Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient‐specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient‐specific combined therapy should be designed. Large‐scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.

中文翻译：

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使用批量和单细胞方法将癌症分子变异性转化为个性化信息。

癌症研究正在努力为特定患者分配正确的个性化药物的新领域。然而，广泛的肿瘤异质性构成了主要障碍。由于患者特异性分子畸变和/或非靶向肿瘤亚群，相同类型的肿瘤通常对治疗的反应不同。通常不可能预先确定哪些患者会对某种疗法产生反应，或者应该如何设计有效的患者特异性联合疗法。大规模数据集一直在加速增长，并且正在开发用于单细胞和批量分析的各种技术和分析工具，以从此类异构数据中提取重要的个性化信号。然而，显着改变疾病进程的个性化疗法仍然很少，大多数肿瘤对医疗护理的反应仍然很差。在这篇综述中，讨论了批量和单细胞方法的基本概念，以及它们在个体化药物治疗设计中的新兴作用，包括它们在个性化医疗中应用的优势和局限性。