Whole Genome Sequence-guided targeting of colorectal and oesophageal cancers

新药的开发是一项长期和高成本的工作，失败率非常高。在所有主要疾病的适应症中，肿瘤学始终是最具挑战性的。目前的药品成本和失败率是不可持续的。选择正确的目标是造成这些流失率的主要原因。越来越多的人认识到，针对显示基因与潜在疾病状态相关的目标开发一种药物，将使该药物进入市场的可能性增加2倍以上。尽管CRISPR作为一种强大的遗传干扰工具出现，但高通量靶标识别方法一直受到缺乏体外细胞模型的限制，这些细胞模型既能真实地概括临床肿瘤对干扰的反应，又能适应高通量筛选方法。全基因组测序(WGS)的历史成本也限制了高度特色化的细胞模型的大型面板的可用性，从而限制了在颗粒水平上了解癌症遗传驱动因素的能力。患者来源的肿瘤有机化合物是3D高级体外细胞模型。越来越多的人认识到，患者来源的肿瘤有机化合物是一种比癌细胞系更具预测性的体外药物发现模型，同时仍然服从高通量药物筛选模式。使用深具特色的患者来源的肿瘤有机化合物，我们将应用全基因组合成致命扰动方法和专有的数据聚合、分析和可视化平台来识别和优先处理新的合成致命癌症目标。合成致命性利用了这样一个事实，即癌细胞中允许它们不受限制地生长的突变通常使它们依赖于健康细胞不需要的“替代”生物过程。针对这些替代过程将允许有选择地摧毁癌症，同时使健康细胞不受影响。针对DNA损伤修复缺陷的癌症的PARP抑制剂已经证明，这种靶向癌症的策略是有效的。将同时制定将最有希望的目标迅速纳入药物发现方案的路线图。

The development of new medicines is a long term and high cost endeavor with exceptionally high failure rates. Across all major disease indications oncology consistently ranks as the most challenging. Current pharmaceutical costs and failure rates are not sustainable. Selection of the correct target is a major reason for these attrition rates. It is increasingly recognised that developing a medicine against a target that shows genetic association with the underlying disease state increases the likelihood of that medicine reaching the market by over 2-fold. Despite the emergence of CRISPR as a powerful genetic perturbation tool, high throughput target identification approaches have been constrained by the lack of in vitro cellular models that both faithfully recapitulate clinical tumour responses to perturbation and are amenable to high throughput screening approaches. The historical cost of whole genome sequencing ("WGS") has also limited the availability of large panels of highly characterised cellular models and therefore the ability to understand the genetic drivers of cancer at a granular level. Patient-derived tumour organoids are 3D advanced _in vitro_ cellular models. There is a growing appreciation that patient-derived tumour organoids are a more predictive _in vitro_ model for drug discovery than cancer cell lines whilst at the same time remaining amenable to high-throughput drug screening formats. Using deeply characterised patient-derived tumour organoids we will apply genome-wide synthetic lethal perturbation approaches and a proprietary data aggregation, analysis and visualisation platform to identify and prioritize novel synthetic lethal cancer targets. Synthetic lethality takes advantage of the fact that mutations in cancer cells which allow them to grow unrestricted often makes them dependent on "alternative" biological processes which healthy cells do not need. Targeting these alternative processes would allow selective destruction of cancer while leaving healthy cells unaffected. PARP inhibitors, which target cancers with defects in DNA damage repair, have proven this strategy for targeting cancers can work. Roadmaps to rapidly progress the most promising targets into drug discovery programmes will be developed in parallel.