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Yurkovich JT, Tian Q, Price ND, et al. A systems approach to clinical oncology uses deep phenotyping to deliver personalized care. Nat Rev Clin Oncol. 2019;17(3):183-194doi: 10.1038/s41571-019-0273-6.
Yurkovich, J. T., Tian, Q., Price, N. D., & Hood, L. (2020). A systems approach to clinical oncology uses deep phenotyping to deliver personalized care. Nature reviews. Clinical oncology, 17(3), 183-194. https://doi.org/10.1038/s41571-019-0273-6
Yurkovich, James T, et al. "A systems approach to clinical oncology uses deep phenotyping to deliver personalized care." Nature reviews. Clinical oncology vol. 17,3 (2020): 183-194. doi: https://doi.org/10.1038/s41571-019-0273-6
Yurkovich JT, Tian Q, Price ND, Hood L. A systems approach to clinical oncology uses deep phenotyping to deliver personalized care. Nat Rev Clin Oncol. 2020 Mar;17(3):183-194. doi: 10.1038/s41571-019-0273-6. Epub 2019 Oct 16. PMID: 31619755.
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Nat Rev Clin Oncol. 2020 Mar;17(3):183-194. doi: 10.1038/s41571-019-0273-6. Epub 2019 Oct 16.

A systems approach to clinical oncology uses deep phenotyping to deliver personalized care.

Nature reviews. Clinical oncology

James T Yurkovich, Qiang Tian, Nathan D Price, Leroy Hood

Affiliations

  1. Institute for Systems Biology, Seattle, WA, USA.
  2. National Research Center for Translational Medicine (Shanghai), Shanghai Jiao Tong University School of Medicine Affiliated Ruijin Hospital, Shanghai, China.
  3. Institute for Systems Biology, Seattle, WA, USA. [email protected].
  4. Institute for Systems Biology, Seattle, WA, USA. [email protected].
  5. Providence St. Joseph Health, Renton, WA, USA. [email protected].

PMID: 31619755 DOI: 10.1038/s41571-019-0273-6

Abstract

Cancer encompasses a complex, heterogeneous and dynamic group of diseases that arise from perturbations to multiple biological networks within the body. A systems biology-based approach would help to decipher this complexity, to deeply characterize the pathophysiology of the disease and to stratify cancers into appropriate molecular subtypes to facilitate the development of personalized therapies. Technological advances made over the past decade have enabled multiscale, longitudinal measurements ('snapshots') of human biology, from single-cell analyses to whole-body monitoring. In this Perspective, we discuss some of these technologies and how they have (and will) contributed to our understanding of cancer biology as well as to the development of early diagnostics and personalized therapies. We argue that the integration of molecular profiling of cancerous tissues with deep, longitudinal profiling of the physiological state of an individual ('deep phenotyping') is key to understanding the prevention, initiation, progression and response to treatment of cancers. Systems biology-based approaches can provide an unprecedented trove of data for early detection of disease transitions, prediction of therapeutic responses and clinical outcomes, and for the design of personalized treatments.

References

  1. Hood, L. & Friend, S. H. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat. Rev. Clin. Oncol. 8, 184–187 (2011). - PubMed
  2. Jones, D. S., Podolsky, S. H. & Greene, J. A. The burden of disease and the changing task of medicine. N. Engl. J. Med. 366, 2333–2338 (2012). - PubMed
  3. Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011). - PubMed
  4. Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001). - PubMed
  5. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001). - PubMed
  6. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007). - PubMed
  7. Denny, J. C. et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 26, 1205–1210 (2010). - PubMed
  8. Ideker, T., Galitski, T. & Hood, L. A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001). - PubMed
  9. Tian, Q., Price, N. D. & Hood, L. Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine. J. Intern. Med. 271, 111–121 (2012). - PubMed
  10. Sagner, M. et al. The P4 health spectrum — a predictive, preventive, personalized and participatory continuum for promoting healthspan. Prog. Cardiovasc. Dis. 59, 506–521 (2017). - PubMed
  11. Schork, N. J. Personalized medicine: time for one-person trials. Nature 520, 609–611 (2015). - PubMed
  12. Lillie, E. O. et al. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per. Med. 8, 161–173 (2011). - PubMed
  13. Larson, E. B. N-of-1 clinical trials. A technique for improving medical therapeutics. West. J. Med. 152, 52–56 (1990). - PubMed
  14. Westerhoff, H. V. & Palsson, B. O. The evolution of molecular biology into systems biology. Nat. Biotechnol. 22, 1249–1252 (2004). - PubMed
  15. Karczewski, K. J. & Snyder, M. P. Integrative omics for health and disease. Nat. Rev. Genet. 19, 299–310 (2018). - PubMed
  16. Swan, M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data 1, 85–99 (2013). - PubMed
  17. Smarr, L. Quantifying your body: a how-to guide from a systems biology perspective. Biotechnol. J. 7, 980–991 (2012). - PubMed
  18. Hood, L. & Price, N. D. Demystifying disease, democratizing health care. Sci. Transl Med. 6, 225ed5 (2014). - PubMed
  19. Plasqui, G. & Westerterp, K. R. Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Obesity 15, 2371–2379 (2007). - PubMed
  20. Wong, L. J. et al. Extended use of a new continuous glucose monitoring system with wireless data transmission in children with type 1 diabetes mellitus. Diabetes Technol. Ther. 8, 139–145 (2006). - PubMed
  21. Jaffee, E. M. et al. Future cancer research priorities in the USA: a Lancet Oncology Commission. Lancet Oncol. 18, e653–e706 (2017). - PubMed
  22. Kuai, R., Ochyl, L. J., Bahjat, K. S., Schwendeman, A. & Moon, J. J. Designer vaccine nanodiscs for personalized cancer immunotherapy. Nat. Mater. 16, 489–496 (2017). - PubMed
  23. Farkona, S., Diamandis, E. P. & Blasutig, I. M. Cancer immunotherapy: the beginning of the end of cancer? BMC Med. 14, 73 (2016). - PubMed
  24. David, L. A. et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15, R89 (2014). - PubMed
  25. Kolker, E. et al. Toward more transparent and reproducible omics studies through a common metadata checklist and data publications. OMICS 18, 10–14 (2014). - PubMed
  26. Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307 (2012). - PubMed
  27. Piening, B. D. et al. Integrative personal omics profiles during periods of weight gain and loss. Cell Syst. 6, 157–170.e8 (2018). - PubMed
  28. Schüssler-Fiorenza Rose, S. M. et al. A longitudinal big data approach for precision health. Nat. Med. 25, 792–804 (2019). - PubMed
  29. Wilmanski, T. et al. Blood metabolome signature predicts gut microbiome α-diversity in health and disease. Nat. Biotechnol. 8, 69 (2019). - PubMed
  30. Price, N. D. et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat. Biotechnol. 35, 747–756 (2017). - PubMed
  31. Knijnenburg, T. A. et al. Genomic and molecular characterization of preterm birth. Proc. Natl Acad. Sci. USA 62, 201716314 (2019). - PubMed
  32. Sugawara, J. et al. Maternity log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy. BMJ Open 9, e025939 (2019). - PubMed
  33. Yurkovich, J. T. & Palsson, B. O. Quantitative -omic data empowers bottom-up systems biology. Curr. Opin. Biotechnol. 51, 130–136 (2018). - PubMed
  34. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008). - PubMed
  35. Weinstein, J. N. et al. The Cancer Genome Atlas pan-cancer analysis project. Nat. Genet. 45, 1113–1120 (2013). - PubMed
  36. Hoadley, K. A. et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173, 291–304.e6 (2018). - PubMed
  37. Malta, T. M. et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 173, 338–354.e15 (2018). - PubMed
  38. Berger, A. C. et al. A comprehensive pan-cancer molecular study of gynecologic and breast cancers. Cancer Cell 33, 690–705.e9 (2018). - PubMed
  39. Jiang, Y. et al. Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma. Nature 567, 257–261 (2019). - PubMed
  40. Liu, Y. et al. Comparative molecular analysis of gastrointestinal adenocarcinomas. Cancer Cell 33, 721–735.e8 (2018). - PubMed
  41. Laganà, A. et al. Precision medicine for relapsed multiple myeloma on the basis of an integrative multiomics approach. JCO Precis. Oncol. 2018, (2018). - PubMed
  42. Taglang, G. & Jackson, D. B. Use of ‘big data’ in drug discovery and clinical trials. Gynecol. Oncol. 141, 17–23 (2016). - PubMed
  43. Gopalakrishnan, V., Helmink, B. A., Spencer, C. N., Reuben, A. & Wargo, J. A. The influence of the gut microbiome on cancer, immunity, and cancer immunotherapy. Cancer Cell 33, 570–580 (2018). - PubMed
  44. Yu, L.-X. & Schwabe, R. F. The gut microbiome and liver cancer: mechanisms and clinical translation. Nat. Rev. Gastroenterol. Hepatol. 14, 527–539 (2017). - PubMed
  45. Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018). - PubMed
  46. Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018). - PubMed
  47. Kauffman, S. Homeostasis and differentiation in random genetic control networks. Nature 224, 177–178 (1969). - PubMed
  48. del Sol, A., Balling, R., Hood, L. & Galas, D. Diseases as network perturbations. Curr. Opin. Biotechnol. 21, 566–571 (2010). - PubMed
  49. Yi, S. et al. Functional variomics and network perturbation: connecting genotype to phenotype in cancer. Nat. Rev. Genet. 18, 395–410 (2017). - PubMed
  50. Santolini, M. & Barabási, A.-L. Predicting perturbation patterns from the topology of biological networks. Proc. Natl Acad. Sci. USA 115, E6375–E6383 (2018). - PubMed
  51. Heath, J. R., Ribas, A. & Mischel, P. S. Single-cell analysis tools for drug discovery and development. Nat. Rev. Drug Discov. 15, 204–216 (2016). - PubMed
  52. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013). - PubMed
  53. Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016). - PubMed
  54. Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015). - PubMed
  55. Schwartzman, O. & Tanay, A. Single-cell epigenomics: techniques and emerging applications. Nat. Rev. Genet. 16, 716–726 (2015). - PubMed
  56. Duncan, K. D., Fyrestam, J. & Lanekoff, I. Advances in mass spectrometry based single-cell metabolomics. Analyst 144, 782–793 (2019). - PubMed
  57. Specht, H. & Slavov, N. Transformative opportunities for single-cell proteomics. J. Proteome Res. 17, 2565–2571 (2018). - PubMed
  58. Bock, C., Farlik, M. & Sheffield, N. C. Multi-omics of single cells: strategies and applications. Trends Biotechnol. 34, 605–608 (2016). - PubMed
  59. Xue, M. et al. Chemical methods for the simultaneous quantitation of metabolites and proteins from single cells. J. Am. Chem. Soc. 137, 4066–4069 (2015). - PubMed
  60. Karaayvaz, M. et al. Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq. Nat. Commun. 9, 3588 (2018). - PubMed
  61. Liberali, P., Snijder, B. & Pelkmans, L. Single-cell and multivariate approaches in genetic perturbation screens. Nat. Rev. Genet. 16, 18–32 (2015). - PubMed
  62. Hochane, M. et al. Single-cell transcriptomics reveals gene expression dynamics of human fetal kidney development. PLOS Biol. 17, e3000152 (2019). - PubMed
  63. Marusyk, A., Almendro, V. & Polyak, K. Intra-tumour heterogeneity: a looking glass for cancer? Nat. Rev. Cancer 12, 323–334 (2012). - PubMed
  64. Nguyen, A., Yoshida, M., Goodarzi, H. & Tavazoie, S. F. Highly variable cancer subpopulations that exhibit enhanced transcriptome variability and metastatic fitness. Nat. Commun. 7, 11246 (2016). - PubMed
  65. Sharma, A. et al. Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy. Nat. Commun. 9, 4931 (2018). - PubMed
  66. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016). - PubMed
  67. Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997.e24 (2018). - PubMed
  68. Su, Y. et al. Single-cell analysis resolves the cell state transition and signaling dynamics associated with melanoma drug-induced resistance. Proc. Natl Acad. Sci. USA 114, 13679–13684 (2017). - PubMed
  69. Shi, Q. et al. Single-cell proteomic chip for profiling intracellular signaling pathways in single tumor cells. Proc. Natl Acad. Sci. USA 109, 419–424 (2012). - PubMed
  70. Hu, Y. et al. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol. 17, 88 (2016). - PubMed
  71. Genshaft, A. S. et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 17, 188 (2016). - PubMed
  72. Hou, Y. et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319 (2016). - PubMed
  73. Zhang, Y. et al. Single-cell codetection of metabolic activity, intracellular functional proteins, and genetic mutations from rare circulating tumor cells. Anal. Chem. 87, 9761–9768 (2015). - PubMed
  74. Mimitou, E. P. et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 6, 28 (2019). - PubMed
  75. Hwang, B., Lee, J. H. & Bang, D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp. Mol. Med. 50, 96 (2018). - PubMed
  76. Mitra, A. K. et al. Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors. Leukemia 30, 1094–1102 (2016). - PubMed
  77. Tabassum, D. P. & Polyak, K. Tumorigenesis: it takes a village. Nat. Rev. Cancer 15, 473–483 (2015). - PubMed
  78. Yurkovich, J. T. & Hood, L. Blood is a window into health and disease. Clin. Chem. 65, 1204–1206 (2019). - PubMed
  79. Shi, L. et al. A decade of blood biomarkers for Alzheimer’s disease research: an evolving field, improving study designs, and the challenge of replication. J. Alzheimers Dis. 62, 1181–1198 (2018). - PubMed
  80. van der Pol, A. et al. Accumulation of 5-oxoproline in myocardial dysfunction and the protective effects of OPLAH. Sci. Transl Med. 9, eaam8574 (2017). - PubMed
  81. Dudley, J. T. & Butte, A. J. Identification of discriminating biomarkers for human disease using integrative network biology. Pac. Symp. Biocomput. 2009, 27–38 (2009). - PubMed
  82. Goldenberg, D. M. et al. Use of radiolabeled antibodies to carcinoembryonic antigen for the detection and localization of diverse cancers by external photoscanning. N. Engl. J. Med. 298, 1384–1386 (1978). - PubMed
  83. Stamey, T. A. et al. Prostate-specific antigen as a serum marker for adenocarcinoma of the prostate. N. Engl. J. Med. 317, 909–916 (1987). - PubMed
  84. Kim, E. H. & Andriole, G. L. Prostate-specific antigen-based screening: controversy and guidelines. BMC Med. 13, 61 (2015). - PubMed
  85. Sørensen, C. G., Karlsson, W. K., Pommergaard, H.-C., Burcharth, J. & Rosenberg, J. The diagnostic accuracy of carcinoembryonic antigen to detect colorectal cancer recurrence — a systematic review. Int. J. Surg. 25, 134–144 (2016). - PubMed
  86. Kearney, P., Boniface, J. J., Price, N. D. & Hood, L. The building blocks of successful translation of proteomics to the clinic. Curr. Opin. Biotechnol. 51, 123–129 (2018). - PubMed
  87. Li, X.-J. et al. A blood-based proteomic classifier for the molecular characterization of pulmonary nodules. Sci. Transl Med. 5, 207ra142 (2013). - PubMed
  88. Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012). - PubMed
  89. Houssami, N., Ciatto, S., Martinelli, F., Bonardi, R. & Duffy, S. W. Early detection of second breast cancers improves prognosis in breast cancer survivors. Ann. Oncol. 20, 1505–1510 (2009). - PubMed
  90. Shah, C. et al. The impact of early detection and intervention of breast cancer-related lymphedema: a systematic review. Cancer Med. 5, 1154–1162 (2016). - PubMed
  91. Ilbawi, A. M. & Anderson, B. O. Cancer in global health: how do prevention and early detection strategies relate? Sci. Transl Med. 7, 278cm1 (2015). - PubMed
  92. Hiom, S. C. Diagnosing cancer earlier: reviewing the evidence for improving cancer survival. Br. J. Cancer 112 (Suppl 1), S1–S5 (2015). - PubMed
  93. Tamborero, D. et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci. Rep. 3, 2650 (2013). - PubMed
  94. Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385.e18 (2018). - PubMed
  95. Iranzo, J., Martincorena, I. & Koonin, E. V. Cancer-mutation network and the number and specificity of driver mutations. Proc. Natl Acad. Sci. USA 115, E6010–E6019 (2018). - PubMed
  96. Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013). - PubMed
  97. Ciriello, G., Cerami, E., Sander, C. & Schultz, N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 22, 398–406 (2012). - PubMed
  98. Canisius, S., Martens, J. W. M. & Wessels, L. F. A. A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence. Genome Biol. 17, 261 (2016). - PubMed
  99. Nussinov, R., Jang, H., Tsai, C.-J. & Cheng, F. Review: precision medicine and driver mutations: computational methods, functional assays and conformational principles for interpreting cancer drivers. PLOS Comput. Biol. 15, e1006658 (2019). - PubMed
  100. Nussinov, R., Jang, H., Tsai, C.-J. & Cheng, F. Precision medicine review: rare driver mutations and their biophysical classification. Biophys. Rev. 11, 5–19 (2019). - PubMed
  101. Wang, K. et al. The spectrum of circulating RNA: a window into systems toxicology. Toxicol. Sci. 132, 478–492 (2013). - PubMed
  102. Zhang, F. & Chen, J. Y. HOMER: a human organ-specific molecular electronic repository. BMC Bioinformatics 12 (Suppl 10), S4 (2011). - PubMed
  103. Qin, S. et al. SRM targeted proteomics in search for biomarkers of HCV-induced progression of fibrosis to cirrhosis in HALT-C patients. Proteomics 12, 1244–1252 (2012). - PubMed
  104. Qin, S. et al. Identification of organ-enriched protein biomarkers of acute liver injury by targeted quantitative proteomics of blood in acetaminophen- and carbon-tetrachloride-treated mouse models and acetaminophen overdose patients. J. Proteome Res. 15, 3724–3740 (2016). - PubMed
  105. Steffen, P. et al. Protein species as diagnostic markers. J. Proteomics 134, 5–18 (2016). - PubMed
  106. Zubair, N. et al. Genetic predisposition impacts clinical changes in a lifestyle coaching program. Sci. Rep. 9, 524 (2019). - PubMed
  107. Ohgaki, H. & Kleihues, P. The definition of primary and secondary glioblastoma. Clin. Cancer Res. 19, 764–772 (2013). - PubMed
  108. Kersten, K., de Visser, K. E., van Miltenburg, M. H. & Jonkers, J. Genetically engineered mouse models in oncology research and cancer medicine. EMBO Mol. Med. 9, 137–153 (2017). - PubMed
  109. Hwang, D. et al. A systems approach to prion disease. Mol. Syst. Biol. 5, 252 (2009). - PubMed
  110. Song, Y. et al. Evolutionary etiology of high-grade astrocytomas. Proc. Natl Acad. Sci. USA 110, 17933–17938 (2013). - PubMed
  111. GLASS Consortium. Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium. Neuro Oncol. 20, 873–884 (2018). - PubMed
  112. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011). - PubMed
  113. Liu, R. et al. Identifying critical transitions and their leading biomolecular networks in complex diseases. Sci. Rep. 2, 813 (2012). - PubMed
  114. Zhou, J. X., Aliyu, M. D. S., Aurell, E. & Huang, S. Quasi-potential landscape in complex multi-stable systems. J. R. Soc. Interface 9, 3539–3553 (2012). - PubMed
  115. Li, Q. et al. Dynamics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape. Proc. Natl Acad. Sci. USA 113, 2672–2677 (2016). - PubMed
  116. Inde, Z. & Dixon, S. J. The impact of non-genetic heterogeneity on cancer cell death. Crit. Rev. Biochem. Mol. Biol. 53, 99–114 (2018). - PubMed
  117. Ramirez, M. et al. Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells. Nat. Commun. 7, 10690 (2016). - PubMed
  118. Wraith, D. C. The future of immunotherapy: a 20-year perspective. Front. Immunol. 8, 1668 (2017). - PubMed
  119. van der Vlist, M., Kuball, J., Radstake, T. R. D. & Meyaard, L. Immune checkpoints and rheumatic diseases: what can cancer immunotherapy teach us? Nat. Rev. Rheumatol. 12, 593–604 (2016). - PubMed
  120. Leach, D. R., Krummel, M. F. & Allison, J. P. Enhancement of antitumor immunity by CTLA-4 blockade. Science 271, 1734–1736 (1996). - PubMed
  121. Buchbinder, E. & Stephen Hodi, F. Cytotoxic T lymphocyte antigen-4 and immune checkpoint blockade. J. Clin. Invest. 125, 3377–3383 (2015). - PubMed
  122. Cho, S.-F., Anderson, K. C. & Tai, Y.-T. Targeting B cell maturation antigen (BCMA) in multiple myeloma: potential uses of BCMA-based immunotherapy. Front. Immunol. 9, 1821 (2018). - PubMed
  123. Gubin, M. M. et al. High-dimensional analysis delineates myeloid and lymphoid compartment remodeling during successful immune-checkpoint cancer therapy. Cell 175, 1014–1030.e19 (2018). - PubMed
  124. Xue, Q. et al. Single-cell multiplexed cytokine profiling of CD19 CAR-T cells reveals a diverse landscape of polyfunctional antigen-specific response. J. Immunother. Cancer 5, 85 (2017). - PubMed
  125. Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018). - PubMed
  126. Chung, W. et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat. Commun. 8, 15081 (2017). - PubMed
  127. Chen, Q. et al. A novel approach for rapid high-throughput selection of recombinant functional rat monoclonal antibodies. BMC Immunol. 19, 35 (2018). - PubMed
  128. Ye, B., Smerin, D., Gao, Q., Kang, C. & Xiong, X. High-throughput sequencing of the immune repertoire in oncology: applications for clinical diagnosis, monitoring, and immunotherapies. Cancer Lett. 416, 42–56 (2018). - PubMed
  129. Costa-Nunes, C. et al. High-throughput screening of human tumor antigen-specific CD4 T cells, including neoantigen-reactive T cells. Clin. Cancer Res. 25, 4320–4331 (2019). - PubMed
  130. Jenkins, R. W., Barbie, D. A. & Flaherty, K. T. Mechanisms of resistance to immune checkpoint inhibitors. Br. J. Cancer 118, 9–16 (2018). - PubMed
  131. Arts, E. J. & Hazuda, D. J. HIV-1 antiretroviral drug therapy. Cold Spring Harb. Perspect. Med. 2, a007161 (2012). - PubMed
  132. Deisboeck, T. S., Wang, Z., Macklin, P. & Cristini, V. Multiscale cancer modeling. Annu. Rev. Biomed. Eng. 13, 127–155 (2011). - PubMed
  133. Wang, Z. & Deisboeck, T. S. Dynamic targeting in cancer treatment. Front. Physiol. 10, 96 (2019). - PubMed
  134. Zhang, L., Strouthos, C. G., Wang, Z. & Deisboeck, T. S. Simulating brain tumor heterogeneity with a multiscale agent-based model: linking molecular signatures, phenotypes and expansion rate. Math. Comput. Model. 49, 307–319 (2009). - PubMed
  135. Araujo, R. P., Petricoin, E. F. & Liotta, L. A. A mathematical model of combination therapy using the EGFR signaling network. Biosystems 80, 57–69 (2005). - PubMed
  136. Fitzgerald, J. B., Schoeberl, B., Nielsen, U. B. & Sorger, P. K. Systems biology and combination therapy in the quest for clinical efficacy. Nat. Chem. Biol. 2, 458–466 (2006). - PubMed
  137. Kitano, H. A robustness-based approach to systems-oriented drug design. Nat. Rev. Drug Discov. 6, 202–210 (2007). - PubMed
  138. Estey, E. et al. Use of all-trans retinoic acid plus arsenic trioxide as an alternative to chemotherapy in untreated acute promyelocytic leukemia. Blood 107, 3469–3473 (2006). - PubMed
  139. Wolchok, J. D. et al. Nivolumab plus ipilimumab in advanced melanoma. N. Engl. J. Med. 369, 122–133 (2013). - PubMed
  140. Ravandi, F., Walter, R. B. & Freeman, S. D. Evaluating measurable residual disease in acute myeloid leukemia. Blood Adv. 2, 1356–1366 (2018). - PubMed
  141. Grimwade, D. & Freeman, S. D. Defining minimal residual disease in acute myeloid leukemia: which platforms are ready for ‘prime time’? Blood 124, 3345–3355 (2014). - PubMed
  142. Schuurhuis, G. J. et al. Minimal/measurable residual disease in AML: a consensus document from the European LeukemiaNet MRD Working Party. Blood 131, 1275–1291 (2018). - PubMed
  143. Green, E. D., Guyer, M. S. & National Human Genome Research Institute. Charting a course for genomic medicine from base pairs to bedside. Nature 470, 204–213 (2011). - PubMed
  144. Simon, R. The use of genomics in clinical trial design. Clin. Cancer Res. 14, 5984–5993 (2008). - PubMed
  145. Caskey, C. T. Using genetic diagnosis to determine individual therapeutic utility. Annu. Rev. Med. 61, 1–15 (2010). - PubMed
  146. Gagan, J. & Van Allen, E. M. Next-generation sequencing to guide cancer therapy. Genome Med. 7, 80 (2015). - PubMed
  147. Vargas, A. J. & Harris, C. C. Biomarker development in the precision medicine era: lung cancer as a case study. Nat. Rev. Cancer 16, 525–537 (2016). - PubMed
  148. McShane, L. M. et al. Criteria for the use of omics-based predictors in clinical trials. Nature 502, 317–320 (2013). - PubMed
  149. Antman, E., Weiss, S. & Loscalzo, J. Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 4, 367–383 (2012). - PubMed
  150. Butcher, E. C., Berg, E. L. & Kunkel, E. J. Systems biology in drug discovery. Nat. Biotechnol. 22, 1253–1259 (2004). - PubMed
  151. Senn, S. Statistical pitfalls of personalized medicine. Nature 563, 619–621 (2018). - PubMed
  152. Beckman, R. A. & Chen, C. New evidence-based adaptive clinical trial methods for optimally integrating predictive biomarkers into oncology clinical development programs. Chin. J. Cancer 32, 233–241 (2013). - PubMed
  153. Duan, N., Kravitz, R. L. & Schmid, C. H. Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research. J. Clin. Epidemiol. 66, S21–S28 (2013). - PubMed
  154. Raz, I. et al. Personalized management of hyperglycemia in type 2 diabetes: reflections from a Diabetes Care Editors’ Expert Forum. Diabetes Care 36, 1779–1788 (2013). - PubMed
  155. Meric-Bernstam, F. & Mills, G. B. Overcoming implementation challenges of personalized cancer therapy. Nat. Rev. Clin. Oncol. 9, 542–548 (2012). - PubMed
  156. Santos, R. et al. A comprehensive map of molecular drug targets. Nat. Rev. Drug Discov. 16, 19–34 (2017). - PubMed
  157. Hopkins, A. L. Network pharmacology. Nat. Biotechnol. 25, 1110–1111 (2007). - PubMed
  158. Yu, K.-H. et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. 7, 12474 (2016). - PubMed
  159. Jiang, C. et al. Dynamic human environmental exposome revealed by longitudinal personal monitoring. Cell 175, 277–291.e31 (2018). - PubMed
  160. Kinross, J. M., Holmes, E., Darzi, A. W. & Nicholson, J. K. Metabolic phenotyping for monitoring surgical patients. Lancet 377, 1817–1819 (2011). - PubMed
  161. Wang, J., Moore, N. E., Deng, Y.-M., Eccles, D. A. & Hall, R. J. MinION nanopore sequencing of an influenza genome. Front. Microbiol. 6, 766 (2015). - PubMed
  162. Blakeman, K. H., Wolfe, D. W., Cavanaugh, C. A. & Ramsey, J. M. High pressure mass spectrometry: the generation of mass spectra at operating pressures exceeding 1 Torr in a microscale cylindrical ion trap. Anal. Chem. 88, 5378–5384 (2016). - PubMed
  163. Gallagher, R. A. & Levy, J. A. Advances in point-of-care ultrasound in pediatric emergency medicine. Curr. Opin. Pediatr. 26, 265–271 (2014). - PubMed
  164. Castro-Wallace, S. L. et al. Nanopore DNA sequencing and genome assembly on the international space station. Sci. Rep. 7, 18022 (2017). - PubMed
  165. Deisboeck, T. S. Personalizing medicine: a systems biology perspective. Mol. Syst. Biol. 5, 249 (2009). - PubMed
  166. Emery, J. C. H. & Zwicker, J. D. Innovation, productivity, and pricing: capturing value from precision medicine technology in Canada. Healthc. Manage. Forum 30, 197–199 (2017). - PubMed
  167. Louis, R. & Roche, N. Personalised medicine: are we ready? Eur. Respir. Rev. 26, 170088 (2017). - PubMed
  168. Spoonamore, K. G. & Johnson, N. M. Who pays? Coverage challenges for cardiovascular genetic testing in U.S. patients. Front. Cardiovasc. Med 3, 14 (2016). - PubMed
  169. Hyman, M. A. Finding the money for healthcare reform. Altern. Ther. Health Med. 15, 20–23 (2009). - PubMed
  170. Taw, M. B. Integrative medicine, or not integrative medicine: that is the question. J. Integr. Med. 13, 350–352 (2015). - PubMed
  171. Joyner, M. J. & Paneth, N. Seven questions for personalized medicine. JAMA 314, 999–1000 (2015). - PubMed
  172. National Human Genome Research Institute. DNA sequencing costs: data (NHGRI, 2019). - PubMed
  173. Shevchenko, Y. & Bale, S. Clinical versus research sequencing. Cold Spring Harb. Perspect. Med. 6, a025809 (2016). - PubMed
  174. Crow, D. A new wave of genomics for all. Cell 177, 5–7 (2019). - PubMed
  175. Institute of Medicine (US). The Healthcare Imperative. Lowering Costs and Improving Outcomes: Workshop Series Summary (eds Yong, P. L., Saunders, R. S. & Olsen L. A.) (National Academies Press, 2010). - PubMed
  176. Van Dyke, K. The incredible costs of chronic diseases: why they occur and possible preventions and/or treatments. J. Health Edu. Res. Dev. 4, 182 (2016). - PubMed
  177. Chapel, J. M., Ritchey, M. D., Zhang, D. & Wang, G. Prevalence and medical costs of chronic diseases among adult Medicaid beneficiaries. Am. J. Prev. Med. 53, S143–S154 (2017). - PubMed
  178. Miki, Y. et al. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 266, 66–71 (1994). - PubMed
  179. Nguyen, D. X. & Massagué, J. Genetic determinants of cancer metastasis. Nat. Rev. Genet. 8, 341–352 (2007). - PubMed
  180. Garraway, L. A. & Lander, E. S. Lessons from the cancer genome. Cell 153, 17–37 (2013). - PubMed
  181. Civelek, M. & Lusis, A. J. Systems genetics approaches to understand complex traits. Nat. Rev. Genet. 15, 34–48 (2014). - PubMed
  182. Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018). - PubMed
  183. Aly, M. et al. Polygenic risk score improves prostate cancer risk prediction: results from the Stockholm-1 cohort study. Eur. Urol. 60, 21–28 (2011). - PubMed
  184. Mavaddat, N. et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am. J. Hum. Genet. 104, 21–34 (2019). - PubMed
  185. Rahman, N. Mainstreaming genetic testing of cancer predisposition genes. Clin. Med. 14, 436–439 (2014). - PubMed
  186. Stoutenberg, M., Sogor, A., Arheart, K., Cutrono, S. E. & Kornfeld, J. A wellness program for cancer survivors and caregivers: developing an integrative pilot program with exercise, nutrition, and complementary medicine. J. Cancer Educ. 31, 47–54 (2016). - PubMed
  187. Haslem, D. S. et al. Precision oncology in advanced cancer patients improves overall survival with lower weekly healthcare costs. Oncotarget 9, 12316–12322 (2018). - PubMed
  188. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017). - PubMed
  189. Schroeder, S. A. Shattuck Lecture. We can do better — improving the health of the American people. N. Engl. J. Med. 357, 1221–1228 (2007). - PubMed
  190. Karaca, S., Erge, S., Cesuroglu, T. & Polimanti, R. Nutritional habits, lifestyle, and genetic predisposition in cardiovascular and metabolic traits in Turkish population. Nutrition 32, 693–701 (2016). - PubMed
  191. Wheate, N. J., Walker, S., Craig, G. E. & Oun, R. The status of platinum anticancer drugs in the clinic and in clinical trials. Dalton Trans. 39, 8113–8127 (2010). - PubMed
  192. Oxnard, G. R. et al. Response rate as a regulatory end point in single-arm studies of advanced solid tumors. JAMA Oncol. 2, 772–779 (2016). - PubMed
  193. Chen, B. H. et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging 8, 1844–1865 (2016). - PubMed
  194. Chaudhury, S. et al. Age-specific biological and molecular profiling distinguishes paediatric from adult acute myeloid leukaemias. Nat. Commun. 9, 5280 (2018). - PubMed
  195. Goggins, M., Canto, M. & Hruban, R. Can we screen high-risk individuals to detect early pancreatic carcinoma? J. Surg. Oncol. 74, 243–248 (2000). - PubMed
  196. Futreal, P. A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004). - PubMed
  197. Szulkin, R. et al. Prediction of individual genetic risk to prostate cancer using a polygenic score. Prostate 75, 1467–1474 (2015). - PubMed
  198. Pashayan, N. et al. Implications of polygenic risk-stratified screening for prostate cancer on overdiagnosis. Genet. Med. 17, 789–795 (2015). - PubMed
  199. Chatterjee, N., Shi, J. & García-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17, 392–406 (2016). - PubMed
  200. Hart, P. A. Early detection of pancreatic cancer in high-risk individuals: where do we go from here? Am. J. Gastroenterol. 114, 560–561 (2019). - PubMed
  201. Best, M. G. et al. RNA-Seq of tumor-educated platelets enables blood-based pan-cancer, multiclass, and molecular pathway cancer diagnostics. Cancer Cell 28, 666–676 (2015). - PubMed
  202. Donaldson, J. & Park, B. H. Circulating tumor DNA: measurement and clinical utility. Annu. Rev. Med. 69, 223–234 (2018). - PubMed
  203. Mamdouhi, T., Twomey, J. D., McSweeney, K. M. & Zhang, B. Fugitives on the run: circulating tumor cells (CTCs) in metastatic diseases. Cancer Metastasis Rev. 38, 297–305 (2019). - PubMed
  204. FUTURE II Study Group. Quadrivalent vaccine against human papillomavirus to prevent high-grade cervical lesions. N. Engl. J. Med. 356, 1915–1927 (2007). - PubMed
  205. Tanyi, J. L. et al. Personalized cancer vaccine effectively mobilizes antitumor T cell immunity in ovarian cancer. Sci. Transl Med. 10, eaao5931 (2018). - PubMed
  206. Berzofsky, J. A. et al. Cancer vaccine strategies: translation from mice to human clinical trials. Cancer Immunol. Immunother. 67, 1863–1869 (2018). - PubMed
  207. Iorio, F. et al. A landscape of pharmacogenomic interactions in Cancer. Cell 166, 740–754 (2016). - PubMed
  208. Huang, L. et al. Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell- and patient-derived tumor organoids. Nat. Med. 21, 1364–1371 (2015). - PubMed
  209. Skehan, P. et al. New colorimetric cytotoxicity assay for anticancer-drug screening. J. Natl Cancer Inst. 82, 1107–1112 (1990). - PubMed

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