Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2017 Nov 21;8(1):1632.
doi: 10.1038/s41467-017-01775-y.

Germline Variation in ADAMTSL1 Is Associated With Prognosis Following Breast Cancer Treatment in Young Women

Affiliations
Free PMC article
Meta-Analysis

Germline Variation in ADAMTSL1 Is Associated With Prognosis Following Breast Cancer Treatment in Young Women

Latha Kadalayil et al. Nat Commun. .
Free PMC article

Abstract

To identify genetic variants associated with breast cancer prognosis we conduct a meta-analysis of overall survival (OS) and disease-free survival (DFS) in 6042 patients from four cohorts. In young women, breast cancer is characterized by a higher incidence of adverse pathological features, unique gene expression profiles and worse survival, which may relate to germline variation. To explore this hypothesis, we also perform survival analysis in 2315 patients aged ≤ 40 years at diagnosis. Here, we identify two SNPs associated with early-onset DFS, rs715212 (P meta = 3.54 × 10-5) and rs10963755 (P meta = 3.91 × 10-4) in ADAMTSL1. The effect of these SNPs is independent of classical prognostic factors and there is no heterogeneity between cohorts. Most importantly, the association with rs715212 is noteworthy (FPRP <0.2) and approaches genome-wide significance in multivariable analysis (P multivariable = 5.37 × 10-8). Expression quantitative trait analysis provides tentative evidence that rs715212 may influence AREG expression (P eQTL = 0.035), although further functional studies are needed to confirm this association and determine a mechanism.

Conflict of interest statement

The authors declare no competing financial interests.

Figures

Fig. 1
Fig. 1
Genome-wide analysis of breast cancer survival. The Manhattan plot shows the result of the stage-1 meta-analysis. Results are plotted as –log10 of the P-value from Cox regression. For each SNP the most significant P-value is selected from the analysis of either overall survival (OS) or disease-free survival (DFS) in all patients or the subset with early onset. The four most significant SNPs after meta-analysis of stages 1 and 2 are highlighted in green (rs410155 and rs12302097 associated with OS and DFS respectively in the whole cohort and rs715212 and rs10963755 associated with DFS in patients with early onset). This plot was produced using the qqman R package
Fig. 2
Fig. 2
Forest plot and meta-analysis for the four most significant SNPs associated with overall survival (OS) or disease-free survival (DFS). Forest plot showing the event rate, hazard ratio (HR), 95% confidence interval (CI) and significance level (P-value) from Cox regression in each cohort and the combined analysis for the most significant SNPs associated with DFS and OS. ABCFS*: evidence for association with OS in the ABCFS cohort is shown for each SNP but these results are excluded from the meta-analyses of DFS. The SNP subtotal rows show the result for a fixed effects meta-analysis across four studies for rs715212, rs10963755 and rs12302097 and five studies for rs410155 using I 2 and Cochran Q-statistic to assess heterogeneity in effect sizes between cohorts
Fig. 3
Fig. 3
Kaplan–Meir survival plots for the four most significant SNPs identified by meta-analyses. Kaplan–Meier plots from univariate analysis of the most significant SNP associated with disease-free survival (DFS) in cases with early onset (a, rs715212 and b, rs10963755), DFS in all cases (c, rs12302097) and overall survival (OS) in all cases (d, rs410155). For OS, the data from all five cohorts (ABCFS, HEBCS, POSH stages 1 and 2 and SUCCESS-A) was pooled whereas for DFS data were pooled across four cohorts because DFS was not recorded in the ABCFS cohort. HR: hazard ratio with 95% confidence interval
Fig. 4
Fig. 4
Regional plots of association with survival (OS or DFS) at stage-1 meta-analysis, recombination rate and gene context for the most significant SNPs. Results from the stage-1 meta-analyses in a region surrounding the most significant SNP associated with DFS in patients with early onset (a, rs715212 and rs10963755), DFS in all patients (b, rs12302097) and OS in all patients (c, rs410155). In each plot, a purple diamond identifies the index SNP and the colour of other SNPs represent their linkage disequilibrium (r 2) with the index SNP from light blue (r 2 ≤ 0.4) to red (r 2 ≥ 0.8). The middle panel displays the 15 state chromatin segmentation track (ChromHMM) in breast variant human mammary epithelial cells (vHMECs, E028), mammary epithelial primary cells (HMECs, E119) and breast myoepithelial primary cells (E027) using data from the HapMap ENCODE Project. The lower panels show genes and their direction of transcription (arrows). Physical positions are relative to build 37 (hg19) of the human genome

Similar articles

  • Common germline polymorphisms associated with breast cancer-specific survival.
    Pirie A, Guo Q, Kraft P, Canisius S, Eccles DM, Rahman N, Nevanlinna H, Chen C, Khan S, Tyrer J, Bolla MK, Wang Q, Dennis J, Michailidou K, Lush M, Dunning AM, Shah M, Czene K, Darabi H, Eriksson M, Lambrechts D, Weltens C, Leunen K, van Ongeval C, Nordestgaard BG, Nielsen SF, Flyger H, Rudolph A, Seibold P, Flesch-Janys D, Blomqvist C, Aittomäki K, Fagerholm R, Muranen TA, Olsen JE, Hallberg E, Vachon C, Knight JA, Glendon G, Mulligan AM, Broeks A, Cornelissen S, Haiman CA, Henderson BE, Schumacher F, Le Marchand L, Hopper JL, Tsimiklis H, Apicella C, Southey MC, Cross SS, Reed MW, Giles GG, Milne RL, McLean C, Winqvist R, Pylkäs K, Jukkola-Vuorinen A, Grip M, Hooning MJ, Hollestelle A, Martens JW, van den Ouweland AM, Marme F, Schneeweiss A, Yang R, Burwinkel B, Figueroa J, Chanock SJ, Lissowska J, Sawyer EJ, Tomlinson I, Kerin MJ, Miller N, Brenner H, Butterbach K, Holleczek B, Kataja V, Kosma VM, Hartikainen JM, Li J, Brand JS, Humphreys K, Devilee P, Tollenaar RA, Seynaeve C, Radice P, Peterlongo P, Manoukian S, Ficarazzi F, Beckmann MW, Hein A, Ekici AB, Balleine R, Phillips KA; kConFab Investigators, Benitez J, Zamora MP, Perez JI, Menéndez P, Jakubowska A, Lubinski J, Gronwald J, Durda K, Hamann U, Kabisch M, Ulmer HU, Rüdiger T, Margolin S, Kristensen V, Nord S; NBCS Investigators, Evans DG, Abraham J, Earl H, Poole CJ, Hiller L, Dunn JA, Bowden S, Yang R, Campa D, Diver WR, Gapstur SM, Gaudet MM, Hankinson S, Hoover RN, Hüsing A, Kaaks R, Machiela MJ, Willett W, Barrdahl M, Canzian F, Chin SF, Caldas C, Hunter DJ, Lindstrom S, Garcia-Closas M, Couch FJ, Chenevix-Trench G, Mannermaa A, Andrulis IL, Hall P, Chang-Claude J, Easton DF, Bojesen SE, Cox A, Fasching PA, Pharoah PD, Schmidt MK. Pirie A, et al. Breast Cancer Res. 2015 Apr 22;17(1):58. doi: 10.1186/s13058-015-0570-7. Breast Cancer Res. 2015. PMID: 25897948 Free PMC article.
  • The relationship between eight GWAS-identified single-nucleotide polymorphisms and primary breast cancer outcomes.
    Bayraktar S, Thompson PA, Yoo SY, Do KA, Sahin AA, Arun BK, Bondy ML, Brewster AM. Bayraktar S, et al. Oncologist. 2013;18(5):493-500. doi: 10.1634/theoncologist.2012-0419. Epub 2013 May 1. Oncologist. 2013. PMID: 23635555 Free PMC article.
  • A genome-wide association study of prognosis in breast cancer.
    Azzato EM, Pharoah PD, Harrington P, Easton DF, Greenberg D, Caporaso NE, Chanock SJ, Hoover RN, Thomas G, Hunter DJ, Kraft P. Azzato EM, et al. Cancer Epidemiol Biomarkers Prev. 2010 Apr;19(4):1140-3. doi: 10.1158/1055-9965.EPI-10-0085. Epub 2010 Mar 23. Cancer Epidemiol Biomarkers Prev. 2010. PMID: 20332263 Free PMC article.
  • Association of the germline TP53 R72P and MDM2 SNP309 variants with breast cancer survival in specific breast tumor subgroups.
    van den Broek AJ, Broeks A, Horlings HM, Canisius SV, Braaf LM, Langerød A, Van't Veer LJ, Schmidt MK. van den Broek AJ, et al. Breast Cancer Res Treat. 2011 Nov;130(2):599-608. doi: 10.1007/s10549-011-1615-y. Epub 2011 Jun 11. Breast Cancer Res Treat. 2011. PMID: 21667122
  • The impact of ERBB-family germline single nucleotide polymorphisms on survival response to adjuvant trastuzumab treatment in HER2-positive breast cancer.
    Toomey S, Madden SF, Furney SJ, Fan Y, McCormack M, Stapleton C, Cremona M, Cavalleri GL, Milewska M, Elster N, Carr A, Fay J, Kay EW, Kennedy S, Crown J, Gallagher WM, Hennessy BT, Eustace AJ. Toomey S, et al. Oncotarget. 2016 Nov 15;7(46):75518-75525. doi: 10.18632/oncotarget.12782. Oncotarget. 2016. PMID: 27776352 Free PMC article.
See all similar articles

Cited by 3 articles

  • High WDR34 mRNA expression as a potential prognostic biomarker in patients with breast cancer as determined by integrated bioinformatics analysis.
    Hu DJ, Shi WJ, Yu M, Zhang L. Hu DJ, et al. Oncol Lett. 2019 Sep;18(3):3177-3187. doi: 10.3892/ol.2019.10634. Epub 2019 Jul 18. Oncol Lett. 2019. PMID: 31452794 Free PMC article.
  • Genome-wide association study of germline variants and breast cancer-specific mortality.
    Escala-Garcia M, Guo Q, Dörk T, Canisius S, Keeman R, Dennis J, Beesley J, Lecarpentier J, Bolla MK, Wang Q, Abraham J, Andrulis IL, Anton-Culver H, Arndt V, Auer PL, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Bernstein L, Blomqvist C, Boeckx B, Bojesen SE, Bonanni B, Børresen-Dale AL, Brauch H, Brenner H, Brentnall A, Brinton L, Broberg P, Brock IW, Brucker SY, Burwinkel B, Caldas C, Caldés T, Campa D, Canzian F, Carracedo A, Carter BD, Castelao JE, Chang-Claude J, Chanock SJ, Chenevix-Trench G, Cheng TD, Chin SF, Clarke CL; NBCS Collaborators, Cordina-Duverger E, Couch FJ, Cox DG, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dunn JA, Dunning AM, Durcan L, Dwek M, Earl HM, Ekici AB, Eliassen AH, Ellberg C, Engel C, Eriksson M, Evans DG, Figueroa J, Flesch-Janys D, Flyger H, Gabrielson M, Gago-Dominguez M, Galle E, Gapstur SM, García-Closas M, García-Sáenz JA, Gaudet MM, George A, Georgoulias V, Giles GG, Glendon G, Goldgar DE, González-Neira A, Alnæs GIG, Grip M, Guénel P, Haeberle L, Hahnen E, Haiman CA, Håkansson N, Hall P, Hamann U, Hankinson S, Harkness EF, Harrington PA, Hart SN, Hartikainen JM, Hein A, Hillemanns P, Hiller L, Holleczek B, Hollestelle A, Hooning MJ, Hoover RN, Hopper JL, Howell A, Huang G, Humphreys K, Hunter DJ, Janni W, John EM, Jones ME, Jukkola-Vuorinen A, Jung A, Kaaks R, Kabisch M, Kaczmarek K, Kerin MJ, Khan S, Khusnutdinova E, Kiiski JI, Kitahara CM, Knight JA, Ko YD, Koppert LB, Kosma VM, Kraft P, Kristensen VN, Krüger U, Kühl T, Lambrechts D, Le Marchand L, Lee E, Lejbkowicz F, Li L, Lindblom A, Lindström S, Linet M, Lissowska J, Lo WY, Loibl S, Lubiński J, Lux MP, MacInnis RJ, Maierthaler M, Maishman T, Makalic E, Mannermaa A, Manoochehri M, Manoukian S, Margolin S, Martinez ME, Mavroudis D, McLean C, Meindl A, Middha P, Miller N, Milne RL, Moreno F, Mulligan AM, Mulot C, Nassir R, Neuhausen SL, Newman WT, Nielsen SF, Nordestgaard BG, Norman A, Olsson H, Orr N, Pankratz VS, Park-Simon TW, Perez JIA, Pérez-Barrios C, Peterlongo P, Petridis C, Pinchev M, Prajzendanc K, Prentice R, Presneau N, Prokofieva D, Pylkäs K, Rack B, Radice P, Ramachandran D, Rennert G, Rennert HS, Rhenius V, Romero A, Roylance R, Saloustros E, Sawyer EJ, Schmidt DF, Schmutzler RK, Schneeweiss A, Schoemaker MJ, Schumacher F, Schwentner L, Scott RJ, Scott C, Seynaeve C, Shah M, Simard J, Smeets A, Sohn C, Southey MC, Swerdlow AJ, Talhouk A, Tamimi RM, Tapper WJ, Teixeira MR, Tengström M, Terry MB, Thöne K, Tollenaar RAEM, Tomlinson I, Torres D, Truong T, Turman C, Turnbull C, Ulmer HU, Untch M, Vachon C, van Asperen CJ, van den Ouweland AMW, van Veen EM, Wendt C, Whittemore AS, Willett W, Winqvist R, Wolk A, Yang XR, Zhang Y, Easton DF, Fasching PA, Nevanlinna H, Eccles DM, Pharoah PDP, Schmidt MK. Escala-Garcia M, et al. Br J Cancer. 2019 Mar;120(6):647-657. doi: 10.1038/s41416-019-0393-x. Epub 2019 Feb 21. Br J Cancer. 2019. PMID: 30787463 Free PMC article.
  • ADAMTSL4, a Secreted Glycoprotein, Is a Novel Immune-Related Biomarker for Primary Glioblastoma Multiforme.
    Zhao Z, Zhang KN, Chai RC, Wang KY, Huang RY, Li GZ, Wang YZ, Chen J, Jiang T. Zhao Z, et al. Dis Markers. 2019 Jan 8;2019:1802620. doi: 10.1155/2019/1802620. eCollection 2019. Dis Markers. 2019. PMID: 30728876 Free PMC article.

References

    1. Ferlay J, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer. 2015;136:E359–E386. doi: 10.1002/ijc.29210. - DOI - PubMed
    1. Wishart GC, et al. PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer. Breast Cancer Res. 2010;12:R1. doi: 10.1186/bcr2464. - DOI - PMC - PubMed
    1. Welter D, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–D1006. doi: 10.1093/nar/gkt1229. - DOI - PMC - PubMed
    1. Michailidou K, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat. Genet. 2013;45:361e1–362e1. doi: 10.1038/ng.2563. - DOI - PMC - PubMed
    1. Hartman M, et al. Is breast cancer prognosis inherited? Breast Cancer Res. 2007;9:R39. doi: 10.1186/bcr1737. - DOI - PMC - PubMed

Publication types

MeSH terms

Feedback