Abdel-Rehim Abbi, Orhobor Oghenejokpeme, Griffiths Gareth, Soldatova Larisa, King Ross D
Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
ValiRx Plc, Nottingham, UK.
NPJ Precis Oncol. 2025 Jun 13;9(1):180. doi: 10.1038/s41698-025-00937-2.
The concept of personalised medicine in cancer therapy is becoming increasingly important. There already exist drugs administered specifically for patients with tumours presenting well-defined genetic mutations. However, the field is still in its infancy, and personalised treatments are far from being standard of care. Personalised medicine is often associated with the utilisation of omics data. Yet, implementation of multi-omics data has proven difficult, due to the variety and scale of the information within the data, as well as the complexity behind the myriad of interactions taking place within the cell. An alternative approach to precision medicine is to employ a function-based profile of cells. This involves screening a range of drugs against patient-derived cells (or derivative organoids and xenograft models). Here we demonstrate a proof-of-concept, where a collection of drug screens against a highly diverse set of patient-derived cell lines, are leveraged to identify putative treatment options for a 'new patient'. We show that this methodology is highly efficient in ranking the drugs according to their activity towards the target cells. We argue that this approach offers great potential, as activities can be efficiently imputed from various subsets of the drug-treated cell lines that do not necessarily originate from the same tissue type.
个性化医疗在癌症治疗中的概念正变得越来越重要。已经有专门针对具有明确基因突变的肿瘤患者施用的药物。然而，该领域仍处于起步阶段，个性化治疗远未成为标准治疗方法。个性化医疗通常与组学数据的利用相关联。然而，由于数据中信息的多样性和规模，以及细胞内发生的无数相互作用背后的复杂性，多组学数据的实施已被证明是困难的。精准医学的另一种方法是采用基于功能的细胞图谱。这涉及针对患者来源的细胞（或衍生的类器官和异种移植模型）筛选一系列药物。在这里，我们展示了一个概念验证，即利用针对高度多样化的患者来源细胞系的一系列药物筛选，为“新患者”确定推定的治疗方案。我们表明，这种方法在根据药物对靶细胞的活性对药物进行排名方面非常有效。我们认为这种方法具有巨大潜力，因为可以从不一定源自相同组织类型的药物处理细胞系的各个子集中有效地推断出活性。
