Interpretable statistical machine learning approaches for the molecular investigation of cancer

用于癌症分子研究的可解释统计机器学习方法

• 批准号: 2728935
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2728935
• 关键词: Interpretable; statistical; machine; learning; approaches

Ovarian cancer is the 6th most common cancer for women in the UK. High-grade serous ovarian cancer (HGSOC) accounts for most cases, with a low 30% 5-year survival. The two main factors that contribute to this poor prognosis are: 1) late diagnosis of the disease, and 2) a high proportion of relapse despite initial response to treatment. The latter suggests that small populations of treatment resistant cancer cells may exist that can repopulate the disease. It is therefore of interest to identify such cancer cells and understanding how they different from other cancer cell types that might also affect why patients respond differently to treatment and differ in how long they survive. The molecular basis of ovarian cancer can be unravelled using a plethora of modern technologies such as sequencing and imaging at both bulk tissue and single-cell level. This is creating an unprecedented opportunity to use a data-driven approach to enable the precise characterisation of ovarian cancer and the possibility of developing targeted treatment options. However, to make effective use of the molecular data, robust analytical approaches are required to characterise cell populations of interest (particularly rare ones) and to integrate heterogeneous data modalities.This research aims to:1) To develop a robust and interpretable approach to identify rare cell populations from high-dimensional molecular data,2) To develop a statistical framework for the integration of multimodal data for survival prediction.Novelty of the research methodology We will develop statistical techniques that are specifically designed to identify rare cell types from molecular data. Classical statistical discovery methods tend to be biased toward the most common cell populations as there is more information about them. There is often a penalty associated with suggesting a rare cell type as these may not be real so a balance must be struck between proposing new cell types and the chance that these proposals may turn out to be false after further examination. We will develop techniques that allow us to control the balance between these competing needs allowing experimental scientists to adjust expectations based on the level of acceptable risk available to them. We will also develop techniques to combine different sources of data such as clinical record, magnetic resonance imaging and whole genome sequencing. These techniques will examine a specific limitation of existing approaches which typically do not account for the information imbalance between different types of data. For example, a clinical record might contain 30-40 data entries describing a patient's condition, but a whole genome sequence might reveal 10,000s of cancer mutations. If naively combined, the sheer number of mutations can overwhelm the importance of the clinical information, which can cause biases in analysis and interpretation, for instance, by failing to consider important socioeconomic or ethnicity information. We will develop approaches that equalise the important placed on different sources of data such that they can be combined in a fair and equitable way. This project falls within the EPSRC Healthcare Technologies research area' where "Optimising disease prediction, diagnosis and intervention" is one of the themes or research areas listed on this website.It will create new methods for analysing large data sets, underpin patient-specific predictive models, and support the identification of opportunities for prevention of disease or its recurrence.This project will involve a collaboration with the Oxford-based cancer immunology company, Singula Bio.

卵巢癌是英国女性第六大常见癌症。高级别浆液性卵巢癌（HGSOC）占大多数病例，5年生存率低至30%。导致这种不良预后的两个主要因素是：1)疾病的诊断较晚，2)尽管最初对治疗有反应，但复发率很高。后者表明，可能存在少量耐药癌细胞，它们可以重新繁殖这种疾病。因此，识别这些癌细胞并了解它们与其他类型的癌细胞有何不同，这可能也会影响患者对治疗的不同反应以及存活时间的不同。卵巢癌的分子基础可以利用大量的现代技术，如测序和成像，在大组织和单细胞水平上解开。这创造了一个前所未有的机会，使用数据驱动的方法来实现卵巢癌的精确特征，并有可能开发有针对性的治疗方案。然而，为了有效地利用分子数据，需要强大的分析方法来表征感兴趣的细胞群（特别是罕见的细胞群）并整合异构数据模式。本研究旨在：1)建立一种可靠且可解释的方法，从高维分子数据中识别稀有细胞群体；2)建立一个统计框架，整合多模态数据进行生存预测。研究方法的新颖性我们将开发专门用于从分子数据中识别稀有细胞类型的统计技术。经典的统计发现方法往往偏向于最常见的细胞群，因为有更多关于它们的信息。提出一种罕见的细胞类型通常会受到惩罚，因为这些细胞类型可能不是真的，所以必须在提出新的细胞类型和这些建议在进一步检查后可能被证明是错误的机会之间取得平衡。我们将开发技术，使我们能够控制这些相互竞争的需求之间的平衡，使实验科学家能够根据他们可接受的风险水平调整预期。我们还将开发技术，以结合不同来源的数据，如临床记录，磁共振成像和全基因组测序。这些技术将检查现有方法的具体限制，这些方法通常没有考虑到不同类型数据之间的信息不平衡。例如，一个临床记录可能包含30-40个描述患者病情的数据条目，但一个完整的基因组序列可能会揭示成千上万的癌症突变。如果天真地结合在一起，突变的数量可能会压倒临床信息的重要性，这可能会导致分析和解释中的偏差，例如，由于没有考虑重要的社会经济或种族信息。我们将制定办法，平等对待不同数据来源的重要性，使它们能够以公平和公正的方式结合起来。该项目属于EPSRC医疗技术研究领域，其中“优化疾病预测、诊断和干预”是本网站列出的主题或研究领域之一。它将创造分析大型数据集的新方法，巩固针对特定患者的预测模型，并支持确定预防疾病或疾病复发的机会。该项目将与牛津癌症免疫学公司Singula Bio合作。