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- O'Mara, TA, et al.
(författare)
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Identification of nine new susceptibility loci for endometrial cancer
- 2018
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9:1, s. 3166-
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Tidskriftsartikel (refereegranskat)abstract
- Endometrial cancer is the most commonly diagnosed cancer of the female reproductive tract in developed countries. Through genome-wide association studies (GWAS), we have previously identified eight risk loci for endometrial cancer. Here, we present an expanded meta-analysis of 12,906 endometrial cancer cases and 108,979 controls (including new genotype data for 5624 cases) and identify nine novel genome-wide significant loci, including a locus on 12q24.12 previously identified by meta-GWAS of endometrial and colorectal cancer. At five loci, expression quantitative trait locus (eQTL) analyses identify candidate causal genes; risk alleles at two of these loci associate with decreased expression of genes, which encode negative regulators of oncogenic signal transduction proteins (SH2B3 (12q24.12) and NF1 (17q11.2)). In summary, this study has doubled the number of known endometrial cancer risk loci and revealed candidate causal genes for future study.
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| 2. |
- Fonnes, T., et al.
(författare)
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Asparaginase-like protein 1 expression in curettage independently predicts lymph node metastasis in endometrial carcinoma: a multicentre study
- 2018
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Ingår i: Bjog-an International Journal of Obstetrics and Gynaecology. - : Wiley. - 1470-0328 .- 1471-0528. ; 125:13, s. 1695-1703
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Tidskriftsartikel (refereegranskat)abstract
- Objective Design Correct preoperative identification of high-risk patients is important to optimise surgical treatment and improve survival. We wanted to explore if asparaginase-like protein 1 (ASRGL1) expression in curettage could predict lymph node metastases and poor outcome, potentially improving preoperative risk stratification. Multicentre study. Setting Population Ten hospitals in Norway, Sweden and Belgium. Women diagnosed with endometrial carcinoma. Methods Main outcome measures ASRGL1 expression in curettage specimens from 1144 women was determined by immunohistochemistry. ASRGL1 status related to disease-specific survival, lymph node status, preoperative imaging parameters and clinicopathological data. Results Conclusions ASRGL1 expression had independent prognostic value in multivariate survival analyses, both in the whole patient population (hazard ratio (HR) 1.63, 95% CI 1.11-2.37, P = 0.012) and in the low-risk curettage histology subgroup (HR 2.54, 95% CI 1.44-4.47, P = 0.001). Lymph node metastases were more frequent in women with low expression of ASRGL1 compared with women with high ASRGL1 levels (23% versus 10%, P < 0.001), and low ASRGL1 level was found to independently predict lymph node metastases (odds ratio 2.07, 95% CI 1.27-3.38, P = 0.003). Low expression of ASRGL1 in curettage independently predicts lymph node metastases and poor disease-specific survival.
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| 3. |
- Janssen, J. M., et al.
(författare)
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Semi-physiological Enriched Population Pharmacokinetic Modelling to Predict the Effects of Pregnancy on the Pharmacokinetics of Cytotoxic Drugs
- 2023
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Ingår i: Clinical Pharmacokinetics. - : Springer Nature. - 0312-5963 .- 1179-1926. ; 62:8, s. 1157-1167
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Tidskriftsartikel (refereegranskat)abstract
- Background and Objective As a result of changes in physiology during pregnancy, the pharmacokinetics (PK) of drugs can be altered. It is unclear whether under- or overexposure occurs in pregnant cancer patients and thus also whether adjustments in dosing regimens are required. Given the severity of the malignant disease and the potentially high impact on both the mother and child, there is a high unmet medical need for adequate and tolerable treatment of this patient population. We aimed to develop and evaluate a semi-physiological enriched model that incorporates physiological changes during pregnancy into available population PK models developed from non-pregnant patient data.Methods Gestational changes in plasma protein levels, renal function, hepatic function, plasma volume, extracellular water and total body water were implemented in existing empirical PK models for docetaxel, paclitaxel, epirubicin and doxorubicin. These models were used to predict PK profiles for pregnant patients, which were compared with observed data obtained from pregnant patients.Results The observed PK profiles were well described by the model. For docetaxel, paclitaxel and doxorubicin, an overprediction of the lower concentrations was observed, most likely as a result of a lack of data on the gestational changes in metabolizing enzymes. For paclitaxel, epirubicin and doxorubicin, the semi-physiological enriched model performed better in predicting PK in pregnant patients compared with a model that was not adjusted for pregnancy-induced changes.Conclusion By incorporating gestational changes into existing population pharmacokinetic models, it is possible to adequately predict plasma concentrations of drugs in pregnant patients which may inform dose adjustments in this population.
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