Gene Expression Profiles Accurately Predict Outcome Following Liver Resection in Patients with Metastatic Colorectal Cancer

<div><p>Purpose</p><p>The aim of this study was to build a molecular prognostic model based on gene signatures for patients with completely resected hepatic metastases from colorectal cancer (MCRC).</p><p>Methods</p><p>Using the Illumina HumanHT-12 gene chip, RNA samples from the liver metastases of 96 patients who underwent R0 liver resection were analyzed. Patients were randomly assigned to a training (n = 60) and test (n = 36) set. The genes associated with disease-specific survival (DSS) and liver-recurrence-free survival (LRFS) were identified by Cox-regression and selected to construct a molecular risk score (MRS) using the supervised principle component method on the training set. The MRS was then evaluated in the independent test set.</p><p>Results</p><p>Nineteen and 115 genes were selected to construct the MRS for DSS and LRFS, respectively. Each MRS was validated in the test set; 3-year DSS/LRFS rates were 42/32% and 79/80% for patients with high and low MRS, respectively (<i>p</i> = 0.007 for DSS and p = 0.046 for LRFS). In a multivariate model controlling for a previously validated clinical risk score (CRS), the MRS remained a significant predictor of DSS (<i>p</i> = 0.001) and LRFS (<i>p</i> = 0.03). When CRS and MRS were combined, the patients were discriminated better with 3-year DSS/LRFS rates of 90/89% in the low risk group (both risk scores low) vs 42/26% in the high risk group (both risk scores high), respectively (<i>p</i> = 0.002/0.004 for DSS/LRFS).</p><p>Conclusion</p><p>MRS based on gene expression profiling has high prognostic value and is independent of CRS. This finding provides a potential strategy for better risk-stratification of patients with liver MCRC.</p></div

Association of clinicopathological variables with high- or low-risk signature for cancer death and liver recurrence in training and test cohort patient (N = 60/36).

<p>Association of clinicopathological variables with high- or low-risk signature for cancer death and liver recurrence in training and test cohort patient (N = 60/36).</p

Univariate and multivariate analysis of DSS and LRFS.

<p>UV, univariate, MV, multivariate.</p

Patient demographics, tumor characteristics and perioperative chemotherapy.

<p>CRS: Sum of points for each variable marked as *on the table, ≥3 considered as high risk.</p>**<p>training set vs test set, ^#Among overall cancer death.</p

Study profile.

<p>Ninety-six samples were selected from our departmental tissue bank. The patients were randomly assigned to the training set and test set by 2∶1. The molecular score was constructed based on the data in the training set cohort and validated using the data in the test set cohort.</p

Disease-specific survival (DSS) and Liver recurrence-free survival (LRFS) of patients following curative liver resection stratified by molecular risk scores (MRS).

<p>A. Kaplan-Meier estimates of DSS (left panel) and LRFS (right panel) for the patients in high-risk and low-risk groups among the training set cohort (N = 60) B. Kaplan-Meier estimates of DSS (left panel) and LRFS (right panel) for the patients in high-risk and low-risk groups among the test set cohort; Of note, the threshold values to discriminate the high-risk and low-risk group were the same as used in the analysis for the training set cohort.</p

Risk stratification by combination of CRS and MRS for DSS and LRFS.

<p>A. Kaplan-Meier estimates of DSS (left panel) and LRFS (right panel) for patients in the high, intermediate, and low risk groups among the training set cohort (N = 60) B. Kaplan-Meier estimates of DSS (left panel) and LRFS (right panel) for patients in the high, intermediate and low risk group among the test set cohort (N = 36) C. Kaplan-Meier estimates of DSS (left panel) and LRFS (right panel) for patients in the high, intermediate and super-low risk group among the entire cohort (N = 96).</p

Scatterplot of <i>p</i>-values for genes associated with liver-specific recurrence by the competing risk analysis and the Cox-regression analysis.

<p>Each dot represents <i>p</i>-values for gene in both analyses.</p