Personalised therapies based on simultaneous targeting of complex oncogenic networks identified by WGS.

从理论上讲，要找到有效的癌症治疗方法，我们只需识别致癌突变，并用一种具有良好特征的药物靶向它。然而，在实践中，每种癌症都是由以独特组合发生的多个突变驱动的，这些突变在每个患者中产生了复杂的肿瘤网络。当肿瘤学家试图实施单靶点精准医学时，由于没有捕获肿瘤的全部遗传复杂性，有希望的初步结果就会消失。虽然全基因组测序（WGS）提供了识别肿瘤相关基因突变的机会，但其目前在个性化癌症治疗中的实用性相当有限。为了使WGS具有有效的临床意义，必须使用基因组信息来建立模型，使癌症患者与最佳药物混合物相匹配。我的个人治疗（MPT）使用果蝇（_Drosophila）_及其复杂的遗传工具库来模拟个体患者的肿瘤特征。我们在苍蝇肠道中重建了WGS鉴定的肿瘤遗传复杂性。这些苍蝇“化身”将发展患者的肿瘤，并将死于相同的癌症，除非他们采取正确的药物组合。然后，我们用这些癌症化身苍蝇来测试数千种批准的化合物，以确定最佳的药物组合，拯救癌症化身免于死亡。通过这种方式，我们可以精确地描绘出整个动物体内生长的肿瘤与测试药物组合之间的相互作用。通过这种称为个人发现过程（PDP）的方法，MPT为胃肠癌（GIC）患者提供超个性化的药物治疗建议。在这个项目中，MPT将与伦敦IVD合作，一个英国NIHR中心，聚集英国顶级GIC专家和最好的基础设施，以积累GIC患者。我们的工艺目前需要四到六个月的时间，而且价格昂贵，不太可能被广泛采用。在这个项目中，我们将创建患者化身模型，并生成药物筛选数据，这些数据可用于训练机器学习工具，该工具将输入患者的肿瘤特征与以前患者的数据相匹配。通过这种方式，使用人工智能，我们将减少所需的时间和成本。该工具将预测可能的最佳组合疗法，并可作为癌症诊断和治疗的一部分被广泛采用。

In theory, to find an efficient treatment for cancer we have only to identify an oncogenic mutation and target it with a well characterized drug. In practice, however, every cancer is driven by multiple mutations occurring in unique combinations, which generate a complex tumour network in each patient. When oncologists have tried to practice single-target precision medicine, promising initial results fade as the full genetic complexity of the tumour is not captured. Although whole-genome sequencing (WGS) offers the opportunity to identify tumour-associated gene mutations, its present utility in personalised cancer therapy is rather limited. To give WGS an effective clinical significance it is essential to use the genomic information to build models that can match cancer patients to the optimum drug mixtures.My Personal Therapeutics (MPT) use fruit flies (_Drosophila)_ and its arsenal of sophisticated genetic tools to model individual patient's tumours features. We reconstruct tumour genetic complexity identified by WGS, in the intestine of flies. These fly "avatars" will develop the patient's tumour and will die by the same cancer, unless they take the right combination of drugs. Then we use these cancer avatar flies to test thousands of approved compounds to identify the best drug combinations that rescue cancerous avatars from lethality. In this way, we produce a precise picture of the interaction between a growing tumour within an entire animal and the drug combinations tested.Via this method, called Personal Discovery Process (PDP), MPT delivers ultra-personalised drug treatment recommendations for patients with gastro-intestinal cancers (GIC), a hard-to-treat malignancy diagnosed for 180 people every day in the UK. In this project, MPT will partner with the London IVD Co-operative, a UK NIHR centre that conglomerate UK top GIC specialists and the best infrastructure to accrue GIC patients. Our process currently takes four to six months and is expensive, making widespread uptake unlikely. In this project, we will create patient avatar models and produce drug screening data that can be used to train a machine learning tool that will match incoming patient's tumour profile with previous patient's data. In this way, using artificial intelligence, we will achieve a reduction in time taken and cost. This tool will predict the best possible combination therapies and could be widely adopted as part of cancer diagnosis and treatment.