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 which derives from both the tumor and the germline genome, and 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 integrate PGx data in cancer therapy with a special meaning for interdisciplinary molecular tumor boards, where cancer patients are discussed to provide evidence-based recommendations for clinical management based on individual tumor profiles.
Significance StatementThe era of personalized oncology has seen the emergence of drugs tailored to genetic variants associated with cancer biology. However, the 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. article has not been copyedited and formatted. The final version may differ from this version. A. Genetic Variation in ADME genes B. Genotype-Phenotype correlation in selected ADME genes C. In silico prediction of functional consequences of genetic variation D. Validation of in silico functional predictions E. Implementation of pharmacogenomics F. Pharmacogenomics-guided supportive care G. Polygenic risk scores and prediction of drug response III. Somatic variation and cancer therapy A. Somatic mutations in cancer B. Targetability of somatic alterations C. Somatic alterations in pharmacogenes D. Risk prediction and drug treatment of cancer IV. Integration of germline and somatic variation for drug therapy A. Precision oncology B. Additional data layers and multi-omics integration C. Tumor heterogeneity and combination therapies D.
