Introduction: The main risk factor for the development of oesophageal adenocarcinoma is Barrett’s oesophagus (BE). To diagnose those patients who will progress to cancer early to improve the dismal survival rate of oesophageal adenocarcinoma, patients with BE undergo regular endoscopic surveillance. The vast majority of patients, however, will never progress and are therefore monitored unnecessarily. Copy number changes have been shown to be important in the progression of BE to oesophageal adenocarcinoma (Li et al., 2014). Shallow whole genome sequencing (sWGS) has been established as a cost-effective method of investigating copy number changes in formalin fixed paraffin embedded (FFPE) tissue (Scheinin et al., 2014). We hypothesised that copy number alterations may be valuable markers in disease progression and aimed to characterise them in the progression of Barrett’s using sWGS in order to predict progression in patients from a point in time as close to baseline endoscopy as possible and to integrate p53 staining. Methods: To optimise sWGS we compared 50X WGS on frozen tissue with 0.1X WGS from FFPE tumour material from the same patient. To address poor cellularity in endoscopic biopsies, shallow WGS data from a 50% cellularity biopsy with a 90% frozen sample from a single patient were compared. Accounting for poor biopsy cellularity 0.4X coverage was used. We performed FFPE shallow WGS on 806 samples from an 89-patient cohort comprising a 1:1 ratio of patients who progressed to high grade dysplasia (HGD) and patients who never progressed. 1-31 samples per patient were collected over time and space throughout surveillance. Non-progressors had significantly longer follow-up (p-value = 0.0008). Data was processed based on published bioinformatic pipelines. Copy number analysis was carried out using a generalised linear model (GLM) in order to develop a predictive algorithm. Results: During optimisation, ˃85% of copy number changes were detected in both frozen and FFPE samples from spatially distinct regions of an individual tumour. We found 91% and 93% agreement in copy number calls using orthogonal platforms between 90% (frozen) and 50% (FFPE) cellularity samples from one tumour. In the 806 sample Barrett’s cohort, we observed larger copy number alterations in patients who progressed to cancer compared with non-progressors and significantly more CN alterations in progressor patients (p-value ˂ 0.001). More cancer-associated genes were affected in progressors and we observed significant heterogeneity between patients. There was also a greater level of complexity seen in the progressor patients when analysed using affinity propagation clustering. These data allowed us to develop a regression model to predict progression. Using the GLM model, we successfully classified samples as early as progressor or not with an AUC of 85.75% and a sensitivity and specificity of 84 and 79% respectively. At the patient level 94% progressor patients had at least one sample classified as at risk of progression and non-dysplastic progressor samples were classified as early as 13 years prior to HGD diagnosis. Depending on the classification threshold used, all samples over time and space were not classified as being at risk of progression in at least 60% patients who have not yet progressed to HGD/cancer. We observed 2 pathways to progression supporting previous observations. 90% of progressors had samples prior to their HGD or cancer diagnosis classified as being predisposed to progression suggestive of genetically unstable lesions from early on in surveillance that progressed to HGD over time. The remaining 10% appeared as non-progressors until their diagnosis of HGD. We investigated p53 expression in our patient cohort as the only biomarker to have successfully transitioned into the clinic for Barrett’s surveillance. Whilst we found our cohort to be representative in staining compared to other published cohorts, it did not contribute to the GLM and the copy number data out-performed the use of p53 IHC in the context of Barrett’s surveillance. Conclusions: We have optimised the use of shallow WGS in oesophageal adenocarcinoma and Barrett’s. Using these copy number data, we can confidently distinguish between patients who will progress to cancer and the majority of patients who will never progress. This approach has led to the development of a model for predicting progression in the clinical setting which is promising for further clinical validation.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:753403 |
| Date | January 2018 |
| Creators | Gregson, Eleanor |
| Contributors | Fitzgerald, Rebecca |
| Publisher | University of Cambridge |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.repository.cam.ac.uk/handle/1810/278652 |
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