cliniCIN: Targeting the roots of chromosomal instability in cancer

目标：通过使用正在进行的突变过程的特征作为药物反应的生物标志物，克服具有高染色体不稳定性的癌症的基因组复杂性。背景：染色体不稳定性是大多数致死性癌症的标志。高度不稳定的肿瘤几乎没有生物标志物来指导治疗决策，而且几十年来患者的生存率没有提高。我的团队开创了使用全基因组DNA拷贝数模式来剖析染色体不稳定性的方法，即所谓的CIN特征，这是不同类型的不稳定性和潜在突变过程的特征。在初步研究中，我们发现CIN特征可以预测药物反应。特征分析侧重于染色体不稳定性的原因，而不是结果，因此代表了与以前的个性化医疗方法的根本背离。方法：在这里，我正在构建我已经开发的基因组和计算技术，以设计用于治疗具有高染色体不稳定性的癌症的生物标志物。我提出了一种新的单细胞DNA测序方法，从单个细胞的独特事件中识别正在进行的突变过程。这种药物基因组学测试是一项重大进步，因为它可以将正在进行的、对癌症发展起积极作用的突变过程与已经灭绝的、在基因组中留下标记但不再活跃的突变过程分离开来。我将在五种高度不稳定的癌症（食管癌、肺癌、胰腺癌、卵巢癌和三阴性乳腺癌）的综合实验计划中系统地确定药物反应的稳健和预测性生物标志物，并测量接受新辅助治疗的患者肿瘤微环境中CIN的异质性。影响：这个开创性的项目将对具有不同类型染色体不稳定性的癌症如何对药物反应以及如何利用基因组复杂性为未满足临床需求的癌症患者带来益处产生根本性的见解。

GOAL: Overcoming the genomic complexity of cancers with high chromosomal instability by using signatures of ongoing mutational processes as biomarkers for drug response.BACKGROUND: Chromosomal instability is a hallmark of most lethal cancers. Highly unstable tumours have few biomarkers to guide treatment decisions and patient survival has not improved for decades. My team pioneered approaches to dissect chromosomal instability using genome-wide DNA copy number patterns, so called CIN signatures, which are characteristic for different types of instability and underlying mutational processes. In pilot studies we found that CIN signatures predict drug response. Signature analysis focuses on the cause, rather than the consequence of chromosomal instability, and thus represents a radical departure from previous personalised medicine approaches. APPROACH: Here, I am building on genomic and computational technologies I have developed to design biomarkers for treating cancers with high chromosomal instability. I propose a novel single cell DNA sequencing approach to identify ongoing mutational processes from unique events in individual cells. This pharmacogenomic test is a major advance, because it allows to separate ongoing mutational processes, which are actively contributing to cancer development, from extinct mutational processes, which left marks in the genome but are no longer active. I will systematically identify robust and predictive biomarkers of drug response in a comprehensive experimental plan across five highly unstable cancers (esophageal, lung, pancreatic, ovarian and triple negative breast cancer) and measure the heterogeneity of CIN across tumour microenvironments in patients undergoing neo-adjuvant treatment. IMPACT: This pioneering project will result in fundamental insights into how cancers with different types of chromosomal instability react to drugs and how to exploit genomic complexity for patient benefit in cancers of unmet clinical need.