Prognostic significance of cellular vascular endothelial growth factor (VEGF) expression in the course of chronic myeloid leukaemia

VEGFR2-targeted fusion antibody improved NK cell-mediated immunosurveillance against K562 cells

MHC class I polypeptide-related sequence A (MICA), which is normally expressed on cancer cells, activates NK cells via NK group 2-member D pathway. However, some cancer cells escape NK-mediated immune surveillance by shedding membrane MICA causing immune suppression. To address this issue, we designed an antibody-MICA fusion targeting tumor-specific antigen (vascular endothelial growth factor receptor 2, VEGFR2) based on our patented antibody (mAb04) against VEGFR2. In vitro results demonstrate that the fusion antibody retains both the antineoplastic and the immunomodulatory activity of mAb04. Further, we revealed that it enhanced NK-mediated immunosurveillance against K562 cells through increasing degranulation and cytokine production of NK cells. The overall data suggest our new fusion protein provides a promising approach for cancer-targeted immunotherapy and has prospects for potential application of chronic myeloid leukemia.

GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data

MOTIVATION

RNA-seq has been widely used in transcriptome analysis to effectively measure gene expression levels. Although sequencing costs are rapidly decreasing, almost 70% of all the human RNA-seq samples in the gene expression omnibus do not have biological replicates and more unreplicated RNA-seq data were published than replicated RNA-seq data in 2011. Despite the large amount of single replicate studies, there is currently no satisfactory method for detecting differentially expressed genes when only a single biological replicate is available.

RESULTS

We present the GFOLD (generalized fold change) algorithm to produce biologically meaningful rankings of differentially expressed genes from RNA-seq data. GFOLD assigns reliable statistics for expression changes based on the posterior distribution of log fold change. In this way, GFOLD overcomes the shortcomings of P-value and fold change calculated by existing RNA-seq analysis methods and gives more stable and biological meaningful gene rankings when only a single biological replicate is available.

AVAILABILITY

The open source C/C++ program is available at http://www.tongji.edu.cn/∼zhanglab/GFOLD/index.html