Development of a multiomics pipeline to deliver a precision oncology approach in breast cancer

乳腺癌是全球女性癌症相关死亡的最常见和主要原因，据估计，2021年新增病例为230万例。如果诊断及时，70%-80%的早期非转移性疾病患者是可以治愈的，但以目前的治疗方案，已转移到远处器官的晚期患者被认为是无法治愈的。乳腺癌的潜在病因学代表了一组不同的疾病，分为几个组织学/内在亚型以及激素受体表达谱。大约10%的乳腺癌与家族遗传易感性有关，相关的蛋白质/代谢重编程被认为是该疾病的主要表达特征。这反映了支撑细胞恶性肿瘤异常发展的生物分子变化。这些分子特征对检测和治疗具有重要意义。因此，通过了解乳腺癌的联合生物分子适应和新的相关性，有可能对疾病的亚型进行双分子分类，并设计疾病亚型生物标记物和更精确的治疗策略。这种精确方法的要素已经在临床上发挥作用，肿瘤组织学已经被用来根据细胞结构的变化和淋巴结中任何转移的存在来对疾病进行分层。结合乳腺癌易感基因检测(可包括BRCA1/2、Ki67、ATM、CHEK2、PALB2、PTEN、STK11和TP53)，以及激素受体和HER2状态，有可能进一步将疾病划分为5种主要分子类型。但是，由于已知存在19种亚型的疾病，我们在生物分子变化如何转化为病理生理表现和进一步完善诊断和治疗实践方面的知识仍然存在巨大差距。多组学方法(基因/蛋白质/代谢物筛选工具的组合，分析与分子生物学中心教条有关的不同层次的化学)是为了改变我们对不同亚型乳腺癌如何在体内表达和表现的理解。通过能够记录不同乳腺肿瘤样本中存在的复杂蛋白质(蛋白质组学)和代谢物(代谢组学)情况，有可能建立精致详细的分子图谱，突出差异表达的生物分子途径。从这些途径中，不仅可以开始开发新的治疗靶点的新假设，而且当与匹配的基于血液的分析相结合时，有可能开发出侵入性小得多的方法来检测和分类乳腺癌亚型。目的：这项研究的主要结果是利用肿瘤活检/血清样本和来自英国生物库的在线组学数据，建立乳腺癌亚型作为肿瘤临床病理参数函数的差异生物分子特征。这些信息将被用来确定是否可以使用非靶向质谱学方法实现乳腺肿瘤的分层。然后，结果将与匹配的患者血清的签名相关联，以确定是否可以使用可检测的循环生物标记物来确定分子肿瘤的亚型，而不需要手术活检。项目组织样本将来自加州大学圣迭戈分校/国立卫生研究院L生物库，质谱分析将在斯特拉斯克莱德分子生物科学中心进行。

Breast cancer is the most diagnosed, and leading cause of cancer-related mortality worldwide among women with an estimate of 2.3 million new cases in 2021. If diagnosed soon enough it is curable in 70-80% of patients with early stage, non-metastatic disease, but advanced forms that have metastasized to distant organs are considered incurable with current therapeutic options. The underlying aetiology of breast cancer represents a heterogeneous group of diseases classified across several histological/intrinsic subtypes alongside hormone receptor expression profiles. Approximately 10% of all breast cancers correlate with familial genetic predisposition and associated protein/metabolic reprogramming are regarded as primary expressed hallmarks of the disease. This reflects biomolecular changes underpinning the abnormal development of cell malignancy. These molecular characteristics have vital implications for detection and therapy. Thus, by understanding the combined biomolecular adaptations and new dependencies of breast cancer it is possible to bimolecularly classify subtypes of the disease and design disease subtype biomarkers and far more precise therapeutic strategies. Elements of this precision approach are already in action in the clinic, with tumour histology already being used to stratify the disease according to cellular architectural changes and any metastatic presence in lymph nodes. When combined with breast cancer susceptibility genes measurement (that can include BRCA1/2, Ki67, ATM, CHEK2, PALB2, PTEN, STK11 and TP53), alongside hormone receptor and HER2 status, it is possible to further stratify the disease in to the 5 main molecular types of the disease. But, with 19 subtypes of the disease known to exist, there is still a huge gap in our knowledge of how biomolecular changes translate up to pathophysiological presentation and further refinement of diagnostic and therapeutic practise. The multiomics approach (the combination of gene/protein/metabolite screening tools that analyse different layers of the chemistry pertained within the central dogma of molecular biology) is primed to make a step change in our understanding of how different subtypes of breast cancer are expressed and manifest within the body. By being able to documents the complex protein (proteomics) and metabolite (metabolomics) landscape present in different breast tumour samples it is possible to build exquisitely detailed molecular maps that highlight differentially expressed biomolecular pathways. From these pathways, not only can new hypotheses for new therapeutic targets begin to be developed, but, when combined with matched blood-based analyses, it is possible to develop far less invasive ways to detect and classify subtypes of breast cancer. Aims: The primary outcome of the study is to establish the differential biomolecular signatures of breast cancer subtypes as a function of tumor clinicopathologic parameters using tumor biopsy/serum samples in tandem with online omics data from the UK Biobank. This information will be used to establish whether stratification of breast tumors can be achieved using an untargeted mass spectrometry approach. Results will then be correlated to signatures from matched patient sera to determine if detectable circulating biomarkers can be used to define molecular tumors sub-phenotype without surgical biopsy. Project tissue samples will come from the UoS/NHS-L biobank and mass spectrometry analysis will be performed within the Strathclyde Centre for Molecular Biosciences.