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. 2025 Feb 19;11(1):18.
doi: 10.1038/s41523-025-00733-y.

Lessons learned from a candidate gene study investigating aromatase inhibitor treatment outcome in breast cancer

Reiner Hoppe #  1   2 Stefan Winter #  3   4 Wing-Yee Lo  5 Kyriaki Michailidou  6   7 Manjeet K Bolla  7 Renske Keeman  8 Qin Wang  7 Joe Dennis  7 Michael Lush  7 Krishna R Kalari  9 Matthew P Goetz  9 Liewei Wang  10 Junmei Cairns  10 Richard Weinshilboum  10 Lois Shepherd  11 Bingshu E Chen  11 Lothar Häberle  12 Matthias Ruebner  12 Matthias W Beckmann  12 Wei He  13 Nicole L Larson  14 Sebastian M Armasu  15 Werner Schroth  3   4 Balram Chowbay  16   17   18 Chiea Chuen Khor  19   20   21 Mustapha Abubakar  22 Antonis C Antoniou  7 Thomas Brüning  23 Jose E Castelao  24 Jenny Chang-Claude  25   26 Nbcs Collaborators  27   28   29   30   31   32   33   34   35 Thilo Dörk  36 Diana M Eccles  37 Jonine D Figueroa  22   38   39 Manuela Gago-Dominguez  40 José A García-Sáenz  41 Melanie Gündert  42   43   44 Carolin C Hack  12 Ute Hamann  45 Sileny Han  46 Maartje J Hooning  47 Hanna Huebner  12 Abctb Investigators  48 Esther M John  49   50 Yon-Dschun Ko  51 Vessela N Kristensen  27   29 Sabine Linn  8   52   53 Sara Margolin  54   55 Dimitrios Mavroudis  56 Heli Nevanlinna  57 Patrick Neven  58 Nadia Obi  59   60 Tjoung-Won Park-Simon  36 Katri Pylkäs  61   62 Muhammad U Rashid  45   63 Atocha Romero  64 Emmanouil Saloustros  65 Elinor J Sawyer  66 William J Tapper  37 Ian Tomlinson  67 Camilla Wendt  55 Robert Winqvist  61 Alison M Dunning  68 Jacques Simard  69 Per Hall  13   54 Paul D P Pharoah  70 Matthias Schwab  3   4   71   72   73 Fergus J Couch  74 Kamila Czene  13 Peter A Fasching  12 Douglas F Easton  7   68 Marjanka K Schmidt  8   75   76 James N Ingle  9 Hiltrud Brauch  3   4   72   73
Affiliations

Lessons learned from a candidate gene study investigating aromatase inhibitor treatment outcome in breast cancer

Reiner Hoppe et al. NPJ Breast Cancer. .

Abstract

The role of germline genetics in adjuvant aromatase inhibitor (AI) treatment efficacy in ER-positive breast cancer is poorly understood. We employed a two-stage candidate gene approach to examine associations between survival endpoints and common germline variants in 753 endocrine resistance-related genes. For a discovery cohort, we screened the Breast Cancer Association Consortium database (n ≥ 90,000 cases) and retrieved 2789 AI-treated patients. Cox model-based analysis revealed 125 variants associated with overall, distant relapse-free, and relapse-free survival (p-value ≤ 1E-04). In validation analysis using five independent cohorts (n = 8857), none of the six selected candidates representing major linkage blocks at CELA2B/CASP9, NR1I2/GSK3B, LRP1B, and MIR143HG (CARMN) were validated. We discuss potential reasons for the failed validation and replication of published findings, including study/treatment heterogeneity and other limitations inherent to genomic treatment outcome studies. For the future, we envision prospective longitudinal studies with sufficiently long follow-up and endpoints that reflect the dynamic nature of endocrine resistance.

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Conflict of interest statement

Competing interests: Peter A. Fasching reports personal fees from Novartis, Pfizer, Daiichi-Sankyo, AstraZeneca, Eisai, Merck Sharp & Dohme, Lilly, Pierre Fabre, SeaGen, Roche, Hexal, Agendia, and Gilead and grants from Biontech and Cepheid. Carolin C. Hack received Honoraria from AstraZeneca, Daiichi Sankyo, Eisai, Novartis, Pfizer, Roche, Gilead and MSD as well as support for attending meetings from Daiichi Sankyo. Matthias Schwab received funding from Agena Bioscience GmbH, HepaRegenix GmbH, Robert Bosch GmbH, and CORAT Therapeutics GmbH, as well as honoraria for oral presentations at academically organized congresses and meetings; he received payment for expert testimony from the Research Impact Fund Committee (RIF), Research Grant Council (RCG), Hong Kong, and from the German Federal Ministry of Education and Research (BMBF); he is (section-) editor for Pharmacogenetics and Genomics, Drug Research, and Genome Medicine. Diana M. Eccles has received research support from AstraZeneca. Matthew P. Goetz reports consulting fees to Mayo Clinic from ARC Therapeutics, AstraZeneca, Biotheranostics, Blueprint Medicines, Lilly, Novartis, Rna Diagnostics, Sanofi Genzyme, Seattle Genetics, Sermonix, Engage Health Media, Laekna and TerSera Therapeutics/Ampity Health; grant funding to Mayo Clinic from Lilly, Pfizer, Sermonix, Loxo, AstraZeneca and ATOSSA Therapeutics; personal fees for CME activities from Research to Practice, Clinical Education Alliance, Medscape, and MJH Life Sciences; personal fees serving as a panelist for a panel discussion from Total Health Conferencing and personal fees for serving as a moderator for Curio Science; and travel support from Lilly. All other authors report no conflict of interest.

Figures

Fig. 1
Fig. 1. Flow diagram of BCAC patients with primary invasive ER-positive breast cancer, early-stage disease characteristics and AI therapy.
The BCAC database (version 12) comprised 92,933 patients with a confirmed diagnosis of invasive ER-positive breast cancer who are of European descent (based on genotype data) and older than or equal to 18 years of age. Of these, 27,887 patients had early-stage disease with documented adjuvant endocrine therapy (AI, Tamoxifen, switch therapy, or unspecified) and complete follow-up data. The subgroup of AI-treated patients analysed in this study comprised 2789 women of which 1335 had received anastrozole, 689 letrozole, and 114 exemestane. For 570 patients, AI treatment was not further specified and 81 patients had received an additional endocrine treatment. AI Aromatase Inhibitor, OS Overall survival, DRFS Distant relapse free survival, RFS Relapse free survival.
Fig. 2
Fig. 2. Candidate gene association study of 15-year overall, distant relapse-free, and relapse-free survival in AI-treated BCAC patients (discovery cohort).
The Manhattan plots display the results of unadjusted analyses (stratified by country and corrected for genetic principal components). The y-axis shows the –log10 meta-analysis p-values and the x-axis the chromosomal positions of the variants. Variants are located within 753 selected candidate genes or surrounding gene region (defined as +/- 10 kb from gene start/end). For variants with association p-value ≤ 1E-04, the corresponding gene symbols are displayed on top (“intergenic” in case no gene was annotated). Genes marked in bold were part of the 753 candidate genes. Analysis of (a) patients treated with any AI (n = 2789) and approximately 170,000 variants with MAF ≥ 0.05, (b) patients treated with anastrozole (n = 1335) and approximately 138,000 variants with MAF ≥ 0.1, and (c) patients treated with letrozole (n = 689) and approximately 138,000 variants with MAF ≥ 0.1. Survival endpoints include OS (overall survival), DRFS (distant relapse-free survival), and RFS (relapse-free survival).
Fig. 3
Fig. 3. Results of meta-analyses for selected candidate variants in validation cohorts and BCAC patients (discovery cohort).
Forest plots depicting per-allele hazard ratios (x-axis) and corresponding confidence intervals for (a) rs3107669, (b) rs353298, and (c) rs353296 in eligible validation cohorts. The size of the squares reflects the study size. The KARMA/pKARMA studies were omitted in DRFS and RFS analyses due to delayed study entry of nearly all 643 patients ( > 0.5 years after diagnosis). Diamonds represent results of random effects (RE) meta-analyses in validation cohorts and of the discovery analyses in BCAC patients, respectively. Endpoints are OS (overall survival), DRFS (distant relapse-free survival), and RFS (relapse-free survival).

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