The value of genomic analysis of breast cancer in drug development

Journal of the National Cancer Institute - Monographs

Volumn , Issue 43, 2011, Pages 60-62

  Szekely, Borbala ^a,b   Pusztai, Lajos ^a  

^a UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^b SEMMELWEIS UNIVERSITY   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

ANTINEOPLASTIC AGENT; DASATINIB; EPIDERMAL GROWTH FACTOR RECEPTOR 2; ESTROGEN RECEPTOR; TUMOR MARKER; DNA;

ARTICLE; BREAST CANCER; DIAGNOSTIC VALUE; DRUG DESIGN; DRUG RESEARCH; DRUG TARGETING; GENETIC ANALYSIS; HIGH THROUGHPUT SCREENING; MOLECULARLY TARGETED THERAPY; MUTATIONAL ANALYSIS; PRIORITY JOURNAL; BREAST TUMOR; DNA SEQUENCE; DRUG DEVELOPMENT; FEMALE; GENETICS; GENOMICS; HUMAN;

ANTINEOPLASTIC AGENTS; BREAST NEOPLASMS; DNA, NEOPLASM; DRUG DISCOVERY; FEMALE; GENOMICS; HUMANS; SEQUENCE ANALYSIS, DNA;

EID: 84855181544     PISSN: 10526773     EISSN: 17456614     Source Type: Book Series    
DOI: 10.1093/jncimonographs/lgr039     Document Type: Article

References (13)

1. Nass S, Moses H, Mendelsohn J Committee. A National Cancer Clinical Trials System for the 21st Century: Reinvigorating the NCI Cooperative Group Program. Institute of Medicine The National Academies. Washington, DC: The National Academies Press, 2010.
2. Hess KR, Anderson K, Symmans WF, et al. Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. J Clin Oncol. 2006, 24(26): 4236-4244.
3. Shi L, Campbell G, Jones WD, et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol. 2010, 28(8): 827-838.
4. Popovici V, Chen W, Gallas BG, et al. Effect of training sample size and classification difficulty on the accuracy of genomic predictors. Breast Cancer Res. 2010, 12(1): R5.
5. Huang F, Reeves K, Han X, et al. Identification of candidate molecular markers predicting sensitivity in solid tumors to dasatinib: rationale for patient selection. Cancer Res. 2007, 67(5): 2226-2238.
6. Dry JR, Pavey S, Pratilas CA, et al. Transcriptional pathway signatures predict MEK addiction and response to selumetinib (AZD6244). Cancer Res. 2010, 70(6): 2264-2273.
7. Bild AH, Yao G, Chang JT, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature. 2006, 439(7330): 353-357.
8. Pusztai L, Anderson K, Hess KR. Pharmacogenomic predictor discovery in phase II clinical trials for breast cancer. Clin Cancer Res. 2007, 13(20): 6080-6086.
9. Moulder S, Yan K, Huang F, et al. Development of candidate genomic markers to select breast cancer patients for dasatinib therapy. Mol Cancer Ther. 2010, 9(5): 1120-1127.
10. Ashworth AA. Synthetic lethal therapeutic approach: poly(ADP) ribose polymerase inhibitors for the treatment of cancers deficient in DNA double-strand break repair. J Clin Oncol. 2008, 26(22): 3785-3790.
11. Bianchini G, Iwamoto T, Coutant C, et al. Prognostic and therapeutic implications of distinct kinase expression patterns in different subtypes of breast cancer. Cancer Res. 2010, 70(21): 8852-8862.
12. Wang J, Wen S, Symmans WF, Pusztai L, Coombes KR. The bimodality index: a criterion for discovering and ranking bimodal signatures from cancer gene expression profiling data. Cancer Inform. 2009, 7: 199-216.
13. Shiang CY, Qi Y, Wang B, et al. Amplification of fibroblast growth factor receptor-1 in breast cancer and the effects of brivanib alaninate. Breast Cancer Res Treat. 2010, 123(3): 747-755.