Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma

Prognostic Impact and Genomic Backgrounds of Renal Parenchymal Infiltration or Micronodular Spread in Nonmetastatic Clear Cell Renal Cell Carcinoma

A subset of clear cell renal cell carcinomas (ccRCCs) exhibits various growth patterns that infiltrate the normal renal parenchyma; however, our understanding of its association with cancer aggressiveness is incomplete. Here, we show that the morphology of the tumor interface with normal renal parenchyma is robustly associated with cancer recurrence after surgery, even when compared with the TNM staging system or the World Health Organization/International Society of Urological Pathology (WHO/ISUP) nuclear grade in nonmetastatic ccRCC. Hematoxylin and eosin-stained slides of whole tissue sections from surgical specimens were analyzed using a cohort of 331 patients with nonmetastatic ccRCC treated with radical nephrectomy. The patients were classified into 10 subgroups based on our classification algorithms for assessing the tumor interface with normal renal parenchyma. Among the 10 subgroups, 4 subgroups consisting of 40 patients (12%) were identified to have aggressive forms of nonmetastatic ccRCC associated with poor prognosis and unified as renal parenchymal infiltration or micronodular spread (RPI/MNS) phenotypes. Multivariable analyses showed that RPI/MNS phenotypes were robustly associated with shorter disease-free survival, independently of existing pathological factors including the TNM staging system and WHO/ISUP nuclear grade. The hazard ratio was highest for RPI/MNS (4.62), followed by WHO/ISUP grades 3 to 4 (2.11) and ≥pT3a stage (2.05). In addition, we conducted genomic analyses using next-generation sequencing of infiltrative lesions in 18 patients with RPI/MNS and tumor lesions in 33 patients without RPI/MNS. Results showed that alterations in SETD2 and TSC1 might be associated with RPI/MNS phenotypes, whereas alterations in PBRM1 might be associated with non-RPI/MNS phenotypes. These data suggest that RPI/MNS may be associated with aggressive genomic backgrounds of ccRCC, although more comprehensive analyses with a larger sample size are required. Future studies may further elucidate the clinical implications of RPI/MNS, particularly for deciding the indication of adjuvant treatment after nephrectomy.

Renal cell carcinoma

The landscape of the management of renal cell carcinoma has evolved substantially in the last decade, leading to improved survival in localised and advanced disease. We review the epidemiology, pathology, and diagnosis of renal cell carcinoma and discuss the evidence for current management strategies from localised to metastatic disease. Developments in adjuvant therapies are discussed, including use of pembrolizumab-the first therapy to achieve overall survival benefit in the adjuvant setting. The treatment of advanced disease, including landmark trials that have established immune checkpoint inhibition as a standard of care, are also reviewed. We also discuss the current controversies that exist surrounding the management of metastatic renal cell carcinoma, including the use of risk assessment models for disease stratification and treatment selection for frontline therapy. Management of non-clear cell renal cell carcinoma subtypes is also reviewed. Future directions of research, including a discussion of ongoing clinical trials and the need for reliable biomarkers to guide treatment in kidney cancer, are also highlighted.

Predicting Recurrence After Radical Surgery for High-Risk Renal Cell Carcinoma: Development and Internal Validation of the "TOWARDS" Score

BACKGROUND

Considering the reported greater benefits of immunotherapy and its unignorable adverse events in adjuvant therapy for high-risk renal cell carcinoma (hrRCC), accurate prediction may optimize drug use.

METHODS

The primary objective of this study was to generate a score-based prognostic model of recurrence-free survival in hrRCC. The study retrospectively evaluated 456 patients at two institutions who underwent radical surgery for nonmetastatic pT3-4 and/or N1-2 or pT2 and G4 disease. Clinical variables deemed universally available were selected through backward stepwise analysis and fitted by a multivariable Cox proportional hazards regression model. A point-based score was derived from regression coefficients. Discrimination, calibration, and decision curve analyses were conducted to evaluate predictive performance. Internal validation with bootstrapping was performed to correct for optimism.

RESULTS

The mean follow-up period was 55.3 months, and the median follow-up period was 28.0 months. During the follow-up period, the recurrence rate was 48.2% (n = 220) during a median of 75.7 months. Stepwise variable selection retained age, Eastern Cooperative Oncology Group (ECOG) performance status, presence or absence of symptoms, size of the primary tumor, pathologic T stage, pathologic N stage, tumor grade, and histology. Subsequently, the TOWARDS score (range 0-53) was developed from these variables. Internal validation showed an optimism-corrected C-index of 0.723 and a calibration slope of 0.834. The decision curve analysis showed the superiority of this score over the University of California, Los Angeles (UCLA) Integrated Staging System and GRade, Age, Nodes, and Tumor score.

CONCLUSIONS

The authors' novel TOWARDS scoring model had good accuracy for predicting disease recurrence in patients with hrRCC, and the clinical practicability was superior to that of the existing models.

Advanced renal cell carcinoma management: the Latin American Cooperative Oncology Group (LACOG) and the Latin American Renal Cancer Group (LARCG) consensus update

PURPOSE

Renal cell carcinoma is an aggressive disease with a high mortality rate. Management has drastically changed with the new era of immunotherapy, and novel strategies are being developed; however, identifying systemic treatments is still challenging. This paper presents an update of the expert panel consensus from the Latin American Cooperative Oncology Group and the Latin American Renal Cancer Group on advanced renal cell carcinoma management in Brazil.

METHODS

A panel of 34 oncologists and experts in renal cell carcinoma discussed and voted on the best options for managing advanced disease in Brazil, including systemic treatment of early and metastatic renal cell carcinoma as well as nonclear cell tumours. The results were compared with the literature and graded according to the level of evidence.

RESULTS

Adjuvant treatments benefit patients with a high risk of recurrence after surgery, and the agents used are pembrolizumab and sunitinib, with a preference for pembrolizumab. Neoadjuvant treatment is exceptional, even in initially unresectable cases. First-line treatment is mainly based on tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs); the choice of treatment is based on the International Metastatic Database Consortium (IMCD) risk score. Patients at favourable risk receive ICIs in combination with TKIs. Patients classified as intermediate or poor risk receive ICIs, without preference for ICI + ICIs or ICI + TKIs. Data on nonclear cell renal cancer treatment are limited. Active surveillance has a place in treating favourable-risk patients. Either denosumab or zoledronic acid can be used for treating metastatic bone disease.

CONCLUSION

Immunotherapy and targeted therapy are the standards of care for advanced disease. The utilization and sequencing of these therapeutic agents hinge upon individual risk scores and responses to previous treatments. This consensus reflects a commitment to informed decision-making, drawn from professional expertise and evidence in the medical literature.

UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120)

Renal cell carcinoma (RCC) is most often diagnosed at a localized stage, where surgery is the standard of care. Existing prognostic scores provide moderate predictive performance, leading to challenges in establishing follow-up recommendations after surgery and in selecting patients who could benefit from adjuvant therapy. In this study, we developed a model for individual postoperative disease-free survival (DFS) prediction using machine learning (ML) on real-world prospective data. Using the French kidney cancer research network database, UroCCR, we analyzed a cohort of surgically treated RCC patients. Participating sites were randomly assigned to either the training or testing cohort, and several ML models were trained on the training dataset. The predictive performance of the best ML model was then evaluated on the test dataset and compared with the usual risk scores. In total, 3372 patients were included, with a median follow-up of 30 months. The best results in predicting DFS were achieved using Cox PH models that included 24 variables, resulting in an iAUC of 0.81 [IC95% 0.77-0.85]. The ML model surpassed the predictive performance of the most commonly used risk scores while handling incomplete data in predictors. Lastly, patients were stratified into four prognostic groups with good discrimination (iAUC = 0.79 [IC95% 0.74-0.83]). Our study suggests that applying ML to real-world prospective data from patients undergoing surgery for localized or locally advanced RCC can provide accurate individual DFS prediction, outperforming traditional prognostic scores.

Prognostic Significance of Grade Discrepancy Between Primary Tumor and Venous Thrombus in Nonmetastatic Clear-cell Renal Cell Carcinoma: Analysis of the REMEMBER Registry and Implications for Adjuvant Therapy

BACKGROUND

Further stratification of the risk of recurrence of clear-cell renal cell carcinoma (ccRCC) with venous tumor thrombus (VTT) will facilitate selection of candidates for adjuvant therapy.

OBJECTIVE

To assess the impact of tumor grade discrepancy (GD) between the primary tumor (PT) and VTT in nonmetastatic ccRCC on disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS).

DESIGN, SETTING, AND PARTICIPANTS

This was a retrospective analysis of a multi-institutional nationwide data set for patients with pT3N0M0 ccRCC who underwent radical nephrectomy and thrombectomy.

OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSIS

Pathology slides were centrally reviewed. GD, a bidirectional variable (upgrading or downgrading), was numerically defined as the VTT grade minus the PT grade. Multivariable models were built to predict DFS, OS, and CSS.

RESULTS AND LIMITATIONS

We analyzed data for 604 patients with median follow-up of 42 mo (excluding events). Tumor GD between VTT and PT was observed for 47% (285/604) of the patients and was an independent risk factor with incremental value in predicting the outcomes of interest (all p < 0.05). Incorporation of tumor GD significantly improved the performance of the ECOG-ACRIN 2805 (ASSURE) model. A GD-based model (PT grade, GD, pT stage, PT sarcomatoid features, fat invasion, and VTT consistency) had a c index of 0.72 for DFS. The hazard ratios were 8.0 for GD = +2 (p < 0.001), 1.9 for GD = +1 (p < 0.001), 0.57 for GD = -1 (p = 0.001), and 0.22 for GD = -2 (p = 0.003) versus GD = 0 as the reference. According to model-converted risk scores, DFS, OS, and CSS significantly differed between subgroups with low, intermediate, and high risk (all p < 0.001).

CONCLUSIONS

Routine reporting of VTT upgrading or downgrading in relation to the PT and use of our GD-based nomograms can facilitate more informed treatment decisions by tailoring strategies to an individual patient's risk of progression.

PATIENT SUMMARY

We developed a tool to improve patient counseling and guide decision-making on other therapies in addition to surgery for patients with the clear-cell type of kidney cancer and tumor invasion of a vein.

Predicting recurrence and survival in patients with non-metastatic renal-cell carcinoma after nephrectomy: a prospective population-based study with multicenter validation

BACKGROUND

Accurate prognostication of oncological outcomes is crucial for the optimal management of patients with renal cell carcinoma (RCC) after surgery. Previous prediction models were developed mainly based on retrospective data in the Western populations, and their predicting accuracy remains limited in contemporary, prospective validation. We aimed to develop contemporary RCC prognostic models for recurrence and overall survival (OS) using prospective population-based patient cohorts and compare their performance with existing, mostly utilized ones.

METHODS

In this prospective analysis and external validation study, the development set included 11  128 consecutive patients with non-metastatic RCC treated at a tertiary urology center in China between 2006 and 2022, and the validation set included 853 patients treated at 13 medical centers in the USA between 1996 and 2013. The primary outcome was progression-free survival (PFS), and the secondary outcome was OS. Multivariable Cox regression was used for variable selection and model development. Model performance was assessed by discrimination [Harrell's C-index and time-dependent areas under the curve (AUC)] and calibration (calibration plots). Models were validated internally by bootstrapping and externally by examining their performance in the validation set. The predictive accuracy of the models was compared with validated models commonly used in clinical trial designs and with recently developed models without extensive validation.

RESULTS

Of the 11  128 patients included in the development set, 633 PFS and 588 OS events occurred over a median follow-up of 4.3 years [interquartile range (IQR) 1.7-7.8]. Six common clinicopathologic variables (tumor necrosis, size, grade, thrombus, nodal involvement, and perinephric or renal sinus fat invasion) were included in each model. The models demonstrated similar C-indices in the development set (0.790 [95% CI 0.773-0.806] for PFS and 0.793 [95% CI 0.773-0.811] for OS) and in the external validation set (0.773 [0.731-0.816] and 0.723 [0.731-0.816]). A relatively stable predictive ability of the models was observed in the development set (PFS: time-dependent AUC 0.832 at 1 year to 0.760 at 9 years; OS: 0.828 at 1 year to 0.794 at 9 years). The models were well calibrated and their predictions correlated with the observed outcome at 3, 5, and 7 years in both development and validation sets. In comparison to existing prognostic models, the present models showed superior performance, as indicated by C-indices ranging from 0.722 to 0.755 (all P <0.0001) for PFS and from 0.680 to 0.744 (all P <0.0001) for OS. The predictive accuracy of the current models was robust in patients with clear-cell and non-clear-cell RCC.

CONCLUSIONS

Based on a prospective population-based patient cohort, the newly developed prognostic models were externally validated and outperformed the currently available models for predicting recurrence and survival in patients with non-metastatic RCC after surgery. The current models have the potential to aid in clinical trial design and facilitate clinical decision-making for both clear-cell and non-clear-cell RCC patients at varying risk of recurrence and survival.

Patients with high nuclear grade pT1-ccRCC are more suitable for radical nephrectomy than partial nephrectomy: a multicenter retrospective study using propensity score

BACKGROUND

Partial nephrectomy (PN) is usually recommended for T1 stage clear cell renal cell carcinoma (ccRCC) regardless of the nuclear grades. However, the question remains unresolved as to whether PN is non-inferior to RN in patients with T1-ccRCC at higher risk of recurrence. In fact, we found that patients with high nuclear grades treated with PN had poorer prognosis compared with those treated with radical nephrectomy (RN). Therefore, this study was designed to evaluate the associations of PN and RN in the four nuclear grade subsets with oncologic outcomes.

METHODS

A retrospective study was conducted in three Chinese urological centers that included 1,714 patients who underwent PN or RN for sporadic, unilateral, pT1, N0, and M0 ccRCC without positive surgical margins and neoadjuvant therapy between 2010 and 2019. Associations of nephrectomy type with local ipsilateral recurrence, distant metastases, and all-cause mortality (ACM) were evaluated using the Kaplan-Meier method and multivariable Cox proportional hazards regression models after overlap weighting (OW).

RESULTS

A total of 1675 patients entered the OW cohort. After OW, in comparison to PN, RN associated with a reduced risk of local ipsilateral recurrence in the G2 subset (HR = 0.148, 95% CI 0.046-0.474; p < 0.05), G3 subset (HR = 0.097, 95% CI 0.021-0.455; p < 0.05), and G4 subset (HR = 0.091, 95% CI 0.011-0.736; p < 0.05), and resulting in increased five-year local recurrence-free survival rates of 7.0%, 17.9%, and 36.2%, respectively. An association between RN and a reduced risk of distant metastases in the G4 subset (HR = 0.071, 95% CI 0.016-0.325; p < 0.05), with the five-year distant metastases-free survival rate increasing by 33.1% was also observed. No significant difference in ACM between PN and RN was identified.

CONCLUSIONS

Our findings substantiate that opting for RN, as opposed to PN, is more advantageous for local recurrence-free survival and distant metastases-free survival in patients with high nuclear grade (especially G4) pT1-ccRCC. We recommend placing a heightened emphasis on enhancing preoperative nuclear grade assessment, as it can significantly influence the choice of surgical plan.

TRIAL REGISTRATION

This study was registered at Chinese Clinical Trial Registry (ID: ChiCTR2200063333).

Perioperative systemic therapy in renal cell carcinoma

PURPOSE OF REVIEW

Renal cell carcinoma (RCC) is the most common kidney neoplasm. Localized RCC can be cured with nephrectomy. However, a proportion of patients will recur with incurable distant metastatic disease. There is a clear need for treatments to reduce the risk of RCC recurrence and thus improve survival. This review describes the landscape of perioperative therapy for RCC, focusing on more recent trials involving immune checkpoint inhibitors (ICIs).

RECENT FINDINGS

ICIs have significantly changed outcomes in advanced RCC. Four trials investigating the role of perioperative ICI for RCC are now reported. Only one trial utilizing adjuvant pembrolizumab (Keynote-564) has shown a disease-free survival benefit in resected RCC.

SUMMARY

Patients with resected RCC should be counselled on their risk of recurrence and the potential option of adjuvant pembrolizumab, recognizing that overall survival data are not yet available.

Adjuvant therapy for renal cell carcinoma: A systematic review of current landscape and future directions

The advent of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) has been transformative for the treatment of advanced renal cell carcinoma (RCC). Their efficacy post-surgical resection remains a contentious point. Various phase 3 RCTs have assessed their potency. Amongst evaluated agents, sunitinib and pembrolizumab have demonstrated notable disease-free survival benefits. Sunitinib's potential is diminished due to absence of clear overall survival (OS) benefits and side-effect profile. Pembrolizumab shows better tolerance, conclusive OS data are forthcoming. This scenario underscores the pressing need for advanced risk stratification methods and discovery of novel biomarkers. Existing strategies, largely pre-dating TKI and ICI therapeutic era, lack sufficient accuracy in predicting relapse-risk. Our review offers a comprehensive analysis of key phase 3 RCTs, focusing on TKIs, mTOR-inhibitors, and ICIs for adjuvant RCC treatment. The intent is to shed light on the intricate landscape of RCC treatment, guiding future research directions for optimizing patient outcomes.

Adjuvant everolimus after surgery for renal cell carcinoma (EVEREST): a double-blind, placebo-controlled, randomised, phase 3 trial

BACKGROUND

Patients undergoing resection of renal cell carcinoma are at risk of disease relapse. We evaluated the effectiveness of the mammalian target of rapamycin inhibitor everolimus administered after surgery.

METHODS

In this randomised, double-blind, phase 3 trial, we enrolled adults with histologically confirmed renal cell carcinoma who had undergone a full surgical resection and were at intermediate-high or very high risk of recurrence at 398 academic and community institution centres in the USA. After nephrectomy, patients were randomly assigned (1:1) via a central web-based application using a dynamic balancing algorithm to receive 10 mg oral everolimus daily or placebo for 54 weeks. The primary endpoint was recurrence-free survival. Efficacy analyses included all eligible, randomly assigned patients; safety analysis included all patients who received treatment. This trial is registered with ClinicalTrials.gov, NCT01120249 and is closed to new participants.

FINDINGS

Between April 1, 2011, and Sept 15, 2016, a total of 1545 patients were randomly assigned to receive everolimus (n=775) or placebo (n=770), of whom 755 assigned to everolimus and 744 assigned to placebo were eligible for inclusion in the efficacy analysis. With a median follow-up of 76 months (IQR 61-92), recurrence-free survival was longer with everolimus than with placebo (5-year recurrence-free survival 67% [95% CI 63-70] vs 63% [60-67]; stratified log-rank p=0·050; stratified hazard ratio [HR] 0·85, 95% CI 0·72-1·00; p=0·051) but did not meet the prespecified p value for statistical significance of 0·044. Recurrence-free survival was longer with everolimus than with placebo in the very-high-risk group (HR 0·79, 95% CI 0·65-0·97; p=0·022) but not in the intermediate-high-risk group (0·99, 0·73-1·35; p=0·96). Grade 3 or higher adverse events occurred in 343 (46%) of 740 patients who received everolimus and 79 (11%) of 723 who received placebo.

INTERPRETATION

Postoperative everolimus did not improve recurrence-free survival compared with placebo among patients with renal cell carcinoma at high risk of recurrence after nephrectomy. These results do not support the adjuvant use of everolimus for renal cell carcinoma after surgery.

FUNDING

US National Institutes of Health, National Cancer Institute, National Clinical Trials Network, Novartis Pharmaceuticals Corporation, and The Hope Foundation.

Prognostic significance of the preoperative platelet-lymphocyte ratio in nonmetastatic renal cell carcinoma: cross-sectional study

The prognostic significance of the platelet-lymphocyte ratio (PLR) in nonmetastatic renal cell carcinoma (RCC) is controversial, although it has been established as a prognostic factor in several cancers.

OBJECTIVE

The objective of our study was to evaluate the prognostic significance of the PLR in patients with nonmetastatic RCC.

PATIENTS AND METHODS

The authors performed a retrospective analysis of patients with nonmetastatic RCC who were operated between 2004 and 2020. Five years recurrence-free survival and metastasis-free survival were calculated. The prognostic significance of the preoperative PLR was assessed. The Kaplan-Meier method was utilized to graphically display survivor functions. Univariate and multivariate Cox's proportional hazards regression models were utilized to analyze the association between PLR and oncological outcomes. Differences were considered significant if P<0.05.

RESULTS

Two hundred and two patients were included. The mean follow-up was 56.8±3 months. Patients with a higher PLR had larger tumors (P=0.02), higher ASA score (P=0.001), symptomatic forms (P=0.01), and more frequent tumor necrosis (P=0.02). Recurrence-free survival and metastasis-free survival rates were significantly lower in patients with high PLR than in those with low ratios (each P<0.005). Multivariate analysis identified PLR as an independent predictor of recurrence-free survival (P=0.002) and metastasis-free survival (P<0.001).

CONCLUSION

A higher PLR was associated with aggressive renal cancer. In addition, the PLR was a significant prognostic factor for both recurrence-free survival and metastasis-free survival in patients with nonmetastatic RCC.

Critical Appraisal of Leibovich 2018 and GRANT Models for Prediction of Cancer-Specific Survival in Non-Metastatic Chromophobe Renal Cell Carcinoma

Within the Surveillance, Epidemiology, and End Results database (2000–2019), we identified 5522 unilateral surgically treated non-metastatic chromophobe kidney cancer (chRCC) patients. This population was randomly divided into development vs. external validation cohorts. In the development cohort, the original Leibovich 2018 and GRANT categories were applied to predict 5- and 10-year cancer-specific survival (CSS). Subsequently, a novel multivariable nomogram was developed. Accuracy, calibration and decision curve analyses (DCA) tested the Cox regression-based nomogram as well as the Leibovich 2018 and GRANT risk categories in the external validation cohort. The accuracy of the Leibovich 2018 and GRANT models was 0.65 and 0.64 at ten years, respectively. The novel prognostic nomogram had an accuracy of 0.78 at ten years. All models exhibited good calibration. In DCA, Leibovich 2018 outperformed the novel nomogram within selected ranges of threshold probabilities at ten years. Conversely, the novel nomogram outperformed Leibovich 2018 for other values of threshold probabilities. In summary, Leibovich 2018 and GRANT risk categories exhibited borderline low accuracy in predicting CSS in North American non-metastatic chRCC patients. Conversely, the novel nomogram exhibited higher accuracy. However, in DCA, all examined models exhibited limitations within specific threshold probability intervals. In consequence, all three examined models provide individual predictions that might be suboptimal and be affected by limitations determined by the natural history of chRCC, where few deaths occur within ten years from surgery. Further investigations regarding established and novel predictors of CSS and relying on large sample sizes with longer follow-up are needed to better stratify CSS in chRCC.

The Continuing Question of Adjuvant Therapy in Clear Cell Renal Cell Carcinoma

Treatment advances in kidney cancer continually evolve. The focus of treatment options continues with oral vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKIs) or intravenous immune checkpoint inhibitors (ICIs). Multiple trials exploring the role of adjuvant treatment after cytoreductive nephrectomy in high-risk clear cell renal cell carcinoma are currently ongoing. The discovery of biomarkers may help determine which patients benefit from these treatments, but these are not yet available outside clinical studies. Trials with combination therapies are also ongoing, especially using novel therapies with new mechanisms of action, and will hopefully provide more clues on proper patient and therapy selection in the adjuvant setting.

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

The significance of sarcomatoid and rhabdoid dedifferentiation in renal cell carcinoma

Dedifferentiation in renal cell carcinoma (RCC), either sarcomatoid or rhabdoid, is an infrequent event that may occur heterogeneously in the setting of any RCC histology and is associated with poor outcomes. Sarcomatoid dedifferentiation is associated with inferior survival with angiogenesis targeted therapy and infrequent responses to cytotoxic chemotherapy. However, immune checkpoint therapy has significantly improved outcomes for patients with sarcomatoid dedifferentiation. Biologically, sarcomatoid dedifferentiation has increased programmed death-ligand 1 (PD-L1) expression and an inflamed tumor microenvironment, in addition to other distinct molecular alterations. Less is known about rhabdoid dedifferentiation from either a clinical, biological, or therapeutic perspective. In this focused review, we will discuss the prognostic implications, outcomes with systemic therapy, and underlying biology in RCC with either sarcomatoid or rhabdoid dedifferentiation present.

A Causal Framework for Making Individualized Treatment Decisions in Oncology

Simple Summary

Physicians routinely make individualized treatment decisions by accounting for the joint effects of patient prognostic covariates and treatments on clinical outcomes. Ideally, this is performed using historical randomized clinical trial (RCT) data. Randomization ensures that unbiased estimates of causal treatment effect parameters can be obtained from the historical RCT data and used to predict each new patient’s outcome based on the joint effect of their baseline covariates and each treatment being considered. However, this process becomes problematic if a patient seen in the clinic is very different from the patients who were enrolled in the RCT. That is, if a new patient does not satisfy the entry criteria of the RCT, then the patient does not belong to the population represented by the patients who were studied in the RCT. In such settings, it still may be possible to utilize the RCT data to help choose a new patient’s treatment. This may be achieved by combining the RCT data with data from other clinical trials, or possibly preclinical experiments, and using the combined dataset to predict the patient’s expected outcome for each treatment being considered. In such settings, combining data from multiple sources in a way that is statistically reliable is not entirely straightforward, and correctly identifying and estimating the effects of treatments and patient covariates on clinical outcomes can be complex. Causal diagrams provide a rational basis to guide this process. The first step is to construct a causal diagram that reflects the plausible relationships between treatment variables, patient covariates, and clinical outcomes. If the diagram is correct, it can be used to determine what additional data may be needed, how to combine data from multiple sources, how to formulate a statistical model for clinical outcomes as a function of treatment and covariates, and how to compute an unbiased treatment effect estimate for each new patient. We use adjuvant therapy of renal cell carcinoma to illustrate how causal diagrams may be used to guide these steps.

Abstract

We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT’s entry criteria, or a treatment’s effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment–covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.

Evolving Role of Adjuvant Systemic Therapy for Kidney and Urothelial Cancers

The role of adjuvant therapy in renal cell carcinoma and urothelial carcinoma is rapidly evolving. To date, the U.S. Food and Drug Administration has approved sunitinib and pembrolizumab in the adjuvant setting for renal cell carcinoma and nivolumab for urothelial carcinoma based on disease-free survival benefit. The U.S. Food and Drug Administration held a joint workshop with the National Cancer Institute and the Society of Urologic Oncology in 2017 to harmonize design elements, including eligibility and radiologic assessments across adjuvant trials in renal cell carcinoma and urothelial carcinoma. Considerations from the discussion at these workshops led the U.S. Food and Drug Administration to draft guidances to help inform subsequent adjuvant trial design for renal cell carcinoma and urothelial carcinoma. Patient-centered decision-making is crucial when determining therapeutic choices in the adjuvant setting; utility functions can be used to help quantify each patient's goals, values, and risk/benefit trade-offs to ensure that the decision regarding adjuvant therapy is informed by their preferences and the evolving outcomes data.

The Four-Feature Prognostic Models for Cancer-Specific and Overall Survival after Surgery for Localized Clear Cell Renal Cancer: Is There a Place for Inflammatory Markers?

We aimed at a determination of the relevance of comorbidities and selected inflammatory markers to the survival of patients with primary non-metastatic localized clear cell renal cancer (RCC). We retrospectively analyzed data from a single tertiary center on 294 patients who underwent a partial or radical nephrectomy in the years 2012–2018. The following parameters were incorporated in the risk score: tumor stage, grade, size, selected hematological markers (SIRI—systemic inflammatory response index; SII—systemic immune-inflammation index) and a comorbidities assessment tool (CCI—Charlson Comorbidity Index). For further analysis we compared our model with existing prognostic tools. In a multivariate analysis, tumor stage (p = 0.01), tumor grade (p = 0.03), tumor size (p = 0.006) and SII (p = 0.02) were significant predictors of CSS, while tumor grade (p = 0.02), CCI (p = 0.02), tumor size (p = 0.01) and SIRI (p = 0.03) were significant predictors of OS. We demonstrated that our model was characterized by higher accuracy in terms of OS prediction compared to the Leibovich and GRANT models and outperformed the GRANT model in terms of CSS prediction, while non-inferiority to the VENUSS model was revealed. Four different features were included in the predictive models for CSS (grade, size, stage and SII) and OS (grade, size, CCI and SIRI) and were characterized by adequate or even superior accuracy when compared with existing prognostic tools.

Adjuvant therapy for patients with renal cell carcinoma following surgery: a focus on pembrolizumab

INTRODUCTION

Many patients with renal cell carcinoma (RCC) who undergo surgery with curative intent have a high risk of disease recurrence and until recently no palatable adjuvant systemic therapy options. Blocking the programmed death ligand (PD-1) immune checkpoint pathway with pembrolizumab has robust clinical efficacy in patients with metastatic RCC. Results from the KEYNOTE 564 trial demonstrate that adjuvant pembrolizumab significantly improves disease- free survival after nephrectomy or metastatectomy.

AREAS COVERED

We provide an overview of efforts to develop an adjuvant therapy in patients with high-risk RCC. This includes a critical review of efficacy, toxicity, and clinical implications from a large phase III trial leading to the FDA and EMA approvals of adjuvant pembrolizumab.

EXPERT OPINION

Pembrolizumab offers an effective and well-tolerated adjuvant therapy for patients with surgically resected RCC at high-risk of disease recurrence. Future research will focus on optimal patient selection and biomarkers that predict benefit and/or toxicity from therapy.

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.

External Validation of the ASSURE Model for Predicting Oncological Outcomes After Resection of High-risk Renal Cell Carcinoma (RESCUE Study: UroCCR 88)

A prognostic model based on the population of the ASSURE phase 3 trial has recently been described. The ASSURE model stratifies patients into risk groups to predict survival after surgical resection of intermediate- and high-risk localised kidney cancer. We evaluated this model in an independent cohort of 1372 patients using discrimination, calibration, and decision curve analysis. Regarding disease-free survival, the ASSURE model showed modest discrimination (65%), miscalibration, and poor net benefit compared with the UCLA Integrated Staging System (UISS) and Leibovich 2018 models. Similarly, the ability of the ASSURE model to predict overall survival was poor in terms of discrimination (63%), with overestimation on calibration plots and a modest net benefit for the probability threshold of between 10% and 40%. Overall, our results show that the performance of the ASSURE model was less optimistic than expected, and not associated with a clear improvement in patient selection and clinical usefulness in comparison to with available models. We propose an updated version using the recalibration method, which leads to a (slight) improvement in performance but should be validated in another external population.

Patient summary

The recent ASSURE model evaluates survival after surgery for nonmetastatic kidney cancer. We found no clear improvement in patient classification when we compared ASSURE with older models, so use of this model for patients with nonmetastatic kidney cancer still needs to be clarified.

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

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