Cancer is a complex process in which the abnormalities of many genes appear to be involved. The combinatorial patterns of gene mutations may reveal the functional relations between genes and pathways in tumorigenesis as well as identify targets for treatment. We examined the patterns of somatic mutations of cancers from Catalog of Somatic Mutations in Cancer (COSMIC), a large-scale database curated by the Wellcome Trust Sanger Institute. The frequently mutated genes are well-known oncogenes and tumor suppressors that are involved in generic processes of cell-cycle control, signal transduction, and stress responses. These "signatures" of gene mutations are heterogeneous when the cancers from different tissues are compared. Mutations in genes functioning in different pathways can occur in the same cancer (i.e., co-occur), whereas those in genes functioning in the same pathway are rarely mutated in the same sample. This observation supports the view of tumorigenesis as derived from a process like Darwinian evolution. However, certain combinatorial mutational patterns violate these simple rules and demonstrate tissue-specific variations. For instance, mutations of genes in the Ras and Wnt pathways tend to co-occur in the large intestine but are mutually exclusive in cancers of the pancreas. The relationships between mutations in different samples of a cancer can also reveal the temporal orders of mutational events. In addition, the observed mutational patterns suggest candidates of new cosequencing targets that can either reveal novel patterns or validate the predictions deduced from existing patterns. These combinatorial mutational patterns provide guiding information for the ongoing cancer genome projects.

We develop a new framework for inferring models of transcriptional regulation. The models, which we call physical network models, are annotated molecular interaction graphs. The attributes in the model correspond to verifiable properties of the underlying biological system such as the existence of protein-protein and protein-DNA interactions, the directionality of signal transduction in protein-protein interactions, as well as signs of the immediate effects of these interactions. Possible configurations of these variables are constrained by the available data sources. Some of the data sources, such as factor-binding data, involve measurements that are directly tied to the variables in the model. Other sources, such as gene knock-outs, are functional in nature and provide only indirect evidence about the variables. We associate each observed knock-out effect in the deletion mutant data with a set of causal paths (molecular cascades) that could in principle explain the effect, resulting in aggregate constraints about the physical variables in the model. The most likely settings of all the variables, specifying the most likely graph annotations, are found by a recursive application of the max-product algorithm. By testing our approach on datasets related to the pheromone response pathway in S. cerevisiae, we demonstrate that the resulting model is consistent with previous studies about the pathway. Moreover, we successfully predict gene knock-out effects with a high degree of accuracy in a cross-validation setting. When applying this approach genome-wide, we extract submodels consistent with previous studies. The approach can be readily extended to other data sources or to facilitate automated experimental design.

Cancers are heterogeneous and genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type. However, it does not yet systematically address heterogeneity at the single-cell level within a single individual's cancer or the dynamic nature of cancer due to genetic and epigenetic change as well as transient functional changes. We have developed a mathematical model of personalized cancer therapy incorporating genetic evolutionary dynamics and single-cell heterogeneity, and have examined simulated clinical outcomes. Analyses of an illustrative case and a virtual clinical trial of over 3 million evaluable "patients" demonstrate that augmented (and sometimes counterintuitive) nonstandard personalized medicine strategies may lead to superior patient outcomes compared with the current personalized medicine approach. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression, generally focusing on the average, static, and current properties of the sample. Nonstandard strategies also consider minor subclones, dynamics, and predicted future tumor states. Our methods allow systematic study and evaluation of nonstandard personalized medicine strategies. These findings may, in turn, suggest global adjustments and enhancements to translational oncology research paradigms.systems biology | evolution | treatment strategy | targeted therapy | combinations