Abstract
Cancer researchers and clinicians speak of both cancer “drivers” and “actionable” mutations. In this paper, we explore how these two concepts are overlapping, and how they are different. Cases like the BCR-ABL1 gene fusion found in people with chronic myelogenous leukemia (CML) have served as exemplars in clinical teaching and research about the value of cancer genomics cancer diagnosis and treatment, but we argue that there are good reasons to think that the CML case is exceptional. With the completion of the cancer genome atlas project (TCGA) there is a growing realization that there are many more “drivers” than anticipated, placing an ever-larger wedge between the notion of “drivers” and “actionable” genes, in ways that have shifted the conversation about the relevance of cancer genomic data to diagnosis, prognosis and treatment. Clinicians now require a more fine-grained, contextual, and hierarchical ranking of significant variants for cancer diagnosis and treatment. We document here the shifts in the presuppositions driving the use of AI and genomic data in cancer diagnosis. We delineate the different ways that variants can be used in clinical activities and explain how this maps on to the distinction between "actionable" vs. "driver" mutations. For instance, the “driver” concept initially emerged in cases where molecular features of particular cancers were well-characterized, such as CML. In this case, a specific mutation provided important clinical information. However, the concept has since expanded to cover a broader set of genes found to be recurrently mutated in specific cancers using “Big Data” and AI approaches. Identification of “driver” mutations in this manner led to the splitting off of “driver” from the concept of “actionable” mutations. The latter refers to a subset of mutations which serve as biomarkers for particular treatments. While these concepts overlap in certain cancers, in others, it is crucial to keep them distinct. For instance, in molecularly heterogenous diseases, such as myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML), it is very important to not conflate them. Although genetic risk stratification often guides treatment decisions, variants in such models are not “actionable” in the sense of being specific treatment targets. This debate over how to demarcate “drivers,” versus “actionable” mutations is tied to a larger debate about the proper role of AI in biomedicine. The use of AI to identify “driver” genes does a dual service: on the one hand, it provides at best correlative, predictive information; on the other, it also indicates a potential causal role. The concept of “actionable” mutations attempts to move beyond the correlative. In this way, the trajectory of cancer research aims to move from identifying “drivers” to distinguishing “actionable” mutations. AI approaches may tell us little as yet about the specific causal role they play, or whether we might expect to successfully intervene their downstream products or associated pathways, raising questions regarding the scope and limits of these methods in translational cancer research.
