RasV12; scrib-/- Tumors: A Cooperative Oncogenesis Model Fueled by Tumor/Host Interactions

The phenomenon of how oncogenes and tumor-suppressor mutations can synergize to promote tumor fitness and cancer progression can be studied in relatively simple animal model systems such as Drosophila melanogaster. Almost two decades after the landmark discovery of cooperative oncogenesis between oncogenic Ras^V12 and the loss of the tumor suppressor scribble in flies, this and other tumor models have provided new concepts and findings in cancer biology that has remarkable parallels and relevance to human cancer. Here we review findings using the Ras^V12; scrib^-/- tumor model and how it has contributed to our understanding of how these initial simple genetic insults cooperate within the tumor cell to set in motion the malignant transformation program leading to tumor growth through cell growth, cell survival and proliferation, dismantling of cell-cell interactions, degradation of basement membrane and spreading to other organs. Recent findings have demonstrated that cooperativity goes beyond cell intrinsic mechanisms as the tumor interacts with the immediate cells of the microenvironment, the immune system and systemic organs to eventually facilitate malignant progression.

In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer

The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.