Pilot studies for personalized cancer medicine: focusing on the patient for treatment selection

Leticia De Mattos-Arruda1, Jordi Rodon

  • 1Molecular Therapies Research Unit, Vall d'Hebron Institute of Oncology, Vall d'Hebron University Hospital, Barcelona, Spain; Universitat Autònoma de Barcelona, Barcelona, Spain.

The Oncologist
|October 19, 2013
PubMed

Insights

Personalized cancer medicine (PCM) uses tumor molecular analysis to match targeted therapies. Lessons from early PCM pilot studies are guiding the design of new clinical trials for tailored cancer care.

Area of Science:

  • Oncology
  • Genomics
  • Translational Medicine

Background:

  • Advances in diagnostics and targeted therapies have revolutionized oncology.
  • Stratified medicine has emerged, linking targeted therapies to specific tumor molecular aberrations.
  • Large-scale sequencing initiatives (e.g., Cancer Genome Atlas) highlight the importance of genome analysis in understanding cancer.

Purpose of the Study:

  • To describe lessons learned from initial personalized cancer medicine (PCM) pilot initiatives.
  • To explain how this knowledge is informing the design of novel clinical trials.
  • To discuss the transformation of cancer care through molecular analysis and collaborative efforts.

Main Methods:

  • Review of findings from early personalized cancer medicine pilot studies.
  • Analysis of data from large-scale genomic projects and cancer mutation catalogs.
  • Examination of collaborative efforts in cancer research and treatment.

Main Results:

  • Pilot studies are transforming the analysis of tumor molecular aberrations.
  • Clinical trial design and efficacy measurement are being redefined by PCM principles.
  • Early initiatives provide valuable insights for future PCM research and application.

Conclusions:

  • Personalized cancer medicine (PCM) is advancing through integrated genomic analysis and targeted therapies.
  • Lessons from pilot studies are crucial for developing effective, personalized cancer treatment strategies.
  • Collaborative research and data sharing are essential for the widespread adoption of PCM.

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