Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review

Abstract

Introduction: The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets.

Methods: A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples.

Results: Five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system.

Conclusions: The published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic.

Figure 1. PRISMA Diagram which depicts the flow of information through the different phases of the prognosis signatures studies systematic review.

Figure 2. Heatmap showing Matthews Correlation Coefficient (MCC) values for each signature in each dataset as result of analyses with Random Forest.

Rows correspond to signatures and columns to datasets. Last column shows a pooled MCC across datasets using sample size as weights. Black lines delimit the first five signatures for which training datasets were available (cells highlighted in black). Cells representing signatures and datasets used to validate them are highlighted in blue. Color scale represents the MCC values: the darker the color, the higher MCC (see the legend). Negative values were collapsed to zero.

Figure 3. Differences between positive and negative post-test probabilities of recurrence and their 95% confidence interval for stage II (A) and stage III (B).

Prevalence probability of recurrence for stage II and III were assumed to be 20 and 34% respectively. Signatures are listed in decreasing order of post-tests probability differences.