The Need for Multi-Omics Biomarker Signatures in Precision Medicine

Michael Olivier, Reto Asmis, Gregory A Hawkins, Timothy D Howard, Laura A Cox, Michael Olivier, Reto Asmis, Gregory A Hawkins, Timothy D Howard, Laura A Cox

Abstract

Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide array of complex human diseases, including cancer. As a result, multi-omics analyses-which take advantage of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics areas-have been proposed and heralded as the key to advancing precision medicine in the clinic. In the field of precision oncology, genomics approaches, and, more recently, other omics analyses have helped reveal several key mechanisms in cancer development, treatment resistance, and recurrence risk, and several of these findings have been implemented in clinical oncology to help guide treatment decisions. However, truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine. Additional efforts are needed to develop the analytical infrastructure necessary to generate, analyze, and annotate multi-omics data effectively to inform precision medicine-based decision-making.

Keywords: epigenomics; genomics; integrated multi-omics; metabolomics; precision medicine; precision oncology; proteomics; transcriptomics.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Summary of the applications of individual omics technologies to study cancer and other human disorders.

References

    1. Yadav S.P. The wholeness in suffix -omics, -omes, and the word om. J. Biomol. Tech. 2007;18:277.
    1. Jiang Z., Zhou X., Li R., Michal J.J., Zhang S., Dodson M.V., Zhang Z., Harland R.M. Whole transcriptome analysis with sequencing: Methods, challenges and potential solutions. Cell Mol. Life Sci. 2015;72:3425–3439. doi: 10.1007/s00018-015-1934-y.
    1. Mutz K.O., Heilkenbrinker A., Lonne M., Walter J.G., Stahl F. Transcriptome analysis using next-generation sequencing. Curr. Opin. Biotechnol. 2013;24:22–30. doi: 10.1016/j.copbio.2012.09.004.
    1. Kalisky T., Oriel S., Bar-Lev T.H., Ben-Haim N., Trink A., Wineberg Y., Kanter I., Gilad S., Pyne S. A brief review of single-cell transcriptomic technologies. Brief. Funct. Genom. 2018;17:64–76. doi: 10.1093/bfgp/elx019.
    1. Aebersold R., Mann M. Mass-spectrometric exploration of proteome structure and function. Nature. 2016;537:347–355. doi: 10.1038/nature19949.
    1. Aslam B., Basit M., Nisar M.A., Khurshid M., Rasool M.H. Proteomics: Technologies and Their Applications. J. Chromatogr. Sci. 2017;55:182–196. doi: 10.1093/chromsci/bmw167.
    1. Schrimpe-Rutledge A.C., Codreanu S.G., Sherrod S.D., McLean J.A. Untargeted Metabolomics Strategies-Challenges and Emerging Directions. J. Am. Soc. Mass Spectrom. 2016;27:1897–1905. doi: 10.1007/s13361-016-1469-y.
    1. Li B., He X., Jia W., Li H. Novel Applications of Metabolomics in Personalized Medicine: A Mini-Review. Molecules. 2017;22:1173. doi: 10.3390/molecules22071173.
    1. Wang K.C., Chang H.Y. Epigenomics: Technologies and Applications. Circ. Res. 2018;122:1191–1199. doi: 10.1161/CIRCRESAHA.118.310998.
    1. Stricker S.H., Koferle A., Beck S. From profiles to function in epigenomics. Nat. Rev. Genet. 2017;18:51–66. doi: 10.1038/nrg.2016.138.
    1. Han X., Gross R.W. Shotgun lipidomics: Multidimensional MS analysis of cellular lipidomes. Expert Rev. Proteom. 2005;2:253–264. doi: 10.1586/14789450.2.2.253.
    1. Wenk M.R. The emerging field of lipidomics. Nat. Rev. Drug Discov. 2005;4:594–610. doi: 10.1038/nrd1776.
    1. Riesenfeld C.S., Schloss P.D., Handelsman J. Metagenomics: Genomic analysis of microbial communities. Annu. Rev. Genet. 2004;38:525–552. doi: 10.1146/annurev.genet.38.072902.091216.
    1. Raman R., Raguram S., Venkataraman G., Paulson J.C., Sasisekharan R. Glycomics: An integrated systems approach to structure-function relationships of glycans. Nat. Methods. 2005;2:817–824. doi: 10.1038/nmeth807.
    1. Sporns O., Tononi G., Kotter R. The human connectome: A structural description of the human brain. PLoS Comput. Biol. 2005;1:e42. doi: 10.1371/journal.pcbi.0010042.
    1. Primiceri E., Chiriaco M.S., Rinaldi R., Maruccio G. Cell chips as new tools for cell biology-results, perspectives and opportunities. Lab Chip. 2013;13:3789–3802. doi: 10.1039/c3lc50550b.
    1. Braconi D., Bernardini G., Millucci L., Santucci A. Foodomics for human health: Current status and perspectives. Expert Rev. Proteom. 2018;15:153–164. doi: 10.1080/14789450.2018.1421072.
    1. Cifuentes A. Food analysis and foodomics. J. Chromatogr. A. 2009;1216:7109. doi: 10.1016/j.chroma.2009.09.018.
    1. Gallo Cantafio M.E., Grillone K., Caracciolo D., Scionti F., Arbitrio M., Barbieri V., Pensabene L., Guzzi P.H., Di Martino M.T. From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology. High Throughput. 2018;7:33. doi: 10.3390/ht7040033.
    1. Alexandrov L.B., Nik-Zainal S., Wedge D.C., Aparicio S.A., Behjati S., Biankin A.V., Bignell G.R., Bolli N., Borg A., Borresen-Dale A.L., et al. Signatures of mutational processes in human cancer. Nature. 2013;500:415–421. doi: 10.1038/nature12477.
    1. Burrell R.A., McGranahan N., Bartek J., Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013;501:338–345. doi: 10.1038/nature12625.
    1. Tomczak K., Czerwinska P., Wiznerowicz M. The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Contemp. Oncol. 2015;19:A68–A77. doi: 10.5114/wo.2014.47136.
    1. Silva F.C., Valentin M.D., Ferreira Fde O., Carraro D.M., Rossi B.M. Mismatch repair genes in Lynch syndrome: A review. Sao Paulo Med. J. 2009;127:46–51. doi: 10.1590/S1516-31802009000100010.
    1. Guha T., Malkin D. Inherited TP53 Mutations and the Li-Fraumeni Syndrome. Cold Spring Harb. Perspect. Med. 2017;7 doi: 10.1101/cshperspect.a026187.
    1. Kurian A.W. BRCA1 and BRCA2 mutations across race and ethnicity: Distribution and clinical implications. Curr. Opin. Obstet. Gynecol. 2010;22:72–78. doi: 10.1097/GCO.0b013e328332dca3.
    1. Sud A., Kinnersley B., Houlston R.S. Genome-wide association studies of cancer: Current insights and future perspectives. Nat. Rev. Cancer. 2017;17:692–704. doi: 10.1038/nrc.2017.82.
    1. Gibson G. On the utilization of polygenic risk scores for therapeutic targeting. PLoS Genet. 2019;15:e1008060. doi: 10.1371/journal.pgen.1008060.
    1. Ben-Neriah Y., Daley G.Q., Mes-Masson A.M., Witte O.N., Baltimore D. The chronic myelogenous leukemia-specific P210 protein is the product of the bcr/abl hybrid gene. Science. 1986;233:212–214. doi: 10.1126/science.3460176.
    1. Hu T., Kumar Y., Shazia I., Duan S.J., Li Y., Chen L., Chen J.F., Yin R., Kwong A., Leung G.K., et al. Forward and reverse mutations in stages of cancer development. Hum. Genom. 2018;12:40. doi: 10.1186/s40246-018-0170-6.
    1. Bailey M.H., Tokheim C., Porta-Pardo E., Sengupta S., Bertrand D., Weerasinghe A., Colaprico A., Wendl M.C., Kim J., Reardon B., et al. Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell. 2018;173:371–385.e18. doi: 10.1016/j.cell.2018.02.060.
    1. Bolton K.L., Chenevix-Trench G., Goh C., Sadetzki S., Ramus S.J., Karlan B.Y., Lambrechts D., Despierre E., Barrowdale D., McGuffog L., et al. Association between BRCA1 and BRCA2 mutations and survival in women with invasive epithelial ovarian cancer. JAMA. 2012;307:382–390. doi: 10.1001/jama.2012.20.
    1. Gao Q., Liang W.W., Foltz S.M., Mutharasu G., Jayasinghe R.G., Cao S., Liao W.W., Reynolds S.M., Wyczalkowski M.A., Yao L., et al. Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Rep. 2018;23:227–238.e3. doi: 10.1016/j.celrep.2018.03.050.
    1. Davoli T., Uno H., Wooten E.C., Elledge S.J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science. 2017;355 doi: 10.1126/science.aaf8399.
    1. Zack T.I., Schumacher S.E., Carter S.L., Cherniack A.D., Saksena G., Tabak B., Lawrence M.S., Zhsng C.Z., Wala J., Mermel C.H., et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 2013;45:1134–1140. doi: 10.1038/ng.2760.
    1. Lee E., Iskow R., Yang L., Gokcumen O., Haseley P., Luquette L.J., III, Lohr J.G., Harris C.C., Ding L., Wilson R.K., et al. Landscape of somatic retrotransposition in human cancers. Science. 2012;337:967–971. doi: 10.1126/science.1222077.
    1. Killock D. CNS cancer: Molecular classification of glioma. Nat. Rev. Clin. Oncol. 2015;12:502. doi: 10.1038/nrclinonc.2015.111.
    1. Cancer Genome Atlas Research Network. Brat D.J., Verhaak R.G., Aldape K.D., Yung W.K., Salama S.R., Cooper L.A., Rheinbay E., Miller C.R., Vitucci M., et al. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. N. Engl. J. Med. 2015;372:2481–2498. doi: 10.1056/NEJMoa1402121.
    1. Setia N., Agoston A.T., Han H.S., Mullen J.T., Duda D.G., Clark J.W., Deshpande V., Mino-Kenudson M., Srivastava A., Lennerz J.K., et al. A protein and mRNA expression-based classification of gastric cancer. Mod. Pathol. 2016;29:772–784. doi: 10.1038/modpathol.2016.55.
    1. Wagle N., Grabiner B.C., Van Allen E.M., Hodis E., Jacobus S., Supko J.G., Stewart M., Choueiri T.K., Gandhi L., Cleary J.M., et al. Activating mTOR mutations in a patient with an extraordinary response on a phase I trial of everolimus and pazopanib. Cancer Discov. 2014;4:546–553. doi: 10.1158/-13-0353.
    1. Grabiner B.C., Nardi V., Birsoy K., Possemato R., Shen K., Sinha S., Jordan A., Beck A.H., Sabatini D.M. A diverse array of cancer-associated MTOR mutations are hyperactivating and can predict rapamycin sensitivity. Cancer Discov. 2014;4:554–563. doi: 10.1158/-13-0929.
    1. Cancer Genome Atlas Research Network. Weinstein J.N., Collisson E.A., Mills G.B., Shaw K.R., Ozenberger B.A., Ellrott K., Shmulevich I., Sander C., Stuart J.M. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 2013;45:1113–1120. doi: 10.1038/ng.2764.
    1. Robinson D., Van Allen E.M., Wu Y.M., Schultz N., Lonigro R.J., Mosquera J.M., Montgomery B., Taplin M.E., Pritchard C.C., Attard G., et al. Integrative clinical genomics of advanced prostate cancer. Cell. 2015;161:1215–1228. doi: 10.1016/j.cell.2015.05.001.
    1. Prat A., Ellis M.J., Perou C.M. Practical implications of gene-expression-based assays for breast oncologists. Nat. Rev. Clin. Oncol. 2011;9:48–57. doi: 10.1038/nrclinonc.2011.178.
    1. Duarte C.W., Willey C.D., Zhi D., Cui X., Harris J.J., Vaughan L.K., Mehta T., McCubrey R.O., Khodarev N.N., Weichselbaum R.R., et al. Expression signature of IFN/STAT1 signaling genes predicts poor survival outcome in glioblastoma multiforme in a subtype-specific manner. PLoS ONE. 2012;7:e29653. doi: 10.1371/journal.pone.0029653.
    1. Li W., Wang R., Yan Z., Bai L., Sun Z. High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles. PLoS ONE. 2012;7:e33653. doi: 10.1371/journal.pone.0033653.
    1. Botling J., Edlund K., Lohr M., Hellwig B., Holmberg L., Lambe M., Berglund A., Ekman S., Bergqvist M., Ponten F., et al. Biomarker discovery in non-small cell lung cancer: Integrating gene expression profiling, meta-analysis, and tissue microarray validation. Clin. Cancer Res. 2013;19:194–204. doi: 10.1158/1078-0432.CCR-12-1139.
    1. Song Q., Hawkins G.A., Wudel L., Chou P.C., Forbes E., Pullikuth A.K., Liu L., Jin G., Craddock L., Topaloglu U., et al. Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq. Cancer Med. 2019;8:3072–3085. doi: 10.1002/cam4.2113.
    1. Suva M.L., Tirosh I. Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges. Mol. Cell. 2019;75:7–12. doi: 10.1016/j.molcel.2019.05.003.
    1. Li Q., Seo J.H., Stranger B., McKenna A., Pe’er I., Laframboise T., Brown M., Tyekucheva S., Freedman M.L. Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell. 2013;152:633–641. doi: 10.1016/j.cell.2012.12.034.
    1. Hoffman J.D., Graff R.E., Emami N.C., Tai C.G., Passarelli M.N., Hu D., Huntsman S., Hadley D., Leong L., Majumdar A., et al. Cis-eQTL-based trans-ethnic meta-analysis reveals novel genes associated with breast cancer risk. PLoS Genet. 2017;13:e1006690. doi: 10.1371/journal.pgen.1006690.
    1. Fehringer G., Kraft P., Pharoah P.D., Eeles R.A., Chatterjee N., Schumacher F.R., Schildkraut J.M., Lindstrom S., Brennan P., Bickeboller H., et al. Cross-Cancer Genome-Wide Analysis of Lung, Ovary, Breast, Prostate, and Colorectal Cancer Reveals Novel Pleiotropic Associations. Cancer Res. 2016;76:5103–5114. doi: 10.1158/0008-5472.CAN-15-2980.
    1. Horning A.M., Wang Y., Lin C.K., Louie A.D., Jadhav R.R., Hung C.N., Wang C.M., Lin C.L., Kirma N.B., Liss M.A., et al. Single-Cell RNA-seq Reveals a Subpopulation of Prostate Cancer Cells with Enhanced Cell-Cycle-Related Transcription and Attenuated Androgen Response. Cancer Res. 2018;78:853–864. doi: 10.1158/0008-5472.CAN-17-1924.
    1. Chung W., Eum H.H., Lee H.O., Lee K.M., Lee H.B., Kim K.T., Ryu H.S., Kim S., Lee J.E., Park Y.H., et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat. Commun. 2017;8:15081. doi: 10.1038/ncomms15081.
    1. Tirosh I., Izar B., Prakadan S.M., Wadsworth M.H., II, Treacy D., Trombetta J.J., Rotem A., Rodman C., Lian C., Murphy G., et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352:189–196. doi: 10.1126/science.aad0501.
    1. Kulis M., Esteller M. DNA methylation and cancer. Adv. Genet. 2010;70:27–56. doi: 10.1016/B978-0-12-380866-0.60002-2.
    1. Audia J.E., Campbell R.M. Histone Modifications and Cancer. Cold Spring Harb. Perspect. Biol. 2016;8:a019521. doi: 10.1101/cshperspect.a019521.
    1. Kohlhapp F.J., Mitra A.K., Lengyel E., Peter M.E. MicroRNAs as mediators and communicators between cancer cells and the tumor microenvironment. Oncogene. 2015;34:5857–5868. doi: 10.1038/onc.2015.89.
    1. Bhan A., Soleimani M., Mandal S.S. Long Noncoding RNA and Cancer: A New Paradigm. Cancer Res. 2017;77:3965–3981. doi: 10.1158/0008-5472.CAN-16-2634.
    1. Peng W.X., Koirala P., Mo Y.Y. LncRNA-mediated regulation of cell signaling in cancer. Oncogene. 2017;36:5661–5667. doi: 10.1038/onc.2017.184.
    1. Hegi M.E., Diserens A.C., Gorlia T., Hamou M.F., de Tribolet N., Weller M., Kros J.M., Hainfellner J.A., Mason W., Mariani L., et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N. Engl. J. Med. 2005;352:997–1003. doi: 10.1056/NEJMoa043331.
    1. El-Awady R.A., Hersi F., Al-Tunaiji H., Saleh E.M., Abdel-Wahab A.H., Al Homssi A., Suhail M., El-Serafi A., Al-Tel T. Epigenetics and miRNA as predictive markers and targets for lung cancer chemotherapy. Cancer Biol. Ther. 2015;16:1056–1070. doi: 10.1080/15384047.2015.1046023.
    1. Neureiter D., Jager T., Ocker M., Kiesslich T. Epigenetics and pancreatic cancer: Pathophysiology and novel treatment aspects. World J. Gastroenterol. 2014;20:7830–7848. doi: 10.3748/wjg.v20.i24.7830.
    1. Teplyuk N.M., Uhlmann E.J., Gabriely G., Volfovsky N., Wang Y., Teng J., Karmali P., Marcusson E., Peter M., Mohan A., et al. Therapeutic potential of targeting microRNA-10b in established intracranial glioblastoma: First steps toward the clinic. EMBO Mol. Med. 2016;8:268–287. doi: 10.15252/emmm.201505495.
    1. Washburn M.P., Koller A., Oshiro G., Ulaszek R.R., Plouffe D., Deciu C., Winzeler E., Yates J.R., III Protein pathway and complex clustering of correlated mRNA and protein expression analyses in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. USA. 2003;100:3107–3112. doi: 10.1073/pnas.0634629100.
    1. Swiatly A., Horala A., Matysiak J., Hajduk J., Nowak-Markwitz E., Kokot Z.J. Understanding Ovarian Cancer: iTRAQ-Based Proteomics for Biomarker Discovery. Int. J. Mol. Sci. 2018;19:2240. doi: 10.3390/ijms19082240.
    1. Yanovich G., Agmon H., Harel M., Sonnenblick A., Peretz T., Geiger T. Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification. Cancer Res. 2018;78:6001–6010. doi: 10.1158/0008-5472.CAN-18-1079.
    1. Ali M., Khan S.A., Wennerberg K., Aittokallio T. Global proteomics profiling improves drug sensitivity prediction: Results from a multi-omics, pan-cancer modeling approach. Bioinformatics. 2018;34:1353–1362. doi: 10.1093/bioinformatics/btx766.
    1. Cruz I.N., Coley H.M., Kramer H.B., Madhuri T.K., Safuwan N.A., Angelino A.R., Yang M. Proteomics Analysis of Ovarian Cancer Cell Lines and Tissues Reveals Drug Resistance-associated Proteins. Cancer Genom. Proteom. 2017;14:35–51. doi: 10.21873/cgp.20017.
    1. Chaturvedi A., Araujo Cruz M.M., Jyotsana N., Sharma A., Yun H., Gorlich K., Wichmann M., Schwarzer A., Preller M., Thol F., et al. Mutant IDH1 promotes leukemogenesis in vivo and can be specifically targeted in human AML. Blood. 2013;122:2877–2887. doi: 10.1182/blood-2013-03-491571.
    1. Zhang Y., He C., Qiu L., Wang Y., Qin X., Liu Y., Li Z. Serum Unsaturated Free Fatty Acids: A Potential Biomarker Panel for Early-Stage Detection of Colorectal Cancer. J. Cancer. 2016;7:477–483. doi: 10.7150/jca.13870.
    1. Giskeodegard G.F., Bertilsson H., Selnaes K.M., Wright A.J., Bathen T.F., Viset T., Halgunset J., Angelsen A., Gribbestad I.S., Tessem M.B. Spermine and citrate as metabolic biomarkers for assessing prostate cancer aggressiveness. PLoS ONE. 2013;8:e62375. doi: 10.1371/journal.pone.0062375.
    1. Mayers J.R., Wu C., Clish C.B., Kraft P., Torrence M.E., Fiske B.P., Yuan C., Bao Y., Townsend M.K., Tworoger S.S., et al. Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nat. Med. 2014;20:1193–1198. doi: 10.1038/nm.3686.
    1. Ferguson J.F., Wang T.J. Branched-Chain Amino Acids and Cardiovascular Disease: Does Diet Matter? Clin. Chem. 2016;62:545–547. doi: 10.1373/clinchem.2016.254318.
    1. Giesbertz P., Daniel H. Branched-chain amino acids as biomarkers in diabetes. Curr. Opin. Clin. Nutr. Metab. Care. 2016;19:48–54. doi: 10.1097/MCO.0000000000000235.
    1. Rappoport N., Shamir R. Multi-omic and multi-view clustering algorithms: Review and cancer benchmark. Nucleic Acids. Res. 2018;46:10546–10562. doi: 10.1093/nar/gky889.
    1. Langfelder P., Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559. doi: 10.1186/1471-2105-9-559.
    1. Misra B.B., Langefeld C.D., Olivier M., Cox L.A. Integrated Omics: Tools, Advances, and Future Approaches. J. Mol. Endocrinol. 2018 doi: 10.1530/JME-18-0055.
    1. Turanli B., Karagoz K., Gulfidan G., Sinha R., Mardinoglu A., Arga K.Y. A Network-Based Cancer Drug Discovery: From Integrated Multi-Omics Approaches to Precision Medicine. Curr. Pharm. Des. 2018;24:3778–3790. doi: 10.2174/1381612824666181106095959.
    1. Hasin Y., Seldin M., Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18:83. doi: 10.1186/s13059-017-1215-1.
    1. Sun Y.V., Hu Y.J. Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases. Adv. Genet. 2016;93:147–190. doi: 10.1016/bs.adgen.2015.11.004.
    1. Kellogg R.A., Dunn J., Snyder M.P. Personal Omics for Precision Health. Circ. Res. 2018;122:1169–1171. doi: 10.1161/CIRCRESAHA.117.310909.
    1. Stanberry L., Mias G.I., Haynes W., Higdon R., Snyder M., Kolker E. Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile. Metabolites. 2013;3:741–760. doi: 10.3390/metabo3030741.
    1. Piening B.D., Zhou W., Contrepois K., Rost H., Gu Urban G.J., Mishra T., Hanson B.M., Bautista E.J., Leopold S., Yeh C.Y., et al. Integrative Personal Omics Profiles during Periods of Weight Gain and Loss. Cell Syst. 2018;6:157–170.e8. doi: 10.1016/j.cels.2017.12.013.
    1. Schussler-Fiorenza Rose S.M., Contrepois K., Moneghetti K.J., Zhou W., Mishra T., Mataraso S., Dagan-Rosenfeld O., Ganz A.B., Dunn J., Hornburg D., et al. A longitudinal big data approach for precision health. Nat. Med. 2019;25:792–804. doi: 10.1038/s41591-019-0414-6.

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