Screening drug effects in patient-derived cancer cells links organoid responses to genome alterations.
Screening drug effects in patient-derived cancer cells links organoid responses to genome alterations.
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DOI：
10.15252/msb.20177697
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发表时间：
2017-11-27
影响因子：
9.9
通讯作者：
Conrad C
中科院分区：
文献类型：
--
作者：
Jabs J;Zickgraf FM;Park J;Wagner S;Jiang X;Jechow K;Kleinheinz K;Toprak UH;Schneider MA;Meister M;Spaich S;Sütterlin M;Schlesner M;Trumpp A;Sprick M;Eils R;Conrad C
患者来源的细胞中的癌症药物筛选为个性化肿瘤学和药物发现带来了巨大希望，但缺乏标准化。无论细胞是传统的单层培养还是先进的基质依赖性类器官培养，都会影响药物效果，从而影响药物选择和临床成功。为了精确比较不同培养的原代细胞中的药物分布，我们开发了 DeathPro，这是一种基于自动显微镜的检测方法，用于解决药物诱导的细胞死亡和增殖抑制问题。使用DeathPro，我们用临床相关药物在单层或类器官培养物中筛选卵巢癌患者的细胞。药物诱导的生长停滞和细胞抑制剂的功效在两种培养系统之间存在差异。有趣的是，类器官中的药物作用更加多样化，并且治疗潜力较低。基因组分析揭示了药物敏感性与类器官 DNA 修复缺陷之间的新联系，而这些联系在单层细胞中是无法检测到的。因此，我们的结果强调了细胞抑制剂和药物基因组学关联对培养系统的依赖性，并指导药物测试的培养物选择。
Cancer drug screening in patient‐derived cells holds great promise for personalized oncology and drug discovery but lacks standardization. Whether cells are cultured as conventional monolayer or advanced, matrix‐dependent organoid cultures influences drug effects and thereby drug selection and clinical success. To precisely compare drug profiles in differently cultured primary cells, we developed DeathPro, an automated microscopy‐based assay to resolve drug‐induced cell death and proliferation inhibition. Using DeathPro, we screened cells from ovarian cancer patients in monolayer or organoid culture with clinically relevant drugs. Drug‐induced growth arrest and efficacy of cytostatic drugs differed between the two culture systems. Interestingly, drug effects in organoids were more diverse and had lower therapeutic potential. Genomic analysis revealed novel links between drug sensitivity and DNA repair deficiency in organoids that were undetectable in monolayers. Thus, our results highlight the dependency of cytostatic drugs and pharmacogenomic associations on culture systems, and guide culture selection for drug tests.
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影响因子：
48
作者：
Conrad, Christian;Wuensche, Annelie;Tan, Tze Heng;Bulkescher, Jutta;Sieckmann, Frank;Verissimo, Fatima;Edelstein, Arthur;Walter, Thomas;Liebel, Urban;Pepperkok, Rainer;Ellenberg, Jan
通讯作者：
Ellenberg, Jan
影响因子：
5.8
作者：
Gu, Zuguang;Eils, Roland;Schlesner, Matthias
通讯作者：
Schlesner, Matthias
影响因子：
82.9
作者：
Gao, Hui;Korn, Joshua M.;Sellers, William R.
通讯作者：
Sellers, William R.
影响因子：
64.5
作者：
Gao D;Vela I;Sboner A;Iaquinta PJ;Karthaus WR;Gopalan A;Dowling C;Wanjala JN;Undvall EA;Arora VK;Wongvipat J;Kossai M;Ramazanoglu S;Barboza LP;Di W;Cao Z;Zhang QF;Sirota I;Ran L;MacDonald TY;Beltran H;Mosquera JM;Touijer KA;Scardino PT;Laudone VP;Curtis KR;Rathkopf DE;Morris MJ;Danila DC;Slovin SF;Solomon SB;Eastham JA;Chi P;Carver B;Rubin MA;Scher HI;Clevers H;Sawyers CL;Chen Y
通讯作者：
Chen Y
影响因子：
2.7
作者：
Gao, Shan;Mobley, Aaron;Chandra, Joya
通讯作者：
Chandra, Joya