Comparisons of outcome and prognostic features among histologic subtypes of renal cell carcinoma

Diagnostic Workup for Patients with Solid Renal Masses: A Cost-Effectiveness Analysis

Background: For patients with solid renal masses, a precise differentiation between malignant and benign tumors is crucial for forward treatment management. Even though MRI and CT are often deemed as the gold standard in the diagnosis of solid renal masses, CEUS may also offer very high sensitivity in detection. The aim of this study therefore was to evaluate the effectiveness of CEUS from an economical point of view. Methods: A decision-making model based on a Markov model assessed expenses and utilities (in QALYs) associated with CEUS, MRI and CT. The utilized parameters were acquired from published research. Further, a Monte Carlo simulation-based deterministic sensitivity analysis of utilized variables with 30,000 repetitions was executed. The willingness-to-pay (WTP) is at USD 100,000/QALY. Results: In the baseline, CT caused overall expenses of USD 10,285.58 and an efficacy of 11.95 QALYs, whereas MRI caused overall expenses of USD 7407.70 and an efficacy of 12.25. Further, CEUS caused overall expenses of USD 5539.78, with an efficacy of 12.44. Consequently, CT and MRI were dominated by CEUS, and CEUS remained cost-effective in the sensitivity analyses. Conclusions: CEUS should be considered as a cost-effective imaging strategy for the initial diagnostic workup and assessment of solid renal masses compared to CT and MRI.

Development and Validation of a Competitive Risk Model in Elderly Patients With Chromophobe Cell Renal Carcinoma: A Population-Based Study

Background

Renal cell carcinoma (RCC) is the most common renal malignancy in adults, and chromophobe renal cell carcinoma (chRCC) is the third most common subtype of RCC. We aimed to construct a competitive risk model to predict cancer-specific survival (CSS) in elderly patients with chRCC.

Methods

The clinicopathological information of the patients was downloaded from the SEER database, and the patients were randomly divided into the training and validation cohorts. Patients' risk factors for cancer-specific death (CSM) were analyzed using proportional subdistribution hazard (SH). We constructed a competitive risk model to predict the CSS of elderly chRCC patients. Consistency index (C-index), the area under receiver operating curve (AUC), and a calibration curve were used to validate the model's accuracy. Decision curve analysis (DCA) was used to test the clinical value of the model.

Results

A total of 3,522 elderly patients with chRCC were included in the analysis. Patients were randomly assigned to either the training cohort (N = 2,474) or the validation cohort (N = 1,048). SH analysis found that age, race, T, N, and M stage, tumor size, and surgery were risk factors for CSM. We constructed a competitive risk model to predict patients' CSS. In the training set, the model predicted patients' 1-, 3-, and 5-year CSS with C-indices of 82.2, 80.8, and 78.2, respectively. The model predicted patient 1-, 3-, and 5-year CSS in the validation cohort with C-indices of 84.7, 83.4, and 76.9, respectively. The calibration curve showed that the model's predicted value is almost consistent with the observed value, which indicated that the model has good accuracy. The AUC of the training set and validation queue also suggested that the model has good discrimination. The clinical utility of the DCA model in predicting patients' CSS is higher than that of traditional TNM staging.

Conclusions

We constructed a competitive risk model to predict CSS in elderly patients with chRCC. The model has good accuracy and reliability, which can help doctors and patients to make clinical decisions and follow-up strategies.

The Diagnostic and Prognostic Values of HOXA Gene Family in Kidney Clear Cell Renal Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most common cancers with high mortality worldwide. As members of the homeobox (HOX) family, homeobox-A (HOXA) genes have been reported to play an increasingly important role in tumorigenesis and the progression of multiple cancers. However, limited studies have investigated the potential diagnostic and prognostic roles of HOXA genes in KIRC. In this research, we explored the expression pattern of the HOXA gene family in KIRC progression by differential analysis of expression profiles from The Cancer Genome Atlas (TCGA). By using univariate Cox analysis and lasso regression analysis, we comprehensively evaluated the prognostic value of HOXA genes and eventually identified a prognostic risk model consisting of five HOXA genes (HOXA2, HOXA3, HOXA7, HOXA11, and HOXA13). The risk model was further validated as a novel independent prognostic factor for KIRC patients based on the calculated risk score by Kaplan-Meier analysis, univariate and multivariate Cox regression analyses, and time-dependent receiver operating characteristic (ROC) curve analysis. Moreover, to explore the potential mechanism of tumorigenesis and clinical application of KIRC, we also developed the HOXA-based competing endogenous RNA (ceRNA) regulatory network and machine learning classification model. Valproic acid and tretinoin were predicted to be the most promising small molecules to adjuvant treatment of KIRC by mining the CMAP and DGIdb drug database. Subsequently, pathway and functional enrichment analyses provided us with new ways to search for a possible mechanism of action of drugs. Taken together, our study demonstrated the nonnegligible role of HOXA genes in KIRC and constructed an effective prognostic and diagnostic model, which offers novel insights into KIRC prognosis.

Prognostic Significance of Percentage Necrosis in Clear Cell Renal Cell Carcinoma

OBJECTIVES

The consensus conference of the International Society of Urological Pathology (ISUP), held in 2012, made recommendations regarding prognostic parameters of renal tumors. There was a strong consensus that tumor morphotype, pathologic tumor stage, and tumor grade are prognostic indicators of poor outcome. It was also agreed upon that prognostic significance of tumor necrosis is in evolution, and both microscopic and macroscopic tumor necrosis should be documented in percentages. The aim of our study was to explore the impact of tumor necrosis on metastasis-free survival in clear cell renal carcinomas (ccRCCs) in Pakistani patients.

METHODS

We retrieved 318 consecutive in-house cases of ccRCC resections from 2014 to 2020 through hospital archives. Histologic slide review was done for assessment of tumor necrosis, tumor stage, and World Health Organization/ISUP grade. The follow-up data to assess metastasis-free survival were available in hospital archives.

RESULTS

In multivariable analysis performed by logistic regression model, tumor necrosis was an independent poor prognostic indicator (P = .0001): group 1 (reference group), 0% necrosis; group 2, 1% to 10% necrosis (adjusted odds ratio [AOR], 8.71; 95% confidence interval [CI], 3.62-20.98); and group 3, more than 10% necrosis (AOR, 9.48; 95% CI, 3.99-22.725).

CONCLUSIONS

Tumor necrosis is an independent predictor of poor outcome in ccRCCs.

Pharmacometabolomics Applied to Personalized Medicine in Urological Cancers

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most common urological cancers, and their incidence has been rising over time. Surgery is the standard treatment for these cancers, but this procedure is only effective when the disease is localized. For metastatic disease, PCa is typically treated with androgen deprivation therapy, while BCa is treated with chemotherapy, and RCC is managed primarily with targeted therapies. However, response rates to these therapeutic options remain unsatisfactory due to the development of resistance and treatment-related toxicity. Thus, the discovery of biomarkers with prognostic and predictive value is needed to stratify patients into different risk groups, minimizing overtreatment and the risk of drug resistance development. Pharmacometabolomics, a branch of metabolomics, is an attractive tool to predict drug response in an individual based on its own metabolic signature, which can be collected before, during, and after drug exposure. Hence, this review focuses on the application of pharmacometabolomic approaches to identify the metabolic responses to hormone therapy, targeted therapy, immunotherapy, and chemotherapy for the most prevalent urological cancers.

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.

Head-to-head comparison of all the prognostic models recommended by the European Association of Urology Guidelines to predict oncologic outcomes in patients with renal cell carcinoma

OBJECTIVES

European Urology Association guidelines suggest the use of integrated prognostic systems to assess oncologic outcomes after surgery in patients with localized renal cell carcinoma (RCC). We performed a head-to-head comparison among all the EAU guidelines recommended prognostic models in RCC.

METHODS

The study included 2,014 patients treated with surgery for clinically localized RCC. Patients were classified into prognostic risk groups, based on each of the five EAU guidelines recommended prognostic model definition, namely UISS, Leibovich 2003, VENUSS, GRANT, and Leibovich 2018 score. Prognostic accuracy of each prognostic model to predict clinical progression or cancer-specific mortality (CSM) was assessed, and ROC curves were calculated, according to histological subtype, namely clear-cell, papillary, and chromophobe RCC.

RESULTS

Of 2,014 patients, 1,575 (78%) harboured clear-cell, 312 (16%) papillary, and 127 (6%) chromophobe RCC. Median follow-up was 66 months [Interquartile range (IQR): 29-120]. In clear-cell RCC, low-risk patients rates ranged from 21% to 64%, according prognostic model. The same phenomenon was observed for papillary and chromophobe RCC. In clear-cell RCC, Leibovich 2018 resulted the most accurate model in predicting clinical progression (88.1%) and CSM (86.8%). Conversely, VENUSS or UISS prognostic models predicting oncologic outcomes represented the most accurate in papillary (88.7% and 84.8%) or chromophobe (87.8% and 89.1%) RCC, respectively.

CONCLUSIONS

A non-negligible difference in terms of performance accuracy exists among the EAU guidelines recommended prognostic models. Thus, their adoption in RCC should be histology-specific and follow-up strategies based on prognostic risk class appear justified only if the appropriate model is used to stratify patients into prognostic risk groups.

Immunotherapy: A new standard in the treatment of metastatic clear cell renal cell carcinoma

Renal cell cancer (RCC) represents 2%-3% of all adulthood cancers and is the most common malignant neoplasm of the kidney (90%). In the mid-nineties of the last century, the standard of treatment for patients with metastatic RCC was cytokines. Sunititib and pazopanib were registered in 2007 and 2009, respectively, and have since been the standard first-line treatment for metastatic clear cell RCC (mccRCC). Renal cell cancer is a highly immunogenic tumor with tumor infiltrating cells, including CD8+ T lymphocytes, dendritic cells, natural killer cells (NK) and macrophages. This observation led to the design of new clinical trials in which patients were treated with immunotherapy. With the growing evidence that proangiogenic factors can have immunomodulatory effects on the host's immune system, the idea of combining angiogenic drugs with immunotherapy has emerged, and new clinical trials have been designed. In the last few years, several therapeutic options have been approved [immunotherapy and immunotherapy/tyrosine kinase inhibitors (TKI)] for the first-line treatment of mccRCC. Nivolumab/ipilimumab is approved for the treatment of patients with intermediate and poor prognoses. Several checkpoint inhibitors (pembrolizumab, nivolumab, avelumab) in combination with TKI (axitinib, lenvatinib, cabozantinib) are approved for the treatment of patients regardless of their International mRCC Database Consortium prognostic group and PD-L1 expression. There is no specific and ideal biomarker that could help in selecting the ideal patient for the appropriate first-line treatment.

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.

Grading of clear cell renal cell carcinoma using diffusion MRI with a fractional order calculus model

BACKGROUND

The fractional order calculus (FROC) model has been developed to describe restrained motion of water molecules as well as microstructural heterogeneity, providing a novel tool for non-invasive tumor grading.

PURPOSE

To evaluate the role of the FROC model in characterizing clear cell renal cell carcinoma (ccRCC) grades.

MATERIAL AND METHODS

A total of 59 patients diagnosed with ccRCC were included in this prospective study. The diffusion metrics derived from the mono-exponential model (apparent diffusion coefficient [ADC]), intra-voxel incoherent motion [IVIM] model [D, D*, f], and FROC model [D_froc, β, μ]) were calculated and compared between low- and high-grade ccRCCs. Binary logistic regression analysis was performed to establish the diagnostic models. Receiver operating characteristic (ROC) analysis and DeLong test were performed to evaluate and compare the diagnostic performance of metrics in grading ccRCC.

RESULTS

All the metrics except D* and f exhibited statistical differences between low- and high-grade ccRCCs. ROC analysis showed individual FROC parameters, μ, D_froc, and β, outperformed ADC and IVIM parameters in grading ccRCC. For single parameter, μ demonstrated the highest AUC value, sensitivity, and diagnostic accuracy in discriminating the two ccRCC groups while β exhibited the optimal specificity. Importantly, the combination of D_froc, μ, and β could further improve the diagnostic performance.

CONCLUSION

The FROC parameters were superior to ADC and IVIM parameters in grading ccRCC, indicating the great potential of the FROC model in distinguishing low- and high-grade ccRCCs.

Hyperpolarized 13C-Pyruvate Metabolism as a Surrogate for Tumor Grade and Poor Outcome in Renal Cell Carcinoma-A Proof of Principle Study

Differentiating aggressive clear cell renal cell carcinoma (ccRCC) from indolent lesions is challenging using conventional imaging. This work prospectively compared the metabolic imaging phenotype of renal tumors using carbon-13 MRI following injection of hyperpolarized [1-13C]pyruvate (HP-13C-MRI) and validated these findings with histopathology. Nine patients with treatment-naïve renal tumors (6 ccRCCs, 1 liposarcoma, 1 pheochromocytoma, 1 oncocytoma) underwent pre-operative HP-13C-MRI and conventional proton (1H) MRI. Multi-regional tissue samples were collected using patient-specific 3D-printed tumor molds for spatial registration between imaging and molecular analysis. The apparent exchange rate constant (kPL) between 13C-pyruvate and 13C-lactate was calculated. Immunohistochemistry for the pyruvate transporter (MCT1) from 44 multi-regional samples, as well as associations between MCT1 expression and outcome in the TCGA-KIRC dataset, were investigated. Increasing kPL in ccRCC was correlated with increasing overall tumor grade (ρ = 0.92, p = 0.009) and MCT1 expression (r = 0.89, p = 0.016), with similar results acquired from the multi-regional analysis. Conventional 1H-MRI parameters did not discriminate tumor grades. The correlation between MCT1 and ccRCC grade was confirmed within a TCGA dataset (p < 0.001), where MCT1 expression was a predictor of overall and disease-free survival. In conclusion, metabolic imaging using HP-13C-MRI differentiates tumor aggressiveness in ccRCC and correlates with the expression of MCT1, a predictor of survival. HP-13C-MRI may non-invasively characterize metabolic phenotypes within renal cancer.

Imaging Tool for Predicting Renal Clear Cell Carcinoma Fuhrman Grade: Comparing R.E.N.A.L. Nephrometry Score and CT Texture Analysis

Background

Clear cell renal cell carcinoma (ccRCC) is the most common renal malignant tumor. Preoperative imaging boasts advantages in diagnosing and choosing treatment methods for ccRCC.

Purpose

This study is aimed at building models based on R.E.N.A.L. nephrometry score (RNS) and CT texture analysis (CTTA) to estimate the Fuhrman grade of ccRCC and comparing the advantages and disadvantages of the two models.

Materials and Methods

143 patients with pathologically confirmed ccRCC were enrolled. All patients were stratified into Fuhrman low-grade and high-grade groups with complete CT data and R.E.N.A.L. nephrometry scores. CTTA features were extracted from the ROI delineated at the largest tumor level, and RNS and CTTA features were included in the logistic regression model, respectively.

Results

RNS model constructed based on multivariate logistic regression analysis showed that 3 pts for R-scores, 2 pts for E-scores, and 3 pts for L-scores were significant indicators to predict high-grade ccRCC, the AUC of RNS model was 0.911, and the sensitivity and specificity were 71.11% and 83.67%, respectively. The CTTA-model confirmed energy, kurtosis, and entropy as independent predictive factors, and the AUC of CTTA model was 0.941, with an optimal sensitivity and specificity of 84.44% and 93.88%.

Conclusions

R.E.N.A.L. nephrometry score has a certain provocative effect on the Fuhrman pathological grading of ccRCC. As a potential emerging technology, CTTA is expected to replace R.E.N.A.L. nephrometry score in evaluating patients' Fuhrman classification, and this approach might become an available method for assisting clinicians in choosing appropriate operation.

Recent Trends in the Incidence of Clear Cell Adenocarcinoma and Survival Outcomes: A SEER Analysis

BACKGROUND

Clear cell adenocarcinoma (CCA) is considered a relatively rare tumor with a glycogen-rich phenotype. The prognosis of CCA patients is unclear. In this study, recent trends in the epidemiological and prognostic factors of CCA were comprehensively investigated.

METHODS

Patients with CCA from years 2000 to 2016 were identified from the Surveillance, Epidemiological, and End Results (SEER) database. Relevant population data were used to analyze the rates age-adjusted incidence, age-standardized 3-year and 5-year relative survivals, and overall survival (OS).

RESULTS

The age-adjusted incidence of CCA increased 2.7-fold from the year 2000 (3.3/100,000) to 2016 (8.8/100,000). This increase occurred across all ages, races, stages, and grades. Of all these subgroups, the increase was largest in the grade IV group. The age-standardized 3-year and 5-year relative survivals increased during this study period, rising by 9.1% and 9.5% from 2000 to 2011, respectively. Among all the stages and grades, the relative survival increase was greatest in the grade IV group. According to multivariate analysis of all CCA patients, predictors of OS were: age, gender, year of diagnosis, marital status, race, grade, stage, and primary tumor site (P < 0.001). The OS of all CCA patients during the period 2008 to 2016 was significantly higher than that from 2000 to 2007 (P < 0.001).

CONCLUSIONS

The incidence of CCA and survival of these patients improved over time. In particular, the highest increases were reported for grade IV CCA, which may be due to an earlier diagnosis and improved treatment.

Role of non-contrast magnetic resonance imaging in pre-surgical evaluation of renal masses in renal impairment patients

Background

The aim of this work is to study the role of non-contrast MRI in pre-surgical evaluation of renal masses in renal impairment patients as confirmed by both intraoperative and histopathological findings. Intraoperative and histopathological findings were correlated with radiological data.

Methods

This prospective study included 20 patients comprising 25 renal masses. The data were collected in a period from April 2018 to September 2019. All patients underwent partial or radical nephrectomy by the same surgeon.

Results

Based on MRI findings, 9 masses (36%) and 8 masses (32%) were found to be associated with collecting system invasion and perinephric fat invasion, respectively. Histopathological assessment confirmed only 6 cases (24%) with collecting system invasion and 7 cases (28%) demonstrated perinephric fat. Seven masses (28%) had intralesional hemorrhage detected by MRI and confirmed by pathological findings. The MRI detected 6 cases (24%) with lymph nodes invasion, while the histopathological assessment confirmed lymphatic invasion in 7 cases (28%). Only 2 cases (8%) had vascular invasion detected by preoperative MRI and confirmed by histopathology and surgery. The final histopathological examination confirmed 20 malignant neoplasms (80%: RCC = 18, leiomyosarcoma = 2), 3 benign neoplasms (12%: angiomyolipoma = 1, oncocytoma = 2) and 2 non-neoplastic benign masses (8%: renal abscess = 1, xanthogranulomatous pyelonephritis = 1).

Conclusion

Non-contrast MRI is a crucial imaging tool in renal impairment patients who cannot be examined with contrast-enhanced CT or MRI. It assesses the extent of the renal sinus fat and the perinephric fat invasion.

A Ferroptosis-Related Prognostic Risk Score Model to Predict Clinical Significance and Immunogenic Characteristics in Glioblastoma Multiforme

Background

Ferroptosis is a recently identified cell death pathway, and the susceptibility to ferroptosis inducers varies among cancer cell types. There have been recent attempts to clarify the mechanisms implicated in ferroptosis, glioma invasion, and the immune microenvironment but little is known about ferroptosis regulation in GBM.

Methods

Screening ferroptosis-related genes from published reports and existing databases, we constructed an integrated model based on the RNA-sequencing data in GBM. The association of FRGPRS and overall survival is identified and validated across several different datasets. Genomic and clinical characteristics, immune infiltration, enriched pathways, pan-cancer, drug resistance, and immune checkpoint inhibitor therapy are compared among various FRGPRS subgroups.

Results

We identified and confirmed the influences of five ferroptosis key hub genes in the FRGPRS model. The FRGPRS model could serve to predict overall survival and progression-free survival in GBM patients, and high FRGPRS was associated with comparatively stronger immunity, higher proportions of tumour tissue, and good cytolytic immune and chemotherapeutics response in GBM patients.

Conclusions

The five ferroptosis key hub genes constituting the FRGPRS model could serve to predict overall survival and progression-free survival in patients with GBM and help guide timely and efficacious therapeutic strategies customised and optimised for each individual patient. This discovery may lay the foundation for the development and optimisation of other iterations of this model for the improved forecasting, detection, and treatment of other malignancies notorious for their drug resistance and immune escape.

The WIRE study a phase II, multi-arm, multi-centre, non-randomised window-of-opportunity clinical trial platform using a Bayesian adaptive design for proof-of-mechanism of novel treatment strategies in operable renal cell cancer - a study protocol

BACKGROUND

Window-of-opportunity trials, evaluating the engagement of drugs with their biological target in the time period between diagnosis and standard-of-care treatment, can help prioritise promising new systemic treatments for later-phase clinical trials. Renal cell carcinoma (RCC), the 7th commonest solid cancer in the UK, exhibits targets for multiple new systemic anti-cancer agents including DNA damage response inhibitors, agents targeting vascular pathways and immune checkpoint inhibitors. Here we present the trial protocol for the WIndow-of-opportunity clinical trial platform for evaluation of novel treatment strategies in REnal cell cancer (WIRE).

METHODS

WIRE is a Phase II, multi-arm, multi-centre, non-randomised, proof-of-mechanism (single and combination investigational medicinal product [IMP]), platform trial using a Bayesian adaptive design. The Bayesian adaptive design leverages outcome information from initial participants during pre-specified interim analyses to determine and minimise the number of participants required to demonstrate efficacy or futility. Patients with biopsy-proven, surgically resectable, cT1b+, cN0-1, cM0-1 clear cell RCC and no contraindications to the IMPs are eligible to participate. Participants undergo diagnostic staging CT and renal mass biopsy followed by treatment in one of the treatment arms for at least 14 days. Initially, the trial includes five treatment arms with cediranib, cediranib + olaparib, olaparib, durvalumab and durvalumab + olaparib. Participants undergo a multiparametric MRI before and after treatment. Vascularised and de-vascularised tissue is collected at surgery. A ≥ 30% increase in CD8+ T-cells on immunohistochemistry between the screening and nephrectomy is the primary endpoint for durvalumab-containing arms. Meanwhile, a reduction in tumour vascular permeability measured by Ktrans on dynamic contrast-enhanced MRI by ≥30% is the primary endpoint for other arms. Secondary outcomes include adverse events and tumour size change. Exploratory outcomes include biomarkers of drug mechanism and treatment effects in blood, urine, tissue and imaging.

DISCUSSION

WIRE is the first trial using a window-of-opportunity design to demonstrate pharmacological activity of novel single and combination treatments in RCC in the pre-surgical space. It will provide rationale for prioritising promising treatments for later phase trials and support the development of new biomarkers of treatment effect with its extensive translational agenda.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT03741426 / EudraCT: 2018-003056-21 .

Adverse Histopathologic Characteristics in Small Papillary Renal Cell Carcinomas Have Minimal Impact on Prognosis

OBJECTIVES

Tumor size has long been used in the management decision-making of patients with renal masses. Active surveillance had recently gained traction in selected patients with tumor size of 4 cm or less. Adverse histopathologic characteristics in papillary renal cell carcinoma (PRCC) have been shown to correlate with worse prognosis. We aimed to study whether such features in small PRCCs provide additional prognostic information.

METHODS

Nephrectomies from our institution were collected and reviewed to evaluate for adverse histopathologic features. Clinical follow-up information was collected for all cases. Relationships between the variables were examined by Wilcoxon test and logistic regression.

RESULTS

We identified 291 consecutive cases of PRCC. Adverse tumor histopathologic characteristics were significantly related to size. In PRCCs with size greater than 4 cm, there were more cases with high World Health Organization/International Society of Urological Pathology grade and necrosis. Adverse histologic features are less commonly seen in small PRCC and are not associated with lower disease-free survival or disease-specific survival.

CONCLUSIONS

Identification of these features in small PRCCs (≤4 cm) through needle core biopsy examination would not provide additional prognostic information in patients for whom active surveillance is considered. Clinical and radiologic follow-up in patients with small renal masses that have a known histologic diagnosis of PRCC should be sufficient.

Low Preoperative Mean Platelet Volume/Platelet Count Ratio Indicates Worse Prognosis in Non-Metastatic Renal Cell Carcinoma

Objectives: Multiple blood parameters are used to determine the prognosis of renal cell carcinoma (RCC). Mean platelet volume/platelet count (MPV/PC) ratio is related to disease progression in various cancers. Our study tried to evaluate the prognostic value of the MPV/PC ratio in RCC patients who underwent surgery. Methods: We retrospectively reviewed 89 patients who underwent radical or partial nephrectomy for RCC in a single institution. Baseline characteristics and MPV/PC ratios were analyzed. The optimal cut-off value of the MPV/PC ratio was determined by a receiver operating characteristic (ROC) curve, and our patients were divided into low and high MPV/PC ratio groups. The Kaplan–Meier survival curve and Cox proportional hazards model were applied for progression-free survival (PFS) and overall survival (OS) analyses. Harell’s C-index was used to compare the prognostic values of the MPV/PC ratio, MPV and PC. Results: Lower MPV/PC ratios were correlated with more advanced tumor stages and worse outcomes. The optimal cut-off value of the preoperative MPV/PC ratio was 0.034 (sensitivity 84.6%, specificity 56.6%). The Kaplan–Meier survival curve revealed that low MPV/PC ratios were associated with worse PFS (p = 0.007) and OS (p = 0.017). Multivariate analysis showed that low MPV/PC ratios were an independent unfavorable factor for PFS (p = 0.044) and OS (p = 0.015). Harell’s C-indexes showed that the prognostic value of the MPV/PC ratio was significantly better than MPV and PC (p < 0.001). Conclusion: Low MPV/PC ratios are an independent, unfavorable risk factor for disease progression and overall survival in patients undergoing surgery for RCC.

Feasibility and Outcomes of Renal Mass Biopsy for Anatomically Complex Renal Tumors

OBJECTIVE

To compare the feasibility and outcomes of renal mass biopsies (RMB) of anatomically complex vs non-complex renal masses.

METHODS

Our institutional renal tumor database was queried for patients who underwent RMB between 2005 and 2019 and with available nephrometry score. Complex masses were: (1) small (<2 cm), (2) entirely endophytic (nephrometry E=3), (3) hilar (h) or (4) partially endophytic (E=2) and anterior. Demographic and pathologic data were compared. Biopsies were deemed adequate if they resulted in a diagnosis. Concordance with surgical pathology was assessed. These were both presented using proportions. Factors associated with biopsy outcomes were identified using multivariable logistic regression. RMB sensitivity and specificity were calculated using contingency methods.

RESULTS

A total of 306 RBMs were included, 179 complex and 127 non-complex. A total of 199 (65%) had an extirpative procedure. Complex lesions were less likely to have an adequate biopsy (89% vs 96%, P = .03), and to be concordant with final surgical pathology from an oncologic standpoint (89% vs 97%, P = .03). There was no significant difference in concordance of histology (76% vs 86%, P = .10) or grade (48 vs 51%, P = .66). On multivariable analyses, only male gender was associated with biopsy adequacy (OR 3.31, 95% CI 1.28-8.55, P = .01). Our overall sensitivity was 93%, specificity 93%, and accuracy 93%. There were no significant differences over time in biopsy outcomes during the study period.

CONCLUSION

RMB of complex lesions is associated with excellent diagnostic yield, albeit lower than non-complex lesions. RMB should not be deferred in cases of anatomically complex lesions where additional data could improve clinical decision-making.

Structure and functions of T-cell immunoglobulin-domain and mucin- domain protein 3 in cancer

BACKGROUND

T-cell immunoglobulin (Ig)-domain and mucin-domain (TIM) proteins represent a family of receptors expressed on T-cells that play essential cellular immunity roles. The TIM proteins span across the membrane belonging to type I transmembrane proteins. The N terminus contains an Ig-like V-type domain and a Ser/Thr-rich mucin stalk as a co-inhibitory receptor. The C-terminal tail oriented toward the cytosol predominantly mediates intracellular signaling.

METHODS

This review discusses the structural features and functions of TIM-3, specifically on its role in mediating immune responses in different cell types, and the rationale for TIM-3-targeted cancer immunotherapy.

RESULTS

TIM-3 has gained significant importance to be a potential biomarker in cancer immunotherapy. It has been shown that blockade with checkpoint inhibitors promotes anti-tumor immunity and inhibits tumor growth in several preclinical tumor models.

CONCLUSION

TIM-3 is an immune regulating molecule expressed on several cell types, including IFNγ-producing T-cells, FoxP3+ Treg cells, and innate immune cells. The roles of TIM-3 in immunosuppression support its merit as a target for cancer immunotherapy.

A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors

Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of 95.3%±2.0%, a specificity of 99.9%±0.4%, and Dice similarity coefficient of 0.98±0.01 in differentiating malignant from benign tumors, as well as an overall accuracy of 89.6%±5.0% in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors.

Radiomics models based on enhanced computed tomography to distinguish clear cell from non-clear cell renal cell carcinomas

This study was to assess the effect of the predictive model for distinguishing clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC) by establishing predictive radiomic models based on enhanced-computed tomography (CT) images of renal cell carcinoma (RCC). A total of 190 cases with RCC confirmed by pathology were retrospectively analyzed, with the patients being randomly divided into two groups, including the training set and testing set according to the ratio of 7:3. A total of 396 radiomic features were computationally obtained and analyzed with the Correlation between features, Univariate Logistics and Multivariate Logistics. Finally, 4 features were selected, and three machine models (Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR)) were established to discriminate RCC subtypes. The radiomics performance was compared with that of radiologist diagnosis. In the testing set, the RF model had an area under the curve (AUC) value of 0.909, a sensitivity of 0.956, and a specificity of 0.538. The SVM model had an AUC value of 0.841, a sensitivity of 1.0, and a specificity of 0.231, in the testing set. The LR model had an AUC value of 0.906, a sensitivity of 0.956, and a specificity of 0.692, in the testing set. The sensitivity and specificity of radiologist diagnosis to differentiate ccRCC from non-ccRCC were 0.850 and 0.581, respectively, with the AUC value of the radiologist diagnosis as 0.69. In conclusion, radiomics models based on CT imaging data show promise for augmenting radiological diagnosis in renal cancer, especially for differentiating ccRCC from non-ccRCC.

The presence of tumour-infiltrating neutrophils is an independent adverse prognostic feature in clear cell renal cell carcinoma

Tumour-promoting inflammation is an emerging hallmark of cancer that is increasingly recognised as a therapeutic target. As a constituent measure of inflammation, tumour-infiltrating neutrophils (TINs) have been associated with inferior prognosis in several cancers. We analysed clinically annotated cohorts of clear cell renal cell carcinoma (ccRCC) to assess the presence of neutrophils within the tumour microenvironment as a function of outcome. We centrally reviewed ccRCC surgical resection and fine-needle aspiration (FNA) specimens, including primary and metastatic sites, from three centres. TINs were scored based on the presence of neutrophils in resection and FNA specimens by two pathologists. TIN count was correlated with tumour characteristics including stage, WHO/ISUP grade, and immunohistochemistry (IHC). In parallel, we performed CIBERSORT analysis of the tumour microenvironment in a cohort of 516 ccRCCs from The Cancer Genome Atlas (TCGA). We included 102 ccRCC cases comprising 65 resection specimens (37 primary and 28 metastatic resection specimens) and 37 FNAs from primary lesions. High TINs were significantly associated with worse overall survival (p = 0.009) independent of tumour grade and stage. In ccRCCs sampled via FNA, all cases with high TINs had distant metastasis, whereas they were seen in only 19% of cases with low TINs (p = 0.0003). IHC analysis showed loss of E-cadherin in viable tumour cells in areas with high TINs, and neutrophil activation was associated with elastase and citrullinated histone H3 expression (cit-H3). In the TCGA cohort, neutrophilic markers were also associated with worse survival (p < 0.0001). TINs are an independent predictor of worse prognosis in ccRCC, which have the potential to be assessed at the time of first biopsy or FNA. Neutrophils act directly on tumour tissue by releasing elastase, a factor that contributes to the breakdown of cell-cell adhesion and to facilitate tumour dissemination.

Tumor necrosis: A synergistic consequence of metabolic stress and inflammation

Tumor necrosis is a common histological feature and poor prognostic predictor in various cancers. Despite its significant clinical implications, the mechanism underlying tumor necrosis remains largely unclear due to lack of appropriate pre-clinical modeling. We propose that tumor necrosis is a synergistic consequence of metabolic stress and inflammation, which lead to oxidative stress-induced cell death, such as ferroptosis. As a natural consequence of tumor expansion, tumor cells are inevitably stripped of vascular supply, resulting in deprivation of oxygen and nutrients. The resulting metabolic stress has commonly been considered the cause of tumor necrosis. Recent studies found that immune cells, such as neutrophils, when recruited to tumors, can directly trigger ferroptosis in tumor cells, suggesting that immune cells can be involved in amplifying tumor necrosis. This article will discuss potential mechanisms underlying tumor necrosis development and its impact on tumor progression as well as the immune response to tumors.

New developments in existing WHO entities and evolving molecular concepts: The Genitourinary Pathology Society (GUPS) update on renal neoplasia

The Genitourinary Pathology Society (GUPS) reviewed recent advances in renal neoplasia, particularly post-2016 World Health Organization (WHO) classification, to provide an update on existing entities, including diagnostic criteria, molecular correlates, and updated nomenclature. Key prognostic features for clear cell renal cell carcinoma (RCC) remain WHO/ISUP grade, AJCC/pTNM stage, coagulative necrosis, and rhabdoid and sarcomatoid differentiation. Accrual of subclonal genetic alterations in clear cell RCC including SETD2, PBRM1, BAP1, loss of chromosome 14q and 9p are associated with variable prognosis, patterns of metastasis, and vulnerability to therapies. Recent National Comprehensive Cancer Network (NCCN) guidelines increasingly adopt immunotherapeutic agents in advanced RCC, including RCC with rhabdoid and sarcomatoid changes. Papillary RCC subtyping is no longer recommended, as WHO/ISUP grade and tumor architecture better predict outcome. New papillary RCC variants/patterns include biphasic, solid, Warthin-like, and papillary renal neoplasm with reverse polarity. For tumors with 'borderline' features between oncocytoma and chromophobe RCC, a term "oncocytic renal neoplasm of low malignant potential, not further classified" is proposed. Clear cell papillary RCC may warrant reclassification as a tumor of low malignant potential. Tubulocystic RCC should only be diagnosed when morphologically pure. MiTF family translocation RCCs exhibit varied morphologic patterns and fusion partners. TFEB-amplified RCC occurs in older patients and is associated with more aggressive behavior. Acquired cystic disease (ACD) RCC-like cysts are likely precursors of ACD-RCC. The diagnosis of renal medullary carcinoma requires a negative SMARCB1 (INI-1) expression and sickle cell trait/disease. Mucinous tubular and spindle cell carcinoma (MTSCC) can be distinguished from papillary RCC with overlapping morphology by losses of chromosomes 1, 4, 6, 8, 9, 13, 14, 15, and 22. MTSCC with adverse histologic features shows frequent CDKN2A/2B (9p) deletions. BRAF mutations unify the metanephric family of tumors. The term "fumarate hydratase deficient RCC" ("FH-deficient RCC") is preferred over "hereditary leiomyomatosis and RCC syndrome-associated RCC". A low threshold for FH, 2SC, and SDHB immunohistochemistry is recommended in difficult to classify RCCs, particularly those with eosinophilic morphology, occurring in younger patients. Current evidence does not support existence of a unique tumor subtype occurring after chemotherapy/radiation in early childhood.

CT-based multi-phase Radiomic models for differentiating clear cell renal cell carcinoma

Background

The aim of the study is to compare the diagnostic value of models that based on a set of CT texture and non-texture features for differentiating clear cell renal cell carcinomas(ccRCCs) from non-clear cell renal cell carcinomas(non-ccRCCs).

Methods

A total of 197 pathologically proven renal tumors were divided into ccRCC(n = 143) and non-ccRCC (n = 54) groups. The 43 non-texture features and 296 texture features that extracted from the 3D volume tumor tissue were assessed for each tumor at both Non-contrast Phase, NCP; Corticomedullary Phase, CMP; Nephrographic Phase, NP and Excretory Phase, EP. Texture-score were calculated by the Least Absolute Shrinkage and Selection Operator (LASSO) to screen the most valuable texture features. Model 1 contains the three most distinctive non-texture features with p < 0.001, Model 2 contains texture scores, and Model 3 contains the above two types of features.

Results

The three models shown good discrimination of the ccRCC from non-ccRCC in NCP, CMP, NP, and EP. The area under receiver operating characteristic curve (AUC)values of the Model 1, Model 2, and Model 3 in differentiating the two groups were 0.748–0.823, 0.776–0.887 and 0.864–0.900, respectively. The difference in AUC between every two of the three Models was statistically significant (p < 0.001).

Conclusions

The predictive efficacy of ccRCC was significantly improved by combining non-texture features and texture features to construct a combined diagnostic model, which could provide a reliable basis for clinical treatment options.

Biorepositories and Databanks for the Development of Novel Biomarkers for Genitourinary Cancer Prevention and Management

CONTEXT

Translational research in uro-oncology depends on the availability of high-quality biospecimens and associated data to advance precision medicine and improve clinical outcomes. Procurement, storage, and annotation of these specimens represent critical steps towards this end.

OBJECTIVE

To review best-practice experiences gained via the McCain GU BioBank, a repository of more than 750 000 biospecimens obtained from more than 16 000 patients attending clinics at the University Health Network in Toronto, Canada.

EVIDENCE ACQUISITION

The review summarizes our experiences at a large single-institution genitourinary oncology biorepository.

EVIDENCE SYNTHESIS

Key findings are placed in the context of emerging trends in genitourinary oncology, with a focus on integration of molecular profiling and clinical data with traditional biorepository management. Proposed approaches provide high-quality biospecimens with comprehensive and reliable clinical data that can fuel innovation and discovery in research.

CONCLUSIONS

Biorepositories are vital for improving clinical outcomes and advancing personalized medicine. High-quality biospecimens and their associated clinical data are crucial for validation of biomarkers in oncology. Efforts to procure, store, and annotate clinical specimens represent critical steps in translational research. Elements such as biobank size, biospecimen types, disease cohorts, predetermined collection protocols, broad informed consent, sample handling and storage protocols, and available infrastructure directly influence the effectiveness and capacity of a biobank.

PATIENT SUMMARY

Biorepositories, or biobanks, are facilities that store biospecimens such as blood, urine, or tissue (usually collected from humans) for use in research. Biobanks have become an important resource in medical research, as they provide high-quality specimens to support different types of contemporary research such as genomics, biomarker discovery, and personalized medicine. Clinical management and treatment of genitourinary cancers, such as prostate, kidney, and bladder cancers, are particularly suited for biomarker research. The provision of biospecimens and their associated clinical data have become crucial for validation of biomarkers in these cancers.

Deep learning for end-to-end kidney cancer diagnosis on multi-phase abdominal computed tomography

In 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors including both benign and malignant tumors on multi-phase CT. Our model is a unified framework to simultaneously identify lesions and classify subtypes for the diagnosis without manual intervention. We trained and tested the model using CT data from 308 patients who underwent nephrectomy for renal tumors. The model achieved an area under the curve (AUC) of 0.889, and outperformed radiologists for most subtypes. We further validated the model on an independent dataset of 184 patients from The Cancer Imaging Archive (TCIA). The AUC for this dataset was 0.855, and the model performed comparably to the radiologists. These results indicate that our model can achieve similar or better diagnostic performance than radiologists in differentiating a wide range of renal tumors on multi-phase CT.

The first competing risk survival nomogram in patients with papillary renal cell carcinoma

There is still a lack of competing risk analysis of patients with papillary renal cell carcinoma (pRCC) following surgery. We performed the cumulative incidence function (CIF) to estimate the absolute risks of cancer-specific mortality (CSM) and other-cause mortality (OCM) of pRCC over time, and constructed a nomogram predicting the probability of 2-, 3- and 5-year CSM based on competing risk regression. A total of 5993 pRCC patients who underwent nephrectomy between 2010 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The 2-, 3-, 5-year CSM rates were 3.2%, 4.4% and 6.5%, respectively, and that of OCM were 3.2%, 5.0% and 9.3%, respectively. The estimates of 5-year cumulative mortality were most pronounced among patients aged > 75 years in OCM (17.0%). On multivariable analyses, age, tumor grade, T stage, N stage, and with or without bone, liver and lung metastases were identified as independent predictors of CSM following surgery and were integrated to generate the nomogram. The nomogram achieved a satisfactory discrimination with the AUC_t of 0.730 at 5-year, and the calibration curves presented impressive agreements. Taken together, age-related OCM is a significant portion of all-cause mortality in elderly patients and our nomogram can be used for decision-making and patient counselling.

Sonic Hedgehog signaling pathway in gynecological and genitourinary cancer (Review)

Cancers of the urinary tract, as well as those of the female and male reproductive systems, account for a large percentage of malignancies worldwide. Mortality is frequently affected by late diagnosis or therapeutic difficulties. The Sonic Hedgehog (SHH) pathway is an evolutionary conserved molecular cascade, which is mainly associated with the development of the central nervous system in fetal life. The present review aimed to provide an in‑depth summary of the SHH signaling pathway, including the characterization of its major components, the mechanism of its upstream regulation and non‑canonical activation, as well as its interactions with other cellular pathways. In addition, the three possible mechanisms of the cellular SHH cascade in cancer tissue are discussed. The aim of the present review was to summarize significant findings with regards to the expression of the SHH pathway components in kidney, bladder, ovarian, cervical and prostate cancer. Reports associated with common deficits and de‑regulations of the SHH pathway were summarized, despite the differences in molecular and histological patterns among these malignancies. However, currently, neither are SHH pathway elements included in panels of prognostic/therapeutic molecular patterns in any of the discussed cancers, nor have the drugs targeting SMO or GLIs been approved for therapy. The findings of the present review may support future studies on the treatment of and/or molecular targets for gynecological and genitourinary cancers.

Increased CDC6 Expression Associates With Poor Prognosis in Patients With Clear Cell Renal Cell Carcinoma

Background

CDC6 (Cell division control protein 6), located at chromosome 17q21.3, plays an important role in the early stage of DNA replication and has unique functions in various malignant tumors. Here, we evaluate the relationship between CDC6 expression and oncology outcomes in patients with clear cell renal cell carcinoma (ccRCC).

Methods

A retrospective analysis of 118 ccRCC patients in Affiliated Hospital of Nantong University from 2015 to 2017 was performed. Triplicate tissue microarrays (TMA) were prepared from formalin-fixed and paraffin-embedded specimens. Immunohistochemistry (IHC) was conducted to evaluate the relationship between CDC6 expression and standard pathological features and prognosis. The RNA sequencing data and corresponding clinical information were acquired from the TCGA database. GSEA was used to identify signal pathways related to CDC6. Cox regression analysis was used to assess independent prognostic factors. In addition, the relationship between CDC6 and immunity was also investigated.

Results

The results of Kaplan–Meier curve indicated that the OS of the patients with high expression of CDC6 was shorter than that of the patients with low CDC6 expression. Integrating the TCGA database and IHC staining, the results showed that CDC6 in ccRCC tissue was obviously up-regulated compared with adjacent normal kidney tissue. The results of Logistic regression analysis demonstrated that ccRCC patients with high expression of CDC6 are more likely to develop advanced disease than ccRCC patients with low CDC6 expression. The results of GSEA showed that the high expression of CDC6 was related to multiple signaling pathways. As for immunity, it was also related to TMB, immune checkpoint molecules, tumor microenvironment and immune infiltration. There were significantly correlations with CDC6 and immune cell infiltration levels and tumor microenvironment. The results of further results of the TCGA database showed that CDC6 was obviously related to immune checkpoint molecules and immune cells.

Conclusions

Increased expression of CDC6 is a potentially prognostic factor of poor prognosis in ccRCC patients.

Glycolysis on F-18 FDG PET/CT Is Superior to Amino Acid Metabolism on C-11 Methionine PET/CT in Identifying Advanced Renal Cell Carcinoma at Staging

Simple Summary

Alteration of metabolism, including glycolysis and glutaminolysis in malignant tumours, has become a hallmark of cancer and related biological aggressiveness. The metabolic signature of each cancer has been actively investigated for potential new drug development. Of the metabolic imaging biomarkers, F-18 fluorodeoxyglucose (FDG) and C-11 methionine positron emission tomography/computed tomography (PET/CT) are widely studied to evaluate the degree of glucose metabolism and amino acid metabolism, respectively. In this prospective study, we found that both F-18 FDG and C-11 methionine uptakes on PET/CT were heterogeneous in renal cell carcinomas, and increased uptake was associated with higher grades of both radiotracers. Additionally, metabolic tumour volume on F-18 FDG PET/CT but not C-11 methionine PET/CT was significant in predicting advanced-stage renal cell carcinoma. These metabolic features derived with PET/CT may help in the development of new drugs targeting glucose and amino acid metabolic pathways.

Abstract

We evaluated the value of F-18 fluorodeoxyglucose (FDG) and C-11 methionine positron emission tomography/computed tomography (PET/CT) to predict high-Fuhrman grade and advanced-stage tumours in patients with renal cell carcinoma (RCC). Forty patients with RCC underwent F-18 FDG and C-11 methionine PET/CT between September 2016 and September 2018. They were classified into limited (stages I and II, n = 15) or advanced stages (stages III and IV, n = 25) according to pathological staging. Logistic regressions were used to predict the advanced stage using various parameters, including maximum standardised uptake value (SUV_max) and metabolic tumour volume (MTV). Receiver operating characteristic analyses were performed to predict high-grade tumours (Fuhrman 3 and 4). On univariate analysis, tumour size, SUV_max and MTV of F-18 FDG and C-11 methionine, and Fuhrman grades were significant predictors for the advanced stage. On multivariate analysis, F-18 FDG MTV > 21.3 cm³ was the most significant predictor (p < 0.001). The area under the curve for predicting high-grade tumours was 0.830 for F-18 FDG (p < 0.001) and 0.726 for C-11 methionine PET/CT (p = 0.014). In conclusion, glycolysis on F-18 FDG PET/CT and amino acid metabolism on C-11 methionine PET/CT were variable but increased in high-grade RCCs. Increased MTV on F-18 FDG PET/CT is a powerful predictor of advanced-stage tumours.

Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides

Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC on biopsy and surgical resection slides under a microscope remains a heavily specialized, error-prone, and time-consuming task for pathologists. In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal. In addition to the whole-slide classification pipeline, we visualized the identified indicative regions and features on slides for classification by reprocessing patch-level classification results to ensure the explainability of our diagnostic model. We evaluated our model on independent test sets of 78 surgical resection whole slides and 79 biopsy slides from our tertiary medical institution, and 917 surgical resection slides from The Cancer Genome Atlas (TCGA) database. The average area under the curve (AUC) of our classifier on the internal resection slides, internal biopsy slides, and external TCGA slides is 0.98 (95% confidence interval (CI): 0.97-1.00), 0.98 (95% CI: 0.96-1.00) and 0.97 (95% CI: 0.96-0.98), respectively. Our results suggest that the high generalizability of our approach across different data sources and specimen types. More importantly, our model has the potential to assist pathologists by (1) automatically pre-screening slides to reduce false-negative cases, (2) highlighting regions of importance on digitized slides to accelerate diagnosis, and (3) providing objective and accurate diagnosis as the second opinion.

Single-cell analysis reveals metastatic cell heterogeneity in clear cell renal cell carcinoma

Renal cell carcinoma (RCC) is one of the leading causes of cancer-related death worldwide. Tumour metastasis and heterogeneity lead to poor survival outcomes and drug resistance in patients with metastatic RCC (mRCC). In this study, we aimed to assess intratumoural heterogeneity (ITH) in mRCC cells by performing a combined analysis of bulk data and single-cell RNA-sequencing data, and develop novel biomarkers for prognosis prediction on the basis of the potential molecular mechanisms underlying tumorigenesis. Eligible single-cell cohorts related to mRCC were acquired using the Gene Expression Omnibus (GEO) dataset to identify potential mRCC subpopulations. We then performed gene set variation analysis to understand the differential function in primary RCC and mRCC samples. Subsequently, we applied weighted correlation network analysis to identify coexpressing gene modules that were related to the external trait of metastasis. Protein-protein interactions were used to screen hub subpopulation-difference (sub-dif) markers (ACTG1, IL6, CASP3, ACTB and RAP1B) that might be involved in the regulation of RCC metastasis and progression. Cox regression analysis revealed that ACTG1 was a protective factor (HR < 1), whereas the other four genes (IL6, CASP3, ACTB and RAP1B) were risk factors (HR > 1). Kaplan-Meier survival analysis suggested the potential prognostic value of these sub-dif markers. The expression of sub-dif markers in mRCC was further evaluated in clinical samples by immunohistochemistry (IHC). Additionally, the genetic features of sub-dif marker expression patterns, such as genetic variation profiles, correlations with tumour-infiltrating lymphocytes (TILs), and targeted signalling pathway activities, were assessed in bulk RNA-seq datasets. In conclusion, we established novel subpopulation markers as key prognostic factors affecting EMT-related signalling pathway activation in mRCC, which could facilitate the implementation of a treatment for mRCC patients.

LncRNA HCG18 Promotes Clear Cell Renal Cell Carcinoma Progression by Targeting miR-152-3p to Upregulate RAB14

Background

Long noncoding RNAs (lncRNAs) have been regarded as crucial regulators in many cancers, including clear cell renal cell carcinoma (ccRCC). This research aimed to explore the biological role and molecular mechanism of lncRNA HCG18 in ccRCC.

Materials and Methods

The expression levels of HCG18, miR-152-3p and RAB14 were examined by RT-qPCR. Cell viability, migration and invasion were examined by CCK-8 and transwell assays. Luciferase reporter and RIP assays were adopted to verify the interaction between miR-152-3p and HCG18 or RAB14.

Results

It was found that HCG18 expression was highly expressed in ccRCC tissues and cells, and patients with high expression of HCG18 had a short overall survival time. Moreover, HCG18 depletion attenuated ccRCC cell viability, migration and invasion. In addition, miR-152-3p was confirmed as a downstream target of HCG18 and was inversely regulated by HCG18, and RAB14 was a target of miR-152-3p. Functional assays demonstrated that miR-152-3p silencing or RAB14 addition abolished the inhibitory effects of HCG18 knockdown on ccRCC progression.

Conclusion

The results of the present study indicated that HCG18 accelerated the development and progression of ccRCC by upregulating RAB14 via sponging miR-152-3p, suggesting a potential therapeutic target for patients with ccRCC.

Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.

Methods

Patients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.

Results

A total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765–0.9585) and 0.8088 (95% CI: 0.7064–0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353–0.8987) and 0.8017 (95% CI: 0.6878–0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646–0.9824) and an AUC of 0.9099 (95% CI: 0.8324–0.9873) for the training cohort and validation cohort, respectively.

Conclusion

We developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.

Imaging Characterization of Renal Masses

The detection of a renal mass is a relatively frequent occurrence in the daily practice of any Radiology Department. The diagnostic approaches depend on whether the lesion is cystic or solid. Cystic lesions can be managed using the Bosniak classification, while management of solid lesions depends on whether the lesion is well-defined or infiltrative. The approach to well-defined lesions focuses mainly on the differentiation between renal cancer and benign tumors such as angiomyolipoma (AML) and oncocytoma. Differential diagnosis of infiltrative lesions is wider, including primary and secondary malignancies and inflammatory disease, and knowledge of the patient history is essential. Radiologists may establish a possible differential diagnosis based on the imaging features of the renal masses and the clinical history. The aim of this review is to present the contribution of the different imaging techniques and image guided biopsies in the diagnostic management of cystic and solid renal lesions.

Validation of the World Health Organization/International Society of Urological Pathology grading for Chinese patients with clear cell renal cell carcinoma

Background

This study aimed to compare the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system and the Fuhrman grading system and to verify the WHO/ISUP grade as a prognostic parameter of clear cell renal cell carcinoma (ccRCC) in a Chinese population.

Methods

The study consisted of 753 ccRCC patients treated with curative surgery between 2010 and 2018 at Xiangya Hospital Central South University (Changsha, China). All pathologic data were retrospectively reviewed by two pathologists. Cancer-specific survival (CSS) and recurrence-free survival (RFS) were examined as clinical outcomes.

Results

According to the WHO/ISUP grading system (ISUP group), nephrectomy type, pT stage and WHO/ISUP grade were independent risk factors for CSS (P<0.0001, P=0.0127 and P<0.0001, respectively) and RFS (P<0.0001, P=0.0077, and P<0.0001, respectively). In the Fuhrman group, nephrectomy type, pT stage and Fuhrman grade were independent risk factors for CSS (P<0.0001, P=0.0004, and P<0.0001, respectively) and RFS (P<0.0001, P=0.0001, and P<0.0001, respectively). The C-index for CSS and RFS using the Fuhrman grading system was 0.6323 and 0.6342, respectively, and that using the WHO/ISUP grading system was 0.6983 and 0.7005, respectively, both higher than the former (P=0.0185, and P=0.0172, respectively). In addition, upgrading from Fuhrman grade 2 to ISUP grade 3 resulted in worse CSS and RFS for ccRCC patients (P=0.0033 and P =0.0003, respectively).

Conclusions

We first verified correlations between the postoperative prognosis and WHO/ISUP grade of ccRCC in a Chinese population and confirmed that the ability to predict clinical outcomes with the WHO/ISUP grading system was superior to that with the Fuhrman grading system.

Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients

PURPOSE

The aim of this study was to develop radiomics-based machine learning models based on extracted radiomic features and clinical information to predict the risk of death within 5 years for prognosis of clear cell renal cell carcinoma (ccRCC) patients.

METHODS

According to image quality and clinical data availability, we eventually selected 70 ccRCC patients that underwent CT scans. Manual volume-of-interest (VOI) segmentation of each image was performed by an experienced radiologist using the 3D slicer software package. Prior to feature extraction, image pre-processing was performed on CT images to extract different image features, including wavelet, Laplacian of Gaussian, and resampling of the intensity values to 32, 64 and 128 bin levels. Overall, 2544 3D radiomics features were extracted from each VOI for each patient. Minimum Redundancy Maximum Relevance (MRMR) algorithm was used as feature selector. Four classification algorithms were used, including Generalized Linear Model (GLM), Support Vector Machine (SVM), K-nearest Neighbor (KNN) and XGBoost. We used the Bootstrap resampling method to create validation sets. Area under the receiver operating characteristic (ROC) curve (AUROC), accuracy, sensitivity, and specificity were used to assess the performance of the classification models.

RESULTS

The best single performance among 8 different models was achieved by the XGBoost model using a combination of radiomic features and clinical information (AUROC, accuracy, sensitivity, and specificity with 95% confidence interval were 0.95-0.98, 0.93-0.98, 0.93-0.96 and ~1.0, respectively).

CONCLUSIONS

We developed a robust radiomics-based classifier that is capable of accurately predicting overall survival of RCC patients for prognosis of ccRCC patients. This signature may help identifying high-risk patients who require additional treatment and follow up regimens.

Application of Chromosome Microarray Analysis for the Differential Diagnosis of Low-grade Renal Cell Carcinoma With Clear Cell and Papillary Features

Clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) are the 2 most common RCCs. However, some RCCs can have both clear cell and papillary features, including clear cell papillary RCC (ccpRCC). They can be a diagnostic challenge in daily practice. Accurate diagnosis of these tumors is important for both patient prognosis and appropriate treatment. Fourteen RCCs with papillary architecture, clear cytoplasm and low Fuhrman grade were analyzed by SNP-based chromosome microarray (CMA). Seven cases had pathologic features of ccpRCC, and all had normal genomic profiles except one that had copy neutral loss of heterozygosity (cnLOH) of chromosome 3 and loss of one copy of the X chromosome. The remaining 7 cases also had papillae and clear cytoplasm. Two of these cases showed losses of chromosome 3 which are typically found in ccRCC. One had a gain of chromosome 7, which is commonly seen in pRCC. The remaining 4 had no alterations of chromosome 3 or 7. However, 3 of these 4 had monosomy 8, which are consistent with RCC with monosomy 8. The remaining case had no copy number alterations. This study shows that low-grade RCC with papillae and clear cell phenotype represents a heterogeneous group, including ccpRCC, ccRCC, pRCC, and RCC with monosomy 8. CMA analysis can be useful for the differential diagnosis of these neoplasms.

Contributions of genetics to the evolution of the diagnostic classification of renal cell neoplasia: a personal perspective

The classification system for neoplasms of the cells lining the renal tubules (renal cell neoplasms) has expanded greatly over the last five decades. The criteria for recognising an entity and including it in the classification have changed from being purely morphological and clinical to include genetics; presently, some are defined purely on genetics. Expansion of the number of entities included in the classification has many of the newly included entities and those under consideration for inclusion being very rare. The clinical utility of including entities which are extremely rare, based mainly upon genetic information, is unclear.

Identification of the miRNA-mRNA regulatory pathways and a miR-21-5p based nomogram model in clear cell renal cell carcinoma

Background

The purpose of this study was to determine the key microRNAs (miRNAs) and their regulatory networks in clear cell renal cell carcinoma (ccRCC).

Methods

Five mRNA and three microRNA microarray datasets were downloaded from the Gene Expression Omnibus database and used to screen the differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs). Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed with Metascape. A miRNA-mRNA network was mapped with the Cytoscape tool. The results were validated with data from The Cancer Genome Atlas (TCGA) and qRT-PCR. A nomogram model based on independent prognostic key DEMs, stage and grade was constructed for further investigation.

Results

A total of 26 key DEMs and 307 DEGs were identified. Dysregulation of four key DEMs (miR-21-5p, miR-142-3p, miR-155-5p and miR-342-5p) was identified to correlate with overall survival. The results were validated with TCGA data and qRT-PCR. The nomogram model showed high accuracy in predicting the prognosis of patients with ccRCC.

Conclusion

We identified 26 DEMs that may play vital roles in the regulatory networks of ccRCC. Four miRNAs (miR-21-5p, miR-142-3p, miR-155-5p and miR-342-5p) were considered as potential biomarkers in the prognosis of ccRCC, among which only miR-21-5p was found to be an independent prognostic factor. A nomogram model was then created on the basis of independent factors for better prediction of prognosis for patients with ccRCC. Our results suggest a need for further experimental validation studies.

Grading Chromophobe Renal Cell Carcinoma: Evidence for a Four-tiered Classification Incorporating Coagulative Tumor Necrosis

BACKGROUND

Although grading systems have been proposed for chromophobe renal cell carcinoma (ChRCC), including a three-tiered system by Paner et al (Paner GP, Amin MB, Alvarado-Cabrero I, et al. A novel tumor grading scheme for chromophobe renal cell carcinoma: prognostic utility and comparison with Fuhrman nuclear grade. Am J Surg Pathol 2010;34:1233-40), none have gained clinical acceptance, and the World Health Organization (WHO) currently recommends against grading ChRCC.

OBJECTIVE

To validate a previously published grading scheme and propose a scheme that includes tumor necrosis.

DESIGN, SETTING, AND PARTICIPANTS

A total of 266 patients who underwent nephrectomy for nonmetastatic ChRCC between 1970 and 2012 were reviewed for ChRCC grade according to the Paner system and coagulative tumor necrosis.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Associations with cancer-specific survival (CSS) were evaluated using Cox proportional hazard regression models and summarized with hazard ratios (HRs).

RESULTS AND LIMITATIONS

Twenty-nine patients died from RCC; the median follow-up was 11.0 (interquartile range 7.9-15.9) yr. ChRCC grade according to the Paner system was significantly associated with CSS, including the difference in outcome between grade 1 and 2 tumors. Among patients with grade 2 tumors, the presence of tumor necrosis helped delineate patients with worse CSS. As such, the Paner system was expanded to four tiers separating grade 2 into those with and without tumor necrosis. HRs for associations of the proposed grade 2, 3, and 4 tumors with CSS were 4.63 (p=0.007), 17.8 (p<0.001), and 20.9 (p<0.001), respectively. The study is limited by the lack of multivariable analysis including additional pathologic features.

CONCLUSIONS

The expansion of a previously reported ChRCC grading system from three to four tiers by the inclusion of tumor necrosis helps further delineate patient outcome and can, therefore, enhance patient counseling following surgery. It also aligns the number of ChRCC grades with the WHO/International Society of Urologic Pathology four-tiered grading systems for clear cell and papillary RCC.

PATIENT SUMMARY

Chromophobe renal cell carcinoma is the third most common type of renal cancer, and unlike other renal cancers, there is no accepted prognostic grading system. In this study, we found that a grading system that included a pathologic feature of tumor necrosis could better define outcomes for patients with chromophobe renal cell carcinoma.

A case of sarcomatoid renal collecting duct carcinoma with paraneoplastic syndrome and peripheral adhesions

Renal sarcoma-like collecting duct cancer is a rare tumor. A 68-year-old man was admitted to the hospital due to chest tightness and back swelling. Computed tomography showed solid occupying of the superior cyst of the left kidney. He underwent radical resection of left kidney cancer. The pathological result is collecting duct carcinoma with sarcoma-like changes. This report helps to understand the clinical manifestations and treatment of the disease.

Neutrophil-induced ferroptosis promotes tumor necrosis in glioblastoma progression

Tumor necrosis commonly exists and predicts poor prognoses in many cancers. Although it is thought to result from chronic ischemia, the underlying nature and mechanisms driving the involved cell death remain obscure. Here, we show that necrosis in glioblastoma (GBM) involves neutrophil-triggered ferroptosis. In a hyperactivated transcriptional coactivator with PDZ-binding motif-driven GBM mouse model, neutrophils coincide with necrosis temporally and spatially. Neutrophil depletion dampens necrosis. Neutrophils isolated from mouse brain tumors kill cocultured tumor cells. Mechanistically, neutrophils induce iron-dependent accumulation of lipid peroxides within tumor cells by transferring myeloperoxidase-containing granules into tumor cells. Inhibition or depletion of myeloperoxidase suppresses neutrophil-induced tumor cell cytotoxicity. Intratumoral glutathione peroxidase 4 overexpression or acyl-CoA synthetase long chain family member 4 depletion diminishes necrosis and aggressiveness of tumors. Furthermore, analyses of human GBMs support that neutrophils and ferroptosis are associated with necrosis and predict poor survival. Thus, our study identifies ferroptosis as the underlying nature of necrosis in GBMs and reveals a pro-tumorigenic role of ferroptosis. Together, we propose that certain tumor damage(s) occurring during early tumor progression (i.e. ischemia) recruits neutrophils to the site of tissue damage and thereby results in a positive feedback loop, amplifying GBM necrosis development to its fullest extent.

Tumour necrosis is associated with tumour aggressiveness and poor outcomes in patients with glioblastomas, but the underlying mechanisms remain poorly understood. Here, the authors show that in a xenograft mouse model of glioblastoma, tumour-infiltrating neutrophils amplify necrosis by promoting myeloperoxidase-induced tumour cell ferroptosis.

The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method

In this research, we exploit an image-based deep learning framework to distinguish three major subtypes of renal cell carcinoma (clear cell, papillary, and chromophobe) using images acquired with computed tomography (CT). A biopsy-proven benchmarking dataset was built from 169 renal cancer cases. In each case, images were acquired at three phases(phase 1, before injection of the contrast agent; phase 2, 1 min after the injection; phase 3, 5 min after the injection). After image acquisition, rectangular ROI (region of interest) in each phase image was marked by radiologists. After cropping the ROIs, a combination weight was multiplied to the three-phase ROI images and the linearly combined images were fed into a deep learning neural network after concatenation. A deep learning neural network was trained to classify the subtypes of renal cell carcinoma, using the drawn ROIs as inputs and the biopsy results as labels. The network showed about 0.85 accuracy, 0.64–0.98 sensitivity, 0.83–0.93 specificity, and 0.9 AUC. The proposed framework which is based on deep learning method and ROIs provided by radiologists showed promising results in renal cell subtype classification. We hope it will help future research on this subject and it can cooperate with radiologists in classifying the subtype of lesion in real clinical situation.