Should patients with low-risk renal cell carcinoma be followed differently after nephron-sparing surgery vs radical nephrectomy?

Oncologic surveillance after surgical treatment for clinically localized kidney cancer: UroCCR study n. 129

BACKGROUND

In 2021, the EAU Guidelines implemented a novel, expert opinion-based follow-up scheme, with a three-risk-category system for clear cell (cc) and non-cc renal cell carcinoma (non-ccRCC) after surgery with curative intent. We aimed to validate the novel follow-up scheme and provide data-driven recurrence estimates according to risk groups, to confirm or implement the oncologic surveillance strategy.

METHODS

We identified 5,320 patients from a prospectively maintained database involving 28 French referral centers. The risk of recurrence, as either loco-regional or distant, was evaluated with the Kaplan-Meier method for each group (low- intermediate- or high-risk) according to ccRCC or non-ccRCC histology. The noncumulative distribution of recurrences was graphically investigated through the LOWESS smoother.

RESULTS

Two thousand two hundred ninety-three (58%), 926 (23%), and 738 (19%) had low-, intermediate, and high-risk ccRCC, and 683 (50%), 297 (22%), and 383 (28%) had low-, intermediate, and high-risk non-ccRCC, respectively. Median follow-up for survivors was 46 months. Overall, 661 patients experienced recurrence. Over time, the noncumulative risk of recurrence was approximately 10% for low-risk cc-RCC, non-ccRCC, and intermediate-risk non-ccRCC, with non-significant difference among the three recurrence functions (P=0.9). At 5-year, time point after which imaging should be de-intensified to biennial, the noncumulative risks of recurrence were: for intermediate risk ccRCC and non-ccRCC: 15% and 11%, respectively; for high-risk ccRCC and non-ccRCC: 24% and 8%, respectively. Among high-risk non-ccRCC patients there were 9 recurrences at 3-month. There was no significant difference between the recurrence function of high-risk non-ccRCC patients with negative imaging at 3-month and the one of intermediate-risk ccRCC (P=0.3).

CONCLUSIONS

Given the relatively low recurrence risk of patients with intermediate-risk non-ccRCC, those individuals could be followed up with a similar strategy to the low-risk category. Similarly, patients with high-risk non-ccRCC with negative imaging at 3-month, could be followed up similarly to intermediate-risk ccRCC after the 3-month time point.

The Role of CT Imaging in Characterization of Small Renal Masses

Small renal masses (SRM) are increasingly detected incidentally during imaging. They vary widely in histology and aggressiveness, and include benign renal tumors and renal cell carcinomas that can be either indolent or aggressive. Imaging plays a key role in the characterization of these small renal masses. While a confident diagnosis can be made in many cases, some renal masses are indeterminate at imaging and can present as diagnostic dilemmas for both the radiologists and the referring clinicians. This review focuses on CT characterization of small renal masses, perhaps helping us understand small renal masses. The following aspects were considered for the review: (a) assessing the presence of fat, (b) assessing the enhancement, (c) differentiating renal tumor subtype, and (d) identifying valuable CT signs.

Identification of a Somatic Mutation-Derived Long Non-Coding RNA Signatures of Genomic Instability in Renal Cell Carcinoma

Background

Renal cell carcinoma (RCC) is a malignant tumor with high morbidity and mortality. It is characterized by a large number of somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) are widely involved in the expression of genomic instability in renal cell carcinoma. But no studies have identified the genome instability-related lncRNAs (GInLncRNAs) and their clinical significances in RCC.

Methods

Clinical data, gene expression data and mutation data of 943 RCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Based on the mutation data and lncRNA expression data, GInLncRNAs were screened out. Co-expression analysis, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore their potential functions and related signaling pathways. A prognosis model was further constructed based on genome instability-related lncRNAs signature (GInLncSig). And the efficiency of the model was verified by receiver operating characteristic (ROC) curve. The relationships between the model and clinical information, prognosis, mutation number and gene expression were analyzed using correlation prognostic analysis. Finally, the prognostic model was verified in clinical stratification according to TCGA dataset.

Results

A total of 45 GInLncRNAs were screened out. Functional analysis showed that the functional genes of these GInLncRNAs were mainly enriched in chromosome and nucleoplasmic components, DNA binding in molecular function, transcription and complex anabolism in biological processes. Univariate and Multivariate Cox analyses further screened out 11 GInLncSig to construct a prognostic model (AL031123.1, AC114803.1, AC103563.7, AL031710.1, LINC00460, AC156455.1, AC015977.2, 'PRDM16-dt', AL139351.1, AL035661.1 and LINC01606), and the coefficient of each GInLncSig in the model was calculated. The area under the curve (AUC) value of the ROC curve was 0.770. Independent analysis of the model showed that the GInLncSig model was significantly correlated with the RCC patients' overall survival. Furthermore, the GInLncSig model still had prognostic value in different subgroups of RCC patients.

Conclusion

Our study preliminarily explored the relationship between genomic instability, lncRNA and clinical characteristics of RCC patients, and constructed a GInLncSig model consisted of 11 GInLncSig to predict the prognosis of patients with RCC. At the same time, our study provided theoretical support for the exploration of the formation and development of RCC.

Assessment of predictors of renal cell carcinoma progression after nephrectomy at short and intermediate term follow-up and implication on surveillance protocols

BACKGROUND

Prediction of risk of RCC progression after surgery is important for follow-up planning. We identified predictors of progression-free survival (PFS) and cancer-specific survival (CSS) in a large single institutional cohort and investigated patterns and sites of progression according to stage and grade.

METHODS

Node-negative non-metastatic clear-cell RCC (ccRCC) patients treated with radical or partial nephrectomy from 2000 to 2020 were included. Sites of progression were defined as thoracic, abdominal and others (bone/brain). Kaplan Meier curves and multivariable Cox regression (MCR) models tested for PFS and CSS.

RESULTS

Of 384 clear cell RCC N0M0 patients, 301 (78.4%) vs. 83 (21.6%) were pT1-2 vs. pT3-4, respectively; 253 (65.9%) vs. 130 (33.9%) were G1-G2 vs. G3-G4. Thoracic progressions occurred in 2.7% pT1-T2 vs. 21.7% pT3-T4 and 2.8% G1-G2 vs. 14.6% G3-G4 tumors. Abdominal progressions occurred in 4.0% pT1-T2 vs. 13.3% pT3-T4 and 4.3% G1-G2 vs. 9.2% G3-G4. Other progressions occurred in 0.3% pT1-T2 vs. 9.6% pT3-T4 and 0.8% G1-G2 vs. 5.4% G3-G4 (5.4%). Five-year PFS and CSS were 81.7 and 90.6%, respectively. At MCR models, pT3-4 (HR 9.1, p<0.001), G3-G4 (HR 2.7, p=0.003) and PSMs (HR 6.1, p<0.001) independently predicted PFS. Similarly, pT3-4 (HR 10.1, p<0.001), G3-G4 (HR 4.1, p=0.02), and PSMs (HR 5.2, p=0.04) independently predicted CSS.

CONCLUSIONS

In ccRCC N0M0 patients, G3-G4, pT3-4, PSMs were independent predictors of progression after surgery. Lower stage and grade ccRCCs progress predominantly in the abdominal sites and may be followed with less frequent extra-abdominal imaging compared to more advanced/aggressive tumors.