Comprehensive image and transcriptomic analysis of circulating tumor cells
- The presence of circulating tumor cells (CTCs) in the blood of metastatic cancer patients can be an indicator of poor prognosis and survival. However, CTC detection assays that enable non-invasive "liquid biopsy" are generally limited by the lack of high-throughput, high-efficiency enrichment of CTCs from whole blood and rapid, accurate enumeration of the collected CTCs. Moreover, molecular analysis of single CTCs, which may recapitulate primary and metastatic tumor biology, remains challenging because current platforms have limited throughput, are expensive, and are not easily translatable to the clinic. This doctoral dissertation describes research work that aims to address these challenges by enabling a comprehensive image and transcriptomic analysis of CTCs from cancer patients. Image analysis of CTCs by immunocytochemistry comprises the immunofluorescent staining of CTCs followed by image analysis to identify CTCs. After CTCs were immunomagnetically enriched from whole blood samples by MagSifter and immunofluorescently stained, fluorescence microscope images of CTCs were then acquired on prepared cytospin slides. To achieve automated image cytometry of CTCs, CTC candidate images were first identified using an automated cell image segmentation method. Subsequently, a Random Forest machine learning algorithm was developed to classify each CTC candidate image as positive or negative, based on parameters such as size, shape, and fluorescence intensities in different channels. This approach was applied to identify and enumerate CTCs enriched from non-small cell lung cancer (NSCLC) patients' blood samples with high correlation to pathological assessment. This approach for automated image cytometry has demonstrated high-throughput CTC identification with excellent accuracy and reproducibility. For downstream molecular analysis of CTCs at the single-cell level, a massively parallel, multiplex gene expression profiling platform has been developed to enable compartmentalization and analysis of hundreds of single CTCs. After magnetic collection of CTC from blood, a single-cell Nanowell assay performs CTC mutation profiling using modular gene panels. Using this approach, multigene expression profiling of individual CTCs from NSCLC patients has been demonstrated with unprecedented sensitivity. These results represent the first demonstration of a high-throughput, multiplexed strategy for comprehensive single-cell mutation profiling of individual lung cancer CTCs toward non-invasive cancer therapy prediction and disease monitoring.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Wong, Dawson J
|Stanford University, Department of Electrical Engineering.
|De la Zerda, Adam
|De la Zerda, Adam
|Statement of responsibility
|Dawson J. Wong.
|Submitted to the Department of Electrical Engineering.
|Thesis (Ph.D.)--Stanford University, 2016.
- © 2016 by Dawson J. Wong
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