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Mayo Clinic: Gene expression profiling not quite perfected in predicting lung cancer prognosis

authors wrote.

Few studies have compared conventional methods of lung cancer prediction with gene profiling. It remains to be seen whether gene expression profiling of lung cancer cases can replace or augment the existing assessment tools and, furthermore, whether it can lead to improved patient care.

In terms of problems associated with gene expression profiling in lung cancer research, the authors found:

  • The accuracy of gene expression-based outcome prediction varies greatly among studies.
  • Most studies lacked independent validation.
  • Clinical outcome prediction between gene expression profiles and pathological features overlap significantly.
  • Current analytical algorithms favor genes at high expression or genes highly differentially expressed, most of which are related to tumor differentiation and may not correlate with clinical outcomes; conversely, genes expressed at low levels or in a subtle difference are often overlooked, which may be quite relevant biologically to clinical questions.

The authors of the study recommend that medical scientists engaged in gene expression profiling should:

  • Clearly define a study aim. The main focus in microarray studies should explore the molecular explanations for varied clinical outcomes given a group of patients with similar clinical and pathological characteristics.
  • Lay out and compare alternative study designs
  • Carefully select samples in terms of size, quality and unambiguous clinical outcomes
  • Understand the limitations of DNA microarray
  • Provide clinically relevant interpretation from the study results and address the value added in practice


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Source:Mayo Clinic


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