An argument for mechanism-based statistical inference in cancer.
Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.
Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21210, USA, geman@jhu.edu.
Donald
D
Geman
Michael
M
Ochs
Nathan D
ND
Price
Cristian
C
Tomasetti
Laurent
L
Younes
D014408:Biomarkers, Tumor; D063646:Carcinogenesis / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics / Q000502:physiology; D009154:Mutation / Q000235:genetics; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology; D010641:Phenotype; D015398:Signal Transduction / Q000235:genetics / Q000502:physiology; D049490:Systems Biology / Q000379:methods*; D057170:Translational Research, Biomedical / Q000379:methods* / Q000639:trends
D014408:Biomarkers, Tumor