A polymorphism in the base excision repair gene PARP2 is associated with differential prognosis by chemotherapy among postmenopausal breast cancer patients.
Personalized therapy considering clinical and genetic patient characteristics will further improve breast cancer survival. Two widely used treatments, chemotherapy and radiotherapy, can induce oxidative DNA damage and, if not repaired, cell death. Since base excision repair (BER) activity is specific for oxidative DNA damage, we hypothesized that germline genetic variation in this pathway will affect breast cancer-specific survival depending on treatment.We assessed in 1,408 postmenopausal breast cancer patients from the German MARIE study whether cancer specific survival after adjuvant chemotherapy, anthracycline chemotherapy, and radiotherapy is modulated by 127 Single Nucleotide Polymorphisms (SNPs) in 21 BER genes. For SNPs with interaction terms showing p<0.1 (likelihood ratio test) using multivariable Cox proportional hazard analyses, replication in 6,392 patients from nine studies of the Breast Cancer Association Consortium (BCAC) was performed.rs878156 in PARP2 showed a differential effect by chemotherapy (p=0.093) and was replicated in BCAC studies (p=0.009; combined analysis p=0.002). Compared to non-carriers, carriers of the variant G allele (minor allele frequency=0.07) showed better survival after chemotherapy (combined allelic hazard ratio (HR)=0.75, 95% 0.53-1.07) and poorer survival when not treated with chemotherapy (HR=1.42, 95% 1.08-1.85). A similar effect modification by rs878156 was observed for anthracycline-based chemotherapy in both MARIE and BCAC, with improved survival in carriers (combined allelic HR=0.73, 95% CI 0.40-1.32). None of the SNPs showed significant differential effects by radiotherapy.Our data suggest for the first time that a SNP in PARP2, rs878156, may together with other genetic variants modulate cancer specific survival in breast cancer patients depending on chemotherapy. These germline SNPs could contribute towards the design of predictive tests for breast cancer patients.
Seibold, P; Schmezer, P; Behrens, S; Michailidou, K; Bolla, MK; Wang, Q; Flesch-Janys, D; Nevanlinna, H; Fagerholm, R; Aittomäki, K; Blomqvist, C; Margolin, S; Mannermaa, A; Kataja, V; Kosma, V-M; Hartikainen, JM; Lambrechts, D; Wildiers, H; Kristensen, V; Alnæs, GG; Nord, S; Borresen-Dale, A-L; Hooning, MJ; Hollestelle, A; Jager, A; Seynaeve, C; Li, J; Liu, J; Humphreys, K; Dunning, AM; Rhenius, V; Shah, M; Kabisch, M; Torres, D; Ulmer, H-U; Hamann, U; Schildkraut, JM; Purrington, KS; Couch, FJ; Hall, P; Pharoah, P; Easton, DF; Schmidt, MK; Chang-Claude, J; Popanda, O
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