Longitudinal analysis of organ-specific tumor lesion sizes in metastatic colorectal cancer patients receiving first line standard chemotherapy in combination with anti-angiogenic treatment.

ABSTRACT

The purpose of this work is to assess the heterogeneity across organs of response to treatment in metastatic colorectal patient based on longitudinal individual target lesion diameters (ILD) in comparison to sum of tumor lesion diameters (SLD). Data were from the McCAVE trial, in which 189 previously untreated patients with metastatic colorectal carcinoma (mCRC) received either bevacizumab (control, C) or vanucizumab (experimental, E), on top of standard chemotherapy. Bayesian hierarchical longitudinal non-linear mixed effect models were fitted to the data using Hamilton Monte Carlo algorithm to characterize the time dynamics of the tumor burden, and to obtain estimates of the tumor shrinkage and regrowth rates. The ILD model brought more nuanced results than to the SLD model. Besides substantial differences in tumor size at baseline (with lesions located in liver more than twice as large as the ones in lungs), it revealed a more durable response in lesions located in lymph nodes and 'other organs' compared to liver and lungs. Specifically, in lymph nodes and 'other organs', the projected time to nadir was doubled in group E (2.12 and 2.44 years respectively) compared to group C (1.07 and 1.20 years respectively). This long period of tumor shrinkage associated with a slightly larger change from baseline at nadir (- 51.4% in lymph nodes and - 62.6% in 'other organs' in the group E, compared to - 46.2% and - 46.9% in group C) resulted in a clinically meaningful difference in the tumor dynamics of patients in group E compared to the group C. The proportion of variance explained by the inter-lesion variability for each model parameter was large (ranging between 10 and 56%), reflecting the heterogeneity in tumor dynamics across organs. These findings suggest that there is value in understanding both within- and between-patient variability in tumor size's time dynamics using an appropriate modeling framework, as this information may help in pairing the right treatment with individual patient profile.