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. 2023 Oct;41(10):2735-2742.
doi: 10.1007/s00345-023-04545-2. Epub 2023 Aug 8.

On-treatment risk model for predicting treatment response in advanced renal cell carcinoma

Melis Guer  1 , Andreas Janitzky  2 , Martin Schostak  2
Affiliations

On-treatment risk model for predicting treatment response in advanced renal cell carcinoma

Melis Guer et al. World J Urol. 2023 Oct.

Abstract

Purpose: The field of immunotherapy combinations for advanced renal cell carcinoma (aRCC) has been expanded in recent years. However, the treatment response varies widely among individual patients. It is still a challenge to predict oncological outcome in clinical practice. We assessed the impact of an activated immune system reflected by changes in C-reactive protein (CRP) levels and the early onset of treatment-related adverse events (TRAEs) on the treatment response.

Methods: In this retrospective analysis of 57 aRCC patients, CRP kinetics based on previous descriptions of CRP flare-response, CRP response or CRP non-response, and the TRAEs, which occurred within a month after therapy initiation, were obtained for this study. According to logistic regression analysis of both factors, we stratified the patients into risk groups: the presence of CRP flare-response/response and early onset of TRAE (low-risk group); the presence of a single factor (intermediate-risk group); and without both factors (high-risk group).

Results: Ten patients (17%) experienced primary disease progression. No progressive disease was observed in the low-risk group, while 60% (n = 6/10) of the high-risk group showed a primary disease progression. Significantly, an increased risk of disease progression was observed by patients without CRP response and TRAEs (p < 0.001).

Conclusion: The present analysis displays the predictive value of the on-treatment risk model based on CRP kinetics and the early onset of TRAEs, which can be easy to implement in clinical practice to optimize the treatment monitoring.

Keywords: Advanced renal cell carcinoma; C-Reactive protein; Checkpoint inhibitors; Predictive biomarker; Treatment-related adverse events.

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Conflict of interest statement

MS: Honoraria: Bayer, Merck, Astra Zeneca/MedImmune, Janssen Oncology, Pfizer; Consulting Role: MSD, Astra Zeneca, Bayer, Bristol-Myers Squibb, Pfizer, Ipsen, EDAP TMS, Sanofi, Janssen Oncology, Astellas Pharma; Research Funding: Bristol-Myers Squibb Foundation, Astra zeneca, Ipsen, MSD Oncology, Janssen Oncology, Sanofi, Bayer. AJ: Honoraria: Apogepha, Astellas Pharma, Eisai, EUSA Pharma, Ipsen, Janssen-Cilag, Merck, Novartis, Pfizer, Pierre Fabre, Roche, UOAK Sachsen-Anhalt; Consulting Role: Bayer, Bristol-Myers Squibb, Coloplast, Eisai, EUSA Pharma, Ipsen, Janssen-Cilag, Roche, Pnn Medical A/S. MG: No conflict of interest.

Figures

Fig. 1
Fig. 1
On-treatment risk model with three defined risk groups
Fig. 2
Fig. 2
Longitudinal CRP changes from baseline after treatment initiation within 3 months
Fig. 3
Fig. 3
Association between primary treatment response and the subgroups of CRP kinetics
Fig. 4
Fig. 4
(A) Comparison of the primary treatment response and the risk groups of the on-treatment risk model. (B) Kaplan–Meier curve of the progression-free survival for the risk groups of the on-treatment model. The vertical axis represents the progression-free survival rate, and the horizontal axis represents the progression-free survival time (weeks) after treatment initiation
See this image and copyright information in PMC

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