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Meta-Analysis
. 2023 Jun:92:104616.
doi: 10.1016/j.ebiom.2023.104616. Epub 2023 May 18.

Dissecting the genetic heterogeneity of gastric cancer

Timo Hess  1 , Carlo Maj  2 , Jan Gehlen  3 , Oleg Borisov  4 , Stephan L Haas  5 , Ines Gockel  6 , Michael Vieth  7 , Guillaume Piessen  8 , Hakan Alakus  9 , Yogesh Vashist  10 , Carina Pereira  11 , Michael Knapp  12 , Vitalia Schüller  3 , Alexander Quaas  13 , Heike I Grabsch  14 , Jessica Trautmann  15 , Ewa Malecka-Wojciesko  16 , Anna Mokrowiecka  16 , Jan Speller  12 , Andreas Mayr  12 , Julia Schröder  15 , Axel M Hillmer  13 , Dominik Heider  17 , Florian Lordick  18 , Ángeles Pérez-Aísa  19 , Rafael Campo  20 , Jesús Espinel  21 , Fernando Geijo  22 , Concha Thomson  23 , Luis Bujanda  24 , Federico Sopeña  25 , Ángel Lanas  26 , María Pellisé  27 , Claudia Pauligk  28 , Thorsten Oliver Goetze  28 , Carolin Zelck  3 , Julian Reingruber  3 , Emadeldin Hassanin  4 , Peter Elbe  5 , Sandra Alsabeah  5 , Mats Lindblad  29 , Magnus Nilsson  29 , Nicole Kreuser  6 , René Thieme  6 , Francesca Tavano  30 , Roberta Pastorino  31 , Dario Arzani  32 , Roberto Persiani  33 , Jin-On Jung  34 , Henrik Nienhüser  35 , Katja Ott  36 , Ralf R Schumann  37 , Oliver Kumpf  38 , Susen Burock  39 , Volker Arndt  40 , Anna Jakubowska  41 , Małgorzta Ławniczak  42 , Victor Moreno  43 , Vicente Martín  44 , Manolis Kogevinas  45 , Marina Pollán  46 , Justyna Dąbrowska  47 , Antonio Salas  48 , Olivier Cussenot  49 , Anne Boland-Auge  50 , Delphine Daian  50 , Jean-Francois Deleuze  50 , Erika Salvi  51 , Maris Teder-Laving  52 , Gianluca Tomasello  53 , Margherita Ratti  54 , Chiara Senti  55 , Valli De Re  56 , Agostino Steffan  57 , Arnulf H Hölscher  9 , Katharina Messerle  9 , Christiane Josephine Bruns  9 , Armands Sīviņš  58 , Inga Bogdanova  58 , Jurgita Skieceviciene  59 , Justina Arstikyte  59 , Markus Moehler  60 , Hauke Lang  61 , Peter P Grimminger  61 , Martin Kruschewski  62 , Nikolaos Vassos  63 , Claus Schildberg  64 , Philipp Lingohr  65 , Karsten Ridwelski  66 , Hans Lippert  67 , Nadine Fricker  68 , Peter Krawitz  4 , Per Hoffmann  68 , Markus M Nöthen  68 , Lothar Veits  7 , Jakob R Izbicki  69 , Adrianna Mostowska  47 , Federico Martinón-Torres  70 , Daniele Cusi  71 , Rolf Adolfsson  72 , Geraldine Cancel-Tassin  49 , Aksana Höblinger  73 , Ernst Rodermann  74 , Monika Ludwig  75 , Gisela Keller  76 , Andres Metspalu  52 , Hermann Brenner  77 , Joerg Heller  78 , Markus Neef  79 , Michael Schepke  79 , Franz Ludwig Dumoulin  80 , Lutz Hamann  37 , Renato Cannizzaro  81 , Michele Ghidini  53 , Dominik Plaßmann  82 , Michael Geppert  83 , Peter Malfertheiner  84 , Olivier Gehlen  85 , Tomasz Skoczylas  86 , Marek Majewski  86 , Jan Lubiński  87 , Orazio Palmieri  30 , Stefania Boccia  88 , Anna Latiano  30 , Nuria Aragones  89 , Thomas Schmidt  34 , Mário Dinis-Ribeiro  90 , Rui Medeiros  91 , Salah-Eddin Al-Batran  28 , Mārcis Leja  92 , Juozas Kupcinskas  59 , María A García-González  93 , Marino Venerito  94 , Johannes Schumacher  95
Affiliations
Meta-Analysis

Dissecting the genetic heterogeneity of gastric cancer

Timo Hess et al. EBioMedicine. 2023 Jun.

Erratum in

  • Corrigendum to "Dissecting the genetic heterogeneity of gastric cancer".
    Hess T, Maj C, Gehlen J, Borisov O, Haas SL, Gockel I, Vieth M, Piessen G, Alakus H, Vashist Y, Pereira C, Knapp M, Schüller V, Quaas A, Grabsch HI, Trautmann J, Malecka-Wojciesko E, Mokrowiecka A, Speller J, Mayr A, Schröder J, Hillmer AM, Heider D, Lordick F, Pérez-Aísa Á, Campo R, Espinel J, Geijo F, Thomson C, Bujanda L, Sopeña F, Lanas Á, Pellisé M, Pauligk C, Goetze TO, Zelck C, Reingruber J, Hassanin E, Elbe P, Alsabeah S, Lindblad M, Nilsson M, Kreuser N, Thieme R, Tavano F, Pastorino R, Arzani D, Persiani R, Jung JO, Nienhüser H, Ott K, Schumann RR, Kumpf O, Burock S, Arndt V, Jakubowska A, Ławniczak M, Moreno V, Martín V, Kogevinas M, Pollán M, Dąbrowska J, Salas A, Cussenot O, Boland-Auge A, Daian D, Deleuze JF, Salvi E, Teder-Laving M, Tomasello G, Ratti M, Senti C, De Re V, Steffan A, Hölscher AH, Messerle K, Bruns CJ, Sīviņš A, Bogdanova I, Skieceviciene J, Arstikyte J, Moehler M, Lang H, Grimminger PP, Kruschewski M, Vassos N, Schildberg C, Lingohr P, Ridwelski K, Lippert H, Fricker N, Krawitz P, Hoffmann P, Nöthen MM, Veits L, Izbicki JR, Mostowska A, Martinón-Torres F, Cusi D, Adolfsson R, Cancel-Tassin G, Höblinger A, Rodermann E, Ludwig M, Keller G, Metspalu A, … See abstract for full author list ➔ Hess T, et al. EBioMedicine. 2023 Aug;94:104709. doi: 10.1016/j.ebiom.2023.104709. Epub 2023 Jul 20. EBioMedicine. 2023. PMID: 37480624 Free PMC article. No abstract available.

Abstract

Background: Gastric cancer (GC) is clinically heterogenous according to location (cardia/non-cardia) and histopathology (diffuse/intestinal). We aimed to characterize the genetic risk architecture of GC according to its subtypes. Another aim was to examine whether cardia GC and oesophageal adenocarcinoma (OAC) and its precursor lesion Barrett's oesophagus (BO), which are all located at the gastro-oesophageal junction (GOJ), share polygenic risk architecture.

Methods: We did a meta-analysis of ten European genome-wide association studies (GWAS) of GC and its subtypes. All patients had a histopathologically confirmed diagnosis of gastric adenocarcinoma. For the identification of risk genes among GWAS loci we did a transcriptome-wide association study (TWAS) and expression quantitative trait locus (eQTL) study from gastric corpus and antrum mucosa. To test whether cardia GC and OAC/BO share genetic aetiology we also used a European GWAS sample with OAC/BO.

Findings: Our GWAS consisting of 5816 patients and 10,999 controls highlights the genetic heterogeneity of GC according to its subtypes. We newly identified two and replicated five GC risk loci, all of them with subtype-specific association. The gastric transcriptome data consisting of 361 corpus and 342 antrum mucosa samples revealed that an upregulated expression of MUC1, ANKRD50, PTGER4, and PSCA are plausible GC-pathomechanisms at four GWAS loci. At another risk locus, we found that the blood-group 0 exerts protective effects for non-cardia and diffuse GC, while blood-group A increases risk for both GC subtypes. Furthermore, our GWAS on cardia GC and OAC/BO (10,279 patients, 16,527 controls) showed that both cancer entities share genetic aetiology at the polygenic level and identified two new risk loci on the single-marker level.

Interpretation: Our findings show that the pathophysiology of GC is genetically heterogenous according to location and histopathology. Moreover, our findings point to common molecular mechanisms underlying cardia GC and OAC/BO.

Funding: German Research Foundation (DFG).

Keywords: Gastric cancer; Genome-wide association study (GWAS); Oesophageal adenocarcinoma; Transcriptome-wide association study (TWAS).

PubMed Disclaimer

Conflict of interest statement

Declaration of interests MDR reports consulting fees from Roche Diagnostics and Medtronic; leadership or fiduciary role in the European Society of Gastrointestinal Endoscopy (ESGE) and World Endoscopy Organization (WEO). All other authors report no conflict of interest.

Figures

Fig. 1
Fig. 1
Regional association plots of GC risk loci. Disease associations are shown for non-cardia and cardia GC on chromosome 1q22 (a) and chromosome 8q24 (b). In addition, disease associations are shown for intestinal and diffuse GC on chromosome 2p23 (c) and chromosome 17q12 (d). At each locus, associations (−log10 (p values)) are shown for SNPs flanking 400 kb on either side of the lead associated SNP (Position in hg19). The lead variant is shown in purple. Other markers at each locus are displayed by different colours, which indicates different levels of LD (r2) to the lead SNP. Furthermore, all annotated genes within each region are shown with arrows indicating their transcription direction. The location- and Lauren-specific effects at each locus are also shown in a case-case comparison in the Appendix (p 12).
Fig. 2
Fig. 2
Genetic correlation between GC and risk factors. Genetic correlations determined with LDSR between GC and 20 traits belonging to five phenotype-categories that represent risk factors for GC development are shown. For each trait the genetic correlation (dot) and the standard deviation (line) is given. The significance levels of the genetic correlation are indicated by asterisks (∗p value < 0.05, ∗∗p value < 0.0025). Body mass index (rg = 0.303, p = 6.0 × 10−4), hip circumference (rg = 0.269, p = 2.3 × 10−3) and weight (rg = 0.262, p = 2.4 × 10−3) showed Bonferroni-corrected positive GC correlation along with pack years of adult smoking (rg = 0.352, p = 2.0 × 10−3) and alcohol intake 10 years previously (rg = 0.361, p = 2.0 × 10−3).
Fig. 3
Fig. 3
Polygenic risk score associations for OAC in the target GC subtypes. The association-values in dependence of different significance thresholds for PRS variant selection are given. The horizontal black line represents the Bonferroni correction threshold. All PRS associations for OAC/BO in the target GC subtypes are shown in the Appendix (p 17).
See this image and copyright information in PMC

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Publication types

Supplementary concepts