Chengcong Chen(1), Yan Summer Feng(2), Ziyi Wang(1), Manish Gupta(2), Xu Steven Xu(2)*, Xiaoyu Yan(1)*
(1)School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR; (2) Clinical Pharmacology and Quantitative Science, Genmab Inc., Princeton, NJ, USA
Objectives:
Several tumor growth inhibition (TGI) models have been developed to evaluate the longitudinal trajectories of tumor size and their associated metrics (e.g., tumor growth rate, KG)[1]. KG is a strong predictor of long-term efficacy outcomes such as overall survival (OS), which can serve as an exploratory endpoint in early proof-of-concept or phase II studies and support the design of phase III trials[2]. The TGI models are usually developed based on the time-course data of the sum of the longest diameter (SLD)[2]. However, heterogeneity in tumor dynamics across organs is typically seen in many cancer types (Figure 1)[3]. We aim to compare the prognostic value of organ-specific dynamics (KG, early tumor response) with SLD dynamics in patients with metastatic colorectal cancer (mCRC).
Methods:
All datasets are accessible in Project Data Sphere, an open-access platform. The lesions were classified into four metastatic sites based on the organ information: liver, lymph nodes, lung, and other (e.g., bone, skin)[4]. The tumor growth inhibition models developed based on organ-level SLD and SLD were used to estimate the organ-specific KGs and SLD KG. The early tumor response from baseline to the first measurement after treatment (≈ 8 weeks) was also evaluated. Patients with a tumor size reduction of over 30% were defined as responders. The relationship between organ-specific dynamics, SLD dynamics, and survival outcomes (overall survival, OS; progression-free survival, PFS) was quantified using Kaplan-Meier analysis and Cox regression.
Results:
This study included 3905 patients from 6 phase III mCRC trials. The liver emerged as the most frequent metastatic site (3058/3905, 78.3%), with variable KGs across different organs in individual patients. For patients with multiple metastatic organs (n=1343), the liver and lung have a higher KG (0.024 and 0.0201 [week-1], respectively) than the lymph node (0.0126 [week-1]) and other (0.0117 [week-1]). Notably, the dynamics for different organs did not equally contribute to predicting survival outcomes. In liver metastasis cases, liver KG proved to be a superior prognostic biomarker for OS and surpasses the predictive performance of SLD (Figure 2). In addition, the univariate Cox regression showed that liver KG presented higher HR (LIVER KG 2.244 vs. SLD KG 2.109), indicating a greater risk of poor survival. Similar results were observed for PFS. What’s more, early liver dynamics also outperform early SLD dynamics in predicting survival. Cox regression analysis confirmed the independence of liver KG and early liver response as variables in survival prediction. In addition, liver KG was identified as the most significant covariate in both multivariate analyses of OS and PFS (Figure 3).
Conclusions:
In mCRC patients with liver metastasis, liver dynamics is the primary prognostic indicator for both PFS and OS. In future drug development for mCRC, greater emphasis should be directed towards understanding the dynamics of liver metastasis development.
References:
[1] Stein, W.D., et al., Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: the growth rate constant as an indicator of therapeutic efficacy. Clin Cancer Res, 2011. 17(4): p. 907-17.
[2] Shemesh, C.S., et al., Early Decision Making in a Randomized Phase II Trial of Atezolizumab in Biliary Tract Cancer Using a Tumor Growth Inhibition-Survival Modeling Framework. Clin Pharmacol Ther, 2023. 114(3): p. 644-651.
[3] Zhou, J., et al., Mapping lesion-specific response and progression dynamics and inter-organ variability in metastatic colorectal cancer. Nat Commun, 2023. 14(1): p. 417.
[4] Mercier, F., et al., Longitudinal analysis of organ-specific tumor lesion sizes in metastatic colorectal cancer patients receiving first line standard chemotherapy in combination with anti-angiogenic treatment. J Pharmacokinet Pharmacodyn, 2020. 47(6): p. 613-625.
Reference: PAGE 32 (2024) Abstr 11095 [www.page-meeting.org/?abstract=11095]
Poster: Drug/Disease Modelling - Oncology