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

跨疾病类型的药物基因组信息的迁移学习和用于药物敏感性预测的临床前模型。

• 批准号: EP/V029045/1
• 负责人: Dennis Wang
• 金额: $ 58.84万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV029045%2F1
• 关键词: Transfer; learning; pharmacogenomic; information; across

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.

进入临床的新药失败率超过90%，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，导致某些患者出现严重的药物副作用和高度变化的结果。最近，通过发现和使用可以解释患者对药物反应的遗传标记，个性化治疗变得流行起来。如果个性化医疗的目标是给正确的患者正确的药物，我们也许可以将药物基因组学与机器学习联合收割机，以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样本数量有限的疾病类型的药物反应方面表现不佳。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），因为患者很少或他们的样本难以收集。通过扩展机器学习来从不同的疾病背景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。为了克服疾病中训练案例很少的问题，我们将开发一个迁移学习框架，该框架将使用具有更多药物反应数据的其他疾病的知识来解决数据较少的疾病中的问题。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具有更多数据的疾病和具有有限数据的疾病的药物反应; 2）开发一个推理模型，用于预测具有有限数据的疾病的药物反应; 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来预测膀胱癌的药物反应; 4）从细胞系中的药物反应中学习，并预测小鼠肿瘤模型中的反应; 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解决新疾病药物测试挑战的机会。我们正在进行这项跨学科研究，作为一个由计算机科学家，临床医生和细胞生物学家组成的团队，拥有机器学习，癌症和神经科学的专业知识。最终目标是最终开发一套软件工具，可供药物开发社区灵活使用，将迁移学习应用于许多不同的问题。

项目成果

Assessment of Alzheimer-related Pathologies of Dementia Using Machine Learning Feature Selection

使用机器学习特征选择评估阿尔茨海默病相关的痴呆症病理

• DOI: 10.21203/rs.3.rs-1584607/v1
• 发表时间: 2022
• 作者: Rajab M

Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features

具有基于 ODE 随机特征的非线性动力系统的组合建模

• 发表时间: 2021
• 作者: McDonald T.

Large scale multi-output multi-class classification using Gaussian processes

• DOI: 10.1007/s10994-022-06289-3
• 发表时间: 2023-02
• 期刊: Machine Learning
• 影响因子: 7.5
• 作者: Chunchao Ma;Mauricio A Álvarez

Learning Nonparametric Volterra Kernels with Gaussian Processes

• 发表时间: 2021-06
• 作者: M. Ross;M. Smith;Mauricio A Álvarez

Shallow and Deep Nonparametric Convolutions for Gaussian Processes

高斯过程的浅层和深层非参数卷积

• DOI: 10.48550/arxiv.2206.08972
• 发表时间: 2022
• 作者: McDonald T