Development, management and analysis of flow cytometry based cell signaling assays in a translational research environment to diagnose juvenile myelomonocytic leukemia
Recent advances in molecular technologies are promising great change for the clinic. Patient physiology can be measured at molecular resolutions that were previously not available thus providing clinicians with novel ways of assessing disease state and treatment and ushering in the era of translational medicine. Realizing the potential of such technologies in a clinical setting however is challenging. Clinical scientists are often overwhelmed with information coming from these molecular technologies and gaining insight into human disease often requires multi-disciplinary teams that include clinicians, molecular biologists, statisticians and informatics understanding in biology and medicine. This is specifically seen with phosphoflow cytometry, a novel technique that allows simultaneous single cell measurements of cell type and signal. The ability to do this in primary cells has it poised to become an important clinical analysis tool for human disease. Standing in the way of this potential however are challenges such as complicated experimental designs, ensuring sufficient annotation, incorporating novel statistics and collaborating across wide-ranging disciplines like statistics, bioinformatics, biochemistry, immunology, and medicine.
The challenges listed above are addressed in this dissertation with the Cytobank, a web-based approach that grew out of the need to collaborate on clinical flow cytometry data sets and follow a line of investigation from patient sample to single cell proteomics. A key driver in the usage and requirements for Cytobank was ongoing work with phosphoflow cytometry to assess signaling activity in juvenile myelomonocytic leukemia (JMML)--an aggressive and difficult to diagnose myeloid malignancy. The results from this work was an assay that reduces the time to confirm diagnosis from 3-4 weeks to 1-2 days and can be used to follow patients over time and monitor disease status including remission, relapse and transformation.
0984: Computer science