A mathematical model for pancreatic cancer growth and treatments

Abstract

Pancreatic cancer is one of the most deadly types of cancer and has extremely poor prognosis. This malignancy typically induces only limited cellular immune responses, the magnitude of which can increase with the number of encountered cancer cells. On the other hand, pancreatic cancer is highly effective at evading immune responses by inducing polarization of pro-inflammatory M1 macrophages into anti-inflammatory M2 macrophages, and promoting expansion of myeloid derived suppressor cells, which block the killing of cancer cells by cytotoxic T cells. These factors allow immune evasion to predominate, promoting metastasis and poor responsiveness to chemotherapies and immunotherapies. In this paper we develop a mathematical model of pancreatic cancer, and use it to qualitatively explain a variety of biomedical and clinical data. The model shows that drugs aimed at suppressing cancer growth are effective only if the immune induced cancer cell death lies within a specific range, that is, the immune system has a specific window of opportunity to effectively suppress cancer under treatment. The model results suggest that tumor growth rate is affected by complex feedback loops between the tumor cells, endothelial cells and the immune response. The relative strength of the different loops determines the cancer growth rate and its response to immunotherapy. The model could serve as a starting point to identify optimal nodes for intervention against pancreatic cancer.

Local and Systemic Expression of Immunomodulatory Factors in Chronic Pancreatitis

“…Data from this study may also be used to support development of advanced mathematical models that simulate cytokine interactions on cells within the pancreas microenvironment. Similar theoretical studies have been conducted by our group for pancreatic cancer and are currently underway as an extension into CP 48 . Finally, these data may also aid efforts in implementing new diagnostic or prognostic strategies in CP.…”

Bistability of the cytokine-immune cell network in a cancer microenvironment

“…But they did not investigate the role of the innate partners of Treg, such as alternatively activated macrophages and MDSCs, and their corresponding interactions with other cell types. Conversely, Louzoun et al [39] considered the interconversion between classically activated macrophages (M 1 -like, immune-promoting) and alternatively activated macrophages (M 2 -like, cancer-promoting), but did not consider the role of adaptive partners of M 2 such as Treg. They showed that the enhancement of M 1 to M 2 conversion by factors secreted by cancer cells and/ or cancer-associated fibroblasts and the positive feedback between cancer cells and CAFs are essential to give rise to a bistable switch from an immune-promoting (cancer-killing) state to an immune-modulating/suppressing (cancer-promoting) state as the cancer-killing rate of CD8 + cells decreases.…”

Towards the Design of a Patient-Specific Virtual Tumour

“…Finally, cancer is an evolutive and very heterogeneous disease with many different stages involving temporal variability on the tumour dynamic and on the patient state that cannot be easily predicted. This requires the development of pathophysiological models that integrate the underlying key mechanisms precisely describing the evolution of the disease for predicting and understanding its behaviour and its response to treatment [10–14]. …”