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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.

SUBMITTER: Lawlor PN

PROVIDER: S-EPMC4172566 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Conceptualizing cancer drugs as classifiers.

Lawlor Patrick Nathan PN Kalisky Tomer T Rosner Robert R Rosner Marsha Rich MR Kording Konrad Paul KP

PloS one 20140923 9

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 specificall ...[more]

PMID: 25248130

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Project description:Introduction"Radiomics" extracts and mines a large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic characteristics of an entire tumor. In order to enhance applicability of radiomics in clinical oncology, highly accurate and reliable machine-learning approaches are required. In this radiomic study, 13 feature selection methods and 11 machine-learning classification methods were evaluated in terms of their performance and stability for predicting overall survival in head and neck cancer patients.MethodsTwo independent head and neck cancer cohorts were investigated. Training cohort HN1 consisted of 101 head and neck cancer patients. Cohort HN2 (n?=?95) was used for validation. A total of 440 radiomic features were extracted from the segmented tumor regions in CT images. Feature selection and classification methods were compared using an unbiased evaluation framework.ResultsWe observed that the three feature selection methods minimum redundancy maximum relevance (AUC?=?0.69, Stability?=?0.66), mutual information feature selection (AUC?=?0.66, Stability?=?0.69), and conditional infomax feature extraction (AUC?=?0.68, Stability?=?0.7) had high prognostic performance and stability. The three classifiers BY (AUC?=?0.67, RSD?=?11.28), RF (AUC?=?0.61, RSD?=?7.36), and NN (AUC?=?0.62, RSD?=?10.52) also showed high prognostic performance and stability. Analysis investigating performance variability indicated that the choice of classification method is the major factor driving the performance variation (29.02% of total variance).ConclusionOur study identified prognostic and reliable machine-learning methods for the prediction of overall survival of head and neck cancer patients. Identification of optimal machine-learning methods for radiomics-based prognostic analyses could broaden the scope of radiomics in precision oncology and cancer care.

Dataset Information

Conceptualizing cancer drugs as classifiers.

Publications

Conceptualizing cancer drugs as classifiers.

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