Is molecular profiling ready for use in clinical decision making?

Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina 45110, Greece.
The Oncologist (Impact Factor: 4.54). 04/2007; 12(3):301-11. DOI: 10.1634/theoncologist.12-3-301
Source: PubMed

ABSTRACT Molecular profiling, the classification of tissue or other specimens for diagnostic, prognostic, and predictive purposes based on multiple gene expression, is a technology that holds major promise for optimizing the management of patients with cancer. However, the use of these tests for clinical decision making presents many challenges to overcome. Assay development and data analysis in this field have been largely exploratory, and leave numerous possibilities for the introduction of bias. Standardization of profiles remains the exception. Classifier performance is usually overinterpreted by presenting the results as p-values or multiplicative effects (e.g., relative risks), while the absolute sensitivity and specificity of classification remain modest at best, especially when tested in large validation samples. Validation has often been done with suboptimal attention to methodology and protection from bias. The postulated classifier performance may be inflated compared to what these profiles can achieve. With the exception of breast cancer, we have little evidence about the incremental discrimination that molecular profiles can provide versus classic risk factors alone. Clinical trials have started to evaluate the utility of using molecular profiles for breast cancer management. Until we obtain data from these trials, the impact of these tests and the net benefit under real-life settings remain unknown. Optimal incorporation into clinical practice is not straightforward. Finally, cost-effectiveness is difficult to appreciate until these other challenges are addressed. Overall, molecular profiling is a fascinating and promising technology, but its incorporation into clinical decision making requires careful planning and robust evidence.

  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Some biosamples obtained from biobanks may go through thawing before processing. We aim to evaluate the effects of thawing at room temperature for different time periods on gene expression analysis. A time course study with four time points was conducted to investigate the expression profiling on 10 thawed normal mice renal tissue samples through Affymetrix GeneChip mouse gene 2.0 st array. Microarray results were validated by quantitative real time polymerase chain reactions (qPCR) on 6 candidate reference genes and 11 target genes. Additionally, we used geNorm plus and NormFinder to identify the most stably expressed reference genes over time. The results showed RNA degraded more after longer incubation at room temperature. However, microarray results showed only 240 genes (0.91%) altered significantly in response to thawing at room temperature. The signal of majority altered probe sets decreased with thawing time, and the crossing point (Cp) values of all candidate reference genes correlated positively with the thawing time (p<0.05). The combination of B2M, ACTB and PPIA was identified as the best choice for qPCR normalization. We found most target genes were stable by using this normalization method. However, serious gene quantification errors were resulted from improper reference genes. In conclusion, thirty minutes of thawing at room temperature has a limited impact on microarray and qPCR analysis, gene expression variations due to RNA degradation in early period after thawing can be largely reduced by proper normalization.
    PLoS ONE 03/2014; 9(3):e93175. DOI:10.1371/journal.pone.0093175 · 3.53 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Background Tissue handling can alter global gene expression potentially affecting the analytical performance of genomic signatures, but such effects have not been systematically evaluated. Methods Tissue samples from 11 previously untreated breast tumors were minced and aliquots were either snap frozen or placed in RNAlater immediately or after 20, 40, 60, 120 or 180 minutes at room temperature. RNA was profiled on Affymetrix HG-U133A arrays. We used probe-set-wise hierarchical models to evaluate the effect of preservation method on transcript expression and linear mixed effects models to assess the effect of cold ischemic delay on the expression of individual probe sets. Gene set enrichment analysis identified pathways overrepresented in the affected transcripts. We combined the levels of 41 most sensitive transcripts to develop an index of ischemic stress. Results Concordance in global gene expression between the baseline and 40 min delay was higher for samples preserved in RNAlater (average concordance correlation coefficient CCC = 0.92 compared to 0.88 for snap frozen). Overall, 481 transcripts (3%) were significantly affected by the preservation method, most of them involved in processes important in cancer. Prolonged cold ischemic delay of up to 3 hours induced marginal global gene expression changes (average CCC=0.90 between baseline and 3 hour delay). However 41 transcripts were significantly affected by cold ischemic delay. Among the induced transcripts were stress response genes, apoptotic response genes; among the downregulated were genes involved in metabolism, protein processing and cell cycle regulation. An index combining the expression levels of these genes was proportional to the cold ischemic delay. Conclusions Prolonged cold ischemia induces significant transcriptional changes in a small subset of transcripts in the tissue. Furthermore, the expression level of about 3% of the transcripts is affected by the preservation method. These sensitive transcripts should not be included in genomic signatures for more reliable analytical performance.
    Molecular oncology 05/2014; DOI:10.1016/j.molonc.2014.02.002 · 5.94 Impact Factor