Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Adi L Tarca; Bálint Ármin Pataki; Roberto Romero; Marina Sirota; Yuanfang Guan; Rintu Kutum; Nardhy Gomez-Lopez; Bogdan Done; Gaurav Bhatti; Thomas Yu; Gaia Andreoletti; Tinnakorn Chaiworapongsa; DREAM Preterm Birth Prediction Challenge Consortium; Sonia S Hassan; Chaur-Dong Hsu; Nima Aghaeepour; Gustavo Stolovitzky; Istvan Csabai; James C Costello

doi:10.1016/j.xcrm.2021.100323

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Cell Rep Med. 2021 Jun 15;2(6):100323. doi: 10.1016/j.xcrm.2021.100323.

Authors

Adi L Tarca^{1

2

3}, Bálint Ármin Pataki⁴, Roberto Romero^{1

5

6

7

8

9}, Marina Sirota^{10

11}, Yuanfang Guan¹², Rintu Kutum¹³, Nardhy Gomez-Lopez^{1

2

14}, Bogdan Done^{1

2}, Gaurav Bhatti^{1

2}, Thomas Yu¹⁵, Gaia Andreoletti^{10

11}, Tinnakorn Chaiworapongsa²; DREAM Preterm Birth Prediction Challenge Consortium; Sonia S Hassan^{2

16

17}, Chaur-Dong Hsu^{1

2

17}, Nima Aghaeepour¹⁸, Gustavo Stolovitzky^{19

20}, Istvan Csabai⁴, James C Costello²¹

Affiliations

¹ Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, US Department of Health and Human Services, Detroit, MI 48201, USA.
² Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48201 USA.
³ Department of Computer Science, Wayne State University College of Engineering, Detroit, MI 48202, USA.
⁴ Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary.
⁵ Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI 48109, USA.
⁶ Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.
⁷ Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA.
⁸ Detroit Medical Center, Detroit, MI 48201, USA.
⁹ Department of Obstetrics and Gynecology, Florida International University, Miami, FL 33199, USA.
¹⁰ Department of Pediatrics, University of California, San Francisco, San Francisco, CA 94143, USA.
¹¹ Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
¹² Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
¹³ Informatics and Big Data Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India.
¹⁴ Department of Biochemistry, Microbiology, and Immunology, Wayne State University School of Medicine, Detroit, MI 48201 USA.
¹⁵ Sage Bionetworks, Seattle, WA, USA.
¹⁶ Office of Women's Health, Integrative Biosciences Center, Wayne State University, Detroit, MI 48202, USA.
¹⁷ Department of Physiology, Wayne State University School of Medicine, Detroit, MI 48201, USA.
¹⁸ Department of Anesthesiology, Perioperative, and Pain Medicine, Department of Pediatrics, and Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA.
¹⁹ Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
²⁰ IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA.
²¹ Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.

Abstract

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27-33 weeks of gestation).

Keywords: aptamers; collaborative competition; human transcriptome arrays; machine learning; plasma proteomics; predictive modeling; preterm labor and delivery; spontaneous preterm birth; whole blood transcriptomics.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Adult
Asymptomatic Diseases
Biomarkers / blood
Blood Proteins / classification
Blood Proteins / genetics*
Blood Proteins / metabolism
Cell-Free Nucleic Acids / blood
Cell-Free Nucleic Acids / classification
Cell-Free Nucleic Acids / genetics*
Crowdsourcing / methods
Female
Gestational Age*
Humans
Infant, Newborn
Longitudinal Studies
Pre-Eclampsia / blood
Pre-Eclampsia / diagnosis
Pre-Eclampsia / genetics*
Pregnancy
Premature Birth / blood
Premature Birth / diagnosis
Premature Birth / genetics*
Proteomics / methods
ROC Curve
Transcriptome*

Substances

Biomarkers
Blood Proteins
Cell-Free Nucleic Acids

Abstract

Publication types

MeSH terms

Substances

Grants and funding