Calculating Cancer

Mathematical models to predict and fight cancer

How can mathematical models be used in order to improve cancer prognosis and therapy? In the studies presented here, e:Med scientists from SYSIMIT generated models to improve prediction of breast cancer development and for a personalized treatment of bacterial infection against cancer.

Highlight from the e:Med Newsletter:

In healthy breast tissue, the presence of immune cells reflecting immunological surveillance or bystander effects during normal tissue turnover is perfectly normal. In contrast, lymphocytic lobulitis (LLO) is a recurrent immune cell pattern, characterized by lymphoid cells infiltrating lobular structures. This pattern has been associated with increased familial breast cancer risk and has been observed with increased frequency in non-malignant prophylactically removed breast tissue. It is difficult to distinguish LLO from common variations in immune surveillance by means of conventional microscopy. To better understand the underlying mechanisms leading to LLO, and with the intention of using this knowledge for the development of prognostic markers, e:Med scientists of the consortia SYSIMIT, around Dr. Haralampos Hatzikirou and Professor Friedrich Feuerhake, developed a mathematical model to optimize the prognostic power of immune cell infiltration in cancer. This model integrates personal patient data (menstrual cycle length, hormone status, genetic predisposition) and advanced image analysis of biopsies. The findings indicate that the immunological context, defined by immune cell density, functional orientation and spatial distribution, contains prognostic information previously not captured by conventional diagnostic approaches. The work suggests new parameters which help to improve predictive tools for the development of cancer and have the long-term potential to improve precision of prognosis in high-risk groups, e.g. women with BRCA 1/2 mutation, regarding the risk to develop neoplasia. [1, 2]

In a further project, the scientists studied the influence of bacteria on tumor development. The phenomenon, that bacterial infections can produce efficacious anti-tumor responses, has been discovered already 200 years ago by the French physician Arsène-Hippolyte Vautier. He observed that the patient’s tumors shrank when the patients additionally suffered from gas gangrene (C. perfringens) infection. However, the intense side-effects of a bacterial infection, the limited background knowledge and the low overall success rates, prevented such approaches from being employed against cancer. The scientists have now performed the first systematic study combining in vivo experiments and in silico modeling towards the mechanistic understanding of the therapeutic potential of bacterial infections against solid tumors. By means of mathematical modeling, they elucidated that bacterial infections can increase the anti-tumor response and can also change the vascularization within tumors. The model allows calculating an optimal bacterial load based on the tumor size and the corresponding immune context. This is a first step towards a personalized treatment protocol using bacterial infection. [3]

Original publications:

Contact:

Prof. Dr. med. Friedrich Feuerhake
MH-Hannover
Institut für Pathologie
feuerhake.friedrich@mh-hannover.de

Dr. Haralampos Hatzikirou
Technische Universität Dresden
Center for Advancing Electronics Dresden
haralambos.hatzikirou@tu-dresden.de

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