828 related articles for article (PubMed ID: 24580837)
1. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines.
Geeleher P; Cox NJ; Huang RS
Genome Biol; 2014 Mar; 15(3):R47. PubMed ID: 24580837
[TBL] [Abstract][Full Text] [Related]
2. Identifying anti-cancer drug response related genes using an integrative analysis of transcriptomic and genomic variations with cell line-based drug perturbations.
Sun Y; Zhang W; Chen Y; Ma Q; Wei J; Liu Q
Oncotarget; 2016 Feb; 7(8):9404-19. PubMed ID: 26824188
[TBL] [Abstract][Full Text] [Related]
3. Development of candidate genomic markers to select breast cancer patients for dasatinib therapy.
Moulder S; Yan K; Huang F; Hess KR; Liedtke C; Lin F; Hatzis C; Hortobagyi GN; Symmans WF; Pusztai L
Mol Cancer Ther; 2010 May; 9(5):1120-7. PubMed ID: 20423993
[TBL] [Abstract][Full Text] [Related]
4. Predicting breast cancer drug response using a multiple-layer cell line drug response network model.
Huang S; Hu P; Lakowski TM
BMC Cancer; 2021 May; 21(1):648. PubMed ID: 34059012
[TBL] [Abstract][Full Text] [Related]
5. Integrated pan-cancer gene expression and drug sensitivity analysis reveals SLFN11 mRNA as a solid tumor biomarker predictive of sensitivity to DNA-damaging chemotherapy.
Shee K; Wells JD; Jiang A; Miller TW
PLoS One; 2019; 14(11):e0224267. PubMed ID: 31682620
[TBL] [Abstract][Full Text] [Related]
6. Gene expression patterns that predict sensitivity to epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer cell lines and human lung tumors.
Balko JM; Potti A; Saunders C; Stromberg A; Haura EB; Black EP
BMC Genomics; 2006 Nov; 7():289. PubMed ID: 17096850
[TBL] [Abstract][Full Text] [Related]
7. Network-based drug sensitivity prediction.
Ahmed KT; Park S; Jiang Q; Yeu Y; Hwang T; Zhang W
BMC Med Genomics; 2020 Dec; 13(Suppl 11):193. PubMed ID: 33371891
[TBL] [Abstract][Full Text] [Related]
8. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels.
Geeleher P; Cox N; Huang RS
PLoS One; 2014; 9(9):e107468. PubMed ID: 25229481
[TBL] [Abstract][Full Text] [Related]
9. Pharmacogenomic strategies provide a rational approach to the treatment of cisplatin-resistant patients with advanced cancer.
Hsu DS; Balakumaran BS; Acharya CR; Vlahovic V; Walters KS; Garman K; Anders C; Riedel RF; Lancaster J; Harpole D; Dressman HK; Nevins JR; Febbo PG; Potti A
J Clin Oncol; 2007 Oct; 25(28):4350-7. PubMed ID: 17906199
[TBL] [Abstract][Full Text] [Related]
10. Molecular profiling of afatinib-resistant non-small cell lung cancer cells in vivo derived from mice.
Chung CT; Yeh KC; Lee CH; Chen YY; Ho PJ; Chang KY; Chen CH; Lai YK; Chen CT
Pharmacol Res; 2020 Nov; 161():105183. PubMed ID: 32896579
[TBL] [Abstract][Full Text] [Related]
11. Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival.
Györffy B; Serra V; Jürchott K; Abdul-Ghani R; Garber M; Stein U; Petersen I; Lage H; Dietel M; Schäfer R
Oncogene; 2005 Nov; 24(51):7542-51. PubMed ID: 16044152
[TBL] [Abstract][Full Text] [Related]
12. Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method.
Huang HH; Dai JG; Liang Y
Cell Physiol Biochem; 2018; 51(5):2073-2084. PubMed ID: 30522095
[TBL] [Abstract][Full Text] [Related]
13. Predictive performance of microarray gene signatures: impact of tumor heterogeneity and multiple mechanisms of drug resistance.
Ng CKY; Weigelt B; A'Hern R; Bidard FC; Lemetre C; Swanton C; Shen R; Reis-Filho JS
Cancer Res; 2014 Jun; 74(11):2946-2961. PubMed ID: 24706696
[TBL] [Abstract][Full Text] [Related]
14. RNA interference (RNAi) screening approach identifies agents that enhance paclitaxel activity in breast cancer cells.
Bauer JA; Ye F; Marshall CB; Lehmann BD; Pendleton CS; Shyr Y; Arteaga CL; Pietenpol JA
Breast Cancer Res; 2010; 12(3):R41. PubMed ID: 20576088
[TBL] [Abstract][Full Text] [Related]
15. Gene expression profiles of tumor biology provide a novel approach to prognosis and may guide the selection of therapeutic targets in multiple myeloma.
Anguiano A; Tuchman SA; Acharya C; Salter K; Gasparetto C; Zhan F; Dhodapkar M; Nevins J; Barlogie B; Shaughnessy JD; Potti A
J Clin Oncol; 2009 Sep; 27(25):4197-203. PubMed ID: 19636021
[TBL] [Abstract][Full Text] [Related]
16. Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.
Zhang X; Li B; Han H; Song S; Xu H; Hong Y; Yi N; Zhuang W
BMC Cancer; 2018 May; 18(1):551. PubMed ID: 29747599
[TBL] [Abstract][Full Text] [Related]
17. A pharmacogenomic method for individualized prediction of drug sensitivity.
Cohen AL; Soldi R; Zhang H; Gustafson AM; Wilcox R; Welm BE; Chang JT; Johnson E; Spira A; Jeffrey SS; Bild AH
Mol Syst Biol; 2011 Jul; 7():513. PubMed ID: 21772261
[TBL] [Abstract][Full Text] [Related]
18. Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance.
Grasse S; Lienhard M; Frese S; Kerick M; Steinbach A; Grimm C; Hussong M; Rolff J; Becker M; Dreher F; Schirmer U; Boerno S; Ramisch A; Leschber G; Timmermann B; Grohé C; Lüders H; Vingron M; Fichtner I; Klein S; Odenthal M; Büttner R; Lehrach H; Sültmann H; Herwig R; Schweiger MR
Genome Med; 2018 Jul; 10(1):55. PubMed ID: 30029672
[TBL] [Abstract][Full Text] [Related]
19. tRNA-Derived Fragments as Novel Predictive Biomarkers for Trastuzumab-Resistant Breast Cancer.
Sun C; Yang F; Zhang Y; Chu J; Wang J; Wang Y; Zhang Y; Li J; Li Y; Fan R; Li W; Huang X; Wu H; Fu Z; Jiang Z; Yin Y
Cell Physiol Biochem; 2018; 49(2):419-431. PubMed ID: 30153663
[TBL] [Abstract][Full Text] [Related]
20. Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib.
Klammer M; Dybowski JN; Hoffmann D; Schaab C
PLoS One; 2015; 10(6):e0128542. PubMed ID: 26083411
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]