A miniaturized neural network enabled nanoplasmonic spectroscopy platform for label-free cancer detection in biofluids

微型神经网络支持纳米等离子体光谱平台，用于生物流体中的无标记癌症检测

• 批准号: 10658204
• 负责人: Randy Carney
• 金额: $ 62.9万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658204
• 关键词: Address; Algorithms; Benign; Biological Markers; Biopsy; Biosensor; Blinded; Blood; Calibration; Cancer Control; Cancer Detection; Classification; Clinical; Complex; Computer software; Consumption; Data; Detection; Devices; Diabetes Mellitus; Diagnosis; Diagnostic; Diagnostic Sensitivity; Disease; Dryness; Early Diagnosis; Economics; Electromagnetics; Elements; Fingerprint; Foundations; Fourier Transform; Fourier transform infrared spectrometry; Future; Head and Neck Cancer; Head and Neck Surgery; Head and neck structure; Health Status; Heart Diseases; Heterogeneity; Histopathology; Image; Immunohistochemistry; Individual; Interferometry; Label; Libraries; Light; Liquid substance; Machine Learning; Malignant Neoplasms; Mass Spectrum Analysis; Measurement; Measures; Metabolic; Metabolic Diseases; Methods; Modeling; Monitor; Monitoring for Recurrence; Neck Cancer; Neural Network Simulation; Otolaryngology; Outcome; Output; Pathology; Patients; Performance; Plasma; Process; Raman Spectrum Analysis; Rapid diagnostics; Recurrence; Risk; Saliva; Sampling; Signal Transduction; Solid; Specificity; Spectroscopy, Fourier Transform Infrared; Spectrum Analysis; Spottings; Staging; Statistical Data Interpretation; Survival Rate; System; Techniques; Technology; Testing; Time; Translating; Update; Width; Work; absorption; accurate diagnostics; cancer diagnosis; cancer type; circulating biomarkers; clinical diagnosis; cohort; data streams; deep neural network; design; detection limit; detector; effective therapy; head and neck cancer patient; high reward; high risk; imaging modality; improved; improved outcome; infrared spectroscopy; innovation; interdisciplinary approach; learning network; liquid biopsy; machine learning algorithm; metabolomics; miniaturize; multidisciplinary; multiplex detection; nano; nanopattern; nanoplasmonic; neural network; neural network architecture; novel; patient stratification; plasmonics; point of care; portability; rapid testing; routine screening; segregation; sensor; smartphone application; tool; voltage; wearable device

PROJECT SUMMARY/ABSTRACT State of the art methods for the early detection and monitoring of cancer are either invasive, time-consuming, expensive, or frequently inaccurate, which hinders the routine screening of at risk-patients to improve survival rates. The multiplexed detection of oncometabolites circulating in minimally or non-invasive biofluids, such as saliva, blood plasma, or sweat, could provide significant clinical and economic benefits. Metabolites and related circulating biomarkers are structurally unique elements with distinctive absorptive fingerprints in the infrared (IR) portion of the electromagnetic spectrum. Common approaches that provide multiplexed metabolite detection, such as mass spectrometry (MS), Raman spectroscopy, and Fourier transform infrared (FTIR) spectroscopy, are expensive and difficult to miniaturize. On the other hand, inexpensive miniaturized electrochemical techniques lack specificity, sensitivity, ease, and suffer from limited multiplexing. Portable technologies capable of rapid and accurate diagnostics of early/late-stage cancer are not readily available. To address this challenge, our multidisciplinary team proposes an innovative Neural Network Enabled Cancer Spectroscopy (NNECS) liquid biopsy platform based on plasmonic nano-micro electromechanical systems (NMEMS) to diagnose and monitor early/late-stage head neck cancer (HNC). Instead of targeting individual metabolites, we propose to process the entire IR spectrum of saliva, blood plasma, and sweat as a biomarker. Our focus is head and neck cancer (HNC), a highly metabolic disease where stratification of patients according to better diagnostic information would greatly improve outcomes. Our platform combines IR NMEMS sensors to accurately detect IR spectral fingerprints with neural network (NN) frameworks to find the appropriate combinations of spectral bands that will inform the design of highly multiplexed miniaturized biosensor. We will take a novel, interdisciplinary approach within the framework of five key components: (i) collecting and analyzing (FTIR, MS, histopathology/imaging) biofluids (saliva, sweat, blood) from a large number of early/late stage HNC patients and healthy subjects per year; (ii) developing powerful NN architectures and diagnosis tools for segregating early/late-stage HNC samples from controls, considering IR data streams from each individual biofluid as well as their potential combinations; (iii) developing a NNECS platform using arrays of plasmonic NMEMS targeting specific IR bands resolved by ML algorithms; (iv) determining NNECS early/late-stage cancer detection performance in terms of specificity, sensitivity, and accuracy; and (v) elucidating which metabolites drive the changes in the IR absorption of cancer biofluids supported by MS. The expected outcome is a miniaturized, label-free, affordable, and accurate technology able to radically improve the ability to diagnose early- stage HNC as well as the monitoring of recurrent HNC patients. Moving beyond, NNECS can be adapted for the diagnosis and monitoring of a wide range of metabolic conditions, including many types of cancer, diabetes, and heart-diseases.

项目摘要/摘要 早期检测和监测癌症的最新方法是侵入性的，耗时的， 昂贵或经常不准确，这阻碍了AT风险患者的常规筛查以提高生存率 费率。在最小或非侵入性生物流体中循环的oncometabolites的多路复用检测，例如 唾液，血浆或汗水可以提供巨大的临床和经济益处。代谢物及相关 循环生物标志物在结构上是独特的元素，在红外线中具有独特的吸收指纹（IR） 电磁光谱的一部分。提供多重代谢物检测的常见方法， 例如质谱法（MS），拉曼光谱和傅立叶变换红外（FTIR）光谱法， 昂贵且难以小型。另一方面，廉价的微型电化学技术 缺乏特异性，敏感性，轻松和有限的多路复用性。便携式技术能够快速和 早期/晚期癌症的准确诊断不容易获得。 为了应对这一挑战，我们的多学科团队提出了创新的神经网络启用癌症 基于等离子体纳米 - 巨化机电系统的光谱（NNECS）液体活检平台 （NMEMS）诊断和监测早期/晚期头颈癌（HNC）。而不是针对个人 代谢物，我们建议处理唾液，血浆和汗水作为生物标志物的整个红外光谱。 我们的重点是头颈癌（HNC），这是一种高度代谢性疾病，患者的分层 更好的诊断信息将大大改善结果。我们的平台将IR NMEMS传感器结合到 准确地检测使用神经网络（NN）框架的红外光谱指纹以找到合适的 光谱带的组合将为高度多重的微型生物传感器的设计提供信息。 我们将在五个关键组成部分的框架内采用一种新颖的跨学科方法：（i）收集和 分析（FTIR，MS，组织病理学/成像）生物流体（唾液，汗水，血液）来自大量早期/晚期 HNC患者和健康受试者每年； （ii）开发强大的NN架构和诊断工具 用于将早期/晚期HNC样品隔离到对照组中，考虑每个人的IR数据流 生物流体及其潜在组合； （iii）使用等离子阵列开发NNECS平台 针对由ML算法解决的特定IR频段的NMEM； （iv）确定NNEC早期/晚期癌症 从特异性，灵敏度和准确性方面的检测性能； （v）阐明哪种代谢物 推动MS支持的癌症生物流体吸收的IR吸收变化。预期的结果是 微型，无标签，负担得起且准确的技术能够从根本上提高诊断早期诊断的能力。 HNC期以及复发性HNC患者的监测。超越了NNEC可以适应 诊断和监测广泛的代谢疾病，包括多种类型的癌症，糖尿病和 心脏疾病。