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

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. The model is simulated stochastically by sampling the parameter values from a joint probability distribution function. The machine learning model is applied to calibrate the mathematical model and to fit it to the overall survival curve. The model simulations faithfully reproduce the clinical cohort at three levels long-term response (OS), the initial response (according to RECIST criteria), and the relationship between the number of chemotherapy cycles and time between two consecutive chemotherapy cycles. In addition, we investigated the relationship between initial and long-term response. We showed that those two variables do not correlate which means that we cannot predict patient survival solely based on the initial response. We also tested several chemotherapy schedules to find the best one for patients treated with palliative intent. We found that the optimal treatment schedule depends, among others, on the strength of competition among various subclones in a tumor. The computational platform developed allows optimizing chemotherapy protocols, within admissible limits of toxicity, for palliative treatment of metastatic NSCLC. The simplicity of the method allows its application to chemotherapy optimization in different cancers.

What do cellular responses to acidity tell us about cancer?

“…For example, according to Lotka-Volterra models, therapies that selectively kill the majority of cancer cells cannot, in the end, change overall outcomes. This is because the 'ecosystem' eventually arrives at the same equilibrium point once the surviving population of cancer cells expands [57][58][59]. In other words, even a small number of surviving cells is able to repopulate tissues and continue along the prior trajectory.…”

Salvianolic Acid B Suppresses Non-Small-Cell Lung Cancer Metastasis through PKM2-Independent Metabolic Reprogramming

“…For resectable NSCLC tumors, primary treatment includes surgery with or without neoadjuvant therapy [ 6 ]. The mainstay of treatment for nonresectable tumors is radiotherapy and systemic treatments that are also offered for palliative treatment [ 7 ]. Systemic treatment contains conventional chemotherapy that is usually linked to remarkable side effects, targeted molecular treatment (such as tyrosine kinase inhibitors), as well as immunotherapies (such as immune-checkpoint inhibitors) [ 8 ].…”

A model for the intrinsic limit of cancer therapy: duality of treatment-induced cell death and treatment-induced stemness

“…For simplicity, we assume that each of these parameters are uniform random variables. One possible way of updating this assumed prior with a more appropriate posterior distribution based on clinical data would be to use an expectation-maximization approach [46]. We assume that the remaining parameters, tumor size at diagnosis and the pharmacodynamic constants, remain fixed across all patients, as they are intrinsic properties of standard clinical practice and a given drug, respectively (Table S2).…”

“…More recently, the “competitive release” of the resistant cells, when these cells expand into the niche freed by the killing of sensitive cells by treatment, has been proposed as mechanism of tumor recurrence, adding the non-intuitive twist that “more killing” is not better [4, 10, 11, 12, 13, 39]. Nonlinear models of competition between sensitive and resistant subpopulations, such as that presented by Kozlowska et al ., lead to similar saddle-point dynamics of tumor depletion and progression as our model, albeit at a longer time scale that encompasses multiple cell generations [46].…”