Novel clinical trial designs emerging from the molecular reclassification of cancer

IF 503.1 1区 医学 Q1 ONCOLOGY
Mina Nikanjam, Shumei Kato, Teresa Allen, Jason K. Sicklick, Razelle Kurzrock
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引用次数: 0

Abstract

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 × 1012 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.
新的临床试验设计从癌症的分子重新分类出现
新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不同的基因组景观,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,主要是疾病的分子基础,而不是起源组织,驱动了结果。因此,肿瘤临床试验已经从标准的1、2和3期组织特异性研究发展;组织特异性、生物标志物驱动的试验;不受组织学限制的组织不可知试验(所有以药物为中心的设计);最终,以患者为中心,N-of-1的精准医学研究,每个患者接受个性化的、生物标志物匹配的治疗/药物组合。基因组学之外的创新技术,包括转录组学、免疫组学、蛋白质组学、功能影响、表观遗传变化和代谢组学,正在进一步完善和定制治疗。分散的研究有可能改善服务不足的少数群体获得试验和精确医疗方法的机会。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、对特殊应答者的询问以及合成武器的利用都有助于个性化治疗方法。有超过1 × 1012种潜在的基因组改变模式和超过450万种可能的三种药物组合,人工智能/机器学习的部署对于优化个体治疗可能是必要的,并且在不久的将来,也可能允许实时发现新的治疗方法。
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来源期刊
CiteScore
873.20
自引率
0.10%
发文量
51
审稿时长
1 months
期刊介绍: CA: A Cancer Journal for Clinicians" has been published by the American Cancer Society since 1950, making it one of the oldest peer-reviewed journals in oncology. It maintains the highest impact factor among all ISI-ranked journals. The journal effectively reaches a broad and diverse audience of health professionals, offering a unique platform to disseminate information on cancer prevention, early detection, various treatment modalities, palliative care, advocacy matters, quality-of-life topics, and more. As the premier journal of the American Cancer Society, it publishes mission-driven content that significantly influences patient care.
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