Novel clinical trial designs emerging from the molecular reclassification of cancer

Mina Nikanjam1,2, Shumei Kato1,2, Teresa Allen3

  • 1Division of Hematology-Oncology, University of California San Diego, La Jolla, California, USA.

PubMed

Insights

Advanced cancers have complex genomic landscapes, driving a shift towards personalized, biomarker-matched therapies. Precision medicine, utilizing diverse molecular data and AI, optimizes individual cancer treatment and drug discovery.

Area of Science:

  • Oncology
  • Genomics
  • Precision Medicine

Background:

  • Next-generation sequencing reveals complex, individual genomic landscapes in advanced cancers.
  • Molecular characteristics, not tissue of origin, increasingly dictate cancer patient outcomes.
  • This necessitates a paradigm shift in cancer treatment strategies and clinical trial design.

Purpose of the Study:

  • To outline the evolution of oncology clinical trials towards precision medicine.
  • To highlight the role of multi-omic technologies and data sources in personalizing cancer therapy.
  • To discuss the potential of artificial intelligence in optimizing individualized treatment and discovering new therapies.

Main Methods:

  • Review of evolving clinical trial designs from tissue-specific to patient-centered, N-of-1 studies.
  • Integration of diverse molecular data (genomics, transcriptomics, proteomics, etc.).
  • Leveraging real-world data, patient-reported outcomes, and advanced computational approaches like AI/machine learning.

Main Results:

  • Oncology trials have progressed to biomarker-driven, tissue-agnostic, and N-of-1 precision medicine models.
  • Multi-omic technologies enable refined and customized therapeutic strategies.
  • AI/machine learning is crucial for navigating complex genomic data and optimizing personalized treatments.

Conclusions:

  • Cancer treatment is increasingly personalized, moving beyond histology to molecular underpinnings.
  • A multi-modal, data-driven approach, including AI, is essential for optimizing precision oncology.
  • Future directions include real-time treatment discovery and improved trial accessibility through decentralized studies.

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