Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The Cancer Moonshot, the role of in vitro models, model accuracy, and the need for validation

Nature Nanotechnology volume 18, pages 1121–1123 (2023)Cite this article

Subjects

Reducing cancer-related deaths can only happen with a better understanding of cancer biology and the development of improved, new therapeutics and delivery mechanisms. Nearly all cancer research is dependent upon the models being used, the model’s accuracy, and appropriate validation and benchmarking. Here the need for such considerations is discussed in line with the goal of the Cancer Moonshot.

The Cancer Moonshot was launched in 2016 with the mission to accelerate the rate of progress against cancer1. In 2022, the Cancer Moonshot was rebooted with the specific goal of reducing the rate of cancer-related deaths by at least 50% over the subsequent 25 years2. The current death rate is about 140 per 100,000 in the USA, and hence the target for the Moonshot is around 70 deaths per 100,000. According to the NIH National Cancer Institute, research aimed at achieving this goal can be categorized into eight areas: cancer biology, cancer genomics, causes of cancer, detection and diagnosis, cancer prevention, cancer treatment, the role of cancer in population health, and cancer health disparities. Recent advances in tissue engineering have significantly extended the repertoire of in vitro models in cancer research, and these models will play an important role, as one class of many tools, in this effort. However, the impact of in vitro models in improving cancer outcomes will be dependent on establishing accuracy and validation of a subset of critical models for specific context-of-use cases aligned with these defined research areas.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

References

  1. Sharpless, N. E. & Singer, D. S. Cancer Cell 39, 889–894 (2021).

    Article  CAS  Google Scholar 

  2. Singer, D. S. Nat. Med. 28, 1345–1347 (2022).

    Article  CAS  Google Scholar 

  3. Reilly, M. M. & Rossor, A. M. J. Neurol. Neurosurg. Psychiatry 91, 1132–1136 (2020).

    Article  Google Scholar 

  4. Perlman, R. L. Evol. Med. Public Health 2016, 170–176 (2016).

    Google Scholar 

  5. Kleiber, M. Physiol. Rev. 27, 511–541 (1947).

    Article  CAS  Google Scholar 

  6. Wong, C. H., Siah, K. W. & Lo, A. W. Biostatistics 20, 366–366 (2019).

    Article  Google Scholar 

  7. Gasparini, G. Int. J. Biol. Marker 23, 63–63 (2008).

    CAS  Google Scholar 

  8. Mak, I. W. Y., Evaniew, N. & Ghert, M. Am. J. Transl. Res. 6, 114–118 (2014).

    Google Scholar 

  9. Frangogiannis, N. G. J. Cardiovasc. Aging 2, 22 (2022).

    Google Scholar 

  10. Katt, M. E., Placone, A. L., Wong, A. D., Xu, Z. S. & Searson, P. C. Front. Bioeng. Biotechnol. 4, 12 (2016).

    Article  Google Scholar 

  11. Hosseini, V. et al. Lab Chip 21, 641–659 (2021).

    Article  CAS  Google Scholar 

  12. Ingber, D. E. Nat. Rev. Genet. 23, 467–491 (2022).

    Article  CAS  Google Scholar 

  13. Silvestri, V. L. et al. Cancer Res. 80, 4288–4301 (2020).

    Article  CAS  Google Scholar 

  14. Wong, A. D., Russell, L. M., Katt, M. E. & Searson, P. C. Acs Biomater. Sci. Eng. 5, 633–643 (2019).

    Article  CAS  Google Scholar 

  15. Wong, A. D. & Searson, P. C. Cancer Res. 74, 4937–4945 (2014).

    Article  CAS  Google Scholar 

  16. Wong, A. D. & Searson, P. C. Cancer Res. 77, 6453–6461 (2017).

    Article  CAS  Google Scholar 

  17. Russell, W. M. S. & Burch, R. L. The Principles of Humane Experimental Technique (Methuen, 1959).

  18. Baran, S. W. et al. Altex-Altern Anim. Ex. 39, 297–314 (2022).

    Google Scholar 

  19. Ekert, J. E. et al. SLAS Discov. 25, 1174–1190 (2020).

    Article  CAS  Google Scholar 

  20. Baker, M. Nature 533, 452–454 (2016).

    Article  CAS  Google Scholar 

  21. Nosek, B. A. & Errington, T. M. eLife 6, e23383 (2017).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD, USA

    Peter C. Searson

  2. Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA

    Peter C. Searson

  3. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Peter C. Searson

  4. Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, USA

    Peter C. Searson

Authors
  1. Peter C. Searson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter C. Searson.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Searson, P.C. The Cancer Moonshot, the role of in vitro models, model accuracy, and the need for validation. Nat. Nanotechnol. 18, 1121–1123 (2023). https://doi.org/10.1038/s41565-023-01486-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41565-023-01486-0

Search

Advanced search

Quick links

Find nanotechnology articles, nanomaterial data and patents all in one place. Visit Nano by Nature Research