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: 10684240
• 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 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10684240
• 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; 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; Pancreas; Pancreatic Ductal Adenocarcinoma; 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; biomarker identification; 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; technology platform; 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.

项目总结与摘要