Obesity alters endoxifen plasma levels in young breast cancer patients: A pharmacometric simulation approach

Endoxifen is one of the most important metabolites of the prodrug tamoxifen. High interindividual variability in endoxifen steady‐state concentrations (CSS,min ENDX) is observed under tamoxifen standard dosing and patients with breast cancer who do not reach endoxifen concentrations above a proposed therapeutic threshold of 5.97 ng/mL may be at a 26% higher recurrence risk compared with patients with endoxifen concentrations exceeding this value. In this investigation, 10 clinical tamoxifen studies were pooled (1,388 patients) to investigate influential factors on CSS,min ENDX using nonlinear mixed‐effects modeling. Age and body weight were found to significantly impact CSS,min ENDX in addition to CYP2D6 phenotype. Compared with postmenopausal patients, premenopausal patients had a 30% higher risk for subtarget CSS,min ENDX at tamoxifen 20 mg per day. In treatment simulations for distinct patient subpopulations, young overweight patients had a 3.1–13.8‐fold higher risk for subtarget CSS,min ENDX compared with elderly low‐weight patients. Considering ever‐rising obesity rates and the clinical importance of tamoxifen for premenopausal patients, this subpopulation may benefit most from individualized tamoxifen dosing

ERBB4 Promoter Polymorphism Is Associated with Poor Distant Disease-Free Survival in High-Risk Early Breast Cancer

Peer reviewe

Obesity Alters Endoxifen Plasma Levels in Young Breast Cancer Patients: A Pharmacometric Simulation Approach

Endoxifen is the most important metabolite of the prodrug tamoxifen. High interindividual variability in endoxifen steady-state concentrations (CSS,min ENDX) is observed under tamoxifen standard dosing breast cancer patients that do not reach endoxifen concentrations above a proposed therapeutic threshold of 5.97 ng/mL may be at higher recurrence risk. In this investigation, 10 clinical tamoxifen studies were pooled (nPatients=1388) to investigate influential factors on CSS,min ENDX using nonlinear mixed-effects modelling. Age and body weight were found to significantly impact CSS,min ENDX in addition to CYP2D6 phenotype. Compared to post-menopausal patients, pre-menopausal patients had a 30% higher risk for subtarget CSS,min ENDX at tamoxifen 20 mg per day. In treatment simulations for distinct patient subpopulations, young overweight patients had a 3.1-13.8-fold higher risk for subtarget CSS,min ENDX compared to elderly low-weight patients. Considering ever-rising obesity rates and the clinical importance of tamoxifen for pre-menopausal patients, this subpopulation may benefit most from individualised tamoxifen dosing

Nonlinear Mixed-Effects Model of Z-Endoxifen Concentrations in Tamoxifen-Treated Patients from the CEPAM Cohort

Tamoxifen is widely used in patients with hormone receptor-positive breast cancer. The polymorphic enzyme CYP2D6 is primarily responsible for metabolic activation of tamoxifen, resulting in substantial interindividual variability of plasma concentrations of its most important metabolite, Z-endoxifen. The Z-endoxifen concentration thresholds below which tamoxifen treatment is less efficacious have been proposed but not validated, and prospective trials of individualized tamoxifen treatment to achieve Z-endoxifen concentration thresholds are considered infeasible. Therefore, we aim to validate the association between Z-endoxifen concentration and tamoxifen treatment outcomes, and identify a Z-endoxifen concentration threshold of tamoxifen efficacy, using pharmacometric modeling and simulation. As a first step, the CYP2D6 Endoxifen Percentage Activity Model (CEPAM) cohort was created by pooling data from 28 clinical studies (&gt; 7,000 patients) with measured endoxifen plasma concentrations. After cleaning, data from 6,083 patients were used to develop a nonlinear mixed-effect (NLME) model for tamoxifen and Z-endoxifen pharmacokinetics that includes a conversion factor to allow inclusion of studies that measured total endoxifen but not Z-endoxifen. The final parent-metabolite NLME model confirmed the primary role of CYP2D6, and contributions from body weight, CYP2C9 phenotype, and co-medication with CYP2D6 inhibitors, on Z-endoxifen pharmacokinetics. Future work will use the model to simulate Z-endoxifen concentrations in patients receiving single agent tamoxifen treatment within large prospective clinical trials with long-term survival to identify the Z-endoxifen concentration threshold below which tamoxifen is less efficacious. Identification of this concentration threshold would allow personalized tamoxifen treatment to improve outcomes in patients with hormone receptor-positive breast cancer.</p

Estrogen Metabolism and Exposure in a Genotypic–Phenotypic Model for Breast Cancer Risk Prediction

Abstract Background: Current models of breast cancer risk prediction do not directly reflect mammary estrogen metabolism or genetic variability in exposure to carcinogenic estrogen metabolites. Methods: We developed a model that simulates the kinetic effect of genetic variants of the enzymes CYP1A1, CYP1B1, and COMT on the production of the main carcinogenic estrogen metabolite, 4-hydroxyestradiol (4-OHE2), expressed as area under the curve metric (4-OHE2-AUC). The model also incorporates phenotypic factors (age, body mass index, hormone replacement therapy, oral contraceptives, and family history), which plausibly influence estrogen metabolism and the production of 4-OHE2. We applied the model to two independent, population-based breast cancer case–control groups, the German GENICA study (967 cases, 971 controls) and the Nashville Breast Cohort (NBC; 465 cases, 885 controls). Results: In the GENICA study, premenopausal women at the 90th percentile of 4-OHE2-AUC among control subjects had a risk of breast cancer that was 2.30 times that of women at the 10th control 4-OHE2-AUC percentile (95% CI: 1.7–3.2, P = 2.9 × 10−7). This relative risk was 1.89 (95% CI: 1.5–2.4, P = 2.2 × 10−8) in postmenopausal women. In the NBC, this relative risk in postmenopausal women was 1.81 (95% CI: 1.3–2.6, P = 7.6 × 10−4), which increased to 1.83 (95% CI: 1.4–2.3, P = 9.5 × 10−7) when a history of proliferative breast disease was included in the model. Conclusions: The model combines genotypic and phenotypic factors involved in carcinogenic estrogen metabolite production and cumulative estrogen exposure to predict breast cancer risk. Impact: The estrogen carcinogenesis–based model has the potential to provide personalized risk estimates. Cancer Epidemiol Biomarkers Prev; 20(7); 1502–15. ©2011 AACR.</jats:p

A network analysis to identify mediators of germline-driven differences in breast cancer prognosis.

Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis

The FANCM:p.Arg658* truncating variant is associated with risk of triple-negative breast cancer.

Breast cancer is a common disease partially caused by genetic risk factors. Germline pathogenic variants in DNA repair genes BRCA1, BRCA2, PALB2, ATM, and CHEK2 are associated with breast cancer risk. FANCM, which encodes for a DNA translocase, has been proposed as a breast cancer predisposition gene, with greater effects for the ER-negative and triple-negative breast cancer (TNBC) subtypes. We tested the three recurrent protein-truncating variants FANCM:p.Arg658*, p.Gln1701*, and p.Arg1931* for association with breast cancer risk in 67,112 cases, 53,766 controls, and 26,662 carriers of pathogenic variants of BRCA1 or BRCA2. These three variants were also studied functionally by measuring survival and chromosome fragility in FANCM -/- patient-derived immortalized fibroblasts treated with diepoxybutane or olaparib. We observed that FANCM:p.Arg658* was associated with increased risk of ER-negative disease and TNBC (OR = 2.44, P = 0.034 and OR = 3.79; P = 0.009, respectively). In a country-restricted analysis, we confirmed the associations detected for FANCM:p.Arg658* and found that also FANCM:p.Arg1931* was associated with ER-negative breast cancer risk (OR = 1.96; P = 0.006). The functional results indicated that all three variants were deleterious affecting cell survival and chromosome stability with FANCM:p.Arg658* causing more severe phenotypes. In conclusion, we confirmed that the two rare FANCM deleterious variants p.Arg658* and p.Arg1931* are risk factors for ER-negative and TNBC subtypes. Overall our data suggest that the effect of truncating variants on breast cancer risk may depend on their position in the gene. Cell sensitivity to olaparib exposure, identifies a possible therapeutic option to treat FANCM-associated tumors