Identifying DNA repair deficiencies across lung cancer progression

This project investigates how metastatic lung cancer develops and evolves over time. DNA sequencing can be utilised to identify patterns in cancer genomes associated with specific sources of mutation (eg: UV rays, tobacco smoke, intrinsic cellular processes). Additionally, cancers may acquire defects in DNA repair mechanisms, resulting in a positive feedback loop of increasing mutation rate.
Recent research from the TRACERx program has helped discover the subpopulations of cells which metastasize in lung cancer. In this project, we will use machine learning to identify patterns in mutations in these lung cancers, and further understand the differences in the patterns of mutations in two populations of cells identified in TRACERx: those which don’t result in metastasis, and those which do.
We aim to identify when in the developmental path of the tumours particular mutational patterns arise and change. Ultimately, the project strives to enhance our understanding of cancer evolution and spread, knowledge that may improve treatment strategies and patient outcomes.

Faculty Supervisor:

Quaid Morris

Student:

Partner:

University College London

Discipline:

Computer science

Sector:

Education

University:

University of Toronto

Program: