Stimulation of an anti-tumor immune response with "chromatin-damaging" therapy.

Curaxins are small molecules that bind genomic DNA and interfere with DNA-histone interactions leading to the loss of histones and decondensation of chromatin. We named this phenomenon 'chromatin damage'. Curaxins demonstrated anti-cancer activity in multiple pre-clinical tumor models. Here, we present data which reveals, for the first time, a role for the immune system in the anti-cancer effects of curaxins. Using the lead curaxin, CBL0137, we observed elevated expression of several group of genes in CBL0137-treated tumor cells including interferon sensitive genes, MHC molecules, some embryo-specific antigens suggesting that CBL0137 increases tumor cell immunogenicity and improves recognition of tumor cells by the immune system. In support of this, we found that the anti-tumor activity of CBL0137 was reduced in immune deficient SCID mice when compared to immune competent mice. Anti-tumor activity of CBL0137 was abrogated in CD8+ T cell depleted mice but only partially lost when natural killer or CD4+ T cells were depleted. Further support for a key role for the immune system in the anti-tumor activity of CBL0137 is evidenced by an increased antigen-specific effector CD8+ T cell and NK cell response, and an increased ratio of effector T cells to Tregs in the tumor and spleen. CBL0137 also elevated the number of CXCR3-expressing CTLs in the tumor and the level of interferon-γ-inducible protein 10 (IP-10) in serum, suggesting IP-10/CXCR3 controls CBL0137-elicited recruitment of effector CTLs to tumors. Our collective data underscores a previously unrecognized role for both innate and adaptive immunity in the anti-tumor activity of curaxins.

SCNVSim: somatic copy number variation and structure variation simulator.

BACKGROUND: Somatically acquired structure variations (SVs) and copy number variations (CNVs) can induce genetic changes that are directly related to tumor genesis. Somatic SV/CNV detection using next-generation sequencing (NGS) data still faces major challenges introduced by tumor sample characteristics, such as ploidy, heterogeneity, and purity. A simulated cancer genome with known SVs and CNVs can serve as a benchmark for evaluating the performance of existing somatic SV/CNV detection tools and developing new methods. RESULTS: SCNVSim is a tool for simulating somatic CNVs and structure variations SVs. Other than multiple types of SV and CNV events, the tool is capable of simulating important features related to tumor samples including aneuploidy, heterogeneity and purity. CONCLUSIONS: SCNVSim generates the genomes of a cancer cell population with detailed information of copy number status, loss of heterozygosity (LOH), and event break points, which is essential for developing and evaluating somatic CNV and SV detection methods in cancer genomics studies.

Structural variation discovery in the cancer genome using next generation sequencing: computational solutions and perspectives.

Somatic Structural Variations (SVs) are a complex collection of chromosomal mutations that could directly contribute to carcinogenesis. Next Generation Sequencing (NGS) technology has emerged as the primary means of interrogating the SVs of the cancer genome in recent investigations. Sophisticated computational methods are required to accurately identify the SV events and delineate their breakpoints from the massive amounts of reads generated by a NGS experiment. In this review, we provide an overview of current analytic tools used for SV detection in NGS-based cancer studies. We summarize the features of common SV groups and the primary types of NGS signatures that can be used in SV detection methods. We discuss the principles and key similarities and differences of existing computational programs and comment on unresolved issues related to this research field. The aim of this article is to provide a practical guide of relevant concepts, computational methods, software tools and important factors for analyzing and interpreting NGS data for the detection of SVs in the cancer genome.