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.

中文翻译：

---

通过综合考虑种系和肿瘤基因组实现癌症药物治疗的创新。


精准癌症医学已得到广泛应用，许多针对各种肿瘤实体的分子靶向药物已获批准或正在开发中。迄今为止，肿瘤学中的个性化药物治疗主要基于肿瘤特征，例如体细胞突变。然而，对药物治疗的反应也取决于 ADME （吸收、分布、代谢和排泄） 一词下总结的药理过程。考虑到个体患者的基因构成，ADME 基因的变异一直是五十多年来深入研究的主题，称为药物基因组学 （PGx）。患者的肿瘤和种系基因组的综合影响仅被部分了解，并且在癌症治疗中往往没有得到充分考虑。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，同时考虑到现有的药物治疗 PGx 指南。此外，此类策略应提供考虑以前未知功能意义的遗传变异的机会。需要开发生物信息学分析方法和相应的数据解释算法，以在跨学科分子肿瘤委员会中考虑 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循证建议。意义声明 个性化肿瘤学时代见证了针对与癌症生物学相关的遗传变异量身定制的药物的出现。然而，由于主要关注获得性肿瘤特异性改变，靶向治疗的潜力仍未得到充分开发。 优化的癌症护理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必要先决条件是开发生物信息学工具，用于对现代精准肿瘤学项目中生成的所有数据层进行全面分析。