Approximately 1,600 human cancer cell lines are available to scientists worldwide and these are a useful resource for cancer research and drug development. Over the past decade the Sanger Institute has played a central role in the genetic and functional analysis of these cancer cell lines. As we enter the era of precision cancer medicine our current cell model collection limits our ability to treat more patients in a molecularly defined manner due to poor representation of some cancer types, insufficient numbers to capture the genetic diversity of cancer, lack of clinical outcome data and lack of comparison to normal reference samples.

We aim to generate a new comprehensive and diverse collection of highly-annotated cancer cell models. This will include derivation and genomic characterisation of new patient-derived 3D organoid cultures, 2D cell lines and the generation of genetically-engineered isogenic cancer models. Cell model passports provide a gateway to access all relevant information about our cell models.D

Targeted molecular therapeutics are increasingly used for the treatment of cancer patients. These strategies are based on our understanding of the genes that are causally implicated in cancer and clinical observations that molecular alterations in cancer genomes strongly influence patient response to drugs. Many cancer drugs currently used in the clinic or in clinical development have not been linked with specific genetic events that may specify their optimal therapeutic effectiveness.

We are performing large-scale differential drug sensitivity screens in cancer cell models and incorporating detailed genomic analyses to systematically identify drug response biomarkers. This includes both single agent and drug combinations screens. Large numbers of cancer cell models are required to encompass the heterogeneity of human cancer to discover novel efficacious therapeutic strategies in defined patient populations. This information can be used to inform the optimal clinical application of cancer drugs, as well as having significant effects on the design, cost and ultimate success of new cancer drug development.

The complexity and diversity of cancer genomes represents a significant challenge when developing new cancer therapies. Specifically, identifying cellular signalling pathways and processes whose perturbation selectively kills cancer cells while sparing normal cells remains acutely difficult. This is because our understanding of which proteins are necessary for cancer cell survival is incomplete. Furthermore, our understanding of cellular networks and processes is relatively poor in normal cells, let alone in the context of cancer cells with their myriad of molecular alterations. Systematic and unbiased approaches to identify critical dependencies in cancer cells could significantly expand the repertoire of new drug targets for future development.

Genome-editing technologies such as CRISPR-Cas9 are a powerful tool for studying gene function in normal and diseased cells. This approach takes advantages of the programmable nature of single guide RNAs (sgRNAs) to perform genome-wide functional genetic screens across a diverse array of cellular models and systems. We are using CRISPR-Cas9 synthetic-lethal genetic screens to map gene function and identify dependencies across a diverse collection of cancer cell models.

The development of a Cancer Dependency Map will require the development of novel computational algorithms for the processing and analysis of new datasets. Furthermore, to fully exploit our large-scale multi-dimensional genomic and functional datasets we will require completely novel approaches for data integration and visualization beyond what is currently available. We are developing new tools and software to support the creation of a DepMap and these will be made available to the research community.