| Summary: | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2013. === "September 2013." Page 173 blank. Cataloged from PDF version of thesis. === Includes bibliographical references (pages 152-163). === Although tumor cells obtained from human patients by surgical biopsy, image-guided intervention, blood draws or fluid drainage (paracentesis, thoracentesis) are a valuable source for analyzing tumor cells, conventional means of proteomic analysis are limited. Highly sensitive and quantitative technologies for point-of-care and multiplexed analysis on small sample sizes are in great demand. To this end, we developed three technologies to improve our understanding of the molecular signatures of cancer in clinical samples. In the first section, we describe a diagnostic magnetic resonance (DMR) device that was developed for point-of-care analyses of human tumors. We optimized a magnetic nanoparticle assay to improve sensitivity and robustness of the DMR approach. The DMR device was tested by analyzing samples from 50 patients. The results were then validated in an independent cohort of 20 additional patients. DMR enabled quantification of multiple protein markers in all patients. Using a four-protein signature enabled us to achieve 96% accuracy for establishing cancer diagnosis, surpassing conventional clinical analysis by immunohistochemistry. Results also show that protein expression patterns decay with time, underscoring the temporal need for rapid sampling and diagnoses. Also, a surprising degree of heterogeneity in protein expression both across different patient samples and even within the same tumor was observed, which has important implications for molecular diagnostics and therapeutic drug targeting. In the second section we molecularly profiled tumor cells in ascites - peritoneal fluid frequently drained for symptomatic relief in advanced ovarian cancer (OvCA) patients. First, we profiled a comprehensive panel of 85 biomarkers in ovarian cancer and benign cell lines. From this data set, 31 markers were identified and profiled in a training set of human ascites samples (n=1 8). We identified an ascites-derived tumor signature termed ATCdx containing four markers which was then validated in a cohort of 47 patients (33 ovarian cancer and 14 control) and correctly identified all 33 ovarian cancer patients. Serial samples were obtained from a subset of patients' serial samples (n=7) and profiled, demonstrating that ATCs can be used to measure treatment response and differentiate responders from non-responders. Finally, we specifically designed a novel microfluidic enrichment chip that allows rapid visualization of cancer cells in heterogeneous ascites fluid. This chip requires small sample volumes (< 1 mL) and has single cell detection sensitivity. Furthermore, it is inexpensive to construct and can be easily fabricated using soft lithographic techniques, providing a point-of-care method that could potentially find widespread use for ATC analyses and diagnosis. In the final section, a multiplexed proteomic assay using a photocleavable DNA barcoding method was developed to multiplex protein detection in single cells. We tested 94 antibodies against common cancer markers to examine different treatment responses and heterogeneity at the single cell level. We then extended our analysis to human clinical samples to demonstrate the potential of protein-based measurements to assist in monitoring cancer therapy through differential changes before and after treatment. We show that protein based tumor profiles can provide sufficient information to predict treatment response. Finally, we examined interpatient variability and intratumoral heterogeneity of single cells with this highly sensitive assay. Together, these technologies can help overcome current clinical limitations and expedite advancements in cancer treatment. === by Vanessa M. Peterson. === Ph. D.
|
|---|
