Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies.
Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies.
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DOI：
10.1016/j.cels.2018.01.009
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发表时间：
2018-03-28
期刊：
影响因子：
9.3
通讯作者：
Liu XS
中科院分区：
文献类型：
--
作者：
Jiang P;Lee W;Li X;Johnson C;Liu JS;Brown M;Aster JC;Liu XS
寻找可靠的药物反应生物标记物是癌症研究中的一个重大挑战。我们提出了CARE，这是一种专注于靶向治疗的计算方法，用于从细胞系化合物筛选中推断药物疗效的全基因组转录签名。CARE输出基因组级别的分数来衡量药物目标基因如何与其他基因相互作用，以影响化合物筛选中的抑制剂效果。以前的研究没有考虑到药物靶标和其他基因之间的统计相互作用，但这对识别预测生物标记物至关重要。当使用来自临床研究的转录组数据进行评估时，CARE可以比其他计算方法和基因组学实验中的签名更好地预测治疗结果。此外，PLX4720 BRAF抑制剂的CARE特征与抗PD1临床反应相关，这表明靶向治疗和免疫治疗之间存在共同的疗效特征。在搜索与拉帕替尼耐药相关的基因时，CARE发现PRKD3是首选候选基因。通过siRNA和化合物对PRKD3的抑制，显著增加了乳腺癌细胞对拉帕替尼的敏感性。因此，CARE应该能够使用复合筛查数据对靶向治疗的反应生物标志物和药物组合进行大规模推断。来自细胞系化合物筛选的数据可以通过测试药物靶基因如何与其他基因相互作用来影响药物疗效，从而获得靶向癌症治疗的临床预测生物标记物。
Identifying reliable drug response biomarkers is a significant challenge in cancer research. We present CARE, a computational method focused on targeted therapies, to infer genome-wide transcriptomic signatures of drug efficacy from cell line compound screens. CARE outputs genome-scale scores to measure how the drug target gene interacts with other genes to affect the inhibitor efficacy in the compound screens. Such statistical interactions between drug targets and other genes were not considered in previous studies but are critical in identifying predictive biomarkers. When evaluated using transcriptome data from clinical studies, CARE can predict the therapy outcome better than signatures from other computational methods and genomics experiments. Moreover, the CARE signatures for the PLX4720 BRAF inhibitor are associated with an anti-PD1 clinical response, suggesting a common efficacy signature between a targeted therapy and immunotherapy. When searching for genes related to lapatinib resistance, CARE identified PRKD3 as the top candidate. PRKD3 inhibition, by both siRNA and compounds, significantly sensitized breast cancer cells to lapatinib. Thus, CARE should enable large-scale inference of response biomarkers and drug combinations for targeted therapies using compound screen data. Data from cell line compound screens could derive clinically predictive biomarkers for targeted cancer therapies by testing how drug target genes interact with other genes to affect drug efficacy.
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DOI：
10.1001/jama.2011.593
发表时间：
2011-05-11
期刊：
JAMA
影响因子：
--
作者：
Hatzis C;Pusztai L;Valero V;Booser DJ;Esserman L;Lluch A;Vidaurre T;Holmes F;Souchon E;Wang H;Martin M;Cotrina J;Gomez H;Hubbard R;Chacón JI;Ferrer-Lozano J;Dyer R;Buxton M;Gong Y;Wu Y;Ibrahim N;Andreopoulou E;Ueno NT;Hunt K;Yang W;Nazario A;DeMichele A;O'Shaughnessy J;Hortobagyi GN;Symmans WF
通讯作者：
Symmans WF
影响因子：
64.5
作者：
Hugo W;Shi H;Sun L;Piva M;Song C;Kong X;Moriceau G;Hong A;Dahlman KB;Johnson DB;Sosman JA;Ribas A;Lo RS
通讯作者：
Lo RS
影响因子：
10.3
作者：
Haas-Kogan, DA;Prados, MD;Stokoe, D
通讯作者：
Stokoe, D
影响因子：
5.4
作者：
Brambor, T;Clark, WR;Golder, M
通讯作者：
Golder, M
影响因子：
64.8
作者：
Garnett, Mathew J.;Edelman, Elena J.;Heidorn, Sonja J.;Greenman, Chris D.;Dastur, Anahita;Lau, King Wai;Greninger, Patricia;Thompson, I. Richard;Luo, Xi;Soares, Jorge;Liu, Qingsong;Iorio, Francesco;Surdez, Didier;Chen, Li;Milano, Randy J.;Bignell, Graham R.;Tam, Ah T.;Davies, Helen;Stevenson, Jesse A.;Barthorpe, Syd;Lutz, Stephen R.;Kogera, Fiona;Lawrence, Karl;McLaren-Douglas, Anne;Mitropoulos, Xeni;Mironenko, Tatiana;Thi, Helen;Richardson, Laura;Zhou, Wenjun;Jewitt, Frances;Zhang, Tinghu;O'Brien, Patrick;Boisvert, Jessica L.;Price, Stacey;Hur, Wooyoung;Yang, Wanjuan;Deng, Xianming;Butler, Adam;Choi, Hwan Geun;Chang, JaeWon;Baselga, Jose;Stamenkovic, Ivan;Engelman, Jeffrey A.;Sharma, Sreenath V.;Delattre, Olivier;Saez-Rodriguez, Julio;Gray, Nathanael S.;Settleman, Jeffrey;Futreal, P. Andrew;Haber, Daniel A.;Stratton, Michael R.;Ramaswamy, Sridhar;McDermott, Ultan;Benes, Cyril H.
通讯作者：
Benes, Cyril H.