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DNA-framework-based multidimensional molecular classifiers for cancer diagnosis

Nature Nanotechnology volume 18, pages 677–686 (2023)Cite this article

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Abstract

A molecular classification of diseases that accurately reflects clinical behaviour lays the foundation of precision medicine. The development of in silico classifiers coupled with molecular implementation based on DNA reactions marks a key advance in more powerful molecular classification, but it nevertheless remains a challenge to process multiple molecular datatypes. Here we introduce a DNA-encoded molecular classifier that can physically implement the computational classification of multidimensional molecular clinical data. To produce unified electrochemical sensing signals across heterogeneous molecular binding events, we exploit DNA-framework-based programmable atom-like nanoparticles with n valence to develop valence-encoded signal reporters that enable linearity in translating virtually any biomolecular binding events to signal gains. Multidimensional molecular information in computational classification is thus precisely assigned weights for bioanalysis. We demonstrate the implementation of a molecular classifier based on programmable atom-like nanoparticles to perform biomarker panel screening and analyse a panel of six biomarkers across three-dimensional datatypes for a near-deterministic molecular taxonomy of prostate cancer patients.

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Fig. 1: A PAN-reporter-based multidimensional molecular classifier for cancer diagnosis.
Fig. 2: The design of valence-encoded signal reporters using PANs.
Fig. 3: A PAN reporter-based weighting system for multidimensional molecules.
Fig. 4: In silico training of linear molecular classifier to discriminate PCa patients and healthy individuals.
Fig. 5: Multidimensional molecular classifier for PCa diagnosis.
Fig. 6: Diagnosis panel screening using PAN reporters for PCa.

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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, mRNA, PSA and SO data used in this study are available in ref. 47 and the National Center for Biotechnology Information database, https://www.ncbi.nlm.nih.gov/genome.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (T2188102, 22025404, 22001168); National Key R&D Program of China (2021YFF1200300); China National Postdoctoral Program for Innovative Talents (BX2021190) by the China Postdoctoral Science Foundation; Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZLCX20212602); 2022 Shanghai ‘Science and Technology Innovation Action Plan’ Fundamental Research Project (22JC1401202); Shanghai Jiao Tong University Scientific and Technological Innovation Funds (21X010202096) and Shanghai Municipal Health Commission (2022JC027).

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Author notes

  1. These authors contributed equally: Fangfei Yin, Haipei Zhao, Shasha Lu, Juwen Shen.

Authors and Affiliations

  1. Institute of Molecular Medicine, Department of Urology, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Fangfei Yin, Min Li, Xiuhai Mao, Fan Li, Baijun Dong, Wei Xue, Xiaolei Zuo & Chunhai Fan

  2. Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Zhangjiang Institute for Advanced Study, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China

    Haipei Zhao, Shasha Lu, Xiaolei Zuo, Xiurong Yang & Chunhai Fan

  3. School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou, China

    Shasha Lu

  4. Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China

    Juwen Shen

  5. Division of Physical Biology, CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai, China

    Jiye Shi & Jiang Li

  6. The Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China

    Jiang Li

  7. State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, China

    Xiurong Yang

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Contributions

X.Z., C.F. and F.Y. conceived the study. F.Y., H.Z. and S.L. performed the experiments. F.Y performed the TIRFM imaging and nucleic acid information translation. H.Z. performed the TEM imaging and SO information translation. S.L. performed the AFM imaging and PSA information translation. J. Shen performed the target screen and data training. B.D. and W.X. provided samples and analysed the clinical data. F.Y., H.Z. and S.L. performed the clinical sample detection. F.Y., J. Shi, M.L., X.M., F.L. and J.L. carried out the assays and analysed the results. X.Z. and C.F. directed the research. X.Z., C.F. and F.Y. wrote the paper. X.Z., C.F. and X.Y. supervised the project. All authors read the paper and provided comments.

Corresponding author

Correspondence to Xiaolei Zuo.

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Nature Nanotechnology thanks Hao Yan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–41, Tables 1–23, Discussion, Notes and References.

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Yin, F., Zhao, H., Lu, S. et al. DNA-framework-based multidimensional molecular classifiers for cancer diagnosis. Nat. Nanotechnol. 18, 677–686 (2023). https://doi.org/10.1038/s41565-023-01348-9

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