Supplementary Material for: Key prognostic factors create a composite risk score to stratify patients into high- and low treatment benefit groups: A multicenter, retrospective data analysis of 84 mCRC patients treated with regorafenib as part of the CORRECT and CONSIGN trials

Abstract

Introduction: In further-line mCRC treatment, median progression-free survival (PFS) is rather short, and many patients do not benefit from any anti-tumor treatment and should therefore treated according to best-supportive care (BSC). A risk score based on standard laboratory values using markers of tumor inflammation aims to define a patient cohort with high treatment benefit and might offer insights into tumor biology. As regorafenib has been dropped off the German market due to an unfavorable risk-benefit ratio, patient selection is key for any further-line treatment option. Methods: We used cox regression analysis to determine lab markers that are independent prognostic factors of OS and PFS outcome. The influence of these variables was weighted using an estimator, which was calculated using cox regression analysis. The estimators were implemented as multiplication factors, resulting in a risk score. A cut-off value for the resulting risk values was then determined via cox regression analysis resulting in a low- and high-risk subgroup. Results: Using data of 82 patients, a risk score identifying long-term survival in patients with last line mCRC treatment could be calculated. The following parameters were associated with significantly longer survival in multivariate analysis: NLR ≤ 5 (p = 1.4) survived only 3.3 months after starting therapy with regorafenib (n=43, p<0.001, HR=3.76). Discussion/Conclusions: The presented composite risk score stratifies patients into two prognostic subgroups characterized by standard laboratory values. Patients with signs of systemic characterized by elevated NLR, AP, and CRP have a high composite risk score and a significant shorter overall survival. Although this score needs to be prospectively validated in larger cohorts, it may be used to stratify patients suitable for further-line treatment studies