Research Projects
Re-defining mutational processes in cancers

Programme(s) to which this project applies:

☑ MPhil/PhD ☒ MRes[Med] ☒ URIS

Cancer cells rely on a precarious balance between DNA damage and repair. Deficiencies in DNA repair allow cancer cells to acquire mutations that promote progression or resistance. Conversely, excessive DNA damage can cause cell death or produce cancer-specific antigens (neoantigens) that induce immune response against cancer cells. For example, defects in mismatch repair cause microsatellite instability and lead to sensitivity to topoisomerase inhibitors1; this defect also increases the burden of neoantigens, which confer sensitivity to immunotherapy. Similarly, mutations in DNA repair genes BRCA1/2 potentiate cancer progression, but concomitantly, the cancer cells become vulnerable to platinum-based therapy and PARP inhibition. Recent studies have begun to characterize patterns of mutations by decomposing pan-cancer mutational profiles into signatures of mutational processes. Characterizing these mutational processes can identify DNA repair deficiency or damage hyperactivity in cancers irrespective of the upstream causes (e.g. point mutation, rearrangement, DNA methylation). However, several of the discovered mutational signatures are redundant, and most have unknown etiology. It is also now recognized that existing algorithms produce inconsistent results due to statistical challenges (non-identifiability) that confound interpretation. Due to quantitative issues with current methods, mutational processes in cancer are inadequately determined and poorly understood. Overcoming these critical barriers can unlock the potential application of identifying active mutational processes in tumors in order to predict cancer-specific vulnerabilities and select the optimal therapy for each patient.

We seek motivated candidates with interest in developing Bayesian models on cancer mutation data for mutational process discovery and quantification. Ideal candidates will have strong statistical modelling skills and familiarity with R. Prior experience in computational biology is not necessary.

Dr DJH Shih, School of Biomedical Sciences


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

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.