Individual cancers are heterogeneous and dynamic at the single cell level, continuously evolving an ever-changing tree of related subclones. Yet, current precision medicine matches therapy to a molecular profile at diagnosis or at relapse/progression, focusing on average, static, and current properties. Simulations, representing a pan-oncology survey based on literature and clinical experience, have demonstrated that novel dynamic precision medicine strategies for optimizing therapeutic sequences have the potential to double median survival and dramatically increase cure rates. These strategies account for minor subclones and evolutionary dynamics, adjust therapy frequently, and plan ahead multiple steps rather than simply matching the current predominant subclone. Further increases in cure rate are possible by adapting based on long-term evolutionary projections. The optimal temporal sequences are extremely complex and non-intuitive. Effective treatment of cancer will require very high order combination therapy, not generally feasible due to toxicities. Such higher order combinations may be given as complex adaptive sequences of lower order combinations and monotherapy pulses.