Current Status of Predictive Biomarker Development in Metastatic Renal Cell Carcinoma

Personalized treatment using predictive biomarkers in solid organ malignancies: A review

In recent years, the influence of specific biomarkers in the diagnosis and prognosis of solid organ malignancies has been increasingly prominent. The relevance of the use of predictive biomarkers, which predict cancer response to specific forms of treatment provided, is playing a more significant role than ever before, as it affects diagnosis and initiation of treatment, monitoring for efficacy and side effects of treatment, and adjustment in treatment regimen in the long term. In the current review, we explored the use of predictive biomarkers in the treatment of solid organ malignancies, including common cancers such as colorectal cancer, breast cancer, lung cancer, prostate cancer, and cancers associated with high mortalities, such as pancreatic cancer, liver cancer, kidney cancer and cancers of the central nervous system. We additionally analyzed the goals and types of personalized treatment using predictive biomarkers, and the management of various types of solid organ malignancies using predictive biomarkers and their relative efficacies so far in the clinical settings.

Real World Analysis of Peritoneal Metastasis From Renal Cell Carcinoma. Meet-Uro27

BACKGROUND

Peritoneal metastases (PM) have been reported in approximately 1% of patients with metastatic Renal Cell Carcinoma (mRCC). Outcome data are limited due to the rarity of this metastatic site. Therefore, the aim of our study is to describe renal cell carcinoma (RCC) patients with PM treated as per clinical practice.

MATERIALS AND METHODS

Baseline characteristics and outcome data of patients with PM from RCC were retrospectively collected from 18 Italian oncological referral centers adhering to the Meet-Uro group, from January 2016 to January 2023.

RESULTS

We collect 81 RCC patients with PM. 78/81 received systemic treatment, 3/81 only best supportive care. First line treatment included tyrosine-kinase inhibitors (TKI) (46/78), ImmuneOncology (IO)-TKI (26/78) and IO-IO (6/78), with different Objective Response Rate (ORR) (43.4% in TKI monotherapy group vs 50% in IO-TKI group, respectively) and Disease Control Rate (DCR) (60.8% in TKI treated patients vs. 76.9% in IO-TKI treated patients). Median PFS was 6.4 months (95%CI 4.18-14.8) in patients treated with TKI monotherapy vs 23.7 months (95%CI 11.1-NR) in patients treated with IO-TKI (p < 0.015). The median OS (mOS) was 22.7 months (95%CI 13.32 - 64.7) in the TKI monotherapy group vs 34.5 mo (95%CI NR-NR) in the IO-TKI group with 53.8% of patients alive at 1 years in the latter group, (p < 0.16). Primary refractory patients were 36.9% for TKI and 15.3% for IO-TKI. According to International Metastatic renal cell carcinoma Database Consortium (IMDC) score, mPFS and mOS were consistent among risk categories. Median PFS was 36.6 months (95%CI 10.9-NR) for good risk patients compared to 10 months (95%CI 7.5-29.8) for intermediate risk and 2.96 months (95%CI 2.43-11.28) for poor risk population (p < 0.0005) whereas mOS was NR (95%CI 28.65-NR) for good risk patients compared to 35.3 months (95%CI 24.6-NA) and 12.4 months (95%CI 3.52-NR) for intermediate and poor risk population, respectively, (p < 0.0002). Only 34/78 (43.5%) received a second line treatment that was TKI (ORR 8.3% and DCR 41.6%) or IO (ORR 18.1% and DCR 40.9%).

CONCLUSION

We report one of the largest case series regarding PM from RCC. Characteristics of patients suggest a more aggressive behavior of PM from mRCC. Outcome data suggest that TKI-IO as first line treatment, and TKI as second line, confirm their activity for these patients with dismal prognosis.

External validation of a red cell-based blood prognostic score in patients with metastatic renal cell carcinoma treated with first-line immunotherapy combinations

Immunotherapy combinations with tyrosine-kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) had significantly improved outcomes of patients with mRCC. Predictive and prognostic factors are crucial to improve patients' counseling and management. The present study aimed to externally validate the prognostic value of a previously developed red cell-based score, including hemoglobin (Hb), mean corpuscular volume (MCV) and red cell distribution width (RDW), in patients with mRCC treated with first-line immunotherapy combinations (TKI plus ICI or ICI plus ICI). We performed a sub-analysis of a multicentre retrospective observational study (ARON-1 project) involving patients with mRCC treated with first-line immunotherapy combinations. Uni- and multivariable Cox regression models were used to assess the correlation between the red cell-based score and progression-free survival (PFS), and overall survival (OS). Logistic regression were used to estimate the correlation between the score and the objective response rate (ORR). The prognostic impact of the red cell-based score on PFS and OS was confirmed in the whole population regardless of the immunotherapy combination used [median PFS (mPFS): 17.4 vs 8.2 months, HR 0.66, 95% CI 0.47-0.94; median OS (mOS): 42.0 vs 17.3 months, HR 0.60, 95% CI 0.39-0.92; p < 0.001 for both]. We validated the prognostic significance of the red cell-based score in patients with mRCC treated with first-line immunotherapy combinations. The score is easy to use in daily clinical practice and it might improve patient counselling.

Serum uric acid and triglycerides in clear cell renal cell carcinoma: A restrospective cross-sectional study

The available evidence on the relationship between serum uric acid and triglycerides in clear cell renal cell carcinoma (ccRCC) is limited. The objective of this study is to investigate whether there is an independent association between serum uric acid and triglycerides in ccRCC, while adjusting for other variables. This cross-sectional study involved 1018 participants with ccRCC, who were admitted to a hospital in China from December 1st, 2013 to January 1st, 2023. The main independent variable investigated was serum uric acid, which was measured at baseline. The dependent variable was triglycerides. Covariates considered in this study included age, sex, body mass index, smoking status, hypertension, diabetes, coronary disease, High-density lipoprotein cholesterol, Low-density lipoprotein cholesterol, Total cholesterol, Blood urea nitrogen, and Creatinine. The study included 1018 participants with an average age of 56.92 ± 10.88 years old, and approximately 68% of them were male. The fully-adjusted linear regression analysis indicated a positive association between serum uric acid levels (100μmol/L) and triglyceride levels (mmol/L) after adjusting for confounding factors (β = 0.13, 95% CI [0.07, 0.18]). Furthermore, a smooth curve was constructed between serum uric acid and triglycerides based on the fully-adjusted model. In patients with ccRCC, there was a positive association between serum uric acid and triglycerides.