Special Issue: Quantitative Cancer Biology TrendsTalk Integrating Quantitative Approaches in Cancer Research and Oncology Cancer is a complex disease that requires a multidisciplinary approach to address the mechanisms by which cancer progresses, evolves, and causes treatment resistance in patients. Quantitative and systems biology approaches can propel our understanding of the physical, biological, and evolutionary principles that drive cancer progression and treatment resistance. Here, we ask experts what they see as the challenges and opportunities for incorporating physical science concepts into cancer biology and oncology. Deconvoluting the Complexity of Cancer Depends on Understanding the Dynamics of Dysregulated Information Flow across Biological Scales Anna D. Barker, PhD University of Southern California, Los Angeles, CA, USA and Arizona State University, Tempe, AZ, USA Cancer comprises many ‘agents’ in a self-organizing complex adaptive system (CAS) that generally operates via simple rules at scale, exhibits redundancy, and operates far from equilibrium at the edge of chaos. Viewing and studying cancer as a CAS requires that the investigator understand that these elements may function somewhat independently or together to drive the development of emergent properties. Cancer exhibits two of the defining features of a CAS, emergence and coevolution, which are inexorably linked through information. Driven by advanced technologies, cancer research is now awash in data from dysregulated molecular pathways and networks, but this data ‘tsunami’ has yet to produce much useful information to address the two most challenging problems in cancer: metastatic disease and therapeutic resistance. Thus, realizing the concept of precision oncology remains elusive. Over the next decade, the application (and merger) of information [1] and evolutionary theories will become a primary theoretical organizing model for developing a fundamen- tal understanding of cancer across scales and time. Cancer is defined by the dynamic nature of the digital and analog computing that drives the myriad functions that occur to support complex decision-making and other functions at scale and across scales. It is remarkable that, since the sequencing of the human genome, we have accumulated vast amounts of data in oncology, but still know very little about the quantitative and mathematical aspects of how to identity, monitor, and predict the management and flow of dysregulated information in cancer. To address these challenges in oncology will require unprecedented advances in fundamentally understanding the quantitative aspects of the dysregulated information driving cancer, including long-needed progress in context-dependent algorithm development, computational modeling, simulation, and visualization. Additional advances in computing and the convergence of disciplines will also be needed to decode the nature and dynamics of this information, paving the way for new targets and strategies to prevent and cure cancer. Cancer Biology and Treatment: Is the Obvious Answer also Correct? Robert Gatenby, MD H. Lee Moffitt Cancer Center, Tampa, FL, USA In 1756, Benjamin Franklin’s plan to view a lunar eclipse was disrupted when a violent nor’easter (i.e., a storm with winds coming from the north east) struck Philadelphia. Franklin, like all scientists of his time, assumed that the wind carried the storm so that his brother in Boston would similarly have missed the eclipse. He was shocked to learn the storm actually arrived in Boston after the eclipse, leading him to develop new models of storm movement guided by atmospheric pressures. Franklin was neither the first nor the last person to experience the divergence of linear human intuition from the nonlinear dynamics of complex dynamic systems. That is, his 270 Trends in Cancer, April 2021, Vol. 7, No. 4 https://doi.org/10.1016/j.trecan.2021.01.011 © 2021 Elsevier Inc. All rights reserved. Trends in Cancer