Histological Subtype is an Independent Predictor of Outcome for Patients With Renal Cell Carcinoma

Prediction of the Prognosis of Clear Cell Renal Cell Carcinoma by Cuproptosis-Related lncRNA Signals Based on Machine Learning and Construction of ceRNA Network

Background

Clear cell renal cell carcinoma's (ccRCC) occurrence and development are strongly linked to the metabolic reprogramming of tumors, and thus far, neither its prognosis nor treatment has achieved satisfying clinical outcomes.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively, provided us with information on the RNA expression of ccRCC patients and their clinical data. Cuproptosis-related genes (CRGS) were discovered in recent massive research. With the help of log-rank testing and univariate Cox analysis, the prognostic significance of CRGS was examined. Different cuproptosis subtypes were identified using consensus clustering analysis, and GSVA was used to further investigate the likely signaling pathways between various subtypes. Univariate Cox, least absolute shrinkage and selection operator (Lasso), random forest (RF), and multivariate stepwise Cox regression analysis were used to build prognostic models. After that, the models were verified by means of the C index, Kaplan-Meier (K-M) survival curves, and time-dependent receiver operating characteristic (ROC) curves. The association between prognostic models and the tumor immune microenvironment as well as the relationship between prognostic models and immunotherapy were next examined using ssGSEA and TIDE analysis. Four online prediction websites-Mircode, MiRDB, MiRTarBase, and TargetScan-were used to build a lncRNA-miRNA-mRNA ceRNA network.

Results

By consensus clustering, two subgroups of cuproptosis were identified that represented distinct prognostic and immunological microenvironments.

Conclusion

A prognostic risk model with 13 CR-lncRNAs was developed. The immune microenvironment and responsiveness to immunotherapy are substantially connected with the model, which may reliably predict the prognosis of patients with ccRCC.

Can Molecular Classifications Help Tailor First-line Treatment of Metastatic Renal Cell Carcinoma? A Systematic Review of Available Models

Context

The advent of immune check inhibitors (ICIs) has tremendously changed the prognosis of metastatic renal cell carcinoma (mRCC), adding an unseen substantial overall survival benefit. These agents could be administered alone or in combination with anti-vascular endothelial growth factor (anti-VEGF) therapies. So far, treatment allocation is based only on clinical stratification risk models.

Objective

Herein, we aimed to report the different molecular classifications reported in the first-line treatment of mRCC and discuss the awaited clinical implications in terms of treatment selection.

Evidence acquisition

Medline database as well as European Society for Medical Oncology (ESMO)/American Society of Clinical Oncology (ASCO) conference proceedings were searched to identify biomarker studies. Inclusion criteria comprised randomized and nonrandomized clinical trials that included patients treated in the first line of mRCC setting, patients treated with anti-VEGF therapies or ICIs, biological modeling, and available survival outcomes.

Evidence synthesis

Four classification models were identified with subsequent clinical implications: Beuselinck model (34 gene signatures), IMmotion150, Hakimi, and JAVELIN 101 model. Tumor profiling shows distinct outcomes when treated with one or other combination. Patients are clustered into two gene signatures: angiogenic and proinflammatory (as per JAVELIN). The first is more likely to respond to therapy that includes anti-VEGF agents, while the best outcomes are obtained with an ICI combination with the second.

Conclusions

The findings presented here were mostly derived from ancillary registered studies of new drugs in the setting of mRCC. Further validation is needed, which sets new paradigms for investigation in clinical research based on tumor biology for treatment allocation and not only on clinical stratification tools.

Patient summary

First-line treatment of metastatic kidney includes immunotherapy alone or in combination with antiangiogenic therapy. However, clinical practice demonstrated that the "one treatment fits all" strategy might not be the best approach. In fact, recent studies showed that the addition of immunotherapy agents will not benefit all patients equally, and some still respond either equally to or better than anti-vascular endothelial growth factor alone. This review revealed biomarker modeling that impacts treatment selection. Recent tumor profiling into "angiogenic signature" more sensitive to angiogenic agents versus "immune signature" more likely to achieve the best response with immunotherapy should be validated. Tumor biology features might be more powerful than clinical classification for a tailored treatment approach.

Divulging a Pleiotropic Role of Succinate Receptor SUCNR1 in Renal Cell Carcinoma Microenvironment

The succinate receptor, SUCNR1, has been attributed to tumor progression, metastasis, and immune response modulation upon its activation via the oncometabolite succinate. Nonetheless, little is known about the prognostic relevance of SUCNR1 and its association with tumor immune infiltrates and microbiota in renal cell carcinoma (RCC). Herein, publicly available platforms including Human Protein Atlas, cBioPortal, TIMER2.0, and TISIDB were utilized to depict a divergent implication of SUCNR1 in the immune microenvironment of clear cell RCC (KIRC) and papillary RCC (KIRP); the two major subtypes of RCC. Our results showed that the SUCNR1 expression level was augmented in RCC compared to other solid cancers, yet with opposite survival rate predictions in RCC subtypes. Consequently, a higher expression level of SUCNR1 was associated with a good disease-specific survival rate (p = 5.797 × 10^-5) in KIRC patients albeit a poor prognostic prediction in KIRP patients (p = 1.9282 × 10^-3). Intriguingly, SUCNR1 was mainly correlated to immunomodulators and diverse immune infiltrates in KIRP. Additionally, the SUCNR1 was mostly associated with a repertoire of microbes including beneficial bacteria that likely influenced a better disease-specific survival rate in KIRC. Our findings illustrate a significant novel subtype-specific role of SUCNR1 in RCC which potentially modulates tumor immune infiltration and microbiome signature, hence altering the prognosis of cancer patients.

Renal Cell Carcinoma Presenting as Syncope due to Saddle Pulmonary Tumor Embolism

Pulmonary embolism (PE) is defined as the obstruction of the pulmonary artery or one of its branches by a blood clot, tumor, air, or fat emboli originating elsewhere in the body. A saddle PE occurs when the obstruction affects the bifurcation of the main pulmonary artery trunk. We present a case of a 46-year-old man who presented to our hospital due to an episode of syncope. Computed tomography angiography (CTA) of the chest showed extensive PE and abdominal CT scan showed a large 8 cm left renal mass with inferior vena cava (IVC) thrombus. Emergent embolectomy, left total nephrectomy, and IVC tumor removal were performed yielding the diagnosis of clear cell renal cell carcinoma (RCC). Interestingly, our patient did not experience any symptoms related to his RCC until the diagnosis of PE due to syncope, and the asymptomatic tumor was found out to be the possible cause of this PE due to the presence of tumor cells constituting the tumor embolus. It is thus recommended to improve the early screening process for RCC. Besides, clinicians should pay attention to patients presenting with uncharacteristic symptoms of RCC who might present with symptoms of saddle PE.

Prognostic Significance of 18F-FDG PET/CT Imaging in Survival Outcomes in Patients with Renal Cell Carcinoma

Objectives:

Renal cell carcinoma (RCC) comprises 85%-90% of primary renal malignant tumors originating from the renal tubular epithelium and has different genetic characteristics. This study aimed to investigate the potential predictive role of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and metabolic parameters in overall survival (OS) analysis in patients with RCC.

Methods:

¹⁸F-FDG PET/CT images of 100 patients performed for initial staging before surgical or oncological treatments were analyzed retrospectively. Maximum standard uptake value (SUV_max-T) of the primary tumor was calculated and its relationship to patient survival was analyzed. The median follow-up time was 5.61 years (0.01-8.7 years).

Results:

SUV_max-T levels in the patients ranged from 2.1 to 48.9 (median 5.9, mean 9.0±7.9). SUV_max-T was significantly higher in RCC-related death more positive than in the negative cases (p<0.001). However, there was not any statistical significance for gender and pathological subtypes on the survival outcomes of patients (p=0.264 and p=0.784). The patients’ 1-year, 3-year, and 5-year OS rates were 71%, 61%, and 57%, respectively. The highest action of SUV_max-T for estimating OS was a cut-off level of 5.4, which maintained sensitivity and specificity of 81% and 75%, respectively. However, cancer staging remained independent significance for OS (p<0.001).

Conclusion:

SUV_max of primary tumor and cancer stage were demonstrated as significant prognostic factors for OS in patients with RCC. Evaluation of 18F-FDG accumulation with PET/CT may help plan treatment strategies and predict survival outcomes of these patients at diagnosis.

Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols

Introduction

This study aimed to construct a radiomics-based machine learning (ML) model for differentiation between non-clear cell and clear cell renal cell carcinomas (ccRCC) that is robust against institutional imaging protocols and scanners.

Materials and methods

Preoperative unenhanced (UN), corticomedullary (CM), and excretory (EX) phase CT scans from 209 patients diagnosed with RCCs were retrospectively collected. After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. For the ML analysis, the cases were randomly split into training and test sets with a 3:1 ratio. Highly correlated RFs were filtered out based on Pearson’s correlation coefficient (r > 0.95). Intraclass correlation coefficient analysis was used to select RFs with excellent reproducibility (ICC ≥ 0.90). The most predictive RFs were selected by the least absolute shrinkage and selection operator (LASSO). A support vector machine algorithm-based binary classifier (SVC) was constructed to predict tumor types and its performance was evaluated based-on receiver operating characteristic curve (ROC) analysis. The “Kidney Tumor Segmentation 2019” (KiTS19) publicly available dataset was used during external validation of the model. The performance of the SVC was also compared with an expert radiologist’s.

Results

The training set consisted of 121 ccRCCs and 38 non-ccRCCs, while the independent internal test set contained 40 ccRCCs and 13 non-ccRCCs. For external validation, 50 ccRCCs and 23 non-ccRCCs were identified from the KiTS19 dataset with the available UN, CM, and EX phase CTs. After filtering out the highly correlated and poorly reproducible features, the LASSO algorithm selected 10 CM phase RFs that were then used for model construction. During external validation, the SVC achieved an area under the ROC curve (AUC) value, accuracy, sensitivity, and specificity of 0.83, 0.78, 0.80, and 0.74, respectively. UN and/or EX phase RFs did not further increase the model’s performance. Meanwhile, in the same comparison, the expert radiologist achieved similar performance with an AUC of 0.77, an accuracy of 0.79, a sensitivity of 0.84, and a specificity of 0.69.

Conclusion

Radiomics analysis of CM phase CT scans combined with ML can achieve comparable performance with an expert radiologist in differentiating ccRCCs from non-ccRCCs.

An external validation of the nocera nomogram: Predicting non-organ confined stage of ≥pT3 in cT1 clear cell renal cell carcinoma

Background

Only one previously published study by Nocera et al. addressed the risk of upstaging to ≥pT3 in cT1 clear cell renal cell carcinoma (ccRCC) by using characteristics of the R.E.N.A.L and PADUA score (age, tumor size, rim location, exophytic rate, polar involvement) developing an accurate nomogram. However, this nomogram has never been externally validated yet.

Material and methods

The study cohort consisted of 288 patients with cT1a-b ccRCC, diagnosed between 2008-2021 at the University Hospital Frankfurt, Germany. Analyses addressed clinical, tumor and radiographic characteristics. The external validation of the nomogram relied on accuracy calculations derived from the area under the curve of the receiver operator characteristic analysis.

Results

Overall, 11.8% (n=34) patients harbored ≥pT3 ccRCC. Median radiographic tumor size (3.6 vs. 5.3cm), R.E.N.A.L. (8 vs. 9 points) and PADUA score (9 vs. 11 points), as well as proportions of renal sinus involvement (82.4% vs. 51.6%), renal hilus involvement (44.1 vs. 13.0%), and medial rim location significantly differed between the pT1-2 and ≥pT3 group (all p ≤ 0.01). In subgroup analyses of small renal mass ccRCC patients (<4cm, cT1a), only 3.8% (n=6) patients had ≥pT3 pathology. Upstaged patients were significantly older and more frequently had endophytic tumor than pT1-2 counterparts (p<0.05). The external validation of the Nocera nomogram showed a good accuracy of 76.6%. Using the suggested cut-off of 21%, 26.5% of patients exhibited ≥pT3 ccRCC. Conversely, within patients below cut-off, 5.9% patients exhibited ≥pT3 ccRCC.

Conclusion

We reported the first external validation of the nomogram addressing the risk of ≥pT3 in cT1 ccRCC patients, demonstrating a good accuracy, with a low false-negative rate. Therefore, the nomogram can accurately be used for patients’ counselling and treatment decision making.

Qualification of Necroptosis-Related lncRNA to Forecast the Treatment Outcome, Immune Response, and Therapeutic Effect of Kidney Renal Clear Cell Carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is considered as a highly immune infiltrative tumor. Necroptosis is an inflammatory programmed cell death associated with a wide range of diseases. Long noncoding RNAs (lncRNAs) play important roles in gene regulation and immune function. lncRNA associated with necroptosis could systematically explore the prognostic value, regulate tumor microenvironment (TME), etc.

Method

The patients' data was collected from TCGA datasets. We used the univariate Cox regression (UCR) to select prediction lncRNAs that are related to necroptosis. Meanwhile, risk models were constructed using LASSO Cox regression (LCR). Kaplan–Meier (KM) analysis, accompanied with receiver operating characteristic (ROC) curves, was performed to assess the independent risk factors of different clinical characteristics. The evaluated factors are age, gender, disease staging, grade, and their related risk score. Databases such as Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSEA) were used to search the probable biological characteristics that could influence the risk groups, containing signaling pathway and immue-related pathways. The single-sample gene set enrichment analysis (ssGSEA) was chosen to perform gene set variation analysis (GSVA), and the GSEABase package was selected to detect the immune and inflammatory infiltration profiles. The TIDE and IC₅₀ evaluation were used to estimate the effectiveness of clinical treatment on KIRC.

Results

Based on the above analysis, we have got a conclusion that patients who show high risk had higher immune infiltration, immune checkpoint expression, and poorer prognosis. We identified 19 novel prognostic necroptosis-related lncRNAs, which could offer opinions for a deeper study of KIRC.

Conclusion

The risk model we constructed makes it possible to predict the prognosis of KIRC patients and offers directions for further research on the prognostication and treatment strategies for KIRC.

A newly defined basement membrane-related gene signature for the prognosis of clear-cell renal cell carcinoma

Background: Basement membranes (BMs) are associated with cell polarity, differentiation, migration, and survival. Previous studies have shown that BMs play a key role in the progression of cancer, and thus could serve as potential targets for inhibiting the development of cancer. However, the association between basement membrane-related genes (BMRGs) and clear cell renal cell carcinoma (ccRCC) remains unclear. To address that gap, we constructed a novel risk signature utilizing BMRGs to explore the relationship between ccRCC and BMs.

Methods: We gathered transcriptome and clinical data from The Cancer Genome Atlas (TCGA) and randomly separated the data into training and test sets to look for new potential biomarkers and create a predictive signature of BMRGs for ccRCC. We applied univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses to establish the model. The risk signature was further verified and evaluated through principal component analysis (PCA), the Kaplan-Meier technique, and time-dependent receiver operating characteristics (ROC). A nomogram was constructed to predict the overall survival (OS). The possible biological pathways were investigated through functional enrichment analysis. In this study, we also determined tumor mutation burden (TMB) and performed immunological analysis and immunotherapeutic drug analysis between the high- and low-risk groups.

Results: We identified 33 differentially expressed genes and constructed a risk model of eight BMRGs, including COL4A4, FREM1, CSPG4, COL4A5, ITGB6, ADAMTS14, MMP17, and THBS4. The PCA analysis showed that the signature could distinguish the high- and low-risk groups well. The K-M and ROC analysis demonstrated that the model could predict the prognosis well from the areas under the curves (AUCs), which was 0.731. Moreover, the nomogram showed good predictability. Univariate and multivariate Cox regression analysis validated that the model results supported the hypothesis that BMRGs were independent risk factors for ccRCC. Furthermore, immune cell infiltration, immunological checkpoints, TMB, and the half-inhibitory concentration varied considerably between high- and low-risk groups.

Conclusion: Employing eight BMRGs to construct a risk model as a prognostic indicator of ccRCC could provide us with a potential progression trajectory as well as predictions of therapeutic response.

Analysis of genomes and transcriptomes of clear cell renal cell carcinomas identifies mutations and gene expression changes in the TGF-beta pathway

The occurrence of clear cell renal cell carcinoma (ccRCC) is related to changes in the transforming growth factor-β (TGF-β) signaling pathway. In this study, we adopted an integrated approach to identify and verify the effects of changes in this pathway on ccRCC and provide a guide for identifying new therapeutic targets. We performed transcriptome analysis of 539 ccRCC cases from The Cancer Genome Atlas (TCGA) and divided the samples into different TGF-β clusters according to unsupervised hierarchical clustering. We found that 76 of the 85 TGF-β pathway genes were dysregulated, and 55 genes were either protective or risk factors affecting the prognosis of ccRCC. The survival time of patients with tumors with low TGF-β scores was shorter than that of patients with tumors with high TGF-β scores. The overall survival (OS) of patients with ccRCC with high TGF-β scores was better than that of patients with low TGF-β scores. The TGF-β score correlated with the expression of key ccRCC and deacetylation genes. The sensitivity of tumor patients to targeted drugs differed between the high and low TGF-β score groups. Therefore, a prognostic model based on the TGF-β gene pathway can predict the prognosis of ccRCC patients. Grouping patients with ccRCC according to their TGF-β score is of great significance for evaluating the prognosis of patients, selecting targeted drugs, and identifying new therapeutic targets.

Real world data of diagnosis, survival, and treatment outcomes in patients with metastatic non clear cell renal cell carcinoma

INTRODUCTION

Metastatic non clear cell renal cell carcinoma (nccRCC) is an heterogenous group, usually excluded from phase 3 trials. We report real life data of prognosis and systemic management of those patients.

METHODS

We retrospectively included 102 metastatic nccRCC patients (unspecified papillary, n = 10; type 1 and 2 papillary n = 10 and n = 32; translocation RCC, n = 9; chromophobe, n = 14; collecting duct, n = 14) treated between 2006 and 2020. Objective response rate (ORR), progression-free survival (PFS) and overall survival (OS) were evaluated.

RESULTS

Among patients who underwent pathological review, 40.8% presented a complete histological discordance. First line treatments were mainly tyrosine kinase inhibitor (60.8%), combination including immunotherapy (7.8%) or combination of chemotherapy (13.7%). Median ORR ranged from 0% in unspecified papillary RCC to 42.9% in type 1 papillary RCC. Median PFS ranged from 2.9 months in collecting duct carcinoma to 10.9 months in type 1 papillary RCC. Median OS ranged from 6.8 months in collecting duct carcinoma to 29.1 months in MiT family translocation RCC. Thirty (29.4%) patients were included in a treatment trial during their treatment course.

CONCLUSION

Metastatic nccRCC patients have variable prognosis due to heterogeneity of histological subtypes. Their diagnosis and access to therapeutic innovation remain suboptimal. Dedicated prospective trials are needed.

Sodium selenite inhibits proliferation and metastasis through ROS-mediated NF-κB signaling in renal cell carcinoma

Background

Sodium selenite (SSE) has been reported to exert anti-tumor effects in several cancer cells. However, the underlying mechanisms in renal cancer are yet to be elucidated. The effects of SSE on the proliferation, metastasis, and apoptosis of renal cancer cells, as well as its mechanism, were investigated in this study.

Methods

ACHN and 786-O renal cancer cells were treated with different concentrations of SSE, MTT, and colony formation assays were used to detect the proliferation ability of cells. The migration of cells was detected using scratch-wound-healing and transwell-migration assays. The effect of SSE on apoptosis was assessed by AnnexinV-FITC/PI double staining. Besides, Western blotting was employed to detect the protein-expression level and elucidate the underlying pathways. We also made subcutaneous xenografts in athymic mice to verify the effect of SSE on tumor growth in vivo.

Results

Our results demonstrated that treatment with SSE resulted in significant inhibition of cell proliferation and migration. Flow cytometry and Western blot confirmed that SSE induced apoptosis via the endogenous apoptotic pathway. We also confirmed that SSE treatment causes an increase in intracellular reactive oxygen species (ROS) levels, resulting in the inhibition of nuclear transcription factor-κB (NF-κB) signaling. Modulation of the ROS level by the chemical inhibitor N-acetyl-cysteine reversed the effect of SSE on cells. Similarly, subcutaneous xenografts in athymic mice models showed that SSE inhibits tumor growth in vivo.

Conclusion

These results indicate that SSE inhibits proliferation and migration and induces apoptosis via ROS mediated inhibition of NF-κB signaling in renal cancer cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09965-8.

Does histologic subtype impact overall survival in observed T1a kidney cancers compared with competing risks? Implications for biopsy as a risk stratification tool

OBJECTIVES

We sought to assess if adding a biopsy proven histologic subtype to a model that predicts overall survival that includes variables representing competing risks in observed, biopsy proven, T1a renal cell carcinomas, enhances the model's performance.

METHODS

The National Cancer Database was assessed (years 2004-2015) for patients with observed T1a renal cell carcinoma who had undergone renal mass biopsy. Kaplan-Meier curves were utilized to estimate overall survival stratified by histologic subtype. We utilized C-index from a Cox proportional hazards model to evaluate the impact of adding histologic subtypes to a model to predict overall survival for each stage.

RESULTS

Of 132 958 T1a renal masses identified, 1614 had biopsy proven histology and were managed non-operatively. Of those, 61% were clear cell, 33% papillary, and 6% chromophobe. Adjusted Kaplan-Meier curves demonstrated a difference in overall survival between histologic subtypes (P = 0.010) with greater median overall survival for patients with chromophobe (85.1 months, hazard rate 0.45, P = 0.005) compared to clear cell (64.8 months, reference group). Adding histology to a model with competing risks alone did not substantially improve model performance (C-index 0.65 vs 0.64 respectively).

CONCLUSIONS

Incorporation of histologic subtype into a risk stratification model to determine prognostic overall survival did not improve modeling of overall survival compared with variables representing competing risks in patients with T1a renal cell carcinoma managed with observation. These results suggest that performing renal mass biopsy in order to obtain tumor histology may have limited utility. Future studies should further investigate the overall utility of renal mass biopsy for observed T1a kidney cancers.

The value measurement of emerging therapeutics in renal cell carcinoma: ASCO value framework and ESMO-MCBS

Purpose

Rapid development of novel therapeutics in renal cell carcinoma (RCC) has led to financial burden for patients and society. Value including clinical benefit, toxicity affecting quality of life and cost-effectiveness are a concern, prompting the need for tools to facilitate value assessment of therapeutics. This study reviews the value assessment tools, and evaluates the value of emerging therapeutics in RCC.

Materials and methods

Two medical oncologists used American Society of Clinical Oncology value framework (ASCO VF) v2.0 and European Society for Medical Oncology-magnitude of clinical benefit scale (ESMO-MCBS) v1.1 to phase 3 trials evaluating first-line therapy in patients with metastatic RCC. Follow-up (FU) reports and extended survival data were included. Equivocal aspects and limitations of the tools were discussed.

Results

Six trials (COMPARZ, CheckMate 214, JAVELIN renal 101, Keynote 426, CLEAR, and CheckMate 9ER) were assessed. The control arm was standard-of-care sunitinib in all trials. ASCO VF’s net health benefit, calculated as clinical benefit, toxicity and other bonus point was 11 in pazopanib, 41.9 in nivolumab plus ipilimumab, 22.4 in axitinib plus avelumab, 48.7 in axitinib plus pembrolizumab, 35.2 in lenvatinib plus pembrolizumab, and 50.8 in cabozantinib plus nivolumab. A higher score means a greater treatment benefit. ESMO-MCBS gave grade 5 to nivolumab plus ipilimumab, 4 to pazopanib, lenvatinib plus pembrolizumab and cabozantinib plus nivolumab, 3 to axitinib plus avelumab or pembrolizumab. Both tools had unclear aspects to be applied to clinical practice, and should be more clearly defined, such as endpoint for determining survival benefits or how to standardize quality of life and toxicity.

Conclusions

ASCO VF and ESMO-MCBS were applied to evaluate the newly emerging drugs in RCC and assessed their value. In-depth discussion by experts in various fields is required for appropriate clinical application in a real-world setting.

Synergic Effect of Metformin and Everolimus on Mitochondrial Dynamics of Renal Cell Carcinoma

Renal cell carcinoma (RCC) frequently recurs or metastasizes after surgical resection. Everolimus, an mTOR inhibitor, is used as a second-line treatment, but the response of RCC to everolimus is insufficient. Metformin is an antidiabetic drug; recent reports have indicated its anti-cancer effects in various cancers, and it is known to have synergistic effects with other drugs. We investigated the possibility of coadministering everolimus and metformin as an effective treatment for RCC. RCC cells treated with a combination of the two drugs showed significantly inhibited cell viability, cell migration, and invasion, and increased apoptosis compared to those treated with each drug alone. An anti-cancer synergistic effect was also confirmed in the xenograft model. Transcriptome analysis for identifying the underlying mechanism of the combined treatment showed the downregulation of mitochondrial fusion genes and upregulation of mitochondrial fission genes by the combination treatment. Changes in mitochondrial dynamics following the combination treatment were observed using LysoTracker, LysoSensor, and JC-1 staining. In conclusion, the combination of everolimus and metformin inhibited RCC growth by disrupting mitochondrial dynamics. Therefore, we suggest that a treatment combining metformin and everolimus disrupts mitochondrial dynamics in RCC, and may be a novel strategy for RCC treatment.

Impact of Renal Cell Carcinoma Histological Variants on Recurrence After Partial Nephrectomy: A Multi-Institutional, Prospective Study (UROCCR Study 82)

BACKGROUND

The prognostic impact of renal cell carcinoma (RCC) morphotype remains unclear in patients who undergo partial nephrectomy (PN). Our objective was to determine the risk factors for recurrence after PN, including RCC morphotype.

METHODS

Patients with RCC who had undergone PN were extracted from the prospective, national French database, UroCCR. Patients with genetic predisposition, bilateral or multiple tumours, and those who had undergone secondary totalization were excluded. Primary endpoint was 5-year, recurrence-free survival (RFS), and secondary endpoint was overall survival (OS). Risk factors for recurrence were assessed by multivariable Cox regression analysis.

RESULTS

Overall, 2,767 patients were included (70% male; median age: 61 years [interquartile range (IQR) 51-69]). Most (71.5%) of the PN procedures were robot-assisted. Overall, 2,573 (93.0%) patients were recurrence free, and 74 died (2.7%). Five-year RFS was 84.9% (IQR 82.4-87.4). A significant difference in RFS was observed between RCC morphotypes (p < 0.001). Surgical margins (hazard ratio [HR] = 2.0 [95% confidence interval (CI): 1.3-3.2], p < 0.01), pT stage >1 (HR = 2.6 [95% CI: 1.8-3.7], p < 0.01]) and Fuhrmann grade >2 (HR = 1.9 [95% CI: 1.4-2.6], p < 0.001) were risk factors for recurrence, whereas chromophobe subtype was a protective factor (HR = 0.08 [95% CI: 0.01-0.6], p = 0.02). Five-year OS was 94.0% [92.4-95.7], and there were no significant differences between RCC subgroups (p = 0.06). The main study limitation was its design (multicentre national database), which may be responsible for declarative bias.

CONCLUSIONS

Chromophobe morphotype was significantly associated with better RFS in RCC patients who underwent PN. Conversely, pT stage, Fuhrman group and positive surgical margins were risk factors for recurrence.

KLF2-induced circZKSCAN1 potentiates the tumorigenic properties of clear cell renal cell carcinoma by targeting the miR-1294/PIM1 axis

Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of urologic cancer. With low survival rates among patients in advanced stages of disease, and increasing rate of morbidity and mortality worldwide, novel therapeutic targets for ccRCC clinical intervention are necessary. In this study, we investigated the functional role of circZKSCAN1 in ccRCC progression. Our results suggested that circZKSCAN1 was abundantly expressed in ccRCC tumor tissues and cells. CircZKSCAN1 knockdown significantly inhibited cell proliferation, migration, invasion, and epithelial-to-mesenchymal transition of renal cell carcinoma (RCC) cells, whereas potentiated Natural Killer (NK) cell-mediated cytotoxicity against RCC cells in vitro and repressed tumor growth in vivo. Furthermore, we identified a novel circZKSCAN1/miR-1294/PIM1 axis was identified in RCC progression, showing that the expression of circZKSCAN1 expression in RCC cells was transcriptionally regulated by Kruppel-like factor 2. The results of our study may provide new insights for ccRCC basic research.Abbreviations: ccRCC: clear cell renal cell carcinoma; ChIP: chromatin immunoprecipitation; circRNA: circular RNA; EDU: 5-ethynyl-2'-deoxyuridine; EMT: epithelial-mesenchymal transition; FBS: fetal bovine serum; FISH: RNA fluorescent in situ hybridization; KLF2: Kruppel-like factor 2; NC: normal control; NK cell: natural killer cell; NOD/SCID: nonobese severe diabetic/severe combined immunodeficiency; PIM1: Pim-1 proto-oncogene, serine/threonine kinase; RCC: renal cell carcinoma; ZKSCAN1: zinc finger with KRAB and SCAN domains 1.

Use of Circular RNAs in Diagnosis, Prognosis and Therapeutics of Renal Cell Carcinoma

Renal cell carcinoma is the most common type of kidney cancer, representing 90% of kidney cancer diagnoses, and the deadliest urological cancer. While the incidence and mortality rates by renal cell carcinoma are higher in men compared to women, in both sexes the clinical characteristics are the same, and usually unspecific, thereby hindering and delaying the diagnostic process and increasing the metastatic potential. Regarding treatment, surgical resection remains the main therapeutic strategy. However, even after radical nephrectomy, metastasis may still occur in some patients, with most metastatic renal cell carcinomas being resistant to chemotherapy and radiotherapy. Therefore, the identification of new biomarkers to help clinicians in the early detection, and treatment of renal cell carcinoma is essential. In this review, we describe circRNAs related to renal cell carcinoma processes reported to date and propose the use of some in therapeutic strategies for renal cell carcinoma treatment.

Papillary renal cell carcinoma: a single institutional study of 199 cases addressing classification, clinicopathologic and molecular features, and treatment outcome

The morphologic spectrum of type 1 papillary renal cell carcinoma (PRCC) is not well-defined, since a significant proportion of cases have mixed type 1 and 2 histology. We analyzed 199 cases of PRCC with any (even if focal) type 1 features, with a median follow-up of 12 years, to identify clinicopathological features associated with outcome. Ninety-five tumors (48%) of the cohort contained some type 2 component (median amount: 25%; IQR: 10%, 70%). As a group they showed high rates of progression-free (PFS) and cancer-specific survival (CSS). Tumor size, mitotic rate, lymphovascular invasion, sarcomatoid differentiation, sheet-like architecture, and lack of tumor circumscription were significantly associated with CSS (p ≤ 0.015) on univariate analysis. While predominant WHO/ISUP nucleolar grade was associated with PFS (p = 0.013) and CSS (p = 0.030), the presence of non-predominant (<50%) nucleolar grade did not show association with outcome (p = 0.7). PFS and CSS showed no significant association with the presence or the amount of type 2 morphology. We compared the molecular alterations in paired type 1 and type 2 areas in a subset of 22 cases with mixed type 1 and 2 features and identified 12 recurrently mutated genes including TERT, ARID1A, KDM6A, KMT2D, NFE2L2, MET, APC, and TP53. Among 78 detected somatic mutations, 61 (78%) were shared between the paired type 1 and type 2 areas. Copy number alterations, including chromosome 7 and 17 gains, were similar between type 1 and 2 areas. These findings support that type 2 features in a PRCC with mixed histology represent either morphologic variance or clonal evolution. Our study underscores the notion that PRCC with any classic type 1 regions is best considered as type 1 PRCC and assigned the appropriate WHO/ISUP nucleolar grade. It provides additional evidence that type 2 PRCC as a separate category should be re-assessed and likely needs to be abandoned.

A Visualized Dynamic Prediction Model for Overall Survival in Elderly Patients With Pancreatic Cancer for Smart Medical Services

Background

Pancreatic cancer (PC) is a highly malignant tumor of the digestive system. The number of elderly patients with PC is increasing, and older age is related to a worse prognosis. Accurate prognostication is crucial in treatment decisions made for people diagnosed with PC. However, an accurate predictive model for the prognosis of these patients is still lacking. We aimed to construct nomograms for predicting the overall survival (OS) of elderly patients with PC.

Methods

Patients with PC, older than 65 years old from 2010 to 2015 in the Surveillance, Epidemiology, and End Results database, were selected and randomly divided into training cohort (n = 4,586) and validation cohort (n = 1,966). Data of patients in 2016-2018 (n = 1,761) were used for external validation. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent prognostic factors. We used significant variables in the training set to construct nomograms predicting prognosis. The performance of the models was evaluated for their discrimination and calibration power based on the concordance index (C-index), calibration curve, and the decision curve analysis (DCA).

Results

Age, insurance, grade, surgery, radiation, chemotherapy, T, N, and American Joint Commission on Cancer were independent predictors for OS and thus were included in our nomogram. In the training cohort and validation cohort, the C-indices of our nomogram were 0.725 (95%CI: 0.715-0.735) and 0.711 (95%CI: 0.695-0.727), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves showed similar results. The calibration curves showed a high consensus between observations and predictions. In the external validation cohort, C-index (0.797, 95%CI: 0.778-0.816) and calibration curves also revealed high consistency between observations and predictions. The nomogram-related DCA curves showed better clinical utility compared to tumor-node-metastasis staging. In addition, we have developed an online prediction tool for OS.

Conclusions

A web-based prediction model for OS in elderly patients with PC was constructed and validated, which may be useful for prognostic assessment, treatment strategy selection, and follow-up management of these patients.

A web-based prognostic nomogram for the cancer specific survival of elderly patients with T1-T3N0M0 renal pelvic transitional cell carcinoma based on the surveillance, epidemiology, and end results database

Background

At present, there are few studies on renal pelvic transitional cell carcinoma (RPTCC) in elderly patients in the literature. The study aims to establish a new nomogram of cancer-specific survival (CSS) in elderly patients with T1-T3N0M0 RPTCC and validate its reliability.

Methods

This study downloaded the data of 1375 elderly patients with T1-T3N0M0 RPTCC in the Surveillance, Epidemiology, and Final Results (SEER) database from 2004 to 2018. Patients were randomly divided into training cohort (n = 977) and validation cohort (n = 398). Proportional subdistribution hazard analyse was applied to determine independent prognostic factors. Based on these factors, we constructed a compting risk model nomogram. We use the calibration plots, the area under the receiver operating characteristics curve (AUC), concordance index (C-index), and decision curve analysis (DCA) to validate predictive performance and clinical applicability. Patients were divided into low-risk group and high-risk group based on nomogram risk score. Kaplan–Meier curve was applied to analyze the difference in survival curve between the two groups of patients.

Results

We found that the risk factors affecting CSS in elderly patients with T1-T3N0M0 RPTCC are surgery, AJCC stage, laterality, tumor size, histological grade, and tumour laterality. Based on these factors, we established a nomogram to predict the CSS of RPTCC patients at 1-, 3-, and 5-year. The calibration plots showed that the predicted value was highly consistent with the observed value. In the training cohort and validation cohort, the C-index of the nomogram were 0.671(95% CI 0.622–0.72) and 0.679(95% CI 0.608–0.750), respectively, the AUC showed similar results. The DCA suggests that namogram performs better than the AJCC stage system. The Kaplan–Meier curve showed that CSS of patients was significantly higher in the low-risk group.

Conclusions

In this study, the SEER database was used for the first time to create and validate a new nomogram prediction model for elderly patients with T1-T3N0M0 RPTCC. Compared with the traditional AJCC stage system, our new nomogram can more accurately predict the CSS of elderly patients with T1-T3N0M0 RPTCC, which is helpful for patient prognosis assessment and treatment strategies selection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01028-1.

Deep Learning Algorithm for Fully Automated Detection of Small (≤4 cm) Renal Cell Carcinoma in Contrast-Enhanced Computed Tomography Using a Multicenter Database

OBJECTIVES

Renal cell carcinoma (RCC) is often found incidentally in asymptomatic individuals undergoing abdominal computed tomography (CT) examinations. The purpose of our study is to develop a deep learning-based algorithm for fully automated detection of small (≤4 cm) RCCs in contrast-enhanced CT images using a multicenter database and to evaluate its performance.

MATERIALS AND METHODS

For the algorithmic detection of RCC, we retrospectively selected contrast-enhanced CT images of patients with histologically confirmed single RCC with a tumor diameter of 4 cm or less between January 2005 and May 2020 from 7 centers in the Japan Medical Image Database. A total of 453 patients from 6 centers were selected as dataset A, and 132 patients from 1 center were selected as dataset B. Dataset A was used for training and internal validation. Dataset B was used only for external validation. Nephrogenic phase images of multiphase CT or single-phase postcontrast CT images were used. Our algorithm consisted of 2-step segmentation models, kidney segmentation and tumor segmentation. For internal validation with dataset A, 10-fold cross-validation was applied. For external validation, the models trained with dataset A were tested on dataset B. The detection performance of the models was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC).

RESULTS

The mean ± SD diameters of RCCs in dataset A and dataset B were 2.67 ± 0.77 cm and 2.64 ± 0.78 cm, respectively. Our algorithm yielded an accuracy, sensitivity, and specificity of 88.3%, 84.3%, and 92.3%, respectively, with dataset A and 87.5%, 84.8%, and 90.2%, respectively, with dataset B. The AUC of the algorithm with dataset A and dataset B was 0.930 and 0.933, respectively.

CONCLUSIONS

The proposed deep learning-based algorithm achieved high accuracy, sensitivity, specificity, and AUC for the detection of small RCCs with both internal and external validations, suggesting that this algorithm could contribute to the early detection of small RCCs.

Personalizing First-Line Management of Metastatic Renal Cell Carcinoma: Leveraging Current and Novel Therapeutic Options

The treatment of metastatic renal cell carcinoma (RCC) has been revolutionized by advances in immunotherapeutic and targeted agents. Therapeutic approaches to RCC in these categories have recently evolved to include immune checkpoint inhibitors, novel vascular endothelial growth factor receptor-targeting tyrosine kinase inhibitors, and combinations of those agents. Multiple regimens within each category have been approved for use in the first-line treatment of clear cell and non-clear cell RCC. However, few of these regimens have been directly compared, leading to a new clinical challenge for physicians: how to select a first-line treatment regimen for an individual patient from among multiple approved options. In the modern era of RCC management, the initial treatment selection therefore becomes highly personalized and depends on numerous patient-specific factors, including histopathologic and clinical features of the disease, comorbid conditions, and psychosocial and economic factors. This review details current first-line treatment options for the management of metastatic RCC and proposes a framework whereby treatment selection can be optimized for individual patients.

Association between preoperative serum Cystatin-C levels and postsurgical oncological prognosis in patients with PRCC: A retrospective cohort study

OBJECTIVE

Cystatin-C (Cys-C) is a predictor of several malignancies. However, whether Cys-C levels predict prognosis in patients with papillary renal cell carcinoma (PRCC) remains uncertain. The aim of this study was to assess the correlation between Cys-C and clinical outcomes in patients with PRCC.

METHODS

The medical records of 137 patients with PRCC who underwent surgery at our institution from January 2008 to December 2020 were retrospectively analyzed. Data were divided into two subgroups based on cutoff values and the relationship between the Cys-C group and their clinical outcomes was assessed.

RESULTS

By the last follow-up, 62 patients had died of various causes, 53 of whom died from PRCC. Sixty patients suffered recurrence or metastasis during follow-up. Based on the cutoff value, the patients were divided into two groups: low Cys-C group (Cys-C < 1.25 mg/L, n = 92) and high Cys-C group (Cys-C ≥ 1.25 mg/L, n = 45). Pathological classification and serum Cys-C levels were shown to be independent prognostic factors affecting clinical outcomes, according to multivariate Cox regression analysis (p < 0.05). After adjusting the Cox proportional hazards model, the risk of death was elevated in the high Cys-C group. The results of the area under the curve for time-dependent receiver operating characteristics analysis indicated that Cys-C is a stable and reliable prognostic biomarker for predicting survival in patients with PRCC. Forest plots, constructed to better reflect the comparison of hazard ratios between the two groups, confirmed that Cys-C levels were significantly associated with worsening overall survival.

CONCLUSION

This study is the first to examine the relationship between preoperative serum Cys-C levels and prognostic overall survival in patients with PRCC. Cys-C may be a useful biomarker for preoperative screening of high-risk patients who may require adjuvant therapy.

Co-Expression and Functional Interactions of Death Receptor 3 and E-Selectin in Clear Cell Renal Cell Carcinoma

Similar to the behavior of inflamed tubular epithelial cells, clear cell renal cell carcinoma (ccRCC) cells express death receptor 3 (DR3 or TNFSFR25) in situ, and expression increases with tumor grade. Surprisingly, E-selectin, which can be induced in endothelial cells by DR3 signaling, is also expressed by ccRCC cells and increases with tumor grade. In ccRCC organ cultures, addition of tumor necrosis factor-like 1A (TL1A or TNFSF15), the ligand for DR3, activates NF-κB and mitogen-activated protein kinases, induces both DR3 and E-selectin expression in an NF-κB-dependent manner, and promotes cell cycle entry. DR3 immunoprecipitated from ccRCC tissue contains sialyl Lewis X moieties (the ligand recognized by E-selectin), proximity ligation assays reveal DR3, and E-selectin interacts on ccRCC cells. Similar to that with the addition of TL1A, the addition of soluble E-selectin to ccRCC organ cultures activates NF-κB and mitogen-activated protein kinases in ccRCC cells and increases both DR3 and E-selectin expression and cell-cycle entry. In contrast, normal renal tubular epithelium, which poorly expresses DR3, is minimally responsive to either of these ligands. These data suggest a functional role for autocrine/paracrine DR3/E-selectin interactions in ccRCC and its progression, revealing a potential new target for therapeutic intervention.

Decoding the multicellular ecosystem of vena caval tumor thrombus in clear cell renal cell carcinoma by single-cell RNA sequencing

BACKGROUND

Vascular invasion with tumor thrombus frequently occurs in advanced renal cell carcinoma (RCC). Thrombectomy is one of the most challenging surgeries with high rate of perioperative morbidity and mortality. However, the mechanisms driving tumor thrombus formation are poorly understood which is required for designing effective therapy for eliminating tumor thrombus.

RESULTS

We perform single-cell RNA sequencing analysis of 19 surgical tissue specimens from 8 clear cell renal cell carcinoma (ccRCC) patients with tumor thrombus. We observe tumor thrombus has increased tissue resident CD8+ T cells with a progenitor exhausted phenotype compared with the matched primary tumors. Remarkably, macrophages, malignant cells, endothelial cells and myofibroblasts from TTs exhibit enhanced remodeling of the extracellular matrix. The macrophages and malignant cells from primary tumors represent proinflammatory states, but also increase the expression of immunosuppressive markers compared to tumor thrombus. Finally, differential gene expression and interaction analyses reveal that tumor-stroma interplay reshapes the extracellular matrix in tumor thrombus associated with poor survival.

CONCLUSIONS

Our comprehensive picture of the ecosystem of ccRCC with tumor thrombus provides deeper insights into the mechanisms of tumor thrombus formation, which may aid in the design of effective neoadjuvant therapy to promote downstaging of tumor thrombus and decrease the perioperative morbidity and mortality of thrombectomy.

Tumor-Educated Platelets as a Promising Biomarker for Blood-Based Detection of Renal Cell Carcinoma

Purpose

Tumor-educated platelets (TEPs) are a promising liquid biopsy in many cancers. However, their role in renal cell carcinoma (RCC) is unknown. Thus, this study explored the diagnostic value of TEPs in RCC patients.

Methods

Platelets were prospectively collected from 24 RCC patients and 25 controls. RNA-seq was performed to identify the differentially expressed genes (DEGs) between RCC patients and controls. Besides, RNA-seq data of pan-cancer TEPs were downloaded and randomly divided into training and validation sets. A pan-cancer TEP model was developed in the training set using the support vector machine (SVM) and validated in the validation set and our RCC dataset. Finally, an RCC-based TEP model was developed and optimized through the SVM algorithms and recursive feature elimination (RFE) method.

Result

Two hundred three DEGs, 64 (31.5%) upregulated and 139 (68.5%) downregulated, were detected in the platelets of RCC patients compared with controls. The pan-cancer TEP model had a high accuracy in detecting cancer in the internal validation (training set, accuracy 98.8%, AUC: 0.999; validation set, accuracy 95.4%, AUC: 0.972; different tumor subtypes, accuracy 86.6%–96.1%, AUC: 0.952–1.000). However, the pan-cancer TEP model in the external validation had a scarce diagnostic value in RCC patients (accuracy 48.7%, AUC: 0.615). Therefore, to develop the RCC-based TEP model, the gene biomarkers mostly contributing to the model were selected using the RFE method. The RCC-based TEP model containing 68 gene biomarkers reached a diagnostic accuracy of 100% (AUC: 1.000) in the training set, 88.9% (AUC: 0.963) in the validation set, and 95.9% (AUC: 0.988) in the overall cohort.

Conclusion

TEPs could function as a minimally invasive blood biomarker in the detection of RCC.

TIMP1 Indicates Poor Prognosis of Renal Cell Carcinoma and Accelerates Tumorigenesis via EMT Signaling Pathway

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system. The mortality of advanced RCC remains high despite advances in systemic therapy of RCC. Considering the misdiagnosis of early-stage RCC, the identification of effective biomarkers is of great importance. Tissue inhibitor matrix metalloproteinase 1 (TIMP1), which belongs to TIMP gene family, is a natural inhibitor of the matrix metalloproteinases (MMPs). In this study, we found TIMP1 was significantly up-regulated in cell lines and RCC tissues. Kaplan-Meier analysis revealed that high expression of TIMP1 indicated a poor prognosis. Multivariate analysis further indicated that TIMP1 overexpression was an independent prognostic factor of RCC patients. Furthermore, knockdown of TIMP1 in vitro suppressed the proliferation, migration, and invasion of RCC cells, while upregulating TIMP1 accelerated the proliferation, migration, and invasion of RCC cells. In addition, we also found that TIMP1 prompted the progression of RCC via epithelial-to-mesenchymal transition (EMT) signaling pathway. In conclusion, the present results suggested that TIMP1 indicated poor prognosis of renal cell carcinoma and could serve as a potential diagnostic and prognostic biomarker for RCC.

A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study

Background

The number of elderly patients with early renal cell carcinoma (RCC) is on the rise. However, there is still a lack of accurate prediction models for the prognosis of early RCC in elderly patients. It is necessary to establish a new nomogram to predict the prognosis of elderly patients with early RCC.

Methods

The data of patients aged above 65 years old with TNM stage I and II RCC were downloaded from the SEER database between 2010 and 2018. The patients from 2010 to 2017 were randomly assigned to the training cohort (n = 7233) and validation cohort (n = 3024). Patient data in 2018(n = 1360) was used for external validation. We used univariable and multivariable Cox regression model to evaluate independent prognostic factors and constructed a nomogram to predict the 1-, 3-, and 5-year overall survival (OS) rates of patients with early-stage RCC. Multiple parameters were used to validate the nomogram, including the consistency index (C-index), the calibration plots, the area under the receiver operator characteristics (ROC) curve, and the decision curve analysis (DCA).

Results

The study included a total of 11,617 elderly patients with early RCC. univariable and multivariable Cox regression analysis based on predictive variables such as age, sex, histologic type, Fuhrman grade, T stage, surgery type, tumors number, tumor size, and marriage were included to establish a nomogram. The C-index of the training cohort and validation cohort were 0.748 (95% CI: 0.760–0.736) and 0.744 (95% CI: 0.762–0.726), respectively. In the external validation cohort, C-index was 0.893 (95% CI: 0.928–0.858). The calibration plots basically coincides with the diagonal, indicating that the observed OS was almost equal to the predicted OS. It was shown in DCA that the nomogram has more important clinical significance than the traditional TNM stage.

Conclusion

A novel nomogram was developed to assess the prognosis of an elderly patient with early RCC and to predict prognosis and formulate treatment and follow-up strategies.

Diagnostic performance of MRI, SPECT, and PET in detecting renal cell carcinoma: a systematic review and meta-analysis

Background

Renal cell carcinoma (RCC) is one of the most common malignancies worldwide. Noninvasive imaging techniques, such as magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and positron emission tomography (PET), have been involved in increasing evolution to detect RCC. This meta-analysis aims to compare to compare the performance of MRI, SPECT, and PET in the detection of RCC in humans, and to provide evidence for decision-making in terms of further research and clinical settings.

Methods

Electronic databases including PubMed, Web of Science, Embase, and Cochrane Library were systemically searched. The keywords such as “magnetic resonance imaging”, “MRI”, “single-photon emission computed tomography”, “SPECT”, “positron emission tomography”, “PET”, “renal cell carcinoma” were used for the search. Studies concerning MRI, SPECT, and PET for the detection of RCC were included. Pooled sensitivity, specificity, and the area under the summary receiver operating characteristic (SROC) curve (AUC), etc. were calculated.

Results

A total of 44 articles were finally detected for inclusion in this study. The pooled sensitivities of MRI, ¹⁸F-FDG PET and ¹⁸F-FDG PET/CT were 0.80, 0.83, and 0.89, respectively. Their respective overall specificities were 0.90, 0.86, and 0.88. The pooled sensitivity and specificity of MRI studies at 1.5 T were 0.86 and 0.94, respectively. With respect to prospective PET studies, the pooled sensitivity, specificity and AUC were 0.90, 0.93 and 0.97, respectively. In the detection of primary RCC, PET studies manifested a pooled sensitivity, specificity, and AUC of 0.77, 0.80, and 0.84, respectively. The pooled sensitivity, specificity, and AUC of PET/CT studies in detecting primary RCC were 0.80, 0.85, and 0.89.

Conclusion

Our study manifests that MRI and PET/CT present better diagnostic value for the detection of RCC in comparison with PET. MRI is superior in the diagnosis of primary RCC.

Safety and efficacy of nivolumab plus ipilimumab in patients with advanced non-clear cell renal cell carcinoma: results from the phase 3b/4 CheckMate 920 trial

BACKGROUND

CheckMate 920 (NCT02982954) is a multicohort, phase 3b/4 clinical trial of nivolumab plus ipilimumab treatment in predominantly US community-based patients with previously untreated advanced renal cell carcinoma (RCC) and clinical features mostly excluded from phase 3 trials. We report safety and efficacy results from the advanced non-clear cell RCC (nccRCC) cohort of CheckMate 920.

METHODS

Patients with previously untreated advanced/metastatic nccRCC, Karnofsky performance status ≥70%, and any International Metastatic Renal Cell Carcinoma Database Consortium risk received up to four doses of nivolumab 3 mg/kg plus ipilimumab 1 mg/kg every 3 weeks followed by nivolumab 480 mg every 4 weeks for ≤2 years or until disease progression/unacceptable toxicity. The primary endpoint was incidence of grade ≥3 immune-mediated adverse events (AEs) within 100 days of last dose of study drug. Key secondary endpoints included objective response rate (ORR), progression-free survival (PFS; both investigator-assessed), time to response (TTR), and duration of response (DOR), all using RECIST V.1.1. Overall survival (OS) was exploratory.

RESULTS

Fifty-two patients with nccRCC (unclassified histology, 42.3%; papillary, 34.6%; chromophobe, 13.5%; translocation-associated, 3.8%; collecting duct, 3.8%; renal medullary, 1.9%) received treatment. With 24.1 months minimum study follow-up, median duration of therapy (range) was 3.5 (0.0-25.8) months for nivolumab and 2.1 (0.0-3.9) months for ipilimumab. Median (range) number of doses received was 4.5 (1-28) for nivolumab and 4.0 (1-4) for ipilimumab. Grade 3-4 immune-mediated AEs were diarrhea/colitis (7.7%), rash (5.8%), nephritis and renal dysfunction (3.8%), hepatitis (1.9%), adrenal insufficiency (1.9%), and hypophysitis (1.9%). No grade 5 immune-mediated AEs occurred. ORR (n=46) was 19.6% (95% CI 9.4 to 33.9). Two patients achieved complete response (papillary, n=1; unclassified, n=1), seven achieved partial response (papillary, n=4; unclassified, n=3), and 17 had stable disease. Median TTR was 2.8 (range 2.1-14.8) months. Median DOR was not reached (range 0.0+-27.8+); eight of nine responders remain without reported progression. Median PFS (n=52) was 3.7 (95% CI 2.7 to 4.6) months. Median OS (n=52) was 21.2 (95% CI 16.6 to not estimable) months.

CONCLUSIONS

Nivolumab plus ipilimumab for previously untreated advanced nccRCC showed no new safety signals and encouraging antitumor activity.

TRIAL REGISTRATION NUMBER

NCT02982954.

Everolimus accelerates Erastin and RSL3-induced ferroptosis in renal cell carcinoma

Renal cell carcinoma (RCC) is a common type of urological cancer and is often diagnosed at an advanced stage. Everolimus, an inhibitor of mammalian target of rapamycin (mTOR), is used as second-line therapy for sorafenib- or sunitinib-refractory metastatic RCC. However, the clinical benefits of Everolimus are often hampered by drug resistance. Ferroptosis is a novel form of regulated cell death that has recently been implicated in the development and therapeutic responses to different cancers. RSL3 ((1S,3R)-RSL3) and Erastin are two experimental compounds that can induce ferroptosis. In the present study, we evaluated the anti-tumor effects of Everolimus in combination with RSL3 or Erastin in RCC. Everolimus and RSL3/Erastin could synergistically inhibit the viability and induce ferroptosis in RCC cells. Mechanistically, the inhibition of the mTOR-4EBP1 axis was found to be essential for the synergistic effects of Everolimus and RSL3/Erastin. Moreover, the forced expression of GPX4 abrogated ferroptosis induced by the combined treatment of Everolimus and RSL3/Erastin. Taken together, these results demonstrated that Everolimus in combination with RSL3/Erastin is a promising therapeutic option for RCC treatment and it may also help to overcome the limitation in clinical applicability of Everolimus.

piRNAs and PIWI Proteins as Diagnostic and Prognostic Markers of Genitourinary Cancers

piRNAs (PIWI-interacting RNAs) are small non-coding RNAs capable of regulation of transposon and gene expression. piRNAs utilise multiple mechanisms to affect gene expression, which makes them potentially more powerful regulators than microRNAs. The mechanisms by which piRNAs regulate transposon and gene expression include DNA methylation, histone modifications, and mRNA degradation. Genitourinary cancers (GC) are a large group of neoplasms that differ by their incidence, clinical course, biology, and prognosis for patients. Regardless of the GC type, metastatic disease remains a key therapeutic challenge, largely affecting patients’ survival rates. Recent studies indicate that piRNAs could serve as potentially useful biomarkers allowing for early cancer detection and therapeutic interventions at the stage of non-advanced tumour, improving patient’s outcomes. Furthermore, studies in prostate cancer show that piRNAs contribute to cancer progression by affecting key oncogenic pathways such as PI3K/AKT. Here, we discuss recent findings on biogenesis, mechanisms of action and the role of piRNAs and the associated PIWI proteins in GC. We also present tools that may be useful for studies on the functioning of piRNAs in cancers.

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.

Kidney Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology

The NCCN Guidelines for Kidney Cancer focus on the screening, diagnosis, staging, treatment, and management of renal cell carcinoma (RCC). Patients with relapsed or stage IV RCC typically undergo surgery and/or receive systemic therapy. Tumor histology and risk stratification of patients is important in therapy selection. The NCCN Guidelines for Kidney Cancer stratify treatment recommendations by histology; recommendations for first-line treatment of ccRCC are also stratified by risk group. To further guide management of advanced RCC, the NCCN Kidney Cancer Panel has categorized all systemic kidney cancer therapy regimens as "Preferred," "Other Recommended Regimens," or "Useful in Certain Circumstances." This categorization provides guidance on treatment selection by considering the efficacy, safety, evidence, and other factors that play a role in treatment selection. These factors include pre-existing comorbidities, nature of the disease, and in some cases consideration of access to agents. This article summarizes surgical and systemic therapy recommendations for patients with relapsed or stage IV RCC.

A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma

Objective

This study aims to develop and validate a CT-based radiomics nomogram integrated with clinic-radiological factors for preoperatively differentiating high-grade from low-grade clear cell renal cell carcinomas (CCRCCs).

Methods

370 patients with complete clinical, pathological, and CT image data were enrolled in this retrospective study, and were randomly divided into training and testing sets with a 7:3 ratio. Radiomics features were extracted from nephrographic phase (NP) contrast-enhanced images, and then a radiomics model was constructed by the selected radiomics features using a multivariable logistic regression combined with the most suitable feature selection algorithm determined by the comparison among least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE) and ReliefF. A clinical model was established using clinical and radiological features. A radiomics nomogram was constructed by integrating the radiomics signature and independent clinic-radiological features. Performance of these three models was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

Results

Using multivariate logistic regression analysis, three clinic-radiological features including intratumoral necrosis (OR=3.00, 95% CI=1.30-6.90, p=0.049), intratumoral angiogenesis (OR=3.28, 95% CI=1.22-8.78, p=0.018), and perinephric metastasis (OR=2.90, 95% CI=1.03-8.17, p=0.044) were found to be independent predictors of WHO/ISUP grade in CCRCC. Incorporating the above clinic-radiological predictors and radiomics signature constructed by LASSO, a CT-based radiomics nomogram was developed, and presented better predictive performance than clinic-radiological model and radiomics signature model, with an AUC of 0.891 (95% CI=0.832-0.962) and 0.843 (95% CI=0.718-0.975) in the training and testing sets, respectively. DCA indicated that the nomogram has potential clinical usefulness.

Conclusion

The CT-based radiomics nomogram is a promising tool to predict WHO/ISUP grade of CCRCC preoperatively and noninvasively.

Tumor antigens and immune subtypes guided mRNA vaccine development for kidney renal clear cell carcinoma

Current treatment strategy for kidney renal clear cell carcinoma (KIRC) is limited. Tumor-associated antigens, especially neoantigen-based personalized mRNA vaccines represent new strategies and manifest clinical benefits in solid tumors, but only a small proportion of patients could benefit from them, which prompts us to identify effective antigens and suitable populations to facilitate mRNA vaccines application in cancer therapy. Through performing expression, mutation, survival and correlation analyses in TCGA-KIRC dataset, we identified four genes including DNA topoisomerase II alpha (TOP2A), neutrophil cytosol factor 4 (NCF4), formin-like protein 1 (FMNL1) and docking protein 3 (DOK3) as potential KIRC-specific neoantigen candidates. These four genes were upregulated, mutated and positively associated with survival and antigen-presenting cells in TCGA-KIRC. Furthermore, we identified two immune subtypes, named renal cell carcinoma immune subtype 1 (RIS1) and RIS2, of KIRC. Distinct clinical, molecular and immune-related signatures were observed between RIS1 and RIS2. Patients of RIS2 had better survival outcomes than those of RIS1. Further comprehensive immune-related analyses indicated that RIS1 is immunologically “hot” and represent an immunosuppressive phenotype, whereas RIS2 represents an immunologically “cold” phenotype. RIS1 and RIS2 also showed differential features with regard to tumor infiltrating immune cells and immune checkpoint-related genes. Moreover, the immune landscape construction identified the immune cell components of each KIRC patient, predicted their survival outcomes, and assisted the development of personalized mRNA vaccines. In summary, our study identified TOP2A, NCF4, FMNL1 and DOK3 as potential effective neoantigens for KIRC mRNA vaccine development, and patients with RIS2 tumor might benefit more from mRNA vaccination.

A CT-based radiomics nomogram for differentiation of small masses (< 4 cm) of renal oncocytoma from clear cell renal cell carcinoma

PURPOSE

Renal oncocytoma (RO) is the most commonly resected benign renal tumor because of misdiagnosis as renal cell carcinoma. This misdiagnosis is generally owing to overlapping imaging features. This study describes the building of a radiomics nomogram based on clinical data and radiomics signature for the preoperative differentiation of RO from clear cell renal cell carcinoma (ccRCC) on tri-phasic contrast-enhanced CT.

METHODS

A total of 122 patients (85 in training set and 37 in external validation set) with ROs (n = 46) or ccRCCs (n = 76) were enrolled. Patient characteristics and tri-phasic contrast-enhanced CT imaging features were evaluated to build a clinical factors model. A radiomics signature was constructed by extracting radiomics features from tri-phasic contrast-enhanced CT images and a radiomics score (Rad-score) was calculated. A radiomics nomogram was then built by incorporating the Rad-score and significant clinical factors according to a multivariate logistic regression analysis. The diagnostic performance of the above three models was evaluated in training and validation sets.

RESULTS

Central stellate area and perirenal fascia thickening were selected to build the clinical factors model. Eleven radiomics features were combined to construct the radiomics signature. The AUCs of the radiomics nomogram, which was based on the selected clinical factors and Rad-score, were 0.960 and 0.898 in the training and validation sets, respectively. The decision curves of the radiomics nomogram and radiomics signature in the validation set indicated an overall net benefit over the clinical factors model.

CONCLUSION

Our radiomics nomogram can effectively predict the preoperative diagnosis of ROs and may therefore be of assistance in sparing unnecessary surgery and tailoring precise therapy. The ROC curves of the clinical model, the radiomics signature and the radiomics nomogram for the validation set. RO = Renal oncocytoma; ccRCC = Clear cell renal cell carcinoma.

Development and validation of nomogram based on a novel platelet index score to predict prognosis in patients with renal cell carcinoma

Purpose: This study aims to develop and validate a nomogram based on a novel platelet index score (PIS) to predict prognosis in patients with renal cell carcinoma (RCC).

Patients and methods: We retrospectively analyzed the data of 759 consecutive patients with RCC. The Kaplan-Meier curves were performed to analyze the platelet parameters and PIS was established. The patients were randomly divided into training (N=456, 60%) and validation cohorts (N=303, 40%). The nomogram was created based on the factors determined by multivariable Cox proportional hazard regression of the training cohort. We assessed the discrimination and calibration of our nomogram in both training and validation cohorts. And then the nomogram was compared with other reported models.

Results: High platelet count (PLT>285×10⁹/L) and low platelet distribution width (PDW≤10.95fL) were associated with shorter progression-free survival (PFS). Thus, PLT and PDW were incorporated in a novel score system called PIS. On multivariable analysis of training cohort, PIS, American Joint Committee on Cancer (AJCC) stage, and sarcomatoid differentiation were independent prognostic factors, which were all selected into the nomogram. The nomogram exhibited good discrimination in both training (C-index: 0.835) and validation cohorts (C-index: 0.883). The calibration curves also showed good agreement between prediction and observation in both cohorts. The C-index of the nomogram (C-index: 0.810~0.902) for predicting 2-year, 3-year, and 4-year PFS were significantly higher than Leibovich (C-index: 0.772~0.813), SSIGN (C-index: 0.775~0.876), Cindolo (C-index: 0.642~0.798), Yaycioglu (C-index: 0.648~0.804), MSKCC (C-index: 0.761~0.862), Karakiewicz (C-index: 0.747~0.851), and AJCC stage models (C-index: 0.759~0.864).

Conclusion: The nomogram based on a novel PIS could offer better risk stratification in patients with RCC.

Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis

OBJECTIVE

To investigate the influence of different volume of interest (VOI) delineation strategies on machine learning-based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT.

METHODS

This study retrospectively collected 177 patients with pathologically proven ccRCC (124 low-grade; 53 high-grade). Tumor VOI was manually segmented, followed by artificially introducing uncertainties as: (i) contour-focused VOI, (ii) margin erosion of 2 or 4 mm, and (iii) margin dilation (2, 4, or 6 mm) inclusive of perirenal fat, peritumoral renal parenchyma, or both. Radiomics features were extracted from four-phase CT images (unenhanced phase (UP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP)). Different combinations of four-phasic features for each VOI delineation strategy were used to build 176 classification models. The best VOI delineation strategy and superior CT phase were identified and the top-ranked features were analyzed.

RESULTS

Features extracted from UP and EP outperformed features from other single/combined phase(s). Shape features and first-order statistics features exhibited superior discrimination. The best performance (ACC 81%, SEN 67%, SPE 87%, AUC 0.87) was achieved with radiomics features extracted from UP and EP based on contour-focused VOI.

CONCLUSION

Shape and first-order features extracted from UP + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear grading.

KEY POINTS

• Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading. • Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC. • Shape features and first-order statistics features showed superior discriminative capability compared to texture features.

Comprehensive Analysis of the Immune Infiltrates of Pyroptosis in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) has long been identified as a highly immune-infiltrated tumor. However, the underlying role of pyroptosis in the tumor microenvironment (TME) of KIRC remains poorly described. Herein, we systematically analyzed the prognostic value, role in the TME, response to ICIs, and drug sensitivity of pyroptosis-related genes (PRGs) in KIRC patients based on The Cancer Genome Atlas (TCGA) database. Cluster 2, by consensus clustering for 24 PRGs, presented a poor prognosis, likely because malignancy-related hallmarks were remarkably enriched. Additionally, we constructed a prognostic prediction model that discriminated well between high- and low-risk patients and was further confirmed in external E-MTAB-1980 cohort and HSP cohort. By further analyzing the TME based on the risk model, higher immune cell infiltration and lower tumor purity were found in the high-risk group, which presented a poor prognosis. Patients with high risk scores also exhibited higher ICI expression, indicating that these patients may be more prone to profit from ICIs. The sensitivity to anticancer drugs that correlated with model-related genes was also identified. Collectively, the pyroptosis-related prognosis risk model may improve prognostic information and provide directions for current research investigations on immunotherapeutic strategies for KIRC patients.

Development and validation of a PBRM1-associated immune prognostic model for clear cell renal cell carcinoma

Alteration in the polybromo‐1 (PBRM1) protein encoding gene PBRM1 is the second most frequent mutation in clear cell renal cell carcinoma (ccRCC). It causes a series of changes in the tumorigenesis, progression, prognosis, and immune response of ccRCC patients. This study explored the PBRM1‐associated immunological features and identified the immune‐related genes (IRGs) linked to PBRM1 mutation using bioinformatics methods. A total of 37 survival IRGs associated with PBRM1 mutation in ccRCC patients were identified. To further explore the role of these IRGs in ccRCC and their association with immune status, eight IRGs with remarkable potential as individual targets were selected. An immune model that was constructed showed good performance in stratifying patients into different subgroups, showing clinical application potential compared to traditional clinical factors. Patients in the high‐risk group were inclined to have more advanced stage and higher grade tumors with node metastasis, distant metastasis, and poorer prognosis. Furthermore, these patients had high percentages of regulatory T cells, follicular helper T cells, and M0 macrophages and exhibited high expression levels of immune checkpoints proteins, such as CTLA‐4, PD‐1, LAG‐3, TIGIT, and CD47. Finally, a nomogram integrating the model and clinical factors for clinical application showed a more robust predictive performance for prognosis. The prediction model associated with PBRM1 mutation status and immunity can serve as a promising tool to stratify patients depending upon their immune status, thus facilitating immunotherapy in the future.

A costimulatory molecule-related signature in regard to evaluation of prognosis and immune features for clear cell renal cell carcinoma

Costimulatory molecules have been proven to enhance antitumor immune responses, but their roles in clear cell renal cell carcinoma (ccRCC) remain unexplored. In this study, we aimed to explore the gene expression profiles of costimulatory molecule genes in ccRCC and construct a prognostic signature to improve treatment decision-making and clinical outcomes. We performed the first comprehensive analysis of costimulatory molecules in patients with ccRCC and identified 13 costimulatory molecule genes with prognostic values and diagnostic values. Consensus clustering analysis based on these 13 costimulatory molecular genes showed different distribution patterns and prognostic differences for the two clusters identified. Then, a costimulatory molecule-related signature was constructed based on these 13 costimulatory molecular genes, and validated in an external dataset, showing good performance for predicting a patient’s prognosis. The signature was an independent risk factor for ccRCC patients and was significantly correlated with patients’ clinical factors, which could be used as a complement for clinical factors. In addition, the signature was associated with the tumor immune microenvironment and the response to immunotherapy. Patients identified as high-risk based on our signature exhibited a high mutation frequency, a high level of immune cell infiltration, and an immunosuppressive microenvironment. High-risk patients tended to have high cytolytic activity scores and immunophenoscore of CTLA4 and PD1/PD-L1/PD-L2 blocker than low-risk patients, suggesting these patients may be more suitable for immunotherapy. Therefore, our signature could provide clinicians with prognosis predictions and help guide treatment for ccRCC patients.

Influence of Histologic Types and Subtypes on Survival Outcomes of Intermediate-High and High-Risk Renal Cell Carcinoma Following Nephrectomy: Findings From the SEER Database

OBJECTIVE

To understand the influence of histologic subtypes on the survival outcomes of intermediate-high and high-risk renal cell carcinoma (RCC) following nephrectomy.

METHODS

This study employed data files from the SEER Program to identify patients diagnosed with intermediate-high or high risk RCC and treated with nephrectomy. Unadjusted Kaplan Meier curves, and multivariable Cox regression analyses were applied to estimate the hazards of histologic types for overall survival (OS) and cancer-specific survival (CSS).

RESULTS

OS was higher for chromophobe (HR=0.58, 95% CI 0.47-0.70; P<.0001), similar for papillary (HR=0.90, 95% CI 0.80-1.02; P=.11) and worse for sarcomatoid (HR=3.17, 95% CI 2.70-3.72; P<.0001) subtypes relative to the clear cell subtype. OS was lower in the high-risk disease (HR=2.35, 95% CI 2.01-2.74; P <.0001) versus intermediate-high risk disease. CSS was higher for chromophobe (HR=0.47, 95% CI 0.35-0.63; P<.0001), similar for papillary (HR=0.91, 95% CI 0.77-1.08; P=.28) and worse for sarcomatoid (HR=4.19, 95% CI 3.50-5.02; P<.0001) subtypes relative to the clear cell subtype. CSS was lower for the high-risk disease (HR=2.86, 95%CI 2.39-3.43; P <.0001) relative to intermediate-high risk disease.

Surgical Insights for the Management of Variant Histology in Renal Cell Carcinoma

PURPOSE

To review the current literature regarding variant (non-clear) histology of renal cell carcinoma (RCC) and the clinical management of these renal tumors.

MATERIAL AND METHODS

A PubMed database search was performed in May 2020 focusing on variant RCC, its diagnosis and associated syndromes, tumor characteristics, and options for management.

RESULTS

A broad range of pathological, clinical and diagnostic characteristics amongst non-ccRCC variants were found to have an impact on the overall management of these tumors. The imaging modalities, frequency of surveillance, and timing for intervention were found to be dependent on the type of genetic alterations, type of histology, and tumor growth rates. The timing and type of surgery as well as the systemic therapy are tailored to the specific tumor type and patient.

CONCLUSION

The findings of this review suggest that clinical management should be considered and adjusted for patients with non-ccRCC histological variants based on tumor subtype and genetic alterations.

Determining the immunohistochemical expression of GLUT1 in renal cell carcinoma using the HSCORE method

The aim of the present study was to compare the immunohistochemical expression of glucose transporter 1 (GLUT1) between the most common histological types of renal cell carcinoma (RCC), and to determine whether a correlation between GLUT1 expression and nuclear grade or tumor size exists. A total of 19 RCC samples were selected for the study, consisting of 8 clear cell (cc)RCC and 11 non-ccRCC tissues. Immunohistochemistry for GLUT1 was performed on formalin-fixed and paraffin-embedded sections using GLUT1 antibodies. All data analyses were performed using the MedCalc software. There was a higher immunohistochemical expression of GLUT1 in the ccRCC group compared with the non-cc group, but there was no difference in GLUT1 expression between groups of RCCs with differing nuclear grades. No significant correlation between GLUT1 expression and tumor size was found. The higher immunohistochemical expression of GLUT1 in ccRCC may be a contributing factor to the clinical characteristics and behavior of that group of carcinomas. These results suggest that GLUT1 expression cannot be used as a prognostic factor for RCC, but it may be used as a predictive factor in the future.

CT Radiomics for the Prediction of Synchronous Distant Metastasis in Clear Cell Renal Cell Carcinoma

PURPOSE

The aim of this study was to construct and verify a computed tomography (CT) radiomics model for preoperative prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC) patients.

METHODS

Overall, 172 patients with ccRCC were enrolled in the present research. Contrast-enhanced CT images were manually sketched, and 2994 quantitative radiomic features were extracted. The radiomic features were then normalized and subjected to hypothesis testing. Least absolute shrinkage and selection operator (LASSO) was applied to dimension reduction, feature selection, and model construction. The performance of the predictive model was validated through analysis of the receiver operating characteristic curve. Multivariate and subgroup analyses were performed to verify the radiomic score as an independent predictor of SDM.

RESULTS

The patients randomized into a training (n = 104) and a validation (n = 68) cohort in a 6:4 ratio. Through dimension reduction using LASSO regression, 9 radiomic features were used for the construction of the SDM prediction model. The model yielded moderate performance in both the training (area under the curve, 0.89; 95% confidence interval, 0.81-0.97) and the validation cohort (area under the curve, 0.83; 95% confidence interval, 0.69-0.95). Multivariate analysis showed that the CT radiomic signature was an independent risk factor for clinical parameters of ccRCC. Subgroup analysis revealed a significant connection between the SDM and radiomic signature, except for the lower pole of the kidney subgroup.

CONCLUSIONS

The CT-based radiomics model could be used as a noninvasive, personalized approach for SDM prediction in patients with ccRCC.