Conceptualizing cancer drugs as classifiers

• Published in: PloS one Vol. 9; no. 9; p. e106444
• Main Authors: Lawlor, Patrick Nathan, Kalisky, Tomer, Rosner, Robert, Rosner, Marsha Rich, Kording, Konrad Paul
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.09.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Antineoplastic agents; Antineoplastic Agents - pharmacology; Antineoplastic Agents - therapeutic use; Artificial intelligence; Biology and Life Sciences; Biomarkers, Tumor - metabolism; Breast cancer; Cancer; Cancer screening; Cancer therapies; Classification; Classifiers; DNA methylation; Drug development; Drug discrimination; Drugs; Epigenetics; Evolution & development; Gene expression; Gene Expression Regulation, Neoplastic - drug effects; Humans; Markers; Medical research; Medicine and Health Sciences; Metastasis; Models, Chemical; Neoplasms - drug therapy; Neoplasms - metabolism; Physical Sciences; Physiology; Principal components analysis; Rehabilitation; Research and Analysis Methods; ROC Curve; New York; Chicago Illinois; United States > US; Illinois
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0106444

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.