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There is currently no screening test available for ovarian cancer. This is largely due to the lack of understanding of mechanisms of tumour initiation. Using single cell sequencing, we recently showed that there are four secretory subtypes of fallopian tube epithelial cells. We found that the molecular signatures that define these subtypes are recapitulated in individual subtypes of HGSOC. This is compelling evidence that HGSOC originates in the fallopian tubes and this gives credit to previously published work highlighting the fallopian tube as a potential tissue of origin.

Studies exploring evolutionary timelines for HGSOC have suggested a latency of around 6.5 years between the development of premalignant fallopian tube lesions and ovarian cancer. Interestingly, the recently published PCAWG studies suggest that ovarian cancer has the longest latency period of all cancers studied, with a minimum of 10 years, but more likely spanning several decades. In order to utilise the latency period between the initial neoplastic changes and cancer, we must gain a better understanding of the earliest mutations that occur in the phenotypically and physiologically normal fallopian tubes.

Exploring mutational processes in normal tissue is central to our understanding of the cancers that originate in these tissues. I have performed ultra-deep targeted sequencing of a collection of over 360 samples that were laser capture microdissected from the normal fallopian tube epithelium.

In this talk, I will discuss the complexities of normal tissue sequencing, how machine learning can offer effective solutions, and share insights gained into the mutational processes that underpin positive selection and clonal expansion of fallopian tube epithelial cells. This will be an important stepping stone towards establishing screening tools for early detection of ovarian cancer.