Conceptualizing Cancer Drugs as Classifiers

<div><p>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.</p></div

Discriminability of healthy versus cancer cells as a function of the number of genes considered.

<p>When measuring accuracy of cell classification as cancerous or healthy, one should consider both types of errors: false positives and false negatives (or more conventionally, true positives). This is illustrated by the Receiver Operating Characteristic (ROC) Curve. Lines indicate mean values, and error bars indicate bootstrapped 95% confidence intervals. Accuracy was measured using cross-validation; and chance value was determined using shuffle control.</p

Drug sensitivity heatmap as a function of the two most important genes.

<p>Drug activity is to some extent predictable using molecular markers.</p

Cancer treatment optimization.

<p>Better discrimination between cell populations is achieved by including an additional drug. The classification threshold line shown, in reality, represents a gradient related to “probability of cell death” which is indicated by shading. See text for full description.</p

The idea of a classifier.

<p>This illustrates how one can combine information from two cellular markers to construct a classifier that separates the two populations (cancerous and healthy cells) better than either marker alone.</p

Classification accuracy showing the improvements achieved by using an additional drug.

<p>Accuracy (AUC) achieved by both drugs together is better than either drug alone.</p

Discriminability of healthy versus cancer cells as a function of the number of cells and genes measured.

<p>Classification performance was measured as area under the curve (AUC) of the ROC curve. A perfect classifier would achieve an AUC of 1, whereas a random classifier would achieve an AUC of.5. Each colored line represents a different number of cells used to train the classifier, showing that performance improves as more cells are used. Lines indicate mean values, and shaded areas indicate bootstrapped 95% confidence intervals. Accuracy was measured using cross-validation; and chance value was determined using shuffle control.</p