Using label-free Raman microscopy to predict therapeutic resistance of TNBC cells

使用无标记拉曼显微镜预测 TNBC 细胞的治疗耐药性

基本信息

批准号：
10831127
负责人：
Amy Brock
金额：
$ 14.38万
依托单位：
UNIVERSITY OF TEXAS AT AUSTIN
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-08 至 2025-08-31
项目状态：
未结题

项目摘要

This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA-23-045. Cellular heterogeneity has become critically important, limiting the effectiveness of therapeutics in cancer treatment. The manifestations of cellular heterogeneity can be observed at the biochemical (molecular composition and structure), morphological, and mechanical level, ultimately affecting cell function. When considering non-invasive single-cell methods, the primary methods to assess population biochemical heterogeneity are based on fluorescence activated cell sorting (FACS) of cell surface markers. While extremely rapid, this method quantifies only specific molecules chosen by the user; but what if the marker is unreliable? For example, studies have shown that enriching cells based on FACS of CD44 surface markers only provides a population of cells in which 5% show stem-like behavior. What about all the other potential molecular changes in lipids or nucleic acid chemistry that were invisible in the FACS experiment, which might be informative as differentiating features in cells? In this supplement request, we propose to develop a label-free, molecular imaging approach to characterize cellular Raman fingerprints and train a convolutional neural network (CNN) to predict therapeutic resistance in heterogenous populations. We will use high-speed, label-free nonlinear Raman scattering, which captures a holistic molecular fingerprint of a cell by quantifying abundance of metabolites, lipids, proteins, and nucleic acids and couple this data with CNN training and functional drug screening. Building off our preliminary findings, a CNN will be developed and trained by culturing different breast cancer subpopulations with increasing concentrations of doxorubicin and measuring both live and dead cell Raman fingerprints (Aim 1). The CNN developed in Aim 1 will be tested for its ability to predict subpopulation therapeutic susceptibility in co-cultures of the two subpopulations (Aim 2). This project offers not only a new take on phenotyping heterogeneous cancer cells, but it will also be fully compatible with downstream functional or molecular testing such as tumor initiation or RNAseq measurements.

本申请是为了响应特殊利益通知（NOSI）而提交的，该通知被确定为不是-CA-23-045。细胞异质性已变得至关重要，限制了治疗的有效性癌症治疗。细胞异质性的表现可以在生化指标上观察到。（分子组成和结构）、形态和机械水平，最终影响细胞功能。在考虑非侵入性单细胞方法时，评估的主要方法群体生化异质性基于细胞的荧光激活细胞分选（FACS）表面标记。虽然速度非常快，但该方法仅量化由用户;但如果标记不可靠怎么办？例如，研究表明，基于富集细胞 CD44表面标记的FACS仅提供了其中5%显示干细胞样的细胞群行为。脂质或核酸化学中所有其他潜在的分子变化又如何呢？在 FACS 实验中是不可见的，这可能作为细胞的分化特征提供信息？在此补充请求中，我们建议开发一种无标记的分子成像方法表征细胞拉曼指纹并训练卷积神经网络 (CNN) 进行预测异质人群中的治疗耐药性。我们将使用高速、无标签非线性拉曼散射，通过量化细胞的丰度来捕获细胞的整体分子指纹代谢物、脂质、蛋白质和核酸，并将这些数据与 CNN 训练和功能相结合药物筛选。基于我们的初步发现，CNN 将通过培养不同乳腺癌亚群的阿霉素浓度增加并测量活细胞和死细胞拉曼指纹（目标 1）。目标 1 中开发的 CNN 将接受测试预测两个亚群共培养物中亚群治疗敏感性的能力（目标 2）。该项目不仅提供了异质癌细胞表型分析的新思路，而且还将与下游功能或分子测试（例如肿瘤起始或 RNAseq）完全兼容测量。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Designing clinical trials for patients who are not average.

为非平均患者设计临床试验。

DOI：
发表时间：
2024-01-19
期刊：
影响因子：
5.8
作者：
Yankeelov, Thomas E;Hormuth 2nd, David A;Lima, Ernesto A B F;Lorenzo, Guillermo;Wu, Chengyue;Okereke, Lois C;Rauch, Gaiane M;Venkatesan, Aradhana M;Chung, Caroline
通讯作者：
Chung, Caroline

Applications of high-resolution clone tracking technologies in cancer.

高分辨率克隆追踪技术在癌症中的应用。