question: SIMPLE SUMMARY: The immune system protects the human body against threats such as emerging cancers or infections, e.g., COVID-19. Mutated malignant cells may in many cases be controlled by the immune system to be kept at an unnoticed low amount. However, a severe infection may compromise the immune system in controlling such malignant clones leading to escape and fatal cancer progression. A novel mechanism based computational model coupling cancer and infection to the adaptive immune system is presented and analyzed. The model pin-points important physiological mechanisms responsible for cancer progression and explains numerous medical observations. The progression of a cancers and the effects of treatments depend on cancer burden, the level of infection and on the efficiency of the adaptive immune system. The model exhibits bi-stability, i.e., gravitate towards one of two stable steady states: a harmless dormant state or a full-blown cancer-infection disease state. A borderline exists and if infection exceeds this for a sufficiently long period of time the cancer escapes. Early treatment is vital for remission and may control the cancer back into the stable dormant state. CAR T-cell immunotherapy is investigated by help of the model. The therapy significantly improves its efficacy in combination with antibiotics or immunomodulation. ABSTRACT: Background: The immune system attacks threats like an emerging cancer or infections like COVID-19 but it also plays a role in dealing with autoimmune disease, e.g., inflammatory bowel diseases, and aging. Malignant cells may tend to be eradicated, to appraoch a dormant state or escape the immune system resulting in uncontrolled growth leading to cancer progression. If the immune system is busy fighting a cancer, a severe infection on top of it may compromise the immunoediting and the comorbidity may be too taxing for the immune system to control. Method: A novel mechanism based computational model coupling a cancer-infection development to the adaptive immune system is presented and analyzed. The model maps the outcome to the underlying physiological mechanisms and agree with numerous evidence based medical observations. Results and Conclusions: Progression of a cancer and the effect of treatments depend on the cancer size, the level of infection, and on the efficiency of the adaptive immune system. The model exhibits bi-stability, i.e., virtual patient trajectories gravitate towards one of two stable steady states: a dormant state or a full-blown cancer-infection disease state. An infectious threshold curve exists and if infection exceed this separatrix for sufficiently long time the cancer escapes. Thus, early treatment is vital for remission and severe infections may instigate cancer progression. CAR T-cell Immunotherapy may sufficiently control cancer progression back into a dormant state but the therapy significantly gains efficiency in combination with antibiotics or immunomodulation.