摘要翻译：
癌症和健康细胞有不同的分子特性分布，因此对药物的反应不同。抗癌药物理想地杀死癌细胞，同时限制对健康细胞的伤害。然而，癌症和健康细胞群体中细胞之间固有的差异增加了选择性药物作用的难度。在这里，我们提出了一个分类框架的基础上的想法，一个理想的癌症药物应该最大限度地区分癌症和健康细胞。我们首先探索如何在单个细胞基础上使用分子标记来区分癌细胞和健康细胞，然后如何通过这些分子标记来统计预测药物的效果。然后，我们将这两个想法结合起来，展示如何最优地将药物与肿瘤细胞匹配。我们发现少数基因的表达水平足以区分癌症和健康组织中的单个细胞。我们还发现基因表达预测了抗癌药物的疗效，提示抗癌药物利用基因图谱作为分类器。与我们的第一个发现一致，少量基因可以很好地预测药物疗效。最后，我们制定了一个框架，定义了一个最佳药物，并预测了药物鸡尾酒可能比单独的单个药物更准确地靶向癌症。将抗癌药物概念化为解决分子标记高维空间中的鉴别问题，有望为新型抗癌药物和药物鸡尾酒的设计提供信息。
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英文标题：
《Designing the statistically optimal drug for cancer therapy》
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作者：
Patrick N. Lawlor, Tomer Kalisky, Stephen Quake, Robert Rosner, Marsha
  Rich Rosner, Konrad P. Kording
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最新提交年份：
2013
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  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 propose a classification framework based on the idea that an ideal cancer drug should maximally discriminate between cancer and healthy cells. We first explore how molecular markers can be used to discriminate cancer cells from healthy cells on a single cell basis, and then how the effects of drugs are statistically predicted by these molecular markers. We then combine these two ideas to show how to optimally match drugs to tumor cells. 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 cancer drugs, suggesting that the cancer drugs act as classifiers using gene profiles. In agreement with our first finding, a small number of genes predict drug efficacy well. 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.
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PDF链接：
https://arxiv.org/pdf/1308.1087