精准癌症医学已广泛建立，许多针对各种肿瘤实体的分子靶向药物已被批准或正在开发。迄今为止，肿瘤学中的个体化药物治疗主要基于肿瘤特征，例如体细胞突变。然而，对药物治疗的反应还取决于术语 ADME（吸收、分布、代谢和排泄）下概括的药理学过程。五十多年来，ADME 基因的变异一直是深入研究的主题，考虑到个体患者的基因组成，称为药物基因组学 (PGx)。患者肿瘤和种系基因组的综合影响仅被部分了解，并且在癌症治疗中通常没有得到充分考虑。这可能部分归因于缺乏对两个数据层进行组合分析的方法。因此，优化的个性化癌症治疗应旨在整合有关肿瘤和种系的分子信息，同时考虑到现有的 PGx 药物治疗指南。此外，此类策略应该提供考虑先前未知功能意义的遗传变异的机会。需要开发生物信息分析方法和相应的数据解释算法，以考虑跨学科分子肿瘤委员会中的 PGx 数据，其中讨论癌症患者，以根据个体肿瘤概况为临床管理提供基于证据的建议。意义陈述个性化肿瘤学时代已经出现了针对与癌症生物学相关的遗传变异的药物。然而，由于主要关注获得性肿瘤特异性改变，靶向治疗的全部潜力仍未得到开发。优化的癌症治疗必须在药物基因组学原理的指导下整合肿瘤和患者基因组。实现癌症患者真正个性化药物治疗的一个重要先决条件是开发生物信息工具，用于综合分析现代精准肿瘤学项目中生成的所有数据层。

Precision cancer medicine is widely established, and numerous molecularly targeted drugs for various tumor entities are approved or in development. Personalized pharmacotherapy in oncology has so far been based primarily on tumor characteristics, e.g., somatic mutations. However, the response to drug treatment also depends on pharmacological processes summarized under the term ADME (absorption, distribution, metabolism, and excretion). Variations in ADME genes have been the subject of intensive research for more than five decades, considering individual patients' genetic makeup, referred to as pharmacogenomics (PGx). The combined impact of a patient's tumor and germline genome is only partially understood and often not adequately considered in cancer therapy. This may be attributed, in part, to the lack of methods for combined analysis of both data layers. Optimized personalized cancer therapies should, therefore, aim to integrate molecular information about the tumor and the germline, taking into account existing PGx guidelines for drug therapy. Moreover, such strategies should provide the opportunity to consider genetic variants of previously unknown functional significance. Bioinformatic analysis methods and corresponding algorithms for data interpretation need to be developed to consider PGx data in interdisciplinary molecular tumor boards, where cancer patients are discussed to provide evidence-based recommendations for clinical management based on individual tumor profiles. Significance Statement The era of personalized oncology has seen the emergence of drugs tailored to genetic variants associated with cancer biology. However, full potential of targeted therapy remains untapped due to the predominant focus on acquired tumor-specific alterations. Optimized cancer care must integrate tumor and patient genomes, guided by pharmacogenomic principles. An essential prerequisite for realizing truly personalized drug treatment of cancer patients is the development of bioinformatic tools for comprehensive analysis of all data layers generated in modern precision oncology programs.