Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets
Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets
复制标题
DOI：
10.1016/j.chembiol.2019.03.011
复制
发表时间：
2019-07-18
影响因子：
8.6
通讯作者：
Wennerberg, Krister
中科院分区：
文献类型：
--
作者：
Gautam, Prson;Jaiswal, Alok;Wennerberg, Krister
缺乏对癌症中大多数突变的功能理解，加上大多数蛋白质的非药物性，挑战了基于基因组学的肿瘤药物靶标鉴定。我们实施了一种基于机器学习的方法（idTRAX），该方法将基于细胞的小分子化合物筛选与其激酶抑制数据相关联，以直接识别有效且易于药物化的靶标。我们将idTRAX应用于三阴性乳腺癌细胞系，并有效地鉴定了癌症选择性靶点。例如，我们发现抑制AKT选择性地杀死MFM-223和CAL 148细胞，而抑制FGFR 2仅杀死MFM-223。由于催化抑制蛋白质的作用可能与降低其水平的作用不同，因此通过idTRAX鉴定的靶点通常与通过基因敲除/敲低方法鉴定的靶点不同。如果目的是确定小分子药物开发的特异性靶点，这一点至关重要，因此idTRAX可能会产生更少的假阳性。这种方法的快速性表明它可能适用于个性化治疗。
The lack of functional understanding of most mutations in cancer, combined with the non-druggability of most proteins, challenge genomics-based identification of oncology drug targets. We implemented a machine-learning-based approach (idTRAX), which relates cell-based screening of small-molecule compounds to their kinase inhibition data, to directly identify effective and readily druggable targets. We applied idTRAX to triple-negative breast cancer cell lines and efficiently identified cancer-selective targets. For example, we found that inhibiting AKT selectively kills MFM-223 and CAL148 cells, while inhibiting FGFR2 only kills MFM-223. Since the effects of catalytically inhibiting a protein can diverge from those of reducing its levels, targets identified by idTRAX frequently differ from those identified through gene knockout/knockdown methods. This is critical if the purpose is to identify targets specifically for small-molecule drug development, whereby idTRAX may produce fewer false-positives. The rapid nature of the approach suggests that it may be applicable in personalizing therapy.