정보과학회지 (Communications of the Korean Institute of Information Scientists and Engineers)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지

암 개인화 치료를 위한 약물 반응성 예측 연구 동향

  • 발행 : 2014.10.16
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

키워드

참고문헌

  1. TAN, Aik Choon; GILBERT, David. Ensemble machine leaming on gene expression data for cancer classification. 2003.
  2. CROCE, Carlo M. Oncogenes and cancer. New England Journal of Medicine, 2008, 358.5: 502-511. https://doi.org/10.1056/NEJMra072367
  3. WAGLE, Nikhil, et al. Dissecting therapeutic resistance to RAF inhibition in melanoma by tumor genomic profiling. Journal of Clinical Oncology, 2011, 29.22: 3085-3096. https://doi.org/10.1200/JCO.2010.33.2312
  4. HARTWELL, Leland H., et al. Integrating genetic approaches into the discovely of anticancer drugs. Science, 1997, 278.5340: 1064-1068 https://doi.org/10.1126/science.278.5340.1064
  5. PAO, William, et al. KRAS mutations and primary resistance of lung adenocarcinomas to gefitinib or erlotinib. PLoS medicine, 2005, 2. 1: e17 https://doi.org/10.1371/journal.pmed.0020017
  6. GRANT, Richard W., et al. Personalized genetic risk counseling to motivate diabetes prevention a randomized trial. Diabetes care, 2013, 36.1: 13-19. https://doi.org/10.2337/dc12-0884
  7. GOLUB, Todd R., et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. science, 1999, 286.5439: 531-537. https://doi.org/10.1126/science.286.5439.531
  8. SOLIT, David B., et al. BRAF mutation predicts sensitivity to MEK inhibition. Nature, 2005, 439.7074: 358-362.
  9. SHOEMAKER, Robert H. The NCI60 human tumour cell line anticancer drug screen. Nature Reviews Cancer, 2006, 6.10: 813-823. https://doi.org/10.1038/nrc1951
  10. SOS, Martin L., et al. Predicting drug susceptibility of non - small cell lung cancers based on genetic lesions. The Journal of clinical investigation, 2009, 119.6: 1727-1740. https://doi.org/10.1172/JCI37127
  11. DRY, Jonathan R., et al. Transcriptional pathway signatures predict MEK addiction and response to selumetinib (AZD6244). Cancer research, 2010, 70.6: 2264-2273. https://doi.org/10.1158/0008-5472.CAN-09-1577
  12. PAIK, Soonmyung, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. New England Journal of Medicine, 2004, 351.27: 2817-2826. https://doi.org/10.1056/NEJMoa041588
  13. MA, Xiao-Jun, et al. Molecular classification of human cancers using a 92-gene real-time quantitative polymerase chain reaction assay. Archives of pathology & laboratory medicine, 2006, 130.4: 465-473
  14. STAUNTON, Jane E., et al. Chemosensitivity prediction by transcriptional profiling. Proceedings of the National Academy of Sciences, 2001 , 98.19: 10787-10792. https://doi.org/10.1073/pnas.191368598
  15. LEE, Jae K., et al. A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery. Proceedings of the National Academy of Sciences, 2007, 104.32: 13086-13091. https://doi.org/10.1073/pnas.0610292104
  16. SOUKUP, Mat; CHO, Hyungjun; LEE, Jae K. Robust classification modeling on microarray data using misclassification penalized posterior. Bioinformatics, 2005, 21.suppl 1: i423-i430. https://doi.org/10.1093/bioinformatics/bti1020
  17. BERLOW, Noah, et al. A new approach for prediction of tumor sensitivity to targeted drugs based on functional data. BMC bioinformatics, 2013, 14. 1: 239. https://doi.org/10.1186/1471-2105-14-239
  18. PAL, Ranadip; BERLOW, Noah. Akinase inhibition map approach for tumor sensitivity prediction and combination therapy design for targeted drugs. In: Pacific Symposium on Biocomputing. 2012. p. 351-62
  19. BARRETINA, Jordi, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 2012, 483.7391: 603-607. https://doi.org/10.1038/nature11003
  20. KUMAR, Rahul, et al. CancerDR: cancer drug resistance database. Scientific reports, 2013, 3.
  21. GRESHOCK, Joel, et al. Molecular target class is predictive of in vitro response profile. Cancer research, 2010, 70.9: 3677-3686. https://doi.org/10.1158/0008-5472.CAN-09-3788
  22. YANG, Wanjuan, et al. Genornics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic acids research, 2013, 41.D1: D955-D961. https://doi.org/10.1093/nar/gks1111