Nanosensor Array Platform to Capture Whole Disease Fingerprints

捕获整个疾病指纹的纳米传感器阵列平台

• 批准号: 10660707
• 负责人: Daniel Alan Heller
• 金额: $ 69.66万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10660707
• 关键词: Acceleration; Adsorption; Binding; Binding Proteins; Biological; Biological Markers; Blinded; CA-125 Antigen; Cancer Patient; Carbon Nanotubes; Chemicals; Clinical; Collection; Color; DNA; DNA Library; DNA Sequence; Data Set; Detection; Diagnosis; Diagnostic; Disease; Disease remission; Early Diagnosis; Elements; Event; Excision; Exhibits; FDA approved; Fingerprint; Libraries; Machine Learning; Malignant neoplasm of ovary; Measurement; Modeling; Molecular; Operative Surgical Procedures; Optics; Outcome; Patients; Pattern; Perception; Pharmaceutical Preparations; Principal Component Analysis; Process; Property; Protein Analysis; Proteins; Proteomics; Recurrence; Recurrent Malignant Neoplasm; Salts; Sampling; Screening for Ovarian Cancer; Serous; Serum; Source; Specificity; Survival Rate; Testing; Training; Validation; Variant; Work; biomarker discovery; biomarker identification; biomarker validation; burden of illness; cancer recurrence; clinically relevant; design; disease classification; experimental study; improved; innovation; large datasets; machine learning algorithm; machine learning classification; machine learning model; molecular marker; nanosensors; novel marker; predictive modeling; prevent; protein biomarkers; response; screening; sensor; technology platform

SUMMARY This project endeavors to build a nanosensor array platform technology to detect whole disease fingerprints from patient biofluids to facilitate diagnosis, screening, and biomarker discovery efforts. Serum biomarker measure- ments are widely used as diagnostic indicators, but many markers are not sufficient for assessments of disease state. Major factors limiting diagnosis and screening using most biomarkers include their low specificity for dis- eases and the overall dearth of established molecular markers. Innovative approaches are needed to identify new biomarkers and/or improve screening and diagnostic efforts in the absence of validated biomarkers. We believe that the differentiation of diseased from normal biofluids may be achieved by the detection of a “disease fingerprint” through the collection of large data sets of molecular binding interactions to a diverse set of moder- ately-selective sensors, which are used to train machine learning algorithms. We will build a sensor array com- prising organic color centers (OCCs, covalently-modified carbon nanotubes) to transduce subtle differences in physicochemical properties of molecules in biofluids. With sufficient diversity, the sensors can differentiate bi- ofluids by disease status with the aid of machine learning processes. In preliminary experiments, we found that a library of OCC-DNA nanosensors exhibited sensitive and differentiated spectral variation to probe an ensemble of molecular binding events. Via machine learning algorithms, we built a prediction model of nanosensor re- sponses that reliably identified high-grade serous ovarian cancer (HGSC) substantially better than the estab- lished, FDA-approved biomarker, CA125, using an initial set of 264 patient serum samples (Nat Biomed Eng, 2022). Despite advances in the understanding and management of HGSC, survival is currently poor when diag- nosed at later stages, and detection is uncommon at early stages. Surgery is the first-line treatment, and cancer recurs in 70% of patients in remission. Secondary surgery can prolong survival but only if performed early enough to enable complete resection. Improved detection of early-recurrent and early-stage HGSC would therefore markedly increase survival rates. We plan to develop a robust diagnostic sensor platform to improve early de- tection of ovarian cancer and recurrence, and to accelerate biomarker discovery processes. Additionally, quan- titative analysis of proteins bound to the sensors can determine the unique pattern of protein adsorption respon- sible for the disease-specific spectral responses, thereby potentially facilitating biomarker discovery. We propose to investigate: 1) the diversity of molecular sensitivities of OCC-DNA nanosensor elements required to differen- tiate patient samples, 2) machine learning-based classification of disease, focusing on early-recurrence and early-stage HGSC, 3) the molecular mechanism of the sensor response, and 4) the potential of the array to facilitate identification of novel biomarkers. Successful completion of this work will result in a validated platform to enable concomitant identification of disease and acceleration of biomarker discovery processes in HGSC, with applicability to many potential indications.

概括 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，以促进诊断、筛查和生物标志物发现工作。 标记物被广泛用作诊断指标，但许多标记物不足以评估疾病 限制使用大多数生物标志物进行诊断和筛查的主要因素包括其诊断特异性较低。 需要创新的方法来识别分子标记的简化和整体深度。 新的生物标志物和/或在缺乏经过验证的生物标志物的情况下改进筛查和诊断工作。 相信可以通过检测“疾病”来区分患病的生物体液和正常的生物体液 指纹”通过收集分子结合相互作用的大数据集到不同的现代 我们将构建一个传感器阵列组件，用于训练机器学习算法。 重视有机色心（OCC，共价修饰的碳纳米管）来传递细微的差异 凭借足够的多样性，传感器可以区分生物流体中分子的物理化学特性。 在机器学习过程的帮助下，我们发现根据疾病状态来确定液体。 OCC-DNA 纳米传感器库，表现出灵敏且差异化的光谱变化，可探测整体 通过机器学习算法，我们建立了纳米传感器重新预测模型。 可靠地识别高级别浆液性卵巢癌（HGSC）的反应远远好于现有的 使用最初的 264 份患者血清样本（Nat Biomed Eng， 2022）尽管对 HGSC 的理解和管理取得了进展，但目前诊断时的生存率很差 晚期才发现，早期检测并不常见，手术是癌症的一线治疗方法。 70% 的缓解期患者会复发，二次手术可以延长生存期，但前提是尽早进行。 因此，能够改进早期复发和早期 HGSC 的检测。 我们计划开发一个强大的诊断传感器平台来改善早期诊断。 保护卵巢癌和复发，并加速生物标志物的发现过程。 对与传感器结合的蛋白质进行滴定分析可以确定蛋白质吸附响应的独特模式 我们建议，可以针对疾病特异性的光谱响应，从而潜在地促进生物标志物的发现。 研究：1) OCC-DNA 纳米传感器元件分子敏感性的多样性，需要区分 患者样本，2）基于机器学习的疾病分类，重点关注早期复发和 早期 HGSC，3）传感器响应的分子机制，以及 4）阵列的潜力 促进新型生物标志物的识别，成功完成这项工作将产生一个经过验证的平台。 使 HGSC 能够同时识别疾病并加速生物标志物发现过程， 适用于许多潜在的适应症。