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Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome

Abstract

Over the past decade, the development of ‘simple’ blood tests that enable cancer screening, diagnosis or monitoring and facilitate the design of personalized therapies without the need for invasive tumour biopsy sampling has been a core ambition in cancer research. Data emerging from ongoing biomarker development efforts indicate that multiple markers, used individually or as part of a multimodal panel, are required to enhance the sensitivity and specificity of assays for early stage cancer detection. The discovery of cancer-associated molecular alterations that are reflected in blood at multiple dimensions (genome, epigenome, transcriptome, proteome and metabolome) and integration of the resultant multi-omics data have the potential to uncover novel biomarkers as well as to further elucidate the underlying molecular pathways. Herein, we review key advances in multi-omics liquid biopsy approaches and introduce the ‘nano-omics’ paradigm: the development and utilization of nanotechnology tools for the enrichment and subsequent omics analysis of the blood-circulating cancerome.

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Fig. 1: Translational potential of multi-omics liquid biopsy.
Fig. 2: The nano-omics paradigm.
Fig. 3: Nanomaterial-based isolation of blood EVs and CTCs.

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References

  1. Ferlay, J. et al. Cancer statistics for the year 2020: an overview. Int. J. Cancer 149, 778–789 (2021).

    Article  CAS  Google Scholar 

  2. Lo, Y. M. D. & Lam, W. K. J. Towards multi-cancer screening using liquid biopsies. Nat. Rev. Clin. Oncol. 17, 525–526 (2020).

    Article  CAS  PubMed  Google Scholar 

  3. Hristova, V. A. & Chan, D. W. Cancer biomarker discovery and translation: proteomics and beyond. Expert. Rev. Proteom. 16, 93–103 (2019).

    Article  CAS  Google Scholar 

  4. Duffy, M. J. Serum tumor markers in breast cancer: are they of clinical value? Clin. Chem. 52, 345–351 (2006).

    Article  CAS  PubMed  Google Scholar 

  5. Keedy, V. L. et al. American Society of Clinical Oncology provisional clinical opinion: epidermal growth factor receptor (EGFR) mutation testing for patients with advanced non-small-cell lung cancer considering first-line EGFR tyrosine kinase inhibitor therapy. J. Clin. Oncol. 29, 2121–2127 (2011).

    Article  PubMed  Google Scholar 

  6. Tarone, R. E., Chu, K. C. & Brawley, O. W. Implications of stage-specific survival rates in assessing recent declines in prostate cancer mortality rates. Epidemiology 11, 167–170 (2000).

    Article  CAS  PubMed  Google Scholar 

  7. Zhang, Z. & Chan, D. W. The road from discovery to clinical diagnostics: lessons learned from the first FDA-cleared in vitro diagnostic multivariate index assay of proteomic biomarkers. Cancer Epidemiol. Biomark. Prev. 19, 2995 (2010).

    Article  CAS  Google Scholar 

  8. Chen, X. et al. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test. Nat. Commun. 11, 3475 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Olivier, M., Asmis, R., Hawkins, G. A., Howard, T. D. & Cox, L. A. The need for multi-omics biomarker signatures in precision medicine. Int. J. Mol. Sci. 20, 4781 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  10. Campos-Carrillo, A. et al. Circulating tumor DNA as an early cancer detection tool. Pharmacol. Ther. 207, 107458 (2020).

    Article  CAS  PubMed  Google Scholar 

  11. Cayer, D. M., Nazor, K. L. & Schork, N. J. Mission critical: the need for proteomics in the era of next-generation sequencing and precision medicine. Hum. Mol. Genet. 25, R182–R189 (2016).

    Article  CAS  PubMed  Google Scholar 

  12. Calsina, B. et al. Integrative multi-omics analysis identifies a prognostic miRNA signature and a targetable miR-21-3p/TSC2/ mTOR axis in metastatic pheochromocytoma/ paraganglioma. Theranostics 9, 4946–4958 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wang, H. et al. Deep multiomics profiling of brain tumors identifies signaling networks downstream of cancer driver genes. Nat. Commun. 10, 3718 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Ankney, J. A., Xie, L., Wrobel, J. A., Wang, L. & Chen, X. Novel secretome-to-transcriptome integrated or secreto-transcriptomic approach to reveal liquid biopsy biomarkers for predicting individualized prognosis of breast cancer patients. BMC Med. Genomics 12, 78 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513, 382–387 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cohen, J. D. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lennon, A. M. et al. Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention. Science 369, eabb9601 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Liu, M. C. et al. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 31, 745–759 (2020).

    Article  CAS  PubMed  Google Scholar 

  19. NHS-Galleri Trial. https://www.nhs-galleri.org/about-the-trial (2021).

  20. Hayes, D. F. et al. Circulating tumor cells at each follow-up time point during therapy of metastatic breast cancer patients predict progression-free and overall survival. Clin. Cancer Res. 12, 4218–4224 (2006).

    Article  CAS  PubMed  Google Scholar 

  21. Cohen, S. J. et al. Relationship of circulating tumor cells to tumor response, progression-free survival, and overall survival in patients with metastatic colorectal cancer. J. Clin. Oncol. 26, 3213–3221 (2008).

    Article  PubMed  Google Scholar 

  22. De Bono, J. S. et al. Circulating tumor cells predict survival benefit from treatment in metastatic castration-resistant prostate cancer. Clin. Cancer Res. 14, 6302–6309 (2008).

    Article  PubMed  CAS  Google Scholar 

  23. Cristofanilli, M. et al. The clinical use of circulating tumor cells (CTCs) enumeration for staging of metastatic breast cancer (MBC): international expert consensus paper. Crit. Rev. Oncol. Hematol. 134, 39–45 (2019).

    Article  PubMed  Google Scholar 

  24. Lim, S. et al. Liquid biopsy: one cell at a time. NPJ Precis. Oncol. 3, 23 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Dai, J. et al. Exosomes: key players in cancer and potential therapeutic strategy. Signal. Transduct. Target. Ther. 5, 145 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wang, J., Ma, P., Kim, D. H., Liu, B. F. & Demirci, U. Towards microfluidic-based exosome isolation and detection for tumor therapy. Nano Today 37, 101066 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ko, J. et al. Single extracellular vesicle protein analysis using immuno-droplet digital polymerase chain reaction amplification. Adv. Biosyst. 4, 1900307 (2020).

    Article  CAS  Google Scholar 

  28. Zhang, P. et al. Ultrasensitive detection of circulating exosomes with a 3D-nanopatterned microfluidic chip. Nat. Biomed. Eng. 3, 438–451 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chen, Y. et al. Exosome detection via the ultrafast-isolation system: EXODUS. Nat. Methods 18, 212–218 (2021).

    Article  CAS  PubMed  Google Scholar 

  30. Wu, X. et al. Exosome-templated nanoplasmonics for multiparametric molecular profiling. Sci. Adv. 6, eaba2556 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kang, Y.-T. et al. Dual-isolation and profiling of circulating tumor cells and cancer exosomes from blood samples with melanoma using immunoaffinity-based microfluidic interfaces. Adv. Sci. 7, 2001581 (2020).

    Article  CAS  Google Scholar 

  32. Lim, J., Choi, M., Lee, H. J., Han, J. Y. & Cho, Y. A novel multifunctional nanowire platform for highly efficient isolation and analysis of circulating tumor-specific markers. Front. Chem. 7, 664 (2019).

    Article  CAS  Google Scholar 

  33. McLendon, R. et al. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

    Article  CAS  Google Scholar 

  34. Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science 362, eaav1898 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Kahles, A. et al. Comprehensive analysis of alternative splicing across tumors from 8,705 Patients. Cancer Cell 34, 211–224.e6 (2018).

    Article  CAS  PubMed  Google Scholar 

  36. Peng, X. et al. Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers. Cell Rep. 23, 255–269.e4 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hoadley, K. A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rodriguez, H., Zenklusen, J. C., Staudt, L. M., Doroshow, J. H. & Lowy, D. R. The next horizon in precision oncology: proteogenomics to inform cancer diagnosis and treatment. Cell 184, 1661–1670 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Tweedie, S. et al. Genenames.org: the HGNC and VGNC resources in 2021. Nucleic Acids Res. 49, D939–D946 (2021).

    Article  CAS  PubMed  Google Scholar 

  40. Wang, L. B. et al. Proteogenomic and metabolomic characterization of human glioblastoma. Cancer Cell 39, 509–528.e20 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Rudnick, P. A. et al. A description of the clinical proteomic tumor analysis consortium (CPTAC) common data analysis pipeline. J. Proteome Res. 15, 1023–1032 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tariq, M. U. et al. Methods for proteogenomics data analysis, challenges, and scalability bottlenecks: a survey. IEEE Access. 9, 5497–5516 (2021).

    Article  PubMed  Google Scholar 

  43. Eicher, T. et al. Challenges in proteogenomics: a comparison of analysis methods with the case study of the DREAM proteogenomics sub-challenge. BMC Bioinforma. 20, 669 (2019).

    Article  Google Scholar 

  44. Subramanian, I., Verma, S., Kumar, S., Jere, A. & Anamika, K. Multi-omics data integration, interpretation, and its application. Bioinform. Biol. Insights 14, 1177932219899051 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Goble, C. et al. FAIR computational workflows under a creative commons attribution 4.0 international (CC BY 4.0) license. Work. Data Intell. 2, 108–121 (2020).

    Article  Google Scholar 

  46. Chambers, M. C. et al. An accessible proteogenomics informatics resource for cancer researchers. Cancer Res. 77, e43–e46 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Srivastava, A. et al. Semantic workflows for benchmark challenges: enhancing comparability, reusability and reproducibility. Pacific Symposium on Biocomputing vol. 24 208–219 (World Scientific Publishing Co. Pte Ltd, 2019).

  48. Huang, S., Chaudhary, K. & Garmire, L. X. More is better: recent progress in multi-omics data integration methods. Front. Genet. 8, 84 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Manzoni, C. et al. Genome, transcriptome and proteome: The rise of omics data and their integration in biomedical sciences. Brief. Bioinform. 19, 286–302 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Bratulic, S., Gatto, F. & Nielsen, J. The translational status of cancer liquid biopsies. Regen. Eng. Transl. Med. 7, 312–352 (2021).

    Article  Google Scholar 

  51. Ignatiadis, M., Sledge, G. W. & Jeffrey, S. S. Liquid biopsy enters the clinic — implementation issues and future challenges. Nat. Rev. Clin. Oncol. 18, 297–312 (2021).

    Article  PubMed  Google Scholar 

  52. Li, J. et al. The growing impact of micro/nanomaterial-based systems in precision oncology: translating “multiomics” technologies. Adv. Funct. Mater. 30, 1909306 (2020).

    Article  CAS  Google Scholar 

  53. Martín-Gracia, B. et al. Nanoparticle-based biosensors for detection of extracellular vesicles in liquid biopsies. J. Mater. Chem. B 8, 6710 (2020).

    Article  PubMed  Google Scholar 

  54. Viswambari Devi, R., Doble, M. & Verma, R. S. Nanomaterials for early detection of cancer biomarker with special emphasis on gold nanoparticles in immunoassays/sensors. Biosens. Bioelectron. 68, 688–698 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Moro, L., Turemis, M., Marini, B., Ippodrino, R. & Giardi, M. T. Better together: strategies based on magnetic particles and quantum dots for improved biosensing. Biotechnol. Adv. 35, 51–63 (2017).

    Article  CAS  PubMed  Google Scholar 

  56. Kelley, S. O. et al. Advancing the speed, sensitivity and accuracy of biomolecular detection using multi-length-scale engineering. Nat. Nanotechnol. 9, 969–980 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Yaari, Z. et al. A perception-based nanosensor platform to detect cancer biomarkers. Sci. Adv. 7, eabj0852 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kim, M. et al. Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning. Nat. Biomed. Eng. 6, 267–275 (2022).

    Article  CAS  PubMed  Google Scholar 

  59. Hanash, S. M., Pitteri, S. J. & Faca, V. M. Mining the plasma proteome for cancer biomarkers. Nature 452, 571–579 (2008).

    Article  CAS  PubMed  Google Scholar 

  60. Paul, J. & Veenstra, T. D. Separation of serum and plasma proteins for in-depth proteomic analysis. Separations 9, 89 (2022).

    Article  CAS  Google Scholar 

  61. Liotta, L. A., Ferrari, M. & Petricoin, E. Written in blood. Nature 425, 905 (2003).

    Article  CAS  PubMed  Google Scholar 

  62. Fredolini, C. et al. Investigation of the ovarian and prostate cancer peptidome for candidate early detection markers using a novel nanoparticle biomarker capture technology. AAPS J. 12, 504–518 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Longo, C. et al. Core-shell hydrogel particles harvest, concentrate and preserve labile low abundance biomarkers. PLoS One 4, e4763 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Tamburro, D. et al. Multifunctional core-shell nanoparticles: discovery of previously invisible biomarkers. J. Am. Chem. Soc. 133, 19178–19188 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Cedervall, T. et al. Understanding the nanoparticle-protein corona using methods to quntify exchange rates and affinities of proteins for nanoparticles. Proc. Natl Acad. Sci. USA 104, 2050–2055 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Hadjidemetriou, M. & Kostarelos, K. Nanomedicine: evolution of the nanoparticle corona. Nat. Nanotechnol. 12, 288–290 (2017).

    Article  CAS  PubMed  Google Scholar 

  67. García-Álvarez, R., Hadjidemetriou, M., Sánchez-Iglesias, A., Liz-Marzán, L. M. & Kostarelos, K. In vivo formation of protein corona on gold nanoparticles. the effect of their size and shape. Nanoscale 10, 1256–1264 (2018).

    Article  PubMed  Google Scholar 

  68. Hadjidemetriou, M. et al. In vivo biomolecule corona around blood-circulating, clinically used and antibody-targeted lipid bilayer nanoscale vesicles. ACS Nano 9, 8142–8156 (2015).

    Article  CAS  PubMed  Google Scholar 

  69. Kamaly, N., Farokhzad, O. C. & Corbo, C. Nanoparticle protein corona evolution: from biological impact to biomarker discovery. Nanoscale 14, 1606–1620 (2022).

    Article  CAS  PubMed  Google Scholar 

  70. Hadjidemetriou, M. et al. The human in vivo biomolecule corona onto pegylated liposomes: a proof-of-concept clinical study. Adv. Mater. 31, e1803335 (2019).

    Article  PubMed  CAS  Google Scholar 

  71. Hadjidemetriou, M., Al-ahmady, Z., Buggio, M., Swift, J. & Kostarelos, K. A novel scavenging tool for cancer biomarker discovery based on the blood-circulating nanoparticle protein corona. Biomaterials 188, 118–129 (2019).

    Article  CAS  PubMed  Google Scholar 

  72. Kostarlos, K. & Hadjidemetriou, M. Detection of cancer biomarkers using nanoparticles. Patent WO2018046542A1 (2018).

  73. Hadjidemetriou, M. et al. Nano-scavengers for blood biomarker discovery in ovarian carcinoma. Nano Today 34, 100901 (2020).

    Article  CAS  Google Scholar 

  74. Di Domenico, M. et al. Nanoparticle-biomolecular corona: a new approach for the early detection of non-small-cell lung cancer. J. Cell. Physiol. 234, 9378–9386 (2019).

    Article  PubMed  CAS  Google Scholar 

  75. Digiacomo, L. et al. A proteomic study on the personalized protein corona of liposomes. Relevance for early diagnosis of pancreatic DUCTAL adenocarcinoma and biomarker detection. J. Nanotheranostics 2, 82–93 (2021).

    Article  Google Scholar 

  76. Blume, J. E. et al. Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. Nat. Commun. 11, 3662 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Chantada-Vázquez, M. D. P. et al. Protein corona gold nanoparticles fingerprinting reveals a profile of blood coagulation proteins in the serum of her2-overexpressing breast cancer patients. Int. J. Mol. Sci. 21, 8449 (2020).

    Article  PubMed Central  CAS  Google Scholar 

  78. Gómez, B. B. et al. Detection of circulating serum protein biomarkers of non-muscle invasive bladder cancer after protein corona-silver nanoparticles analysis by swath-ms. Nanomaterials 11, 2384 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Gardner, L. et al. The biomolecule corona of lipid nanoparticles contains circulating cell-free DNA. Nanoscale Horiz. 5, 1476–1486 (2020).

    Article  PubMed  Google Scholar 

  80. Lee, H. J. et al. Magnetic nanowire networks for dual-isolation and detection of tumor-associated circulating biomarkers. Theranostics 8, 505–517 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Gwak, H. et al. On-chip isolation and enrichment of circulating cell-free DNA using microfluidic device. Biomicrofluidics 13, 24113 (2019).

    Article  CAS  Google Scholar 

  82. Jeon, S. H. et al. Efficient capture and isolation of tumor-related circulating cell-free dna from cancer patients using electroactive conducting polymer nanowire platforms. Theranostics 6, 828–836 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Sina, A. A. I. et al. Epigenetically reprogrammed methylation landscape drives the DNA self-assembly and serves as a universal cancer biomarker. Nat. Commun. 9, 4915 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Zhou, X. et al. Leukocyte-repelling biomimetic immunomagnetic nanoplatform for high-performance circulating tumor cells isolation. Small 15, 1900558 (2019).

    Article  CAS  Google Scholar 

  85. Hong, W., Lee, S., Chang, H. J., Lee, E. S. & Cho, Y. Multifunctional magnetic nanowires: a novel breakthrough for ultrasensitive detection and isolation of rare cancer cells from non-metastatic early breast cancer patients using small volumes of blood. Biomaterials 106, 78–86 (2016).

    Article  CAS  PubMed  Google Scholar 

  86. Poudineh, M. et al. Tracking the dynamics of circulating tumour cell phenotypes using nanoparticle-mediated magnetic ranking. Nat. Nanotechnol. 12, 274–281 (2017).

    Article  CAS  PubMed  Google Scholar 

  87. Mohamadi, R. M. et al. Nanoparticle-mediated binning and profiling of heterogeneous circulating tumor cell subpopulations. Angew. Chem. Int. Ed. 54, 139–143 (2015).

    Article  CAS  Google Scholar 

  88. Zhou, X. et al. Multifunctional luminescent immuno-magnetic nanoparticles: toward fast, efficient, cell-friendly capture and recovery of circulating tumor cells. J. Mater. Chem. B 7, 393–400 (2019).

    Article  CAS  PubMed  Google Scholar 

  89. Lim, J. et al. Direct isolation and characterization of circulating exosomes from biological samples using magnetic nanowires. J. Nanobiotechnol. 17, 1 (2019).

    Article  Google Scholar 

  90. Park, M. H. et al. Enhanced isolation and release of circulating tumor cells using nanoparticle binding and ligand exchange in a microfluidic chip. J. Am. Chem. Soc. 139, 2741–2749 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Ke, Z. et al. Programming thermoresponsiveness of nanovelcro substrates enables effective purification of circulating tumor cells in lung cancer patients. ACS Nano 9, 62–70 (2015).

    Article  CAS  PubMed  Google Scholar 

  92. Wang, S. et al. Highly efficient capture of circulating tumor cells by using nanostructured silicon substrates with integrated chaotic micromixers. Angew. Chem. Int. Ed. 50, 3084–3088 (2011).

    Article  CAS  Google Scholar 

  93. Dong, J. et al. Bio-inspired NanoVilli chips for enhanced capture of tumor-derived extracellular vesicles: toward non-invasive detection of gene alterations in non-small cell lung cancer. ACS Appl. Mater. Interfaces 11, 13973–13983 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Sun, N. et al. Purification of HCC-specific extracellular vesicles on nanosubstrates for early HCC detection by digital scoring. Nat. Commun. 11, 4489 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Zhang, N. et al. Electrospun TiO2 nanofiber-based cell capture assay for detecting circulating tumor cells from colorectal and gastric cancer patients. Adv. Mater. 24, 2756–2760 (2012).

    Article  CAS  PubMed  Google Scholar 

  96. Yoon, H. J. et al. Sensitive capture of circulating tumour cells by functionalized graphene oxide nanosheets. Nat. Nanotechnol. 8, 735–741 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Loeian, M. S. et al. Liquid biopsy using the nanotube-CTC-chip: capture of invasive CTCs with high purity using preferential adherence in breast cancer patients. Lab. Chip 19, 1899–1915 (2019).

    Article  CAS  PubMed  Google Scholar 

  98. Zhang, P., He, M. & Zeng, Y. Ultrasensitive microfluidic analysis of circulating exosomes using a nanostructured graphene oxide/polydopamine coating. Lab. Chip 16, 3033–3042 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Schweiger, M. R., Kerick, M., Timmermann, B. & Isau, M. The power of NGS technologies to delineate the genome organization in cancer: from mutations to structural variations and epigenetic alterations. Cancer Metastasis Rev. 30, 199–210 (2011).

    Article  CAS  PubMed  Google Scholar 

  100. Papafilippou, L., Claxton, A., Dark, P., Kostarelos, K. & Hadjidemetriou, M. Protein corona fingerprinting to differentiate sepsis from non-infectious systemic inflammation. Nanoscale 12, 10240–10253 (2020).

    Article  CAS  PubMed  Google Scholar 

  101. Chetwynd, A. J. & Lynch, I. The rise of the nanomaterial metabolite corona, and emergence of the complete corona. Environ. Sci. Nano 7, 1041–1060 (2020).

    Article  CAS  Google Scholar 

  102. Chetwynd, A. J., Zhang, W., Thorn, J. A., Lynch, I. & Ramautar, R. The nanomaterial metabolite corona determined using a quantitative metabolomics approach: a pilot study. Small 16, 2000295 (2020).

    Article  CAS  Google Scholar 

  103. Raesch, S. S. et al. Proteomic and lipidomic analysis of nanoparticle corona upon contact with lung surfactant reveals differences in protein, but not lipid composition. ACS Nano 9, 11872–11885 (2015).

    Article  CAS  PubMed  Google Scholar 

  104. Kapralov, A. A. et al. Adsorption of surfactant lipids by single-walled carbon nanotubes in mouse lung upon pharyngeal aspiration. ACS Nano 6, 4147–4156 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Martel, J. et al. Fatty acids and small organic compounds bind to mineralo-organic nanoparticles derived from human body fluids as revealed by metabolomic analysis. Nanoscale 8, 5537–5545 (2016).

    Article  CAS  PubMed  Google Scholar 

  106. Pink, M., Verma, N., Kersch, C. & Schmitz-Spanke, S. Identification and characterization of small organic compounds within the corona formed around engineered nanoparticles. Environ. Sci. Nano 5, 1420 (2018).

    Article  CAS  Google Scholar 

  107. Grintzalis, K., Lawson, T. N., Nasser, F., Lynch, I. & Viant, M. R. Metabolomic method to detect a metabolite corona on amino-functionalized polystyrene nanoparticles. Nanotoxicology 13, 783–794 (2019).

    Article  CAS  PubMed  Google Scholar 

  108. La Barbera, G. et al. A comprehensive analysis of liposomal biomolecular corona upon human plasma incubation: the evolution towards the lipid corona. Talanta 209, 120487 (2020).

    Article  PubMed  CAS  Google Scholar 

  109. Hellstrand, E. et al. Complete high-density lipoproteins in nanoparticle corona. FEBS J. 276, 3372–3381 (2009).

    Article  CAS  PubMed  Google Scholar 

  110. Lee, J. Y. et al. Analysis of lipid adsorption on nanoparticles by nanoflow liquid chromatography-tandem mass spectrometry. Anal. Bioanal. Chem. 410, 6155–6164 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Hadjidemetriou, M. et al. Nanoparticle-enabled enrichment of longitudinal blood proteomic fingerprints in Alzheimer’s disease. ACS Nano 15, 7357–7369 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Lyons, N. et al. Early Detection and Diagnosis of Cancer: A roadmap to the future. Cancer Research UK https://www.cancerresearchuk.org/funding-for-researchers/research-opportunities-in-early-detection-and-diagnosis/early-detection-and-diagnosis-roadmap (2021).

  113. Hou, S. et al. Polymer nanofiber-embedded microchips for detection, isolation, and molecular analysis of single circulating melanoma cells. Angew. Chem. Int. Ed. 52, 3379–3383 (2013).

    Article  CAS  Google Scholar 

  114. Shen, M. Y. et al. Glycan stimulation enables purification of prostate cancer circulating tumor cells on PEDOT NanoVelcro chips for RNA biomarker detection. Adv. Healthc. Mater. https://doi.org/10.1002/adhm.201700701 (2018).

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors acknowledge funding support from the NIHR Manchester Biomedical Research Centre (BRC) and the Cancer Research UK International Alliance for Cancer Early Detection (ACED; EICEDAAP\100013).

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L.G. researched data for the article; L.G., K.K. and M.H. made substantial contributions to the discussion of content; L.G., P.M. and M.H. wrote the manuscript; and L.G., K.K., C.D. and M.H. edited the manuscript before submission. M.H. took responsibility for revising the manuscript after submission.

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Correspondence to Marilena Hadjidemetriou.

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Nature Reviews Clinical Oncology thanks D. Heller, J.X.J. Zhang and the other, anonymous, reviewers for their contribution to the peer review of this work.

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Gardner, L., Kostarelos, K., Mallick, P. et al. Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome. Nat Rev Clin Oncol 19, 551–561 (2022). https://doi.org/10.1038/s41571-022-00645-x

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