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

项目摘要/摘要 用于癌症早期检测和监测的最新技术方法要么是侵入性的，耗时的， 费用昂贵或经常不准确，这阻碍了对高危患者的常规筛查，以提高存活率 费率。在微创或非侵入性生物体液中循环的肿瘤代谢物的多重检测，例如 唾液、血浆或汗液可以提供显著的临床和经济效益。代谢物及相关物质 循环生物标志物是结构独特的元素，在红外(IR)上具有独特的吸收指纹 电磁波谱的一部分。提供多重代谢物检测的常见方法， 例如质谱仪(MS)、拉曼光谱和傅立叶变换红外(FTIR)光谱， 都很昂贵，而且很难小型化。另一方面，廉价的小型化电化学技术 缺乏特异性、敏感性、易感性，并且受到有限的多路传输的困扰。便携技术能够快速和 对早期/晚期癌症的准确诊断并不容易。 为了应对这一挑战，我们的多学科团队提出了一种创新的神经网络使能癌症 基于等离子体纳米机电系统的光谱学(NNECS)液体活检台 (NMEMS)用于诊断和监测早/晚期头颈癌(HNC)。而不是针对个人 对于代谢物，我们建议将唾液、血浆和汗液的整个红外光谱作为生物标记物进行处理。 我们的重点是头颈癌(HNC)，这是一种高度代谢性疾病，根据患者的分层 更好的诊断信息将极大地改善结果。我们的平台结合了红外NMEMS传感器以 使用神经网络(NN)框架准确检测红外光谱指纹，以找到合适的 光谱波段的组合将为设计高度多元化的小型化生物传感器提供信息。 我们将在五个主要组成部分的框架内采取新颖的跨学科方法：(I)收集和 分析(FTIR、MS、组织病理学/成像)大量早期/晚期患者的生物体液(唾液、汗液、血液) 每年对HNC患者和健康受试者进行分期；(Ii)开发强大的神经网络体系结构和诊断工具 用于将早期/后期HNC样本从对照中分离出来，考虑来自每个个体的IR数据流 生物流体及其可能的组合；(Iii)利用等离子体阵列开发NNECS平台 以ML算法解析的特定红外波段为靶点的NMEMS；(Iv)确定NNECS早期/晚期癌症 在特异性、敏感性和准确性方面的检测性能；以及(V)阐明哪些代谢物 在MS的支持下，推动癌症生物液红外吸收的变化。 小型化、无标签、经济实惠和准确的技术能够从根本上提高早期诊断的能力- HNC分期以及对复发HNC患者的监测。在此基础上，NNECS可以适应 诊断和监测广泛的代谢状况，包括多种癌症、糖尿病和 心脏病。

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.