Citations

Pelvicalyceal invasion (PCI) is a relatively novel pT3a staging parameter for renal cell carcinoma (RCC) nephrectomies. While interobserver reproducibility staging studies of sinus/vascular invasion in RCC exist, a similar evaluation for PCI has not been performed. Moreover, in our experience, there is also diagnostic variability in how pathologists interpret PCI. Herein, we explore interobserver reproducibility among genitourinary (GU) pathologists. Twenty hematoxylin and eosin-stained digitized slides from RCCs (all grossly approaching the renal pelvis) were distributed to 31 GU pathologists to classify each as PCI or not PCI based on their respective clinical practices; slides with concomitant sinus/fat/vascular invasion were excluded. Slides were then evaluated for the following 4 morphologic features: tumor abutting renal pelvis, tumor pushing/indenting into the renal pelvis, polypoid configuration of tumor into the renal pelvis, and tumor eroding through renal pelvic urothelium. Interobserver reproducibility was assessed, and the morphologic features were correlated with PCI. Relationships between pathologists’ interpretations, morphologic features, and PCI were evaluated using hierarchical clustering. Although the diagnosis of PCI was relatively uniform with a majority agreement (>67%) reached in 16/20 slides, overall interobserver reproducibility was only moderate (kappa=0.601). While all 4 morphologic features were sensitive for PCI, polypoid configuration of the tumor into the renal pelvis and the tumor eroding through the renal pelvic urothelium were most specific (90%, 100%, respectively). Although we show general consensus among genitourinary pathologists on PCI assessment, clarifying the diagnostic guidelines with specific criteria should be included in pathologic staging systems.

Abstract Background The mechanisms underlying metastatic latency in renal cell carcinoma (RCC) remain poorly understood. Methods This study evaluated 2 large independent cohorts for differences in tumor biology between patients who developed metastases early (≤1 year after nephrectomy) and those with late onset (>3 years). Results In the discovery cohort (n = 161), late metastatic RCC was associated with clear cell histology (88.9% vs 78.7%), lower pathological stage (pT1-2; 40.3% vs 18.0%), and favorable histopathological features including low grade (40.0% vs 2.3%), less sarcomatoid (5.6% vs 21.8%), and reduced necrosis (37.7% vs 78.3%; all P < .02). Late metastatic RCC tumors exhibited increased angiogenesis (63.5% vs 19.4%) and reduced inflammation (78.8% vs 50.0%; all P < .02) profiles. Genomic driver analyses revealed comparable rates of PBRM1 and SETD2 loss in late and early metastatic RCC, while BAP1 loss was significantly less common in late metastatic RCC (7.5% vs 27.1%; P < .02). In multivariable models, BAP1/PBRM1/SETD2 status and tumor necrosis emerged as key discriminators of late metastatic RCCs. These findings were confirmed in the second cohort (n = 307). Late metastatic RCC was enriched for fatty acid oxidation and angiogenesis pathways, supporting a less aggressive phenotype. This was further evidenced by a lower engraftment rate in murine models (0% vs 36.5%; P < .001) and significantly longer overall survival from the time of metastasis (median survival doubled, P < .001). Interestingly, late metastatic RCC shared genomic and phenotypic features with RCC that metastasizes to the pancreas, suggesting a common underlying biology influencing both metastatic latency and pancreatic tropism. Conclusions Overall, these findings advocate for recognition of late metastatic RCC because of its distinct biology and improved prognosis.

The NCCN Guidelines for Kidney Cancer provide multidisciplinary recommendations for diagnostic workup, staging, and treatment of patients with renal cell carcinoma (RCC). These NCCN Guidelines Insights focus on the systemic therapy options for patients with advanced RCC and summarize the new clinical data evaluated by the NCCN panel for the recommended therapies in Version 2.2024 of the NCCN Guidelines for Kidney Cancer.

Abstract Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) algorithms, which can infer molecular features from ubiquitous hematoxylin and eosin (H&E)–stained tissue sections. Although DL algorithms have been developed to predict some driver mutations from H&E images, the ability of these DL algorithms to resolve intratumoral mutation heterogeneity at subclonal spatial resolution is unexplored. Here, we apply DL to a paradigm of intratumoral heterogeneity, clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer. Matched IHC and H&E images were leveraged to develop DL models for predicting intratumoral genetic heterogeneity of the three most frequently mutated ccRCC genes, BAP1, PBRM1, and SETD2. DL models were generated on a large cohort (N = 1,282) and tested on several independent cohorts, including a TCGA cohort (N = 363 patients) and two tissue microarray (TMA) cohorts (N = 118 and 365 patients). These models were also expanded to a patient-derived xenograft (PDX) TMA, affording analysis of homotopic and heterotopic interactions of tumor and stroma. The status of all three genes could be inferred by DL, with BAP1 showing the highest sensitivity and performance within and across tissue samples (AUC = 0.87–0.89 on holdout). BAP1 results were validated on independent human (AUC = 0.77–0.84) and PDX (AUC = 0.80) cohorts. Finally, BAP1 predictions correlated with clinical outputs such as disease-specific survival. Overall, these data show that DL models can resolve intratumoral heterogeneity in cancer with potential diagnostic, prognostic, and biological implications. Significance: This work demonstrates the potential for deep learning analysis of histopathologic images to serve as a fast, low-cost method to assess genetic intratumoral heterogeneity. See related commentary by Song et al., p. 2672

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

Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype, and metastatic ccRCC is associated with 5-year survival rates of 10% to 20%. Genetically, ccRCC originates from sequential losses of multiple tumor suppressor genes. Remarkably, chromosome 3p loss occurs in more than 90% of sporadic ccRCCs. This results in concurrent one-copy loss of four tumor suppressor genes that are also mutated individually at high frequency in ccRCC (ie, VHL, 80%; PBRM1, 29% to 46%; BAP1, 6% to 19%; and SETD2, 8% to 30%). Pathogenically, 3p loss probably represents the first genetic event that occurs in sporadic ccRCC and the second genetic event in VHL-mutated hereditary ccRCC. VHL constitutes the substrate recognition module of the VCB-Cul2 E3 ligase that degrades HIF1/2α, whereas PBRM1, BAP1, and SETD2 are epigenetic modulators that regulate gene transcription. Because 3p loss and VHL inactivation are nearly universal truncal events in ccRCC, the resulting HIF1/2 signaling overdrive and accompanied tumor hypervascularization probably underlie the therapeutic benefits observed with vascular endothelial growth factor receptor inhibitors, including sorafenib, sunitinib, pazopanib, axitinib, bevacizumab, cabozantinib, and lenvatinib. Furthermore, recent marked advances in ccRCC genomics, transcriptomics, proteomics, metabolomics, molecular mechanisms, mouse models, prognostic and predictive biomarkers, and clinical trials have rendered invaluable translational insights concerning precision kidney cancer therapeutics. With an armamentarium encompassing 13 drugs that exploit seven unique therapeutic mechanisms (ie, cytokines, vascular endothelial growth factor receptor, mTORC1, cMET/AXL, fibroblast growth factor receptor, programmed cell death-1 and programmed death-ligand 1, and cytotoxic T-cell lymphocyte associated-4) to treat metastatic renal cell carcinoma, one of the imminent clinical questions concerning care of patients with metastatic ccRCC is how a personalized treatment strategy, through rationally combining and sequencing different therapeutic modalities, can be formulated to offer the best clinical outcome for individual patients. Here, we attempt to integrate recent discoveries of immediate translational impacts and discuss future translational challenges and opportunities.

Purpose Enhancer of zeste homolog 2 (EZH2), a chromatin remodeler, is implicated in the pathogenesis of clear cell renal cell carcinoma (ccRCC). However, the effect of EZH2 on outcomes in localized ccRCC is unclear, and molecular biomarkers are not currently integrated into prognostic models or adjuvant therapy trials. Methods We performed Cox regression to evaluate the association of tumor-based EZH2 gene and protein expression with survival in three independent cohorts: a cohort from The Cancer Genome Atlas (n = 532), a cohort from University of Texas Southwestern Medical Center (n = 122), and a cohort from Mayo Clinic (n = 1,338). Analyses were adjusted for the prognostic stage, size, grade, and necrosis (SSIGN) score as well as within low-, intermediate-, and high-risk SSIGN groups. Results Patients in The Cancer Genome Atlas cohort with EZH2-high gene expression were 1.5 times more likely to experience overall death than patients with EZH2-low expression (95% CI, 1.1 to 2.3; P = .028). Patients in the University of Texas Southwestern Medical Center cohort with EZH2-high protein expression were two times more likely to experience overall death than patients with EZH2-low expression (95% CI, 1.1 to 4.4; P = .034). Similarly, patients in the Mayo Clinic cohort with EZH2-high protein expression were 1.4 times more likely to experience overall death (95% CI, 1.2 to 1.7; P < .001). Patients in the Mayo Clinic cohort with EZH2-high protein expression were nearly two times more likely to experience RCC-specific death (95% CI, 1.5 to 2.6; P < .001); EZH2 protein expression was particularly prognostic among patients with low-risk SSIGN tumors (HR, 6.1; 95% CI, 3.4 to 11.1; P < .001). Conclusion EZH2 expression accurately predicts risk of RCC death beyond existing clinicopathologic models, particularly in low- and intermediate-risk SSIGN tumors. Further studies are required to incorporate molecular biomarkers into surveillance guidelines and adjuvant clinical trials.