AI-Based Liquid Biopsy for Reproductive Precision Medicine
Keywords:
Cell-free DNA, prenatal healthcare, genomics, deep learning, data scienceAbstract
Cell-free DNA (cfDNA) occupies a unique place for various purposes in precision reproductive and preventive healthcare, as a product of metabolism and apoptosis of cells housed in different tissues and organs. Given the hitherto unusual success of deep learning (DL) in the domain of unconstrained problems across scientific fields, its application into cfDNA analytics also appears promising, potentially achieving clinical signals with similar efficiencies. Core principles are defined and linked to cfDNA functions in reproduction, suggesting cfDNA analysis for the achievement of related objectives. In particular, the sought-after conditions for DL introduction are discussed and proposed for the specific case of precision reproductive healthcare, whereby upcoming studies could be designed and assessed using these same elements.
CFDNA is a biological fluid with two main clinical applications: sex determination of the fetus and non-invasive prenatal testing (NIPT) of fetal aneuploidies. Although both applications rely on a similar cfDNA approach, they target different clinical signals and rest on different models of CFDA analysis. Nevertheless, four fundamental requirements are common to both applications, conditions that have led to their indeed successful development and implementation. Two core conditions, related to cfDNA biology and the classification of the clinical signal under consideration, seem to be particularly relevant for DL implementation. These aspects are subsequently addressed, and the conclusions provide a perspective on the future of DL in CFDA.
References
1. Shitara, A., Takahashi, K., Goto, M., Takahashi, H., Iwasawa, T., Onodera, Y., & others. (2021). Cell-free DNA in spent culture medium effectively reflects the chromosomal status of embryos following culturing beyond implantation compared to trophectoderm biopsy. PLOS ONE, 16(2), e0246438.
2. Palei, A. C. (2021). Cell-free DNA as a potential biomarker for preeclampsia. Expert Review of Molecular Diagnostics, 21(12), 1253–1256.
3. Loganathan, R., & Kolla, S. H. (2024). HARNESSING GENERATIVE AI FOR ADAPTIVE RISK POLICY GENERATION IN MULTI-REGULATORY DATA CENTER ENVIRONMENTS. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 505–515. https://doi.org/10.61841/vakvfh91
4. Moufarrej, M. N., Vorperian, S. K., Wong, R. J., Campos, A. A., Quaintance, C. C., Sit, R. V., Tan, M., Detweiler, A. M., Mekonen, H., Neff, N. F., Baruch-Gravett, C., Litch, J. A., Druzin, M. L., Winn, V. D., Shaw, G. M., Stevenson, D. K., & Quake, S. R. (2022). Early prediction of preeclampsia in pregnancy with cell-free RNA. Nature, 602, 689–694.
5. Navarro-Sánchez, L., García-Pascual, C., Rubio, C., & Simón, C. (2022). Non-invasive preimplantation genetic testing for aneuploidies: An update. Reproductive BioMedicine Online, 44(5), 817–828.
6. Alizadegan, A., Dianat-Moghadam, H., Shadman, N., Nouri, M., Hamdi, K., Ghasemzadeh, A., Akbarzadeh, M., Sarvarian, P., Mehdizadeh, A., Dolati, S., & Yousefi, M. (2022). Application of cell free DNA in ART. Placenta, 120, 18–24.
7. Sonehara, H., Matsumoto, R., Nakayama, N., & others. (2022). Aneuploidy and sex concordance rate between cell-free DNA analysis from spent culture media of preimplantation embryo and DNA from whole embryo with respect to different morphological grading. Reproductive Medicine and Biology, 21(1), e12493.
8. Buca, D., D’Antonio, F., Buca, D. V., Di Sebastiano, F., Simeone, P., Di Girolamo, R., Bologna, G., Vespa, S., Catitti, G., Liberati, M., Miscia, S., & Lanuti, P. (2022). Extracellular vesicles in pregnancy: Their potential role as a liquid biopsy. Journal of Reproductive Immunology, 154, 103734.
9. Gray, K. J., Hemberg, M., & Karumanchi, S. A. (2022). Cell-free RNA transcriptome and prediction of adverse pregnancy outcomes. Clinical Chemistry, 68(11), 1358–1360.
10. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
11. García-Pascual, C. M., Navarro-Sánchez, L., Ichikawa-Ceschin, I., Bakalova, D., Martínez-Merino, L., Simón, C., & Rubio, C. (2023). Cell-free deoxyribonucleic acid analysis in preimplantation genetic testing. F&S Science, 4(2S), 7–16.
12. Handayani, N., Aubry, D., Boediono, A., Sirait, B., Sini, I., Polim, A. A., Dwiranti, A., & Bowolaksono, A. (2023). The origin and possible mechanism of embryonic cell-free DNA release in spent embryo culture media: A review. Journal of Assisted Reproduction and Genetics, 40(6), 1231–1242.
13. Shi, H., Pan, M., Jia, E., Lu, W., Zhou, Y., Sheng, Y., Zhao, X., Cai, L., & Ge, Q. (2023). A comprehensive characterization of cell-free RNA in spent blastocyst medium and quality prediction for blastocyst. Clinical Science, 137(2), 129–143.
14. Kolla, S. K., & Reddy, V. A. R. (2024). Evaluating Cloud-Native vs. Hybrid Architectures for Health Benefit Administration Systems. International Journal of Medical Toxicology and Legal Medicine, 27(5), 1042-1053.
15. De Borre, M., Che, H., Yu, Q., Lannoo, L., De Ridder, K., Vancoillie, L., Dreesen, P., Van Den Ackerveken, M., Aerden, M., Galle, E., Breckpot, J., Van Keirsbilck, J., Gyselaers, W., Devriendt, K., Vermeesch, J. R., Van Calsteren, K., & Thienpont, B. (2023). Cell-free DNA methylome analysis for early preeclampsia prediction. Nature Medicine, 29(9), 2206–2215.
16. Zhou, S., Li, J., Yang, W., Xue, P., Yin, Y., Wang, Y., Tian, P., Peng, H., Jiang, H., Xu, W., Huang, S., Zhang, R., Wei, F., Sun, H.-X., Zhang, J., & Zhao, L. (2023). Noninvasive preeclampsia prediction using plasma cell-free RNA signatures. American Journal of Obstetrics and Gynecology, 229(5), 553.e1–553.e16.
17. Zhao, Z., Li, B., Xiao, X., Liu, J., & Zheng, W. (2023). Cell-free RNA and fully convolutional dense network-based early preeclampsia prediction. Clinical and Translational Medicine, 13(8), e1371.
18. Paul, N., Sultana, Z., Fisher, J. J., Maiti, K., & Smith, R. (2023). Extracellular vesicles—Crucial players in human pregnancy. Placenta, 140, 30–38.
19. Buca, D., Lucidi, A., Buca, D. V., Di Sebastiano, F., D’Angelo, E., Vespa, S., Liberati, M., Lanuti, P., & D’Antonio, F. (2023). Extracellular vesicles during the three trimesters of pregnancy. Journal of Reproductive Immunology, 159, 103987.
20. Gahlawat, A. W., Witte, T., Sinn, P., & Schott, S. (2023). Circulating cf-miRNA as a more appropriate surrogate liquid biopsy marker than cfDNA for ovarian cancer. Scientific Reports, 13, 5503.
21. Yu, S. C. Y., Choy, L. Y. L., & Lo, Y. M. D. (2023). “Longing” for the next generation of liquid biopsy: The diagnostic potential of long cell-free DNA in oncology and prenatal testing. Molecular Diagnosis & Therapy, 27(5), 563–571.
22. Bandi, V. D. V. K. (2024). Intelligent Data Platforms For Personalized Retail Analytics At Scale. Metallurgical and Materials Engineering, 30 (4), 1011–1027.
23. Moufarrej, M. N., Bianchi, D. W., Shaw, G. M., Stevenson, D. K., & Quake, S. R. (2023). Noninvasive prenatal testing using circulating DNA and RNA: Advances, challenges, and possibilities. Annual Review of Biomedical Data Science, 6, 397–418.
24. Li, N., Gu, Y., Tang, J., Li, Y., Chen, D., & Xu, Z. (2023). Circulating non-coding RNAs and exosomes: Liquid biopsies for monitoring preeclampsia. Methods in Molecular Biology, 2664, 327–341.
25. Warintaksa, P., Romero, R., Lertrat, W., Yuenyongdechawat, N., Mongkolsuk, P., Chaiyakarn, S., Settacomkul, R., Pongchaikul, P., Vivithanaporn, P., & Chaemsaithong, P. (2024). Quantitative cervicovaginal fluid fetal fibronectin: A liquid biopsy for intra-amniotic inflammation. Acta Obstetricia et Gynecologica Scandinavica, 103(11), 2252–2263.
26. Khalil, A., Bellesia, G., Norton, M. E., Jacobsson, B., Haeri, S., Egbert, M., Malone, F. D., Wapner, R. J., Roman, A., Faro, R., Madankumar, R., Strong, N., Silver, R. M., Vohra, N., Hyett, J., MacPherson, C., Prigmore, B., Ahmed, E., Demko, Z., Ortiz, J. B., Souter, V., & Dar, P. (2024). The role of cell-free DNA biomarkers and patient data in the early prediction of preeclampsia: An artificial intelligence model. American Journal of Obstetrics and Gynecology, 231(5), 554.e1–554.e18.
27. Yu, Y., Xu, W., Zhang, S., Feng, S., Feng, F., Dai, J., Zhang, X., Tian, P., Wang, S., Zhao, Z., Zhao, W., Guan, L., Qiu, Z., Zhang, J., Peng, H., Lin, J., Zhang, Q., Chen, W., Li, H., Zhou, S., et al. (2024). Non-invasive prediction of preeclampsia using the maternal plasma cell-free DNA profile and clinical risk factors. Frontiers in Medicine, 11, 1254467.
28. Aerden, M., De Borre, M., & Thienpont, B. (2024). Cell-free DNA methylation-based preeclampsia prediction: A journey to improve maternal health. Prenatal Diagnosis, 44(4), 418–421.
29. Pan, M., Shi, H., Qi, T., Cai, L., & Ge, Q. (2024). The biological characteristics of long cell-free DNA in spent embryos culture medium as noninvasive biomarker in in-vitro embryo selection. Gene, 927, 148667.
30. Handayani, N., Aubry, D., Boediono, A., Bowolaksono, A., Sini, I., Haq, N. M. D., Sirait, B., Periastiningrum, G., Mutia, K., & Wiweko, B. (2024). Non-invasive pre-implantation genetic testing's reliability for aneuploidy using cell-free DNA in embryo culture media. Journal of Gynecology Obstetrics and Human Reproduction, 53(8), 102808.
31. Popova, A. K., Vashukova, E. S., & Illarionov, R. A. (2024). Extracellular vesicles as biomarkers of pregnancy complications. International Journal of Molecular Sciences, 25(22), 11944.
32. Mangalampalli, B. M. Intelligent Data Profiling for Healthcare Data Lakes Using AI-Enhanced Analytics.
33. Della Corte, L., Russo, G., Pepe, F., Pisapia, P., Dell'Aquila, M., Malapelle, U., Troncone, G., Bifulco, G., & Giampaolino, P. (2024). The role of liquid biopsy in epithelial ovarian cancer: State of the art. Critical Reviews in Oncology/Hematology, 194, 104263.
34. Trevisi, E., Sessa, C., & Colombo, I. (2024). Clinical relevance of circulating tumor DNA in ovarian cancer: Current issues and future opportunities. Exploration of Targeted Anti-tumor Therapy, 5(3), 627–640.
35. Zhang, Y., & Tian, L. (2024). Advances and challenges in the use of liquid biopsy in gynaecological oncology. Heliyon, 10(20), e39148.
