4.7 Article

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

期刊

出版社

MDPI
DOI: 10.3390/ijms232416014

关键词

endometrial cancer; recurrence; prediction; machine learning

资金

  1. NIH
  2. Department of Defense [5R01CA99908-18]
  3. Research Fund of the Gynecologic Oncology Division of the University of Iowa Hospitals and Clinics [OC190352]
  4. Department of Obstetrics and Gynecology research fund of the University of Iowa

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Prediction models incorporating clinical and genomic data can help distinguish patients at risk of disease recurrence in endometrial cancer.
Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case-control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 55 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine-learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone.

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