Mathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doublet

Emilia Kozłowska , Rafał Suwiński, Monika Giglok, Andrzej Świerniak, Marek Kimmel

Abstract

We developed a computational platform including machine learning and a mechanistic mathematical model to find the optimal protocol for administration of platinum-doublet chemotherapy in a palliative setting. The platform has been applied to advanced metastatic non-small cell lung cancer (NSCLC). The 42 NSCLC patients treated with palliative intent at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, were collected from a retrospective cohort of patients diagnosed in 2004–2014. Patients were followed-up, for three years. Clinical data collected include complete information about the clinical course of the patients including treatment schedule, response according to RECIST classification, and survival. The core of the platform is the mathematical model, in the form of a system of ordinary differential equations, describing dynamics of platinum-sensitive and platinum-resistant cancer cells and interactions reflecting competition for space and resources.

Introduction

Resistance to treatment is a major challenge in oncology [1,2]. Even though the majority of patients initially respond to primary treatment, cancer relapse is frequently observed, sometimes after a short-time-interval [3]. One cause of treatment resistance is tumor heterogeneity and the mode of tumor evolution [4,5]. The treatment causes the death of cells that are sensitive and results in the selective advantage for resistant cells, which contribute to the residual disease and affect final outcome. As a result, when the tumor reoccurs, the patient is already resistant to drugs with similar model of action, i.e., multi-drug resistance is present.

Materials and methods

Ethic statement

The study was approved by the Local Bioethical Commitee at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, Poland in accordance with national regulations. The approval was granted by the named board according to national regulations. A formal written consent was obtained from all participants of the study. The clinical data were anonymized before the computational analysis.

Discussion

Drug resistance is one of the major causes of lung cancer death, and thus new chemotherapy treatment protocols to overcome treatment resistance are urgently needed. There exist several drug scheduling schemes, such as metronomic chemotherapy protocol, based on suggestions from mathematical modeling studies to tackle this clinical problem [15]. For example, metronomic therapy, which involves a low-dose frequent-time chemotherapy protocol, was tested in many clinical trials including breast[21], prostate [22], and lung cancer[23]. In most cases, mathematical models focus on the application of chemotherapy to lung cancer patients with a curative intent. However, a lot of clinical research in oncology focuses on patients with a poor prognosis who are treated in a palliative setting.

Citation: Kozłowska E, Suwiński R, Giglok M, Świerniak A, Kimmel M (2020) Mathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doublet. PLoS Comput Biol 16(10): e1008234. https://doi.org/10.1371/journal.pcbi.1008234

Editor: Attila Csikász-Nagy, King’s College London, UNITED KINGDOM

Received: April 22, 2020; Accepted: August 10, 2020; Published: October 5, 2020

Copyright: © 2020 Kozłowska et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data are presented in the paper and supplementary materials of the article.

Funding: This work has been supported by National Science Centre, Poland (https://ncn.gov. pl), grant DEC2016/21/B/ST7/02241 (AS,RS,EK) and Foundation for Polish Science (FNP) under START scholarship (EK). The authors acknowledge The Silesian University of Technology for financial support (02/040/RGZ20/1006-06 and BKM 746/Rau1/2020/02/040/BKM20/1005). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

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