Mathematical Oncology to Cancer Systems Medicine: Translation from Academic Pursuit to Individualized Therapy with MORA

Abstract

Aim & Objective: This article is aimed to understand the gradual development of cancer systems medicine and how this provides a better therapeutic strategy (in terms of drug selection, dose and duration) and patients care. Hence, this study is focused to understand the need and the evolving nature of the analytical models for the assessment of the outcome of different cancer therapeutics. Background: Presently, cancer is viewed from a quantitative standpoint; hence, several analytical models on different cancers have developed. From the information of cancer development to therapeutic advantage, mathematical oncology has contributed significantly. With a fewer number of variables, models in this area have successfully synchronized the model output with real-life dynamical data. However, with the availability of large scale data for different cancers, systems biology has gained importance. It provides biomedical insights among a large number of variables. And to get information for clinically relevant variables especially, the controlling variable(s), cancer systems medicine is suggested. Methods: In this article, we have reviewed the gradual development of the field from mathematical oncology to cancer systems biology to cancer systems medicine. An intensive search with PubMed, IEEE Xplorer and Google for cancer model, analytical model and cancer systems biology was made and the latest developments have been noted. Results: Gradual development of cancer systems biology entails the importance of the development of models towards a unified model of cancer treatment. For this, the model should be flexible so that different types of cancer and/or its therapy can be included within the same model. With the existing knowledge, relevant variables are included in the same model, followed by simulation studies that will enrich the knowledge base further. Such a deductive approach in the modelling and simulations efforts can help to tackle the adversity of individual cancer cases in future. This approach is indeed important to encompass the fourth industrial revolution in health sector. Conclusion: Towards the development of a unified modelling effort, a multi-scale modelling approach could be suitable; so that different researchers across the globe can add their contribution to enrich the same model. Moreover, with this, the identification of controlling variables may be possible. Towards this goal, middle-out rationalist approach (MORA) is working on analytical models for cancer treatment.