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Predictive Immune Modeling of Solid Tumors
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Predictive Immune Modeling of Solid Tumors

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Moving from correlative science to predictive oncology

Richard Simon1

  • 1National Cancer Institute, 9000 Rockville Pike, Bethesda, MD, 20892-7434 USA.

The EPMA Journal
|December 4, 2012
PubMed

Insights

Genomic technologies enable precision medicine in oncology by identifying patients for targeted therapies. This requires new clinical trial designs to evaluate biomarkers effectively.

Area of Science:

  • Oncology
  • Genomics
  • Clinical Trial Design

Background:

  • Many diseases are molecularly heterogeneous, leading to ineffective treatments and large trials with minimal average benefits.
  • Genomic technologies offer tools for patient stratification and drug selection in oncology.
  • Drug development with companion diagnostics complicates clinical trials, necessitating novel design and analysis approaches.

Purpose of the Study:

  • To address challenges in clinical development and trial design driven by tumor genomics.
  • To comment on designing clinical studies for evaluating prognostic and predictive biomarkers.

Main Methods:

  • Review of current challenges in clinical development and trial design.
  • Discussion of the impact of genomic technologies on oncology drug development.
  • Analysis of approaches for evaluating biomarker utility and robustness.

Main Results:

  • Traditional disease classifications are insufficient due to molecular heterogeneity.
  • Genomic profiling is crucial for personalized medicine and effective cancer treatment.
  • New paradigms are needed for clinical and statistical investigators to adapt to tumor genomics.

Conclusions:

  • Adapting to tumor genomics requires paradigm shifts in clinical research.
  • Robust evaluation of biomarkers is essential for advancing precision oncology.
  • Innovative clinical trial designs are necessary to realize the full potential of genomic medicine.

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