CancerSim: A Cancer Simulation Package for python3
| Main Authors: | Luka Opasic, Jacob G. Scott, Arne Traulsen, Carsten Fortmann-Grote |
|---|---|
| Format: | info software Journal |
| Bahasa: | eng |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4034071 |
Daftar Isi:
- Cancer is a group of complex diseases characterized by excessive cell proliferation, invasion, and destruction of the surrounding tissue [Kumar, 2017] . Its high division and mutation rates lead to excessive intratumour genetic heterogeneity which makes cancer highly adaptable to environmental pressures such as therapy [Turajlic, 2019] . This process is known as somatic evolution of cancer. Throughout most of its existence a tumour is inaccessible to direct observation and experimental evaluation. Therefore, computational modelling can be useful to study many aspects of cancer. Some examples where theoretical models can be of great use include early carcinogenesis, as lesions are clinically observable when they already contain millions of cells, seeding of metastases, and cancer cell dormancy [Altrock, 2015] . Here, we present CancerSim, a software that simulates somatic evolution of tumours. The software produces virtual spatial tumours with variable extent of intratumour genetic heterogeneity and realistic mutational profiles. Simulated tumours can be subjected to multi-region sampling to obtain mutation profiles that are realistic representation of the sequencing data. This makes the software useful for studying various sampling strategies in clinical cancer diagnostics. An early version of this cancer evolution model was used to simulate tumours subjected to sampling for classification of mutations based on their abundance [Opasic, 2019]. Target users of CancerSim are scientists working in the field of mathematical oncology. Simplicity and accessibility of our model in comparison to more advanced models (see e.g. Ref. [Waclaw, 2015]) makes it particularly suitable for students with interest in somatic evolution of cancer. Our model is abstract, not specific to any neoplasm type, and does not consider a variety of biological features commonly found in neoplasm such as vasculature, immune contexture, availability of nutrients, and architecture of the tumour surroundings. It most closely resembles the superficially spreading tumours like carcinoma in situ, skin cancers, or gastric cancers, but it can be used to model any tumour on this abstract level.