Prognostic and predictive biomarkers in renal cell carcinoma

A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA

BACKGROUND

Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features.

METHODS

In this work, a gene signature selection strategy for TCGA data was proposed by integrating the gene expression data, the methylation data and the prior knowledge about cancer biomarkers. Different from the traditional integration method, the expanded 450 K methylation data were applied instead of the original 450 K array data, and the reported biomarkers were weighted in the feature selection. Fuzzy rule based classification method and cross validation strategy were applied in the model construction for performance evaluation.

RESULTS

Our selected gene features showed prediction accuracy close to 100% in the cross validation with fuzzy rule based classification model on 6 cancers from TCGA. The cross validation performance of our proposed model is similar to other integrative models or RNA-seq only model, while the prediction performance on independent data is obviously better than other 5 models. The gene signatures extracted with our fuzzy rule based integrative feature selection strategy were more robust, and had the potential to get better prediction results.

CONCLUSION

The results indicated that the integration of expanded methylation data would cover more genes, and had greater capacity to retrieve the signature genes compared with the original 450 K methylation data. Also, the integration of the reported biomarkers was a promising way to improve the performance. PTCHD3 gene was selected as a discriminating gene in 3 out of the 6 cancers, which suggested that it might play important role in the cancer risk and would be worthy for the intensive investigation.

Aberrant methylation of PCDH8 is a potential prognostic biomarker for patients with clear cell renal cell carcinoma

BACKGROUND

PCDH8 is a tumor suppressor that regulates cell adhesin, proliferation, and migration. It is often inactivated by aberrant promoter methylation in several human cancers, including clear cell renal cell carcinoma (CCRCC). The clinical significance of PCDH8 methylation in CCRCC remains unclear. The aim of this study was to investigate the relationship between PCDH8 methylation and clinicopathological characteristics as well as outcome of patients with CCRCC.

MATERIAL/METHODS

The methylation status of PCDH8 in 153 CCRCC tissues and 97 paired adjacent normal renal tissues were examined using methylation-specific PCR (MSP). Then the relationships between PCDH8 methylation and clinicopathological features as well as progression-free survival of CCRCC patients were evaluated.

RESULTS

PCDH8 methylation was significantly more frequent in CCRCC tissues compared with normal renal tissues. Moreover, PCDH8 methylation was significantly correlated with advanced clinical stage (P=0.0141), higher grade (P=0.0190), and lymph node metastasis (P=0.0098). In addition, multivariate analysis showed that PCDH8 methylation was independently associated with poor progression-free survival (P=0.0316).

CONCLUSIONS

PCDH8 methylation is a frequent event in CCRCC and is correlated with unfavorable clinicopathological features. Moreover, PCDH8 methylation may be a useful biomarker to predict the progression of CCRCC.

Tumor-associated macrophages subvert T-cell function and correlate with reduced survival in clear cell renal cell carcinoma

Although malignant cells can be recognized and controlled by the immune system, in patients with clinically apparent cancer immunosurveillance has failed. To better understand local immunoregulatory processes that impact on cancer progression, we correlated intratumoral immunological profiles with the survival of patients affected by primary clear cell renal cell carcinoma (ccRCC). A retrospective analysis of 54 primary ccRCC samples for 31 different immune response-related transcripts, revealed a negative correlation of CD68 (a marker of tumor-associated macrophages, TAMs) and FOXP3 (a marker of regulatory T cells, Tregs) with survival. The subsequent analysis of 12 TAM-related transcripts revealed an association between the genes coding for CD163, interferon regulatory factor 4 (IRF4) and fibronectin 1 (FN1), all of which have been linked to the M2 TAM phenotype, with reduced survival and increased tumor stage, whereas the opposite was the case for the M1-associated gene coding for inducible nitric oxide synthetase (iNOS). The M2 signature of (CD68⁺) TAMs was found to correlate with CD163 expression, as determined in prospectively collected fresh ccRCC tissue samples. Upon co-culture with autologous tumor cells, CD11b⁺ cells isolated from paired blood samples expressed CD163 and other M2-associated proteins, suggesting that the malignant cells promote the accumulation of M2 TAMs. Furthermore, the tumor-associated milieu as well as isolated TAMs induced the skewing of autologous, blood-derived CD4⁺ T cells toward a more immunosuppressive phenotype, as shown by decreased production of effector cytokines, increased production of interleukin-10 (IL-10) and enhanced expression of the co-inhibitory molecules programmed death 1 (PD-1) and T-cell immunoglobulin mucin 3 (TIM-3). Taken together, our data suggest that ccRCC progressively attracts macrophages and induces their skewing into M2 TAMs, in turn subverting tumor-infiltrating T cells such that immunoregulatory functions are increased at the expense of effector functions.

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.

The urinary proteome and peptidome of renal cell carcinoma patients: a comparison of different techniques

Renal cell carcinomas, originating from the renal cortex, account for about 80% of kidney primary malignancies. Small localized tumors rarely produce symptoms and diagnosis is often delayed until the disease is advanced. In contrast to other urological cancers, renal cell carcinomas are associated with a high degree of metastases and a low 5-year survival rate. The identification of diagnostic and prognostic markers, especially in the urine, remains an area of intense investigation. Different proteomic strategies have been applied so far to biomarker discovery in urine at the proteome or the peptidome level. Gel-based and gel-free strategies combined with mass spectrometry are the most-used strategies, have different success rates, and will be depicted here. We also prefigure a scenario in which the limitations of a single approach are overcome by applying new and complementary research strategies, relying on the excellent availability coupled to the intrinsic richness typical of urine samples.

Spontaneous peripheral T-cell responses toward the tumor-associated antigen cyclin D1 in patients with clear cell renal cell carcinoma

Renal cell carcinoma (RCC) is a heterogeneous group of kidney cancers with clear cell RCC (ccRCC) as the major subgroup. To expand the number of clinically relevant tumor-associated antigens (TAA) that can be targeted by immunotherapy, we analyzed samples from 23 patients with primary ccRCC for the expression and immunogenicity of various TAAs. We found high-frequency expression of MAGE-A9 and NY-ESO-1 in 36% and 55% of samples, respectively, and overexpression of PRAME, RAGE-1, CA-IX, Cyclin D1, ADFP, C-MET, and RGS-5 in many of the tumor samples. We analyzed the blood of patients with HLA-A2(+) ccRCC for the presence of CD8(+) T cells specific for TAA-derived HLA-A2-restricted peptides and found spontaneous responses to cyclin D1 in 5 of 6 patients with Cyclin D1-positive tumors. Cyclin D1-specific CD8(+) T cells secreted TNF-α, IFN-γ, and interleukin-2 (IL-2), and degranulated, indicating the presence of polyfunctional tumor-specific CD8(+) T cells in the blood of these patients with ccRCC. The high frequency (43%) of Cyclin D1 overexpression and the presence of functional cyclin D1-specific T cells in 83% of these patients with ccRCC suggest that cyclin D1 may be a target for immunotherapeutic strategies.

Modeling tumor growth kinetics after treatment with pazopanib or placebo in patients with renal cell carcinoma

PURPOSE

The purpose of this study is to characterize tumor growth kinetics in patients with renal cell carcinoma (RCC) after treatment with pazopanib or placebo and to identify predictive patient-specific covariates.

METHODS

Different tumor growth models that included patient-specific covariates were fit to tumor growth data from Phase 2 (n = 220) and Phase 3 (n = 423) clinical trials using nonlinear mixed-effects modeling. Logistic regression was used to determine whether individual model parameters or covariates were related to occurrence of new lesions.

RESULTS

A modified Wang model that included a quadratic growth term and a mixture model adequately described the data. Patients in Group 1 (93 %) showed treatment-dependent tumor shrinkage followed by treatment-independent regrowth. Patients in Group 2 (7 %) showed treatment-independent tumor shrinkage that did not regrow. In Group 1, pazopanib 800 mg increased the tumor shrinkage rate by 267 % compared to placebo. Baseline tumor size was dependent on baseline hemoglobin, baseline lactate dehydrogenase, study, and prior nephrectomy. Logistic regression analysis showed that prior radiotherapy, baseline tumor size, tumor shrinkage rate, tumor regrowth rate, study, and treatment (P < 0.01 for all) were all important predictors of new lesions. Patients treated with placebo were approximately twice as likely to develop new lesions than patients treated with pazopanib.

CONCLUSIONS

Mathematical modeling of tumor growth kinetics can quantify the effect of anticancer therapies. Pazopanib 800 mg was shown to be an effective treatment for RCC that increased the tumor shrinkage rate by 267 % compared with placebo and reduced the likelihood of developing new lesions.

Differential protein profiling of renal cell carcinoma urinary exosomes

Renal cell carcinoma (RCC) accounts for about 3% of all human malignancies and its incidence is increasing. There are no standard biomarkers currently used in the clinical management of patients with renal cell carcinoma. A promising strategy for new biomarker detection is comparative proteomics of urinary exosomes (UE), nanovesicles released by every epithelial cell facing the urinary space, enriched in renal proteins and excluding high-abundance plasmatic proteins, such as albumin. Aim of the work is to establish the protein profile of exosomes isolated from urines of RCC patient compared with control subjects. We enrolled 29 clear cell RCC patients and 23 control healthy subjects (CTRL), age and sex-matched, for urine collection and vesicle isolation by differential centrifugation. Such vesicles were morphologically and biochemically characterized and proved to share exosome properties. Proteomic analysis, performed on 9 urinary exosome (UE) pooled samples by gel based digestion followed by LC-MS/MS, led to the identification of 261 proteins from CTRL subject UE and 186 from RCC patient UE, and demonstrated that most of the identified proteins are membrane associated or cytoplasmic. Moreover, about a half of identified proteins are not shared between RCC and control UE. Starting from these observations, and from the literature, we selected a panel of 10 proteins, whose UE differential content was subjected to immunoblotting validation. Results show for the first time that RCC UE protein content is substantially and reproducibly different from control UE, and that these differences may provide clues for new RCC biomarker discovery.

Prognostic value of the Leibovich prognosis score supplemented by vascular invasion for clear cell renal cell carcinoma

PURPOSE

We assessed whether supplementing the Leibovich prognosis score with vascular invasion would improve prognostic value to predict metastatic disease in patients with nonmetastatic clear cell renal cell carcinoma.

MATERIALS AND METHODS

We retrospectively evaluated the pathology records of 1,754 patients with nonmetastatic clear cell renal cell carcinoma treated with surgery between 1984 and 2006 at a single tertiary academic center. The Leibovich prognosis score was supplemented by additional scoring for vascular invasion. Metastasis-free survival was assessed using the Kaplan-Meier method for each score category. A Cox regression model was used for multivariate testing. Predictive accuracy was determined by the Harrell concordance index and decision curve analysis.

RESULTS

Median followup was 84 months. Ten-year metastasis-free survival probability for a score of 0 to 1 and 2 to 8 or greater was 95%, 83%, 78%, 81%, 69%, 51%, 15% and 13%, respectively. The concordance index was 0.792 compared to 0.778 from our external validation of the Leibovich prognosis score using routine pathological findings (p <0.05). Decision curve analysis also favored the predictive ability of the novel model.

CONCLUSIONS

Adding vascular invasion improved the predictive accuracy of our validation data by 1.4% over that of the Leibovich prognosis score. Patients with a score of 7 or greater had a more than 85% probability of metastatic disease at 10 years. Thus, they could be considered candidates for adjuvant treatment trials.

Delivering preventive, predictive and personalised cancer medicine for renal cell carcinoma: the challenge of tumour heterogeneity

Recent years have seen major advances in the management of metastatic renal cell carcinoma (mRCC). The tyrosine kinase and mammalian target of rapamycin inhibitors have resulted in disease control and improved survival for many patients with mRCC, but they have not led to preventive, predictive or personalised medicine (PPPM). Failure to achieve this rests ultimately with inadequate knowledge of tissue and molecular heterogeneity; discovery of these drugs was based upon identification of pathogenic molecular pathways in RCC, but research into molecular factors which underpin drug response, resistance and selection of therapy for individual patients has lagged well behind clinical trials of drug development. This review will provide an overview of the development of targeted drug therapies for mRCC, will discuss the challenges which currently impede the delivery of PPPM, including identification of biomarkers, drug resistance and molecular heterogeneity, and will propose research methodologies and technologies required to overcome these obstacles.

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.