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In Silico Estimates of Tissue Components in Surgical Samples May Improve Cancer Research

Strategic Insights

— 1 min read

Researchers from the University of California's Vaccine Research Institute of San Diego have developed a method for estimating tissue components in surgical samples using gene expression profiling data. This approach, which they call "in silico prediction," may help to improve the accuracy of cancer research by reducing the impact of variation in tissue samples. The researchers tested their method using four large gene expression microarray data sets from prostate cancer and found that their predictions were accurate to within 8-17% of the pathologists' estimates.

Key Takeaways:

  • The researchers developed a method for estimating tissue components in surgical samples using gene expression profiling data, which they call "in silico prediction."
  • The method was tested using four large gene expression microarray data sets from prostate cancer and found to be accurate to within 8-17% of the pathologists' estimates.
  • The researchers created a web service called "CellPred" for making in silico predictions of sample tissue components based on expression data.
  • The method may help to improve the accuracy of cancer research by reducing the impact of variation in tissue samples.
  • The researchers found that genes that correlated with tissue percentage generally also correlated with recurrence in cancer patients.
  • The method identified almost a quarter of tumor-enriched samples as having 30% or less tumor cells, indicating a potential bias in the data.
  • There was a 10.5% difference in the average predicted tumor content between recurrent and nonrecurrent cancer patients.

Statistics:

  • The average differences between the pathologists' predictions and the in silico predictions of major tissue components were 8-14% for tumor and 13-17% for stroma.
  • Across independent data sets, the average differences were 11-12% for tumor and 12-17% for stroma.
  • Almost one quarter (23.6%) of the 219 tumor-enriched samples were predicted to have 30% or less tumor cells.
  • The average predicted tumor content was 10.5% higher in recurrent cancer patients than in nonrecurrent patients.

Sources:

  • Y. Wang et al. "In silico estimates of tissue components in surgical samples based on expression profiling data." Cancer Research, 2010; 70(16): 6448-55.
  • University of California's Vaccine Research Institute of San Diego.
  • NewsRx.com.

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