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.

项目概要和摘要 该项目致力于构建纳米传感器阵列平台技术来检测整个疾病的指纹 患者生物体液促进胰腺癌的诊断和生物标志物发现工作。 目前是美国癌症相关死亡的第四大原因。 早期癌症将使许多患者得到积极治疗，从而大大改善他们的治疗结果。 血清生物标志物测量已广泛用作诊断/预后指标，但许多标志物 不足以对疾病状态进行具体评估。限制精确诊断的主要因素。 这些生物标志物包括它们对疾病的低敏感性和高特异性以及建立的总体深度。 因此，提出了提高疾病状态特异性/敏感性的创新方法。 我相信，需要通过生物标志物的发现努力来实现对许多情况的准确识别。 通过检测“疾病指纹”可以将疾病与正常生物体液区分开来 收集分子结合相互作用的大数据集到一组不同的中等选择性传感器。 我将构建一个包含稳定有机色心（共价改性碳纳米管）的传感器阵列 与 DNA 一起转导生物流体中分子物理化学性质的细微差异。 多样性，传感器可以在机器学习亲的帮助下根据疾病状态区分生物体液 在初步数据中，我发现该平台还将用于促进生物标志物的发现工作。 从数百个患者样本中收集并由机器学习算法解释的反应 可以击败已建立的血清生物标志物，我计划利用这项技术开发一种强大的技术。 诊断传感器平台获取患者体液中胰腺癌的疾病指纹，以显着 提高单一生物标志物的敏感性和特异性，并加速生物标志物的发现过程。 提议研究：1）该技术在早期检测胰腺癌方面的潜力，2） 反应的分子机制，以及 3）该平台有可能发现新的 在该奖项的 2 年指导（K99）期间，我的目标是开发一种机器感知纳米传感器。 结合胰腺癌检测的聚焦问题，建立筛选规则 传感器阵列构建和基于机器学习的模型开发工作流程 3 年。 依赖（R00）期间，我的目标是系统地研究如何使机器学习模型跨 家长了解高预测准确度的机制并发现有效的生物标志物组合 临床验证研究的成功完成将产生一个经过验证的平台 能够同时识别早期疾病状态并加速蛋白质生物标志物的发现 胰腺癌的过程。