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.

总结 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，以促进诊断、筛选和生物标记物发现工作。血清生物标志物测量- 部分被广泛用作诊断指标，但许多标记物不足以评估疾病 状态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它们对疾病的低特异性。 病例和已建立的分子标记的总体缺乏。需要采取创新办法， 新的生物标志物和/或在缺乏经验证的生物标志物的情况下改善筛查和诊断工作。我们 我相信可以通过检测“疾病”来区分患病的和正常的生物流体 指纹”，通过收集大量的数据集的分子结合相互作用的一组不同的现代， 自动选择传感器，用于训练机器学习算法。我们将建立一个传感器阵列- 利用有机色心（OCC，共价修饰的碳纳米管）来消除细微差异 生物流体中分子的物理化学性质。有了足够的多样性，传感器可以区分双- 在机器学习过程的帮助下，通过疾病状态来评估体液。在初步实验中，我们发现， OCC-DNA纳米传感器库表现出灵敏和差异化的光谱变化，以探测系综 分子结合的过程。通过机器学习算法，我们建立了一个预测模型的纳米传感器重新， 可靠地识别高级别浆液性卵巢癌（HGSC）的方法明显优于建立的 使用最初的一组264个患者血清样品，列出了FDA批准的生物标志物CA 125（Nat Biomed Eng， 2022年）。尽管对HGSC的理解和管理取得了进展，但目前诊断时生存率很低， 在后期出现，在早期发现是不常见的。手术是一线治疗手段，而癌症 70%的缓解期患者复发。二次手术可以延长生存期，但前提是手术时间足够早 以实现完全切除。因此，改善早期复发和早期HGSC的检测将 显著提高存活率。我们计划开发一个强大的诊断传感器平台，以改善早期诊断， 卵巢癌和复发的检测，并加速生物标志物的发现过程。此外，quan- 结合到传感器的蛋白质的定量分析可以确定蛋白质吸附响应的独特模式， 可用于疾病特异性光谱响应，从而潜在地促进生物标志物发现。我们提出 研究：1）OCC-DNA纳米传感器元件的分子灵敏度的多样性， 验证患者样本，2）基于机器学习的疾病分类，重点关注早期复发， 早期HGSC，3）传感器响应的分子机制，以及4）阵列的潜力， 有助于鉴定新的生物标志物。成功完成这项工作将产生一个经过验证的平台 为了能够在HGSC中同时识别疾病和加速生物标志物发现过程， 适用于许多潜在的适应症。