Martins Maria M, Zhou Alicia Y, Corella Alexandra, Horiuchi Dai, Yau Christina, Rakhshandehroo Taha, Gordan John D, Levin Rebecca S, Johnson Jeff, Jascur John, Shales Mike, Sorrentino Antonio, Cheah Jaime, Clemons Paul A, Shamji Alykhan F, Schreiber Stuart L, Krogan Nevan J, Shokat Kevan M, McCormick Frank, Goga Andrei, Bandyopadhyay Sourav
University of California, San Francisco, San Francisco, California.
Center for the Science of Therapeutics, Broad Institute, Cambridge, Massachusetts.
Cancer Discov. 2015 Feb;5(2):154-67. doi: 10.1158/2159-8290.CD-14-0552. Epub 2014 Dec 12.
There is an urgent need in oncology to link molecular aberrations in tumors with therapeutics that can be administered in a personalized fashion. One approach identifies synthetic-lethal genetic interactions or dependencies that cancer cells acquire in the presence of specific mutations. Using engineered isogenic cells, we generated a systematic and quantitative chemical-genetic interaction map that charts the influence of 51 aberrant cancer genes on 90 drug responses. The dataset strongly predicts drug responses found in cancer cell line collections, indicating that isogenic cells can model complex cellular contexts. Applying this dataset to triple-negative breast cancer, we report clinically actionable interactions with the MYC oncogene, including resistance to AKT-PI3K pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic lethal manner, providing new drug and biomarker pairs for clinical investigation. This scalable approach enables the prediction of drug responses from patient data and can accelerate the development of new genotype-directed therapies.
Determining how the plethora of genomic abnormalities that exist within a given tumor cell affects drug responses remains a major challenge in oncology. Here, we develop a new mapping approach to connect cancer genotypes to drug responses using engineered isogenic cell lines and demonstrate how the resulting dataset can guide clinical interrogation.
肿瘤学领域迫切需要将肿瘤中的分子异常与能够以个性化方式给药的治疗方法联系起来。一种方法是识别癌细胞在特定突变存在时获得的合成致死性基因相互作用或依赖性。利用工程化的同基因细胞，我们生成了一个系统的定量化学-遗传相互作用图谱，该图谱描绘了51个异常癌症基因对90种药物反应的影响。该数据集强烈预测了癌细胞系集合中发现的药物反应，表明同基因细胞可以模拟复杂的细胞环境。将该数据集应用于三阴性乳腺癌，我们报告了与MYC癌基因的临床可操作相互作用，包括对AKT-PI3K通路抑制剂的耐药性以及通过合成致死方式抑制LYN对达沙替尼的意外敏感性，为临床研究提供了新的药物和生物标志物组合。这种可扩展的方法能够根据患者数据预测药物反应，并可加速新的基因型导向疗法的开发。
确定给定肿瘤细胞内存在的大量基因组异常如何影响药物反应仍然是肿瘤学中的一项重大挑战。在这里，我们开发了一种新的图谱绘制方法，使用工程化的同基因细胞系将癌症基因型与药物反应联系起来，并展示了所得数据集如何指导临床研究。
