Articles

Mazzaschi G, Quaini F, Bersanelli M, et al.

Crit Rev Oncol Hematol. 2021 Apr;160:103293.
DOI: 10.1016/j.critrevonc.2021.103293.

Abstract

Background

The role of cytoreductive nephrectomy (CN) in metastatic renal cell carcinoma (mRCC) in the era of targeted- (TT) and immuno- (IT) therapy remains controversial.

Design

The primary objective of the present systematic, performed according to PRISMA guidelines review, was to assess the prevalence of nephrectomy in mRCC patients enrolled in TT/IT randomized phase II/III clinical trials (RCTs) or expanded access programs (EAPs). Medline database was searched from 2003 to 2019 for studies with available nephrectomy data.

Results

We identified 609 studies, subsequently restricted to 57 randomized phase II/III clinical trials and 6 EAPs. Overall, 33,196 patients with mRCC were included, among whom 28,700 (86.4 %) underwent nephrectomy. The trends over time of nephrectomy occurrence remained substantially stable from 2003 to 2019.

Conclusions

Our analysis highlighted that data from RCTs and EAPs driving the clinical practice originate from nephrectomized patient populations. This evidence supports the clinical relevance of CN also in mRCC patients candidate to receive TT/IT.

Marostica E, Barber R, Denize T, et al.

Clin Cancer Res. 2021 May 15;27(10):2868-2878.
DOI: 10.1158/1078-0432.CCR-20-4119.

Abstract

Purpose

Histopathology evaluation is the gold standard for diagnosing clear cell (ccRCC), papillary, and chromophobe renal cell carcinoma (RCC). However, interrater variability has been reported, and the whole-slide histopathology images likely contain underutilized biological signals predictive of genomic profiles.

Experimental design

To address this knowledge gap, we obtained whole-slide histopathology images and demographic, genomic, and clinical data from The Cancer Genome Atlas, the Clinical Proteomic Tumor Analysis Consortium, and Brigham and Women’s Hospital (Boston, MA) to develop computational methods for integrating data analyses. Leveraging these large and diverse datasets, we developed fully automated convolutional neural networks to diagnose renal cancers and connect quantitative pathology patterns with patients’ genomic profiles and prognoses.

Results

Our deep convolutional neural networks successfully detected malignancy (AUC in the independent validation cohort: 0.964-0.985), diagnosed RCC histologic subtypes (independent validation AUCs of the best models: 0.953-0.993), and predicted stage I ccRCC patients’ survival outcomes (log-rank test P = 0.02). Our machine learning approaches further identified histopathology image features indicative of copy-number alterations (AUC > 0.7 in multiple genes in patients with ccRCC) and tumor mutation burden.

Conclusions

Our results suggest that convolutional neural networks can extract histologic signals predictive of patients’ diagnoses, prognoses, and genomic variations of clinical importance. Our approaches can systematically identify previously unknown relations among diverse data modalities.

Strauss A, Schmid M, Rink M, et al.

Future Oncol. 2021 Mar 16.
DOI: 10.2217/fon-2020-1020

Abstract

Aim

Examine outcomes in sunitinib-treated patients by International Metastatic RCC Database Consortium (IMDC) or Memorial Sloan-Kettering Cancer Center (MSKCC) risk factors

Patients & methods

Patients enrolled in STAR-TOR registry (n = 327). End points included overall survival, progression-free survival and objective response rate.

Results

Overall survival was similar for IMDC 0 versus 1 (p = 0.238) or 2 versus ≥3 (p = 0.156), but different for MSKCC (0 vs 1, p = 0.037; 2 vs ≥3, p = 0.001). Progression-free survival was similar for IMDC 2 versus 3 (p = 0.306), but different for MSKCC (p = 0.009). Objective response rate was different for IMDC 1 (41.9%) and 2 (29.5%) and similar for MSKCC 1 (34.4%) and 2 (31.0%). Conclusion: Outcome data varied according to IMDC or MSKCC. MSKCC model accurately stratify patients into risk groups. Clinical trial registration: NCT00700258 (ClinicalTrials.gov).

Correa AF, Jegede OA, Haas NB, et al.

Eur Urol. 2021 Mar 8;S0302-2838(21)00145-7.
DOI: 10.1016/j.eururo.2021.02.025.

Abstract

Background

Risk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts.

Objective

To introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial.

Design, setting, and participants

The model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial.

Outcome measurements and statistical analysis

The primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests.

Results and limitations

A total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFS, DFS validation set, and OS model’s discriminatory ability was seen over time, with a global c-index of 68.0% (95% confidence interval or CI [65.5, 70.4]), 68.6% [65.1%, 72.2%], and 69.4% (95% CI [66.9%, 71.9%], respectively. The EDP model had a c-index of 75.1% (95% CI [71.3, 79.0]).

Conclusions

We introduce a contemporary RCC recurrence model built and internally validated using prospective and highly annotated data from a clinical trial. Performance characteristics of the current model exceed available prognostic models with the added benefit of being histology inclusive and TNM agnostic.

Patient summary

Important decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models.

Chehrazi-Raffle A, Meza L, Alcantara M, et al.

J Immunother Cancer. 2021 Mar;9(3):e002009.
DOI: 10.1136/jitc-2020-002009.

Abstract

Background

Circulating cytokines and angiogenic factors have been associated with clinical outcom es in patients with metastatic renal cell carcinoma (RCC) receiving systemic therapy. However, none have yet examined cytokine concentrations in parallel cohorts receiving either immunotherapy or targeted therapy.

Methods

In this prospective correlative study, we enrolled 56 patients who were planned for treatment with either a vascular endothelial growth factor-tyrosine kinase inhibitor (VEGF-TKI) or immune checkpoint inhibitor (ICI). Eligibility requirements permitted any RCC histologic subtype, International Metastatic Renal Cell Carcinoma risk classification, and line of therapy. Immunologic profile was assessed at baseline and after 1 month on treatment using a Human Cytokine 30-plex protein assay (Invitrogen). Clinical benefit was defined as complete response, partial response, or stable disease ≥6 months per RECIST (Response Evaluation Criteria in Solid Tumors) V.1.1 criteria.

Results

Clinical benefit was similar between VEGF-TKI and ICI arms (65% vs 54%). Patients with clinical benefit from VEGF-TKIs had lower pretreatment levels of interleukin-6 (IL-6) (p=0.02), IL-1RA (p=0.03), and granulocyte colony-stimulating factor (CSF) (p=0.02). At 1 month, patients with clinical benefit from ICIs had higher levels of interferon-γ (IFN-γ) (p=0.04) and IL-12 (p=0.03). Among patients on VEGF-TKIs, those with clinical benefit had lower 1 month IL-13 (p=0.02) and granulocyte macrophage CSF (p=0.01) as well as higher 1 month VEGF (p=0.04) compared with patients with no clinical benefit.

Conclusions

For patients receiving VEGF-TKI or ICI therapy, distinct plasma cytokines were associated with clinical benefit. Our findings support additional investigation into plasma cytokines as biomarkers in metastatic RCC.

Metcalf MR, Cheaib JG, Biles MJ, et al.

J Urol. 2021 May;205(5):1286-1293.
DOI: 10.1097/JU.0000000000001575.

Abstract

Purpose

A paradigm shift in the management of small renal masses has increased utilization of active surveillance. However, questions remain regarding safety and durability in younger patients.

Materials and methods

Patients aged 60 or younger at diagnosis were identified from the Delayed Intervention and Surveillance for Small Renal Masses registry. The active surveillance, primary intervention, and delayed intervention groups were evaluated using ANOVA with Bonferroni correction, χ2 and Fisher’s exact tests, and Kruskal-Wallis and Wilcoxon signed-rank tests. Survival outcomes were calculated using the Kaplan-Meier method and compared with the log-rank test.

Results

Of 224 patients with median followup of 4.9 years 30.4% chose surveillance. There were 20 (29.4%) surveillance progression events, including 4 elective crossovers, and 13 (19.1%) patients underwent delayed intervention. Among patients with initial tumor size ≤2 cm, 15.1% crossed over, compared to 33.3% with initial tumor size 2-4 cm. Overall survival was similar in primary intervention and surveillance at 7 years (94.0% vs 90.8%, log-rank p=0.2). Cancer-specific survival remained at 100% for both groups. There were no significant differences between primary and delayed intervention with respect to minimally invasive or nephron-sparing interventions. Recurrence-free survival at 5 years was 96.0% and 100% for primary and delayed intervention, respectively (log-rank p=0.6).

Conclusions

Active surveillance is a safe initial strategy in younger patients and can avoid unnecessary intervention in a subset for whom it is durable. Crucially, no patient developed metastatic disease on surveillance or recurrence after delayed intervention. This study confirms active surveillance principles can effectively be applied to younger patients.

Krishna C, DiNatale RG, Kuo F, et al.

Cancer Cell. 2021 May 10;39(5):662-677.e6.
DOI: 10.1016/j.ccell.2021.03.007.

Abstract

Clear cell renal cell carcinomas (ccRCCs) are highly immune infiltrated, but the effect of immune heterogeneity on clinical outcome in ccRCC has not been fully characterized. Here we perform paired single-cell RNA (scRNA) and T cell receptor (TCR) sequencing of 167,283 cells from multiple tumor regions, lymph node, normal kidney, and peripheral blood of two immune checkpoint blockade (ICB)-naïve and four ICB-treated patients to map the ccRCC immune landscape. We detect extensive heterogeneity within and between patients, with enrichment of CD8A+ tissue-resident T cells in a patient responsive to ICB and tumor-associated macrophages (TAMs) in a resistant patient. A TCR trajectory framework suggests distinct T cell differentiation pathways between patients responding and resistant to ICB. Finally, scRNA-derived signatures of tissue-resident T cells and TAMs are associated with response to ICB and targeted therapies across multiple independent cohorts. Our study establishes a multimodal interrogation of the cellular programs underlying therapeutic efficacy in ccRCC.

Ghodoussipour S, Reddy SS, Ma R, et al.

J Urol. 2021 May;205(5):1294-1302.
DOI: 10.1097/JU.0000000000001557.

Abstract

Purpose

Automated performance metrics provide a novel approach to the assessment of surgical performance. Herein, we present a construct validation of automated performance metrics during robotic assisted partial nephrectomy.

Materials and methods

Automated performance metrics (instrument motion tracking/system events) and synchronized surgical videos from da Vinci® Si systems during robotic assisted partial nephrectomy were recorded using a system data recorder. Each case was segmented into 7 steps: colon mobilization, ureteral identification/dissection, hilar dissection, exposure of tumor within Gerota’s fascia, intraoperative ultrasound/tumor scoring, tumor excision, and renorrhaphy. Automated performance metrics from each step were compared between expert (≥150 cases) and trainee (<150 cases) surgeons by Mann-Whitney U test (continuous variables) and Pearson’s chi-squared test (categorical variables). Clinical outcomes were collected prospectively and correlated to automated performance metrics and R.E.N.A.L. (radius, exophytic/endophytic, nearness of tumor to collecting system, anterior/posterior, location relative to polar line) nephrometry score by Spearman’s correlation coefficients (r).

Results

A total of 50 robotic assisted partial nephrectomy cases were included for analysis, performed by 7 expert and 10 trainee surgeons. Automated performance metric profiles significantly differed between experts and novices in the initial 5 steps (p <0.05). Specifically, experts exhibited faster dominant instrument movement and greater dominant instrument usage (bimanual dexterity) than trainees in select steps (p ≤0.045). Automated performance metrics during tumor excision and renorrhaphy were significantly correlated with R.E.N.A.L. score (r ≥0.364; p ≤0.041). These included metrics related to instrument efficiency, task duration, and dominant instrument use.

Conclusions

Experts are more efficient and directed in their movement during robotic assisted partial nephrectomy. Automated performance metrics during key steps correlate with objective measures of tumor complexity and may serve as predictors of clinical outcomes. These data help establish a standardized metric for surgeon assessment and training during robotic assisted partial nephrectomy.

Palacios DA, Wilson B, Ascha M, et al.

J Urol. 2021 May;205(5):1310-1320.
DOI: 10.1097/JU.0000000000001549

Abstract

Purpose

Preoperative estimation of new baseline glomerular filtration rate after partial nephrectomy or radical nephrectomy for renal cell carcinoma has important clinical implications. However, current predictive models are either complex or lack external validity. We aimed to develop and validate a simple equation to estimate postoperative new baseline glomerular filtration rate.

Materials and methods

For development and internal validation of the equation, a cohort of 7,860 patients with renal cell carcinoma undergoing partial nephrectomy/radical nephrectomy (2005-2015) at the Veterans Affairs National Health System was analyzed. Based on preliminary analysis of 94,327 first-year postoperative glomerular filtration rate measurements, new baseline glomerular filtration rate was defined as the final glomerular filtration rate within 3 to 12 months after surgery.Multivariable linear regression analyses were applied to develop the equation using two-thirds of the renal cell carcinoma Veterans Administration cohort. The simplest model with the highest coefficient of determination (R2) was selected and tested. This model was then internally validated in the remaining third of the renal cell carcinoma Veterans Administration cohort. Correlation/bias/accuracy/precision of equation were examined. For external validation, a similar cohort of 3,012 patients with renal cell carcinoma from an outside tertiary care center (renal cell carcinoma-Cleveland Clinic) was independently analyzed.

Results

New baseline glomerular filtration rate (in ml/minute/1.73 m2) can be estimated with the following simplified equation: new baseline glomerular filtration rate = 35 + preoperative glomerular filtration rate (× 0.65) – 18 (if radical nephrectomy) – age (× 0.25) + 3 (if tumor size >7 cm) – 2 (if diabetes). Correlation/bias/accuracy/precision were 0.82/0.00/83/-7.5-8.4 and 0.82/-0.52/82/-8.6-8.0 in the internal/external validation cohorts, respectively. Additionally, the area under the curve (95% confidence interval) to discriminate postoperative new baseline glomerular filtration rate ≥45 ml/minute/1.73 m2 from receiver operating characteristic analyses were 0.90 (0.88, 0.91) and 0.90 (0.89, 0.91) in the internal/external validation cohorts, respectively.

Conclusions

Our study provides a validated equation to accurately predict postoperative new baseline glomerular filtration rate in patients being considered for radical nephrectomy or partial nephrectomy that can be easily implemented in daily clinical practice.

Braun DA, Street K, Burke KP, et al.

Cancer Cell. 2021 May 10;39(5):632-648.e8.
DOI: 10.1016/j.ccell.2021.02.013.

Abstract

The tumor immune microenvironment plays a critical role in cancer progression and response to immunotherapy in clear cell renal cell carcinoma (ccRCC), yet the composition and phenotypic states of immune cells in this tumor are incompletely characterized. We performed single-cell RNA and T cell receptor sequencing on 164,722 individual cells from tumor and adjacent non-tumor tissue in patients with ccRCC across disease stages: early, locally advanced, and advanced/metastatic. Terminally exhausted CD8+ T cells were enriched in metastatic disease and were restricted in T cell receptor diversity. Within the myeloid compartment, pro-inflammatory macrophages were decreased, and suppressive M2-like macrophages were increased in advanced disease. Terminally exhausted CD8+ T cells and M2-like macrophages co-occurred in advanced disease and expressed ligands and receptors that support T cell dysfunction and M2-like polarization. This immune dysfunction circuit is associated with a worse prognosis in external cohorts and identifies potentially targetable immune inhibitory pathways in ccRCC.