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Cancer diagnosis with DNA molecular computation

Abstract

Early and precise cancer diagnosis substantially improves patient survival. Recent work has revealed that the levels of multiple microRNAs in serum are informative as biomarkers for the diagnosis of cancers. Here, we designed a DNA molecular computation platform for the analysis of miRNA profiles in clinical serum samples. A computational classifier is first trained in silico using miRNA profiles from The Cancer Genome Atlas. This is followed by a computationally powerful but simple molecular implementation scheme using DNA, as well as an effective in situ amplification and transformation method for miRNA enrichment in serum without perturbing the original variety and quantity information. We successfully achieved rapid and accurate cancer diagnosis using clinical serum samples from 22 healthy people (8) and people with lung cancer (14) with an accuracy of 86.4%. We envision that this DNA computational platform will inspire more clinical applications towards inexpensive, non-invasive and rapid disease screening, classification and progress monitoring.

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Fig. 1: DNA computation platform for NSCLC diagnosis.
Fig. 2: Amplification and transformation of miRNAs to loop DNAs.
Fig. 3: Workflow of the DNA computation.
Fig. 4: Validation of the DNA computation-based diagnostic system with synthetic and clinical samples.

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Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. Furthermore, the miRNA-seq data used in this study are available on TCGA database https://portal.gdc.cancer.gov.

Code availability

The SVM training and validation code used in this study is from ref. 41 and available on https://www.csie.ntu.edu.tw/~cjlin/libsvm/.

References

  1. Allemani, C. et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet 391, 1023–1075 (2018).

    Google Scholar 

  2. Rotunno, M. et al. A gene expression signature from peripheral whole blood for stage I lung adenocarcinoma. Cancer Prev. Res. 4, 1599–1608 (2011).

    CAS  Google Scholar 

  3. Liu, C. et al. Low-cost thermophoretic profiling of extracellular-vesicle surface proteins for the early detection and classification of cancers. Nat. Biomed. Eng. 3, 183–193 (2019).

    CAS  Google Scholar 

  4. Gines, G. et al. Isothermal digital detection of microRNAs using background-free molecular circuit. Sci. Adv. 6, eaay5952 (2020).

    Google Scholar 

  5. Esquela-Kerscher, A. & Slack, F. J. Oncomirs—microRNAs with a role in cancer. Nat. Rev. Cancer 6, 259–269 (2006).

    CAS  Google Scholar 

  6. Calin, G. A. & Croce, C. M. MicroRNA signatures in human cancers. Nat. Rev. Cancer 6, 857–866 (2006).

    CAS  Google Scholar 

  7. Lu, J. et al. MicroRNA expression profiles classify human cancers. Nature 435, 834–838 (2005).

    CAS  Google Scholar 

  8. Chen, X. et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res. 18, 997–1006 (2008).

    CAS  Google Scholar 

  9. Madhavan, D., Cuk, K., Burwinkel, B. & Yang, R. Cancer diagnosis and prognosis decoded by blood-based circulating microRNA signatures. Front. Genet. 4, 116 (2013).

    CAS  Google Scholar 

  10. 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).

    CAS  Google Scholar 

  11. Liu, Q. et al. DNA computing on surfaces. Nature 403, 175–179 (2000).

    CAS  Google Scholar 

  12. Adleman, L. M. Molecular computation of solutions to combinatorial problems. Science 266, 1021–1024 (1994).

    CAS  Google Scholar 

  13. Braich, R. S., Chelyapov, N., Johnson, C., Rothemund, P. W. & Adleman, L. Solution of a 20-variable 3-SAT problem on a DNA computer. Science 296, 499–502 (2002).

    CAS  Google Scholar 

  14. Han, D. et al. A cascade reaction network mimicking the basic functional steps of adaptive immune response. Nat. Chem. 7, 835–841 (2015).

    CAS  Google Scholar 

  15. Srinivas, N., Parkin, J., Seelig, G., Winfree, E. & Soloveichik, D. Enzyme-free nucleic acid dynamical systems. Science 358, eaal2052 (2017).

    Google Scholar 

  16. Green, A. A. et al. Complex cellular logic computation using ribocomputing devices. Nature 548, 117–121 (2017).

    CAS  Google Scholar 

  17. Joesaar, A. et al. DNA-based communication in populations of synthetic protocells. Nat. Nanotechnol. 14, 369–378 (2019).

    CAS  Google Scholar 

  18. Han, D. et al. Engineering a cell-surface aptamer circuit for targeted and amplified photodynamic cancer therapy. ACS Nano 7, 2312–2319 (2013).

    CAS  Google Scholar 

  19. You, M., Zhu, G., Chen, T., Donovan, M. J. & Tan, W. Programmable and multiparameter DNA-based logic platform for cancer recognition and targeted therapy. J. Am. Chem. Soc. 137, 667–674 (2015).

    CAS  Google Scholar 

  20. Rudchenko, M. et al. Autonomous molecular cascades for evaluation of cell surfaces. Nat. Nanotechnol. 8, 580–586 (2013).

    CAS  Google Scholar 

  21. Chang, X. et al. Construction of a multiple-aptamer-based DNA logic device on live cell membranes via associative toehold activation for accurate cancer cell identification. J. Am. Chem. Soc. 141, 12738–12743 (2019).

    CAS  Google Scholar 

  22. Douglas, S. M., Bachelet, I. & Church, G. M. A logic-gated nanorobot for targeted transport of molecular payloads. Science 335, 831–834 (2012).

    CAS  Google Scholar 

  23. Thubagere, A. J. et al. A cargo-sorting DNA robot. Science 357, eaan6558 (2017).

    Google Scholar 

  24. Pei, R., Matamoros, E., Liu, M., Stefanovic, D. & Stojanovic, M. N. Training a molecular automaton to play a game. Nat. Nanotechnol. 5, 773–777 (2010).

    CAS  Google Scholar 

  25. Chao, J. et al. Solving mazes with single-molecule DNA navigators. Nat. Mater. 18, 273–279 (2019).

    CAS  Google Scholar 

  26. Song, J. et al. Reconfiguration of DNA molecular arrays driven by information relay. Science 357, eaan3377 (2017).

    Google Scholar 

  27. Qian, L., Winfree, E. & Bruck, J. Neural network computation with DNA strand displacement cascades. Nature 475, 368–372 (2011).

    CAS  Google Scholar 

  28. Cherry, K. M. & Qian, L. Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks. Nature 559, 370–376 (2018).

    CAS  Google Scholar 

  29. Lopez, R., Wang, R. & Seelig, G. A molecular multi-gene classifier for disease diagnostics. Nat. Chem. 10, 746–754 (2018).

    CAS  Google Scholar 

  30. Seelig, G., Soloveichik, D., Zhang, D. Y. & Winfree, E. Enzyme-free nucleic acid logic circuits. Science 314, 1585–1588 (2006).

    CAS  Google Scholar 

  31. Benenson, Y., Gil, B., Ben-Dor, U., Adar, R. & Shapiro, E. An autonomous molecular computer for logical control of gene expression. Nature 429, 423–429 (2004).

    CAS  Google Scholar 

  32. Koscielny, S. Why most gene expression signatures of tumors have not been useful in the clinic. Sci. Transl. Med. 2, 14ps12 (2010).

    Google Scholar 

  33. Brown, M. P. et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl Acad. Sci. USA 97, 262–267 (2000).

    CAS  Google Scholar 

  34. Rice, J. E. et al. Monoplex/multiplex linear-after-the-exponential-PCR assays combined with PrimeSafe and Dilute-‘N’-Go sequencing. Nat. Protoc. 2, 2429–2438 (2007).

    CAS  Google Scholar 

  35. Pierce, K. E., Sanchez, J. A., Rice, J. E. & Wangh, L. J. Linear-after-the-exponential (LATE)-PCR: primer design criteria for high yields of specific single-stranded DNA and improved real-time detection. Proc. Natl Acad. Sci. USA 102, 8609–8614 (2005).

    CAS  Google Scholar 

  36. Simmel, F. C., Yurke, B. & Singh, H. R. Principles and applications of nucleic acid strand displacement reactions. Chem. Rev. 119, 6326–6369 (2019).

    CAS  Google Scholar 

  37. Zhang, D. Y. Cooperative hybridization of oligonucleotides. J. Am. Chem. Soc. 133, 1077–1086 (2011).

    CAS  Google Scholar 

  38. Chen, Y. J. et al. Programmable chemical controllers made from DNA. Nat. Nanotechnol. 8, 755–762 (2013).

    CAS  Google Scholar 

  39. Broza, Y. Y. et al. Disease detection with molecular biomarkers: from chemistry of body fluids to nature-inspired chemical sensors. Chem. Rev. 119, 11761–11817 (2019).

    CAS  Google Scholar 

  40. Tregubov, A. A., Nikitin, P. I. & Nikitin, M. P. Advanced smart nanomaterials with integrated logic-gating and biocomputing: dawn of theranostic nanorobots. Chem. Rev. 118, 10294–10348 (2018).

    CAS  Google Scholar 

  41. Chang, C.-C. & Lin, C.-J. LIBSVM: a library for support vector machines. ACM T. Intel. Syst. Tec. 2, 27 (2011).

    Google Scholar 

  42. Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20, 273–297 (1995).

    Google Scholar 

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (grant nos. 21974087 and 31871009), Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support (no. 20181709), Innovative Research Team of High-Level Local Universities in Shanghai, Faculty Start-up Funding Support from the Institute of Molecular Medicine of Shanghai Jiao Tong University and the Recruitment Programme of Global Youth Experts of China. We thank M. Zhang for the help on constructing kinetic models and data simulations. We thank J. Sun for the helpful discussion.

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Authors and Affiliations

Authors

Contributions

C.Z. and D.H. conceived and designed the experiments. C.Z. carried out the assays and analysed the results. C.Z., Y.Z., X.X., H.L., R.X., Y.M. and D.H. supported the optimization of assays and prepared the data. X.T. and Y.D. collected the specimens. D.H., H.-C.L. and C.Z. wrote the manuscript. D.H. supervised the project.

Corresponding author

Correspondence to Da Han.

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The authors declare no competing interests.

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Peer review information Nature Nanotechnology thanks Tom de Greef and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Note, Figs. 1–20, Tables 1–4 and refs. 43–47.

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Zhang, C., Zhao, Y., Xu, X. et al. Cancer diagnosis with DNA molecular computation. Nat. Nanotechnol. 15, 709–715 (2020). https://doi.org/10.1038/s41565-020-0699-0

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