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Development and Validation of a Serum Metabolomic Signature for Endometrial Cancer Screening in Postmenopausal Women.

ABSTRACT: Importance:Endometrial carcinoma (EC) is the most commonly diagnosed gynecologic cancer. Its early detection is advisable because 20% of women have advanced disease at the time of diagnosis. Objective:To clinically validate a metabolomics-based classification algorithm as a screening test for EC. Design, Setting, and Participants:This diagnostic study enrolled 2 cohorts. A multicenter prospective cohort, with 50 cases (postmenopausal women with EC; International Federation of Gynecology and Obstetrics stage I-III and grade G1-G3) and 70 controls (no EC but matched on age, years from menopause, tobacco use, and comorbidities), was used to train multiple classification models. The accuracy of each trained model was then used as a statistical weight to produce an ensemble machine learning algorithm for testing, which was validated with a subsequent prospective cohort of 1430 postmenopausal women. The study was conducted at the San Giovanni di Dio e Ruggi d'Aragona University Hospital of Salerno (Italy) and Lega Italiana per la Lotta contro i Tumori clinic in Avellino (Italy). Data collection was conducted from January 2018 to February 2019, and analysis was conducted from January to March 2019. Main Outcomes and Measures:The presence or absence of EC based on evaluation of the blood metabolome. Metabolites were extracted from dried blood samples from all participants and analyzed by gas chromatography-mass spectrometry. A confusion matrix was used to summarize test results. Performance indices included sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and accuracy. Confirmation or exclusion of EC in women with a positive test result was by means of hysteroscopy. Participants with negative results were followed up 1 year after enrollment to investigate the appearance of EC signs. Results:The study population consisted of 1550 postmenopausal women. The mean (SD) age was 68.2 (11.7) years for participants with no EC in the training cohort, 69.4 (13.8) years for women with EC in the training cohort, and 59.7 (7.7) years for women in the validation cohort. Application of the ensemble machine learning to the validation cohort resulted in 16 true-positives, 2 false-positives, and 0 false-negatives, and it correctly classified more than 99% of samples. Disease prevalence was 1.12% (16 of 1430). Conclusions and Relevance:In this study, dried blood metabolomic profile was used to assess the presence or absence of EC in postmenopausal women not receiving hormonal therapy with greater than 99% accuracy.

SUBMITTER: Troisi J

PROVIDER: S-EPMC7522698 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Development and Validation of a Serum Metabolomic Signature for Endometrial Cancer Screening in Postmenopausal Women.

Troisi Jacopo J Raffone Antonio A Travaglino Antonio A Belli Gaetano G Belli Carmen C Anand Santosh S Giugliano Luigi L Cavallo Pierpaolo P Scala Giovanni G Symes Steven S Richards Sean S Adair David D Fasano Alessio A Bottigliero Vincenzo V Guida Maurizio M

JAMA network open 20200901 9

<h4>Importance</h4>Endometrial carcinoma (EC) is the most commonly diagnosed gynecologic cancer. Its early detection is advisable because 20% of women have advanced disease at the time of diagnosis.<h4>Objective</h4>To clinically validate a metabolomics-based classification algorithm as a screening test for EC.<h4>Design, setting, and participants</h4>This diagnostic study enrolled 2 cohorts. A multicenter prospective cohort, with 50 cases (postmenopausal women with EC; International Federation ...[more]

PMID: 32986110

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Project description:ObjectiveEstrogen is a well-established risk factor for various cancers. It causes endometrial proliferation, which is assessed routinely as endometrial thickness (ET) using transvaginal ultrasound (TVS). Only one previous study, restricted to endometrial and breast cancer, has considered ET and the risk of non-endometrial cancer. The aim of this study was to explore the association between baseline and serial ET measurements and nine non-endometrial hormone-sensitive cancers, in postmenopausal women, using contemporary statistical methodology that attempts to minimize the biases typical of endogenous serial data.MethodsThis was a cohort study nested within the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). In the ultrasound arm of UKCTOCS, 50639 postmenopausal women, aged 50-74, underwent annual TVS examination, of whom 38 105 had a valid ET measurement, no prior hysterectomy and complete covariate data, and were included in this study. All women were followed up through linkage to national cancer registries. The effect of ET on the risk of six estrogen-dependent cancers (breast, ovarian, colorectal, bladder, lung and pancreatic) was assessed using joint models for longitudinal biomarker and time-to-event data, and Cox models were used to assess the association between baseline ET measurement and these six cancers in addition to liver cancer, gastric cancer and non-Hodgkin's lymphoma (NHL). All models were adjusted for current hormone-replacement therapy (HRT) use, body mass index, age at last menstrual period, parity and oral contraceptive pill use.ResultsThe 38 105 included women had a combined total of 267 567 (median, 8; interquartile range, 5-9) valid ET measurements. During a combined total of 407 838 (median, 10.9) years of follow-up, 1398 breast, 351 endometrial, 381 lung, 495 colorectal, 222 ovarian, 94 pancreatic, 79 bladder, 62 gastric, 38 liver cancers and 52 NHLs were registered. Using joint models, a doubling of ET increased significantly the risk of breast (hazard ratio (HR), 1.21; 95% CI, 1.09-1.36; P = 0.001), ovarian (HR, 1.39; 95% CI, 1.06-1.82; P = 0.018) and lung (HR, 1.25; 95% CI, 1.02-1.54; P = 0.036) cancers. There were no statistically significant associations between ET and the remaining six cancers.ConclusionPostmenopausal women with high/increasing ET on TVS are at increased risk of breast, ovarian and lung cancer. It is important that clinicians are aware of these risks, as TVS is a common investigation. © 2019 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Project description:BackgroundCancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker.MethodsWe developed and validated linear and dichotomous (≥35 U/mL) circulating CA125 prediction models in postmenopausal women without ovarian cancer who participated in one of five large population-based studies: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n = 26,981), European Prospective Investigation into Cancer and Nutrition (EPIC, n = 861), the Nurses' Health Studies (NHS/NHSII, n = 81), and the New England Case Control Study (NEC, n = 923). The prediction models were developed using stepwise regression in PLCO and validated in EPIC, NHS/NHSII and NEC.ResultThe linear CA125 prediction model, which included age, race, body mass index (BMI), smoking status and duration, parity, hysterectomy, age at menopause, and duration of hormone therapy (HT), explained 5% of the total variance of CA125. The correlation between measured and predicted CA125 was comparable in PLCO testing dataset (r = 0.18) and external validation datasets (r = 0.14). The dichotomous CA125 prediction model included age, race, BMI, smoking status and duration, hysterectomy, time since menopause, and duration of HT with AUC of 0.64 in PLCO and 0.80 in validation dataset.ConclusionsThe linear prediction model explained a small portion of the total variability of CA125, suggesting the need to identify novel predictors of CA125. The dichotomous prediction model showed moderate discriminatory performance which validated well in independent dataset. Our dichotomous model could be valuable in identifying healthy women who may have elevated CA125 levels, which may contribute to reducing false positive tests using CA125 as screening biomarker.

Dataset Information

Development and Validation of a Serum Metabolomic Signature for Endometrial Cancer Screening in Postmenopausal Women.

Publications

Development and Validation of a Serum Metabolomic Signature for Endometrial Cancer Screening in Postmenopausal Women.

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