A multigene assay identifying distinct prognostic subtypes of clear cell renal cell carcinoma with differential response to tyrosine kinase inhibition

Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma

PURPOSE

Develop and validate gene expression-based biomarker associated with recurrent disease to facilitate risk stratification of clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (discovery cohort). Patients who recurred were matched on the basis of grade/stage to patients without recurrence. Capture whole-transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence-free survival/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNA-seq data for each patient. Kaplan-Meier (KM) curves and multivariable Cox proportional hazard testing were used to validate the independent prognostic impact of the gene-expression signature on DFS, disease-specific survival (DSS), and overall survival (OS) in two validation data sets (combined n = 761).

RESULTS

After quality control, the discovery cohort comprised 50 patients with recurrence and 41 patients without, with a median follow-up of 26 and 36 months, respectively. We developed a 15-gene (15G) signature, which was independently associated with worse DFS and DSS (DFS: hazard ratio [HR], 11.08 [95% CI, 4.9 to 25.1]; DSS: HR, 9.67 [95% CI, 3.4 to 27.7]) in a multivariable model adjusting for clinicopathologic parameters (including stage, size, grade, and necrosis [SSIGN] score and Memorial Sloan Kettering Cancer Center nomogram) and mxCCP score. The 15G signature was also independently associated with worse DFS and DSS in both validation data sets (Validation A [n = 382], DFS: HR, 2.6 [95% CI, 1.6 to 4.3]; DSS: HR, 3 [95% CI, 1.4 to 6.1] and Validation B (n = 379), DFS: HR, 2.1 [95% CI, 1.2 to 3.6]; OS: HR, 3 [95% CI, 1.6 to 5.7]) adjusting for clinicopathologic variables and mxCCP score.

CONCLUSION

We developed and validated a novel 15G prognostic signature to improve risk stratification of patients with ccRCC. Pending further validation, this signature has the potential to facilitate optimal treatment allocation.

Characterizing the molecular heterogeneity of clear cell renal cell carcinoma subgroups classified by miRNA expression profile

Clear cell renal cell carcinoma (ccRCC) is a heterogeneous disease that is associated with poor prognosis. Recent works have revealed the significant roles of miRNA in ccRCC initiation and progression. Comprehensive characterization of ccRCC based on the prognostic miRNAs would contribute to clinicians’ early detection and targeted treatment. Here, we performed unsupervised clustering using TCGA-retrieved prognostic miRNAs expression profiles. Two ccRCC subtypes were identified after assessing principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and consensus heatmaps. We found that the two subtypes are associated with distinct clinical features, overall survivals, and molecular characteristics. C1 cluster enriched patients in relatively early stage and have better prognosis while patients in C2 cluster have poor prognosis with relatively advanced state. Mechanistically, we found the differentially expressed genes (DEGs) between the indicated subgroups dominantly enriched in biological processes related to transmembrane transport activity. In addition, we also revealed a miRNA-centered DEGs regulatory network, which severed as essential regulators in both transmembrane transport activity control and ccRCC progression. Together, our work described the molecular heterogeneity among ccRCC cancers, provided potential targets served as effective biomarkers for ccRCC diagnosis and prognosis, and paved avenues to better understand miRNA-directed regulatory network in ccRCC progression.

Prognostic and Predictive Factors in Metastatic Renal Cell Carcinoma: Current Perspective and a Look Into the Future

Metastatic renal cell carcinoma (mRCC) comprises a highly heterogeneous group of diseases with varied clinical outcomes. As a result, models to estimate prognosis were developed in an attempt to aid patient counseling, treatment selection, and clinical trial design. Contemporary prognostic models have been mostly generated based on clinical factors because of their ease of use. Recent advances in molecular techniques have allowed unprecedented molecular profiling of RCC and the discovery of genomic and proteotranscriptomic factors that may contribute to disease trajectory. With the advent of multiple systemic therapies in mRCC in recent years, predictive biomarkers have become increasingly relevant in treatment selection. In this review, we discuss the existing staging systems and prognostic models in mRCC. We also highlight various promising molecular biomarkers according to the subtypes of RCC and explore their integration into the traditional prognostic models. In addition, we discuss emerging predictive biomarkers in the era of immuno-oncology. Lastly, we explore future directions with a focus on liquid biopsies and composite biomarkers.

Automated Renal Cancer Grading Using Nuclear Pleomorphic Patterns

PURPOSE

Nuclear pleomorphic patterns are essential for Fuhrman grading of clear cell renal cell carcinoma (ccRCC). Manual observation of renal histopathologic slides may lead to subjective and inconsistent assessment between pathologists. An automated, image-based system that classifies ccRCC slides by quantifying nuclear pleomorphic patterns in an objective and consistent interpretable fashion can aid pathologists in histopathologic assessment.

METHODS

In the current study, histopathologic tissue slides of 59 patients with ccRCC who underwent surgery at Singapore General Hospital were assembled retrospectively. An automated image classification pipeline detects and analyzes prominent nucleoli in ccRCC images to classify them as either low (Fuhrman grade 1 and 2) or high (Fuhrman grade 3 and 4). The pipeline uses machine learning and image pixel intensity-based feature extraction techniques for nuclear analysis. We trained classification systems that concurrently analyze different permutations of multiple prominent nucleoli image patches.

RESULTS

Given the parameters for feature combination and extraction, we present experimental results across various configurations for the classification of a given ccRCC histopathologic image. We also demonstrate that the image score used by the pipeline, termed fraction value, is correlated ( R = 0.59) with an existing multigene assay-based scoring system that has previously been demonstrated to be a strong indicator of prognosis in patients with ccRCC.

CONCLUSION

The current method provides an objective and fully automated way by which to process pathologic slides. The correlation study with a multigene assay-based scoring system also allows us to provide quantitative interpretation for already established nuclear pleomorphic patterns in ccRCC. This method can be extended to other cancers whose corresponding grading systems use nuclear pattern information.

miR-9-5p in Nephrectomy Specimens is a Potential Predictor of Primary Resistance to First-Line Treatment with Tyrosine Kinase Inhibitors in Patients with Metastatic Renal Cell Carcinoma

Approximately 20–30% of patients with metastatic renal cell carcinoma (mRCC) in first-line treatment with tyrosine kinase inhibitors (TKIs) do not respond due to primary resistance to this drug. At present, suitable robust biomarkers for prediction of a response are not available. Therefore, the aim of this study was to evaluate a panel of microRNAs (miRNAs) in nephrectomy specimens for use as predictive biomarkers for TKI resistance. Archived formalin-fixed, paraffin embedded nephrectomy samples from 60 mRCC patients treated with first-line TKIs (sunitinib, n = 51; pazopanib, n = 6; sorafenib, n = 3) were categorized into responders and non-responders. Using the standard Response Evaluation Criteria in Solid Tumors, patients with progressive disease within 3 months after the start of treatment with TKI were considered as non-responders and those patients with stable disease and complete or partial response under the TKI treatment for at least 6 months as responders. Based on a miRNA microarray expression profile in the two stratified groups of patients, seven differentially expressed miRNAs were validated using droplet digital reverse-transcription quantitative real-time polymerase chain reaction (RT-qPCR) assays in the two groups. Receiver operating characteristic curve analysis and binary logistic regression of response prediction were performed. MiR-9-5p and miR-489-3p were able to discriminate between the two groups. MiR-9-5p, as the most significant miRNA, improved the correct prediction of primary resistance against TKIs in comparison to that of conventional clinicopathological variables. The results of the decision curve analyses, Kaplan-Meier analyses and Cox regression analyses confirmed the potential of miR-9-5p in the prediction of response to TKIs and the prediction of progression-free survival after the initiation of TKI treatment.

Molecular Subtypes of Clear Cell Renal Cell Carcinoma Are Associated With Outcome During Pazopanib Therapy in the Metastatic Setting

BACKGROUND

We previously described 4 molecular subtypes of metastatic clear cell renal cell carcinoma (mccRCC), named ccrcc1-4 (Beuselinck et al, 2015). These have both prognostic and predictive value for patients treated with first-line sunitinib, with distinctive objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). The ccrcc2 and ccrcc3 tumors have the best outcomes, followed by ccrcc1 and then ccrcc4. We hypothesized that these molecular subtypes would show similar outcomes with first-line pazopanib treatment.

PATIENTS AND METHODS

We classified 28 mccRCC tumors treated with pazopanib as first-line therapy, as described previously. The primary endpoints were PFS and OS from the start of pazopanib. A secondary endpoint was ORR. Because there were only 2 ccrcc3 tumors, they were pooled with the ccrcc2 tumors for outcome analysis.

RESULTS

PFS was 9 months for the ccrcc2 and ccrcc3 tumors, 5 months for ccrcc1 tumors, and 3 months for the ccrcc4 tumors (P = .011). The corresponding OS duration was 69, 19, and 5 months (P = .003). The corresponding ORR was 50%, 33%, and 0%. The corresponding mean tumor size decreased by 34%, 6%, and 2% (P = .032). The ccrcc1-4 classification was a stronger predictor of outcome than the International Metastatic Renal Cell Carcinoma Database Consortium score on univariate analysis (P = .011 vs. P = .094 for PFS and P = .003 vs. .013 for OS). Both remained independent on bivariate analysis.

CONCLUSION

The molecular subtypes of mccRCC are associated with outcome on pazopanib as first-line therapy. The prognostic and predictive value of the ccrcc1-4 molecular classification requires validation in prospective trials.

Recognizing the Continuous Nature of Expression Heterogeneity and Clinical Outcomes in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) has been previously classified into putative discrete prognostic subtypes by gene expression profiling. To investigate the robustness of these proposed subtype classifications, we evaluated 12 public datasets, together with a new dataset of 265 ccRCC gene expression profiles. Consensus clustering showed unstable subtype and principal component analysis (PCA) showed a continuous spectrum both within and between datasets. Considering the lack of discrete delineation and continuous spectrum observed, we developed a continuous quantitative prognosis score (Continuous Linear Enhanced Assessment of RCC, or CLEAR score). Prognostic performance was evaluated in independent cohorts from The Cancer Genome Atlas (TCGA) (n = 414) and EMBL-EBI (n = 53), CLEAR score demonstrated both superior prognostic estimates and inverse correlation with anti-angiogenic tyrosine-kinase inhibition in comparison to previously proposed discrete subtyping classifications. Inverse correlation with high-dose interleukin-2 outcomes was also observed for the CLEAR score. Multiple somatic mutations (VHL, PBRM1, SETD2, KDM5C, TP53, BAP1, PTEN, MTOR) were associated with the CLEAR score. Application of the CLEAR score to independent expression profiling of intratumoral ccRCC regions demonstrated that average intertumoral heterogeneity exceeded intratumoral expression heterogeneity. Wider investigation of cancer biology using continuous approaches may yield insights into tumor heterogeneity; single cell analysis may provide a key foundation for this approach.

Secreted miR-210-3p as non-invasive biomarker in clear cell renal cell carcinoma

The most common subtype of renal cell carcinoma (RCC) is clear cell RCC (ccRCC). It accounts for 70-80% of all renal malignancies representing the third most common urological cancer after prostate and bladder cancer. The identification of non-invasive biomarkers for the diagnosis and responsiveness to therapy of ccRCC may represent a relevant step-forward in ccRCC management. The aim of this study is to evaluate whether specific miRNAs deregulated in ccRCC tissues present altered levels also in urine specimens. To this end we first assessed that miR-21-5p, miR-210-3p and miR-221-3p resulted upregulated in ccRCC fresh frozen tissues compared to matched normal counterparts. Next, we evidenced that miR-210-3p resulted significantly up-regulated in 38 urine specimens collected from two independent cohorts of ccRCC patients at the time of surgery compared to healthy donors samples. Of note, miR-210-3p levels resulted significantly reduced in follow-up samples. These results point to miR-210-3p as a potential non-invasive biomarker useful not only for diagnosis but also for the assessment of complete resection or response to treatment in ccRCC management.

Molecular Subtypes Improve Prognostic Value of International Metastatic Renal Cell Carcinoma Database Consortium Prognostic Model

INTRODUCTION

Gene-expression signatures for prognosis have been reported in localized renal cell carcinoma (RCC). The aim of this study was to test the predictive power of two different signatures, ClearCode34, a 34-gene signature model [Eur Urol 2014;66:77-84], and an 8-gene signature model [Eur Urol 2015;67:17-20], in the setting of systemic therapy for metastatic disease.

MATERIALS AND METHODS

Metastatic RCC (mRCC) patients from five institutions who were part of TCGA were identified and clinical data were retrieved. We trained and implemented each gene model as described by the original study. The latter was demonstrated by faithful regeneration of a figure and results from the original study. mRCC patients were dichotomized to good or poor prognostic risk groups using each gene model. Cox proportional hazard regression and concordance index (C-Index) analysis were used to investigate an association between each prognostic risk model and overall survival (OS) from first-line therapy.

RESULTS

Overall, 54 patients were included in the final analysis. The primary endpoint was OS. Applying the ClearCode34 model, median survival for the low-risk-ccA (n = 17)-and the high-risk-ccB (n = 37)-subtypes were 27.6 and 22.3 months (hazard ratio (HR): 2.33; p = .039), respectively. ClearCode34 ccA/ccB and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) classifications appear to represent distinct risk criteria in mRCC, and we observed no significant overlap in classification (p > .05, chi-square test). On multivariable analyses and adjusting for IMDC groups, ccB remained independently associated with a worse OS (p = .044); the joint model of ccA/ccB and IMDC was significantly more accurate in predicting OS than a model with IMDC alone (p = .045, F-test). This was also observed in C-Index analysis; a model with both ccA and ccB subtypes had higher accuracy (C-Index 0.63, 95% confidence interval [CI] = 0.51-0.75) and 95% CIs of the C-Index that did not include the null value of 0.5 in contrast to a model with IMDC alone (0.60, CI = 0.47-0.72). The 8-gene signature molecular subtype model was a weak but insignificant predictor of survival in this cohort (p = .13). A model that included both the 8-gene signature and IMDC (C-Index 0.62, CI = 0.49-0.76) was more prognostic than IMDC alone but did not reach significance, as the 95% CI included the null value of 0.5. These two genomic signatures share no genes in common and are enriched in different biological pathways. The ClearCode34 included genes ARNT and EPAS1 (also known as HIF2a), which are involved in regulation of gene expression by hypoxia-inducible factor.

CONCLUSION

The ClearCode34 but not the 8-gene molecular model improved the prognostic predictive power of the IMDC model in this cohort of 54 patients with metastatic clear cell RCC. The Oncologist 2017;22:286-292 IMPLICATIONS FOR PRACTICE: The clinical and laboratory factors included in the International Metastatic Renal Cell Carcinoma Database Consortium model provide prognostic information in metastatic renal cell carcinoma (mRCC). The present study shows that genomic signatures, originally validated in localized RCC, may add further complementary prognostic information in the metastatic setting. This study may provide new insights into the molecular basis of certain mRCC subgroups. The integration of clinical and molecular data has the potential to redefine mRCC classification, enhance the understanding of mRCC biology, and potentially predict response to treatment in the future.

A systematic search strategy identifies cubilin as independent prognostic marker for renal cell carcinoma

BACKGROUND

There is an unmet clinical need for better prognostic and diagnostic tools for renal cell carcinoma (RCC).

METHODS

Human Protein Atlas data resources, including the transcriptomes and proteomes of normal and malignant human tissues, were searched for RCC-specific proteins and cubilin (CUBN) identified as a candidate. Patient tissue representing various cancer types was constructed into a tissue microarray (n = 940) and immunohistochemistry used to investigate the specificity of CUBN expression in RCC as compared to other cancers. Two independent RCC cohorts (n = 181; n = 114) were analyzed to further establish the sensitivity of CUBN as RCC-specific marker and to explore if the fraction of RCCs lacking CUBN expression could predict differences in patient survival.

RESULTS

CUBN was identified as highly RCC-specific protein with 58% of all primary RCCs staining positive for CUBN using immunohistochemistry. In venous tumor thrombi and metastatic lesions, the frequency of CUBN expression was increasingly lost. Clear cell RCC (ccRCC) patients with CUBN positive tumors had a significantly better prognosis compared to patients with CUBN negative tumors, independent of T-stage, Fuhrman grade and nodal status (HR 0.382, CI 0.203-0.719, P = 0.003).

CONCLUSIONS

CUBN expression is highly specific to RCC and loss of the protein is significantly and independently associated with poor prognosis. CUBN expression in ccRCC provides a promising positive prognostic indicator for patients with ccRCC. The high specificity of CUBN expression in RCC also suggests a role as a new diagnostic marker in clinical cancer differential diagnostics to confirm or rule out RCC.

Positive intratumoral chemokine (C-C motif) receptor 8 expression predicts high recurrence risk of post-operation clear-cell renal cell carcinoma patients

Chemokine (C-C motif) receptor 8 (CCR8) could drive cancer progress through recruiting certain immune cells. Recent evidences revealed the chemotaxis of CCR8⁺ human malignant tumor cells towards lymph node, and a significantly increased CCR8 expression in renal carcinomas patients. To assess the clinical association between CCR8 expression and the risk of post-surgery recurrence in patients with clear-cell renal cell carcinoma (ccRCC), we detected intratumoral CCR8 expression in 472 post-nephrectomy ccRCC patients retrospectively enrolled. Positive CCR8 staining tumor cell occurred in 26.1% (123 of 472) non-metastatic ccRCC cases, and the positive expression was associated with increased risks of recurrence (Log-Rank P < 0.001). In multivariate analyses, CCR8 expression was identified as an independent prognostic factor (P = 0.008) and entered into a newly-built nomogram together with T stage, Fuhrman grade, tumor size, necrosis and lymphovascular invasion. Calibration curves showed optimal agreement between predictions and observations, while its C-index was higher than that of Leibovich score for predicting recurrence-free survival (RFS) of localised RCC patients (0.854 vs 0.836, respectively; P = 0.044). The practical prognostic nomogram model may help clinicians in decision making and design of clinical studies.

Dectin-1 predicts adverse postoperative prognosis of patients with clear cell renal cell carcinoma

Dectin-1, a classical pattern-recognition receptor, was now identified as an important regulator in immune homeostasis and cancer immunity through its extensive ligands binding functions and subsequent cytokines production. The aim of this study was to assess the clinical significance of dectin-1 expression in 290 patients with clear cell renal cell carcinoma (ccRCC) through immunohistochemistry on tissue microarrays. We found that dectin-1 was predominantly expressed on ccRCC cells, in accordance with several other online databases. Moreover, Kaplan-Meier method was conducted and high expression of tumoral dectin-1 was associated with shorter patient recurrence free survival (RFS) and overall survival (OS) (P < 0.001 for both). In multivariate analyses, tumoral dectin-1 expression was also confirmed as an independent prognostic factor for patients’ survival together with other clinical parameters (P < 0.001 for RFS and OS). After incorporating these characteristics including tumoral dectin-1 expression, two nomograms were constructed to predict ccRCC patients’ RFS and OS (c-index 0.796 and 0.812, respectively) and performed better than existed integrated models (P < 0.001 for all models comparisons). In conclusion, high tumoral dectin-1 expression was an independent predictor of adverse clinical outcome in ccRCC patients. This molecule and established nomograms might help clinicians in future decision making and therapeutic developments.

Expression and clinicopathological correlations of retinoid acid receptor responder protein 1 in renal cell carcinomas

AIM

To evaluate the expression and prognostic value of RARRES1 at protein level in renal cell carcinoma (RCC).

MATERIALS & METHODS

Expression profile of RARRES1 was analyzed in 903 documented RCC followed by clinicopathological correlations and survival analysis.

RESULTS

RARRES1 expression was seen in 72.5% of RCC. A stronger RARRES1 expression was seen in high grade compared with low grade RCC (p < 0.001). Logrank tests revealed shorter overall survival in RARRES1 positive RCC (p = 0.006) and in pT1/2 tumors with RARRES1 expression (p = 0.002).

CONCLUSION

The variable expression profile in low and high grade RCC may reflect and confirm the differences of previous gene expression analysis. There was a significant prognostic value of RARRES1 expression in patients with RCC, especially in pT1/2 tumors.

A decade of pharmacogenomics research on tyrosine kinase inhibitors in metastatic renal cell cancer: a systematic review

OBJECTIVE

The individual response to targeted tyrosine kinase inhibitors (TKIs) in the treatment of metastatic renal cell cancer (mRCC) is highly variable. Outlined in this article are findings on potential biomarkers for TKI treatment outcome in mRCC and an evaluation of the status of clinical implementation.

METHODS

Articles were selected by two independent reviewers using a systematic search in five medical databases on renal cell carcinoma, TKIs, and pharmacogenetics.

RESULTS

Many researchers have focused on predictive biomarkers for treatment outcome of targeted therapies in mRCC patients. Attempts to explain differences in efficacy and toxicity of TKIs by use of genetic variants in genes related to the pharmacokinetics and pharmacodynamics of the drug have been successful.

CONCLUSION

Most findings on potential biomarkers have not been validated and therefore biomarker testing to guide choice of therapy and dose in mRCC is not yet feasible.

The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders

Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare.

Diagnosis of Renal Cell Carcinoma: A Clinician's Perspective

Renal cell carcinoma (RCC) is a heterogeneous disease. A rigorous diagnostic assessment by a pathologist with close communication with the clinician provides more accurate prognostication and informed treatment decisions. In the localized setting, an accurate prognostic assessment directs patients to potential adjuvant clinical trials. For patients with advanced disease, the pathologic assessment may have a direct impact on the systemic therapy algorithm. Additionally, it provides the basis for continuous efforts in biomarker development. In rare histologic subtypes, the interaction between clinicians and pathologists provides an opportunity to offer patients specific clinical trials. Molecular characterization platforms may identify targets for therapeutic intervention.

Postoperative surveillance imaging for patients undergoing nephrectomy for renal cell carcinoma

The American Urological Association and the National Comprehensive Cancer Network guidelines regarding postoperative surveillance for renal cell carcinoma (RCC) have provided a standardized framework for imaging following nephrectomy. These stage-stratified recommendations are based on retrospective studies that identified the timeline and location of RCC recurrences. However, the simplified and generalizable protocols offered by the American Urological Association and the National Comprehensive Cancer Network are not without limitations. Studies have found that RCC recurrences continue to be missed even with perfect compliance to these protocols and that RCC recurrences occur not infrequently after the required surveillance window of 5 years. Furthermore, recent studies evaluating the use of adjuvant systemic therapy in patients who are at a high risk for RCC recurrence or metastasis after nephrectomy have yielded disappointing results. This calls into question what interventions we can offer patients to improve survival once RCC recurrences are detected during postoperative surveillance; an effective surveillance strategy requires effective treatment options. The future of personalized medicine with genetic profiling of patients with RCC may offer a potential solution by providing better risk stratification to determine the intensity of surveillance imaging as well as to determine which patients will actually derive survival benefit from intervention on recurrent disease.

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.

Profilin-1 expression is associated with high grade and stage and decreased disease-free survival in renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is associated with high mortality, although individual outcomes are highly variable. Identification of patients with increased risk of disease progression can guide customizing management plan according to disease severity. Profilin-1 (Pfn1) has been recently identified as overexpressed in metastatic ccRCC compared with primary tumors. We examined Pfn1 expression in a tissue microarray of 384 cases of histologically confirmed primary ccRCC with detailed clinical follow-up. Profilin-1 expression showed both cytoplasmic and nuclear staining patterns. The immunoexpression of Pfn1 was scored in a semiquantitative fashion. There was no significant difference in Pfn1 expression between normal kidney and kidney ccRCC. Our results show that strong cytoplasmic Pfn1 expression is associated with high-grade (P < .001) and high-stage (III-IV) (P = .018) disease. Univariate analysis of the data set showed that higher Pfn1 expression is associated with significantly shorter disease-free survival (hazard ratio 7.36, P = .047) and also lower overall survival. Kaplan-Meier analysis showed that high cytoplasmic expression of Pfn1 was also associated with a statistically significant lower disease-free survival (P = .018). It was also associated with lower overall survival, although this was not statistically significant. Profilin-1 lost its prognostic significance in the multivariate analysis when controlling for grade and stage. Profilin-1 expression was not associated with significant prognostic deference in the subgroup of patients with stage 1 disease. Our results suggest that the evaluation of Pfn1 by immunohistochemistry may help to identify patients with an increased risk of disease progression. We validated our results at the messenger RNA level on an independent patient cohort. Higher messenger RNA expression of Pfn1 is associated with significantly lower survival.