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. 2023 Mar 2;110(3):475-486.
doi: 10.1016/j.ajhg.2023.02.003. Epub 2023 Feb 23.

The impact of coding germline variants on contralateral breast cancer risk and survival

Anna Morra  1 , Nasim Mavaddat  2 , Taru A Muranen  3 , Thomas U Ahearn  4 , Jamie Allen  2 , Irene L Andrulis  5 , Päivi Auvinen  6 , Heiko Becher  7 , Sabine Behrens  8 , Carl Blomqvist  9 , Stig E Bojesen  10 , Manjeet K Bolla  2 , Hiltrud Brauch  11 , Nicola J Camp  12 , Sara Carvalho  2 , Jose E Castelao  13 , Melissa H Cessna  14 , Jenny Chang-Claude  15 , Georgia Chenevix-Trench  16 ; NBCS Collaborators  17 ; Kamila Czene  18 , Brennan Decker  19 , Joe Dennis  2 , Thilo Dörk  20 , Leila Dorling  2 , Alison M Dunning  21 , Arif B Ekici  22 , Mikael Eriksson  18 , D Gareth Evans  23 , Peter A Fasching  24 , Jonine D Figueroa  25 , Henrik Flyger  26 , Manuela Gago-Dominguez  27 , Montserrat García-Closas  4 , Willemina R R Geurts-Giele  28 , Graham G Giles  29 , Pascal Guénel  30 , Melanie Gündert  31 , Eric Hahnen  32 , Per Hall  33 , Ute Hamann  34 , Patricia A Harrington  21 , Wei He  18 , Päivi Heikkilä  35 , Maartje J Hooning  36 , Reiner Hoppe  37 , Anthony Howell  38 , Keith Humphreys  18 ; kConFab Investigators  39 ; Anna Jakubowska  40 , Audrey Y Jung  8 , Renske Keeman  41 , Vessela N Kristensen  42 , Jan Lubiński  43 , Arto Mannermaa  44 , Mehdi Manoochehri  34 , Siranoush Manoukian  45 , Sara Margolin  46 , Dimitrios Mavroudis  47 , Roger L Milne  29 , Anna Marie Mulligan  48 , William G Newman  23 , Tjoung-Won Park-Simon  20 , Paolo Peterlongo  49 , Paul D P Pharoah  50 , Valerie Rhenius  21 , Emmanouil Saloustros  51 , Elinor J Sawyer  52 , Rita K Schmutzler  53 , Mitul Shah  21 , Amanda B Spurdle  54 , Ian Tomlinson  55 , Thérèse Truong  30 , Elke M van Veen  23 , Maaike P G Vreeswijk  56 , Qin Wang  2 , Camilla Wendt  46 , Xiaohong R Yang  4 , Heli Nevanlinna  3 , Peter Devilee  57 , Douglas F Easton  50 , Marjanka K Schmidt  58
Collaborators, Affiliations

The impact of coding germline variants on contralateral breast cancer risk and survival

Anna Morra et al. Am J Hum Genet. .

Abstract

Evidence linking coding germline variants in breast cancer (BC)-susceptibility genes other than BRCA1, BRCA2, and CHEK2 with contralateral breast cancer (CBC) risk and breast cancer-specific survival (BCSS) is scarce. The aim of this study was to assess the association of protein-truncating variants (PTVs) and rare missense variants (MSVs) in nine known (ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C, RAD51D, and TP53) and 25 suspected BC-susceptibility genes with CBC risk and BCSS. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated with Cox regression models. Analyses included 34,401 women of European ancestry diagnosed with BC, including 676 CBCs and 3,449 BC deaths; the median follow-up was 10.9 years. Subtype analyses were based on estrogen receptor (ER) status of the first BC. Combined PTVs and pathogenic/likely pathogenic MSVs in BRCA1, BRCA2, and TP53 and PTVs in CHEK2 and PALB2 were associated with increased CBC risk [HRs (95% CIs): 2.88 (1.70-4.87), 2.31 (1.39-3.85), 8.29 (2.53-27.21), 2.25 (1.55-3.27), and 2.67 (1.33-5.35), respectively]. The strongest evidence of association with BCSS was for PTVs and pathogenic/likely pathogenic MSVs in BRCA2 (ER-positive BC) and TP53 and PTVs in CHEK2 [HRs (95% CIs): 1.53 (1.13-2.07), 2.08 (0.95-4.57), and 1.39 (1.13-1.72), respectively, after adjusting for tumor characteristics and treatment]. HRs were essentially unchanged when censoring for CBC, suggesting that these associations are not completely explained by increased CBC risk, tumor characteristics, or treatment. There was limited evidence of associations of PTVs and/or rare MSVs with CBC risk or BCSS for the 25 suspected BC genes. The CBC findings are relevant to treatment decisions, follow-up, and screening after BC diagnosis.

Keywords: breast cancer susceptibility genes; coding germline variants; contralateral breast cancer risk; survival.

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Conflict of interest statement

Declaration of interests B.D. is a stockholder of Roche, Vaccitech and EQRx. P.A.F. conducts research funded by Amgen, Novartis, and Pfizer outside the submitted work. He received honoraria from Roche, Novartis, and Pfizer outside the submitted work. D.F.E. is an associate editor at The American Journal of Human Genetics (AJHG).

Figures

Figure 1
Figure 1
Forest plot showing the association of protein-truncating variants (PTVs) in ATM, BARD1, CHEK2, PALB2, RAD51C, and RAD51D and of combined PTVs and pathogenic/likely pathogenic rare missense variants (MSVs) in BRCA1, BRCA2, and TP53 with contralateral breast cancer (CBC) risk The black squares and solid lines represent hazard ratio (HR) estimates and 95% confidence intervals (CIs) from the unadjusted analyses, respectively. The gray squares and dashed gray lines represent HR estimates and 95% CIs from the adjusted analyses. We performed the adjusted analyses by including age at diagnosis, nodal status, size category, grade, estrogen receptor (ER) status, ERB-B2 receptor tyrosine kinase 2 (ERBB2) status of the first breast cancer, (neo)adjuvant chemotherapy, endocrine therapy, and trastuzumab as covariates in the Cox regression model. For each gene, the exact numbers of women and CBCs are reported in Table 1. PTVs and pathogenic/likely pathogenic MSVs were defined as in Dorling et al.
Figure 2
Figure 2
Cumulative incidence curves for developing contralateral breast cancer in the presence of competing risk of death for any cause (A–E) Cumulative incidence for carriers (blue line) and non-carriers (red line) of combined protein-truncating variants (PTVs) and pathogenic/likely pathogenic missense variants (MSVs) in BRCA1 (A), combined PTVs and pathogenic/likely MSVs in BRCA2 (B), PTVs in CHEK2 (C), PTVs in PALB2 (D), and combined PTVs and pathogenic/likely pathogenic MSVs in TP53 (E). PTVs and pathogenic/likely pathogenic MSVs as defined in Dorling et al. were considered. We limited the y axis to the range (0.00, 0.30) to better visualize the curves. The x axis is restricted to 15 years from diagnosis because of the low number of carriers after 15 years.
Figure 3
Figure 3
Forest plots showing the association of PTVs in ATM, BARD1, CHEK2, PALB2, RAD51C, and RAD51D and of combined PTVs and pathogenic/likely pathogenic rare missense variants (MSVs) in BRCA1, BRCA2, and TP53, with breast cancer-specific survival, in women from all studies, excluding women who developed a CBC before study entry (A–C) The results of the analysis shown in Table 2 are shown in (A). The results of the analysis based on women diagnosed with an estrogen receptor (ER)-positive first breast cancer (Table S26) are shown in (B). The hazard ratios (HRs) for the association of PTVs in RAD51C with breast cancer-specific survival could not be estimated because of the low number of carriers and the absence of carriers who died of breast cancer (Table S26). The results of the analysis based on women diagnosed with an estrogen ER-negative first breast cancer (Table S27) are shown in (C). The black squares and solid lines represent hazard ratio (HR) estimates and 95% confidence intervals (CIs) from the unadjusted analyses, respectively. The gray squares and dashed gray lines represent HR estimates and 95% CIs from the adjusted analyses. Adjusted analyses shown in (A) included age at diagnosis, nodal status, size category, grade, ER status, ERB-B2 receptor tyrosine kinase 2 (ERBB2) status of the first breast cancer, (neo)adjuvant chemotherapy, endocrine therapy, and trastuzumab as covariates in the Cox regression model. Adjusted analyses in (B) and (C) included the same covariates as in (A) except the ER status of the first breast cancer. PTVs and pathogenic/likely pathogenic MSVs were defined as in Dorling et al.
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