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

结合机器学习和纳流体技术进行胰腺癌的多重诊断

• 批准号: 10613226
• 负责人: Erica Carpenter
• 金额: $ 38.83万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10613226
• 关键词: Address; Algorithms; Benchmarking; Benign; Biological Assay; Biological Markers; Blinded; Blood; Blood specimen; CA-19-9 Antigen; Cancer Etiology; Cell Line; Cessation of life; Classification; Clinical; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Progression; Distant Metastasis; Early Diagnosis; Elements; Endoscopic Biopsy; Endoscopy; Environment; Evaluation; Goals; Hour; Image; Individual; KRAS2 gene; Lesion; Localized Disease; Machine Learning; Magnetic Resonance Imaging; Magnetism; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of pancreas; Membrane Proteins; Messenger RNA; Methods; MicroRNAs; Modeling; Molecular Profiling; Monitor; Mutation Detection; Neoplasm Metastasis; Nucleic Acids; Operative Surgical Procedures; Pancreas; Pancreatic Adenocarcinoma; Pancreatic Diseases; Pancreatic Ductal Adenocarcinoma; Pancreatitis; Patient-Focused Outcomes; Patients; Performance; Physicians; Plasma; Positron-Emission Tomography; Proteins; RNA; RNA Probes; Resectable; Role; Sampling; Sorting; Staging; Stream; Surface; Technology; Testing; Training; Tumor-Derived; Tumor-associated macrophages; United States; Validation; Vesicle; Work; X-Ray Computed Tomography; cell free DNA; cell type; clinical diagnostics; cohort; design; detection limit; detection sensitivity; diagnostic signature; diagnostic value; disorder control; drug efficacy; efficacious treatment; extracellular vesicles; improved; innovation; instrumentation; liquid biopsy; machine learning classification; machine learning classifier; manufacturability; microfluidic technology; multimodality; nanofluidic; nanoparticle; nanopore; nanoscale; neoplastic cell; next generation; noninvasive diagnosis; novel strategies; operation; physical property; predictive panel; prognostic signature; protein expression; screening; standard of care; treatment response; tumor; tumor DNA

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.

胰腺导管腺癌(PDAC)是#年癌症相关死亡的第三大原因。 美国的总5年存活率为9%。诊断和分期继续依赖于 在内窥镜活检和成像上，因此大多数患者被诊断为晚期 舞台。对早期疾病的足够敏感和具体的筛查测试仍然难以捉摸。 此外，尽管治疗意向手术是疾病仅限于 胰腺，区分那些不太可能从手术中受益的转移患者， 由于无法通过成像检测到隐匿性转移，因此仍然具有挑战性。要解决这些问题 挑战，已经开发了几种基于血液的液体活组织检查生物标志物，但显示 检测早期疾病的敏感性。我们最近发现循环中的肿瘤 衍生的细胞外小泡(EV)可以从血液中分离出来，它们的RNA货物用于 诊断早期胰腺癌和分期疾病。这些发现表明有机会 通过开发胰腺的非侵入性诊断改善患者的预后 癌症。然而，正如已经很好地记录的那样，电动汽车在其 蛋白质表面标志物及其核酸和蛋白质的表达，以及起源 来自肿瘤微环境(TME)中的多种细胞类型(例如，肿瘤细胞、肿瘤 相关巨噬细胞)。这项提议的最终目标是解决一个根本的问题 通过进一步开发电动汽车的新方法，在电动汽车诊断方面的技术需求尚未得到满足 使用磁性纳米孔隔离亚种群，它结合了纳米级的优点 以足够快的流速(比典型的纳米流体方法快106倍)进行分选 适用于临床诊断学。在这款R33中，我们将这种方法发展成一款多路电动汽车 允许多个独特的EV亚群的检测-基于表面标记表达- 被分离出来并对他们的RNA货物进行分析。在我们先前工作的基础上，我们演示了 分析单个EV亚群的价值，以及提高多分析物与 单一分析物测试，我们将开发一种基于多分析物EV的检测算法 也结合来自TME的多个循环EV分离株的肿瘤相关EV RNA 作为循环无细胞DNA(CcfDNA)浓度，循环肿瘤DNA为基础的KRAS 突变检测，以及使用机器学习的CA19-9。