Despite substantial advances in the treatment of various cancers, many patients still receive anti-cancer therapies that hardly eradicate tumor cells but inflict considerable side effects. To provide the best treatment regimen for an individual patient, a major goal in molecular oncology is to identify predictive markers for a personalized therapeutic strategy. Regarding novel targeted anti-cancer therapies, there are usually good markers available. Unfortunately, however, targeted therapies alone often result in rather short remissions and little cytotoxic effect on the cancer cells. Therefore, classical chemotherapy with frequent long remissions, cures, and a clear effect on cancer cell eradication remains a corner stone in current anti-cancer therapy. Reliable biomarkers which predict the response of tumors to classical chemotherapy are rare, in contrast to the situation for targeted therapy. For the bulk of cytotoxic therapeutic agents, including DNA-damaging drugs, drugs targeting microtubules or antimetabolites, there are still no reliable biomarkers used in the clinic to predict tumor response. To make progress in this direction, meticulous studies of classical chemotherapeutic drug action and resistance mechanisms are required. For this purpose, novel functional screening technologies have emerged as successful technologies to study chemotherapeutic drug response in a variety of models. They allow a systematic analysis of genetic contributions to a drug-responsive or −sensitive phenotype and facilitate a better understanding of the mode of action of these drugs. These functional genomic approaches are not only useful for the development of novel targeted anti-cancer drugs but may also guide the use of classical chemotherapeutic drugs by deciphering novel mechanisms influencing a tumor’s drug response. Moreover, due to the advances of 3D organoid cultures from patient tumors and in vivo screens in mice, these genetic screens can be applied using conditions that are more representative of the clinical setting. Patient-derived 3D organoid lines furthermore allow the characterization of the “essentialome”, the specific set of genes required for survival of these cells, of an individual tumor, which could be monitored over the course of treatment and help understanding how drug resistance evolves in clinical tumors. Thus, we expect that these functional screens will enable the discovery of novel cancer-specific vulnerabilities, and through clinical validation, move the field of predictive biomarkers forward. This review focuses on novel advanced techniques to decipher the interplay between genetic alterations and drug response.

尽管在各种癌症的治疗方面取得了重大进展，但许多患者仍接受抗癌疗法，这种疗法几乎不会根除肿瘤细胞，但会带来相当大的副作用。为了为单个患者提供最佳治疗方案，分子肿瘤学的主要目标是为个性化治疗策略识别预测标记。关于新型靶向抗癌疗法，通常有良好的标记物。然而不幸的是，仅靶向疗法常常导致相当短的缓解并且对癌细胞几乎没有细胞毒性作用。因此，具有长期长期缓解，治愈和清除癌细胞作用明显的经典化学疗法仍是当前抗癌治疗的基石。预测肿瘤对经典化疗反应的可靠生物标记物很少见，与针对性治疗的情况相反。对于大多数细胞毒性治疗剂，包括破坏DNA的药物，靶向微管或抗代谢物的药物，临床上仍没有可靠的生物标志物用于预测肿瘤反应。为了朝这个方向取得进展，需要对经典化疗药物作用和耐药机制进行认真研究。为此，新颖的功能筛选技术已经成为研究各种模型中化学疗法药物反应的成功技术。它们允许对遗传对药物反应性或敏感性表型的贡献进行系统的分析，并有助于更好地了解这些药物的作用方式。这些功能基因组学方法不仅可用于开发新型靶向抗癌药物，而且还可通过破译影响肿瘤药物反应的新机制来指导经典化疗药物的使用。此外，由于来自患者肿瘤和在小鼠体内筛选中，可以使用更能代表临床背景的条件进行这些基因筛选。患者来源的3D类器官样线还可以表征单个肿瘤的“必需基因组”，即这些细胞存活所需的特定基因集，可以在治疗过程中对其进行监测，并有助于了解耐药性如何演变。临床肿瘤。因此，我们希望这些功能性筛查能够发现新型的癌症特异性漏洞，并通过临床验证使预测性生物标志物的领域向前发展。这篇评论集中在新颖的先进技术，以破译基因改变和药物反应之间的相互作用。