Ve patient’s treatment. For example De Boer et al. [82] developed a detailed model where they were able to show tumor regression and tumor growth dependent on the antigenicity of tumor-immune interaction. Tumourinfiltrating cytotoxic lymphocytes (TICLs) play an important role in tumor-immune interaction. Matzavinos et al.Gallasch et al. Journal of Clinical Bioinformatics 2013, 3:23 6 of[83] developed a spatio-temporal model to investigate the interaction of TICLs and tumors. It is possible to simulate the spatio-temporal dynamics of TICLs in a solid tumor. Kirschner et al. [84] developed a model that includes immunotherapy with cultured immune cells that have anti-tumor reactivity and additionally IL-2. In simulations a total eradiation of the tumor was only possible with the immune therapy. In the model of de Pillis et al. [85] the cytolytic effectiveness of tumor specific T-cells was the most sensitive parameter. Following the simulation results the efficacy of the CD8+ T cells and the response to immunotherapy was correlating. One therapy against superficial bladder cancer PubMed ID: is the treatment with Bacillus Calmette-Guerin (BCG). Rentsch et al. [86] showed with their mathematical model that the dose of BCG and the treatment interval have a Quinagolide (hydrochloride) price positive correlation of tumor extension. Wei [87] investigated this immunotherapy with a mathematical model and showed that the infection rate and the growth rate of the tumor are the most important parameters for a successful treatment. Rihan et al. [88] investigated the effect of adoptive cellular immunotherapy and found out that only a combination of the treatment with IL-2 can be used to clear the tumor. In summary, major contributions for clinical oncology have been made by the modeling community. However, although many models were designed and tested for clinical applications, the use in routine setting is sparse. One way to overcome this is to develop models for very specific applications and rigorously test the performance and the predictive power. Furthermore, the use of the available knowledge should be also part of the decision process. We envision a computational decision support system which is using clinical data, molecular data, publicly available data, as well as simulation results of mathematical models to reach a decision for therapeutic strategy.and eventually be used to stop or at least slow down the processes of tumor initiation, evolution and resistance to therapies.Competing interests The authors declare that they have no competing interests. Authors’ contribution RG, ME, PC, HH, and ZT carried out literature search and wrote the manuscript. ZT conceived the study. All authors read and approved the final manuscript. Acknowledgements This work was supported by the Austria Science Fund (Projects Doktoratskolleg W11 Molecular Cell Biology and Oncology and SFB F21 Cell Proliferation and Cell Death in Tumors), the Tiroler Standortagentur (Bioinformatics Tyrol), and by the Austrian Research Promotion Agency (FFG), project Oncotyrol. We apologize to the authors of papers not cited in this review due to space constraints. Received: 8 October 2013 Accepted: 4 November 2013 Published: 7 November 2013 References 1. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr, Kinzler KW: Cancer genome landscapes. Science 2013, 339:1546?558. 2. Pabinger S, Dander A, Fischer M, Snajder R, Sperk M, Efremova M, Krabichler B, Speicher MR, Zs.