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. 2022 Jan 4;24(1):2.
doi: 10.1186/s13058-021-01484-x.

Common variants in breast cancer risk loci predispose to distinct tumor subtypes

Thomas U Ahearn #  1 , Haoyu Zhang #  1   2 , Kyriaki Michailidou  3   4   5 , Roger L Milne  6   7   8 , Manjeet K Bolla  4 , Joe Dennis  4 , Alison M Dunning  9 , Michael Lush  4 , Qin Wang  4 , Irene L Andrulis  10   11 , Hoda Anton-Culver  12 , Volker Arndt  13 , Kristan J Aronson  14 , Paul L Auer  15   16 , Annelie Augustinsson  17 , Adinda Baten  18 , Heiko Becher  19 , Sabine Behrens  20 , Javier Benitez  21   22 , Marina Bermisheva  23   24 , Carl Blomqvist  25   26 , Stig E Bojesen  27   28   29 , Bernardo Bonanni  30 , Anne-Lise Børresen-Dale  31   32 , Hiltrud Brauch  33   34   35 , Hermann Brenner  13   36   37 , Angela Brooks-Wilson  38   39 , Thomas Brüning  40 , Barbara Burwinkel  41   42 , Saundra S Buys  43 , Federico Canzian  44 , Jose E Castelao  45 , Jenny Chang-Claude  20   46 , Stephen J Chanock  1 , Georgia Chenevix-Trench  47 , Christine L Clarke  48 ; NBCS Collaborators; J Margriet Collée  49 , Angela Cox  50 , Simon S Cross  51 , Kamila Czene  52 , Mary B Daly  53 , Peter Devilee  54   55 , Thilo Dörk  56 , Miriam Dwek  57 , Diana M Eccles  58 , D Gareth Evans  59   60 , Peter A Fasching  61 , Jonine Figueroa  62   63 , Giuseppe Floris  18 , Manuela Gago-Dominguez  64   65 , Susan M Gapstur  66 , José A García-Sáenz  67 , Mia M Gaudet  66 , Graham G Giles  6   7   8 , Mark S Goldberg  68   69 , Anna González-Neira  21 , Grethe I Grenaker Alnæs  31 , Mervi Grip  70 , Pascal Guénel  71 , Christopher A Haiman  72 , Per Hall  52   73 , Ute Hamann  74 , Elaine F Harkness  75   76   77 , Bernadette A M Heemskerk-Gerritsen  78 , Bernd Holleczek  79 , Antoinette Hollestelle  78 , Maartje J Hooning  78 , Robert N Hoover  1 , John L Hopper  7 , Anthony Howell  80 ; ABCTB Investigators; kConFab/AOCS Investigators; Milena Jakimovska  81 , Anna Jakubowska  82   83 , Esther M John  84   85 , Michael E Jones  86 , Audrey Jung  20 , Rudolf Kaaks  20 , Saila Kauppila  87 , Renske Keeman  88 , Elza Khusnutdinova  23   89 , Cari M Kitahara  90 , Yon-Dschun Ko  91 , Stella Koutros  1 , Vessela N Kristensen  32   92 , Ute Krüger  17 , Katerina Kubelka-Sabit  93 , Allison W Kurian  84   85 , Kyriacos Kyriacou  5   94 , Diether Lambrechts  95   96 , Derrick G Lee  97   98 , Annika Lindblom  99   100 , Martha Linet  90 , Jolanta Lissowska  101 , Ana Llaneza  102 , Wing-Yee Lo  33   103 , Robert J MacInnis  6   7 , Arto Mannermaa  104   105   106 , Mehdi Manoochehri  74 , Sara Margolin  73   107 , Maria Elena Martinez  65 , Catriona McLean  108 , Alfons Meindl  109 , Usha Menon  110 , Heli Nevanlinna  111 , William G Newman  59   60 , Jesse Nodora  65   112 , Kenneth Offit  113 , Håkan Olsson  17 , Nick Orr  114 , Tjoung-Won Park-Simon  56 , Alpa V Patel  66 , Julian Peto  115 , Guillermo Pita  116 , Dijana Plaseska-Karanfilska  81 , Ross Prentice  15 , Kevin Punie  117 , Katri Pylkäs  118   119 , Paolo Radice  120 , Gad Rennert  121 , Atocha Romero  122 , Thomas Rüdiger  123 , Emmanouil Saloustros  124 , Sarah Sampson  125 , Dale P Sandler  126 , Elinor J Sawyer  127 , Rita K Schmutzler  128   129   130 , Minouk J Schoemaker  86 , Ben Schöttker  13   131 , Mark E Sherman  132 , Xiao-Ou Shu  133 , Snezhana Smichkoska  134 , Melissa C Southey  6   8   135 , John J Spinelli  136   137 , Anthony J Swerdlow  86   138 , Rulla M Tamimi  139 , William J Tapper  58 , Jack A Taylor  126   140 , Lauren R Teras  66 , Mary Beth Terry  141 , Diana Torres  74   142 , Melissa A Troester  143 , Celine M Vachon  144 , Carolien H M van Deurzen  145 , Elke M van Veen  59   60 , Philippe Wagner  17 , Clarice R Weinberg  146 , Camilla Wendt  73   107 , Jelle Wesseling  88   147 , Robert Winqvist  118   119 , Alicja Wolk  148   149 , Xiaohong R Yang  1 , Wei Zheng  133 , Fergus J Couch  150 , Jacques Simard  151 , Peter Kraft  152   153 , Douglas F Easton  4   9 , Paul D P Pharoah  4   9 , Marjanka K Schmidt  88   154 , Montserrat García-Closas #  155 , Nilanjan Chatterjee #  156   157
Collaborators, Affiliations

Common variants in breast cancer risk loci predispose to distinct tumor subtypes

Thomas U Ahearn et al. Breast Cancer Res. .

Abstract

Background: Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear.

Methods: Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes.

Results: Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate < 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p < 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions.

Conclusion: This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.

Keywords: Breast cancer; Common breast cancer susceptibility variants; Etiologic heterogeneity; Genetic predisposition.

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Conflict of interest statement

The authors have no competing interests to declare.

Figures

Fig. 1
Fig. 1
Overview of the analytic strategy and results from the investigation of 173 known breast cancer susceptibility variants for evidence of heterogeneity of effect according to the estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and grade. aWe evaluated 173 breast cancer risk variants identified in or replicated by prior BCAC GWAS [6, 7], see Methods and Additional file 3: Methods sections for more details. bModel 1 (primary analyses): Mixed-effect two-stage polytomous model (ER as fixed-effect, and PR, HER2 and grade as random-effects) for global heterogeneity tests (i.e. case-case comparisons from stage 2 of the two-stage model) between each individual risk variant and any of the tumor features (separate models were fit for each variant). cModel 2: Fixed-effect two-stage polytomous model for marker-specific tumor heterogeneity tests (i.e. case-case comparisons from stage 2 of the two-stage model) between each individual variant and each of the tumor features (ER, PR, HER2, and grade), mutually adjusted for each other (separate models were fit for each variant). dModel 3: Fixed effect two-stage polytomous model for risk associations with intrinsic-like subtypes (i.e. case–control comparisons from stage 1 of the two-stage model): luminal A-like, luminal B-like/HER2-negative, luminal B-like/HER2-positive, HER2-positive/non-luminal, and triple-negative. eModel 4: Fixed effect two-stage polytomous model for risk associations with tumor grade (i.e. case–control comparisons from stage 1 of the two-stage model) for the 12 variants associated at p < 0.05 only with grade in case-case comparisons (from model 2): grade 1, grade 2, and grade 3
Fig. 2
Fig. 2
Heatmap of the z-values from the fixed-effects two-stage polytomous model for marker-specific heterogeneity tests (case-case comparison from model 2) for the association between each of the 173 breast cancer susceptibility variants and estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) or grade, adjusting for principal components and each tumor marker. Columns represent individual variants. For more detailed information on the context of the figure, see Additional file 1: Fig. S1
Fig. 3
Fig. 3
Results from fixed-effects two-stage polytomous models for risk associationsa with intrinsic-like subtypes (model 3) for variants with evidence of heterogeneity by tumor markers in the two-stage model (model1)b; panels show examples of variants (a) most strongly associated with luminal-like subtypes, (b) most strongly associated with TN subtypes, (c) associated with all subtypes with varying strengths of association, and (d) associated with luminal A-like and TN subtypes in different directions. See Additional file 1: Fig. S2 for more details
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