title: Conceptualizing cancer drugs as classifiers.
abstract: Cancer and healthy cells have distinct distributions of molecular properties and thus respond differently to drugs. Cancer drugs ideally kill cancer cells while limiting harm to healthy cells. However, the inherent variance among cells in both cancer and healthy cell populations increases the difficulty of selective drug action. Here we formalize a classification framework based on the idea that an ideal cancer drug should maximally discriminate between cancer and healthy cells. More specifically, this discrimination should be performed on the basis of measurable cell markers. We divide the problem into three parts which we explore with examples. First, molecular markers should discriminate cancer cells from healthy cells at the single-cell level. Second, the effects of drugs should be statistically predicted by these molecular markers. Third, drugs should be optimized for classification performance. We find that expression levels of a handful of genes suffice to discriminate well between individual cells in cancer and healthy tissue. We also find that gene expression predicts the efficacy of some cancer drugs, suggesting that these cancer drugs act as suboptimal classifiers using gene profiles. Finally, we formulate a framework that defines an optimal drug, and predicts drug cocktails that may target cancer more accurately than the individual drugs alone. Conceptualizing cancer drugs as solving a discrimination problem in the high-dimensional space of molecular markers promises to inform the design of new cancer drugs and drug cocktails.
authors.affiliation: Department of Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois, United States of America; Department of Physiology, Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois, United States of America.
authors.forename: Patrick Nathan
authors.initials: PN
authors.lastname: Lawlor
authors.affiliation: Department of Engineering, Bar-Ilan University, Ramat Gan, Israel.
authors.forename: Tomer
authors.initials: T
authors.lastname: Kalisky
authors.affiliation: Department of Physics, University of Chicago, Chicago, Illinois, United States of America.
authors.forename: Robert
authors.initials: R
authors.lastname: Rosner
authors.affiliation: Ben May Department for Cancer Research, University of Chicago, Chicago, Illinois, United States of America.
authors.forename: Marsha Rich
authors.initials: MR
authors.lastname: Rosner
authors.affiliation: Department of Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois, United States of America; Department of Physiology, Rehabilitation Institute of Chicago, Northwestern University, Chicago, Illinois, United States of America.
authors.forename: Konrad Paul
authors.initials: KP
authors.lastname: Kording
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology* / Q000627:therapeutic use; D014408:Biomarkers, Tumor / Q000378:metabolism*; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects; D006801:Humans; D008956:Models, Chemical; D009369:Neoplasms / Q000188:drug therapy / Q000378:metabolism*; D012372:ROC Curve
chemical_list: D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor