The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Human Tumor Xenografts as Predictive Preclinical Models for Anticancer Drug Activity in Humans: Better than Commonly Perceived—But They Can Be Improved
Open accessAbstract
It is not uncommon for new anti-cancer drugs or therapies to show highly effective, and sometimes even spectacular anti-cancer treatment results using transplantable tumors in mice. These models frequently involve human tumor xenografts grown subcutaneously in immune deficient hosts such as athymic (nude) or severe combined immune deficient (SCID) mice. Unfortunately, such preclinical results are often followed by failure of the drug/therapy in clinical trials, or, if the drug is successful, it usually has only modest efficacy results, by comparison. Not surprisingly, this has provoked considerable skepticism about the value of using such preclinical models for early stage in vivo preclinical drug testing. As a result, a shift has occurred towards developing and using spontaneous mouse tumors arising in transgenic and/or knockout mice engineered to recapitulate various genetic alterations thought to be causative of specific types of respective human cancers. Alternatively, the opinion has been expressed of the need to refine and improve the human tumor xenograft models, e.g., by use of orthotopic transplantation and therefore promotion of metastatic spread of the resultant "primary" tumors. Close inspection of retrospective and prospective studies in the literature, however, reveals that human tumor xenografts-even non metastatic ectopic/subcutaneous "primary" tumor transplants-can be remarkably predictive of cytotoxic chemotherapeutic drugs that have activity in humans, when the drugs are tested in mice using pharmacokinetically clinically equivalent or "rational" drug doses. What may be at variance with clinical activity, however, is the magnitude of the benefit observed in mice, both in terms of the degree of tumor responses and overall survival. It is argued that this disparity can be significantly minimized by use of orthotopic transplant/metastatic tumor models in which treatment is initiated after the primary tumor has been removed and the distant metastases are well established and macroscopic-i.e., the bar is raised and treatment is undertaken on advanced, high volume, metastatic disease. In such circumstances, survival should be used as an endpoint; changes in tumor burden using surrogate markers or micro-imaging techniques can be used as well to monitor effects of therapies on tumor response. Adoption of such procedures would more accurately recapitulate the phase I/II/III clinical trial situation in which treatment is initiated on patients with advanced, high-volume metastatic disease.
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Cites
Systematic variation in gene expression patterns in human cancer cell lines
Ross, D. T., Scherf, U., Eisen, M. B., Perou, C. M., Rees, C., Spellman, P., Iyer, V., Jeffrey, S. S., Van de Rijn, M., Waltham, M., et al. (2000) · Nature Genetics · doi:10.1038/73432 · Cited by 2,088
Antiangiogenic therapy of experimental cancer does not induce acquired drug resistance
Boehm, T., Folkman, J., Browder, T., & O'Reilly, M. S. (1997) · Nature · doi:10.1038/37126 · Cited by 1,683
Browder, T. D., Butterfield, C., Kräling, B. M., Shi, B., Marshall, B., OʼReilly, M., & Folkman, J. (2000) · PubMed · Cited by 1,543
Klement, G., Baruchel, S., Rak, J., Man, S., Clark, K., Hicklin, D. J., Bohlen, P., & Kerbel, R. S. (2000) · Journal of Clinical Investigation · doi:10.1172/jci8829 · Cited by 1,168
Self-citationLess is more, regularly: metronomic dosing of cytotoxic drugs can target tumor angiogenesis in mice
Hanahan, D., Bergers, G., & Bergsland, E. (2000) · Journal of Clinical Investigation · doi:10.1172/jci9872 · Cited by 865
Houghton, P. J., Cheshire, P. J., Hallman, J. D., Lutz, L., Friedman, H. S., Danks, M. K., & Houghton, J. A. (1995) · Cancer Chemotherapy and Pharmacology · doi:10.1007/bf00686188 · Cited by 401
Fidler, I. J. (1986) · CANCER AND METASTASIS REVIEW · doi:10.1007/bf00049529 · Cited by 373
A cancer therapy resistant to resistance
Kerbel, R. S. (1997) · Nature · doi:10.1038/36978 · Cited by 369
Self-citationCancer Modeling in the Modern Era
Van Dyke, T., & Jacks, T. (2002) · Cell · doi:10.1016/s0092-8674(02)00621-9 · Cited by 358
Man, S., Bocci, G., Francia, G., Green, S. K., Jothy, S., Hanahan, D., Böhlen, P., Hicklin, D. J., Bergers, G., & Kerbel, R. S. (2002) · PubMed · Cited by 314
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The use of 3-d cultures for high-throughput screening: the multicellular spheroid model.
Kunz-Schughart, L. A., Freyer, J. P., Hofstaedter, F., & Ebner, R. (2004) · SLAS DISCOVERY · doi:10.1177/1087057104265040 · Cited by 797
Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents
Sharma, S. V., Haber, D. A., & Settleman, J. (2010) · Nature Reviews Cancer · doi:10.1038/nrc2820 · Cited by 586
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Talmadge, J. E., Singh, R. K., Fidler, I. J., & Raz, A. (2007) · The American Journal of Pathology · doi:10.2353/ajpath.2007.060929 · Cited by 468
Chen, S., Zhong, Y., Fan, W., Xiang, J., Wang, G., Zhou, Q., Wang, J., Geng, Y., Sun, R., Zhang, Z., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00701-4 · Cited by 325
50 Years of Preclinical Anticancer Drug Screening: Empirical to Target-Driven Approaches
Suggitt, M., & Bibby, M. C. (2005) · Clinical Cancer Research · doi:10.1158/1078-0432.971.11.3 · Cited by 298
Metadata
- Authors
- Kerbel, R. S.
- Venue
- Cancer Biology & Therapy
- Publisher
- Informa UK Limited
- Published
- 2003-03
- Volume
- 2
- Issue
- sup1
- Pages
- 133-138
- ISSN
- 1538-4047 (ISSN-L)
- 1555-8576
- OpenAlex
- W2120191503
- Cited by
- 326
- References
- 33
- FWCI
- 10.67
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Kerbel, R. S. (2003). Human Tumor Xenografts as Predictive Preclinical Models for Anticancer Drug Activity in Humans: Better than Commonly Perceived—But They Can Be Improved. Cancer Biology & Therapy. https://doi.org/10.4161/cbt.213
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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