Early detection of cancer using identification of volatile metabolites

癌症是一种疾病，超过三分之一的世界人口将在他们的生活。不同类型的癌症有不同的患病率和死亡率，已经表明，筛查和诊断工具的进步降低了死亡率。我们现在生活在一个“精确健康和健康”的时代，我们每个人都是不同的，我们变老，生活或生病的方式是独特的，但预测个人的命运是众所周知的困难。然而，新技术为我们提供了一种可能的解决方案：通过准确和精确地测量生物分子，有可能提供一种“方法”来定量分析健康和衰老的潜在生物化学。癌症发现得越早，生存的机会就越大。已经表明，许多类型的癌症的特征在于改变的代谢方案，例如，一些癌症可以通过狗使用它们的嗅觉来检测，这表明特定的挥发性代谢物必须在癌症中产生或增加。在这个项目中，我们将使用两种实体癌类型，卵巢癌和前列腺癌的代谢模型，并使用基于约束的代谢分析技术将它们与健康组织进行比较。这将使我们能够预测健康组织和癌症组织之间代谢通量分布的变化。然后，我们将映射反应的代谢产物的生产，以确定化合物的生产是独特的或显着增加的癌症组织。接下来，这些预测将使用代谢组学和其他实验分析在前列腺癌和卵巢癌样本中进行测试和验证。我们将通过曼彻斯特癌症研究中心生物库获得原代和体外样本;这包括新型卵巢癌培养物，其作为迷你卵巢，沿着其配对匹配的基质细胞培养物。这些是从在真实的世界中真实的时间进行临床分层、治疗和监测的患者中分离出来的。我们预计，培养物将具有独特的代谢通量，有助于其分子表征，并提供额外水平的“组学细节”，这将有助于我们解构其对治疗的反应。此外，我们在实验室中有一系列代表疾病进展途径的前列腺癌细胞，包括正常的良性前列腺增生，直到高级别的侵袭性癌症。确定他们的代谢通量将产生一个侵略性的指纹，可以识别新的代谢途径，我们可以预测，早期诊断和治疗的目标。我们还将通过多组学（蛋白质组学，脂质组学等）评估细胞。通过结合这些信息和我们从人类前列腺癌中获得的数据集，我们将能够从经济学上预测疾病的预测，进展并帮助指导治疗方案。在体外，我们可以采取这些不同的代谢通量途径，并分析药物毒性，调节和存活增殖方面的培养物。卵巢癌和前列腺癌是由卵巢驱动的，很难检测和控制，这个项目将增加详细的代谢特征，这将有助于关联和准确地“映射”到临床数据。

Cancer is a disease which more than a third of the world population will get in their life. Different types of cancers have different prevalence and mortality rates, and it has been shown that advances in screening and diagnostic tools reduce mortality. We now live in an era of 'precision health and wellness' where everyone of us are different and the manner in which we age, live or become ill is unique, but predicting an individual's fate is notoriously difficult. However, new technology offers us a possible solution: by accurately and precisely measuring biomolecules, it may be possible to provide a 'way in' to quantifiably profiling the underlying biochemistry of health and aging. The earlier cancer is detected, the better the chances of survival. It has been shown that many types of cancer are characterised by altered metabolic regimens, for example some cancers can be detected by dogs using their olfactory sense, indicating that specific volatile metabolites must be produced or increased in cancer. In this project, we will use metabolic models of two solid cancer types, ovarian and prostate cancer, and assess them in comparison to their healthy tissue counterparts using constraint-based metabolic analysis techniques. This will allow us to predict alterations in metabolic flux distributions between healthy and cancer tissues. We will then map reactions to the production of metabolites in order to determine compounds whose production is either unique or significantly increased in cancer tissues. Next, these predictions will be tested and validated using metabolomics and other experimental analyses in prostate and ovarian cancer samples. We will have access to primary and in vitro samples via the Manchester Cancer Research Centre Biobank; this includes novel ovarian cancer cultures which act as a mini ovary, along with their paired matched stromal cell cultures. These have been isolated from patients that have been clinically stratified, treated and monitored in real time in the real world. We anticipate that the cultures will have unique metabolic fluxes aiding their molecular characterisation and offers an additional level of 'omic detail which will help us deconstruct their responses to treatment. In addition, we have a series of prostate cancer cells in the lab that represents the disease-progression pathway, encompassing normal, benign prostate hyperplasia, through to high-grade aggressive cancer. Determining their metabolic fluxes will generate a fingerprint of aggression that may identify novel metabolic pathways which we can target for prediction, earlier diagnosis but also treatment. We will also asses the cells by multi 'omics (proteomics, lipidomics etc). By combining this information and mapping against data sets we have from human prostate cancer, we will be able to 'omically characterise disease prediction, progression and help guide treatment options. In vitro we can take these various metabolic flux pathways and analyse cultures in terms of drug toxicity, modulation and survival proliferation.Together, ovary and prostate cancer which are hormonally driven, are difficult to detect and control and this project will add detailed metabolic characterisation which will help correlate and accurately 'map' onto clinical data.