The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine
Open accessAbstract
Precision medicine is an approach that takes into account the influence of individuals' genes, environment, and lifestyle exposures to tailor interventions. Here, we describe the development of a robust precision cancer care platform that integrates whole-exome sequencing with a living biobank that enables high-throughput drug screens on patient-derived tumor organoids. To date, 56 tumor-derived organoid cultures and 19 patient-derived xenograft (PDX) models have been established from the 769 patients enrolled in an Institutional Review Board–approved clinical trial. Because genomics alone was insufficient to identify therapeutic options for the majority of patients with advanced disease, we used high-throughput drug screening to discover effective treatment strategies. Analysis of tumor-derived cells from four cases, two uterine malignancies and two colon cancers, identified effective drugs and drug combinations that were subsequently validated using 3-D cultures and PDX models. This platform thereby promotes the discovery of novel therapeutic approaches that can be assessed in clinical trials and provides personalized therapeutic options for individual patients where standard clinical options have been exhausted. Significance: Integration of genomic data with drug screening from personalized in vitro and in vivo cancer models guides precision cancer care and fuels next-generation research. Cancer Discov; 7(5); 462–77. ©2017 AACR. See related commentary by Picco and Garnett, p. 456. This article is highlighted in the In This Issue feature, p. 443
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Self-citationPhase II and Phase III attrition rates 2011–2012
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“However, an understanding of the functionality of these alterations and the influence they have on treatment response remains limited due to a paucity of personalized preclinical models (14, 15).”
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Cited by
Patient-derived organoids model treatment response of metastatic gastrointestinal cancers
Vlachogiannis, G., Hedayat, S., Vatsiou, A., Jamin, Y., Fernández-Mateos, J., Khan, K., Lampis, A., Eason, K., Huntingford, I., Burke, R., et al. (2018) · Science · doi:10.1126/science.aao2774 · Cited by 2,096
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Three-dimensional in vitro cell culture models in drug discovery and drug repositioning
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The current state and future directions of RNAi-based therapeutics
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Cancer modeling meets human organoid technology
Tuveson, D., & Clevers, H. (2019) · Science · doi:10.1126/science.aaw6985 · Cited by 935
Metadata
- Authors
- Pauli, C., Hopkins, B. D., Prandi, D., Shaw, R., Fedrizzi, T., Sboner, A., Sailer, V., Augello, M., Puca, L., Rosati, R., McNary, T. J., Churakova, Y., Cheung, C., Triscott, J., Pisapia, D., Rao, R., Mosquera, J. M., Robinson, B., Faltas, B. M., Emerling, B. E., Gadi, V. K., Bernard, B., Elemento, O., Beltran, H., Demichelis, F., Kemp, C. J., Grandori, C., Cantley, L. C., & Rubin, M. A.
- Venue
- Cancer Discovery
- Published
- 2017-04-30
- Volume
- 7
- Issue
- 5
- Pages
- 462-477
- ISSN
- 2159-8274 (ISSN-L)
- 2159-8290
- OpenAlex
- W2601380465
- Cited by
- 937
- References
- 10
- FWCI
- 27.02
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Pauli, C., Hopkins, B. D., Prandi, D., Shaw, R., Fedrizzi, T., Sboner, A., Sailer, V., Augello, M., Puca, L., Rosati, R., McNary, T. J., Churakova, Y., Cheung, C., Triscott, J., Pisapia, D., Rao, R., Mosquera, J. M., Robinson, B., Faltas, B. M., . . . Rubin, M. A. (2017). Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine. Cancer Discovery. https://doi.org/10.1158/2159-8290.cd-16-1154
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
