GNA5042 · Cancer genomics

Overview

This unit will enable you to gain knowledge on the application of genome sequencing in the diagnoses and clinical management of cancer. You will learn about the different mechanisms that drive cancers and how integrative -omics can be applied to understanding cancer. You will learn about the types of somatic and germline cancer mutations that develop and how they are identified. You will apply genome curation techniques to analyse cancer genomes and learn how to identify clinically relevant sequence variants. The unit is designed to develop real-world skills in using cancer genome databases and generating clinical genome analysis reports.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. Evaluate the different mechanisms that drive cancer and explain why genomic heterogeneity is common in cancer cells;
2. Evaluate how integrative ‘omics’ can be applied to understanding cancer and evaluate the use of databases in cancer genome analysis and diagnosis;
3. Assess how precision medicine can be used in cancer treatment;
4. Apply genome curation approaches to analyse cancer genomic sequences;
5. Apply skills to critically select different ‘omic’ tools to help answer research questions in cancer;
6. Evaluate how cancer genomics impacts diagnosis, treatment selection, and outcome prediction;
7. Examine the clinical utility of current and emerging genomics applications in the clinical cancer laboratory setting;
8. Evaluate the disease mechanisms and clinical features of specific cancer types, and define and justify their associated genomic testing strategies;
9. Critically review the scientific literature relating to cancer;
10. Communicate research outcomes in verbal and written form using appropriate tools to a scientific audience.

Workload

A total of 24 hours per week including 12 hours of directed learning such as lectures (online), workshops, and online activities and 12 hours of self-directed study.

Student experience, not official rules. Nothing here changes what the Handbook says.