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Computational Models of Melanoma Marco Albrecht1, Philippe Lucarelli1, Dagmar Kulms2 Andthomassauter1*

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Computational Models of Melanoma Marco Albrecht1, Philippe Lucarelli1, Dagmar Kulms2 Andthomassauter1* Albrecht et al. Theoretical Biology and Medical Modelling (2020) 17:8 https://doi.org/10.1186/s12976-020-00126-7 REVIEW Open Access Computational models of melanoma Marco Albrecht1, Philippe Lucarelli1, Dagmar Kulms2 andThomasSauter1* Abstract Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research. Keywords: Melanoma, Systems biology, Physical oncology, Tumor growth Background An approach to better understand causative relations, to Melanoma is a neoplasm of the skin and originates from check hypothesis consistency, but also to reveal miss- transformed melanocytes. It causes the loss of 1.6 mil- ing qualitative information is constructing evidence-based lion disease-adjusted life-years worldwide, and the inci- models of these biological systems [9]. Models depict dence rate will increase in the next decades [1]. Since several interconnected biological elements with a struc- the discovery of the high prevalence of mutations in b- ture, which is derived from the current understanding, Raf proto-oncogene (BRAF) and NRAS protooncogene, and parameters, which are based on data. While many GTPase (NRAS) [2, 3], small-molecule inhibitors such life-scientists still rely on straight-forward relationships as dabrafenib and vemurafenib have been developed. between observation and insight to extend their knowl- More recently, immunotherapies, with antibodies bind- edge, leading scientists report that the direct link between ing immune receptors like the cytotoxic T-lymphocyte observation and insight seems to fade [10]. Thus, experi- associated protein 4 (CTLA4) or the programmed cell mentally proven relationships are increasingly transferred death 1 (PDCD1), have proven to be clinically effec- into the language of mathematics to enhance our under- tive [4]. However, many drug resistance mechanisms standing of experimental findings and underlying reasons. occurred and represent a major problem in both tar- Cancer scientists can benefit from well-designed com- geted therapy and immunotherapy [5–7]. As a result, life putational models, whereby systems biologists deliver expectancy remains low. The two-year survival rate is models of cancer biochemistry, and physical oncolo- 53.5% for combined BRAF + mitogen-activated protein gists provide models of tissues. Systems biology helps kinase kinases (MAP2K) inhibitors and 63% for combined understanding how biochemical pathways change during CTLA4 + PDCD1 immunotherapy [8]. Consequently, melanoma cell proliferation, invasiveness, survival, and a deeper understanding of disease mechanisms is still drug resistance based on network structure and dynamic demanded. behavior [11]. By contrast, physical oncology helps under- standing how transport, growth, and deformations in tis- *Correspondence: [email protected] 1Systems Biology Group, Life Science Research Unit, University of sues occur and is characterized by principles of geometry Luxembourg, 6, avenue du Swing, 4367 Belval, Luxembourg and mechanics [12, 13]. Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/)appliestothedatamade available in this article, unless otherwise stated in a credit line to the data. Albrecht et al. Theoretical Biology and Medical Modelling (2020) 17:8 Page 2 of 16 In this review, we tried to collect all published computa- specific -omics data can be obtained from genomic [15, tional models of melanoma and describe them regarding 16] and proteomic studies [17] but also from the secre- their contribution to the field. In particular, we focus on tome [18] and the metabolome, respectively [19, 20]. the interconnection of system elements or network char- Because multiple -omics data are rarely integrated with a acteristics while omitting classical statistics and bioin- systems-centered approach [21], the following studies and formatics of melanoma. By sorting models and methods repositories are only a starting point. around the topic of melanoma, we intend to support readers in finding the most appropriate mathematical Repositories to inform network models model to address their melanoma-specific research ques- Published knowledge in the form of structured and cen- tions. Additionally, the review shall describe potentials tralized databases facilitates model development. Beside for improvement, encourage readers to discover poten- general sources for system biologists [22], melanoma- tial extensions, and create awareness of missing melanoma specific databases are available (Table 1). The Melanoma topics to be tackled in the next decade. However, even if Molecular Map Project (MMMP) is an open-access, par- some models seem simplistic in biology, they often repre- ticipative project that structures published knowledge sent technically challenging stepping-stones for more bio- about molecules, genes, and pathways to enable trans- logically meaningful models in the future. Consequently, lational perspectives [23]. The MelGene project pro- reviewing the currently existing models might help to vides an easily searchable database of genetic associa- push forward the modeling and computational character- tion studies of cutaneous melanoma, as well as a meta- ization of melanoma. analysis for many polymorphisms [24]. The MelanomaDB The review is structured as follows: Network-based database lists published genomic datasets, including clin- approaches are explained in “Molecular networks”and ical and molecular information, and allows the cre- complemented by melanoma-specific repositories. The ation of gene lists by merging selected studies [25]. complex interaction between molecular players requires The Melanoma Gene Database (MGDB) provides exten- network-based approaches to suggest novel key interven- sive entries about 527 melanoma-associated genes (422 tion strategies, to stratify patients, and to individualize protein-coding), including epigenetic and drug-related patient treatment. In “Cell population models: bridging evidence [26]. Caution is required when using these cell culture to clinics” , the dynamic changes in cell databases, which accumulate data from multiple sources, count of different melanoma cell types, immune cells, sometimes in an automated manner, and are therefore and fibroblasts are modeled and complemented by stim- susceptible to perpetuate the biases and errors of the data ulating or inhibiting effects between cells. Such cellular source [27]. models represent another way to achieve therapy indi- vidualization and patient stratification. “Spatial models Models of melanoma genomics of melanoma” leads to geometric effects which will be The melanoma-specific repositories contain mainly augmented by the mechanics of melanoma in “Mechan- genetic data with not yet fully identified patterns. The ical models of melanoma” . Further aspects of oxygen, mutation pattern within the genome of metastatic nutrient, and drug transport are presented in “Trans- melanoma can be used to find mutually exclusive gene port of oxygen and drugs” sections. The confined, spa- modules [28]. If two proteins are related in an interaction tial, and physiological tissue environment is relevant for network and their genes are mutated in a way that one tumor growth prognosis, drug delivery, surgery, and der- gets amplified while the other gets deleted or only one moscopic pattern recognition. All available computational gets modified without the other, one could presume melanoma models are listed in Supplemental Table 1 and that this happens to intensify cancer pathways at the summarized in Fig. 1. protein level under given pathophysiological pressure. Consequently, one can conclude that a protein inhibits Molecular networks or activates the other in a known interaction network. Molecular networks represent larger sets of molecules in The pathophysiologic pressure on cancer protein path- an interconnected manner
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