Innovative approach to cancer detection and treatment monitoring

描述（由申请人提供）：现在已经确定无细胞肿瘤DNA释放到癌症患者的血液中。从理论上讲，这为开发用于各种临床应用的生物标志物提供了潜在的强大机会，包括癌症检测，对治疗反应的监测和复发性疾病的检测。然而，迄今为止，这一潜力大部分仍未实现。这是由几个因素造成的。首先，在血浆/血清中发现低浓度（ng/ml）的循环无细胞DNA，并且主要以小片段形式存在。这在历史上使得分离足够数量的DNA用于测试变得困难。此外，大多数循环的无细胞DNA来自正常细胞， 肿瘤细胞，从而呈现出“大海捞针”的问题。最后，在很大程度上缺乏能够检测罕见的肿瘤相关DNA改变的广泛可用和强大的分析工具。此外，可用的那些工具需要在血浆中检测特定肿瘤相关突变的先验知识，因此不适合从头癌症检测测试。在本申请中，我们将使用新数据和创新技术来克服这些问题，并确定循环无细胞肿瘤DNA作为食管腺癌（EAC）患者检测和治疗监测生物标志物的潜力。具体地，我们将使用基于多重PCR的预扩增技术（AmpliSeq（tm））来产生下一代测序文库，所述测序文库靶向来自发现在EAC中频繁突变的20个基因的编码区的> 66 Kb的DNA。这项技术是专门设计的，与纳克输入的高度片段化的DNA兼容，我们已经成功地使用它来测序肿瘤样本和血浆中的无细胞DNA中的20个基因。在这些样品中使用Ion Torrent半导体测序的平均每个碱基读取深度> 2000 x，并且可以容易地进一步增加。因此，我们相信，我们可以检测到任何碱基的突变在靶基因的检测灵敏度为0.5%肿瘤DNA fractions. Complementing这种新的测序方法，我们将使用另一种新技术，液滴数字PCR（ddPCR），定量肿瘤相关的突变在血浆中，甚至更低的灵敏度（<0.001%）。在具体目标1中，这些技术的结合将使我们能够确定下一代基于深度测序的癌症检测方法的可行性。接下来，我们将使用这些相同的技术来解决EAC患者管理中的另一个重要临床问题：动态监测对新辅助治疗的反应。高达40%的患者对新辅助治疗没有反应，因此暴露于相关的毒性和成本而没有益处。此外，潜在的治愈性手术治疗在这些患者中被延迟，其中一些患者进展到手术不再是一种选择的程度。在特定目标2中，我们将使用ddPCR来确定循环肿瘤DNA载量是否可用于快速和动态地识别对新辅助治疗的应答或耐药。

DESCRIPTION (provided by applicant): It is now well established that cell-free tumor DNA is released into the blood of cancer patients. In theory, this presents a potentially powerful opportunity to develop biomarkers for a variety of clinical applications including cancer detection, monitoring of response to therapy and detection of recurrent disease. To date however, this potential remains mostly unrealized. This is due to several factors. First, circulating, cell-free DNA is found at low concentrations (ng/ml) in plasma/serum and is mostly present as small fragments. This has historically made isolation of adequate DNA quantities for testing difficult. In addition, the majority of circulating, cell-free DNA is from normal cells not tumor cells, thus presenting a "needle in a haystack" problem. Finally, widely available and robust analytical tools capable of detecting rare, tumor-associated DNA alterations have for the most part been lacking. Furthermore, those tools that are available require a priori knowledge of the specific tumor-associated mutations to be detected in the plasma and therefore are not suitable for de novo cancer detection tests. In this application, we will use novel data and innovative technologies in order to overcome these issues and to determine the potential of circulating, cell-free tumor DNA as a biomarker for detection and treatment monitoring in patients with esophageal adenocarcinoma (EAC). Specifically, we will use a multiplex PCR- based pre-amplification technology (AmpliSeq(tm)) to generate next-generation sequencing libraries targeting >66Kb of DNA from the coding regions of 20 genes found to be frequently mutated in EAC. This technology is specifically designed to be compatible with nanogram inputs of highly fragmented DNA and we have successfully used it to sequence the 20 genes in tumor samples and in cell-free DNA from plasma. Average per-base read depth using Ion Torrent semi-conductor sequencing in these samples is >2000x and can easily be increased further. Thus we believe that we can detect mutations at any base in the target genes with detection sensitivity of 0.5% tumor DNA fraction or less. Complementing this novel sequencing approach, we will use another new technology, droplet digital PCR (ddPCR), to quantify tumor associated mutations in plasma with even lower sensitivity (<0.001%). In Specific Aim 1, these technologies combined will allow us to determine the feasibility of a next-generation, deep sequencing-based approach to cancer detection. Next, we will use these same technologies to address another important clinical issue in the management of patients with EAC: dynamic monitoring of response to neoadjuvant therapy. Up to 40% of patients do not respond to neoadjuvant therapy and are thus exposed to the associated toxicity and cost without benefit. Furthermore, potentially curative surgical therapy is delayed in these patients, some of whom progress to the point where surgery is no longer an option. In Specific Aim 2, we will use ddPCR to determine whether circulating tumor DNA load can be used to rapidly and dynamically identify response or resistance to neoadjuvant therapy.