Uncovering the genetic basis of cancer with multiplex genome editing

A PhD project for the 2022 doctoral clinical fellows programme with Greg Findlay (primary supervisor, Crick) and Clare Turnbull (The Institute of Cancer Research)

Project description

Clare Turnbull

Professor of Translational Cancer Genetics, Institute of Cancer Research

The promise of using a patient’s DNA as a means of guiding clinical interventions is predicated on the ability to interpret rare genetic variants. In the context of cancer, understanding the effects of variants in established oncogenes and tumour suppressor genes is of great importance because such knowledge can inform who is genetically predisposed to cancer and which pharmacologic interventions will optimise outcomes [1, 2].

Despite growing demand for accurate variant interpretation, our ability to assess the functional effects of rare human variants remains highly limited. Even in the most well-studied genes, it is challenging to predict which mutations have molecular consequences that impact our health. This is illustrated by hundreds of thousands of ‘variants of uncertain significance’ in genetic databases such as ClinVar. We also lack a firm understanding of which non-coding regions play critical roles in regulating cancer genes, and relatedly, how variants in such regions contribute to disease risk.

To address this challenge, our lab has developed powerful genome editing tools that allow us to systematically probe the functional consequences of genetic variation. We leverage the latest CRISPR technologies to ask which variants contribute to human disease and how. We perform genome editing in human cells and use next-generation sequencing to track effects of millions of engineered mutations simultaneously. With assays we’ve developed such as Saturation Genome Editing [3, 4] and dual CRISPR guide RNA screening [5], we can identify precisely which variants contribute to cancer phenotypes. Our study of 3,893 BRCA1 variants exemplifies the large clinical impact these methods can have, as this data set has already proven crucial for interpreting genetic test results for many patients and their families.

This clinical PHD project will involve working closely with cancer geneticists tasked with leading variant interpretation efforts nationally to define priority targets for experiments. The student will be trained to generate and analyse their own large experimental data sets using CRISPR genome editing, expanding from ongoing studies in the lab with a range of assays including single-cell transcriptomics. Variants observed in patients will be studied in greater depth to bolster clinical impact. There will also be ample opportunities to investigate the mechanisms driving variant effects in non-coding regions, for instance, through studying RNA splicing and gene regulation. 

This project will ultimately advance our mechanistic understanding of the diverse pathways through which mutations lead to cancer. Furthermore, it promises to have a direct clinical impact by improving our ability to interpret variants seen in patients. The successful applicant will be at the forefront of developing and applying cutting edge genomics technologies, analysing large experimental and clinical genetics data sets, and working with leading clinicians to advance precision medicine.

We welcome applications from clinicians at any stage of scientific training and across specialties.

The partner institution for this project is The Institute of Cancer Research.

References

  1. Findlay, G.M. (2021). Linking genome variants to disease: Scalable approached to test the functional impact of human mutations. Hum. Mol. Genet. ddab219.  PubMed abstract
  2. Shendure, J., Findlay, G.M., and Snyder, M.W. (2019). Genomic Medicine – Progress, Pitfalls, and Promise. Cell 177: 45-57. PubMed abstract
  3. Findlay, G. M., Boyle, E. A., Hause, R. J., Klein, J. C. and Shendure, J. (2014). Saturation editing of genomic regions by multiplex homology-directed repair. Nature 513: 120-123. PubMed abstract
  4. Findlay, G. M., Daza, R. M., Martin, B., Zhang, M. D., Leith, A. P., Gasperini, M., . . . Shendure, J. (2018). Accurate classification of BRCA1 variants with saturation genome editing. Nature 562: 217-222. PubMed abstract
  5. Gasperini, M., Findlay, G. M., McKenna, A., Milbank, J. H., Lee, C., Zhang, M. D., . . . Shendure, J. (2017). CRISPR/Cas9-Mediated Scanning for Regulatory Elements Required for HPRT1 Expression via Thousands of Large, Programmed Genomic Deletions. Am J Hum Genet 101: 192-205. PubMed abstract