Dr Chi Wong

Clinical Fellow

 

Alumni

This person is a member of Sanger Institute Alumni.

Chi is a clinician scientist interested in identifying and understanding the genetic and molecular basis of haematological cancers using a combination of genetic model systems, genomics and cell biology. With these tools, he focuses particularly on cancer subtypes associated with poor prognosis to uncover urgently needed, new therapeutic strategies.

The Sanger Institute has played a pivotal role in identifying gene variants that are important in human disease, such as mutations that predispose individuals to developing cancer or are commonly found in sporadic cancers. For example, large-scale analysis of cancer genomes from diverse cancer types uncovered the CUX1 transcription factor as novel cancer gene that was overlooked by previous, smaller sequencing studies.

A key challenge now is to understand the role of CUX1 and other newly identified gene aberrations in cancer development. By harnessing the Institute’s strength in producing genetically tractable model systems, I have generated several genetic, cancer models to recapitulate cancer-associated mutations using CRISPR/Cas9-mediated genome-editing techniques. With these models, I can study the phenotypic consequences of these mutations and decipher the underlying mechanisms underpinning the observed phenotypes. By understanding the mechanistic action of these gene mutations, I aim ultimately to uncover new therapeutic strategies that can specifically target cellular pathways critical for cancer maintenance. As many of genes under investigation confer poor prognosis in patients when mutated, identifying new treatment modalities is a key priority. Beneficially, my findings will be tested in a complementary approach using primary human cancer samples obtained from my clinical work.

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Histogram showing genes with increased ratios of observed to expected nonsense mutations (Benjamini-Hochberg's false discovery rate-adjusted P value, q < 0.01). Genes are ranked according to the observed-to-expected ratio using a mutation-selection model as described. <em>CUX1</em> is highlighted in red.

Histogram showing genes with increased ratios of observed to expected nonsense mutations (Benjamini-Hochberg's false discovery rate-adjusted P value, q < 0.01). Genes are ranked according to the observed-to-expected ratio using a mutation-selection model as described. CUX1 is highlighted in red.

Predicted consequences of <em>CUX1</em> mutations identified from the analysis of 7,651 human cancer genomes (top) and myeloid malignancies (bottom) are depicted across the CUX1p200 1505 amino acid (aa) isoform (NCBI, NP_853530). CUX1 protein domains are highlighted: ID, inhibitory domain; CC, coiled-coil; CUT, CUT DNA-binding; HD, homeodomain. Mutation type is indicated. In bottom panel, MDS-associated mutations are indicated by solid symbols and AML-associated mutations by outlined symbols.

Predicted consequences of CUX1 mutations identified from the analysis of 7,651 human cancer genomes (top) and myeloid malignancies (bottom) are depicted across the CUX1p200 1505 amino acid (aa) isoform (NCBI, NP_853530). CUX1 protein domains are highlighted: ID, inhibitory domain; CC, coiled-coil; CUT, CUT DNA-binding; HD, homeodomain. Mutation type is indicated. In bottom panel, MDS-associated mutations are indicated by solid symbols and AML-associated mutations by outlined symbols.

Estimated survival curves for 598 patients with MDS or MDS/MPN overlap according to presence of <em>CUX1</em> − truncating mutations or − 7/del(7q) (left) and 1,477 patients with AML according to presence of <em>CUX1</em> − truncating mutations or −7/del(7q) (right).

Estimated survival curves for 598 patients with MDS or MDS/MPN overlap according to presence of CUX1 − truncating mutations or − 7/del(7q) (left) and 1,477 patients with AML according to presence of CUX1 − truncating mutations or −7/del(7q) (right).