Hot topics in renal cancer pathology: implications for clinical management

Development and validation of a prediction model for the prognosis of renal cell carcinoma with liver metastases: a population-based cohort study

Background

Current studies on the establishment of prognostic model for renal cell carcinoma (RCC) with liver metastases (LM) were scarce. This study aimed to develop nomograms to predict the prognosis of RCC with LM.

Methods

Patients diagnosed with RCC between 2010 and 2021 from the Surveillance, Epidemiology, and End Results (SEER) database were selected. The eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning algorithms were used to screen for the most influential factors affecting prognosis, and the Venn diagram method was employed for further refinement. Subsequently, a nomogram related to brain metastases was constructed. The performance of the nomograms was evaluated through receiver operating characteristics (ROC) curves, calibration plots, C-index, time-dependent C-index, and decision curve analysis (DCA). Kaplan-Meier (K-M) survival curves were used to provide additional verification of the clinical efficacy of the nomogram.

Results

This research comprised 2,395 RCC patients with LM. The Venn diagram demonstrated that age, histological type, grade, AJCC T stage, AJCC N stage, surgery, chemotherapy, marital status, and lung metastasis were highly relevant variables to patients with LM. The AUC, C-index, calibration curves, and DCA curves showed excellent performance of the nomogram. Additionally, the prognostic nomogram accurately classified RCC with LM patients into low- and high-risk groups for mortality.

Conclusion

This study developed a novel nomogram to predict the prognostic factors of RCC with LM, providing a valuable reference for making accurate clinical decisions.

Clinical utility of keratin 14 expression measurement in reflecting the tumor properties and prognosis in patients with renal cell carcinoma: a study with long-term follow-up

PURPOSE

Keratin 14 (KRT14) is hypothesized to be involved in the pathogenesis of renal cell carcinoma (RCC) based on its tumorigenic role in various cancers and its relationship with the prognosis of other urinary system malignancies. This study aimed to evaluate the correlation of KRT14 with tumor properties and prognosis in RCC patients.

METHODS

Data from 180 RCC patients who received tumor resection were retrospectively reviewed. The KRT14 was assessed by immunohistochemistry (IHC) staining in tumor tissues and non-tumor tissues.

RESULTS

KRT14 was insufficiently expressed in both tumor and non-tumor tissues, with median (interquartile range) IHC score of 2.0 (0.0-3.4) and 1.0 (0.0-2.0), respectively. While it was relatively higher in tumor versus non-tumor tissues (P < 0.001). Besides, tumor KRT14 was positively correlated with the pathological grade (P = 0.038), tumor size (P = 0.012), T stage (P = 0.006), and TNM stage (P = 0.018). Interestingly, tumor KRT14 high predicted shorter accumulating recurrence-free survival (RFS) (P = 0.003) and accumulating overall survival (OS) (P = 0.001), which was further verified by the multivariate Cox's regression analysis (both P < 0.05). Furthermore, tumor KRT14 high estimated shorter RFS and OS from the Gene Expression Profiling Interactive Analysis and Human Protein ATLAS databases (all P < 0.05). Subgroup analyses indicated that the correlation of tumor KRT14 with accumulating RFS and accumulating OS was more pronounced in RCC patients with better physical status (such as age < 65 years and better eastern cooperative oncology group performance status) and higher tumor stages (such as higher pathological grade).

CONCLUSION

High KRT14 in tumor tissue could reflect an advanced tumor features and unsatisfying survival in RCC patients.

Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives

Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems' outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future.

Targeting hyaluronic acid synthase-3 (HAS3) for the treatment of advanced renal cell carcinoma

BACKGROUND

Hyaluronic acid (HA) promotes cancer metastasis; however, the currently approved treatments do not target HA. Metastatic renal carcinoma (mRCC) is an incurable disease. Sorafenib (SF) is a modestly effective antiangiogenic drug for mRCC. Although only endothelial cells express known SF targets, SF is cytotoxic to RCC cells at concentrations higher than the pharmacological-dose (5-µM). Using patient cohorts, mRCC models, and SF combination with 4-methylumbelliferone (MU), we discovered an SF target in RCC cells and targeted it for treatment.

METHODS

We analyzed HA-synthase (HAS1, HAS2, HAS3) expression in RCC cells and clinical (n = 129), TCGA-KIRC (n = 542), and TCGA-KIRP (n = 291) cohorts. We evaluated the efficacy of SF and SF plus MU combination in RCC cells, HAS3-transfectants, endothelial-RCC co-cultures, and xenografts.

RESULTS

RCC cells showed increased HAS3 expression. In the clinical and TCGA-KIRC/TCGA-KIRP cohorts, higher HAS3 levels predicted metastasis and shorter survival. At > 10-µM dose, SF inhibited HAS3/HA-synthesis and RCC cell growth. However, at ≤ 5-µM dose SF in combination with MU inhibited HAS3/HA synthesis, growth of RCC cells and endothelial-RCC co-cultures, and induced apoptosis. The combination inhibited motility/invasion and an HA-signaling-related invasive-signature. We previously showed that MU inhibits SF inactivation in RCC cells. While HAS3-knockdown transfectants were sensitive to SF, ectopic-HAS3-expression induced resistance to the combination. In RCC models, the combination inhibited tumor growth and metastasis with little toxicity; however, ectopic-HAS3-expressing tumors were resistant.

CONCLUSION

HAS3 is the first known target of SF in RCC cells. In combination with MU (human equivalent-dose, 0.6-1.1-g/day), SF targets HAS3 and effectively abrogates mRCC.