Our project Deep Insight: Integrating germline and somatic genetic profiles through machine learning to understand esophageal cancer etiology has just been granted by the BMBF (031L0267A).

The partner in the project is the University of Cologne.

Abstract

Esophageal adenocarcinoma (EAC), also known as Barrett’s carcinoma, represents a major socio-medical challenge. It is a highly aggressive neoplasm that usually develops at a late stage and is generally resistant
to chemotherapy. Therefore, EAC patients have an extremely poor prognosis with an overall 5-year survival rate of < 15%. The incidence of EAC has been increasing at alarming rates in high-income countries over
the past decades. Gastroesophageal reflux disease (GERD), which refers to the reflux of stomach acid into the lower esophagus, has increased in parallel with dramatic rates. Acid reflux is the major risk factor for the
development of Barrett’s esophagus (BE) in which the normal stratified squamous epithelium of the esophagus is replaced by metaplastic columnar epithelium. As a result, many millions of people are at increased risk of EAC because BE is its precursor lesion. However, not all BE patients develop cancer. Biomarkers are urgently needed to predict which patient has a progression to EAC and require frequent endoscopic monitoring, which is otherwise costly and associated with medical complications. This joint research proposal aims to use intelligence to develop and evaluate a predictive system to improve diagnostics, optimize monitoring of BE patients and explore the role of genetic risks of germline and somatic mutations beyond known and well-studied mechanisms.