Dataset Information

Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.

ABSTRACT: Controlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient's genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched to groups of various MII oocyte counts using ranking, correspondence analysis, and self-organizing map methods. The gradient boosting machine technique was used to train models on a clinical dataset of 8,574 or a clinical-genetic dataset of 516 ovarian stimulations. The clinical-genetic model predicted the number of MII oocytes better than that based on clinical data. Anti-Müllerian hormone level and antral follicle count were the two most important predictors while a genetic feature consisting of sequence variants in the GDF9, LHCGR, FSHB, ESR1, and ESR2 genes was the third. The combined contribution of genetic features important for the prediction was over one-third of that revealed for anti-Müllerian hormone. Predictions of our clinical-genetic model accurately matched individuals' actual outcomes preventing over- or underestimation. The genetic data upgrades the personalized prediction of ovarian stimulation outcomes, thus improving the in vitro fertilization procedure.

SUBMITTER: Zielinski K

PROVIDER: S-EPMC10138216 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.

Zieliński Krystian K Pukszta Sebastian S Mickiewicz Małgorzata M Kotlarz Marta M Wygocki Piotr P Zieleń Marcin M Drzewiecka Dominika D Drzyzga Damian D Kloska Anna A Jakóbkiewicz-Banecka Joanna J

PLoS computational biology 20230427 4

Controlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient's genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched to groups of various MII oocyte counts using ranking, correspondenc ...[more]

PMID: 37104276

Similar Datasets

Project description:PurposeThe aim of this study was to determine whether BRCA1/2 mutation carriers produce fewer mature oocytes after ovarian stimulation for in vitro fertilization (IVF) with preimplantation genetic diagnosis (PGD), in comparison to a PGD control group.MethodsA retrospective, international, multicenter cohort study was performed on data of first PGD cycles performed between January 2006 and September 2015. Data were extracted from medical files. The study was performed in one PGD center and three affiliated IVF centers in the Netherlands and one PGD center in Belgium. Exposed couples underwent PGD because of a pathogenic BRCA1/2 mutation, controls for other monogenic conditions. Only couples treated in a long gonadotropin-releasing hormone (GnRH) agonist-suppressive protocol, stimulated with at least 150 IU follicle stimulating hormone (FSH), were included. Women suspected to have a diminished ovarian reserve status due to chemotherapy, auto-immune disorders, or genetic conditions (other than BRCA1/2 mutations) were excluded. A total of 106 BRCA1/2 mutation carriers underwent PGD in this period, of which 43 (20 BRCA1 and 23 BRCA2 mutation carriers) met the inclusion criteria. They were compared to 174 controls selected by frequency matching.ResultsThirty-eight BRCA1/2 mutation carriers (18 BRCA1 and 20 BRCA2 mutation carriers) and 154 controls proceeded to oocyte pickup. The median number of mature oocytes was 7.0 (interquartile range (IQR) 4.0-9.0) in the BRCA group as a whole, 6.5 (IQR 4.0-8.0) in BRCA1 mutation carriers, 7.5 (IQR 5.5-9.0) in BRCA2 mutation carriers, and 8.0 (IQR 6.0-11.0) in controls. Multiple linear regression analysis with the number of mature oocytes as a dependent variable and adjustment for treatment center, female age, female body mass index (BMI), type of gonadotropin used, and the total dose of gonadotropins administered revealed a significantly lower yield of mature oocytes in the BRCA group as compared to controls (p = 0.04). This finding could be fully accounted for by the BRCA1 subgroup (BRCA1 mutation carriers versus controls p = 0.02, BRCA2 mutation carriers versus controls p = 0.50).ConclusionsOvarian response to stimulation, expressed as the number of mature oocytes, was reduced in BRCA1 but not in BRCA2 mutation carriers. Although oocyte yield was in correspondence to a normal response in all subgroups, this finding points to a possible negative influence of the BRCA1 gene on ovarian reserve.

Project description:Lung cancers with a mutated epidermal growth factor receptor (EGFR) are a major contributor to cancer fatalities globally. Targeted tyrosine kinase inhibitors (TKIs) have been developed against EGFR and show encouraging results for survival rate and quality of life. However, drug resistance may affect treatment plans and treatment efficacy may be lost after about a year. Predicting the response to EGFR-TKIs for EGFR-mutated lung cancer patients is a key research area. In this study, we propose a personalized drug response prediction model (PDRP), based on molecular dynamics simulations and machine learning, to predict the response of first generation FDA-approved small molecule EGFR-TKIs, Gefitinib/Erlotinib, in lung cancer patients. The patient's mutation status is taken into consideration in molecular dynamics (MD) simulation. Each patient's unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. Moreover, additional clinical features were incorporated into machine learning model for drug response prediction. The complete feature set includes demographic and clinical information (DCI), geometrical properties of the drug-target binding site, and the binding free energy of the drug-target complex from the MD simulation. PDRP incorporates an XGBoost classifier, which achieves state-of-the-art performance with 97.5% accuracy, 93% recall, 96.5% precision, and 94% F1-score, for a 4-class drug response prediction task. We found that modeling the geometry of the binding pocket combined with binding free energy is a good predictor for drug response. However, we observed that clinical information had a little impact on the performance of the model. The proposed model could be tested on other types of cancers. We believe PDRP will support the planning of effective treatment regimes based on clinical-genomic information. The source code and related files are available on GitHub at: https://github.com/rizwanqureshi123/PDRP/ .

Dataset Information

Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.

Publications

Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets