Modeling TGF-β in early stages of cancer tissue dynamics

New perspectives in triple-negative breast cancer therapy based on treatments with TGFβ1 siRNA and doxorubicin

Triple-negative breast cancer (TNBC), which accounts for 10-20% of all breast cancers, has the worst prognosis. Although chemotherapy treatment is a standard for TNBC, it lacks a specific target. Therefore, new therapeutic strategies are required to be investigated. In this study, a combined doxorubicin (DOX) and small interfering RNA (siRNA) therapy is proposed as therapeutic strategy for targeting TGFβ1 gene. Hs578T cell line is used as in vitro model for TNBC, wherein TGFβ1siRNA therapy is employed to enhance therapeutic effects. Cell proliferation rate is measured using an MTT test, and morphological alterations are assed using microscopically approached, while gene expression is determined by qRT-PCR analysis. The combined treatment of TGFβ1siRNA and DOX reduced levels of cell proliferation and mitochondrial activity and promoted the alteration of cell morphology (dark-field microscopy). DOX treatment caused downregulation of six genes and upregulation of another six genes. The combined effects of DOX and TGFβ1siRNA resulted in upregulation of 13 genes and downregulation of four genes. Silencing of TGFβ1 resulted in activation of cell death mechanisms in Hs578T cells, to potentiate the effects of DOX, but not in an additive manner, due to the activation of genes involved in resistance to therapy (ABCB1 and IL-6).

Pattern formation in a nonlocal mathematical model for the multiple roles of the TGF-β pathway in tumour dynamics

The growth and invasion of cancer cells are very complex processes, which can be regulated by the cross-talk between various signalling pathways, or by single signalling pathways that can control multiple aspects of cell behaviour. TGF-β is one of the most investigated signalling pathways in oncology, since it can regulate multiple aspects of cell behaviour: cell proliferation and apoptosis, cell-cell adhesion and epithelial-to-mesenchimal transition via loss of cell adhesion. In this study, we use a mathematical modelling approach to investigate the complex roles of TGF-β signalling pathways on the inhibition and growth of tumours, as well as on the epithelial-to-mesenchimal transition involved in the metastasis of tumour cells. We show that the nonlocal mathematical model derived here to describe repulsive and adhesive cell-cell interactions can explain the formation of new tumour cell aggregations at positions in space that are further away from the main aggregation. Moreover, we show that the increase in cell-cell adhesion leads to fewer but larger aggregations, and the increase in TGF-β molecules - whose late-stage effect is to decrease cell adhesion - leads to many small cellular aggregations. Finally, we perform a sensitivity analysis on some parameters associated with TGF-β dynamics, and use it to investigate the relation between the tumour size and its metastatic spread.

Modelling circulating tumour cells for personalised survival prediction in metastatic breast cancer

Ductal carcinoma is one of the most common cancers among women, and the main cause of death is the formation of metastases. The development of metastases is caused by cancer cells that migrate from the primary tumour site (the mammary duct) through the blood vessels and extravasating they initiate metastasis. Here, we propose a multi-compartment model which mimics the dynamics of tumoural cells in the mammary duct, in the circulatory system and in the bone. Through a branching process model, we describe the relation between the survival times and the four markers mainly involved in metastatic breast cancer (EPCAM, CD47, CD44 and MET). In particular, the model takes into account the gene expression profile of circulating tumour cells to predict personalised survival probability. We also include the administration of drugs as bisphosphonates, which reduce the formation of circulating tumour cells and their survival in the blood vessels, in order to analyse the dynamic changes induced by the therapy. We analyse the effects of circulating tumour cells on the progression of the disease providing a quantitative measure of the cell driver mutations needed for invading the bone tissue. Our model allows to design intervention scenarios that alter the patient-specific survival probability by modifying the populations of circulating tumour cells and it could be extended to other cancer metastasis dynamics.