The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Adaptive Randomization of Veliparib–Carboplatin Treatment in Breast Cancer
Open accessAbstract
BACKGROUND: The genetic and clinical heterogeneity of breast cancer makes the identification of effective therapies challenging. We designed I-SPY 2, a phase 2, multicenter, adaptively randomized trial to screen multiple experimental regimens in combination with standard neoadjuvant chemotherapy for breast cancer. The goal is to match experimental regimens with responding cancer subtypes. We report results for veliparib, a poly(ADP-ribose) polymerase (PARP) inhibitor, combined with carboplatin. METHODS: In this ongoing trial, women are eligible for participation if they have stage II or III breast cancer with a tumor 2.5 cm or larger in diameter; cancers are categorized into eight biomarker subtypes on the basis of status with regard to human epidermal growth factor receptor 2 (HER2), hormone receptors, and a 70-gene assay. Patients undergo adaptive randomization within each biomarker subtype to receive regimens that have better performance than the standard therapy. Regimens are evaluated within 10 biomarker signatures (i.e., prospectively defined combinations of biomarker subtypes). Veliparib-carboplatin plus standard therapy was considered for HER2-negative tumors and was therefore evaluated in 3 signatures. The primary end point is pathological complete response. Tumor volume changes measured by magnetic resonance imaging during treatment are used to predict whether a patient will have a pathological complete response. Regimens move on from phase 2 if and when they have a high Bayesian predictive probability of success in a subsequent phase 3 neoadjuvant trial within the biomarker signature in which they performed well. RESULTS: With regard to triple-negative breast cancer, veliparib-carboplatin had an 88% predicted probability of success in a phase 3 trial. A total of 72 patients were randomly assigned to receive veliparib-carboplatin, and 44 patients were concurrently assigned to receive control therapy; at the completion of chemotherapy, the estimated rates of pathological complete response in the triple-negative population were 51% (95% Bayesian probability interval [PI], 36 to 66%) in the veliparib-carboplatin group versus 26% (95% PI, 9 to 43%) in the control group. The toxicity of veliparib-carboplatin was greater than that of the control. CONCLUSIONS: The process used in our trial showed that veliparib-carboplatin added to standard therapy resulted in higher rates of pathological complete response than standard therapy alone specifically in triple-negative breast cancer. (Funded by the QuantumLeap Healthcare Collaborative and others; I-SPY 2 TRIAL ClinicalTrials.gov number, NCT01042379.).
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Siegel, R. L., Miller, K. D., & Jemal, A. (2015) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21254 · Cited by 11,853
Cortazar, P., Zhang, L., Untch, M., Mehta, K., Costantino, J. P., Wolmark, N., Bonnefoi, H., Cameron, D., Gianni, L., Valagussa, P., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)62422-8 · Cited by 4,742
Measurement of Residual Breast Cancer Burden to Predict Survival After Neoadjuvant Chemotherapy
Symmans, W. F., Peintinger, F., Hatzis, C., Rajan, R., Kuerer, H., Valero, V., Assad, L., Poniecka, A., Hennessy, B., Green, M., et al. (2007) · Journal of Clinical Oncology · doi:10.1200/jco.2007.10.6823 · Cited by 1,654
Self-citationvon Minckwitz, G., Schneeweiss, A., Loibl, S., Salat, C., Denkert, C., Rezai, M., Blohmer, J. U., Jackisch, C., Paepke, S., Gerber, B., et al. (2014) · The Lancet Oncology · doi:10.1016/s1470-2045(14)70160-3 · Cited by 1,022
Sikov, W. M., Berry, D. A., Perou, C. M., Singh, B., Cirrincione, C. T., Tolaney, S. M., Kuzma, C. S., Pluard, T. J., Somlo, G., Port, E. R., et al. (2015) · Journal of Clinical Oncology · doi:10.1200/jco.2014.57.0572 · Cited by 1,013
Self-citationDonawho, C. K., Luo, Y., Luo, Y., Penning, T. D., Bauch, J. L., Bouska, J. J., Bontcheva-Diaz, V. D., Cox, B. F., DeWeese, T. L., Dillehay, L. E., et al. (2007) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-06-3039 · Cited by 773
I-SPY 2: An Adaptive Breast Cancer Trial Design in the Setting of Neoadjuvant Chemotherapy
Barker, A., Sigman, C., Kelloff, G., Hylton, N., Berry, D., & Esserman, L. (2009) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2009.68 · Cited by 766
Self-citationBerry, D. A. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd1927 · Cited by 726
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Converting a breast cancer microarray signature into a high-throughput diagnostic test
Glas, A. M., Floore, A., Delahaye, L. J., Witteveen, A. T., Pover, R. C., Bakx, N., Lahti-Domenici, J. S., Bruinsma, T. J., Warmoes, M. O., Bernards, R., et al. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-278 · Cited by 494
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Curigliano, G., Burstein, H. J., Winer, E. P., Gnant, M., Dubsky, P., Loibl, S., Colleoni, M., Regan, M. M., Piccart-Gebhart, M., Senn, H.-J., et al. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx308 · Cited by 1,205
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Metadata
- Authors
- Rugo, H. S., Olopade, O. I., DeMichele, A., Yau, C., van ’t Veer, L. J., Buxton, M. B., Hogarth, M., Hylton, N. M., Paoloni, M., Perlmutter, J., Symmans, W. F., Yee, D., Chien, A. J., Wallace, A. M., Kaplan, H. G., Boughey, J. C., Haddad, T. C., Albain, K. S., Liu, M. C., Isaacs, C., Khan, Q. J., Lang, J. E., Viscusi, R. K., Pusztai, L., Moulder, S. L., Chui, S. Y., Kemmer, K. A., Elias, A. D., Edmiston, K. K., Euhus, D. M., Haley, B. B., Nanda, R., Northfelt, D. W., Tripathy, D., Wood, W. C., Ewing, C., Schwab, R., Lyandres, J., Davis, S. E., Hirst, G. L., Sanil, A., Berry, D. A., & Esserman, L. J.
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2016-07-07
- Volume
- 375
- Issue
- 1
- Pages
- 23-34
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W2474664935
- Cited by
- 554
- References
- 23
- FWCI
- 35.39
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Rugo, H. S., Olopade, O. I., DeMichele, A., Yau, C., van ’t Veer, L. J., Buxton, M. B., Hogarth, M., Hylton, N. M., Paoloni, M., Perlmutter, J., Symmans, W. F., Yee, D., Chien, A. J., Wallace, A. M., Kaplan, H. G., Boughey, J. C., Haddad, T. C., Albain, K. S., Liu, M. C., . . . Esserman, L. J. (2016). Adaptive Randomization of Veliparib–Carboplatin Treatment in Breast Cancer. New England Journal of Medicine. https://doi.org/10.1056/nejmoa1513749
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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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