined by weighted voting based on the score from the prediction results of each classifier. The prediction accuracy of the EMLI-Metastasis algorithm achieved 97.1% and 85.0% in proteome and transcriptome datasets, respectively. Among the 132 feature genes, 21 gene-pair signatures were developed to establish a metastasis-related prognosis risk-stratification model in ICC (EMLI-Prognosis). Based on EMLI-Prognosis algorithm, patients in the high-risk group had significantly dismal overall survival relative to the low-risk group in the clinical cohort (P-value < 0.05). Taken together, the EMLI-ICC algorithm provides a powerful and robust means for accurate metastasis prediction and risk stratification across proteome and transcriptome data types that is superior to currently used clinicopathological features in patients with ICC. Our developed algorithm could have profound implications not just in improved clinical care in cancer metastasis risk prediction, but also more broadly in machine-learning-based multi-cohort diagnosis method development. To make the EMLI-ICC algorithm easily accessible for clinical application, we established a web-based server for metastasis risk prediction (http://ibi.zju.edu.cn/EMLI/).

Comprehensive Analysis of the Expression and Prognosis Value of Chromobox Family Members in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) accounts for 80% of all renal cancers and has a poor prognosis. Chromobox (CBX) family protein expression has been reported in a variety of human malignancies, but the roles of CBXs in ccRCC remain unclear. In this study, by using ONCOMINE, UALCAN, GEPIA, Kaplan-Meier Plotter, cBioPortal, and TIMER, we found the transcriptional levels of CBX3 and CBX4 in ccRCC tissues were significantly higher than those in normal kidney tissues, whereas the transcriptional levels of CBX1, CBX5, CBX6, and CBX7 were significantly reduced in ccRCC tissues. The promoters of CBX2, CBX3, CBX4, CBX5, CBX6, CBX7, and CBX8 were hypermethylated, whereas the CBX1 promoter was hypomethylated in ccRCC. The expression of CBX1, CBX3, CBX4, CBX5, CBX6, and CBX7 was significantly associated with clinicopathological parameters in ccRCC patients. ccRCC patients with high expression levels of CBX3, CBX4, and CBX8 and low expression levels of CBX1, CBX5, CBX6, and CBX7 showed a strong association with poor overall survival. Genetic alterations in CBXs were correlated with poor overall survival and disease-free survival in patients with ccRCC. Moreover, we found significant associations between the expression of CBXs and infiltration of immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells). Our results provide novel insights into the development of CBX-based biomarkers and therapeutic targets for ccRCC.

Genetic variants in MAPK10 modify renal cell carcinoma susceptibility and clinical outcomes

AIMS

The mitogen-activated protein kinase (MAPK) cascades integrate various upstream signals to regulate many cellular functions, including proliferation, differentiation, and survival. Dysregulation of these pathways has been implicated in the occurrence and progression of a variety of cancers.

MAIN METHODS

This study aimed to assess the association of 192 single nucleotide polymorphisms in 22 MAPK cascade genes with renal cell carcinoma (RCC) risk and survival in 312 patients and 318 controls.

KEY FINDINGS

After multiple testing correction and multivariate analysis, the minor T allele of MAPK10 rs12648265 remained associated with a lower risk of RCC (adjusted odds ratio 0.64, 95% confidence interval 0.50-0.82, P = 0.000426) and metastasis (adjusted hazard ratio 0.50, 95% confidence interval 0.30-0.82, P = 0.006). Presence of the rs12648265 T allele demonstrated a trend towards being associated with increased MAPK10 expression, and meta-analysis of four RCC datasets indicated that high MAPK10 expression is associated with a favourable prognosis. Furthermore, activation of MAPK10 by the potent agonist anisomycin inhibited RCC cell growth in vitro, suggesting an involvement of MAPK10 in RCC progression.

SIGNIFICANCE

In conclusion, MAPK10 may be a meaningful biomarker and a potential therapeutic target in RCC.

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

Immune evasion in renal cell carcinoma: biology, clinical translation, future directions

Targeted therapies and immune checkpoint inhibitors have advanced the treatment landscape of Renal Cell Carcinoma (RCC) over the last decade. While checkpoint inhibitors have demonstrated survival benefit and are currently approved in the front-line and second-line settings, primary and secondary resistance is common. A comprehensive understanding of the mechanisms of immune evasion in RCC is therefore critical to the development of effective combination treatment strategies. This article reviews the current understanding of the different, yet coordinated, mechanisms adopted by RCC cells to evade immune killing; summarizes various aspects of clinical translation thus far, including the currently registered RCC clinical trials exploring agents in combination with checkpoint inhibitors; and provides perspectives on the current landscape and future directions for the field.

Developing and testing high‐efficacy patient subgroups within a clinical trial using risk scores

There is the potential for high-dimensional information about patients collected in clinical trials (such as genomic, imaging, and data from wearable technologies) to be informative for the efficacy of a new treatment in situations where only a subset of patients benefits from the treatment. The adaptive signature design (ASD) method has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using genetic data. The method requires selection of three tuning parameters which may be highly computationally expensive. We propose a variation to the ASD method, the cross-validated risk scores (CVRS) design method, that does not require selection of any tuning parameters. The method is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure.We assess the properties of CVRS against the originally proposed cross-validated ASD using simulation data and a real psychiatry trial. CVRS, as assessed for various sample sizes and response rates, has a substantial reduction in the computational time required. In many simulation scenarios, there is a substantial improvement in the ability to correctly identify the sensitive group and the power of the design to detect a treatment effect in the sensitive group.We illustrate the application of the CVRS method on the psychiatry trial.

compound.Cox: Univariate feature selection and compound covariate for predicting survival

BACKGROUND AND OBJECTIVE

Univariate feature selection is one of the simplest and most commonly used techniques to develop a multigene predictor for survival. Presently, there is no software tailored to perform univariate feature selection and predictor construction.

METHODS

We develop the compound.Cox R package that implements univariate significance tests (via the Wald tests or score tests) for feature selection. We provide a cross-validation algorithm to measure predictive capability of selected genes and a permutation algorithm to assess the false discovery rate. We also provide three algorithms for constructing a multigene predictor (compound covariate, compound shrinkage, and copula-based methods), which are tailored to the subset of genes obtained from univariate feature selection. We demonstrate our package using survival data on the lung cancer patients. We examine the predictive capability of the developed algorithms by the lung cancer data and simulated data.

RESULTS

The developed R package, compound.Cox, is available on the CRAN repository. The statistical tools in compound.Cox allow researchers to determine an optimal significance level of the tests, thus providing researchers an optimal subset of genes for prediction. The package also allows researchers to compute the false discovery rate and various prediction algorithms.

Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening

Background

In 2016, it is estimated that there will be 62,700 new cases of kidney cancer in the United States, and 14,240 patients will die from the disease. Because the incidence of kidney renal clear cell carcinoma (KIRC), the most common type of kidney cancer, is expected to continue to increase in the US, there is an urgent need to find effective diagnostic biomarkers for KIRC that could help earlier detection of and customized treatment strategies for the disease. Accordingly, in this study we systematically investigated KIRC’s prognostic biomarkers for survival using the reverse phase protein array (RPPA) data and the high throughput sequencing data from The Cancer Genome Atlas (TCGA).

Results

With comprehensive data available in TCGA, we systematically screened protein expression based survival biomarkers in 10 major cancer types, among which KIRC presented many protein prognostic biomarkers of survival time. This is in agreement with a previous report that expression level changes (mRNAs, microRNA and protein) may have a better performance for prognosis of KIRC. In this study, we also identified 52 prognostic genes for KIRC, many of which are involved in cell-cycle and cancer signaling, as well as 15 tumor-stage-specific prognostic biomarkers. Notably, we found fewer prognostic biomarkers for early-stage than for late-stage KIRC. Four biomarkers (the RPPA protein IDs: FASN, ACC1, Cyclin_B1 and Rad51) were found to be prognostic for survival based on both protein and mRNA expression data.

Conclusions

Through pan-cancer screening, we found that many protein biomarkers were prognostic for patients’ survival in KIRC. Stage-specific survival biomarkers in KIRC were also identified. Our study indicated that these protein biomarkers might have potential clinical value in terms of predicting survival in KIRC patients and developing individualized treatment strategies. Importantly, we found many biomarkers in KIRC at both the mRNA expression level and the protein expression level. These biomarkers shared a significant overlap, indicating that they were technically replicable.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4026-6) contains supplementary material, which is available to authorized users.

VHL promotes immune response against renal cell carcinoma via NF-κB-dependent regulation of VCAM-1

Labrousse-Arias et al. show that VHL expression leads to increased VCAM-1 levels in renal cell carcinoma through an NF-κB–dependent mechanism that seems to contribute to the antitumoral immune response. This study also suggests that VCAM-1 levels might serve as a marker of ccRCC progression in human patients.

Vascular cell adhesion molecule 1 (VCAM-1) is an adhesion molecule assigned to the activated endothelium mediating immune cells adhesion and extravasation. However, its expression in renal carcinomas inversely correlates with tumor malignancy. Our experiments in clear cell renal cell carcinoma (ccRCC) cell lines demonstrated that von Hippel Lindau (VHL) loss, hypoxia, or PHD (for prolyl hydroxylase domain–containing proteins) inactivation decreased VCAM-1 levels through a transcriptional mechanism that was independent of the hypoxia-inducible factor and dependent on the nuclear factor κB signaling pathway. Conversely, VHL expression leads to high VCAM-1 levels in ccRCC, which in turn leads to better outcomes, possibly by favoring antitumor immunity through VCAM-1 interaction with the α4β1 integrin expressed in immune cells. Remarkably, in ccRCC human samples with VHL nonmissense mutations, we observed a negative correlation between VCAM-1 levels and ccRCC stage, microvascular invasion, and symptom presentation, pointing out the clinical value of VCAM-1 levels as a marker of ccRCC progression.

Contrast-Enhanced Ultrasonography with Quantitative Analysis allows Differentiation of Renal Tumor Histotypes

Totally 85 patients with 93 renal lesions who underwent contrast-enhanced ultrasound (CEUS) were retrospectively studied with quantitative analysis to evaluate its value in the differential diagnosis of renal tumor histotypes. CEUS characteristics were analysed including the enhancement patterns, peak intensity, homogeneity of enhancement, and pseudocapsule. Quantitative parameters of peak intensity (P) and time to peak (TP) were measured with QontraXt software, and the index “relative enhancement percentage” ΔP% and “difference in TP between tumor and cortex” ΔTP were used to quantify the CEUS features of renal tumors. There are significant difference in CEUS features between the 46 clear cell renal cell carcinoma (CCRCC) and other types of renal tumors, including 17 low malignant lesions, 11 urothelial carcinoma of the renal pelvis, and 19 renal angiomyolipoma. The differences lie in the peak intensity, the homogeneity, the time of wash-in, peak, clearance and presence of pseudocapsule. The ΔTP and ΔP% of the CCRCC is significantly different from other tumors. With “fast to peak + high peak intensity” as the main criterion, assisted with “heterogeneous enhancement” and “fast wash-in” as the secondary criteria, the diagnostic accuracy of CCRCC is 91.4%, demonstrating quantitative CEUS imaging is highly valuable in differentiating CCRCC from other tumors.

Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice

International initiatives such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) are collecting multiple datasets at different genome-scales with the aim of identifying novel cancer biomarkers and predicting survival of patients. To analyze such data, several statistical methods have been applied, among them Cox regression models. Although these models provide a good statistical framework to analyze omic data, there is still a lack of studies that illustrate advantages and drawbacks in integrating biological information and selecting groups of biomarkers. In fact, classical Cox regression algorithms focus on the selection of a single biomarker, without taking into account the strong correlation between genes. Even though network-based Cox regression algorithms overcome such drawbacks, such network-based approaches are less widely used within the life science community. In this article, we aim to provide a clear methodological framework on the use of such approaches in order to turn cancer research results into clinical applications. Therefore, we first discuss the rationale and the practical usage of three recently proposed network-based Cox regression algorithms (i.e., Net-Cox, AdaLnet, and fastcox). Then, we show how to combine existing biological knowledge and available data with such algorithms to identify networks of cancer biomarkers and to estimate survival of patients. Finally, we describe in detail a new permutation-based approach to better validate the significance of the selection in terms of cancer gene signatures and pathway/networks identification. We illustrate the proposed methodology by means of both simulations and real case studies. Overall, the aim of our work is two-fold. Firstly, to show how network-based Cox regression models can be used to integrate biological knowledge (e.g., multi-omics data) for the analysis of survival data. Secondly, to provide a clear methodological and computational approach for investigating cancers regulatory networks.

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.

Common reduction of the Raf kinase inhibitory protein in clear cell renal cell carcinoma

Despite the recent progress in our understanding of clear cell renal cell carcinomas (ccRCCs), the etiology of ccRCC remains unclear. We reported here a prevailing reduction of the raf kinase inhibitory protein (RKIP) in ccRCC. In our examination of more than 600 ccRCC patients by western blot and immunohistochemistry, RKIP was significantly reduced in 80% of tumors. Inhibition of RKIP transcription in ccRCC occurs to greater levels than VHL transcription based on the quantification analysis of their transcripts in six large datasets of DNA microarray available in Oncomine™ with the median rank of suppression being 582 and 2343 for RKIP and VHL, respectively. Collectively, the magnitude of RKIP reduction and the levels of its downregulation match those of VHL. Furthermore, RKIP displays tumor suppressing activity in ccRCC. While modulation of RKIP expression did not affect the proliferation of A498 and 786-0 ccRCC cells and neither their ability to form xenograft tumors in NOD/SCID mice, ectopic expression or knockdown of RKIP inhibited or enhanced A498 and 786-0 ccRCC cell invasion, respectively. This was associated with robust changes in vimentin expression, a marker of EMT. Taken together, we demonstrate here that downregulation of RKIP occurs frequently at a rate that reaches that of VHL, suggesting RKIP being a critical tumor suppressor for ccRCC. This is consistent with RKIP being a tumor suppressor for other cancers.

Role of IL13RA2 in Sunitinib Resistance in Clear Cell Renal Cell Carcinoma

Vascular endothelial growth factor (VEGF) and mammalian target of rapamycin are well-known therapeutic targets for renal cell carcinoma (RCC). Sunitinib is an agent that targets VEGF receptors and is considered to be a standard treatment for metastatic or unresectable clear cell RCC (ccRCC). However, ccRCC eventually develops resistance to sunitinib in most cases, and the mechanisms underlying this resistance are not fully elucidated. In the present study, we established unique primary xenograft models, KURC1 (Kyoto University Renal Cancer 1) and KURC2, from freshly isolated ccRCC specimens. The KURC1 xenograft initially responded to sunitinib treatment, however finally acquired resistance. KURC2 retained sensitivity to sunitinib for over 6 months. Comparing gene expression profiles between the two xenograft models with different sensitivity to sunitinib, we identified interleukin 13 receptor alpha 2 (IL13RA2) as a candidate molecule associated with the acquired sunitinib-resistance in ccRCC. And patients with high IL13RA2 expression in immunohistochemistry in primary ccRCC tumor tends to have sunitinib-resistant metastatic site. Next, we showed that sunitinib-sensitive 786-O cells acquired resistance in vivo when IL13RA2 was overexpressed. Conversely, shRNA-mediated knockdown of IL13RA2 successfully overcame the sunitinib-resistance in Caki-1 cells. Histopathological analyses revealed that IL13RA2 repressed sunitinib-induced apoptosis without increasing tumor vasculature in vivo. To our knowledge, this is a novel mechanism of developing resistance to sunitinib in a certain population of ccRCC, and these results indicate that IL13RA2 could be one of potential target to overcome sunitinib resistance.

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.

Reprogramming the tumor microenvironment: tumor-induced immunosuppressive factors paralyze T cells

It has become evident that tumor-induced immuno-suppressive factors in the tumor microenvironment play a major role in suppressing normal functions of effector T cells. These factors serve as hurdles that limit the therapeutic potential of cancer immunotherapies. This review focuses on illustrating the molecular mechanisms of immunosuppression in the tumor microenvironment, including evasion of T-cell recognition, interference with T-cell trafficking, metabolism, and functions, induction of resistance to T-cell killing, and apoptosis of T cells. A better understanding of these mechanisms may help in the development of strategies to enhance the effectiveness of cancer immunotherapies.

Whole-Transcriptome profiling of formalin-fixed, paraffin-embedded renal cell carcinoma by RNA-seq

Background

Formalin-fixed paraffin-embedded (FFPE) tissue samples are routinely archived in the course of patient care and can be linked to clinical outcomes with long-term follow-up. However, FFPE tissues have degraded RNA which poses challenges for analyzing gene expression. Next-generation sequencing (NGS) is rapidly becoming accepted as an effective tool for measuring gene expressions for research and clinical use. However, the feasibility of NGS has not been firmly established when using FFPE tissue.

Results

We optimized strategies for whole transcriptome sequencing (RNA-seq) using FFPE tissue. Ribosomal RNA (rRNA) was successfully depleted by competitive hybridization using the Ribo-zero™ Kit (Epicentre Biotechnologies), and rRNA sequence content was less than one percent for each library. Gene expression measured by FFPE RNA-seq was compared to two different standards: RNA-seq from fresh frozen (FF) tissue and quantitative PCR (qPCR). Both FF and FFPE tumors were sequenced on an Illumina Genome Analyzer IIX with an average of 10 million reads. The distribution of FPKMs (fragments per kilobase of exon per million fragments mapped) and number of detected genes were similar between FFPE and FF. RNA-seq expressions from FF and FFPE samples from the same renal cell carcinoma (RCC) correlated highly (r = 0.919 for tumor 1 and r = 0.954 for tumor 2). On hierarchical cluster analysis, samples clustered by patient identity rather than method of preservation. TaqMan qPCR of 424 RCC-related genes correlated highly with FFPE RNA-seq expressions (r = 0.775 for FFPE tumor 1, r = 0.803 for FFPE tumor 2). Expression fold changes were considered, to assess biologic relevance of gene expressions. Expression fold changes between FFPE tumors (tumor 1/tumor 2) correlated well when comparing qPCR and RNA-seq (r = 0.890). Expression fold changes between tumors from different risk groups (our high risk RCC/The Cancer Genome Atlas, TCGA, low risk RCC) also correlated well when comparing RNA-seq from FF and FFPE tumors (r = 0.887).

Conclusions

FFPE RNA-seq provides reliable genes expression data, comparable to that obtained from fresh frozen tissue. It represents a useful tool for discovery and validation of biomarkers.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1087) contains supplementary material, which is available to authorized users.

Heterogeneity and renal mass biopsy: a review of its role and reliability

Increased abdominal imaging has led to an increase in the detection of the incidental small renal mass (SRM). With increasing recognition that the malignant potential of SRMs is heterogeneous, ranging from benign (15%-20%) to aggressive (20%), enthusiasm for more conservative management strategies in the elderly and infirmed, such as active surveillance (AS), have grown considerably. As the management of the SRM evolves to incorporate ablative techniques and AS for low risk disease, the role of renal mass biopsy (RMB) to help guide individualized therapy is evolving. Historically, the role of RMB was limited to the evaluation of suspected metastatic disease, renal abscess, or lymphoma. However, in the contemporary era, the role of biopsy has grown, most notably to identify patients who harbor benign lesions and for whom treatment, particularly the elderly or frail, may be avoided. When performing a RMB to guide initial clinical decision making for small, localized tumors, the most relevant questions are often relegated to proof of malignancy and documentation (if possible) of grade. However, significant intratumoral heterogeneity has been identified in clear cell renal cell carcinoma (ccRCC) that may lead to an underestimation of the genetic complexity of a tumor when single-biopsy procedures are used. Heterogeneous genomic landscapes and branched parallel evolution of ccRCCs with spatially separated subclones creates an illusion of clonal dominance when assessed by single biopsies and raises important questions regarding how tumors can be optimally sampled and whether future evolutionary tumor branches might be predictable and ultimately targetable. This work raises profound questions concerning the genetic landscape of cancer and how tumor heterogeneity may affect, and possibly confound, targeted diagnostic and therapeutic interventions. In this review, we discuss the current role of RMB, the implications of tumor heterogeneity on diagnostic accuracy, and highlight promising future directions.

Emerging antiangiogenics for renal cancer

INTRODUCTION

Antiangiogenic therapy is considered to be the backbone of treatment strategy in metastatic renal cell carcinoma (mRCC). New, more focused, targeted drugs are emerging, while other targeted drugs oriented toward resistance or alternative mechanisms are under development.

AREAS COVERED

Antiangiogenic agents include two types of agents: the monoclonal antibody, targeting vascular endothelial growth factor (VEGF), bevacizumab and the tyrosine kinase inhibitors (TKIs). Data regarding efficacy and safety of these agents are reported. Differences between the first generation of TKIs, sunitinib, sorafenib, and the new generation, pazopanib, axitinib and tivozanib are also detailed. Most of these agents have been approved in the treatment of kidney cancer in specific settings of the disease.

EXPERT OPINION

The class of antiangiogenic drugs for treatment of mRCC is already relatively full. After 'me-too' drugs, more targeted drugs against VEGFR have been developed but have to demonstrate a benefit in first-line treatment. Another option for the development is to combine a known drug with an antiangiogenic inhibition profile and at least one additional target involved in resistance to an antiangiogenic or in an alternative pathway. The cost of approach with targeted drugs, including antiangiogenics, has led to a tremendous increase in the cost of care in mRCC.

Integrated analysis of microRNA-target interactions with clinical outcomes for cancers

Background

Clinical statement alone is not enough to predict the progression of disease. Instead, the gene expression profiles have been widely used to forecast clinical outcomes. Many genes related to survival have been identified, and recently miRNA expression signatures predicting patient survival have been also investigated for several cancers. However, miRNAs and their target genes associated with clinical outcomes have remained largely unexplored.

Methods

Here, we demonstrate a survival analysis based on the regulatory relationships of miRNAs and their target genes. The patient survivals for the two major cancers, ovarian cancer and glioblastoma multiforme (GBM), are investigated through the integrated analysis of miRNA-mRNA interaction pairs.

Results

We found that there is a larger survival difference between two patient groups with an inversely correlated expression profile of miRNA and mRNA. It supports the idea that signatures of miRNAs and their targets related to cancer progression can be detected via this approach.

Conclusions

This integrated analysis can help to discover coordinated expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.

A MAP3k1 SNP predicts survival of gastric cancer in a Chinese population

OBJECTIVES

Genome-wide association studies (GWAS) have demonstrated that the single nucleotide polymorphism (SNP) MAP3K1 rs889312 is a genetic susceptibility marker significantly associated with a risk of hormone-related tumors such as breast cancer. Considering steroid hormone-mediated signaling pathways have an important role in the progression of gastric cancer, we hypothesized that MAP3K1 rs889312 may be associated with survival outcomes in gastric cancer. The purpose of this study was to test this hypothesis.

METHODS

We genotyped MAP3K1 rs889312 using TaqMan in 884 gastric cancer patients who received subtotal or total gastrectomy. Kaplan-Meier survival analysis and Cox proportional hazard regression were used to analyze the association between MAP3K1 rs889312 genotypes and survival outcomes of gastric cancer.

RESULTS

Our findings reveal that the rs889312 heterozygous AC genotype was significantly associated with an increased rate of mortality among patients with diffuse-type gastric cancer (log-rank P = 0.028 for AC versus AA/CC, hazard ratio [HR] = 1.32, 95% confidence interval [CI] = 1.03-1.69), compared to those carrying the homozygous variant genotypes (AA/CC). Additionally, univariate and multivariate Cox regression analysis demonstrate that rs889312 polymorphism was an independent risk factor for poor survival in these patients.

CONCLUSIONS

In conclusion, we demonstrate that MAP3K1 rs889312 is closely correlated with outcome among diffuse-type gastric cancer. This raises the possibility for rs889312 polymorphisms to be used as an independent indicator for predicting the prognosis of diffuse-type gastric cancer within the Chinese population.

Metastasis-Initiating Cells in Renal Cancer

Metastasis is a complex process that propagates cells from the primary or initial site of the cancer occurrence to distant parts of the body. Cancer cells break from the cancer site and circulate through the bloodstream or lymph vessels, allowing them to reach nearly all parts of the body. These circulating tumour cells (CTCs) contain specialized metastasis-initiating cells (MICs) that reside in the biological heterogeneous primary tumour. Researchers have hypothesized that metastasis of renal cell carcinoma is initiated by circulation of MICs in patients’ blood and bone marrow. Based on the cancer stem/progenitor cell concept of carcinogenesis, understanding the molecular phenotypes of metastasis-initiating cells (MICs) in renal cancer could play a vital role in developing strategies for therapeutic interventions in renal cancer. Existence of MICs among CTCs in renal carcinoma has not been proven in large scale. However, some studies have reported that specialized markers are found on the surface of circulating cells from the primary tumour. In mice, MICs have been isolated from CTCs using such markers, which have then been transplanted into xenograft model to show whether they give rise to metastasis in different organs. Considering these findings, in this review we have attempted to summarize the studies connected with MICs and their gene expression profiles that are responsible for metastasis in renal cancer.

Activation of VCAM-1 and its associated molecule CD44 leads to increased malignant potential of breast cancer cells

VCAM-1 (CD106), a transmembrane glycoprotein, was first reported to play an important role in leukocyte adhesion, leukocyte transendothelial migration and cell activation by binding to integrin VLA-1 (α4β1). In the present study, we observed that VCAM-1 expression can be induced in many breast cancer epithelial cells by cytokine stimulation in vitro and its up-regulation directly correlated with advanced clinical breast cancer stage. We found that VCAM-1 over-expression in the NMuMG breast epithelial cells controls the epithelial and mesenchymal transition (EMT) program to increase cell motility rates and promote chemoresistance to doxorubicin and cisplatin in vitro. Conversely, in the established MDAMB231 metastatic breast cancer cell line, we confirmed that knockdown of endogenous VCAM-1 expression reduced cell proliferation and inhibited TGFβ1 or IL-6 mediated cell migration, and increased chemosensitivity. Furthermore, we demonstrated that knockdown of endogenous VCAM-1 expression in MDAMB231 cells reduced tumor formation in a SCID xenograft mouse model. Signaling studies showed that VCAM-1 physically associates with CD44 and enhances CD44 and ABCG2 expression. Our findings uncover the possible mechanism of VCAM-1 activation facilitating breast cancer progression, and suggest that targeting VCAM-1 is an attractive strategy for therapeutic intervention.

Significance of gene expression analysis of renal cell carcinoma

Renal cell carcinoma (RCC) describes a family of epithelial tumors arising from within the kidney. Each subtype of RCC presents a unique clinical picture with varied tumor biology, patient prognosis and response to treatment. Gene expression profiling offers the ability to analyze thousands of candidate genes in high-throughput arrays and has led to a greater knowledge of the molecular genetics of RCC. This powerful technology can identify RCC subtypes, recapitulating and refining the current histological classifications. Gene expression data also promise to advance current staging systems and improve prognostic information for patients and clinicians. Understanding the genetic signature of RCC tumors will allow for sophisticated application of systemic and targeted therapies, improving patient response and minimizing unnecessary exposure of patients to treatment toxicities. This article reviews the significance of gene expression analysis in the understanding of tumor biology and RCC treatment.

Biological resonance for cancer metastasis, a new hypothesis based on comparisons between primary cancers and metastases

Many hypotheses have been proposed to try to explain cancer metastasis. However, they seem to be contradictory and have some limitations. Comparisons of primary tumors and matched metastases provide new insight into metastasis. The results show high concordances and minor differences at multiple scales from organic level to molecular level. The concordances reflect the commonality between primary cancer and metastasis, and also mean that metastatic cancer cells derived from primary cancer are quite conservative in distant sites. The differences reflect variation that cancer cells must acquire new traits to adapt to foreign milieu during the course of evolving into a new tumor in second organs. These comparisons also provided new information on understanding mechanism of vascular metastasis, organ-specific metastasis, and tumor dormancy. The collective results suggest a new hypothesis, biological resonance (bio-resonance) model. The hypothesis has two aspects. One is that primary cancer and matched metastasis have a common progenitor. The other is that both ancestors of primary cancer cells and metastatic cancer cells are under similar microenvironments and receive similar or same signals. When their interactions reach a status similar to primary cancer, metastasis will occur. Compared with previous hypotheses, the bio-resonance hypothesis seems to be more applicable for cancer metastasis to explain how, when and where metastasis occurs. Thus, it has important implications for individual prediction, prevention and treatment of cancer metastasis.

The distribution, clearance, and safety of an anti-MMP-9 DNAzyme in normal and MMTV-PyMT transgenic mice

Catalytic oligonucleotides, known as DNAzymes, are a new class of nucleic acid-based gene therapy that have recently been used in preclinical animal studies to treat various cancers. In this study the systemic distribution, pharmacokinetics, and safety of intravenously administered anti-MMP (matrix metalloproteinase)-9 DNAzyme (AM9D) were determined in healthy FVB and in MMTV-polyoma virus middle T (PyMT) transgenic mice bearing mammary tumors. MMP-9 is known to be involved in tumor cell development, angiogenesis, invasion, and metastasis. Sulfur-35 ((35)S) labeled ([(35)S]-AM9D) administered intravenously, without the use of carrier molecules, to healthy and mammary tumor bearing MMTV-PyMT transgenic mice distributed to all major organs. The order of percentages of [(35)S]-AM9D accumulation in different organs of healthy and MMTV-PyMT mice were blood>liver>kidney>lung>spleen>heart and mammary tumor>blood≈liver>kidney>spleen>lung>heart, respectively. The amount of AM9D accumulated in mammary tumors 2 hours post injection was 0.6% and 0.2% higher than in either blood or liver, respectively, and its rate of initial clearance from mammary tissue was at least 50% slower than the other organs. Approximately 43% of the delivered dosage of [(35)S]-AM9D was cleared from the system via feces and urine over a period of 72 hours. No evidence of acute or chronic cytotoxicity, local or widespread, associated with AM9D treatment (up to 75 mg AM9D /kg of body weight) was observed in the organs examined. These data suggest that DNAzyme in general and AM9D in particular can be used systemically as a therapeutic agent to treat patients with breast cancer or other metastatic and surgically inaccessible tumors.

Survival analysis by penalized regression and matrix factorization

Because every disease has its unique survival pattern, it is necessary to find a suitable model to simulate followups. DNA microarray is a useful technique to detect thousands of gene expressions at one time and is usually employed to classify different types of cancer. We propose combination methods of penalized regression models and nonnegative matrix factorization (NMF) for predicting survival. We tried L1- (lasso), L2- (ridge), and L1-L2 combined (elastic net) penalized regression for diffuse large B-cell lymphoma (DLBCL) patients' microarray data and found that L1-L2 combined method predicts survival best with the smallest logrank P value. Furthermore, 80% of selected genes have been reported to correlate with carcinogenesis or lymphoma. Through NMF we found that DLBCL patients can be divided into 4 groups clearly, and it implies that DLBCL may have 4 subtypes which have a little different survival patterns. Next we excluded some patients who were indicated hard to classify in NMF and executed three penalized regression models again. We found that the performance of survival prediction has been improved with lower logrank P values. Therefore, we conclude that after preselection of patients by NMF, penalized regression models can predict DLBCL patients' survival successfully.

Integrative genome-wide gene expression profiling of clear cell renal cell carcinoma in Czech Republic and in the United States

Gene expression microarray and next generation sequencing efforts on conventional, clear cell renal cell carcinoma (ccRCC) have been mostly performed in North American and Western European populations, while the highest incidence rates are found in Central/Eastern Europe. We conducted whole-genome expression profiling on 101 pairs of ccRCC tumours and adjacent non-tumour renal tissue from Czech patients recruited within the "K2 Study", using the Illumina HumanHT-12 v4 Expression BeadChips to explore the molecular variations underlying the biological and clinical heterogeneity of this cancer. Differential expression analysis identified 1650 significant probes (fold change ≥2 and false discovery rate <0.05) mapping to 630 up- and 720 down-regulated unique genes. We performed similar statistical analysis on the RNA sequencing data of 65 ccRCC cases from the Cancer Genome Atlas (TCGA) project and identified 60% (402) of the downregulated and 74% (469) of the upregulated genes found in the K2 series. The biological characterization of the significantly deregulated genes demonstrated involvement of downregulated genes in metabolic and catabolic processes, excretion, oxidation reduction, ion transport and response to chemical stimulus, while simultaneously upregulated genes were associated with immune and inflammatory responses, response to hypoxia, stress, wounding, vasculature development and cell activation. Furthermore, genome-wide DNA methylation analysis of 317 TCGA ccRCC/adjacent non-tumour renal tissue pairs indicated that deregulation of approximately 7% of genes could be explained by epigenetic changes. Finally, survival analysis conducted on 89 K2 and 464 TCGA cases identified 8 genes associated with differential prognostic outcomes. In conclusion, a large proportion of ccRCC molecular characteristics were common to the two populations and several may have clinical implications when validated further through large clinical cohorts.

Genetic basis of kidney cancer: role of genomics for the development of disease-based therapeutics

Kidney cancer is not a single disease; it is made up of a number of different types of cancer, including clear cell, type 1 papillary, type 2 papillary, chromophobe, TFE3, TFEB, and oncocytoma. Sporadic, nonfamilial kidney cancer includes clear cell kidney cancer (75%), type 1 papillary kidney cancer (10%), papillary type 2 kidney cancer (including collecting duct and medullary RCC) (5%), the microphalmia-associated transcription (MiT) family translocation kidney cancers (TFE3, TFEB, and MITF), chromophobe kidney cancer (5%), and oncocytoma (5%). Each has a distinct histology, a different clinical course, responds differently to therapy, and is caused by mutation in a different gene. Genomic studies identifying the genes for kidney cancer, including the VHL, MET, FLCN, fumarate hydratase, succinate dehydrogenase, TSC1, TSC2, and TFE3 genes, have significantly altered the ways in which patients with kidney cancer are managed. While seven FDA-approved agents that target the VHL pathway have been approved for the treatment of patients with advanced kidney cancer, further genomic studies, such as whole genome sequencing, gene expression patterns, and gene copy number, will be required to gain a complete understanding of the genetic basis of kidney cancer and of the kidney cancer gene pathways and, most importantly, to provide the foundation for the development of effective forms of therapy for patients with this disease.

Cancer association study of aminoacyl-tRNA synthetase signaling network in glioblastoma

Aminoacyl-tRNA synthetases (ARSs) and ARS-interacting multifunctional proteins (AIMPs) exhibit remarkable functional versatility beyond their catalytic activities in protein synthesis. Their non-canonical functions have been pathologically linked to cancers. Here we described our integrative genome-wide analysis of ARSs to show cancer-associated activities in glioblastoma multiforme (GBM), the most aggressive malignant primary brain tumor. We first selected 23 ARS/AIMPs (together referred to as ARSN), 124 cancer-associated druggable target genes (DTGs) and 404 protein-protein interactors (PPIs) of ARSs using NCI’s cancer gene index. 254 GBM affymetrix microarray data in The Cancer Genome Atlas (TCGA) were used to identify the probe sets whose expression were most strongly correlated with survival (Kaplan-Meier plots versus survival times, log-rank t-test <0.05). The analysis identified 122 probe sets as survival signatures, including 5 of ARSN (VARS, QARS, CARS, NARS, FARS), and 115 of DTGs and PPIs (PARD3, RXRB, ATP5C1, HSP90AA1, CD44, THRA, TRAF2, KRT10, MED12, etc). Of note, 61 survival-related probes were differentially expressed in three different prognosis subgroups in GBM patients and showed correlation with established prognosis markers such as age and phenotypic molecular signatures. CARS and FARS also showed significantly higher association with different molecular networks in GBM patients. Taken together, our findings demonstrate evidence for an ARSN biology-dominant contribution in the biology of GBM.

Transient variations in the serum concentrations of cell adhesion molecules following retroperitoneal laparoscopic and open radical nephrectomy for localized renal-cell carcinoma

PURPOSE

To evaluate differences in the serum concentrations of cell adhesion molecules (CAMs) after retroperitoneal laparoscopic and conventional open radical nephrectomies for localized renal-cell carcinoma (RCC).

PATIENTS AND METHODS

A total of 62 patients with stage T(1)N(0)M(0) RCC were randomized to either a retroperitoneal laparoscopic radical nephrectomy group (n=31) or an open group (n=31). Serum levels of soluble cluster of differentiation 44 splice variant 6 (sCD44v6), soluble intercellular adhesion molecule-1 (sICAM-1), soluble vascular cell adhesion molecule-1 (sVCAM-1), and soluble epithelial cadherin (sE-cadherin) were determined independently by enzyme linked immunosorbent assay (ELISA) preoperatively, and on postoperative days 1 and 5. In addition, follow-up results were compared.

RESULTS

On postoperative day 1, sCD44v6, sICAM-1, and sVCAM-1 levels increased significantly compared with preoperative levels in both groups (P<0.05). sE-cadherin levels decreased compared with preoperative levels in both groups without statistically significant differences (P>0.05). sCD44v6 levels in the retro-laparoscopy group were significantly higher than in the open group (P<0.05), while sICAM-1, sVCAM-1, and sE-cadherin levels showed no statistically significant differences between both groups (P>0.05). On postoperative day 5, all parameters in both groups were similar to preoperative values (P>0.05). Follow-up ranged from 7 to 18 months postoperatively in all 62 patients, with a 100% cancer-specific survival rate in each group.

CONCLUSION

Although postoperatively higher serum concentrations of CAMs in both groups and significantly elevated sCD44v6 in the retro-laparoscopy group may be facilitated, the differences in CAMs between both groups are small and transient. Together with the similar follow-up results, this further supports previous studies that failed to show a difference in the oncologic outcomes between open and laparoscopic radical nephrectomy and provides a probable molecular mechanism.

Integrative genome-wide expression profiling identifies three distinct molecular subgroups of renal cell carcinoma with different patient outcome

Background

Renal cell carcinoma (RCC) is characterized by a number of diverse molecular aberrations that differ among individuals. Recent approaches to molecularly classify RCC were based on clinical, pathological as well as on single molecular parameters. As a consequence, gene expression patterns reflecting the sum of genetic aberrations in individual tumors may not have been recognized. In an attempt to uncover such molecular features in RCC, we used a novel, unbiased and integrative approach.

Methods

We integrated gene expression data from 97 primary RCC of different pathologic parameters, 15 RCC metastases as well as 34 cancer cell lines for two-way nonsupervised hierarchical clustering using gene groups suggested by the PANTHER Classification System. We depicted the genomic landscape of the resulted tumor groups by means of Single Nuclear Polymorphism (SNP) technology. Finally, the achieved results were immunohistochemically analyzed using a tissue microarray (TMA) composed of 254 RCC.

Results

We found robust, genome wide expression signatures, which split RCC into three distinct molecular subgroups. These groups remained stable even if randomly selected gene sets were clustered. Notably, the pattern obtained from RCC cell lines was clearly distinguishable from that of primary tumors. SNP array analysis demonstrated differing frequencies of chromosomal copy number alterations among RCC subgroups. TMA analysis with group-specific markers showed a prognostic significance of the different groups.

Conclusion

We propose the existence of characteristic and histologically independent genome-wide expression outputs in RCC with potential biological and clinical relevance.

Kinome expression profiling identifies IKBKE as a predictor of overall survival in clear cell renal cell carcinoma patients

There are 516 known kinases in the human genome. Because of their important role maintaining proper cellular function, they are often misregulated during tumorigenesis and associated with clinical outcomes in cancer patients, including clear cell renal cell carcinoma (ccRCC). However, less is known about the global expression status of these genes in renal cell carcinoma and their association with clinical outcomes. We performed a systematic analysis of gene expression for 503 kinases in 93 tumor samples and adjacent normal tissues. Expression patterns for 41 kinases were able to clearly differentiate tumor and normal samples. Expression of I-kappa-B kinase epsilon (IKBKE) was associated with a 5.3-fold increased risk of dying [95% confidence interval (CI): 1.93-14.59, P-value: 0.0012]. Individuals with high IKBKE expression were at a significantly increased risk of death (hazard ratio: 3.34, 95% CI: 1.07-10.40, P-value: 0.038) resulting in a significantly reduced overall survival time compared with those with low IKBKE tumor expression (P-value: 0.049). These results for IKBKE were validated in a replication population consisting of 237 ccRCC patients (P-value: 0.0021). Furthermore, IKBKE was observed to be higher expressed in tumors compared with adjacent normal tissues (P-value < 10(-7)). IKBKE is a member of the nuclear factor-kappaB (NF-κB) signaling pathway and interestingly, gene expression patterns for other members of the NF-κB pathway were not associated with survival, suggesting that IKBKE gene expression may be an independent marker of variation in overall survival. Overall, these results support a novel role for IKBKE expression in modulating overall survival in ccRCC patients.

Clinical and molecular prognostic factors in renal cell carcinoma: what we know so far

The current treatment paradigm for metastatic renal cell carcinoma (RCC) includes agents that target the vascular endothelial growth factor (VEGF) and mammalian target of rapamycin (mTOR) pathways. Because these agents have revolutionized RCC over the past five years, new clinical and molecular predictive and prognostic tools are required. These are potentially important for therapy selection, patient counseling, and clinical trial stratification. This review examines clinical prognostic models and molecular biomarkers in RCC.

Combined gene expression profiling and RNAi screening in clear cell renal cell carcinoma identify PLK1 and other therapeutic kinase targets

In recent years, several molecularly targeted therapies have been approved for clear cell renal cell carcinoma (ccRCC), a highly aggressive cancer. Although these therapies significantly extend overall survival, nearly all patients with advanced ccRCC eventually succumb to the disease. To identify other molecular targets, we profiled gene expression in 90 ccRCC patient specimens for which tumor grade information was available. Gene set enrichment analysis indicated that cell-cycle-related genes, in particular, Polo-like kinase 1 (PLK1), were associated with disease aggressiveness. We also carried out RNAi screening to identify kinases and phosphatases that when inhibited could prevent cell proliferation. As expected, RNAi-mediated knockdown of PLK1 and other cell-cycle kinases was sufficient to suppress ccRCC cell proliferation. The association of PLK1 in both disease aggression and in vitro growth prompted us to examine the effects of a small-molecule inhibitor of PLK1, BI 2536, in ccRCC cell lines. BI 2536 inhibited the proliferation of ccRCC cell lines at concentrations required to inhibit PLK1 kinase activity, and sustained inhibition of PLK1 by BI 2536 led to dramatic regression of ccRCC xenograft tumors in vivo. Taken together, these findings highlight PLK1 as a rational therapeutic target for ccRCC.

Putative biomarker genes for grading clear cell renal cell carcinoma

OBJECTIVE

The initial objective of this renal cancer study was to identify gene sets in clear cell renal cell carcinoma (ccRCC) to support grading of ccRCC histopathology.

MATERIALS AND METHODS

Preselected ccRCC tumor tissues of grade 1 (G1, n = 14) and grade 3 (G3, n = 15) as well es 14 normal kidney tissues thereof were subjected to microarray expression analysis using Human Genome U133 Plus 2.0 Array. Event ratio scoring, hierarchical clustering and principal component analysis were used to determine gene sets that distinguish expression profiles from normal kidney tissue, G1 and G3 tumor tissues.

RESULTS

An initial set of 73 genes provided seven gene subclusters (SC01 to SC07) that distinguish RNA expression profiles from G1, G3 tumor and normal kidney tissues. A ranked list of 24 genes was determined that separated G1 from G3 tumors in high concordance with histopathological grading confirmed by immunohistochemical analysis of ceruloplasmin protein expression.

CONCLUSION

A final set of 24 genes has been determined awaiting further validation on the RNA as well as on the protein level by studying an additional cohort of ccRCC patients. A reliable separation of G1 and G3 tumor grades will be instrumental to foster and direct the administration of upcoming targeted therapeutics of ccRCC tumors in a more predictive and reliable manner.

Watchful waiting in the treatment of the small renal mass

Objectives:

To evaluate the role and feasibility of observation with regard to the small renal mass.

Methods:

We performed a literature search of MEDLINE, reviewing the world literature relevant to the natural history, role of percutaneous biopsy and surveillance of the small renal mass.

Results:

The average yearly growth rate of most small renal masses ranges from 0.1 to 0.70 cm/yr with obvious exceptions. Clinical predictors of growth such as radiographic size at presentation, age, gender and tumor characteristics are not reliable. Approximately 1% develops metastatic disease while under surveillance. Contemporary series of percutaneous biopsy of small renal masses report sensitivity for malignancy to be 90%-98%. However, false-negative results can occur. For the majority of patients, the gold standard remains surgical extirpation.

Conclusions:

Watchful waiting is an acceptable option for management of small renal masses in the surgically unfit and elderly population. More information regarding the natural history and metastatic potential of small renal masses is needed. Percutaneous needle biopsy can be successful in detecting malignancy in selected patients with small renal masses. The role of needle biopsy for the small renal mass continues to evolve

Staging of renal cell carcinoma: Current concepts

The most important and widely utilized system for providing prognostic information following surgical management for renal cell carcinoma (RCC) is currently the tumor, nodes, and metastasis (TNM) staging system. An accurate and clinically useful staging system is an essential tool used to provide patients with counseling regarding prognosis, select treatment modalities, and determining eligibility for clinical trials. Data published over the last few years has led to significant controversies as to whether further revisions are needed and whether improvements can be made with the introduction of new, more accurate predictive prognostic factors. Staging systems have also evolved with an increase in the understanding of RCC tumor biology. Molecular tumor biomarkers are expected to revolutionize the staging of RCC by providing more effective prognostic ability over traditional clinical variables alone. This review will examine the components of the TNM staging system, current staging modalities including comprehensive integrated staging systems, and predictive nomograms, and introduce the concept of molecular staging for RCC.

Basic research in kidney cancer

CONTEXT

Advances in basic research will enhance prognosis, diagnosis, and treatment of renal cancer patients.

OBJECTIVE

To discuss advances in our understanding of the molecular basis of renal cancer, targeted therapies, renal cancer and immunity, and genetic factors and renal cell carcinoma (RCC).

EVIDENCE ACQUISITION

Data on recently published (2005-2011) basic science papers were reviewed.

EVIDENCE SYNTHESIS

Advances in basic research have shown that renal cancers can be subdivided based on specific genetic profiles. Now that this molecular basis has been established, it is becoming clear that additional events play a major role in the development of renal cancer. For example, aberrant chromatin remodelling appears to be a main driving force behind tumour progression in clear cell RCC. A large number of potential biomarkers have emerged using various high-throughput platforms, but adequate biomarkers for RCC are still lacking. To bring the potential biomarkers and biomarker profiles to the clinical arena is a major challenge for the field. The introduction of tyrosine kinase inhibitors (TKIs) for therapy has shifted the interest away from immunologic approaches. Nevertheless, a wealth of evidence supports immunotherapy for RCC. Interestingly, studies are now appearing that suggest a combination of TKI and immunotherapy may be beneficial. Thus far, little attention has been paid to patient-specific differences. With high-throughput methods becoming cheaper and with the advances in sequencing possibilities, this situation is expected to change rapidly.

CONCLUSIONS

Great strides have been made in the understanding of molecular mechanisms of RCC. This has led this field to the enviable position of having a range of molecularly targeted therapies. Large sequencing efforts are now revealing more and more genes responsible for tumour development and progression, offering new targets for therapy. It is foreseen that through integration of high-throughput platforms, personalised cancer treatment for RCC patients will become possible.

A specific gene expression signature characterizes metastatic potential in clear cell renal cell carcinoma

PURPOSE

The discovery of metastasis markers in clear cell renal cell carcinoma is of critical importance to define individual metastatic risk and select patients for new targeted therapies. We identified potential biomarkers for metastatic clear cell renal cell carcinoma by gene expression analysis.

MATERIALS AND METHODS

We performed transcriptional profiling of 16 primary metastatic and 18 nonmetastatic clear cell renal cell carcinomas with PIQOR™ microarrays. Differentially expressed genes were validated by quantitative real-time polymerase chain reaction.

RESULTS

Genes discriminating between metastatic and nonmetastatic tumors were identified at q <0.001 by significance analysis of microarrays. The metastatic signature contained 127 transcripts. In metastatic samples a greater than 4-fold decrease in expression was detected for the genes CD151 and IKBA (t/F statistic p <0.0001) while the genes MMP16, B7-H1, BCL2L2 and FRA2 showed greater than 4-fold increase of expression in metastatic primary tumors (p <0.0001). Quantitative real-time polymerase chain reaction revealed significant differences in expression among all metastatic tumors, including synchronously and metachronously metastasized tumors, and nonmetastatic tumors for FRA2 (p = 0.032) and CD151 (p = 0.005). In addition, the genes B7-H1 (p = 0.040), FRA2 (p = 0.035), CD151 (p = 0.004) and BCL2L2 (p = 0.035) showed significantly higher expression in early metastasized than in nonmetastatic tumor samples. Different B7-H1 (p = 0.002) and BCL2L2 (p = 0.007) expression levels were found in samples with late metastasis compared to those in synchronously metastasized tumors.

CONCLUSIONS

We determined a metastatic signature of clear cell renal cell carcinoma by microarray analysis. Our data provide the possibility of defining the metastatic potential of primary clear cell renal cell carcinoma based on a select number of genes even in a localized situation.

Co-localization of PCNA, VCAM-1 and caspase-3 with nestin in xenografts derived from human anaplastic astrocytoma and glioblastoma multiforme tumor spheres

The cancer stem cell hypothesis proposes that tumors contain a small subset of cancer cells, the cancer stem cells, which constitute a reservoir of self-sustaining cells with the exclusive ability to self-renew and maintain the tumor. Markers that define cancer stem cells that are capable of recapitulating brain tumors as xenografts in mice has not been described. We investigated the relationship between expression of nestin and that of PCNA, VCAM-1 and caspase-3 in the xenografts developed from human anaplastic astrocytoma and glioblastoma tumor-derived spheres in the brain of nude mouse. Xenografts obtained from astrocytoma tumor stem cells (ATSC) and glioblastoma tumor stem cells (GTSC) have showed a large number of cells positive for both PCNA and the nestin, demonstrating that nestin expressing cells have a high rate of proliferation. Xenografts from GTSC showed heterogeneous staining pattern with cells that express both nestin and VCAM-1, whereas others cells remained complete negative. In this case it was noticed that most tumor cells with large or multinucleated nuclei coexpress nestin and VCAM-1. In xenografts from ATSC most cells positive for nestin express VCAM-1 and in this case the two proteins appear to occupy the same cytoplasmic region. Both GTSC and ATSC derived xenografts showed cells positive for both caspase-3 and for nestin detected mainly as single cells and as cell clusters located near or around a blood vessel.

What can molecular pathology contribute to the management of renal cell carcinoma?

The incidence of renal cell carcinoma (RCC) is increasing and outcomes remain poor. One-third of patients with localized disease will relapse, and 5-year survival for patients with metastatic disease is less than 10%. No molecular test is currently available to identify which patients who have undergone 'curative' surgery will relapse, and which patients will respond to targeted therapy. Some well characterized biochemical pathways, such as those associated with von Hippel-Lindau disease, are aberrantly regulated in RCC and are associated with histological subtype, but the understanding of these pathways contributes little to the clinical management of patients with RCC. Gene expression and sequencing studies have increased our understanding of the genetic basis of the disease but have failed to establish any unified classification to improve molecular stratification or to predict which patients are likely to relapse or respond to targeted therapy. Instead, they have served to highlight that RCC is heterogeneous at histological, morphological, and molecular levels, and that novel approaches are required to resolve the complexity of RCC prognostication and prediction of treatment response.

Renal mass sampling: an enlightened perspective

Renal mass sampling (RMS) can be carried out by core biopsy or fine needle aspiration with each presenting potential advantages and limitations. The literature about RMS is confounded by a lack of standardized techniques, ambiguous terminology, imprecise definitions of accuracy, substantial rates of non-informative biopsies, and recurrent diagnostic challenges with respect to eosinophilic neoplasms. Despite these concerns, RMS has an expanding role in the evaluation and treatment of renal masses, in order to stratify biological aggressiveness and guide management that can range from surgery to active surveillance. Non-informative biopsies can be managed with surgical excision or repeat biopsy, with the latter showing encouraging results in recent studies. We propose a new classification in which all biopsies are categorized as non-informative versus informative, with the latter being subclassified as confirmed accurate, presumed accurate or confirmed inaccurate. This terminology will facilitate the comparison of results from various studies and stimulate progress. Incorporation of novel biomarkers and molecular fingerprinting into RMS protocols will likely allow for more rational management of patients with renal masses in the near future.

Perspectives in drug development for metastatic renal cell cancer

Patients with renal cell carcinoma (RCC) exhibit a spectrum of clinical outcomes, with some patients following an indolent clinical course and others displaying rapidly advancing disease. As evidence points to RCC being largely refractory to traditional chemotherapy and radiotherapy strategies, immunotherapeutic approaches played a dominant role in the management of metastatic RCC for a quarter of a century. Management of this challenging tumor has been revolutionized by the incorporation of molecularly targeted therapies such as inhibitors of pathways involving tyrosine kinase signaling and the mammalian target of rapamycin (mTOR). The improvements in disease stabilization and survival seen with these agents has meant that molecularly targeted therapy now forms the foundation for treating RCC and has resulted in a multitude of studies investigating similar compounds for efficacy in RCC. Despite this, the rationale for using immunomodulatory regimens remains strong and its ongoing place in this era of targeted treatments continues to pose interesting clinical questions. The challenge of maintaining durable responses from our current therapies persists and this review highlights the plethora of options now available in RCC treatment and the directions in which modern management are heading.

Renal cell carcinoma: where will the state-of-the-art lead us?

Less than 20 years ago, the von Hippel-Lindau (VHL) gene was discovered and associated with sporadic renal cell carcinoma (RCC). Since then, researchers and clinicians have labored to better understand the biology driving RCC tumor progression and provide means to predict patient survival and response to therapy. Studies surrounding VHL inactivation and downstream effects continue to provide insights into these areas. Besides studies of this primary pathway, cytogenetic studies, gene expression analyses, tissue microarrays, serum proteomics, genomic resequencing, and microRNA profiling have yielded greater understanding of RCC biology and clinical presentation, and have led to a rich understanding of the heterogeneity of this disease. We review the current state of research investigations into the molecular biology of RCC, and discuss the applications to currently used clinical prognostic nomograms.

Caloric restriction: from soup to nuts

Caloric restriction (CR), reduced protein, methionine, or tryptophan diets; and reduced insulin and/or IGFI intracellular signaling can extend mean and/or maximum lifespan and delay deleterious age-related physiological changes in animals. Mice and flies can shift readily between the control and CR physiological states, even at older ages. Many health benefits are induced by even brief periods of CR in flies, rodents, monkeys, and humans. In humans and nonhuman primates, CR produces most of the physiologic, hematologic, hormonal, and biochemical changes it produces in other animals. In primates, CR provides protection from type 2 diabetes, cardiovascular and cerebral vascular diseases, immunological decline, malignancy, hepatotoxicity, liver fibrosis and failure, sarcopenia, inflammation, and DNA damage. It also enhances muscle mitochondrial biogenesis, affords neuroprotection; and extends mean and maximum lifespan. CR rapidly induces antineoplastic effects in mice. Most claims of lifespan extension in rodents by drugs or nutrients are confounded by CR effects. Transcription factors and co-activators involved in the regulation of mitochondrial biogenesis and energy metabolism, including SirT1, PGC-1alpha, AMPK and TOR may be involved in the lifespan effects of CR. Paradoxically, low body weight in middle aged and elderly humans is associated with increased mortality. Thus, enhancement of human longevity may require pharmaceutical interventions.