Nikanjam Mina, Kato Shumei, Allen Teresa, Sicklick Jason K, Kurzrock Razelle
Division of Hematology-Oncology, University of California San Diego, La Jolla, California, USA.
Moores Cancer Center, University of California San Diego Health, La Jolla, California, USA.
CA Cancer J Clin. 2025 May-Jun;75(3):243-267. doi: 10.3322/caac.21880. Epub 2025 Jan 22.
Next-generation sequencing has revealed the disruptive reality that advanced/metastatic cancers have complex and individually distinct genomic landscapes, necessitating a rethinking of treatment strategies and clinical trial designs. Indeed, the molecular reclassification of cancer suggests that it is the molecular underpinnings of the disease, rather than the tissue of origin, that mostly drives outcomes. Consequently, oncology clinical trials have evolved from standard phase 1, 2, and 3 tissue-specific studies; to tissue-specific, biomarker-driven trials; to tissue-agnostic trials untethered from histology (all drug-centered designs); and, ultimately, to patient-centered, N-of-1 precision medicine studies in which each patient receives a personalized, biomarker-matched therapy/combination of drugs. Innovative technologies beyond genomics, including those that address transcriptomics, immunomics, proteomics, functional impact, epigenetic changes, and metabolomics, are enabling further refinement and customization of therapy. Decentralized studies have the potential to improve access to trials and precision medicine approaches for underserved minorities. Evaluation of real-world data, assessment of patient-reported outcomes, use of registry protocols, interrogation of exceptional responders, and exploitation of synthetic arms have all contributed to personalized therapeutic approaches. With greater than 1 × 10 potential patterns of genomic alterations and greater than 4.5 million possible three-drug combinations, the deployment of artificial intelligence/machine learning may be necessary for the optimization of individual therapy and, in the near future, also may permit the discovery of new treatments in real time.
下一代测序揭示了一个颠覆性的现实,即晚期/转移性癌症具有复杂且个体独特的基因组格局,这就需要重新思考治疗策略和临床试验设计。事实上,癌症的分子重新分类表明,驱动治疗结果的主要是疾病的分子基础,而非起源组织。因此,肿瘤学临床试验已从标准的1、2、3期组织特异性研究,发展到组织特异性、生物标志物驱动的试验,再到不受组织学限制的组织agnostic试验(所有以药物为中心的设计),最终发展到以患者为中心的N-of-1精准医学研究,即每个患者接受个性化的、生物标志物匹配的治疗/药物组合。除基因组学之外的创新技术,包括那些涉及转录组学、免疫组学、蛋白质组学、功能影响、表观遗传变化和代谢组学的技术,正在使治疗进一步精细化和个性化。去中心化研究有潜力改善弱势群体参与试验和精准医学方法的机会。对真实世界数据的评估、对患者报告结果的评估、注册方案的使用、对特殊反应者的研究以及合成臂的利用,都有助于个性化治疗方法的发展。由于存在超过1×10种潜在的基因组改变模式以及超过450万种可能的三药组合,可能需要部署人工智能/机器学习来优化个体治疗,并且在不久的将来,还可能实时发现新的治疗方法。