Predicting oesophageal cancer progression using genomic information in pre-malignant oesophageal tissues
Year: 2017
Session type: Proffered paper sessions
Theme: Diagnosis and therapy
Abstract
Background
Esophageal adenocarcinomas (EAC) have been shown to have high rates of somatic copy number alterations that are likely to drive the progression of the disease. A pre-malignant tissue, known as Barrett’s Esophagus (BE), also develops early somatic copy number changes that appear to be later magnified in EAC suggesting a possible route for new diagnostic tests.
Method
Using a nested case-control cohort of 69 patients diagnosed with BE, and following the standard surveillance protocols, the whole-genome copy number status of the tissue was evaluated in 634 samples from 2-15 years prior to a diagnosis of high-grade dysplasia or cancer. The copy number status of those patients who have progressed to cancer versus those with Barrett’s who have not progressed was used to develop a model which predicted progression per sample.
Results
The model was shown to robustly classify both progressive and non-progressive samples with an AUC of 79% using default cutoffs. At the pathological endpoint (high-grade dysplasia) the model predicted 93% of early cancer samples correctly as progressive, but most important were the predictions for pathologies prior to the endpoint, these predictions included pathological grades prior to current diagnostic guidelines, particularly non-dysplastic BE (see table below). It was also able to accurately predict patients who would progress 6-8 years prior to diagnosis in 88% of endoscopies. In a separate cohort of 20 patients (158 samples) these prediction rates were validated with a sensitivity of 69% and specificity of 76%.
Pathology (samples)
All Patients (69)
Progressors Only (35)
Validation Cohort (10P+10NP)
BE
77%
69%
74%
Low-grade Dysplasia
78%
77%
93%
Indeterminate
92%
88%
77%
High-grade Dysplasia (Progressors only)
N/A
92%
87%
Conclusion
Genomic copy number information generated from current surveillance protocols can improve early diagnosis in this disease, and enable better management for Barrett’s patients.