Machine Perception Nanosensor Array Platform to Capture Whole Disease Fingerprints of Early Stage Pancreatic Cancer

机器感知纳米传感器阵列平台可捕获早期胰腺癌的整个疾病指纹

• 批准号: 10507496
• 负责人: Mijin Kim
• 金额: $ 9.94万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10507496
• 关键词: Acceleration; Algorithms; Award; Binding; Biological Markers; CA-19-9 Antigen; Cancer Detection; Cancer Etiology; Carbon Nanotubes; Cessation of life; Classification; Clinical; Collection; Color; DNA; DNA Library; Data; Data Set; Dependence; Detection; Diagnosis; Diagnostic; Disease; Disease Progression; Distant; Drops; Early Diagnosis; Early identification; Event; Fingerprint; Immunoassay; Individual; Libraries; Machine Learning; Malignant Neoplasms; Malignant neoplasm of pancreas; Measurement; Mentors; Modeling; Molecular; Neoplasm Metastasis; Outcome; Outcome Study; Pancreas; Pancreatic Ductal Adenocarcinoma; Parents; Patients; Perception; Principal Component Analysis; Process; Property; Proteins; Proteomics; Recurrence; Sampling; Sensitivity and Specificity; Serum; Serum Markers; Site; Source; Specificity; Survival Rate; Technology; Testing; Training; United States; Variant; Work; artificial neural network; biomarker discovery; experimental study; high risk; improved; improved outcome; innovation; large datasets; machine learning algorithm; machine learning model; model development; molecular marker; mortality; nanosensors; pancreatic cancer model; potential biomarker; process optimization; prognostic indicator; prospective; protein biomarkers; random forest; response; screening; sensor; sensor technology; support vector machine; temporal measurement; validation studies

PROJECT SUMMARY AND ABSTRACT This project endeavors to build a nanosensor array platform technology to detect whole disease fingerprints from patient biofluids to facilitate diagnosis and biomarker discovery efforts in pancreatic cancer. Pancreatic cancer is currently the fourth leading cause of cancer-related mortality in the US. The ability to diagnose pancreatic cancer at early stages would allow many patients to be actively treated, thereby greatly improving their outcomes. Serum biomarker measurements have been widely used as diagnostic/prognostic indicators, but many markers are not sufficient for specific assessments of disease states. Major factors limiting precise diagnosis us- ing these biomarkers include their low sensitivity at high specificity for diseases and the overall dearth of estab- lished molecular markers. Therefore, innovative approaches to improve disease-state specificity/sensitivity and biomarker discovery efforts are needed to achieve accurate identification of many conditions. I believe that the differentiation of disease from normal biofluids may be achieved by the detection of a “disease fingerprint” by collecting large data sets of molecular binding interactions to a diverse set of moderately selective sensors. I will build a sensor array comprising organic color centers (covalently-modified carbon nanotubes) stabilized with DNA to transduce subtle differences in physicochemical properties of molecules in biofluids. With sufficient diversity, the sensors can differentiate biofluids by disease status with the aid of machine learning pro- cesses. This platform will also be used to facilitate biomarker discovery efforts. In preliminary data, I discovered that the responses collected from hundreds of patient samples and interpreted by machine learning algorithms can beat established serum biomarker measurements. I plan to leverage this technology to develop a robust diagnostic sensor platform to acquire disease fingerprints of pancreatic cancer in patients biofluids to significantly increase sensitivity and specificity over single biomarkers and to accelerate biomarker discovery processes. I propose to investigate: 1) the potential of this technology for the early detection of pancreatic cancer, 2) the molecular mechanism of the response, and 3) the potential for this platform to enable the discovery of new biomarkers. In the 2-year mentored (K99) period of the award, I aim to develop a machine perception nanosen- sor technology with the focusing problem of pancreatic cancer detection and establish the selection rules in the sensor array construction and the workflow of machine learning-based model development. For the 3-year in- dependence (R00) period, I aim to systematically investigate how to render the machine learning models trans- parent to understand the mechanism of high prediction accuracy and discover effective biomarker combinations for clinical validation studies. Successful completion of the proposed work will result in a validated platform to enable concomitant identification of early disease states and acceleration of protein biomarker discovery pro- cesses in pancreatic cancer.

项目摘要和摘要 该项目努力建立纳米传感器阵列平台技术，以检测 患者生物流体促进胰腺癌中的诊断和生物标志物发现工作。胰腺癌 目前是美国与癌症相关死亡率的第四个主要原因。诊断胰腺的能力 早期癌症将使许多患者受到积极治疗，从而大大改善其结果。 血清生物标志物测量已被广泛用作诊断/预后指标，但是许多标记 不足以对疾病状态进行特定评估。限制精度诊断的主要因素美国 - 这些生物标志物包括它们对疾病的高度敏感性以及疾病的总体死亡 租用的分子标记物。因此，提高疾病状态的特异性/敏感性和 需要生物标志物发现工作以实现许多条件的准确识别。我相信 通过检测到正常生物流体的疾病与正常生物流体的分化可以通过检测到“疾病指纹” 收集大量数据集的分子结合相互作用与中度选择性传感器的潜水员集合。 我将构建一个传感器阵列，以稳定有机颜色中心（共价修饰的碳纳米管） 足够 多样性，传感器可以通过疾病状况来区分生物流体，从而在机器学习方面进行 塞斯。该平台还将用于促进生物标志物发现工作。在初步数据中，我发现了 从数百个患者样本中收集的反应，并通过机器学习算法进行解释 可以击败已建立的血清生物标志物测量。我计划利用这项技术开发出强大的 诊断传感器平台以获取患者生物流体的胰腺癌疾病指纹 提高对单个生物标志物的敏感性和特异性，并加速生物标志物发现过程。我 调查的建议：1）这项技术对胰腺癌的早期检测的潜力，2） 响应的分子机制，以及3）该平台有可能使新的发现 生物标志物。在奖励的2年指导期（K99）期间，我的目标是开发机器感知 SOR技术具有胰腺癌检测的重点问题，并在 传感器阵列构建和基于机器学习的模型开发的工作流程。三年 依赖性（R00）时期，我旨在系统地研究如何使机器学习模型变形 父母了解高预测精度的机制并发现有效的生物标志物组合 用于临床验证研究。成功完成拟议的工作将导致经过验证的平台 能够同时鉴定早期疾病状态并加速蛋白质生物标志物发现 胰腺癌中的塞斯。