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

• Published in: International journal of molecular sciences 2022-12, Vol.23 (24), p.16014
• Main Authors: Gonzalez-Bosquet, Jesus, Gabrilovich, Sofia, McDonald, Megan E, Smith, Brian J, Leslie, Kimberly K, Bender, David D, Goodheart, Michael J, Devor, Eric
• Format: Article
• Language: English
• Subjects: Cancer therapies; Carcinoma, Endometrioid - genetics; Case-Control Studies; Datasets; Endometrial cancer; Endometrial Neoplasms - epidemiology; Endometrial Neoplasms - genetics; Endometrium; Female; Gene expression; Genomics; Health services; Humans; machine learning; Mortality; Neoplasm Recurrence, Local - genetics; Non-coding RNA; Obesity; Patients; Pilot Projects; prediction; Prediction models; Radiation; recurrence; Regression analysis; Retrospective Studies; Risk assessment; RNA, Long Noncoding; Surveillance; Transcriptomes; Vagina
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms232416014

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.