#x201C;Use of computational biology in assessing treatment response and disease progression#x201D;

摘要

As advances in technology have spurred innovation and more effective treatments in the field of radiation oncology, so to have increasingly sophisticated tools to understand cancer biology from a therapeutic perspective. It is apparent from nearly a century of treating cancers with ionizing radiation that not all tumors respond similarly to therapy. This heterogeneity of effect leaves malignancies with a spectrum of cure rates. Despite increasing doses of radiation safely delivered to some lesions, there continues to be a failure in controlling growth. Consequently, a greater number of clinicians and scientists have turned to genetics and molecular biology to explain these response differences. With increasing knowledge of the cancer genome, we are beginning to understand why some malignancies are susceptible to radiation and others not. Furthermore, mining of data from tumors versus normal tissue counterparts for mutations, patterns of genomic changes, and other characteristics has allowed us to define new druggable/actionable targets that would sensitize tumors to radiation more efficiently. These new computationally derived data sets will have strong predictive and prognostic value regarding disease states and paradigm shifting treatment outcomes and will help clinicians monitor in advance future needs for therapy adjustments. It is because of the volume of biologic information that is present regarding tumors that the need for computation is an absolute requirement. This talk will provide a synopsis of some of the latest uses of computation in the study of cancer biology and therapy.