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Tumor-agnostic baskets to N-of-1 platform trials and real-world data: Transforming precision oncology clinical trial design.
Fountzilas, Elena; Tsimberidou, Apostolia-Maria; Hiep Vo, Henry; Kurzrock, Razelle.
Afiliação
  • Fountzilas E; Department of Medical Oncology, St Luke's Clinic, Thessaloniki, Greece; European University Cyprus, German Oncology Center, Nicosia, Cyprus.
  • Tsimberidou AM; The University of Texas MD Anderson Cancer Center, Department of Investigational Cancer Therapeutics, Houston, TX, USA. Electronic address: atsimber@mdanderson.org.
  • Hiep Vo H; The University of Texas MD Anderson Cancer Center, Department of Investigational Cancer Therapeutics, Houston, TX, USA.
  • Kurzrock R; WIN Consortium for Precision Medicine, France; Medical College of Wisconsin, USA.
Cancer Treat Rev ; 125: 102703, 2024 Apr.
Article em En | MEDLINE | ID: mdl-38484408
ABSTRACT
Choosing the right drug(s) for the right patient via advanced genomic sequencing and multi-omic interrogation is the sine qua non of precision cancer medicine. Traditional cancer clinical trial designs follow well-defined protocols to evaluate the efficacy of new therapies in patient groups, usually identified by their histology/tissue of origin of their malignancy. In contrast, precision medicine seeks to optimize benefit in individual patients, i.e., to define who benefits rather than determine whether the overall group benefits. Since cancer is a disease driven by molecular alterations, innovative trial designs, including biomarker-defined tumor-agnostic basket trials, are driving ground-breaking regulatory approvals and deployment of gene- and immune-targeted drugs. Molecular interrogation further reveals the disruptive reality that advanced cancers are extraordinarily complex and individually distinct. Therefore, optimized treatment often requires drug combinations and N-of-1 customization, addressed by a new generation of N-of-1 trials. Real-world data and structured master registry trials are also providing massive datasets that are further fueling a transformation in oncology. Finally, machine learning is facilitating rapid discovery, and it is plausible that high-throughput computing, in silico modeling, and 3-dimensional printing may be exploitable in the near future to discover and design customized drugs in real time.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article