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

Aim & Objective: This article aims at understanding the gradual development of cancer systems medicine and how it provides a better therapeutic strategy (in terms of drug selection, dose and duration) and, thus, improves patients' care. Hence, this study focuses on understanding the need and the evolving nature of the analytical models for assessing the outcome of different cancer therapeutics. Background: At present, cancer is viewed from a quantitative standpoint; hence, several analytical models on different cancers have been developed. Mathematical oncology has contributed significantly - from drawing the hypothesis of cancer development to therapeutic advantage. Using fewer 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, the systems biology approach 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 developing 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. In the future, such a deductive approach through the modelling and simulations efforts can not only aid in overcoming the challenges in different individual cancer cases but also help to tackle the drug adversities in individual patients. This approach may help to tune with the fourth industrial revolution in the 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 contributions to enrich the same model. Moreover, with this, the identification of controlling variables may be possible. Towards this goal, the middle-out rationalist approach (MORA) is working on analytical models for cancer treatment.