Developing deep learning models for precision oncology

项目总结/摘要 这项研究的目标是开发机器学习方法，特别是深度学习模型（DLMs），以 学习个体肿瘤中细胞信号传导途径的激活状态的更好表示， 利用这些信息来预测其对抗癌药物的敏感性。癌症主要由体细胞基因组引起 干扰细胞信号通路的变异（SGAs），以及通路中的畸变最终导致 癌症发展精确肿瘤学旨在准确检测和靶向肿瘤特异性畸变， 挑战依然存在。目前，还没有一种成熟的方法来检测信号的激活状态 途径，以及使用靶基因的突变状态作为 分子靶向药物的处方有其局限性。为了克服这种局限性，我们假设， DLM紧密模拟蜂窝信令系统的分层组织，可用于 系统地识别主要的癌症信号传导途径，以检测信号传导中的肿瘤特异性畸变 途径，并预测癌细胞对抗癌药物的敏感性。 我们将开发更精确地代表癌细胞中信号系统状态的模型， 这样的信息，以提高精确肿瘤学。我将设计和应用创新的DLMs癌症大数据， 包括大规模药物基因组学数据和癌症组学数据，以学习 在癌细胞中，尽管它们的生长不同，但驱动SGAs引起的信号系统畸变 条件，如在细胞培养、PDX和真实的肿瘤中。这将使我们能够将使用 细胞系和PDX用于临床环境（真实的肿瘤）。根据药物的性质， 目标蛋白质，我们开发模型DLM-MLT（DLM和多任务学习的组合）来预测 肿瘤样本对多种药物的敏感性。此外，我们将开发模型BioSI-DLM， 各种扰动（例如，SGA/LINCS扰动数据）作为辅助信息来学习更好的表示， 潜在地将DLM中的潜在变量映射到生物实体。我们假设， 从我们设计的模型中学习到的知识将显著提高预测精度， 药物治疗的常规适应症（例如，药物靶向蛋白的突变状态）。总之，我们的 研究使用基于深度学习的机器学习方法来学习更好和简洁的表示 嵌入到癌症组学数据中，以反映个性化的基因组变化， 指导个性化治疗。我们的研究可能会大大有助于癌症的发展 本体论，促进精准医学的发展。

Project Summary/Abstract The goal of this study is to develop machine learning methods, especially deep learning models (DLMs), to learn a better representation of activation states of cellular signaling pathways in an individual tumor and use such information to predict its sensitivity to anti-cancer drugs. Cancer is mainly caused by somatic genome alterations (SGAs) that perturb cellular signaling pathways, and aberrations in pathways eventually lead to cancer development. Precision oncology aims to accurately detect and target tumor-specific aberrations, but challenges remain. Currently, there is no well-established method to detect the activation states of signaling pathways, and the common practice of using mutation status of a targeted gene as the indicator for prescribing a molecularly targeted drug has limitations. To overcome such limitation, we hypothesize that, by closely simulating the hierarchical organization of cellular signaling systems, DLMs can be used to systematically identify major cancer signaling pathways, to detect tumor-specific aberrations in signaling pathways, and to predict cancer cell sensitivity to anti-cancer drugs. We will develop models that more precisely represent the state of signaling systems in cancer cells and use such information to enhance precision oncology. I will design and apply innovative DLMs to cancer big data, including large-scale pharmacogenomic data and cancer omics data to learn unified representation of aberrations in signaling systems caused by driver SGAs in cancer cell, despite of their different growth conditions, such as in cell culture, PDX and real tumor. This will enable us to transfer the models trained using cell lines and PDXs to clinical setting (real tumors) in future. By the nature of drugs that may share common target proteins, we develop model DLM-MLT (the combination of DLM and multi-task learning) to predict the sensitivity of tumor samples to multiple drugs at once. Furthermore, we will develop model BioSI-DLM to use various perturbations (ex. SGA/LINCS perturbation data) as side information to learn better representation that potentially map latent variables in a DLM to biological entities. We hypothesize that the representation learned from our designed models will significantly improve the prediction accuracy compared with the conventional indication for drug treatment (ex. mutation state of the drug targeting protein). In summary, our study uses deep learning based machine learning methods to learn better and concise representation embedded in the cancer omics data to reflect the personalized genomic changes, which could be used to guide the personalized treatment. Our study could significantly contribute to the development of cancer ontology and promote the development of precision medicine.