“Rethinking” Cancer as a Complex Adaptive System”
1Complex Adaptive Systems Institute, Arizona, USA
Progress in cancer genomic sequencing technologies (with its associated "big data") now offers hope that cancer's seemingly unyielding complexity can now be de-convoluted to identify real targets for cancer treatment, detection and prevention. Cancer genomic alterations, and increasingly their prescribed clinical interventions, are now the central focus across virtually all aspects of cancer biomedicine. This "revolution" has unquestionably been enabled by large-scale genome sequencing projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC); as large scale sequencing project have endeavored to profile the genomics alterations in nearly all of the major cancers. Interestingly, data from these comprehensive efforts are beginning to offer a great deal more insight into cancer's complexity than simply providing an "atlas" of these changes. While the "holy grail" of drug development in oncology is the "driver" mutation, increasingly we are learning that although some of these so-called "actionable" alterations may be present to a greater or lesser extent in selected cancers, overall they are relatively rare. Instead, in TCGA and similar efforts, we observed an extraordinarily complex "long tail" of genomic alterations which generally remain unexplored. This landscape of genomic heterogeneity is reflected in cancer's heterogeneity (and the clonal heterogeneity) of most tumors - which makes the reality of achieving cures elusive. Despite an enormous amount of effort by myriad investigators, we are still early in our attempts to functionalize the complex array of interrelated changes that generally comprise the dysfunctional signaling pathways for the range of complex diseases we label as cancer. Recent large scale genomics projects, combined with decades of cancer research, indicate that cancer is not simply a "disease of the genes", and in fact, should more appropriately be viewed and studied as an emergent and evolving complex adaptive system (CAS) (within the tumor and within the individual patient). Employing CAS approaches and models to understanding cancer at a more fundamental level will require research that considers macro- and microenvironments across scales ranging from the molecular level to the patient. Of equal or even greater importance, understanding the emergent properties (hallmarks of complex systems) that are characteristic of cancer will require analysis of the coherent whole vs. the component parts. Approaching the investigation of cancer as a complex adaptive system represents an opportunity to achieve a deeper level of comprehension of the biological, temporal and spatial aspects of this extraordinarily complex disease. Such a conceptual shift will provide new strategies for knowledge-based diagnostic, therapeutic and preventive cancer interventions. For example, the heterogeneity of cancer provides it with redundancy which renders the system "robust; and all too often this "robustness" renders it resistant to even the most "targeted" of therapies. Understanding the evolution of these robust cancer phenotypes (states) both in carcinogenesis per se, and following therapeutic intervention, through appropriate data driven CAS models is becoming increasingly feasible. Predictably altering the "state" of complex, hierarchically organized, systems such as cancer requires embracing new ideas, disciplines and models - all of which will require significant change. However, all indications are that that achieving a future where stable disease (effectively rendering cancer a chronic, treatable condition) and/or cure are achieved for most cancers will require that the cancer research communities embrace complexity.