Transfer learning of pharmacogenomic information across disease types and preclinical models for drug sensitivity prediction.

新药进入临床的失败率超过90%，其中超过四分之一的药物因缺乏疗效而失败。肺癌等复杂疾病的早期治疗决策只考虑了少数患者因素，并为所有患者规定了固定的治疗方案，这导致一些患者出现严重的药物副作用，结果差异很大。最近，通过发现和使用基因标记来解释病人对药物的反应，个性化治疗变得流行起来。如果个性化医疗的目标是为合适的病人提供合适的药物，我们或许可以将药物基因组学与机器学习结合起来，帮助做出更好的治疗决策。由于在实验室中对患者细胞和动物模型测试无效药物的潜在浪费，我们有动力利用机器学习的力量从有限数量的实验中预测药物反应。我们和许多其他药物开发人员已经使用计算方法从体外测量的药物反应中学习，并为临床试验提供证据，然而，现有的机器学习方法在预测疾病类型的药物反应方面做得很差，因为我们的样本数量有限。不幸的是，这种情况经常发生在罕见的癌症和其他疾病，如运动神经元疾病（也称为ALS），因为很少有患者或他们的样本难以收集。通过扩展机器学习以从不同的疾病环境中学习来克服这一限制，将意味着我们可以减少收集生物资源的耗时步骤，然后加速药物开发。在这个项目中，我们将开发机器学习算法，该算法将考虑我们在少数样品中测试的每种药物的所有剂量-反应数据。为了克服一种疾病中训练案例较少的问题，我们将开发一个迁移学习框架，该框架将使用来自具有更多药物反应数据的其他疾病的知识来解决具有较少数据的疾病中的问题。这些算法将分五个阶段进行开发和测试：1)开发一个学习模型，将基因组信息映射到数据较多的疾病和数据有限的疾病的药物反应；2)在数据有限的情况下，建立预测疾病药物反应的推理模型；3)应用学习和推理模型，利用肺癌药物敏感性的基因组关系预测膀胱癌的药物反应；4)从细胞系的药物反应中学习，并预测小鼠肿瘤模型的反应；5)学习和预测描述特定药物在肺癌和运动神经元疾病中的敏感性的生物标志物。基因组信息将被用作预测算法的输入，因为它们可以在实验室和临床中可靠地测量。我们使用越来越困难的预测测试案例，但是在疾病之间转移药物基因组学信息的成功将突出科学家利用现有数据集来解决在新疾病中测试药物的挑战的机会。我们作为一个计算机科学家、临床医生和细胞生物学家组成的团队，在机器学习、癌症和神经科学方面拥有专业知识，正在进行这项跨学科研究。最终目标是最终开发一套软件工具，可以被药物开发社区灵活地使用，将迁移学习应用于许多不同的问题。

The failure rate for new drugs entering clinics is in excess of 90%, with more than a quarter of drugs failing due to lack of efficacy. Earlier treatment decisions for complex diseases like lung cancer considered a small number of patient factors and prescribed a fixed treatment regimen for all patients, resulting in severe drug side effects for some and highly-varying outcomes. Recently, personalised treatments have become popular through the discovery and use of genetic markers that can explain a patient's response to a drug. If the goal of personalised medicine is to give the right drug to the right patient, we may be able to combine pharmacogenomics with machine learning to help make better treatment decisions.Due to the potential waste of testing ineffective drugs on patient cells and animal models in the laboratory, we are motivated to leverage the power of machine learning to predict drug response from a limited number of experiments. We and many others in drug development have used computational methods to learn from drug responses measured in vitro and provide evidence for clinical trials, however, existing machine learning methods do poorly at predicting drug response in disease types where we have a limited number of samples. This situation unfortunately happens quite often for rare cancers and other diseases like motor neurone disease (also known as ALS), because there are few patients or their samples are difficult to collect. Overcoming this limitation by extending machine learning to learn from different disease contexts would mean that we can reduce the time-consuming step of gathering biological resources and then accelerate drug development.In this project, we will develop machine learning algorithms that will take into account all of the dose-response data we have for each drug tested in only a few samples. To overcome the issue of few training cases in a disease, we will develop a transfer learning framework that will use knowledge from other diseases with more drug response data to address the problem in the disease with less data. The algorithms will be developed and tested in five stages: 1) develop a learning model that maps genomic information to drug response in both the disease with more data and the disease with limited data; 2) develop an inference model for predicting drug response in the disease with limited data; 3) apply the learning and inference models to use genomic relationships to drug sensitivity in lung cancer to predict drug response in bladder cancer; 4) learn from drug responses in cell lines and predict response in mice tumour models; 5) learn and predict biomarkers that describe a particular drug's sensitivity in both lung cancer and motor neurone disease. Genomic information will be used as inputs for the prediction algorithms because they can be reliably measured in the laboratory and in the clinic. We use prediction test cases of increasing difficulty, but successes in transferring pharmacogenomics information between diseases will highlight opportunities for scientists to leverage existing data sets to solve challenges of testing a drug in a new disease.We are conducting this interdisciplinary study as a team of computer scientists, clinicians and cell biologists with expertise in machine learning, cancer and neuroscience. The end goal is to eventually develop a suite of software tools that can be readily used flexibly by the drug development community to apply transfer learning to many different problems.