Metabolomic Biomarkers for the Detection of Obesity-Driven Endometrial Cancer

TY - JOUR

T1 - Metabolomic Biomarkers for the Detection of Obesity-Driven Endometrial Cancer

AU - Njoku, Kelechi

AU - Campbell, Amy E.

AU - Geary, Bethany

AU - MacKintosh, Michelle L.

AU - Derbyshire, Abigail E.

AU - Kitson, Sarah J.

AU - Sivalingam, Vanitha N.

AU - Pierce, Andrew

AU - Whetton, Anthony D.

AU - Crosbie, Emma J.

N1 - © 2021 by the authors

PY - 2021/2/10

Y1 - 2021/2/10

N2 - Endometrial cancer is the most common malignancy of the female genital tract and a major cause of morbidity and mortality in women. Early detection is key to ensuring good outcomes but a lack of minimally invasive screening tools is a significant barrier. Most endometrial cancers are obesity-driven and develop in the context of severe metabolomic dysfunction. Blood-derived metabolites may therefore provide clinically relevant biomarkers for endometrial cancer detection. In this study, we analysed plasma samples of women with body mass index (BMI) ≥ 30 kg/m2 and endometrioid endometrial cancer (cases, n = 67) or histologically normal endometrium (controls, n = 69), using a mass spectrometry-based metabolomics approach. Eighty percent of the samples were randomly selected to serve as a training set and the remaining 20% were used to qualify test performance. Robust predictive models (AUC > 0.9) for endometrial cancer detection based on artificial intelligence algorithms were developed and validated. Phospholipids were of significance as biomarkers of endometrial cancer, with sphingolipids (sphingomyelins) discriminatory in post-menopausal women. An algorithm combining the top ten performing metabolites showed 92.6% prediction accuracy (AUC of 0.95) for endometrial cancer detection. These results suggest that a simple blood test could enable the early detection of endometrial cancer and provide the basis for a minimally invasive screening tool for women with a BMI ≥ 30 kg/m2.

AB - Endometrial cancer is the most common malignancy of the female genital tract and a major cause of morbidity and mortality in women. Early detection is key to ensuring good outcomes but a lack of minimally invasive screening tools is a significant barrier. Most endometrial cancers are obesity-driven and develop in the context of severe metabolomic dysfunction. Blood-derived metabolites may therefore provide clinically relevant biomarkers for endometrial cancer detection. In this study, we analysed plasma samples of women with body mass index (BMI) ≥ 30 kg/m2 and endometrioid endometrial cancer (cases, n = 67) or histologically normal endometrium (controls, n = 69), using a mass spectrometry-based metabolomics approach. Eighty percent of the samples were randomly selected to serve as a training set and the remaining 20% were used to qualify test performance. Robust predictive models (AUC > 0.9) for endometrial cancer detection based on artificial intelligence algorithms were developed and validated. Phospholipids were of significance as biomarkers of endometrial cancer, with sphingolipids (sphingomyelins) discriminatory in post-menopausal women. An algorithm combining the top ten performing metabolites showed 92.6% prediction accuracy (AUC of 0.95) for endometrial cancer detection. These results suggest that a simple blood test could enable the early detection of endometrial cancer and provide the basis for a minimally invasive screening tool for women with a BMI ≥ 30 kg/m2.

KW - endometrial cancer

KW - obesity

KW - metabolomics

KW - liquid biopsy

KW - mass spectrometry

KW - plasma biomarkers

KW - artificial intelligence

U2 - 10.3390/cancers13040718

DO - 10.3390/cancers13040718

M3 - Article

SN - 2072-6694

VL - 13

JO - Cancers

JF - Cancers

IS - 4

M1 - 718

ER -