Dormant Tumor Cell Vaccination: A Mathematical Model of Immunological Dormancy in Triple-Negative Breast Cancer

Abstract

Triple-negative breast cancer (TNBC) is a molecular subtype of breast malignancy with a poor clinical prognosis. There is growing evidence that some chemotherapeutic agents induce an adaptive anti-tumor immune response. This reaction has been proposed to maintain the equilibrium phase of the immunoediting process and to control tumor growth by immunological cancer dormancy. We recently reported a model of immunological breast cancer dormancy based on the murine 4T1 TNBC model. Treatment of 4T1 cells in vitro with high-dose chemotherapy activated the type I interferon (type I IFN) signaling pathway, causing a switch from immunosuppressive to cytotoxic T lymphocyte-dependent immune response in vivo, resulting in sustained dormancy. Here, we developed a deterministic mathematical model based on the assumption that two cell subpopulations exist within the treated tumor: one population with high type I IFN signaling and immunogenicity and lower growth rate; the other population with low type I IFN signaling and immunogenicity and higher growth rate. The model reproduced cancer dormancy, elimination, and immune-escape in agreement with our previously reported experimental data. It predicted that the injection of dormant tumor cells with active type I IFN signaling results in complete growth control of the aggressive parental cancer cells injected at a later time point, but also of an already established aggressive tumor. Taken together, our results indicate that a dormant cell population can suppress the growth of an aggressive counterpart by eliciting a cytotoxic T lymphocyte-dependent immune response.

Chemoimmunotherapy Administration Protocol Design for the Treatment of Leukemia through Mathematical Modeling and In Silico Experimentation

“…This represents 10% of the rate value of Guzev et al as this was estimated in vitro in ideal conditions. Further, values of this order of magnitude have been reported in several works concerning mathematical models formulated with in vivo studies of both solid and non-solid tumors [24,[26][27][28][34][35][36].…”

Personalized Immunotherapy Treatment Strategies for a Dynamical System of Chronic Myelogenous Leukemia

“…Results shown in Figure 1 are achieved when immunotherapy treatment is constantly applied, i.e., ∀ as it is shown in the upper panel. The application of the treatment was considered as a constant for the sake of simplicity in the mathematical analysis as has been previously discussed through the years by many authors when modelling the role of immunotherapy in boosting the immune system response to cancer growth, see [ 9 , 12 , 13 , 30 , 31 , 32 , 33 , 34 , 35 ]. Nonetheless, two issues arise in this case, it is not biologically feasible to constantly applied an immunotherapy treatment to a cancer patient and it is evident that the solution of the effector T cells goes to the value 256,596 cells/μL (a concentration close to that of the the maximum carrying capacity of the CML cancer cells) which could produce adverse events in the patient’s health [ 36 ].…”

A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice

“…At the same time, mathematical modeling of cancer development and tumor micro-environment offers insights and can be used in discovering new treatments [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. As the complex spatial cell-to-cell interactions in the tumor micro-environment has attracted many experimental studies, a more thorough mathematical model can help scientists gain a better insight into the mechanisms of cancer growth.…”