BackgroundColorectal cancer (CRC) is 3rd most commonly diagnosed cancer in males and the second in females. PD-1/PD-L1 axis, as an immune checkpoint, is up-regulated in many tumors and their microenvironment. However, the prognostic value of PD-1/PD-L1 in CRC remains unclear.MethodsThe Cancer Genome Atlas (TCGA) database (N = 356) and Fudan University Shanghai Cancer Center (FUSCC) cohort of patients (N = 276) were adopted to analyze the prognostic value of PD-L1 in colorectal tumor cells (TCs) and of PD-1 in tumor infiltrating cells (TILs) for CRC. Subgroup analyses were conducted in FUSCC cohort according to patients’ status of mismatch repair.ResultsIn TCGA cohort, the cut-off values of PD-1 and PD-L1 expression were determined by X-tile program, which were 4.40 and 2.92, respectively. Kaplan-Meier analysis indicated that higher PD-1 and PD-L1 expressions correlated with better OS (P = 0.032 and P = 0.002, respectively). In FUSCC cohort, expressions of PD-1 on TILs and PD-L1 on TCs were analyzed separately by immunohistochemistry (IHC) staining based on a TMA sample (N = 276) and revealed that both TILs-PD-1 and TCs-PD-L1 were associated with OS (P = 0.006 and P = 0.002, respectively) and DFS (P = 0.025 and P = 0.004, respectively) of CRC patients. Multivariate Cox regression analysis indicated TILs-PD-1 was an independent prognostic factor both for OS and DFS of CRC patients (P < 0.05). Subgroup analyses showed that TILs-PD-1 was an independent prognostic factor for both OS and DFS in CRC patients in MSS-proficient subgroup (P < 0.05), while neither of them correlated with OS or DFS in MSS-deficient subgroup (P > 0.05).ConclusionsHigher expressions of PD-1 and PD-L1 correlates with better prognosis of CRC patients. TILs-PD-1 is an independent prognostic factor for OS and DFS of CRC patients, especially for MMR-proficient subgroup.Electronic supplementary materialThe online version of this article (doi:10.1186/s12943-016-0539-x) contains supplementary material, which is available to authorized users.

Tumor metastasis is a hallmark of cancer. Metastatic cancer cells often reside in distal tissues and organs in their dormant state. Mechanisms underlying the pre-metastatic niche formation are poorly understood. Here we show that in a colorectal cancer (CRC) model, primary tumors release integrin beta-like 1 (ITGBL1)-rich extracellular vesicles (EVs) to the circulation to activate resident fibroblasts in remote organs. The activated fibroblasts induce the premetastatic niche formation and promote metastatic cancer growth by secreting proinflammatory cytokine, such as IL-6 and IL-8. Mechanistically, the primary CRC-derived ITGBL1-enriched EVs stimulate the TNFAIP3-mediated NF-κB signaling pathway to activate fibroblasts. Consequently, the activated fibroblasts produce high levels of pro-inflammatory cytokines to promote metastatic cancer growth. These findings uncover a tumor-stromal interaction in the metastatic tumor microenvironment and an intimate signaling communication between primary tumors and metastases through the ITGBL1-loaded EVs. Targeting the EVs-ITGBL1-CAFs-TNFAIP3-NF-κB signaling axis provides an attractive approach for treating metastatic diseases.

ObjectiveTo compare the long-term survival of colorectal cancer (CRC) in young patients with elderly ones.MethodsUsing Surveillance, Epidemiology, and End Results (SEER) population-based data, we identified 69,835 patients with non-metastatic colorectal cancer diagnosed between January 1, 1988 and December 31, 2003 treated with surgery. Patients were divided into young (40 years and under) and elderly groups (over 40 years of age). Five-year cancer specific survival data were obtained. Kaplan-Meier methods were adopted and multivariable Cox regression models were built for the analysis of long-term survival outcomes and risk factors.ResultsYoung patients showed significantly higher pathological grading (p<0.001), more cases of mucinous and signet-ring histological type (p<0.001), later AJCC stage (p<0.001), more lymph nodes (≥12 nodes) dissected (p<0.001) and higher metastatic lymph node ratio (p<0.001). The 5-year colorectal cancer specific survival rates were 78.6% in young group and 75.3% in elderly group, which had significant difference in both univariate and multivariate analysis (P<0.001). Further analysis showed this significant difference only existed in stage II and III patients.ConclusionsCompared with elderly patients, young patients with colorectal cancer treated with surgery appear to have unique characteristics and a higher cancer specific survival rate although they presented with higher proportions of unfavorable biological behavior as well as advanced stage disease.

Background: Accurate lymph node metastasis (LNM) prediction in colorectal cancer (CRC) patients is of great significance for treatment decision making and prognostic evaluation. We aimed to develop and validate a clinicalradiomics nomogram for the individual preoperative prediction of LNM in CRC patients. Methods:We enrolled 766 patients (458 in the training set and 308 in the validation set) with clinicopathologically confirmed CRC. We included nine significant clinical risk factors (age, sex, preoperative carbohydrate antigen 19-9 (CA19-9) level, preoperative carcinoembryonic antigen (CEA) level, tumor size, tumor location, histotype, differentiation and M stage) to build the clinical model. We used analysis of variance (ANOVA), relief and recursive feature elimination (RFE) for feature selection (including clinical risk factors and the imaging features of primary lesions and peripheral lymph nodes), established classification models with logistic regression analysis and selected the respective candidate models by fivefold cross-validation. Then, we combined the clinical risk factors, primary lesion radiomics features and peripheral lymph node radiomics features of the candidate models to establish combined predictive models. Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, decision curve analysis (DCA) and a nomogram were used to evaluate the clinical usefulness of the model. Results:The clinical-primary lesion radiomics-peripheral lymph node radiomics model, with the highest AUC value (0.7606), was regarded as the candidate model and had good discrimination and calibration in both the training and validation sets. DCA demonstrated that the clinical-radiomics nomogram was useful for preoperative prediction in the clinical environment.

miR-139-5p, which has been reported to be underexpressed in several types of cancer, is associated with tumorigenesis by participating in various biological processes via the modulation of different target genes. In the present study, we analyzed mice deficient in miR-139-5p, aiming to investigate its role in intestinal inflammation and colitis-associated colorectal cancer. We show that miR-139-5p knockout (KO) mice are highly susceptible to colitis and colon cancer, accompanied by elevated proliferation and decreased apoptosis, as well as an increased production of inflammatory cytokines, chemokines and tumorigenic factors. Furthermore, enhanced colon inflammation and colorectal tumor development in miR-139-5p KO mice are a result of the regulatory effects of miR-139-5p on its target genes for Rap1b and nuclear factor-kappa B, thus affecting the activity of the mitogen-activated protein kinase, nuclear factor-kappa B and signal transducer and activator of transcription 3 signaling pathways. These results reveal a critical part for miR-139-5p in maintaining intestinal homeostasis and protecting against colitis and colorectal cancer in vivo, providing new insights into the function of miR-139-5p with respect to linking inflammation to carcinogenesis.

The purpose of our study was to develop a multigene signature based on transcriptome profiles of both mRNAs and lncRNAs to identify a group of patients who are at high risk of early relapse in stages II-III colon cancer. Firstly, propensity score matching was conducted between patients in early relapse group and long-term survival group from GSE39582 training series (N = 359) and patients were matched 1:1. Global transcriptome analysis was then performed between the paired groups to identify tumor specific mRNAs and lncRNAs. Finally, using LASSO Cox regression model, we built a multigene early relapse classifier incorporating 15 mRNAs and three lncRNAs. The prognostic and predictive accuracy of the signature was internally validated in 102 colon cancer patients and externally validated in other 241 patients. In the training set, patients with high risk score were more likely to suffer from relapse than those with low risk score (HR: 2.67, 95% CI: 2.07-3.46, P < 0.001). The results were validated in the internal validation set (HR: 2.23, 95% CI: 1.23-3.78, P = 0.003) and external validation (HR 1.88, 95% CI 1.42-2.48; P < 0.001) set. Time-dependent receiver operating curve at 1 year showed that the integrated mRNA-lncRNA signature [area under curve (AUC) = 0.742] had better prognostic accuracy than AJCC TNM stage (AUC = 0.615) in the entire 702 patients. In addition, survival decision curve analyses at 12 months revealed a good clinical usefulness of the integrated mRNA-lncRNA signature. In conclusion, we successfully developed an integrated mRNA-lncRNA signature that can accurately predict early relapse.

Background Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics. Methods We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months’ postoperative follow-up from three hospitals. Through transfer learning, a deep learning radiomic signature (DLRS) based on multiparametric magnetic resonance imaging (MRI) was constructed. A nomogram was established integrating deep MRI information and clinicopathologic factors for better prediction. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) were used as performance metrics. Furthermore, the risk of DM in patients with different response to nCRT was evaluated with the nomogram. Findings DLRS performed well in DM prediction, with a C-index of 0·747 and an area under curve (AUC) at three years of 0·894 in the validation cohort. The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM ( P < 0·05). Interpretation MRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT. Funding The funding bodies that contributed to this study are listed in the Acknowledgements section.

Epigenetic markers based on differential methylation of DNA sequences are used in cancer screening and diagnostics. Detection of abnormal methylation at specific loci by real-time quantitative polymerase chain reaction (RT-qPCR) has been developed to enable high-throughput cancer screening. For tests that combine the results of multiple PCR replicates into a single reportable result, both individual PCR cutoff and weighting of the individual PCR result are essential to test outcome. In this opportunistic screening study, we tested samples from 1133 patients using the triplicate Epi proColon assay with various algorithms and compared it with the newly developed single replicate SensiColon assay that measures methylation status of the same SEPT9 gene sequence. The Epi proColon test approved by the US FDA (1/3 algorithm) showed the highest sensitivity (82.4%) at a lower specificity (82.0%) compared with the Epi proColon 2.0 CE version with 2/3 algorithm (75.1% sensitivity, 97.1% specificity) or 1/1 algorithm (71.3% sensitivity, 92.7% specificity). No significant difference in performance was found between the Epi proColon 2.0 CE and the SensiColon assays. The choice of algorithm must depend on specific test usage, including screening and early detection. These considerations allow one to choose the optimal algorithm to maximize the test performance. We hope this study can help to optimize the methylation detection in cancer screening and early detection.