Modelling tumour growth and progression free survival of breast cancer patients treated with neoadjuvant therapy
Belén P.Solans (1, 2), Marta Santisteban (3), Iñaki F. Trocóniz (1, 2)
(1) Pharmacometrics and Systems Pharmacology, Departament of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona, Spain. (2) IdiSNA; Navarra Institute for Health Research, Pamplona, Spain. (3) Department of Medical Oncology, University Clinic of Navarra, Pamplona, Spain.
Background: Breast cancer (BC) is the most commonly diagnosed malignancy in the US and European women, with 23% (231,840) of new cases and 40,730 estimated deaths in 2015 (1), ranking 5th as cause of death worldwide(2). Although early diagnosis offers the best chance for survival, the identification of new prognostic factors is crucial. Early change in tumour size (CTS) has been related to Progression Free Survival (PFS) and Overall Survival (OS) for a number of malignancies (3–5) and may offer a chance for early evaluation of potential clinical benefit.
Objectives: The aim of this evaluation was to I) establish a semi-mechanistic model for tumour-shrinkage for the period lasting from diagnosis to tumour resection and ii) to evaluate predictive and prognostic factors (including model predicted tumour size related metrics) in relation with PFS.
Methods: Information related to tumour size and survival was obtained from 218 patients diagnosed with BC at the University Clinic of Navarra where neoadjuvant chemotherapy was administered. Tumour size and survival versus time data were linked and described using the population approach with NONMEM 7.3. Model evaluation was performed through predictive checks.
Results: Drug exposure was dealt using the KPD approach. The model used to describe the tumour size dynamics incorporates a drug efficacy part that depends on drug exposure and the administration of immune therapy. However, the incorporation of a disease progression argument or resistance development was not possible. Covariates tested included patient’s characteristics (age, BSA), tumour infiltrating lymphocytes, KI67% and tumour subtype among others. The tumour growth inhibition model was able to individually and accurately describe tumour shrinkage. Patients receiving immune therapy had a shrinkage rate of 29% higher than those who did not receive this treatment. Predicted tumour dynamics over time were linked to the probability of survival as an argument of the hazard function, which was best described using a Weibull model. Predicted 5-year PFS was 84.7% vs observed - 85.35%. The survival model also included tumour subtype and tumour size at diagnosis as covariates.
Conclusions: The modelling exercise predicts the efficacy of the neoadjuvant therapy in terms of tumour growth inhibition and survival of patients with breast cancer. It is expected to have a potential benefit in optimising the standard treatment of patients receiving neoadjuvant therapy, predicting the likelihood of treatment success.
References:
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