Our new article has been accepted in Gut:
Schröder J, Chegwidden L, Maj C, Gehlen J, Speller J, Böhmer AC, Borisov O, Hess T, Kreuser N, Venerito M, Alakus H, May A, Gerges C, Schmidt T, Thieme R, Heider D, Hillmer AM, Reingruber J, Lyros O, Dietrich A, Hoffmeister A, Mehdorn M, Lordick F, Stocker G, Hohaus M, Reim D, Kandler J, Müller M, Ebigbo A, Fuchs C, Bruns CJ, Hölscher AH, Lang H, Grimminger PP, Dakkak D, Vashist Y, May S, Görg S, Franke A, Ellinghaus D, Galavotti S, Veits L, Weismüller J, Dommermuth J, Benner U, Rösch T, Messmann H, Schumacher B, Neuhaus H, Schmidt C, Wissinowski TT, Nöthen MM, Wellcome Trust Case Control Consortium 2 (WTCCC2), Esophageal Adenocarcinoma Genetics Consortium (EAGLE), Barrett’s and Esophageal Adenocarcinoma Consortium (BEACON), Dong J, Ong J-S, Buas MF, Thrift AP, Vaughan TL, Tomlinson I, Whiteman DC, Fitzgerald RC, Jankowski J, Vieth M, Mayr A, Gharahkhani P, MacGregor S, Gockel I, Palles C, Schumacher J: GWAS meta-analysis of 16 790 patients with Barrett’s oesophagus and oesophageal adenocarcinoma identifies 16 novel genetic risk loci and provides insights into disease aetiology beyond the single marker level, Gut 2022, in press. (Link)
Objective Oesophageal cancer (EC) is the sixth leading cause of cancer-related deaths. Oesophageal adenocarcinoma (EA), with Barrett’s oesophagus (BE) as a precursor lesion, is the most prevalent EC subtype in the Western world. This study aims to contribute to better understand the genetic causes of BE/EA by leveraging genome wide association studies (GWAS), genetic correlation analyses and polygenic risk modelling.
Design We combined data from previous GWAS with new cohorts, increasing the sample size to 16 790 BE/EA cases and 32 476 controls. We also carried out a transcriptome wide association study (TWAS) using expression data from disease-relevant tissues to identify BE/EA candidate genes. To investigate the relationship with reported BE/EA risk factors, a linkage disequilibrium score regression (LDSR) analysis was performed. BE/EA risk models were developed combining clinical/lifestyle risk factors with polygenic risk scores (PRS) derived from the GWAS meta-analysis.
Results The GWAS meta-analysis identified 27 BE and/or EA risk loci, 11 of which were novel. The TWAS identified promising BE/EA candidate genes at seven GWAS loci and at five additional risk loci. The LDSR analysis led to the identification of novel genetic correlations and pointed to differences in BE and EA aetiology. Gastro-oesophageal reflux disease appeared to contribute stronger to the metaplastic BE transformation than to EA development. Finally, combining PRS with BE/EA risk factors improved the performance of the risk models.
Conclusion Our findings provide further insights into BE/EA aetiology and its relationship to risk factors. The results lay the foundation for future follow-up studies to identify underlying disease mechanisms and improving risk prediction.