Combining Machine Learning and Nanofluidic Technology for The Multiplexed Diagnosis of Pancreatic Adenocarcinoma

胰腺导管腺癌（PDAC）是胰腺癌患者癌症相关死亡的第三大原因。 美国的5年生存率为9%。诊断和分期仍然依赖于 内窥镜活检和成像，因此大多数患者在晚期诊断 阶段足够敏感和特异的早期疾病筛查试验仍然难以捉摸。 此外，虽然治疗性手术是疾病局限于以下疾病的患者的一种选择， 胰腺，区分那些不太可能从手术中受益的转移患者， 由于影像学无法检测到隐匿性转移，因此仍然具有挑战性。解决这些 为了克服这些挑战，已经开发了几种基于血液的液体活检生物标志物，但显示出低的 检测早期疾病的灵敏度。我们最近发现循环肿瘤 衍生的细胞外囊泡（EV）可以从血液中分离， 诊断早期胰腺癌和分期疾病。这些发现表明， 通过开发胰腺癌非侵入性诊断改善患者预后 癌然而，如已经充分记载的，EV在其性能方面是高度异质的。 蛋白质表面标记物及其核酸和蛋白质货物的表达，以及来源于 来自肿瘤微环境（TME）中的多种细胞类型（例如肿瘤细胞、肿瘤微环境（TME）中的肿瘤细胞）， 相关巨噬细胞）。这项提案的最终目标是解决一个根本问题， 通过进一步开发我们的电动汽车新方法， 使用磁性纳米孔的亚群隔离，其结合了纳米尺度的益处， 以足够快的流速（比典型的纳米流体方法快106倍）进行分选， 对于临床诊断是实用的。在这个R33中，我们将这种方法发展成多路复用EV 允许多个独特EV亚群的测定-基于表面标志物表达- 进行分离并分析它们的RNA货物。基于我们先前的工作， 分析单个EV亚群的价值，以及多分析物与 单分析物测试，我们将开发一种基于多分析物EV的检测方法， 结合了来自TME的多种循环EV分离株的肿瘤相关EV RNA， 作为循环无细胞DNA（ccfDNA）浓度，基于循环肿瘤DNA的KRAS 突变检测和使用机器学习的CA 19 -9。

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death in the United States with an overall 5-year survival of 9%. Diagnosis and staging continue to rely on endoscopic biopsy and imaging, and as such most patients are diagnosed at an advanced stage. Sufficiently sensitive and specific screening tests for early disease remain elusive. Moreover, while curative-intent surgery is an option for patients whose disease is confined to the pancreas, distinguishing patients with metastases who are unlikely to benefit from surgery, remains challenging due to occult metastases not detectable by imaging. To address these challenges, several blood-based liquid biopsy biomarkers have been developed but show low sensitivity for detection of early-stage disease. We have recently shown that circulating tumor derived extracellular vesicles(EVs) can be isolated from blood and their RNA cargo used to diagnose early pancreatic cancer and stage disease. These findings suggest an opportunity to improve patient outcomes through development of a non-invasive diagnostic for pancreatic cancer. However, as has been well documented, EVs are highly heterogeneous in their expression of protein surface markers and their nucleic acid and protein cargo, and originate from multiple cell types in the tumor micro environment (TME) (e.g. tumor cells, tumor associated macrophages). The ultimate goal of this proposal is to address a fundamental technological unmet need in EV diagnostics, by further developing our new approach to EV subpopulation isolation using magnetic nanopores, which combines the benefits of nano-scale sorting with sufficiently fast flow rates (106x faster than typical nanofluidic approaches) to be practical for clinical diagnostics. In this R33, we develop this approach into a multiplexed EV assay that will allow multiple unique EV sub-populations - based on surface marker expression- to be isolated and their RNA cargo profiled. Building on our prior work that demonstrated the value of analyzing single EV-subpopulations, and improved sensitivity of a multi-analyte vs single analyte test, we will develop a multi-analyte EV-based assay that algorithmically combines tumor associated EV RNA from multiple circulating EV isolates from the TME, as well as Circulating cell-free DNA (ccfDNA) concentration, circulating tumor DNA-based KRAS mutation detection, and CA19-9 using machine learning.