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A Raman spectroscopic-based platform using advanced data mining methods for in-situ cancer cell classification and characterization.

机译：基于拉曼光谱的平台，使用高级数据挖掘方法进行原位癌细胞分类和表征。

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摘要

Raman spectroscopy has the potential to significantly aid in the research, diagnosis and treatment of cancer. The information dense, complex spectra generate vast datasets in which subtle correlations among peaks often provide essential clues for biological analysis and interpretation. Thus, the implementation of advanced data mining techniques is imperative for complete, rapid and accurate data analysis of large spectral datasets; particularly in regards to clinical translation of the technology. Standard classification models have shown to perform poorly on such high dimensional datasets, typically transforming the original feature space, and making it unfeasible to ascribe biological relevance to the discriminating features. In the first part of this work, Raman spectroscopy is combined with a novel data mining framework, known as Fisher-based Feature Selection-Support Vector Machines (FFS-SVM), to classify and characterize, in-situ, five breast cell lines based on differences in biochemical composition (e.g. lipids, DNA, protein, carbohydrates). This provides both high classification accuracy of cell type, as well as extraction of biologically relevant 'biomarker-type' information based on selected features from each classification 17 schema. The subsequent phase of this work is based on further broadening the application of this Raman spectroscopic-based platform for developing a non-invasive, real-time, in-vitro assay methodology for the classification and characterization of the effects and efficacy of anti-cancer agents on breast cancer cells. Assessment of efficacy by classification of cell spectra as apoptotic, dead/necrotic, or healthy is achieved. Correlation of the features, or spectral peaks, to the corresponding biology reveals that the Raman-based platform provides a wealth information comparable to that provided by several of the most commonly used conventional assay methods, yet in a more efficient, effective, and non-invasive manner. The extension of FFS-SVM to a multiclass classification framework, along with an optimized cluster analysis, provides classification accuracies of greater than 95%, as well as biologically relevant spectral features associated with the agent's mechanism of action (MOA). Continued development of this platform could improve pre-clinical model predictive capabilities, while concurrently providing insight into the MOA of potential anti-cancer agents, thus increasing drug development and screening efficiency, while decreasing developmental cost.
机译：拉曼光谱法有可能极大地帮助癌症的研究，诊断和治疗。信息密集，复杂的光谱生成庞大的数据集，其中峰之间的细微关联通常为生物学分析和解释提供必要的线索。因此，对于大型光谱数据集的完整，快速和准确的数据分析，必须采用先进的数据挖掘技术。特别是在技术的临床翻译方面。标准分类模型已显示在这种高维数据集上表现不佳，通常会转换原始特征空间，并使将生物学相关性归因于辨别特征变得不可行。在这项工作的第一部分中，将拉曼光谱与一种新颖的数据挖掘框架（称为基于Fisher的特征选择-支持向量机（FFS-SVM））相结合，以对五种乳腺癌细胞系进行原位分类和表征关于生化成分（例如脂质，DNA，蛋白质，碳水化合物）的差异。这提供了细胞类型的高分类精度，以及基于从每个分类17模式中选择的特征提取生物学上相关的“生物标记物类型”信息。这项工作的后续阶段基于进一步拓宽该基于拉曼光谱的平台的应用，以开发非侵入性，实时，体外测定方法，以对抗癌作用和功效进行分类和表征乳腺癌细胞上的药物。通过将细胞光谱分类为凋亡，死亡/坏死或健康来评估功效。特征或光谱峰与相应生物学的相关性表明，基于拉曼的平台提供的财富信息可与几种最常用的常规测定方法提供的财富信息相提并论，但更加高效，有效且非侵入性方式。将FFS-SVM扩展到多类别分类框架，以及优化的聚类分析，可提供超过95％的分类准确度，以及与代理作用机理（MOA）相关的生物学相关光谱特征。继续开发该平台可以提高临床前模型的预测能力，同时提供对潜在抗癌药MOA的见识，从而提高药物开发和筛选效率，同时降低开发成本。

著录项

  • 作者

    Fenn, Michael B.;

  • 作者单位

    University of Florida.;

  • 授予单位 University of Florida.;
  • 学科 Biomedical engineering.;Bioinformatics.;Oncology.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 284 p.
  • 总页数 284
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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