<p>Supplementary Figure S1. Flowchart for developing and validating the clinicopathologic-genomic nomogram in a large-scale CRC cohort. Gene expression signatures concerning CRC prognosis were systematically retrieved from PubMed. CRC microarray datasets with clinically annotated information were retrieved from GEO. After excluding patients with incomplete clinical data and duplications, we combined these datasets into a large-scale CRC cohort, which was further analyzed by PCA and clustering. Then, the CRC cohort was randomly divided into a training set (n = 855) and a validation set (n = 855) to develop and validate the clinicopathologic-genomic nomogram, respectively. Based on the training set, we assessed the prognostic performances of signatures that met the inclusion criteria detailed in the flowchart. Next, a nomogram integrating well-performing signatures with clinical variables was constructed via a backward stepwise Cox proportional hazard model. Based on the validation set, we further evaluated the prognostic value of the clinicopathologic-genomic nomogram to determine whether it could robustly identify patients at high risk of recurrence.</p>
ARTICLE ABSTRACT
The current tumor staging system is insufficient for predicting the outcomes for patients with colorectal cancer because of its phenotypic and genomic heterogeneity. Integrating gene expression signatures with clinicopathologic factors may yield a predictive accuracy exceeding that of the currently available system. Twenty-seven signatures that used gene expression data to predict colorectal cancer prognosis were identified and re-analyzed using bioinformatic methods. Next, clinically annotated colorectal cancer samples (n = 1710) with the corresponding expression profiles, that predicted a patient's probability of cancer recurrence, were pooled to evaluate their prognostic values and establish a clinicopathologic–genomic nomogram. Only 2 of the 27 signatures evaluated showed a significant association with prognosis and provided a reasonable prediction accuracy in the pooled cohort (HR, 2.46; 95% CI, 1.183–5.132, P < 0.001; AUC, 60.83; HR, 2.33; 95% CI, 1.218–4.453, P < 0.001; AUC, 71.34). By integrating the above signatures with prognostic clinicopathologic features, a clinicopathologic–genomic nomogram was cautiously constructed. The nomogram successfully stratified colorectal cancer patients into three risk groups with remarkably different DFS rates and further stratified stage II and III patients into distinct risk subgroups. Importantly, among patients receiving chemotherapy, the nomogram determined that those in the intermediate- (HR, 0.98; 95% CI, 0.255–0.679, P < 0.001) and high-risk (HR, 0.67; 95% CI, 0.469–0.957, P = 0.028) groups had favorable responses.Implications: These findings offer evidence that genomic data provide independent and complementary prognostic information, and incorporation of this information refines the prognosis of colorectal cancer. Mol Cancer Res; 16(9); 1373–84. ©2018 AACR.
