Cancer vulnerabilities

Programme stream(s): Cancer discovery / underpinning research

Chair: Chris Lord, The Institute of Cancer Research, UK
Speaker: Colm Ryan, University College Dublin, Ireland
Speaker: Jason Moffatt, University of Toronto, Canada
Speaker: Rachael Natrajan, The Institute of Cancer Research, UK

2:00 pm-4:00 pm

Room: Boisdale

The molecular re-writing that is associated with the tumorigenic process, as well as driving many of the well-established hallmarks of cancer, also generates a series of vulnerabilities in cells, including gene addiction and synthetic lethal interactions. This session will discuss how these vulnerabilities might be identified, understood and ultimately exploited in the development of new therapeutic approaches for cancer, and will include insights from both computational as well wet lab biology. By the end of this session participants will gain an understanding of methods and approaches to identify and dissect genetic interactions in tumour cells.

Identifying and interpreting robust genetic dependencies in cancer cell lines
Speaker: Colm Ryan
Affiliation: University College Dublin


Genes whose function is selectively essential in the presence of cancer driver gene alterations represent promising targets for the development of precision therapeutics. Over the last decade multiple groups have performed large-scale loss-of-function screens in panels of cancer cell lines in order to identify such genetic dependencies. Despite these systematic efforts, relatively few robust genetic dependencies have been identified, with many ‘hits’ appearing to be cell line or screen specific.  We have previously used protein interaction networks as a means of interpreting the genetic dependencies identified in loss-of-function screens. Here, we show that genetic dependencies involving pairs of genes whose protein products interact are more likely to be reproduced across multiple experiments. Thus the integration of protein-protein interaction networks serves as both a means of interpreting genetic dependencies and a way to identify those genetic dependencies that are likely to be reproduced across multiple experiments.