Raphael Scholl (University of Cambridge)
The goal of personalized medicine is to stratify patient populations into subgroups according to biologically relevant individual variations. In principle, these variations could be in lifestyle or environment; in practice, they are usually genetic. The hope is that subgroups will exhibit meaningful regularities that are directly relevant to individual patients. We may be able to explain why a risk exists or a disease develops in members of a particular subgroup; what course of disease that subgroup should expect; or how the subgroup will respond to different kinds of therapies.
However, this project has turned out to be more challenging than early proponents expected. Around the time of the completion of the human genome project, it was expected that association studies would find a handful of genetic variations with relatively large effects that are relevant to explanation, prognosis, and therapy. But most of our data indicates that medically relevant genetic variations are for the most part rare and heterogeneous. This presents an unexpected epistemological challenge. Correlational studies, such as genome-wide association studies, are often insufficient for investigating causal structures in which the same effect can be produced by a large range of different causes, where each cause occurs only infrequently, and where each cause typically only has a small effect size.
Cancer biology is a paradigm case of this problem. Over the past decades, the genetic causes of tumors have been found to be both extremely rare and strikingly heterogeneous. At the molecular level, the somatic mutations of most tumors differ from those of most other tumors, even when their clinical phenotypes are indistinguishable. Population-based correlational methods are usually insufficient to attach reliable regularities to such rare variants.
A number of strategies have been developed to explore the rare and heterogeneous causes of cancers. This poster will focus on a set of related strategies that can be grouped under the label of "network-based stratification''. The basic idea is the following: While it is often difficult or impossible to attach regularities to rare and heterogeneous gene variants themselves, the sought-after regularities can be discerned more readily at the level of gene network modules. A tumor's prognosis and response to therapy seems to depend significantly on which network modules are altered or disrupted by individual mutations. Thus, network reconstruction promises to deliver information about the causation, prognosis, and response to therapy of individual cancers in individual patients. The poster will put the epistemological principles and ontological presuppositions of network-based stratification in broader philosophical and historical perspective.
