A population model to predict progression free survival in breast cancer patients treated with neoadjuvant chemotherapy ñ vaccines based on tumour growth inhibition metrics.

Abstract

e12114 Background: Breast cancer (BC) is the most commonly diagnosed malignancy in women. Neoadjuvant chemotherapy selects patients with optimal pathological responses and increases tumorectomy versus total mastectomy. Changes in tumor size (CTS) are related to an early clinical benefit and to Progression Free Survival (PFS) and Overall Survival (OS) in BC patients. Thus, the identification of new prognostic factors in the neoadjuvant scenario is crucial. Objectives:To assess the link between tumor growth inhibition metrics (TGI) and progression free survival (PFS) based on data obtained from BC patients with neoadjuvant therapy Methods: Data were obtained from 218 patients with BC treated at the University Clinic of Navarra, diagnosed from January 2008 to February 2016, with a median age of 49 years (range 25 to 84). Classification of molecular subtypes was based on IHC and found as follows: Her2 (6%), LA ( 23%), LB ( 36%), LB-Her2 (10%) and TN ( 25%). Patients were treated with ddECx 4 followed Docetaxel x 4. Her2 patients had therapy with Trastuzumab. 18% of the patients (LB and TN) received vaccination with dendritic cells. Data were analysed under the population approach with NONMEN 7.3. A model accounting for the dynamics of tumor growth and antitumor effect of the neoadjuvant therapy was developed. The model describes tumor size as the sum of the longest diameters of target lesions as a function of time and drug exposure. A PFS model was developed to describe the PFS time distribution as a function of covariates Results: The TGI was able to individually and accurately describe the tumor shrinkage. Vaccinated patients had an increased shrinkage rate of 36% (p < 0.001) than those who were not (p < 0.001). PFS (months) was best described by a Weibull model, and greater tumor size at diagnosis (p < 0.05), smaller CTS (p < 0.05) and TN subtype (p < 0.001) were identified as poor prognostic factors Conclusions: A PFS model that uses a model-based estimate of tumor growth to predict the PFS of BC patients receiving neoadjuvant therapy has been developed. This model helps to gain early understanding of potential clinical benefit to facilitate go/no-go decision making.