Generation of a multivariate stratification model to predict Trametinib-sensitive low-grade serous ovarian cancer tumours

生成多变量分层模型来预测曲美替尼敏感的低级别浆液性卵巢癌肿瘤

• 批准号: 2887341
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887341
• 关键词: Generation; multivariate; stratification; model; predict

Low-grade serous ovarian cancer (LGSOC) represents a distinctive subtype of ovarian cancer, accounting for approximately 4% of all epithelial ovarian cancer cases. What sets LGSOC apart is its characteristic display of reduced nuclear atypia and a lower mitotic index in comparison to high-grade serous carcinoma. At the molecular level, LGSOC is characterized by a notably low mutational burden, diminished p53 expression, and a relative state of chromosomal stability. One of its defining features is the prevalence of mutually exclusive activating mutations within the MAPK (Mitogen-Activated Protein Kinase) pathway.In a clinical trial spearheaded by the Gourley group, the MEK inhibitor Trametinib has demonstrated remarkable effectiveness in enhancing disease-free survival for LGSOC patients. Intriguingly, the presence of activating mutations within the MAPK pathway exhibited a strong correlation with a positive response to Trametinib. However, some patients lacking these mutations also exhibited favorable responses to the treatment.The principal objective of my study is to unravel the intricate molecular landscape of LGSOC by quantifying the activation of the MAPK pathway through a multivariate proteomic signature. By establishing associations between this pathway's activity, mutational status, and proteomic profiles, we aim to pinpoint common and rare genetic and proteomic characteristics linked to MAPK activation and Trametinib sensitivity in LGSOC. Ultimately, we will employ advanced Machine Learning techniques to predict Trametinib response in these patients.My project unfolds in three interconnected phases: data generation, computational analysis, and interpretation. This comprehensive approach encompasses mass spectrometry-based proteomics, genomic data analysis, and the application of advanced statistical and Machine Learning methodologies.

低级别浆液性卵巢癌（LGSOC）是卵巢癌的一种独特亚型，约占所有上皮性卵巢癌病例的4%。与高级别浆液性癌相比，LGSOC的特征性表现是核分裂减少和有丝分裂指数较低。在分子水平上，LGSOC的特征在于显著低的突变负荷、减少的p53表达和染色体稳定性的相对状态。其特征之一是MAPK（丝裂原活化蛋白激酶）通路中相互排斥的激活突变的普遍存在。在Gourley小组牵头的一项临床试验中，MEK抑制剂Trametinib在提高LGSOC患者的无病生存率方面表现出显着的效果。有趣的是，MAPK通路中激活突变的存在与曲美替尼的阳性反应有很强的相关性。然而，一些缺乏这些突变的患者也表现出良好的反应，我的研究的主要目的是解开复杂的分子景观LGSOC通过定量活化的MAPK途径，通过多变量蛋白质组学签名。通过建立该途径的活性、突变状态和蛋白质组学特征之间的关联，我们的目标是查明与LGSOC中MAPK激活和曲美替尼敏感性相关的常见和罕见遗传和蛋白质组学特征。最终，我们将采用先进的机器学习技术来预测这些患者的曲美替尼反应。我的项目分为三个相互关联的阶段：数据生成，计算分析和解释。这种全面的方法包括基于质谱的蛋白质组学，基因组数据分析以及先进的统计和机器学习方法的应用。