A 16-gene assay to predict recurrence after surgery in localised renal cell carcinoma: development and validation studies

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

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

A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma

Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I-III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy.

Approaches to Treating High Risk and Advanced Renal Cell Carcinoma (RCC): Key Trial Data That Impacts Treatment Decisions in the Clinic

The treatment paradigm for high risk localized and advanced kidney cancer has been characterized by ongoing changes, with the introduction of vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR TKIs) and later with immune checkpoint blockade. In this article, we review how current evidence informs our decision-making on post-checkpoint inhibitor systemic therapies, the role of adjuvant and/or neoadjuvant therapies, and the role of cytoreductive nephrectomy in the evolving systemic therapy landscape. While some studies support a post-checkpoint inhibitor benefit from the VEGFR TKIs cabozantinib or axitinib, the benefit of doublet therapies including a VEGF receptor inhibitor and a checkpoint inhibitor remains an area of active investigation, with the combination of lenvatinib plus pembrolizumab showing promise but with a Phase III trial of the combination of atezolizumab plus cabozantinib showing no benefit over cabozantinib alone. The role of adjuvant therapy in patients with high-risk disease who have undergone cytoreductive nephrectomy and potentially metastasectomy is also an area of continuing interest. While the S-TRAC study demonstrated a disease-free survival benefit for adjuvant sunitinib, no overall survival benefit was shown, and multiple other studies of adjuvant VEGFR TKI therapy have been negative. Subsequently, adjuvant pembrolizumab has shown a benefit in overall survival, whereas trials of neoadjuvant and adjuvant nivolumab, adjuvant atezolizumab, and adjuvant ipilimumab plus nivolumab have all been negative. Finally, the role for cytoreductive nephrectomy continues to be an area of active debate. The CARMENA study raised important questions about the role of cytoreductive nephrectomy given the advances in VEGFR TKI therapy but was characterized by accrual difficulties and a significant number of patients not receiving treatment according to the study protocol. Two ongoing studies (NORDIC-SUN and PROBE) seek to further address the role of cytoreductive nephrectomy in the doublet therapy era.

The evolving management of small renal masses

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

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

Predictive and prognostic biomarkers in urological tumours

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

Management of Renal Cell Carcinoma: Promising Biomarkers and the Challenges to Reach the Clinic

The incidence of renal cell carcinoma (RCC) is increasing worldwide, yet research within this field is lagging behind other cancers. Despite increased detection of early disease as a consequence of the widespread use of diagnostic CT scans, 25% of patients have disseminated disease at diagnosis. Similarly, around 25% progress to metastatic disease following curatively intended surgery. Surgery is the cornerstone in the treatment of RCC; however, when the disease is disseminated, immunotherapy or immunotherapy in combination with a tyrosine kinase inhibitor is the patient's best option. Immunotherapy is a potent treatment, with durable treatment responses and potential to cure the patient, but only half of the patients benefit from the administered treatment, and there are currently no methods that can identify which patients will respond to immunotherapy. Moreover, there is a need to identify the patients in greatest risk of relapsing after surgery for localized disease and direct adjuvant treatment there. Even though several molecular biomarkers have been published to date, we are still lacking routinely used biomarkers to guide optimal clinical management. The purpose of this review is to highlight some of the most promising biomarkers, discuss the efforts made within this field to date, and describe the barriers needed to be overcome to have reliable and robust predictive and prognostic biomarkers in the clinic for renal cancer.

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.

The emerging landscape of neo/adjuvant immunotherapy in renal cell carcinoma

Adjuvant and neoadjuvant therapies that reduce the risk of renal cell carcinoma (RCC) recurrence remain an area of unmet need. Advances have been made in metastatic RCC recently by leveraging PD-1/PD-L1 immune checkpoint inhibitors (ICIs). These agents are currently being investigated in the adjuvant and neoadjuvant settings to determine if intervention early in the disease trajectory offers a clinically meaningful benefit. While a disease-free survival benefit has been demonstrated with pembrolizumab, results from other ICI studies have not been positive to date. More mature data from these studies are needed to determine whether there is a survival benefit to ICIs in the curative-intent setting. The success of ICIs has also ushered a new wave of studies combining ICIs with other agents such as targeted therapies and vaccines, which are in early stages of investigation. We review the current state of adjuvant/neoadjuvant therapy in RCC and highlight opportunities for ongoing study.

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.

Current Landscape of Genomic Biomarkers in Clear Cell Renal Cell Carcinoma

CONTEXT

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

OBJECTIVE

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

EVIDENCE ACQUISITION

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

EVIDENCE SYNTHESIS

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

CONCLUSIONS

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

PATIENT SUMMARY

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

Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study

BACKGROUND

Improved markers for predicting recurrence are needed to stratify patients with localised (stage I-III) renal cell carcinoma after surgery for selection of adjuvant therapy. We developed a novel assay integrating three modalities-clinical, genomic, and histopathological-to improve the predictive accuracy for localised renal cell carcinoma recurrence.

METHODS

In this retrospective analysis and validation study, we developed a histopathological whole-slide image (WSI)-based score using deep learning allied to digital scanning of conventional haematoxylin and eosin-stained tumour tissue sections, to predict tumour recurrence in a development dataset of 651 patients with distinctly good or poor disease outcome. The six single nucleotide polymorphism-based score, which was detected in paraffin-embedded tumour tissue samples, and the Leibovich score, which was established using clinicopathological risk factors, were combined with the WSI-based score to construct a multimodal recurrence score in the training dataset of 1125 patients. The multimodal recurrence score was validated in 1625 patients from the independent validation dataset and 418 patients from The Cancer Genome Atlas set. The primary outcome measured was the recurrence-free interval (RFI).

FINDINGS

The multimodal recurrence score had significantly higher predictive accuracy than the three single-modal scores and clinicopathological risk factors, and it precisely predicted the RFI of patients in the training and two validation datasets (areas under the curve at 5 years: 0·825-0·876 vs 0·608-0·793; p<0·05). The RFI of patients with low stage or grade is usually better than that of patients with high stage or grade; however, the RFI in the multimodal recurrence score-defined high-risk stage I and II group was shorter than in the low-risk stage III group (hazard ratio [HR] 4·57, 95% CI 2·49-8·40; p<0·0001), and the RFI of the high-risk grade 1 and 2 group was shorter than in the low-risk grade 3 and 4 group (HR 4·58, 3·19-6·59; p<0·0001).

INTERPRETATION

Our multimodal recurrence score is a practical and reliable predictor that can add value to the current staging system for predicting localised renal cell carcinoma recurrence after surgery, and this combined approach more precisely informs treatment decisions about adjuvant therapy.

FUNDING

National Natural Science Foundation of China, and National Key Research and Development Program of China.

The Promise of Neoadjuvant and Adjuvant Therapies for Renal Cancer

Because metachronous metastatic disease will develop in 20% to 40% of patients with presumed localized renal cell carcinoma (RCC) treated surgically, research is focused on neoadjuvant and adjuvant systemic therapy, to improve disease-free and overall survival. Neoadjuvant therapies trialed include anti-vascular endothelial growth factor (VEGF) tyrosine kinase inhibitor (TKI) agents, or combination therapies (immunotherapy with TKI), and aim to improve resectability of locoregional RCC. Adjuvant therapies trialed include cytokines, anti-VEGF TKI agents, or immunotherapy. These therapeutics can facilitate the surgical extirpation of the primary kidney tumor in the neoadjuvant setting and improve disease-free survival in the adjuvant setting.

Application of Genomic Sequencing to Refine Patient Stratification for Adjuvant Therapy in Renal Cell Carcinoma

PURPOSE

Patients with resected localized clear-cell renal cell carcinoma (ccRCC) remain at variable risk of recurrence. Incorporation of biomarkers may refine risk prediction and inform adjuvant treatment decisions. We explored the role of tumor genomics in this setting, leveraging the largest cohort to date of localized ccRCC tissues subjected to targeted gene sequencing.

EXPERIMENTAL DESIGN

The somatic mutation status of 12 genes was determined in 943 ccRCC cases from a multinational cohort of patients, and associations to outcomes were examined in a Discovery (n = 469) and Validation (n = 474) framework.

RESULTS

Tumors containing a von-Hippel Lindau (VHL) mutation alone were associated with significantly improved outcomes in comparison with tumors containing a VHL plus additional mutations. Within the Discovery cohort, those with VHL+0, VHL+1, VHL+2, and VHL+≥3 tumors had disease-free survival (DFS) rates of 90.8%, 80.1%, 68.2%, and 50.7% respectively, at 5 years. This trend was replicated in the Validation cohort. Notably, these genomically defined groups were independent of tumor mutational burden. Amongst patients eligible for adjuvant therapy, those with a VHL+0 tumor (29%) had a 5-year DFS rate of 79.3% and could, therefore, potentially be spared further treatment. Conversely, patients with VHL+2 and VHL+≥3 tumors (32%) had equivalent DFS rates of 45.6% and 35.3%, respectively, and should be prioritized for adjuvant therapy.

CONCLUSIONS

Genomic characterization of ccRCC identified biologically distinct groups of patients with divergent relapse rates. These groups account for the ∼80% of cases with VHL mutations and could be used to personalize adjuvant treatment discussions with patients as well as inform future adjuvant trial design.

SERPINE1 and its co-expressed genes are associated with the progression of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma(ccRCC) is a frequently occurring malignant tumor of the urinary system. Despite extensive research, the regulatory mechanisms underlying the pathogenesis and progression of ccRCC remain largely unknown.

METHODS

We downloaded 5 ccRCC expression profiles from the Gene Expression Omnibus (GEO) database and obtained the list of differentially expressed genes (DEGs). Using String and Cytoscape tools, we determined the hub genes of ccRCC, and then analyzed their relationship with ccRCC patient survival. Ultimately, we identified SERPINE1 as a prognostic factor in ccRCC. Meanwhile, we confirmed the role of SERPINE1 in 786-O cells by cell transfection and in vitro experiments.

RESULTS

Our analysis yielded a total of 258 differentially expressed genes, comprising 105 down-regulated genes and 153 up-regulated genes. Survival analysis of SERPINE1 expression in The Cancer Genome Atlas (TCGA) confirmed its association with the increase of tumor grade, lymph node metastasis, and tumor stage, as well as with shorter survival. Furthermore, we found that SERPINE1 expression levels were associated with CD8 + T cells, CD4 + T cells, B cells, macrophages, neutrophils, and dendritic cells. Cell experiments showed that knockdown SERPINE1 expression could inhibit the proliferation, migration and invasion of ccRCC cells. Among the co-expressed genes with the highest correlation, ITGA5, SLC2A3, SLC2A14, SHC1, CEBPB, and ADA were overexpressed and associated with shorter overall survival (OS) in ccRCC.

CONCLUSIONS

In this study, we identified hub genes that are strongly related to ccRCC, and highlights the potential utility of overexpressed SERPINE1 and its co-expressed genes could be used as prognostic and diagnostic biomarkers in ccRCC.

Transcriptomic recurrence score improves recurrence prediction for surgically treated patients with intermediate-risk clear cell kidney cancer

BACKGROUND

Risk stratification of kidney cancer patients after nephrectomy may tailor surveillance intensity and selection for adjuvant therapy. Transcriptomic approaches are effective in predicting recurrence, but whether they add value to clinicopathologic models remains unclear.

METHODS

Data from patients with clear cell renal cell carcinoma (ccRCC) was downloaded from The Cancer Genome Atlas. Clinicopathologic variables were used to calculate SSIGN (stage, size, grade, and necrosis) scores. The 16 gene recurrence score (RS) signature was generated using RNA-seq data. Transcriptomic risk groups were calculated using the original thresholds. SSIGN groups were divided into low, intermediate, and high risk. Disease-free status was the primary endpoint assessed.

RESULTS

SSIGN and RS were calculated for 428 patients with non-metastatic ccRCC. SSIGN low-, intermediate-, and high-risk groups demonstrated 2.7%, 15.2%, and 27.5%, 3-year recurrence risk, respectively. On multivariable analysis, the RS was associated with disease-free status (sub-distribution hazard ratio (sHR) 1.43 per 25 RS [95% CI (1.00-1.43)], p = 0.05). By risk groups, RS further risk stratified the SSIGN intermediate-risk group (sHR 2.22 [95% CI 1.10-4.50], p = 0.03). SSIGN intermediate-risk patients with low and high RS had a 3-year recurrence rate of 8.0% and 25.2%, respectively. Within this risk group, the area under the curve (AUC) at 3 years was 0.69 for SSIGN, 0.74 for RS, and 0.78 for their combination.

CONCLUSIONS

Transcriptomic recurrence scores improve risk prediction even when controlling for clinicopathologic factors. Utility may be best suited for intermediate-risk patients who have heterogeneous outcomes and further refinement for clinical utility is warranted.

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

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

Adjuvant therapy in renal cell carcinoma in the immunotherapy era: where do we stand?

Locoregional, as well as metastatic renal cell carcinoma, tends to relapse after nephrectomy or metastasectomy. Adjuvant therapy seems to be an effective strategy to reduce the risk of recurrence. All anti-VEGF tyrosine kinase inhibitors except sunitinib have failed to show any benefit in the adjuvant setting in patients with locally advanced disease and an intermediate-to-high chance of recurrence. On the other hand, immune checkpoint inhibitors, which are now used in the first-line in the metastatic setting, are being tested in the adjuvant setting. Pembrolizumab has shown benefit in the adjuvant setting in patients with a high risk of recurrence or with resected metastatic disease with no evidence of disease. Results for other checkpoint inhibitors are still awaited.

The chromosomal instability 25 gene signature is identified in clear cell renal cell carcinoma and serves as a predictor for survival and Sunitinib response

Background

Chromosomal instability (CIN) is a cancer hallmark and it is difficult to directly measure its phenotype, while a CIN25 gene signature was established to do so in several cancer types. However, it is currently unclear whether there exists this signature in clear cell renal cell carcinoma (ccRCC), and if so, which biological and clinical implications it has.

Methods

Transcriptomic profiling was performed on 10 ccRCC tumors and matched renal non-tumorous tissues (NTs) for CIN25 signature analyses. TCGA and E-MBAT1980 ccRCC cohorts were analyzed for the presence of CIN25 signature, CIN25 score-based ccRCC classification, and association with molecular alterations and overall or progression-free survival (OS or PFS). IMmotion150 and 151 cohorts of ccRCC patients treated with Sunitinib were analyzed for the CIN25 impact on Sunitinib response and survival.

Results

The transcriptomic analysis of 10 patient samples showed robustly upregulated expression of the CIN25 signature genes in ccRCC tumors, which were further confirmed in TCGA and E-MBAT1980 ccRCC cohorts. Based on their expression heterogeneity, ccRCC tumors were categorized into CIN25-C1 (low) and C2 (high) subtypes. The CIN25-C2 subtype was associated with significantly shorter patient OS and PFS, and characterized by increased telomerase activity, proliferation, stemness and EMT. The CIN25 signature reflects not only a CIN phenotype, but also levels of the whole genomic instability including mutation burden, microsatellite instability and homologous recombination deficiency (HRD). Importantly, the CIN25 score was significantly associated with Sunitinib response and survival. In IMmotion151 cohort, patients in the CIN25-C1 group exhibited 2-fold higher remission rate than those in the CIN25-C2 group (P = 0.0004) and median PFS in these two groups was 11.2 and 5.6 months, respectively (P = 7.78E-08). Similar results were obtained from the IMmotion150 cohort analysis. Higher EZH2 expression and poor angiogenesis, well characterized factors leading to Sunitinib resistance, were enriched in the CIN25-C2 tumors.

Conclusion

The CIN25 signature identified in ccRCC serves as a biomarker for CIN and other genome instability phenotypes and predicts patient outcomes and response to Sunitinib treatment. A PCR quantification is enough for the CIN25-based ccRCC classification, which holds great promises in clinical routine application.

Markers of local kidney cancer recurrence: A surgeon's mistake or a pattern? Review

The influence of various morphological, anatomical, genetic and other factors on the local recurrence-free survival of patients who have undergone different renal cell cancer (RCC) treatment is still a rather complex, ambiguous and controversial issue for practicing oncourologists. This review evaluates the effect of several factors on both recurrence-free survival and local recurrence-free survival. The review includes articles, clinical cases, literature reviews, and meta-analyses highlighting the analysis of independent and interrelated predisposing factors for developing local recurrence of RCC from 1984 to 2020. The PubMed, Web of Science, and Scopus databases were searched in English, Spanish, and German. A review of the literature showed the role of the following indices in the local recurrence RCC: microvascular invasion (p = 0.001), tumor necrosis (p = 0.0001), high malignancy (Fuhrman III or IV) (HR = 38.3, 95% CI 3.1-467, p = 0.004) as histological factors, tumor size as an anatomical factor. Thus, the authors state that every centimeter of the tumor increases the risk of local recurrence (p < 0.05). A group from the Mayo Clinic showed the equivalence of different treatment methods in local RCC recurrence. Thus, in the group of patients with cT1a stage kidney cancer, the 5-year local recurrence-free survival rates were 97.7% (96.7-98.6), 95.9% (92.3-99.6), and 95.9% (92.3-99.6) for renal resection, RFA, and cryoablation, respectively. Surgical margin status is the most studied and controversial marker of local renal cell carcinoma recurrence. Researchers found a direct effect of PSM on the risk of local RCC recurrence (p < 0.01). The personalized approach with the search and evaluation of predisposing factors for the local recurrence, as well as further selection of the most optimal treatment, will allow oncourologists to improve both the effectiveness of primary treatment and the recurrence-free survival of patients.

Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer

Tumor behavior is intricately dependent on the oncogenic properties of cancer cells and their multi-cellular interactions. To understand these dependencies within the wider microenvironment, we studied over 270,000 single-cell transcriptomes and 100 microdissected whole exomes from 12 patients with kidney tumors, prior to validation using spatial transcriptomics. Tissues were sampled from multiple regions of the tumor core, the tumor-normal interface, normal surrounding tissues, and peripheral blood. We find that the tissue-type location of CD8+ T cell clonotypes largely defines their exhaustion state with intra-tumoral spatial heterogeneity that is not well explained by somatic heterogeneity. De novo mutation calling from single-cell RNA-sequencing data allows us to broadly infer the clonality of stromal cells and lineage-trace myeloid cell development. We report six conserved meta-programs that distinguish tumor cell function, and find an epithelial-mesenchymal transition meta-program highly enriched at the tumor-normal interface that co-localizes with IL1B-expressing macrophages, offering a potential therapeutic target.

Hot topics in renal cancer pathology: implications for clinical management

INTRODUCTION

The updated European Association of Urology (EAU) Guidelines issued a weak recommendation for adjuvant pembrolizumab for patients with high-risk operable clear cell Renal Cell Carcinoma (ccRCC). High risk of recurrence was defined, as per protocol-criteria, as T2 with nuclear grade 4 or sarcomatoid differentiation, T3 or higher, regional lymph node metastasis, or stage M1 with no evidence of disease. Considering the heterogeneous population included in the recommendation, it has been questioned if adjuvant pembrolizumab may lead to overtreatment of some patients as well as undertreatment of patients with worse prognosis.

AREAS COVERED

In this review, we discuss the issues related to the assessment of pathological features required to identify those patients harboring a high-risk tumor, highlighting the issue related to interobserver variability and discuss the currently available prognostic scoring systems in ccRCC.

EXPERT OPINION

PPathologist assessment of prognostic features suffers from interobserver variability which may depend on gross sampling and the pathologist's expertise. The presence of clear cell feature is not sufficient criteria by itself to define ccRCC since clear cell can be also found in other histotypes. Application of molecular biomarkers may be useful tools in the near future to help clinicians identify patients harboring tumors with worse prognosis.

Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

Contemporary Clinical Definitions, Differential Diagnosis, and Novel Predictive Tools for Renal Cell Carcinoma

Despite significant progress regarding clinical detection/imaging evaluation modalities and genetic/molecular characterization of pathogenesis, advanced renal cell carcinoma (RCC) remains an incurable disease and overall RCC mortality has been steadily rising for decades. Concomitantly, clinical definitions have been greatly nuanced and refined. RCCs are currently viewed as a heterogeneous series of cancers, with the same anatomical origin, but fundamentally different metabolisms and clinical behaviors. Thus, RCC pathological diagnosis/subtyping guidelines have become increasingly intricate and cumbersome, routinely requiring ancillary studies, mainly immunohistochemistry. Meanwhile, RCC-associated-antigen targeted systemic therapy has been greatly diversified and emerging, novel clinical applications for RCC immunotherapy have already reported significant survival benefits, at least in the adjuvant setting. Even so, systemically disseminated RCCs still associate very poor clinical outcomes, with currently available therapeutic modalities only being able to prolong survival. In lack of a definitive cure for advanced RCCs, integration of the amounting scientific knowledge regarding RCC pathogenesis into RCC clinical management has been paramount for improving patient outcomes. The current review aims to offer an integrative perspective regarding contemporary RCC clinical definitions, proper RCC clinical work-up at initial diagnosis (semiology and multimodal imaging), RCC pathological evaluation, differential diagnosis/subtyping protocols, and novel clinical tools for RCC screening, risk stratification and therapeutic response prediction.

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.

Optimal Choice of Adjuvant Treatment for Renal Cell Carcinoma Following Nephrectomy

Renal cell carcinoma (RCC) is the fourteenth most common cancer worldwide. In about 55% of cases, it is diagnosed at a localised and/or locally advanced stage and therefore amenable to a curative approach. Although nephrectomy still represents the cornerstone of non-metastatic RCC (nmRCC) treatment, a relapse is observed in about 25-30% of patients undergoing curative surgery. Prognosis is drastically influenced by lymph nodal involvement. After the first disappointing results with a cytokine-based strategy, tyrosine kinase inhibitors (TKIs) were tested as adjuvant agents. Despite their efficacy in the metastatic setting, results in terms of disease-free survival (DFS) are not unequivocal and the overall survival (OS) benefit has not been demonstrated. Moreover, their toxicity profile induced a remarkable percentage of patients to discontinue the treatment. On the contrary, the KEYNOTE-564 trial showed the benefit of adjuvant pembrolizumab compared with placebo in terms of DFS with promising results in term of OS. Patients included were at intermediate or high risk of relapse, or patients with no evidence of disease after metastasectomy (M1 NED). The updated analysis presented at the American Society of Clinical Oncology Genito-Urinary (ASCO GU) 2022 confirmed the benefit of pembrolizumab versus placebo over time, although OS data are still immature. A longer follow-up and the several ongoing trials with immune checkpoint inhibitors (ICIs) will provide further data about adjuvant immuno-oncology (IO). Furthermore, the patients' selection based on clinical or biological features will be crucial in order to identify who benefits most from treatments.

A novel molecular signature identifies mixed subtypes in renal cell carcinoma with poor prognosis and independent response to immunotherapy

Background

Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes. For therapy selection, precise subtype identification and individualized prognosis are mandatory, but currently limited. Our aim was to refine subtyping and outcome prediction across main subtypes, assuming that a tumor is composed of molecular features present in distinct pathological subtypes.

Methods

Individual RCC samples were modeled as linear combination of the main subtypes (clear cell (ccRCC), papillary (pRCC), chromophobe (chRCC)) using computational gene expression deconvolution. The new molecular subtyping was compared with histological classification of RCC using the Cancer Genome Atlas (TCGA) cohort (n = 864; ccRCC: 512; pRCC: 287; chRCC: 65) as well as 92 independent histopathologically well-characterized RCC. Predicted continuous subtypes were correlated to cancer-specific survival (CSS) in the TCGA cohort and validated in 242 independent RCC. Association with treatment-related progression-free survival (PFS) was studied in the JAVELIN Renal 101 (n = 726) and IMmotion151 trials (n = 823). CSS and PFS were analyzed using the Kaplan–Meier and Cox regression analysis.

Results

One hundred seventy-four signature genes enabled reference-free molecular classification of individual RCC. We unambiguously assign tumors to either ccRCC, pRCC, or chRCC and uncover molecularly heterogeneous tumors (e.g., with ccRCC and pRCC features), which are at risk of worse outcome. Assigned proportions of molecular subtype-features significantly correlated with CSS (ccRCC (P = 4.1E − 10), pRCC (P = 6.5E − 10), chRCC (P = 8.6E − 06)) in TCGA. Translation into a numerical RCC-R(isk) score enabled prognosis in TCGA (P = 9.5E − 11). Survival modeling based on the RCC-R score compared to pathological categories was significantly improved (P = 3.6E − 11). The RCC-R score was validated in univariate (P = 3.2E − 05; HR = 3.02, 95% CI: 1.8–5.08) and multivariate analyses including clinicopathological factors (P = 0.018; HR = 2.14, 95% CI: 1.14–4.04). Heterogeneous PD-L1-positive RCC determined by molecular subtyping showed increased PFS with checkpoint inhibition versus sunitinib in the JAVELIN Renal 101 (P = 3.3E − 04; HR = 0.52, 95% CI: 0.36 − 0.75) and IMmotion151 trials (P = 0.047; HR = 0.69, 95% CI: 0.48 − 1). The prediction of PFS significantly benefits from classification into heterogeneous and unambiguous subtypes in both cohorts (P = 0.013 and P = 0.032).

Conclusion

Switching from categorical to continuous subtype classification across most frequent RCC subtypes enables outcome prediction and fosters personalized treatment strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01105-y.

The cuproptosis-associated 13 gene signature as a robust predictor for outcome and response to immune- and targeted-therapies in clear cell renal cell carcinoma

Cuproptosis, the newly identified form of regulatory cell death (RCD), results from mitochondrial proteotoxic stress mediated by copper and FDX1. Little is known about significances of cuproptosis in oncogenesis. Here we determined clinical implications of cuproptosis in clear cell renal cell carcinoma (ccRCC). Based on the correlation and survival analyses of cuproptosis-correlated genes in TCGA ccRCC cohort, we constructed a cuproptosis-associated 13 gene signature (CuAGS-13) score system. In both TCGA training and two validation cohorts, when patients were categorized into high- and low-risk groups according to a median score as the cutoff, the CuAGS-13 high-risk group was significantly associated with shorter overall survival (OS) and/or progression-free survival (PFS) independently (P<0.001 for all). The CuAGS-13 score assessment could also predict recurrence and recurrence-free survival of patients at stage I - III with a high accuracy, which outperformed the ccAccB/ClearCode34 model, a well-established molecular predictor for ccRCC prognosis. Moreover, patients treated with immune checkpoint inhibitors (ICIs) acquired complete/partial remissions up to 3-time higher coupled with significantly longer PFS in the CuAGS-13 low- than high-risk groups in both training and validation cohorts of ccRCCs (7.2 - 14.1 vs. 2.1 - 3.0 months, P<0.001). The combination of ICI with anti-angiogenic agent Bevacizumab doubled remission rates in CuAGS-13 high-risk patients while did not improve the efficacy in the low-risk group. Further analyses showed a positive correlation between CuAGS-13 and TIDE scores. We also observed that the CuAGS-13 score assessment accurately predicted patient response to Sunitinib, and higher remission rates in the low-risk group led to longer PFS (Low- vs. high-risk, 13.9 vs. 5.8 months, P = 5.0e-12). Taken together, the CuAGS-13 score assessment serves as a robust predictor for survival, recurrence, and response to ICIs, ICI plus anti-angiogenic drugs and Sunitinib in ccRCC patients, which significantly improves patient stratifications for precision medicine of ccRCC.

Application of Machine Learning Models to Predict Recurrence After Surgical Resection of Nonmetastatic Renal Cell Carcinoma

BACKGROUND

Predictive tools can be useful for adapting surveillance or including patients in adjuvant trials after surgical resection of nonmetastatic renal cell carcinoma (RCC). Current models have been built using traditional statistical modelling and prespecified variables, which limits their performance.

OBJECTIVE

To investigate the performance of machine learning (ML) framework to predict recurrence after RCC surgery and compare them with current validated models.

DESIGN, SETTING, AND PARTICIPANTS

In this observational study, we derived and tested several ML-based models (Random Survival Forests [RSF], Survival Support Vector Machines [S-SVM], and Extreme Gradient Boosting [XG boost]) to predict recurrence of patients who underwent radical or partial nephrectomy for a nonmetastatic RCC, between 2013 and 2020, at 21 French medical centres.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary end point was disease-free survival. Model discrimination was assessed using the concordance index (c-index), and calibration was assessed using the Brier score. ML models were compared with four conventional prognostic models, using decision curve analysis (DCA).

RESULTS AND LIMITATIONS

A total of 4067 patients were included in this study (3253 in the development cohort and 814 in the validation cohort). Most tumours (69%) were clear cell RCC, 40% were of high grade (nuclear International Society of Urological Pathology grade 3 or 4), and 24% had necrosis. Of the patients, 4% had nodal involvement. After a median follow-up of 57 mo (interquartile range 29-76), 523 (13%) patients recurred. ML models obtained higher c-index values than conventional models. The RSF yielded the highest c-index values (0.794), followed by S-SVM (c-index 0.784) and XG boost (c-index 0.782). In addition, all models showed good calibration with low integrated Brier scores (all integrated brier scores <0.1). However, we found calibration drift over time for all models, albeit with a smaller magnitude for ML models. Finally, DCA showed an incremental net benefit from all ML models compared with conventional models currently used in practice.

CONCLUSIONS

Applying ML approaches to predict recurrence following surgical resection of RCC resulted in better prediction than that of current validated models available in clinical practice. However, there is still room for improvement, which may come from the integration of novel biological and/or imaging biomarkers.

PATIENT SUMMARY

We found that artificial intelligence algorithms could better predict the risk of recurrence after surgery for a localised kidney cancer. These algorithms may help better select patients who will benefit from medical treatment after surgery.

Exploring synthetic lethal network for the precision treatment of clear cell renal cell carcinoma

The emerging targeted therapies have revolutionized the treatment of advanced clear cell renal cell carcinoma (ccRCC) over the past 15 years. Nevertheless, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles from ccRCC cohorts were explored for integrative analysis. A credible method was developed to identify synthetic lethality (SL) pairs and a list of 72 candidate pairs was determined, which might be utilized to selectively eliminate tumors with genetic aberrations using SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medical targets for patients with BAP1 mutations. After mapping these target genes to the comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potentials in the BAP1 mutant tumors. Overall, our findings provided insight into the overview of ccRCC mutation patterns and offered novel opportunities for improving individualized cancer treatment.

Prognostic Gene Expression-Based Signature in Clear-Cell Renal Cell Carcinoma

The inaccuracy of the current prognostic algorithms and the potential changes in the therapeutic management of localized ccRCC demands the development of an improved prognostic model for these patients. To this end, we analyzed whole-transcriptome profiling of 26 tissue samples from progressive and non-progressive ccRCCs using Illumina Hi-seq 4000. Differentially expressed genes (DEG) were intersected with the RNA-sequencing data from the TCGA. The overlapping genes were used for further analysis. A total of 132 genes were found to be prognosis-related genes. LASSO regression enabled the development of the best prognostic six-gene panel. Cox regression analyses were performed to identify independent clinical prognostic parameters to construct a combined nomogram which includes the expression of CERCAM, MIA2, HS6ST2, ONECUT2, SOX12, TMEM132A, pT stage, tumor size and ISUP grade. A risk score generated using this model effectively stratified patients at higher risk of disease progression (HR 10.79; p < 0.001) and cancer-specific death (HR 19.27; p < 0.001). It correlated with the clinicopathological variables, enabling us to discriminate a subset of patients at higher risk of progression within the Stage, Size, Grade and Necrosis score (SSIGN) risk groups, pT and ISUP grade. In summary, a gene expression-based prognostic signature was successfully developed providing a more precise assessment of the individual risk of progression.

Complementary roles of surgery and systemic treatment in clear cell renal cell carcinoma

Standard-of-care management of renal cell carcinoma (RCC) indisputably relies on surgery for low-risk localized tumours and systemic treatment for poor-prognosis metastatic disease, but a grey area remains, encompassing high-risk localized tumours and patients with metastatic disease with a good-to-intermediate prognosis. Over the past few years, results of major practice-changing trials for the management of metastatic RCC have completely transformed the therapeutic options for this disease. Treatments targeting vascular endothelial growth factor (VEGF) have been the mainstay of therapy for metastatic RCC in the past decade, but the advent of immune checkpoint inhibitors has revolutionized the therapeutic landscape in the metastatic setting. Results from several pivotal trials have shown a substantial benefit from the combination of VEGF-directed therapy and immune checkpoint inhibition, raising new hopes for the treatment of high-risk localized RCC. The potential of these therapeutics to facilitate the surgical extirpation of the tumour in the neoadjuvant setting or to improve disease-free survival in the adjuvant setting has been investigated. The role of surgery for metastatic RCC has been redefined, with results of large trials bringing into question the paradigm of upfront cytoreductive nephrectomy, inherited from the era of cytokine therapy, when initial extirpation of the primary tumour did show clinical benefits. The potential benefits and risks of deferred surgery for residual primary tumours or metastases after partial response to checkpoint inhibitor treatment are also gaining interest, considering the long-lasting effects of these new drugs, which encourages the complete removal of residual masses.

Pathological and genetic markers improve recurrence prognostication with the University of California Los Angeles Integrated Staging System for patients with clear cell renal cell carcinoma

PURPOSE

To elucidate which patients with clear cell renal cell carcinoma have the highest risk for disease relapse after curative nephrectomy is challenging but is acutely relevant in the era of approved adjuvant therapies. Pathological and genetic markers were used to improve the University of California Los Angeles Integrated Staging System (UISS) for the risk stratification and prognostication of recurrence free survival (RFS).

PATIENTS AND METHODS

Necrosis, sarcomatoid features, Rhabdoid features, chromosomal loss 9p, combined chromosomal loss 3p14q and microvascular invasion (MVI) were tested in univariable and multivariable analyses for their ability to improve the discriminatory ability of the UISS.

RESULTS

In the development cohort, during the median follow-up time of 43.4 months (±SD 54.1 months), 50/240 (21%) patients developed disease recurrence. MVI (HR: 2.22; p = 0.013) and the combined loss of chromosome 3p/14q (HR: 2.89; p = 0.004) demonstrated independent association with RFS and were used to improve the assignment to the UISS risk category. In the current UISS high-risk group, only 7/50 (14%) recurrence cases were correctly identified; while in the improved system, 23/50 (45%) were correctly prognosticated. The concordance index meaningfully improved from 0.55 to 0.68 to distinguish patients at intermediate risk versus high risk. Internal validation demonstrated a robust prognostication of RFS. In the external validation cohort, there was no case with disease recurrence in the low-risk group, and the mean RFS times were 13.2 (±1.8) and 8.2 (±0.8) years in the intermediate and high-risk groups, respectively.

CONCLUSIONS

Adding MVI and combined chromosomal loss3p/14q to the UISS improves the ability to define the patient group with clear cell renal cell carcinomawho are at the highest risk for disease relapse after surgical treatment.

Identification of a 5-Nutrient Stress-Sensitive Gene Signature to Predict Survival for Colorectal Cancer

Background

The high heterogeneity and the complexity of the tumor microenvironment of colorectal cancer (CRC) have enhanced the difficulty of prognosis prediction based on conventional clinical indicators. Recent studies revealed that tumor cells could overcome various nutritional deficiencies by gene regulation and metabolic remodeling. However, whether differentially expressed genes (DEGs) in CRC cells under kinds of nutrient deficiency could be used to predict prognosis remained unveiled.

Methods

Three datasets (GSE70976, GSE13548, and GSE116087), in which colon cancer cells were, respectively, cultured in serum-free, glucose-free, or glutamine-free medium, were included to delineate the profiles of gene expression by nutrient stress. DEGs were figured out in three datasets, and gene functional analysis was performed. Survival analyses and Cox proportional hazards model were then used to identify nutrient stress sensitive genes in CRC datasets (GSE39582 and TCGA COAD). Then, a 5-gene signature was constructed and the risk scores were also calculated. Survival analyses, cox analyses, and nomogram were applied to predict the prognosis of patients with colorectal cancer. The effectiveness of the risk model was also tested.

Results

A total of 48 genes were found to be dysregulated in serum, glucose, or glutamine-deprived CRC cells, which were mainly enriched in cell cycle and endoplasmic reticulum stress pathways. After further analyses, 5 genes, MCM5, MCM6, CDCA2, GINS2, and SPC25, were identified to be differentially expressed in CRC and be related to prognosis of in CRC datasets. We used the above nutrient stress-sensitive genes to construct a risk scoring model. CRC samples in the datasets were divided into low-risk and high-risk groups. Data showed that higher risk scores were associated with better outcomes and risk scores decreased significantly with tumor procession. Moreover, the risk score could be used to predict the probability of survival based on nomogram.

Conclusions

The 5-nutrient stress-sensitive gene signature could act as an independent biomarker for survival prediction of CRC patients.

Diagnostics and prognostic evaluation in renal cell tumors: the German S3 guidelines recommendations

The German guidelines on renal cell carcinoma (RCC) have been developed at highest level of evidence based on systematic literature review. In this paper, we are presenting the current recommendations on diagnostics including preoperative imaging and imaging for stage evaluation as well as histopathological classification. The role of tumor biopsy is further discussed. In addition, different prognostic scores and the status of biomarkers in RCC are critically evaluated.

External Validation of the 2003 Leibovich Prognostic Score in Patients Randomly Assigned to SORCE, an International Phase III Trial of Adjuvant Sorafenib in Renal Cell Cancer

The 2003 Leibovich score guides prognostication and selection to adjuvant clinical trials for patients with locally advanced renal cell carcinoma (RCC) after nephrectomy. We provide a robust external validation of the 2003 Leibovich score using contemporary data from SORCE, an international, randomized trial of sorafenib after excision of primary RCC.

Read how we have shown that the 2003 Leibovich score demonstrates discriminative accuracy in contemporary clear-cell and non–clear-cell RCC patient cohorts, supporting its continued use to guide discussions on patient prognosis and risk-stratification in clinical trials.

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.

A 17-Gene Expression Signature for Early Identification of Poor Prognosis in Clear Cell Renal Cell Carcinoma

The Identification of reliable Biomarkers able to predict the outcome after nephrectomy of patients with clear cell renal cell carcinoma (ccRCC) is an unmet need. The gene expression analysis in tumor tissues represents a promising tool for better stratification of ccRCC subtypes and patients' evaluation.

METHODS

In our study we retrospectively analyzed using Next-Generation expression analysis (NanoString), the expression of a gene panel in tumor tissue from 46 consecutive patients treated with nephrectomy for non-metastatic ccRCC at two Italian Oncological Centres. Significant differences in expression levels of selected genes was sought. Additionally, we performed a univariate and a multivariate analysis on overall survival according to Cox regression model.

RESULTS

A 17-gene expression signature of patients with a recurrence-free survival (RFS) < 1 year (unfavorable genomic signature (UGS)) and of patients with a RFS > 5 years (favorable genomic signature (FGS)) was identified and resulted in being significantly correlated with overall survival of the patients included in this analysis (HR 51.37, p < 0.0001).

CONCLUSIONS

The identified Genomic Signatures may serve as potential biomarkers for prognosis prediction of non-metastatic RCC and could drive both follow-up and treatment personalization in RCC management.

Clinicopathological and Molecular Prognostic Classifier for Intermediate/High-Risk Clear Cell Renal Cell Carcinoma

Simple Summary

In this report, we identified biomarkers for tumor progression from tissue samples of intermediate/high-risk ccRCC. Using the molecular findings and the clinical data, we developed an improved prognostic model which could help to provide better individualized management recommendations.

Abstract

The probability of tumor progression in intermediate/high-risk clear cell renal cell carcinoma (ccRCC) is highly variable, underlining the lack of predictive accuracy of the current clinicopathological factors. To develop an accurate prognostic classifier for these patients, we analyzed global gene expression patterns in 13 tissue samples from progressive and non-progressive ccRCC using Illumina Hi-seq 4000. Expression levels of 22 selected differentially expressed genes (DEG) were assessed by nCounter analysis in an independent series of 71 ccRCCs. A clinicopathological-molecular model for predicting tumor progression was developed and in silico validated in a total of 202 ccRCC patients using the TCGA cohort. A total of 1202 DEGs were found between progressive and non-progressive intermediate/high-risk ccRCC in RNAseq analysis, and seven of the 22 DEGs selected were validated by nCounter. Expression of HS6ST2, pT stage, tumor size, and ISUP grade were found to be independent prognostic factors for tumor progression. A risk score generated using these variables was able to distinguish patients at higher risk of tumor progression (HR 7.27; p < 0.001), consistent with the results obtained from the TCGA cohort (HR 2.74; p < 0.002). In summary, a combined prognostic algorithm was successfully developed and validated. This model may aid physicians to select high-risk patients for adjuvant therapy.

A large-scale assay library for targeted protein quantification in renal cell carcinoma tissues

Renal cell carcinoma (RCC) represents 2.2% of all cancer incidences; however, prognostic or predictive RCC biomarkers at protein level are largely missing. To support proteomics research of localized and metastatic RCC, we introduce a new library of targeted mass spectrometry assays for accurate protein quantification in malignant and normal kidney tissue. Aliquots of 86 initially localized RCC, 75 metastatic RCC and 17 adjacent non-cancerous fresh frozen tissue lysates were trypsin digested, pooled, and fractionated using hydrophilic chromatography. The fractions were analyzed using LC-MS/MS on QExactive HF-X mass spectrometer in data-dependent acquisition (DDA) mode. A resulting spectral library contains 77,817 peptides representing 7960 protein groups (FDR = 1%). Further, we confirm applicability of this library on four RCC datasets measured in data-independent acquisition (DIA) mode, demonstrating a specific quantification of a substantially increased part of RCC proteome, depending on LC-MS/MS instrumentation. Impact of sample specificity of the library on the results of targeted DIA data extraction was demonstrated by parallel analyses of two datasets by two pan human libraries. The new RCC specific library has potential to contribute to better understanding the RCC development at molecular level, leading to new diagnostic and therapeutic targets.

Characterization of Genetic Heterogeneity in Recurrent Metastases of Renal Cell Carcinoma

Metastatic renal cell carcinoma (RCC) exhibits poor prognosis. Better knowledge of distant metastases is crucial to foster personalized treatment strategies. Here, we aimed to investigate the genetic landscape of metastases, including synchronous and/or recurrent metastases to elucidate potential drug target genes and clinically relevant mutations in a real-world setting of patients. We assessed 81 metastases from 56 RCC patients, including synchronous and/or recurrent metastases of 19 patients. Samples were analysed through next-generation sequencing with a high coverage (~1000× mean coverage). We therefore established a novel sequencing panel comprising 32 genes with impact on RCC development. We observed a high frequency of mutations in known RCC driver genes (e.g., >40% carriers of VHL and PBRM1 mutations) in metastases irrespective of the metastatic site. The somatic mutational composition was significantly associated with cancer-specific survival (p(logrank) = 0.03). Moreover, we identified in 34 patients at least one drug target gene as well as clinically relevant mutations listed in the VICC Meta-Knowledgebase in 7%. In addition to significantly higher mutational burden in recurrent metastases compared to earlier ones, synchronous and/or recurrent metastases of individual patients, even after a time-period >2 yrs, shared a high proportion of somatic events. Our data demonstrate the importance of somatic profiling in metastases for precision medicine in RCC.

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

BACKGROUND

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

OBJECTIVE

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

DESIGN, SETTING, AND PARTICIPANTS

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

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

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

RESULTS AND LIMITATIONS

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

CONCLUSIONS

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

PATIENT SUMMARY

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

Clinical Significance of ADAMTS19, BMP7, SIM1, and SFRP1 Promoter Methylation in Renal Clear Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney tumors, accounting for the majority of deaths from genitourinary cancers. The currently used nomograms for predicting patient outcomes are based on clinical-pathological characteristics only; however, a significant number of ccRCC survivors with similar radiological and histological features still demonstrate a different clinical course of the disease. This study aimed at the identification of novel DNA methylation biomarkers for the monitoring of patients with ccRCC.

Methods

Gene expression profiling by SurePrint G3 Human GE 8×60K Microarrays was performed in 4 ccRCC tissues and adjacent non-cancerous renal tissue (NRT) samples. Four down-regulated genes were selected for further DNA methylation status analysis in 123 ccRCC and 45 NRT samples using methylation-specific PCR (MSP).

Results

DNA methylation changes of ADAMTS19, BMP7, SIM1, and SFRP1 were cancer-specific with significantly (P<0.050) higher methylation frequency (37%, 20%, 18%, and 42%, respectively) in tumor tissues. The methylated status of at least one gene was significantly related to various clinical-pathological parameters, including tumor size, Fuhrman and WHO/ISUP grades, intravascular invasion, and necrosis. Moreover, the methylated status of multimarker panel ADAMTS19, BMP7 & SFRP1 was predictive for poorer overall survival (HR, 4.11; 95% CI, 1.22–13.86).

Conclusion

In conclusion, DNA methylation of the three-gene panel consisting of ADAMTS19, BMP7 & SFRP1 supposedly predicts the outcome of patients diagnosed with ccRCC and possibly might be used to enrich the current prognostic tools.

Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression

BACKGROUND

Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy.

METHODS

In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data.

RESULTS

Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance.

CONCLUSION

A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

Biomarkers in renal cell carcinoma: Are we there yet?

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

Identification of a Five-Gene Prognostic Signature Related to B Cells Infiltration in Pancreatic Adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is an extremely malignant cancer. Immunotherapy is a promising avenue to increase the survival time of patients with PAAD.

Methods

RNA sequencing and clinical data for PAAD were downloaded from the TCGA database. The ssGSEA method and weighted gene co-expression network analysis were used to calculate the relative abundance of tumor-infiltrating immune cells and identify the related modules. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were used to construct a prognostic model. MCPcounter and EPIC were also used to assess immune cell components using gene expression profiles.

Results

The B cells closely related module was identified, and five genes, including ARID5A, CLEC2B, MICAL1, MZB1, and RAPGEF1, were ultimately selected to establish a prognostic signature to calculate the risk scores of PAAD patients. Kaplan–Meier curves showed worse survival in the high-risk patients (p < 0.05), and the area under the receiver operating characteristic (ROC) curves of risk score for 1-year and 3-year survival were 0.78 and 0.80, respectively, based on the training set. Similar results were verified using the validated and combined sets. Interestingly, the low-risk group presented significantly elevated immune and stromal scores, proportion of B cells, and associations between these five genes and B cells were identified using multiple methods including ssGSEA, MCPcounter, and EPIC.

Conclusion

This is the first attempt to study a B cells-related prognostic signature, which is instrumental in the exploration of novel prognostic biomarkers in PAAD.

[Biomarkers for renal cell tumours]

During the last three decades, renal tumours have become increasingly well differentiated on the basis of their histopathological and molecular features. This subtyping has increasingly impacted clinical practice because more therapeutic options are available in organ-confined and metastatic renal cell tumours. The knowledge of the underlying molecular alterations is essential to develop molecular targeted therapies and to select the most effective systemic therapy for each patient. This manuscript gives an overview of the molecular differentiation on the one hand, and on diagnostic, prognostic and predictive biomarkers on the other hand.

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

Background

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

Methods

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

Results

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

Conclusions

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