A novel oHSV-1 targeting telomerase reverse transcriptase-positive cancer cells via tumor-specific promoters regulating the expression of ICP4

Virotherapy is a promising strategy for cancer treatment. Using the human telomerase reverse transcriptase promoter, we developed a novel tumor-selective replication oncolytic HSV-1. Here we showed that oHSV1-hTERT virus was cytopathic in telomerase-positive cancer cell lines but not in telomerase-negative cell lines. In intra-venous injection in mice, oHSV1-hTERT was safer than its parental oHSV1-17+. In human blood cell transduction assays, both viruses transduced few blood cells and the transduction rate for oHSV1-hTERT was even less than that for its parental virus. In vivo, oHSV1-hTERT inhibited growth of tumors and prolong survival in telomerase-positive xenograft tumor models. Therefore, we concluded that this virus may be a safe and effective therapeutic agent for cancer treatment, warranting clinical trials in humans.

Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach

Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.

Constitutive activation of STAT3 signaling regulates hTERT and promotes stem cell-like traits in human breast cancer cells

Mounting clinical data suggest that high telomerase activity is tightly associated with cancer progression and poor outcomes. Constitutively activated STAT3 is found in ∼60% of human malignancies and shows a dismal prognosis. We previously reported that activated STAT3 promoted epithelial-mesenchymal transition (EMT) and cancer stem cell phenotype in human breast cancer. However, little is known how STAT3 is regulated in the cancer stem cell and by which mechanisms STAT3 contributes to poor prognosis in aggressive breast cancer. Here we demonstrate that STAT3 physically interacts with CD44 and NF-kB and activates the catalytic subunit of telomerase (hTERT) in human breast cancer stem cells. STAT3 plays a role as a signal transducing molecule between CD44 and NF-kB. In addition to functioning as a catalytic subunit of telomerase, hTERT has been reported to function as a transcription co-factor which drives EMT and cancer stem cell phenotype in human cancer. We observed that activated hTERT increases CD44 (+) subpopulation, whereas targeted knock-down of hTERT abolished cancer stem cell phenotype. Targeted STAT3 knock-down cells also down-regulated hTERT and decreased CD44 subpopulation. Finally, CD44 knock-down resulted in the abrogation of cancer stem cell phenotype and concurrent down-regulation of pSTAT3 and hTERT. Our study delineates the signaling pathway where STAT3 functions as a modulator for CD44 and hTERT, promoting a cancer stem cell phenotype. The constitutive activation of STAT3 signaling that leads to regulation of hTERT pathway may provide novel therapeutic targets for human breast cancer stem cells.