Developing deep learning models for precision oncology

开发精准肿瘤学的深度学习模型

• 批准号: 9892632
• 负责人: Lujia Chen
• 金额: $ 9.39万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2021-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9892632
• 关键词: Antineoplastic Agents; Big Data; Biological; Biological Markers; Cancer cell line; Cause of Death; Cell Culture Techniques; Cell Line; Cells; Clinical; Data; Development; Disease; Drug Targeting; Foundations; Future; Gene Expression; Genes; Genome; Genomics; Goals; Growth; Human; Individual; Knowledge; Label; Lead; Learning; Malignant Neoplasms; Maps; Messenger RNA; Methods; MicroRNAs; Modeling; Mutation; Nature; Oncogenic; Ontology; PIK3CA gene; Paper; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacogenomics; Pharmacotherapy; Proteins; Proteomics; Rattus; Research Activity; Resistance; Sampling; Side; Signal Pathway; Signal Transduction; System; Testing; The Cancer Genome Atlas; Training; Work; Xenograft procedure; Yeasts; base; cancer cell; deep learning; design; drug sensitivity; experimental study; improved; innovation; machine learning method; model design; molecular drug target; molecular phenotype; multi-task learning; mutational status; novel; personalized medicine; precision medicine; precision oncology; response; transcription factor; transcriptomics; tumor; tumor xenograft

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.

项目摘要/摘要 本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 了解单个肿瘤中细胞信号通路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 扰乱细胞信号通路的改变(SGA)，以及通路中的异常最终导致 癌症的发展。精确肿瘤学的目标是准确地检测和定位肿瘤特异性的异常，但 挑战依然存在。目前，还没有成熟的方法来检测信令的激活状态 途径，以及使用目标基因突变状态作为指示的常见做法 开出一种分子靶向药物是有局限性的。为了克服这种限制，我们假设，通过 DLMS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通路，以检测信号中的肿瘤特异性异常 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表示癌细胞中信号系统状态的模型，并使用 这些信息有助于提高肿瘤学的精确度。我将设计并应用创新的DLMS到癌症大数据中， 包括大规模药物基因组数据和癌症组学数据，以学习统一表示 驱动SGAs在癌细胞中引起的信号系统异常，尽管它们的生长方式不同 在细胞培养、PDX和真实肿瘤等条件下。这将使我们能够传输使用以下工具训练的模型 细胞系和PDX在未来的临床环境中(真正的肿瘤)。根据毒品的性质，这些药物可能有共同的 对于目标蛋白质，我们建立了DLm-MLT模型(DLm和多任务学习的组合)来预测 肿瘤标本同时对多种药物的敏感性。此外，我们还将开发Biosi-DLm模型以用于 各种扰动(例如SGA/LINCS扰动数据)作为边信息，以学习更好的表示 潜在地将DLm中的潜在变量映射到生物实体。我们假设这一表述 从我们设计的模型中学习的结果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)。总而言之，我们的 学习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 嵌入在癌症组学数据中，以反映个性化的基因组变化，这可以用于 引导个性化治疗。我们的研究可能会对癌症的发展做出重大贡献 本体论，推动精准医学发展。