Identifying the genetic signatures of acquired resistance to Pol I transcription inhibitors in cancer

Transgenic mice with B cell lymphomas (Eμ-myc mouse model) have been shown to develop resistance to CX-5461, a small molecule inhibitor of RNA Polymerase I transcription. The development of this drug resistance may be closely related to changes in gene expression, expression of novel chimeric fusion genes as a result of chromosomal translocations, and/or the accumulation of somatic mutations in genes affecting critical biological pathways. Understanding what mediated CX-5461 resistance might guide patient treatment strategies to prevent or treat this response.

This project, supervised by Dr Maurits Evers, will suit a candidate with an interest in computation and/or bioinformatics.

As part of this project you will:

  1. Implement a reproducible analysis workflow to quantify (changes in) gene expression from available high-throughput RNA sequencing (RNA-seq) data. You will familiarise yourself with critical RNA-seq data analysis steps such as quality control, read alignment, and transcript abundance estimation using gold-standard bioinformatics tools.
  2. Evaluate state-of-the-art computational methods for identifying chimeric fusion transcripts from RNA-seq data. This may involve surveying and testing existing methods, developing a sensible test environment for benchmarking, and comparing results from different tools to obtain rigorous estimates of fusion transcript expression.
  3. Identify genomic/transcriptomic markers that facilitate the development of CX-5461 drug resistance. This may involve identifying affected pathways, and/or collaborating with wet-lab biologists to validate fusion gene expression.
  4. Make use of the high-performance computing environments of the ANU Bioinformatics Consultancy (ABC) unit and the National Computational Infrastructure (NCI)

Time Frame: This project is expected to take 6-18 months

Requirements: Experience with Linux environments; practical experience with programming e.g. using R, Python, Perl etc.; solid understanding of applied statistics