We are interested in the interaction between tumours and the host immune system, with a particular focus on those with a very high mutational load as a result of polymerase proofreading domain mutations. We have recently shown that ultramutated POLE-mutant colorectal and endometrial cancers have an excellent prognosis, plausibly because they elicit a strong anti-tumour T cell response against the many neoantigens they generate. Further study of these and other highly mutated cancers may provide novel insights into mutation as a cancer biomarker and therapeutic target.
|Frequency and location of germline and somatic POLE proofreading exonuclease domain mutations. Schematic of the POLE exonuclease domain (upper panel) showing the conserved exo motifs essential for exonuclease proofreading (numbered I-V, highlighted in pink), and the position and frequency of germline (blue) and somatic (red) mutations found in human cancers. The exo I motif active sites D275 and E277 are shown in magenta. The lower panel shows the location of human POLE exonuclease domain mutations relative to single stranded DNA mapped to the highly conserved S.cerevisiae Pol epsilon crystal structure. The mutations lie close to the DNA binding interface and are likely to perturb exonuclease proofreading (modified from Rayner et al., Nature Reviews Cancer, 2016)|
|Possible mechanism linking POLE proofreading mutation, immune response and favourable endometrial cancer prognosis. POLE encodes the catalytic and proofreading subunit of DNA polymerase ε (Pol ε), the leading strand replicase in humans. Cancer-associated POLE exonuclease domain mutations perturb proofreading activity, resulting in tumour ultramutation. Enhanced presentation of mutated antigenic neopeptides stimulates both a cytolytic T cell response and upregulation of immunosuppressive checkpoints; however, increased effector cytokine expression (not shown) suggests that the T cell response is functional and at least partly contributes to the favorable prognosis of POLE proofreading-mutant endometrial cancers. (modified from Van Gool et al., Oncoimmunology, 2016)|
Accounting for intensity variation in image analysis of large-scale multiplexed clinical trial datasets.
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