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

项目总结和摘要 本项目致力于建立一个纳米传感器阵列平台技术，以检测整个疾病指纹， 患者生物流体有助于胰腺癌的诊断和生物标志物发现工作。胰腺癌 目前是美国癌症相关死亡的第四大原因。诊断胰腺癌的能力 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结果。 血清生物标志物测量已被广泛用作诊断/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是- 使用这些生物标志物包括它们对疾病的高特异性的低敏感性和总体缺乏建立。 分子标记。因此，改善疾病状态特异性/敏感性和 需要进行生物标记物发现的努力以实现许多病症的准确鉴定。我相信 疾病与正常生物流体的区分可以通过检测“疾病指纹”来实现， 收集分子结合相互作用的大数据集到不同的中等选择性传感器集合。 我将建立一个传感器阵列，包括有机色心（共价修饰的碳纳米管）稳定 用DNA来解释生物流体中分子物理化学性质的细微差异。以足够 多样性，传感器可以在机器学习的帮助下根据疾病状态区分生物流体， cesses。该平台还将用于促进生物标志物的发现工作。在初步数据中，我发现 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， 可以胜过已建立的血清生物标志物测量。我计划利用这项技术开发一个强大的 诊断传感器平台，以获取胰腺癌患者生物流体中的疾病指纹， 提高了对单一生物标志物的灵敏度和特异性，并加速了生物标志物的发现过程。我 我建议调查：1）这项技术在胰腺癌早期检测方面的潜力，2） 反应的分子机制，以及3）该平台能够发现新的 生物标志物。在为期2年的指导（K99）期间的奖项，我的目标是开发一个机器感知nanosen- 分类技术与胰腺癌检测的聚焦问题相结合，建立了胰腺癌检测中的选择规则， 传感器阵列构建和基于机器学习的模型开发工作流程。三年来， 依赖（R 00）期间，我的目标是系统地研究如何使机器学习模型的跨- 父母了解高预测准确性的机制，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， 胰腺癌的治疗

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.