A Multigene Signature Based on Cell Cycle Proliferation Improves Prediction of Mortality Within 5 Yr of Radical Nephrectomy for Renal Cell Carcinoma

Prognostic models for predicting oncological outcomes after surgical resection of a nonmetastatic renal cancer: A critical review of current literature

Prognostic models can be valuable for clinicians in counseling and monitoring patients after the surgical resection of nonmetastatic renal cell carcinoma (nmRCC). Over the years, several risk prediction models have been developed, evolving significantly in their ability to predict recurrence and overall survival following surgery. This review comprehensively evaluates and critically appraises current prognostic models for nm-RCC after nephrectomy. The last 2 decades have witnessed a notable increase in the development of various prognostic risk models for RCC, incorporating clinical, pathological, genomic, and molecular factors, primarily using retrospective data. Only a limited number of these models have been developed using prospective data, and their performance has been less effective than expected when applied to broader, real-life patient populations. Recently, artificial intelligence (AI), especially machine learning and deep learning algorithms, has emerged as a significant tool in creating survival prediction models. However, their widespread application remains constrained due to limited external validation, a lack of cost-effectiveness analysis, and unconfirmed clinical utility. Although numerous models that integrate clinical, pathological, and molecular data have been proposed for nm-RCC risk stratification, none have conclusively demonstrated practical effectiveness. As a result, current guidelines do not endorse a specific model. The ongoing development and validation of AI algorithms in RCC risk prediction are crucial areas for future research.

The evolving management of small renal masses

Small renal masses (SRMs) are a heterogeneous group of tumours with varying metastatic potential. The increasing use and improving quality of abdominal imaging have led to increasingly early diagnosis of incidental SRMs that are asymptomatic and organ confined. Despite improvements in imaging and the growing use of renal mass biopsy, diagnosis of malignancy before treatment remains challenging. Management of SRMs has shifted away from radical nephrectomy, with active surveillance and nephron-sparing surgery taking over as the primary modalities of treatment. The optimal treatment strategy for SRMs continues to evolve as factors affecting short-term and long-term outcomes in this patient cohort are elucidated through studies from prospective data registries. Evidence from rapidly evolving research in biomarkers, imaging modalities, and machine learning shows promise in improving understanding of the biology and management of this patient cohort.

Memory/Active T-Cell Activation Is Associated with Immunotherapeutic Response in Fumarate Hydratase-Deficient Renal Cell Carcinoma

PURPOSE

Fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) is a rare and lethal subtype of kidney cancer. However, the optimal treatments and molecular correlates of benefits for FH-deficient RCC are currently lacking.

EXPERIMENTAL DESIGN

A total of 91 patients with FH-deficient RCC from 15 medical centers between 2009 and 2022 were enrolled in this study. Genomic and bulk RNA-sequencing (RNA-seq) were performed on 88 and 45 untreated FH-deficient RCCs, respectively. Single-cell RNA-seq was performed to identify biomarkers for treatment response. Main outcomes included disease-free survival (DFS) for localized patients, objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) for patients with metastasis.

RESULTS

In the localized setting, we found that a cell-cycle progression signature enabled to predict disease progression. In the metastatic setting, first-line immune checkpoint inhibitor plus tyrosine kinase inhibitor (ICI+TKI) combination therapy showed satisfactory safety and was associated with a higher ORR (43.2% vs. 5.6%), apparently superior PFS (median PFS, 17.3 vs. 9.6 months, P = 0.016) and OS (median OS, not reached vs. 25.7 months, P = 0.005) over TKI monotherapy. Bulk and single-cell RNA-seq data revealed an enrichment of memory and effect T cells in responders to ICI plus TKI combination therapy. Furthermore, we identified a signature of memory and effect T cells that was associated with the effectiveness of ICI plus TKI combination therapy.

CONCLUSIONS

ICI plus TKI combination therapy may represent a promising treatment option for metastatic FH-deficient RCC. A memory/active T-cell-derived signature is associated with the efficacy of ICI+TKI but necessitates further validation.

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.

Predictive and prognostic biomarkers in urological tumours

Advancements in cutting-edge molecular profiling techniques, such as next-generation sequencing and bioinformatic analytic tools, have allowed researchers to examine tumour biology in detail and stratify patients based on factors linked with clinical outcome and response to therapy. This manuscript highlights the most relevant prognostic and predictive biomarkers in kidney, bladder, prostate and testicular cancers with recognised impact in clinical practice. In bladder and prostate cancer, new genetic acquisitions concerning the biology of tumours have modified the therapeutic scenario and led to the approval of target directed therapies, increasing the quality of patient care. Thus, it has become of paramount importance to choose adequate molecular tests, i.e., FGFR screening for urothelial cancer and BRCA1-2 alterations for prostate cancer, to guide the treatment plan for patients. While no tissue or blood-based biomarkers are currently used in routine clinical practice for renal cell carcinoma and testicular cancers, the field is quickly expanding. In kidney tumours, gene expression signatures might be the key to identify patients who will respond better to immunotherapy or anti-angiogenic drugs. In testicular germ cell tumours, the use of microRNA has outperformed conventional serum biomarkers in the diagnosis of primary tumours, prediction of chemoresistance, follow-up monitoring, and relapse prediction.

SLFN11 promotes clear cell renal cell carcinoma progression via the PI3K/AKT signaling pathway

SLFN11 is abnormally expressed and associated with survival outcomes in various human cancers. However, the role of SLFN11 in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to investigate the clinical value and potential functions of SLFN11 in ccRCC. Comprehensive bioinformatics analyses were performed using online databases. Quantitative real-time PCR (qPCR) and western blotting were used to validate the expression data. CCK8, flow cytometry analysis, and EdU staining were performed to determine the level of cell proliferation. Flow cytometry analysis was also used to detect cell apoptosis. Wound-healing assay and Transwell assays were performed to assess cell migration and invasion capability, respectively. SLFN11 was overexpressed and was an independent prognostic factor in ccRCC. SLFN11 knockdown inhibited cell proliferation, migration, and invasion and promoted apoptosis. Functional and pathway enrichment analyses suggested that SLFN11 may have an impact on tumorigenesis in ccRCC through regulation of the inflammatory response, the PI3K/AKT signaling pathway and other effectors. Furthermore, SLFN11 knockdown inhibited the phosphorylation of the PI3K/AKT signaling pathway and could be activated by 740 Y-P. Finally, we demonstrated that miR-183 may specifically target SLFN11, and miR-183 expression was correlated with predicted survival. SLFN11 may play a critical role in ccRCC progression and may serve as a novel prognostic biomarker in ccRCC.

An R package for Survival-based Gene Set Enrichment Analysis

Functional enrichment analysis is usually used to assess the effects of experimental differences. However, researchers sometimes want to understand the relationship between transcriptomic variation and health outcomes like survival. Therefore, we suggest the use of Survival-based Gene Set Enrichment Analysis (SGSEA) to help determine biological functions associated with a disease's survival. We developed an R package and corresponding Shiny App called SGSEA for this analysis and presented a study of kidney renal clear cell carcinoma (KIRC) to demonstrate the approach. In Gene Set Enrichment Analysis (GSEA), the log-fold change in expression between treatments is used to rank genes, to determine if a biological function has a non-random distribution of altered gene expression. SGSEA is a variation of GSEA using the hazard ratio instead of a log fold change. Our study shows that pathways enriched with genes whose increased transcription is associated with mortality (NES > 0, adjusted p-value < 0.15) have previously been linked to KIRC survival, helping to demonstrate the value of this approach. This approach allows researchers to quickly identify disease variant pathways for further research and provides supplementary information to standard GSEA, all within a single R package or through using the convenient app.

Current Landscape of Genomic Biomarkers in Clear Cell Renal Cell Carcinoma

CONTEXT

Dramatic gains in our understanding of the molecular biology of clear cell renal cell carcinoma (ccRCC) have created a foundation for clinical translation to improve patient care.

OBJECTIVE

To review and contextualize clinically impactful data surrounding genomic biomarkers in ccRCC.

EVIDENCE ACQUISITION

A systematic literature search was conducted focusing on genomic-based biomarkers with an emphasis on studies assessing clinical outcomes.

EVIDENCE SYNTHESIS

The advancement of tumor sequencing techniques has led to a rapid increase in the knowledge of the molecular underpinnings of ccRCC and with that the discovery of multiple candidate genomic biomarkers. These include somatic gene mutations such as VHL, PBRM1, SETD2, and BAP1; copy number variations; transcriptomic multigene signatures; and specific immune cell populations. Many of these biomarkers have been assessed for their association with survival and a smaller number as potential predictors of a response to systemic therapy. In this scoping review, we discuss many of these biomarkers in detail. Further studies are needed to continue to refine and validate these molecular tools for risk stratification, with the ultimate goal of improving clinical decision-making and patient outcomes.

CONCLUSIONS

While no tissue or blood-based biomarkers for ccRCC have been incorporated into routine clinical practice to date, the field continues to expand rapidly. There remains a critical need to develop and validate these tools in order to improve the care for patients with kidney cancer.

PATIENT SUMMARY

Genomic biomarkers have the potential to better predict outcome and select the most appropriate treatment for patients with kidney cancer; however, further research is needed before any of these currently developed biomarkers are adopted into clinical practice.

Molecular phenotyping of malignant canine mammary tumours: Detection of high-risk group and its relationship with clinicomolecular characteristics

Canine mammary gland tumours (CMTs) constitute the most common cancer in female dogs and comprise approximately 50% of all canine cancers. With the advent of high-throughput technologies such as microarray and next-generation sequencing, the molecular phenotyping (classification) of various cancers has been extensively developed. The present study used a canine RNA-sequencing dataset, namely GSE119810, to classify 113 malignant CMTs and 64 matched normal samples via an unsupervised hierarchical algorithm with a view to evaluating the association between the resulting subtypes (clusters) (n = 4) and clinical and molecular characteristics. Finally, a molecular classifier was developed, and it detected 1 high-risk molecular subtype in the training dataset (GSE119810) and 2 independent validation datasets (GSE20718 and GSE22516). Our results revealed four molecular subtypes (C2-C5) in malignant CMTs. Furthermore, the normal samples constituted a distinct group in the clustering analysis. Marked significant associations were observed between the molecular subtypes (especially C5) and clinical/molecular features, including positive lymphatic invasion, high tumour grades, histopathology diagnoses, short survival and high TP53 mutation rates (ps <.05). The high-risk subtype (C5) was further characterized through the development of a cell cycle-based gene signature, which comprised 37 proliferation-related genes according to the support vector machine algorithm. This signature identified the high-risk group in both training and validation datasets (ps <.001). In the validation analysis, our potential classifier robustly predicted patients with positive lymphatic invasion, metastases and short survival.

Post nephrectomy management of localized renal cell carcinoma. From risk stratification to therapeutic evidence in an evolving clinical scenario

Standard treatment for localized non-metastatic renal cell carcinoma (RCC) is radical or partial nephrectomy. However, after radical surgery, patients with stage II-III have a substantial risk of relapse (around 35%). To date a unique standardized classification for the risk of disease recurrence still lack. Moreover, in the last years great attention has been focused in developing systemic therapies with the aim of improving the disease-free survival (DFS) of high-risk patients, with negative results from adjuvant VEGFR-TKIs. Therefore, there is still a need for developing effective treatments for radically resected RCC patients who are at intermediate/high risk of relapse. Recently, interesting results came from immune-checkpoint inhibitors (ICIs) targeting the PD-1/PD-L1 pathway, with a significant benefit in terms of disease-free survival from adjuvant pembrolizumab. However, the conflicting results of diverse clinical trials investigating different ICI-based regimens in the adjuvant setting, together with the still immature data on the overall survival advantage of immunotherapy, requires careful considerations. Furthermore, several questions remain unanswered, primarily regarding the selection of patients who could benefit the most from immunotherapy. In this review, we have summarized the main clinical trials investigating adjuvant therapy in RCC, with a particular focus on immunotherapy. Moreover, we have analyzed the crucial issue of patients' stratification according to the risk of disease recurrence, and we have described the possible future prospective and novel agents under evaluation for perioperative and adjuvant therapies.

Adjuvant Therapy in Renal Cell Carcinoma: Are we ready for prime time?

The standard of care for localized renal cell carcinoma (RCC) is radical or partial nephrectomy. Despite complete resection, a subset of patients will develop locoregional recurrence or metastatic disease. Adjuvant immunotherapy has been studied since the 1980 s as the primary method to mitigate tumor recurrence after definitive surgery. We herein discuss published and ongoing clinical trials investigating adjuvant therapy in localized or locoregional RCC.

Risk models for recurrence and survival after kidney cancer: a systematic review

OBJECTIVE

To systematically identify and compare the performance of prognostic models providing estimates of survival or recurrence of localized renal cell cancer (RCC) in patients treated with surgery with curative intent.

MATERIALS AND METHODS

We performed a systematic review (PROSPERO CRD42019162349). We searched Medline, EMBASE and the Cochrane Library from 1 January 2000 to 12 December 2019 to identify studies reporting the performance of one or more prognostic model(s) that predict recurrence-free survival (RFS), cancer-specific survival (CSS) or overall survival (OS) in patients who have undergone surgical resection for localized RCC. For each outcome we summarized the discrimination of each model using the C-statistic and performed multivariate random-effects meta-analysis of the logit transformed C-statistic to rank the models.

RESULTS

Of a total of 13 549 articles, 57 included data on the performance of 22 models in external populations. C-statistics ranged from 0.59 to 0.90. Several risk models were assessed in two or more external populations and had similarly high discriminative performance. For RFS, these were the Sorbellini, Karakiewicz, Leibovich and Kattan models, with the UCLA Integrated Staging System model also having similar performance in European/US populations. All had C-statistics ≥0.75 in at least half of the validations. For CSS, they the models with the highest discriminative performance in two or more external validation studies were the Zisman, Stage, Size, Grade and Necrosis (SSIGN), Karakiewicz, Leibovich and Sorbellini models (C-statistic ≥0.80 in at least half of the validations), and for OS they were the Leibovich, Karakiewicz, Sorbellini and SSIGN models. For all outcomes, the models based on clinical features at presentation alone (Cindolo and Yaycioglu) had consistently lower discrimination. Estimates of model calibration were only infrequently included but most underestimated survival.

CONCLUSION

Several models had good discriminative ability, with there being no single 'best' model. The choice from these models for each setting should be informed by both the comparative performance and availability of factors included in the models. All would need recalibration if used to provide absolute survival estimates.

A Targetable Myeloid Inflammatory State Governs Disease Recurrence in Clear-Cell Renal Cell Carcinoma

It is poorly understood how the tumor immune microenvironment influences disease recurrence in localized clear-cell renal cell carcinoma (ccRCC). Here we performed whole-transcriptomic profiling of 236 tumors from patients assigned to the placebo-only arm of a randomized, adjuvant clinical trial for high-risk localized ccRCC. Unbiased pathway analysis identified myeloid-derived IL6 as a key mediator. Furthermore, a novel myeloid gene signature strongly correlated with disease recurrence and overall survival on uni- and multivariate analyses and is linked to TP53 inactivation across multiple data sets. Strikingly, effector T-cell gene signatures, infiltration patterns, and exhaustion markers were not associated with disease recurrence. Targeting immunosuppressive myeloid inflammation with an adenosine A2A receptor antagonist in a novel, immunocompetent, Tp53-inactivated mouse model significantly reduced metastatic development. Our findings suggest that myeloid inflammation promotes disease recurrence in ccRCC and is targetable as well as provide a potential biomarker-based framework for the design of future immuno-oncology trials in ccRCC.

SIGNIFICANCE

Improved understanding of factors that influence metastatic development in localized ccRCC is greatly needed to aid accurate prediction of disease recurrence, clinical decision-making, and future adjuvant clinical trial design. Our analysis implicates intratumoral myeloid inflammation as a key driver of metastasis in patients and a novel immunocompetent mouse model. This article is highlighted in the In This Issue feature, p. 2221.

Development and Validation of a Nomogram to Predict Cancer-Specific Survival in Elderly Patients With Papillary Renal Cell Carcinoma

Objective

Papillary renal cell carcinoma (pRCC) is the second most common type of renal cell carcinoma and an important disease affecting older patients. We aimed to establish a nomogram to predict cancer-specific survival (CSS) in elderly patients with pRCC.

Methods

Patient information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) project, and we included all elderly patients with pRCC from 2004 to 2018. All patients were randomly divided into a training cohort and a validation cohort. Univariate and multivariate Cox proportional risk regression models were used to identify patient independent risk factors. We constructed a nomogram based on a multivariate Cox regression model to predict CSS for 1-, 3-, and 5- years in elderly patients with pRCC. A series of validation methods were used to validate the accuracy and reliability of the model, including consistency index (C-index), calibration curve, and area under the Subject operating curve (AUC).

Results

A total of 13,105 elderly patients with pRCC were enrolled. Univariate and multivariate Cox regression analysis suggested that age, tumor size, histological grade, TNM stage, surgery, radiotherapy and chemotherapy were independent risk factors for survival. We constructed a nomogram to predict patients' CSS. The training and validation cohort's C-index were 0.853 (95%CI: 0.859–0.847) and 0.855 (95%CI: 0.865–0.845), respectively, suggesting that the model had good discrimination ability. The AUC showed the same results. The calibration curve also indicates that the model has good accuracy.

Conclusions

In this study, we constructed a nomogram to predict the CSS of elderly pRCC patients, which has good accuracy and reliability and can help doctors and patients make clinical decisions.

Prognostic Factors for Localized Clear Cell Renal Cell Carcinoma and Their Application in Adjuvant Therapy

Simple Summary

Approximately one fifth of patients with newly diagnosed renal cell carcinoma (RCC) present with metastatic disease and over one third of the remaining patients with localized RCC will eventually have metastases spread to distant sites after complete resection of the primary tumor in the kidney. Usually, disease recurrence is observed within the first five years of follow-up, but late recurrences after five years are seen in up to 10% of patients. Despite novel biomarkers, simple histopathological factors, such as tumor size, tumor grade, and tumor extension into the blood vessels or beyond the kidney, are still valid features in predicting the risk of disease recurrence after surgery. The optimal set of prognostic factors remains unclear. The results from ongoing placebo-controlled adjuvant therapy trials may elucidate prognostic features that help to define high-risk patients for disease recurrence.

Abstract

Approximately 20% of patients with renal cell carcinoma (RCC) present with primarily metastatic disease and over 30% of patients with localized RCC will develop distant metastases later, after complete resection of the primary tumor. Accurate postoperative prognostic models are essential for designing personalized surveillance programs, as well as for designing adjuvant therapy and trials. Several clinical and histopathological prognostic factors have been identified and adopted into prognostic algorithms to assess the individual risk for disease recurrence after radical or partial nephrectomy. However, the prediction accuracy of current prognostic models has been studied in retrospective patient cohorts and the optimal set of prognostic features remains unclear. In addition to traditional histopathological prognostic factors, novel biomarkers, such as gene expression profiles and circulating tumor DNA, are extensively studied to supplement existing prognostic algorithms to improve their prediction accuracy. Here, we aim to give an overview of existing prognostic features and prediction models for localized postoperative clear cell RCC and discuss their role in the adjuvant therapy trials. The results of ongoing placebo-controlled adjuvant therapy trials may elucidate prognostic factors and biomarkers that help to define patients at high risk for disease recurrence.

Molecular Characterization of Clear Cell Renal Cell Carcinoma Reveals Prognostic Significance of Epithelial-mesenchymal Transition Gene Expression Signature

BACKGROUND

There is an ongoing need to develop prognostic biomarkers to improve the management of clear cell renal cell carcinoma (ccRCC).

OBJECTIVE

To leverage enriched pathways in ccRCC to improve risk-stratification.

DESIGN, SETTING, AND PARTICIPANTS

We retrospectively identified two complementary discovery cohorts of patients with ccRCC who underwent (1) radical nephrectomy (RNx) with inferior vena cava tumor thrombectomy (patients = 5, samples = 24) and (2) RNx for localized disease and developed recurrence versus no recurrence (n = 36). Patients with localized ccRCC (M0) in The Cancer Genome Atlas (TCGA, n = 386) were used for validation.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

A differential expression gene (DEG) analysis was performed on targeted RNA next-generation sequencing data from both discovery cohorts. Using TCGA for validation, Kaplan-Meier survival analysis and multivariable Cox proportional hazard testing were utilized to investigate the prognostic impact of DEGs, cell cycle proliferation (CCP), and a novel epithelial-mesenchymal transition (EMT) score on progression-free (PFS) and disease-specific (DSS) survival.

RESULTS AND LIMITATIONS

In the discovery cohorts, we observed overexpression of WT1 and CCP genes in the tumor thrombus versus the primary tumor, as well as in patients with recurrence versus those without recurrence. A hallmark pathway analysis demonstrated enrichment of the EMT- and CCP-related pathways in patients with high WT1 expression in the TCGA (validation) ccRCC cohort. CCP and EMT scores were derived in the validation cohort, which was stratified into four risk groups using Youden Index cut points: CCP^low/EMT^low, CCP^low/EMT^high, CCP^high/EMT^low, and CCP^high/EMT^high. The CCP^high/EMT^high risk group was associated with the worst PFS and DSS (both p < 0.001). In a multivariable analysis, CCP^high/EMT^high was independently associated with poor PFS and DSS (hazard ratio = 4.6 and 10.3, respectively; p < 0.001).

CONCLUSIONS

We demonstrate the synergistic prognostic impact of EMT in tumors with a high CCP score. Our novel EMT score has the potential to improve risk stratification and provide potential novel therapeutic targets.

PATIENT SUMMARY

Genes involved in epithelial-mesenchymal transition provides important prognostic information for patients with clear cell renal cell carcinoma.

Biomarkers in renal cell carcinoma: Are we there yet?

Management of kidney cancer has undergone a paradigm shift with the approval of new therapies over the last two decades. Although these drugs have improved clinical outcomes in patients with kidney cancer, there are still a large number of patients who do not show objective responses. A multitude of investigators, including those for The Cancer Genome Atlas have biologically characterized and sub-classified kidney cancer. However, we have not been able to identify molecular targets to effectively treat patients with kidney cancer. As we familiarize ourselves with newer drugs for patients with kidney cancer, it is important to understand that these drugs may not work in every patient and instead may expose patients to unnecessary toxic effects along with burdening society with the financial impact. As we head toward the era of "precision medicine", validated biomarkers are being utilized to guide treatment choices and help identify pathways of resistance in other tumor types. The current review aims at evaluating the progress made so far in this realm for patients with kidney cancer.

Radiology- and gene-based risk stratification in small renal cell carcinoma: A preliminary study

PURPOSE

Most small renal cell carcinomas (small RCCs) will remain indolent after detection, but some stage I RCCs still metastasize. There are no risk-stratification imaging factors that could be used to identify poor-prognosis patients based on genomic profiling. Here, we evaluated the relationships between imaging parameters and RNA expressions in small RCC and attempted to identify imaging factors that could be used as effective biomarkers.

METHODS

We acquired biopsy specimens of 18 clear cell carcinomas that had undergone perfusion CT (pCT) and MRI between April 2018 and March 2019. We performed RNA sequencing, assessed RNA expressions, and calculated each tumor's cell-cycle progression (CCP) score, which has prognostic value in predicting metastatic progression. We classified the tumors into two groups: clear cell type A (ccA) and type B (ccB). CcA has better survival compared to ccB. We evaluated the following characteristics of each tumor: tumor size, presence of pseudocapsule, and fat. We used the pCT and MRI to measure each tumor's volume transfer constant (Ktrans), rate constant (Kep), extracellular extravascular volume fraction (VE), fractional plasma volume (VP), and apparent diffusion coefficient (ADC). The correlations between these small RCC imaging parameters and the tumor size and RNA expressions were determined.

RESULTS

The tumor size was significantly correlated with Kep and inversely correlated with VE, VP, ADC, and hallmark angiogenesis. The CCP score was significantly inversely correlated with Ktrans and Kep. The ccA tumors tended to show a pseudocapsule on MRI.

CONCLUSION

Tumor size was correlated with low perfusion, but not with prognostic factors based on genomic profiling. Imaging parameters (e.g., Ktrans and Kep) and tumor characteristics (e.g., pseudocapsule) may enable gene-based risk stratification in small RCC.

Identification and validation of dichotomous immune subtypes based on intratumoral immune cells infiltration in clear cell renal cell carcinoma patients

Background

Increasing evidence has elucidated the clinical significance of tumor infiltrating immune cells in predicting outcomes and therapeutic efficacy. In this study, we comprehensively analyze the tumor microenvironment (TME) immune cell infiltrations in clear cell renal cell carcinoma (ccRCC) and correlated the infiltration patterns with anti-tumor immunity and clinical outcomes.

Methods

We analyzed immune cell infiltrations in four independent cohorts, including the KIRC cohort of 533 patients, the Zhongshan ccRCC cohorts of 259 patients, the Zhongshan fresh tumor sample cohorts of 20 patients and the Zhongshan metastatic ccRCC cohorts of 87 patients. Intrinsic patterns of immune cell infiltrations were evaluated for associations with clinicopathological characteristics, underlying biological pathways, genetic changes, oncological outcomes and treatment responses.

Results

Unsupervised clustering of tumor infiltrating immune cells identified two microenvironment subtypes, TMEcluster-A and TMEcluster-B. Gene markers and biological pathways referring to immune evasion were upregulated in TMEcluster-B. TMEcluster-B associated with poor overall survival (p<0.001; HR 2.629) and recurrence free survival (p=0.012; HR 1.870) in ccRCC validation cohort. TMEcluster-B cases had worse treatment response (p=0.009), overall survival (p<0.001; HR 2.223) and progression free survival (p=0.015; HR 2.7762) in metastatic ccRCC cohort. The predictive accuracy of International Metastatic Database Consortium risk score was improved after incorporation of TME clusters.

Conclusions

TMEcluster-A featured increased mast cells infiltration, prolonged survival and better treatment response. TMEcluster-B was a heavily infiltrated but immunosuppressed phenotype enriched for macrophages, CD4⁺ T cells, Tregs, CD8⁺ T cells and B cells. TMEcluster-B predicted dismal survival and worse treatment response in clear cell renal cell carcinoma patients.

Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis

Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in individual tumor samples, the disruption of transcription homeostasis in key pathways or gene sets plays an important role in tumorigenesis and has profound implications for the patient's clinical outcome. We devised a computational method named iPath to identify, at the individual-sample level, which pathways or gene sets significantly deviate from their norms. We conducted a pan-cancer analysis and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications.

Geospatial Cellular Distribution of Cancer-Associated Fibroblasts Significantly Impacts Clinical Outcomes in Metastatic Clear Cell Renal Cell Carcinoma

Simple Summary

Cancer-associated fibroblasts (CAFs) are highly prevalent cells in the clear cell renal cell carcinoma (ccRCC) tumor immune microenvironment. CAFs are thought to potentiate tumor proliferation primarily through paracrine interactions, as evidenced by laboratory-based studies. We sought to corroborate these findings using surgically removed tissue samples from 96 patients with metastatic ccRCC and associate geospatial relationships between CAFs and rapidly proliferating tumor cells with survival outcomes. We found that CAFs exhibited more geospatial clustering with proliferating tumor cells than with dying tumor cells, and patients whose samples exhibited higher tumor cell proliferation had worse overall survival and were more likely to be resistant to systemic tyrosine-kinase-inhibiting targeted therapies. Immunotherapy resistance was not associated with the geospatial metrics measured in this analysis. Overall, these findings suggest that close proximity to CAFs potentiates tumor cell proliferation, worsening survival and conferring resistance to targeted therapies.

Abstract

Cancer-associated fibroblasts (CAF) are highly prevalent cells in the tumor microenvironment in clear cell renal cell carcinoma (ccRCC). CAFs exhibit a pro-tumor effect in vitro and have been implicated in tumor cell proliferation, metastasis, and treatment resistance. Our objective is to analyze the geospatial distribution of CAFs with proliferating and apoptotic tumor cells in the ccRCC tumor microenvironment and determine associations with survival and systemic treatment. Pre-treatment primary tumor samples were collected from 96 patients with metastatic ccRCC. Three adjacent slices were obtained from 2 tumor-core regions of interest (ROI) per patient, and immunohistochemistry (IHC) staining was performed for αSMA, Ki-67, and caspase-3 to detect CAFs, proliferating cells, and apoptotic cells, respectively. H-scores and cellular density were generated for each marker. ROIs were aligned, and spatial point patterns were generated, which were then used to perform spatial analyses using a normalized Ripley’s K function at a radius of 25 μm (nK(25)). The survival analyses used an optimal cut-point method, maximizing the log-rank statistic, to stratify the IHC-derived metrics into high and low groups. Multivariable Cox regression analyses were performed accounting for age and International Metastatic RCC Database Consortium (IMDC) risk category. Survival outcomes included overall survival (OS) from the date of diagnosis, OS from the date of immunotherapy initiation (OS-IT), and OS from the date of targeted therapy initiation (OS-TT). Therapy resistance was defined as progression-free survival (PFS) <6 months, and therapy response was defined as PFS >9 months. CAFs exhibited higher cellular clustering with Ki-67⁺ cells than with caspase-3⁺ cells (nK(25): Ki-67 1.19; caspase-3 1.05; p = 0.04). The median nearest neighbor (NN) distance from CAFs to Ki-67⁺ cells was shorter compared to caspase-3⁺ cells (15 μm vs. 37 μm, respectively; p < 0.001). Multivariable Cox regression analyses demonstrated that both high Ki-67⁺ density and H-score were associated with worse OS, OS-IT, and OS-TT. Regarding αSMA+CAFs, only a high H-score was associated with worse OS, OS-IT, and OS-TT. For caspase-3⁺, high H-score and density were associated with worse OS and OS-TT. Patients whose tumors were resistant to targeted therapy (TT) had higher Ki-67 density and H-scores than those who had TT responses. Overall, this ex vivo geospatial analysis of CAF distribution suggests that close proximity clustering of tumor cells and CAFs potentiates tumor cell proliferation, resulting in worse OS and resistance to TT in metastatic ccRCC.

A 25-year perspective on evaluation and understanding of biomarkers in urologic cancers

The past 25 years have witnessed an explosion of investigative attempts to identify clinically useful biomarkers which can have meaningful impacts for patients with urologic cancers. However, in spite of the enormous amount of research aiming to identify markers with the hope of impacting patient care, only a handful have proven to have true clinical utility. Improvements in targeted imaging, pan-omics evaluation, and genetic sequencing at the tissue and single-cell levels have yielded many potential targets for continued biomarker investigation. This article, as one in this series for the 25th Anniversary Issue of Urologic Oncology: Seminars and Original Investigations, serves to give a perspective on our progress and failures over the past quarter-century in our highest volume urologic cancers: prostate, bladder, and kidney cancers.

Adjuvant Therapy in Renal Cell Carcinoma: Current Status and Future Directions

In recent years, incredible progress has been made in the treatment of metastatic renal cell carcinoma, with a paradigm shift from the use of cytokines to tyrosine kinase inhibitors, and more recently, immune checkpoint inhibitors (ICIs). Despite advances in the metastatic setting, effective therapies in the adjuvant setting are a largely unmet need. Currently, sunitinib (Sutent, Pfizer) is the only therapy for the adjuvant treatment of RCC included in the National Comprehensive Cancer Network guidelines, which was approved by the FDA based on the improvement in disease-free survival (DFS) seen in the S-TRAC trial. However, improvement in DFS has not translated into an overall survival (OS) benefit for patients at high-risk of relapse post-nephrectomy, illustrating the need for more effective therapies. This manuscript will highlight attributes of both historical and current drug trials and their implications on the landscape of adjuvant therapy. Additionally, we will outline strategies for selecting patients in whom treatment would be most beneficial, as optimal patient selection is a crucial step towards improving outcomes in the adjuvant setting. This is especially critical, given the financial cost and pharmacological toxicity of therapeutic agents. Furthermore, we will review the design of clinical trials including the value of utilizing OS as an endpoint over DFS. Finally, we will discuss how the incorporation of genomic data into predictive models, the use of more sensitive imaging modalities for more accurate staging, and more extensive surgical intervention involving lymph node dissection, may impact outcomes.

Outcomes of systemic targeted therapy in recurrent renal cell carcinoma treated with adjuvant sunitinib

OBJECTIVE

To assess the efficacy and tolerability of rechallenge with sunitinib and other targeted therapies (TTs) in patitents with relapsed recurrent renal cell carcinoma (RCC) in the advanced setting.

METHODS

In this multi-institutional retrospective study, patients with relapsed RCC were rechallenged with sunitinib or other systemic TTs as a first-line therapeutic approach after failed adjuvant sunitinib treatment. Patient characteristics, treatments and clinical outcomes were recorded. The primary endpoint was progression-free survival (PFS). Secondary endpoints were objective response rate (ORR) and overall survival (OS).

RESULTS

A total of 34 patients with relapses were recorded, and 25 of these (73.5%) were men. Twenty-five patients were treated with systemic TT: 65% of patients received TT against the vascular endothelial growth factor pathway (including sunitinib), 21.7% received mammalian target of rapamycin inhibitors and 13% received immunotherapy. The median (interquartile range) time to relapse was 20.3 (5.2-20.4) months from diagnosis, and 7.5 months (1.0-8.5) from the end of adjuvant suntinib treatment. At a median follow-up of 23.5 months, 24 of the 25 patients had progressed on first-line systemic therapy. The median PFS was 12.0 months (95% confidence interval [CI] 5.78-18.2). There were no statistical differences in PFS between different treatments or sunitinib rechallenge. PFS was not statistically different in patients relapsing on or after adjuvant suntinib treatment (≤ 6 or >6 months after adjuvant suntinib ending). The ORR was 20.5%. The median OS was 29.1 months (95% CI 16.4-41.8).

CONCLUSIONS

Rechallenge with sunitinib or other systemic therapies is still a feasible therapeutic option that provides patients with advanced or metastastic RCC with additional clinical benefits with regard to PFS and OS after failed response to adjuvant sunitinib.

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

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

Potential biomarkers and risk assessment models to enhance the tumor-node-metastasis (TNM) staging classification of urologic cancers

INTRODUCTION

The anatomic-based TNM classification is considered the benchmark in cancer staging and has been regularly updated since its inception. In the current era of precision medicine, the added intention for future TNM modifications is to heighten its impact in the more 'personalized' level of cancer care. In urologic cancers, this goal may be achieved by incorporating 'non-anatomic' factors into TNM, such as biomarkers (e.g. gene alterations, molecular subtypes, genomic classifiers) and risk assessment models (e.g. nomogram, look-up table), while maintaining the anatomic extent as the foundation of staging. These different prognosticators can be combined and integrated, may serve as substratifiers for T, N, or M categories, and perhaps, incorporated as elements in TNM stage groupings to enhance their prognostic capability in urologic cancers.

AREAS COVERED

This review highlights candidate biomarkers and risk assessment models that can be explored to potentially improve TNM prognostication of bladder, prostate, kidney, and testicular cancers.

EXPERT OPINION

Recent advances in molecular analysis have increased the understanding of the genomic, transcriptomic, and epigenetic features for biomarker use in prognostication of urologic cancers, which together with the available risk assessment models, may complement and overcome the limitations of the traditional TNM staging.

Prognostic Molecular Signatures for Metastatic Potential in Clinically Low-Risk Stage I and II Clear Cell Renal Cell Carcinomas

Introduction: For patients with localized node-negative (Stage I and II) clear cell renal cell carcinomas (ccRCC), current clinicopathological staging has limited predictive capability because of their low risk. Analyzing molecular signatures at the time of nephrectomy can aid in understanding future metastatic potential.

Objective: Develop a molecular signature that can stratify patients who have clinically low risk ccRCC, but have high risk genetic changes driving an aggressive metastatic phenotype.

Patients, Materials, and Methods: Presented is the differential expression of mRNA and miRNA in 44 Stage I and Stage II patients, 21 who developed metastasis within 5 years of nephrectomy, compared to 23 patients who remained disease free for more than 5 years. Extracted RNA from nephrectomy specimens preserved in FFPE blocks was sequenced using RNAseq. MiRNA expression was performed using the TaqMan OpenArray qPCR protocol.

Results: One hundred thirty one genes and 2 miRNA were differentially expressed between the two groups. Canonical correlation (CC) analysis was applied and four CCs (CC32, CC20, CC9, and CC7) have an AUC > 0.65 in our dataset with similar predictive power in the TCGA-KIRC dataset. Gene set enrichment showed CC9 as kidney development/adhesion, CC20 as oxidative phosphorylation pathway, CC32 as RNA binding/spindle and CC7 as immune response. In a multivariate Cox model, the four CCs were able to identify high/low risk groups for metastases in the TCGA-KIRC (p < 0.05) with odds ratios of CC32 = 5.7, CC20 = 4.4, CC9 = 3.6, and CC7 = 2.7.

Conclusion: These results identify molecular signatures for more aggressive tumors in clinically low risk ccRCC patients who have a higher potential of metastasis than would be expected.

PTPRO predicts patient prognosis and correlates with immune infiltrates in human clear cell renal cell carcinoma

BACKGROUND

The tumor-suppressive role of protein tyrosine phosphatase receptor type O (PTPRO) has been described in a variety of human cancers; however, the clinical significance of PTPRO in human clear cell renal cell carcinoma (ccRCC) remains unclear.

METHODS

PTPRO expression in renal cell carcinoma (RCC) was analyzed via the Oncomine database, Gene Expression Omnibus (GEO) datasets, and The Cancer Genome Atlas (TCGA) datasets. The Kaplan-Meier curves and Cox proportional hazards model were used to evaluate the relationship of PTPRO with overall survival in ccRCC. Gene ontology (GO) analysis and gene set enrichment analysis (GSEA) were performed to explore the signaling pathways in which PTPRO may be involved. The correlation between PTPRO and immune infiltrates in ccRCC was investigated via Tumor Immune Estimation Resource (TIMER) database. The association between PTPRO mRNA expression and its methylation in RCC was analyzed using the Cancer Cell Line Encyclopedia (CCLE) dataset, GEO dataset, and cBioPortal database. The impact of PTPRO methylation on overall survival was estimated by the MethSurv database.

RESULTS

We showed that the expression of PTPRO was significantly lower in human RCC. Moreover, the lower expression of PTPRO was associated with worse overall survival in ccRCC, particularly in the advanced stage patients. Multivariate Cox regression analysis revealed the expression of PTPRO as an independent prognostic predictor for overall survival of ccRCC. Of note, PTPRO was found to be associated with the activation of immune signaling and immune cell infiltration. Furthermore, methylation of PTPRO was prevalently observed in ccRCC, and methylation of PTPRO predicted the poor outcome of ccRCC.

CONCLUSIONS

Our findings suggested that PTPRO at both RNA and DNA methylation levels had the potential as a prognostic biomarker for predicting prognosis, and PTPRO expression was closely associated with immune infiltration in ccRCC patients.

Genomic profiling in renal cell carcinoma

The treatment landscape of metastatic renal cell carcinoma (RCC) has been revolutionized over the past two decades, bringing forth an era in which more than a dozen therapeutic agents are now available to treat patients. As a consequence, personalized care has become a critical part of developing effective treatment guidelines and improving patient outcomes. One of the most important emerging aspects of precision medicine in cancer is matching patients and treatments based on the genomic characteristics of an individual and their tumour. Despite the lack of a single genomic predictor of treatment response or prognostication feature in RCC, emerging research suggests that the identification of such markers remains promising. Mutations in VHL and alterations in its downstream pathways are the mainstay of RCC development and progression. However, the predictive value of VHL mutations has been questioned. Further research has examined mutations in genes involved in chromosome remodelling (for example, PBRM1, BAP1 and SETD2), DNA methylation and DNA damage repair, all of which have been associated with clinical outcomes. Here, we provide a comprehensive overview of genomic evidence in the context of RCC and its potential predictive and prognostic value.

MicroRNA profile in stage I clear cell renal cell carcinoma predicts progression to metastatic disease

OBJECTIVE

This study sought to identify microRNA (miRNA) profiles of small, pathologically confirmed stage 1 clear cell renal cell carcinoma (ccRCC) tumors that are associated with progression to metachronous metastatic disease.

MATERIALS AND METHODS

Fifty-five pathologic stage 1 ccRCC tumors ≤5cm, from 2 institutions, were examined in a miRNA screening, followed by a validation study. For the screening phase 752 miRNA were evaluated on each sample to identify those with differential expression between tumors that subsequently did (n = 10) or did not (n = 10) progress to metastatic disease. For the validation, 35 additional samples (20 nonprogressors and 15 with distant progression) were utilized to investigate 20 miRNA to determine if a miRNA panel could differentiate aggressive tumors: associations of miRNA expression with cancer specific survival was also investigated.

RESULTS

In the screening analysis, 35 miRNA were differentially expressed (P < 0.05, FDR < 0.1) between the groups. In the validation, 11 miRNA were confirmed to have differential expression. The miRNA -10a-5p, -23b-3p, and -26a-5p differentiated nonprogressive and distant progressive disease with a sensitivity of 73.3% and a specificity of 85% (AUC=0.893). In addition, levels of miR-30a-3p and -145-5p were identified as independent prognostic factors of cancer specific survival.

CONCLUSIONS

This investigation identified miRNA biomarkers that may differentiate between non-progressive ccRCC tumors and those that progress to metastatic disease in this group of stage I tumors. The miRNA profiles determined in this study have the potential to identify patients with small renal masses who are likely to have progressive ccRCC. Such information may be valuable to incorporate into predictive models.

Development and validation of an individualized DNA repair-related gene signature in localized clear cell renal cell carcinoma

BACKGROUND

To establish a robust, individualized DNA repair-related gene signature to estimate prognosis for patients with localized clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

We retrospectively analyzed gene expression profiles of 541 localized ccRCC patients from two public ccRCC cohorts. The DNA repair-related gene pair index (DRPI) was constructed with the least absolute shrinkage and selection operator (LASSO) regression model. The associations between DRPI, overall survival (OS), and disease-specific survival (DSS) were evaluated by Kaplan-Meier analysis, univariate analysis, and multivariate Cox regression survival analysis. We compared the predictive accuracy of different risk models with Harrel's C-index.

RESULTS

In the primary univariate analysis, patients in DRPI-high-risk group had significantly shorter OS [P < 0.001, HR (95% CI) 2.093 (1.431-3.061)] and DSS [P < 0.001, HR (95% CI) 3.567 (2.017-6.339)]. After adjusted for stage and grade, DRPI-high-risk group remained an independent adverse risk factor for both OS [P = 0.026, HR (95% CI) 1.629 (1.094-2.452)] and DSS [P = 0.010, HR (95% CI) 2.209 (1.217-4.010)]. DPRI showed comparable predictive accuracy with cell cycle proliferation (CCP) score and ccA/ccB signature. Copy number alterations and tumor mutation burden were enriched in DRPI-high tumors. There were elevated number of Treg cells and higher T cell exhaustion marker expression in DRPI-high-risk tumors. The combined DNA repair-clinical score outperformed other risk models in terms of C-index.

CONCLUSION

We validated the proposed DRPI as a predictor of clinical outcome in localized ccRCC patients. It provides an individualized and more accurate risk assessment beyond clinicopathological characteristics.

gga-microRNA-375 negatively regulates the cell cycle and proliferation by targeting Yes-associated protein 1 in DF-1 cells

MicroRNAs (miRNAs/miRs) serve a key role in regulating the cell cycle and inducing tumorigenesis. Subgroup J of the avian leukosis virus (ALV-J) belongs to the family Retroviridae, subfamily Orthoretrovirinae and genus Alpharetrovirus that causes tumors in susceptible chickens. gga-miR-375 is downregulated and Yes-associated protein 1 (YAP1) is upregulated in ALV-J-induced tumors in the livers of chickens, and it has been further identified that YAP1 is the direct target gene of gga-miR-375. In the present study, it was found that ALV-J infection promoted the cell cycle and proliferation in DF-1 cells. As the cell cycle and cell proliferation are closely associated with tumorigenesis, further experiments were performed to determine whether gga-miR-375 and YAP1 were involved in these cellular processes. It was demonstrated that gga-miR-375 significantly inhibited the cell cycle by inhibiting G₁ to S/G₂ stage transition and decreasing cell proliferation, while YAP1 significantly promoted the cell cycle and proliferation. Furthermore, these cellular processes in DF-1 cells were affected by gga-miR-375 through the targeting of YAP1. Collectively, the present results suggested that gga-miR-375, downregulated by ALV-J infection, negatively regulated the cell cycle and proliferation via the targeting of YAP1.

Contemporary Renal Tumor Categorization With Biomarker and Translational Updates: A Practical Review

CONTEXT.—

Renal tumor classification has evolved in recent decades, as evidenced by the comparable complexity of the 2016 revision to the World Health Organization Classification of Tumours of the Urinary System and Male Genital Organs. A recent expansion of the knowledge base surrounding the cells of origin and evolutionary genomic characteristics of renal tumors has led to molecular characterization of novel entities and enriched understanding of established entities. This pace of research and its implementation into clinical practice has again begun to surpass that of our own classification schemata, with significant discoveries having been made since the introduction of the 2016 revision to the World Health Organization classification. In particular, biomarkers for renal tumor diagnosis and prognosis are in translation for future clinical application.

OBJECTIVES.—

To provide a brief framework for clinical characterization of renal tumors rooted in morphologic assessment, to briefly review the current and future status of renal tumor biomarkers with an emphasis on practical use of these ancillary tools for accurate diagnosis, and to discuss the impact of emerging technologies and clinical trials relevant to renal cell carcinoma classification and biomarker development.

DATA SOURCES.—

We review recent literature relevant to renal tumor classification (including established and proposed entities), focusing on molecular characterization and biomarker assessment.

CONCLUSIONS.—

Accurate renal tumor diagnosis requires an up-to-date understanding of renal tumor classification, including an awareness of morphologic clues that should stimulate consideration of molecularly defined entities, as well as the ancillary biomarker testing required to confirm diagnoses.

Transcriptomics in RCC

Improvements in chemistry, molecular biology, genetics, and bioinformatics have allowed broad use of transcriptomic profiling. Understanding the population of ribonucleic acid (RNA) transcripts can provide important clinical information relevant to kidney cancer care. This includes a better understanding of kidney cancer subtype and distinct clusters within these categories. RNA-sequencing (RNA-seq) is typically done on a region within the tumor, which represents thousands to millions of heterogeneous cells and various components of the microenvironment. Computational tools can deconvolute these populations to provide insight into the microenvironment. Specific signatures of hypoxia, proliferation, angiogenesis and immune infiltration can predict response and survival. Prognostic signatures can risk stratify tumors to aid in identification of patients who might derive benefit from adjuvant therapy. As the cost of sequencing continues to decline and improved bioinformatic tools are developed, the barriers to clinical use of transcriptomic data continue to crumble. Here we review the current literature around the use of transcriptomics in kidney cancer diagnosis and management.

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

BACKGROUND

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

OBJECTIVE

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

DESIGN, SETTING, AND PARTICIPANTS

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

INTERVENTION(S)

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

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

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

RESULTS AND LIMITATIONS

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

CONCLUSIONS

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

PATIENT SUMMARY

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

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

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

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

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

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

Validation of risk factors for recurrence of renal cell carcinoma: Results from a large single-institution series

PURPOSE

To validate prognostic factors and determine the impact of obesity, hypertension, smoking and diabetes mellitus (DM) on risk of recurrence after surgery in patients with localized renal cell carcinoma (RCC).

MATERIALS AND METHODS

We performed a retrospective cohort study among patients that underwent partial or radical nephrectomy at Weill Cornell Medicine for RCC and collected preoperative information on RCC risk factors, as well as pathological data. Cases were reviewed for radiographic evidence of RCC recurrence. A Cox proportional-hazards model was developed to determine the contribution of RCC risk factors to recurrence risk. Disease-free survival and overall survival were analyzed using the Kaplan-Meier method and log-rank test.

RESULTS

We identified 873 patients who underwent surgery for RCC between the years 2000-2015. In total 115 patients (13.2%) experienced a disease recurrence after a median follow up of 4.9 years. In multivariate analysis, increasing pathological T-stage (HR 1.429, 95% CI 1.265-1.614) and Nuclear grade (HR 2.376, 95% CI 1.734-3.255) were independently associated with RCC recurrence. In patients with T1-2 tumors, DM was identified as an additional independent risk factor for RCC recurrence (HR 2.744, 95% CI 1.343-5.605). Patients with DM had a significantly shorter median disease-free survival (1.5 years versus 2.6 years, p = 0.004), as well as median overall survival (4.1 years, versus 5.8 years, p<0.001).

CONCLUSIONS

We validated high pathological T-stage and nuclear grade as independent risk factors for RCC recurrence following nephrectomy. DM is associated with an increased risk of recurrence among patients with early stage disease.

Harnessing the Genomic Landscape of the Small Renal Mass to Guide Clinical Management

CONTEXT

Small renal masses (SRMs; tumors <4 cm) encompass a diagnostic and therapeutic challenge. Genomic profiling has the potential to improve risk stratification and personalize treatment selection.

OBJECTIVE

Herein, we review the evidence regarding the utility, challenges, and potential implications of genomic profiling in the management of SRMs.

EVIDENCE ACQUISITION

Pertinent publications available on PubMed database pertaining to kidney cancer, tumor size, genomics, and clinical management were reviewed.

EVIDENCE SYNTHESIS

Compared with larger tumors, SRMs range from benign to lethal, necessitating strategies for improved treatment selection. Recent advances in the molecular characterization of renal cell carcinoma have improved our understanding of the disease; however, utility of these tools for the management of SRMs is less clear. While intratumoral heterogeneity (ITH) reduces the accuracy and reliability of sequencing, relative genomic uniformity of SRMs somewhat lessens the impact of ITH. Therefore, renal mass biopsy of SRMs represents an appealing opportunity to evaluate how incorporation of molecular profiles may improve management strategies.

CONCLUSIONS

Ongoing research into the genomic landscape of SRMs has advanced our understanding of the spectrum of disease aggressiveness and may hold promise in matching disease biology to treatment intensity.

PATIENT SUMMARY

Small renal masses are a clinical challenge, as they range from benign to lethal. Genomic profiling may eventually improve treatment selection, but more research is needed.

Construction and Validation of a 9-Gene Signature for Predicting Prognosis in Stage III Clear Cell Renal Cell Carcinoma

Purpose: Aim of this study was to develop a multi-gene signature to help better predict prognosis for stage III renal cell carcinoma (RCC) patients.

Methods: Fourteen pairs of stage III tumor and normal tissues mRNA expression data from GSE53757 and 16 pairs mRNA expression data from TCGA clear cell RCC database were used to analyze differentially expressed genes between tumor and normal tissues. Common different expressed genes in both datasets were used for further modeling. Lasso Cox regression analysis was performed to select and build prognostic multi-gene signature in TCGA stage III kidney cancer patients (N = 122). Then, the multi-gene signature was validated in stage III renal cancer cases in Fudan University Shanghai Cancer Center (N = 77). C-index and time-dependent ROC were used to test the efficiency of this signature in predicting overall survival.

Results: In total, 1,370 common different expressed genes were found between tumor and normal tissues in both datasets. After Lasso Cox modeling, nine mRNAs were finally identified to build a classifier. Using this classifier, we could classify stage III clear cell RCC patients into high-risk group and low-risk group. Prognosis was significantly different between these groups in discovery TCGA cohort, validation FUSCC cohort and entire set (All P < 0.001). Multivariate cox regression in entire set (N = 199) revealed that risk group classified by 9-gene signature, age of diagnosis, pN stage and ISUP grade were independent prognostic factor of overall survival in stage III kidney cancer patients.

Conclusion: We developed a robust multi-gene classifier that can effectively classify stage III RCC patients into groups with low and high risk of poor prognosis. This signature may help select high-risk patients who require more aggressive adjuvant target therapy or immune therapy.

Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma: a retrospective analysis and multicentre validation study

BACKGROUND

Identification of high-risk localised renal cell carcinoma is key for the selection of patients for adjuvant treatment who are at truly higher risk of reccurrence. We developed a classifier based on single-nucleotide polymorphisms (SNPs) to improve the predictive accuracy for renal cell carcinoma recurrence and investigated whether intratumour heterogeneity affected the precision of the classifier.

METHODS

In this retrospective analysis and multicentre validation study, we used paraffin-embedded specimens from the training set of 227 patients from Sun Yat-sen University (Guangzhou, Guangdong, China) with localised clear cell renal cell carcinoma to examine 44 potential recurrence-associated SNPs, which were identified by exploratory bioinformatics analyses of a genome-wide association study from The Cancer Genome Atlas (TCGA) Kidney Renal Clear Cell Carcinoma (KIRC) dataset (n=114, 906 600 SNPs). We developed a six-SNP-based classifier by use of LASSO Cox regression, based on the association between SNP status and patients' recurrence-free survival. Intratumour heterogeneity was investigated from two other regions within the same tumours in the training set. The six-SNP-based classifier was validated in the internal testing set (n=226), the independent validation set (Chinese multicentre study; 428 patients treated between Jan 1, 2004 and Dec 31, 2012, at three hospitals in China), and TCGA set (441 retrospectively identified patients who underwent resection between 1998 and 2010 for localised clear cell renal cell carcinoma in the USA). The main outcome was recurrence-free survival; the secondary outcome was overall survival.

FINDINGS

Although intratumour heterogeneity was found in 48 (23%) of 206 cases in the internal testing set with complete SNP information, the predictive accuracy of the six-SNP-based classifier was similar in the three different regions of the training set (areas under the curve [AUC] at 5 years: 0·749 [95% CI 0·660-0·826] in region 1, 0·734 [0·651-0·814] in region 2, and 0·736 [0·649-0·824] in region 3). The six-SNP-based classifier precisely predicted recurrence-free survival of patients in three validation sets (hazard ratio [HR] 5·32 [95% CI 2·81-10·07] in the internal testing set, 5·39 [3·38-8·59] in the independent validation set, and 4·62 [2·48-8·61] in the TCGA set; all p<0·0001), independently of patient age or sex and tumour stage, grade, or necrosis. The classifier and the clinicopathological risk factors (tumour stage, grade, and necrosis) were combined to construct a nomogram, which had a predictive accuracy significantly higher than that of each variable alone (AUC at 5 years 0·811 [95% CI 0·756-0·861]).

INTERPRETATION

Our six-SNP-based classifier could be a practical and reliable predictor that can complement the existing staging system for prediction of localised renal cell carcinoma recurrence after surgery, which might enable physicians to make more informed treatment decisions about adjuvant therapy. Intratumour heterogeneity does not seem to hamper the accuracy of the six-SNP-based classifier as a reliable predictor of recurrence. The classifier has the potential to guide treatment decisions for patients at differing risks of recurrence.

FUNDING

National Key Research and Development Program of China, National Natural Science Foundation of China, Guangdong Provincial Science and Technology Foundation of China, and Guangzhou Science and Technology Foundation of China.

Current Management of Small Renal Masses, Including Patient Selection, Renal Tumor Biopsy, Active Surveillance, and Thermal Ablation

Renal cancer represents 2% to 3% of all cancers, and its incidence is rising. The increased use of ultrasonography and cross-sectional imaging has resulted in the clinical dilemma of incidentally detected small renal masses (SRMs). SRMs represent a heterogeneous group of tumors that span the full spectrum of metastatic potential, including benign, indolent, and more aggressive tumors. Currently, no composite model or biomarker exists that accurately predicts the diagnosis of kidney cancer before treatment selection, and the use of renal mass biopsy remains controversial. The management of SRMs has changed dramatically over the last two decades as our understanding of tumor biology and competing risks of mortality in this population has improved. In this review, we critically assess published consensus guidelines and recent literature on the diagnosis and management of SRMs, with a focus on patient treatment selection and use of renal mass biopsy, active surveillance, and thermal ablation. Finally, we highlight important opportunities for leveraging recent research discoveries to identify patients with SRMs at high risk for renal cell carcinoma-related mortality and minimize overtreatment and patient morbidity.

Genomic Heterogeneity and the Small Renal Mass

Purpose: Tumor heterogeneity may represent a barrier to preoperative genomic characterization by needle biopsy in clear cell renal cell carcinoma (ccRCC). The extent of heterogeneity in small renal tumors remains unknown. Therefore, we set out to evaluate heterogeneity in resected large and small renal tumors.Experimental Design: We conducted a study from 2013 to 2016 that evaluated 47 consecutive ccRCC tumors resected during radical or partial nephrectomy. Cases were designated as small (<4 cm) and large (>7 cm) tumors. Each tumor had three regions sampled. Copy-number variation (CNV) was assessed and gene expression analysis was performed to characterize the clear-cell A and B (ccA/ccB) profile and the cell-cycle progression (CCP) score. Genomic heterogeneity between three regions was evaluated using CNV subclonal events, regional expression profiles, and correlation between gene expression.Results: Twenty-three small and 24 large tumors were analyzed. Total CNVs and subclonal CNVs events were less frequent in small tumors (P < 0.001). Significant gene expression heterogeneity was observed for both CCP scores and ccA/ccB classifications. Larger tumors had more variance in CCP scores (P = 0.026). The distribution of ccA/ccB differed between small and large tumors with mixed ccA/ccB tumors occurring more frequently in the larger tumors (P = 0.024). Analysis of five mixed tumors (with both ccA/ccB regions) demonstrated the more aggressive ccB phenotype had greater CNV events (P = 0.014).Conclusions: Small renal tumors have much less genomic complexity and fewer subclonal events. Pretreatment genomic characterization with single-needle biopsy in small tumors may be useful to assess biologic potential and may influence therapy. Clin Cancer Res; 24(17); 4137-44. ©2018 AACR.