Research Projects
Harnessing high-throughput sequencing for cancer precision medicine

Objective and Significance:

High-throughput sequencing is becoming a widely used tool by oncologists to identify specific mutations that inform cancer treatment decisions. In the clinical setting, such sequencing tests usually involve sequencing a targeted panel of known proto-oncogenes and tumour suppressors, and these targeted sequencing tests thus have limited utility when the tumour has no specific mutations for which known treatment options exist. However, the mutational profile of a tumour reflects its evolutionary history and thus records the DNA repair and damage processes that have occurred in the tumour. We will therefore infer the activities of these mutational processes in the tumours and identify treatment responses that are associated with specific mutational processes. Additionally, there are many other obstacles in targeted sequencing tests that prevent these tests from realizing their full potential, such as the failure of standard bioinformatic pipelines to remove sequence artifacts, the difficulty in estimating the cancer cell fractions of mutations, and the lack of matched normal samples. By overcoming these obstacles, we can make better use of the targeted sequencing data and broaden the benefits of high-throughput sequencing for cancer precision medicine.

Research Plan and Methodology:

• Analyze of mutational processes in tumours using targeted sequencing data
  Identify mutational processes that are associated with differential responses to specific treatments by examining electronic health records
• Run bioinformatic pipelines for estimating copy-number profiles, tumour purity, tumour ploidy, and cancer cell fractions of mutations
• Develop methods to distinguish between somatic vs. germline mutations in tumour samples without matched normal

Learning Outcomes:

Through this project, students will learn to

• Interpret reports from targeted sequencing tests
• Distinguish between real cancer mutations and sequencing artifacts
• Identify associations between clinical outcomes and genomic features
• Apply bioinformatics skills in sequencing data analysis

Professor DJH Shih, School of Biomedical Sciences

Biography
HKU Scholars Hub
Lab
ORCID
dshih@hku.hk

For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

1. your CV,
2. a brief description of your research interest and experience, and
3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to rpgmed@hku.hk.

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.