Purpose CHEK2*1100delC is a well-established breast cancer risk variant that is most prevalent in European populations; however, there are limited data on risk of breast cancer by age and tumor subtype, which limits its usefulness in breast cancer risk prediction. We aimed to generate tumor subtypeand age-specific risk estimates by using data from the Breast Cancer Association Consortium, including 44,777 patients with breast cancer and 42,997 controls from 33 studies genotyped for CHEK2*1100delC.Patients and Methods CHEK2*1100delC genotyping was mostly done by a custom Taqman assay. Breast cancer odds ratios (ORs) for CHEK2*1100delC carriers versus noncarriers were estimated by using logistic regression and adjusted for study (categorical) and age. Main analyses included patients with invasive breast cancer from population-and hospital-based studies. ResultsProportions of heterozygous CHEK2*1100delC carriers in controls, in patients with breast cancer from population-and hospital-based studies, and in patients with breast cancer from familial-and clinical genetics center-based studies were 0.5%, 1.3%, and 3.0%, respectively. The estimated OR for invasive breast cancer was 2.26 (95%CI, 1.90 to 2.69; P = 2.3 3 10 220 ). The OR was higher for estrogen receptor (ER)-positive disease (2.55 [95%CI, 2.10 to 3.10; P = 4.9 3 10 221 ]) than it was for ER-negative disease (1.32 [95%CI, 0.93 to 1.88; P = .12]; P interaction = 9.9 3 10 24 ). The OR significantly declined with attained age for breast cancer overall (P = .001) and for ER-positive tumors (P = .001). Estimated cumulative risks for development of ER-positive and ER-negative tumors by age 80 in CHEK2*1100delC carriers were 20% and 3%, respectively, compared with 9% and 2%, respectively, in the general population of the United Kingdom. ConclusionThese CHEK2*1100delC breast cancer risk estimates provide a basis for incorporating CHEK2*1100delC into breast cancer risk prediction models and into guidelines for intensified screening and follow-up. INTRODUCTIONSusceptibility to breast cancer is known to be conferred by rare mutations in high-risk genes, notably BRCA1 and BRCA2, by mutations in several moderate-risk genes, and by a large number of common genetic variants. Among moderate-risk genes, one of the best established is CHEK2 (cell-cycle checkpoint kinase 2).1 The protein encoded by CHEK2 is a cell-cycle checkpoint regulator and putative tumor suppressor and it plays a critical role in the DNA damage repair pathway.2-4 The 1100delC germline mutation in CHEK2, which is located at 22q12.1 (NM_007194.3(CHEK2):c.1100del: p.(Thr367Metfs*15)), is the most frequently found protein-truncating variant in populations of European descent.1,5-7 Deletion of a single cytosine at position 1100 in exon 10 introduces a stop codon and results in a kinase-dead CHEK2 protein.Although the evidence that CHEK2*1100delC is associated with increased breast cancer risk is unequivocal, the magnitude of the risk is still uncertain, in part because the variant is relatively uncommon and i...

LVI and tumour size emerged as the most powerful independent predictors of ALNM, followed by the location of the tumour in the breast and the presence of multiple foci.

Completion axillary lymph node dissection (cALND) is the golden standard if breast cancer involves the sentinel lymph node (SLN). However, most non-sentinel lymph nodes (NSLN) are not involved, cALND has a considerable complication rate and does not improve outcome. We here present and validate our predictive model for positive NSLNs in the cALND if the SLN is positive. Consecutive early breast cancer patients from one center undergoing cALND for a positive SLN were included. We assessed demographic and clinicopathological variables for NSLN involvement. Uni- and multivariate analysis was performed. A predictive model was built and validated in two external centers. 21.9% of 470 patients had at least one involved NSLN. In univariate analysis, seven variables were significantly correlated with NSLN involvement: tumor size, grade, lymphovascular invasion (LVI), number of positive and negative SLNs, size of SLN metastasis and intraoperative positive SLN. In multivariate analysis, LVI, number of negative SLNs, size of SLN metastasis and intraoperative positive pathological evaluation were independent predictors for NSLN involvement. The calculated risk resulted in an AUC of 0.76. Applied to the external data, the model was accurate and discriminating for one (AUC = 0.75) and less for the other center (AUC = 0.58). A discriminative predictive model was constructed to calculate the risk of NSLN involvement in case of a positive SLN. External validation of our model reveals differences in performance when applied to data from other institutions concluding that such a predictive model requires validation prior to use.