Integrating genomic signatures for treatment selection with Bayesian predictive failure time models

Over the past decade, a tremendous amount of resources have been dedicated to the pursuit of developing genomic signatures that effectively match patients with targeted therapies. Although dozens of therapies that target DNA mutations have been developed, the practice of studying single candidate genes has limited our understanding of cancer. Moreover, many studies of multiple-gene signatures have been conducted for the purpose of identifying prognostic risk cohorts, and thus are limited for selecting personalized treatments. Existing statistical methods for treatment selection often model treatment-by-covariate interactions that are difficult to specify, and require prohibitively large patient cohorts. In this article, we describe a Bayesian predictive failure time model for treatment selection that integrates multiple-gene signatures. Our approach relies on a heuristic measure of similarity that determines the extent to which historically treated patients contribute to the outcome prediction of new patients. The similarity measure, which can be obtained from existing clustering methods, imparts robustness to the underlying stochastic data structure, which enhances feasibility in the presence of small samples. Performance of the proposed method is evaluated in simulation studies, and its application is demonstrated through a study of lung squamous cell carcinoma. Our Bayesian predictive failure time approach is shown to effectively leverage genomic signatures to match patients to the therapies that are most beneficial for prolonging their survival.

Taking uncertainty seriously: Classical realism and national security

If we can’t reliably predict the future, how can we be wise when preparing for it? Examining the UK’s ‘Strategic Defence and Security Review’ of 2010, I demonstrate that though planners often rightly invoke uncertainty, they also imply a highly certain ideology about Western power and foresight. Modern ‘national security states’ describe the world as dangerously uncertain, yet fall prey to a misplaced confidence in their ability to anticipate and prevent threats. I argue that classical realism, especially that of Clausewitz and Morgenthau, is a valuable resource for handling uncertainty more reflexively. Classical realism counsels that governments should go beyond attempts to improve foresight. They should try to check against the fallibility of their assumptions, marshal their power more conservatively, insure against the likelihood of predictive failure by developing the intellectual capability to react to the unknown, and avoid misplaced confidence in their ability to bring order into chaos.