The DOI matches its Crossref record; 163 of the 166 references it cites were found in a registry or an index.
Harnessing the predictive power of preclinical models for oncology drug development
Abstract
Recent progress in understanding the molecular basis of cellular processes, identification of promising therapeutic targets and evolution of the regulatory landscape makes this an exciting and unprecedented time to be in the field of oncology drug development. However, high costs, long development timelines and steep rates of attrition continue to afflict the drug development process. Lack of predictive preclinical models is considered one of the key reasons for the high rate of attrition in oncology. Generating meaningful and predictive results preclinically requires a firm grasp of the relevant biological questions and alignment of the model systems that mirror the patient context. In doing so, the ability to conduct both forward translation, the process of implementing basic research discoveries into practice, as well as reverse translation, the process of elucidating the mechanistic basis of clinical observations, greatly enhances our ability to develop effective anticancer treatments. In this Review, we outline issues in preclinical-to-clinical translatability of molecularly targeted cancer therapies, present concepts and examples of successful reverse translation, and highlight the need to better align tumour biology in patients with preclinical model systems including tracking of strengths and weaknesses of preclinical models throughout programme development.
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“In addition to this topographical issue, culture conditions can contribute to selecting specific clones, possibly altering or reducing clonality over time129.”
From Organoids (2022) · cites this paper as [129]
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“While some studies have reported that molecular characteristics can be maintained over long-term culture, others have observed tumor evolution after serial passaging129,132.”
From Organoids (2022) · cites this paper as [129]
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“This has been shown by RNAseq on many tumor types including bladder cancer128 and esophageal cancer129 amongst others.”
From Organoids (2022) · cites this paper as [129]
Citations
Cites
Double-slit photoelectron interference in strong-field ionization of the neon dimer
Kunitski, M., Eicke, N., Huber, P., Köhler, J., Zeller, S., Voigtsberger, J., Schlott, N., Henrichs, K., Sann, H., Trinter, F., et al. (2019) · Institutional Repository of Leibniz Universität Hannover (Leibniz Universität Hannover) · doi:10.15488/5174 · Cited by 8,378
Double-slit photoelectron interference in strong-field ionization of the neon dimer
Kunitski, M., Eicke, N., Huber, P., Köhler, J., Zeller, S., Voigtsberger, J., Schlott, N., Henrichs, K., Sann, H., Trinter, F., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-07882-8 · Cited by 8,162
Improved survival with vemurafenib in melanoma with BRAF V600E mutation
Chapman, P. B., Hauschild, A., Robert, C., Haanen, J. B., Ascierto, P., Larkin, J., Dummer, R., Garbe, C., Testori, A., Maio, M., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1103782 · Cited by 7,720
Osimertinib in Untreated EGFR -Mutated Advanced Non–Small-Cell Lung Cancer
Soria, J.-C., Ohe, Y., Vansteenkiste, J., Reungwetwattana, T., Chewaskulyong, B., Lee, K. H., Dechaphunkul, A., Imamura, F., Nogami, N., Kurata, T., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1713137 · Cited by 5,508
Cancer drug resistance: an evolving paradigm
Holohan, C., Van Schaeybroeck, S., Longley, D. B., & Johnston, P. G. (2013) · Nature Reviews Cancer · doi:10.1038/nrc3599 · Cited by 4,681
Pao, W., Miller, V., Zakowski, M., Doherty, J., Politi, K., Sarkaria, I., Singh, B., Heelan, R., Rusch, V., Fulton, L., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0405220101 · Cited by 4,338
Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., Bindal, N., Beare, D., Smith, J. A., Thompson, I. R., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1111 · Cited by 4,051
Pao, W., Miller, V. A., Politi, K. A., Riely, G. J., Somwar, R., Zakowski, M. F., Kris, M. G., & Varmus, H. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020073 · Cited by 3,527
Defining a Cancer Dependency Map
Tsherniak, A., Vazquez, F., Montgomery, P. G., Weir, B. A., Kryukov, G., Cowley, G. S., Gill, S., Harrington, W. F., Pantel, S., Krill-Burger, J. M., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.010 · Cited by 3,276
Olive, K. P., Jacobetz, M. A., Davidson, C. J., Gopinathan, A., McIntyre, D., Honess, D., Madhu, B., Goldgraben, M. A., Caldwell, M. E., Allard, D., et al. (2009) · Science · doi:10.1126/science.1171362 · Cited by 3,170
Research works printed but not linked: 2
- Lightfoot, J. T., Bamman, M. M. & Booth, F. W. Translation goes both ways: the power of reverse translation from human trials into animal models. Transl. J. Am. Coll. Sports Med. 2, 29–31 (2017).
- Jarnuczak, A. F. et al. An integrated landscape of protein expression in human cancer. Preprint at biorxiv https://doi.org/10.1101/665968v1.abstract (2019). · DOI
Cited by
Sorted by citations
Zhao, Z., Chen, X., Dowbaj, A. M., Sljukic, A., Bratlie, K., Lin, L., Fong, E. L. S., Balachander, G. M., Chen, Z., Soragni, A., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00174-y · Cited by 1,082
Patient-derived xenograft models in cancer therapy: technologies and applications
Liu, Y., Wu, W., Cai, C., Zhang, H., Shen, H., & Han, Y. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01419-2 · Cited by 533
Drug resistance in cancer: molecular mechanisms and emerging treatment strategies
Li, J., Hu, J., Yang, Y., Zhang, H., Liu, Y., Fang, Y., Qu, L., Lin, A., Luo, P., Jiang, A., et al. (2025) · Molecular Biomedicine · doi:10.1186/s43556-025-00352-w · Cited by 120
Coding, or non-coding, that is the question
Poliseno, L., Lanza, M., & Pandolfi, P. P. (2024) · Cell Research · doi:10.1038/s41422-024-00975-8 · Cited by 101
Personalized chordoma organoids for drug discovery studies
Al Shihabi, A., Davarifar, A., Nguyen, H. T. L., Tavanaie, N., Nelson, S. D., Yanagawa, J., Federman, N., Bernthal, N., Hornicek, F., & Soragni, A. (2022) · Science Advances · doi:10.1126/sciadv.abl3674 · Cited by 75
Metadata
- Authors
- Honkala, A., Malhotra, S. V., Kummar, S., & Junttila, M. R.
- Affiliations
- Oregon Health & Science University (US)
- ORIC Pharmaceuticals, Inc. (United States) (US)
- Corresponding author
- Kummar, Shivaani
- Junttila, Melissa R.
- Venue
- Nature Reviews Drug Discovery
- Published
- 2021-10-26
- Volume
- 21
- Issue
- 2
- Pages
- 99-114
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W3210942885
- Cited by
- 121
- References
- 166
- Citations by year
- 2026: 30 · 2025: 31 · 2024: 20 · 2023: 23 · 2022: 16 · 2021: 1
- FWCI
- 2.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Reviews Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Science, Research, and Medicine
- Keywords
- anticancer drug development, preclinical models, prediction model, translational research, forward translation, reverse translation, molecularly targeted cancer therapy, drug attrition, tumor biology
- MeSH terms
- Drug Development, Animals, Antineoplastic Agents, Drug Evaluation, Preclinical, Humans, Neoplasms, Biomarkers, Tumor, Molecular Targeted Therapy
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