Programme(s) to which this project applies:
|☒ MPhil/PhD||☑ MRes[Med]||☒ URIS|
Objective and Significance:
Precision oncology aims to define patient therapy based on the specific mutational profile of a cancer patient. In research settings, both tumor and normal samples are collected from these patients. These samples are then interrogated in genomic assays and co-analyzed to confidently and unequivocally ascertain the somatic (arisen in cancer and thus absent in normal) status of the mutations. In clinical settings often this luxury of matched normal is not available. A key challenge in translating research analytics to clinical application is determining the somatic mutations from a cancer sample alone (without a matched normal). This project aims to compare multiple analytical tools available towards this end.
Research Plan and Methodology:
There are multiple software pipelines designed to identify somatic mutations in cancer samples without a matched patient normal sample. Many of these methodologies differ in fundamental underlying algorithms. In order to compare these pipelines, we will use inhouse matched patient tumor-normal data to first define the true set of somatic mutations. Next the patient normal will be masked and the analysis reconducted. The latter analysis will mimic the common clinical scenarios. Comparing the two results will give a direct estimation of the efficacy of these tools and provide insight on how to fine tune them for best results.
Dr A Javed, School of Biomedical Sciences
Dr Javed is an Assistant Professor in the Li Ka Shing Faculty of Medicine at the University of Hong Kong. He received his PhD in computer science from Rensselaer Polytechnic Institute. After doctorate, he joined Computational Biology Center at IBM TJ Research Center as a postdoctoral researcher where his work was part of the Genographic project. He then joined Genome Institute of Singapore (A*STAR) as a research associate and was promoted to research scientist in 2014. Dr Javed has co-authored more than 50 publications including senior authorships in Nature Methods, Nature Genetics and Blood. His research group focuses on method development and application in active collaboration with other clinical and experimental groups.
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HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.
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