Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for 'Cox' method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656-0.801, 95% CI) and 0.705 (0.628-0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670-0.952, 95%CI) in training and 0.805 (0.638-0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. FUND: This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China.

Purpose: Our study aimed to investigate whether CAF (cancer-associated fibroblasts) were involved in long noncoding RNAs (lncRNA)-regulated radioresponse in esophageal squamous cell carcinoma (ESCC). Experimental Design: By use of lncRNAs PCR array, 38 lncRNAs were screened in esophageal cancer cells and in normal esophageal epithelial cells Het-1A. LncRNA DNM3OS was detected in tumor tissues of patients with ESCC and in matched normal esophageal epithelial tissues by qRT-PCR analysis and in situ hybridization assay. The association of DNM3OS and tumor radioresistance was investigated in vitro and in vivo. The influences of DNM3OS on DNA damage response (DDR) was investigated by Western blotting, immunofluorescence imaging, and comet assay. The mechanisms by which CAFs promoted DNM3OS expression was investigated by kinase inhibitors' screening, luciferase assay, and chromatin immunoprecipitation. Results: Among the 38 lncRNAs tested, DNM3OS was found to have a much higher expression level in esophageal cancer cells than in Het-1A. In tumor tissues of 16 patients with ESCC, the expression level of DNM3OS showed an average increase of 6.3429-fold compared with that in matched normal tissues. DNM3OS conferred significant radioresistance in vitro and in vivo by regulating DDR. CAFs promoted the expression of DNM3OS with a 39.2554-fold and 38.3163-fold increase in KYSE-30 and KYSE-140, respectively. CAFs promoted the expression of DNM3OS in a PDGFβ/PDGFRβ/FOXO1 signaling pathway–dependent manner. FOXO1, a transcription factor downstream of PDGFβ/PDGFRβ signaling pathway, initiated the transcription of DNM3OS by binding to DNM3OS promoter. Conclusions: Our study highlighted CAF-promoted DNM3OS as an attractive target to reverse tumor radioresistance in ESCC.

Five-year survival rate of esophageal squamous cell carcinoma (ESCC) patients treated with radiotherapy is <20%. Our study aimed to investigate whether cancer-associated fibroblasts (CAFs), one major component of tumor microenvironment, were involved in tumor radioresistance in ESCC. By use of human chemokine/cytokine array, human chemokine CXCL1 was found to be highly expressed in CAFs compared with that in matched normal fibroblasts. Inhibition of CXCL1 expression in CAFs significantly reversed CAF-conferred radioresistance in vitro and in vivo. CAF-secreted CXCL1 inhibited the expression of reactive oxygen species (ROS)-scavenging enzyme superoxide dismutase 1, leading to increased ROS accumulation following radiation, by which DNA damage repair was enhanced and the radioresistance was mediated. CAF-secreted CXCL1 mediated the radioresistance also by activation of Mek/Erk pathway. The cross talk of CAFs and ESCC cells induced CXCL1 expression in an autocrine/paracrine signaling loop, which further enhanced tumor radioresistance. Together, our study highlighted CAF-secreted CXCL1 as an attractive target to reverse tumor radioresistance and can be used as an independent prognostic factor of ESCC patients treated with chemoradiotherapy.

Advances in genomic technology have enabled the faithful detection and measurement of mutations and the gene expression profile of cancer cells at the single-cell level. Recently, several single-cell sequencing methods have been developed that permit the comprehensive and precise analysis of the cancer-cell genome, transcriptome, and epigenome. The use of these methods to analyze cancer cells has led to a series of unanticipated discoveries, such as the high heterogeneity and stochastic changes in cancer-cell populations, the new driver mutations and the complicated clonal evolution mechanisms, and the novel identification of biomarkers of variant tumors. These methods and the knowledge gained from their utilization could potentially improve the early detection and monitoring of rare cancer cells, such as circulating tumor cells and disseminated tumor cells, and promote the development of personalized and highly precise cancer therapy. Here, we discuss the current methods for single cancer-cell sequencing, with a strong focus on those practically used or potentially valuable in cancer research, including single-cell isolation, whole genome and transcriptome amplification, epigenome profiling, multi-dimensional sequencing, and next-generation sequencing and analysis. We also examine the current applications, challenges, and prospects of single cancer-cell sequencing. Cancer Res; 76(6); 1305–12. ©2016 AACR.