ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.

BackgroundA key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison.ResultsIn this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next-generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1600 samples, including cell lines, fresh frozen tissues, and formalin-fixed paraffin-embedded tissues; we tested our algorithm with both deep targeted and whole-exome sequencing data. Our algorithm correctly classified between 95 and 98% of somatic mutations with F1-measure ranges from 75.9 to 98.6% depending on the tumor type. We have released the algorithm as a software package called ISOWN (Identification of SOmatic mutations Without matching Normal tissues).ConclusionsIn this work, we describe the development, implementation, and validation of ISOWN, an accurate algorithm for predicting somatic mutations in cancer tissues in the absence of matching normal tissues. ISOWN is available as Open Source under Apache License 2.0 from https://github.com/ikalatskaya/ISOWN

Alterations in tumor necrosis factor signaling pathways are associated with cytotoxicity and resistance to taxanes: a study in isogenic resistant tumor cells

INTRODUCTION: The taxanes paclitaxel and docetaxel are widely used in the treatment of breast, ovarian, and other cancers. Although their cytotoxicity has been attributed to cell-cycle arrest through stabilization of microtubules, the mechanisms by which tumor cells die remains unclear. Paclitaxel has been shown to induce soluble tumor necrosis factor alpha (sTNF-α) production in macrophages, but the involvement of TNF production in taxane cytotoxicity or resistance in tumor cells has not been established. Our study aimed to correlate alterations in the TNF pathway with taxane cytotoxicity and the acquisition of taxane resistance. METHODS: MCF-7 cells or isogenic drug-resistant variants (developed by selection for surviving cells in increasing concentrations of paclitaxel or docetaxel) were assessed for sTNF-α production in the absence or presence of taxanes by enzyme-linked immunosorbent assay (ELISA) and for sensitivity to docetaxel or sTNF-α by using a clonogenic assay (in the absence or presence of TNFR1 or TNFR2 neutralizing antibodies). Nuclear factor (NF)-κB activity was also measured with ELISA, whereas gene-expression changes associated with docetaxel resistance in MCF-7 and A2780 cells were determined with microarray analysis and quantitative reverse transcription polymerase chain reaction (RTqPCR). RESULTS: MCF-7 and A2780 cells increased production of sTNF-α in the presence of taxanes, whereas docetaxel-resistant variants of MCF-7 produced high levels of sTNF-α, although only within a particular drug-concentration threshold (between 3 and 45 nM). Increased production of sTNF-α was NF-κB dependent and correlated with decreased sensitivity to sTNF-α, decreased levels of TNFR1, and increased survival through TNFR2 and NF-κB activation. The NF-κB inhibitor SN-50 reestablished sensitivity to docetaxel in docetaxel-resistant MCF-7 cells. Gene-expression analysis of wild-type and docetaxel-resistant MCF-7, MDA-MB-231, and A2780 cells identified changes in the expression of TNF-α-related genes consistent with reduced TNF-induced cytotoxicity and activation of NF-κB survival pathways. CONCLUSIONS: We report for the first time that taxanes can promote dose-dependent sTNF-α production in tumor cells at clinically relevant concentrations, which can contribute to their cytotoxicity. Defects in the TNF cytotoxicity pathway or activation of TNF-dependent NF-κB survival genes may, in contrast, contribute to taxane resistance in tumor cells. These findings may be of strong clinical significance

Conditioned spin and charge dynamics of a single electron quantum dot

In this article we describe the incoherent and coherent spin and charge dynamics of a single electron quantum dot. We use a stochastic master equation to model the state of the system, as inferred by an observer with access to only the measurement signal. Measurements obtained during an interval of time contribute, by a past quantum state analysis, to our knowledge about the system at any time $t$ within that interval. Such analysis permits precise estimation of physical parameters, and we propose and test a modification of the classical Baum-Welch parameter re-estimation method to systems driven by both coherent and incoherent processes.Comment: 9 pages, 9 figure

Downregulation of histone H2A and H2B pathways is associated with anthracycline sensitivity in breast cancer

Abstract Background Drug resistance in breast cancer is the major obstacle to effective treatment with chemotherapy. While upregulation of multidrug resistance genes is an important component of drug resistance mechanisms in vitro, their clinical relevance remains to be determined. Therefore, identifying pathways that could be targeted in the clinic to eliminate anthracycline-resistant breast cancer remains a major challenge. Methods We generated paired native and epirubicin-resistant MDA-MB-231, MCF7, SKBR3 and ZR-75-1 epirubicin-resistant breast cancer cell lines to identify pathways contributing to anthracycline resistance. Native cell lines were exposed to increasing concentrations of epirubicin until resistant cells were generated. To identify mechanisms driving epirubicin resistance, we used a complementary approach including gene expression analyses to identify molecular pathways involved in resistance, and small-molecule inhibitors to reverse resistance. In addition, we tested its clinical relevance in a BR9601 adjuvant clinical trial. Results Characterisation of epirubicin-resistant cells revealed that they were cross-resistant to doxorubicin and SN-38 and had alterations in apoptosis and cell-cycle profiles. Gene expression analysis identified deregulation of histone H2A and H2B genes in all four cell lines. Histone deacetylase small-molecule inhibitors reversed resistance and were cytotoxic for epirubicin-resistant cell lines, confirming that histone pathways are associated with epirubicin resistance. Gene expression of a novel 18-gene histone pathway module analysis of the BR9601 adjuvant clinical trial revealed that patients with low expression of the 18-gene histone module benefited from anthracycline treatment more than those with high expression (hazard ratio 0.35, 95 % confidence interval 0.13–0.96, p = 0.042). Conclusions This study revealed a key pathway that contributes to anthracycline resistance and established model systems for investigating drug resistance in all four major breast cancer subtypes. As the histone modification can be targeted with small-molecule inhibitors, it represents a possible means of reversing clinical anthracycline resistance. Trial registration ClinicalTrials.gov identifier NCT00003012 . Registered on 1 November 1999

ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.

BackgroundA key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison.ResultsIn this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next-generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1600 samples, including cell lines, fresh frozen tissues, and formalin-fixed paraffin-embedded tissues; we tested our algorithm with both deep targeted and whole-exome sequencing data. Our algorithm correctly classified between 95 and 98% of somatic mutations with F1-measure ranges from 75.9 to 98.6% depending on the tumor type. We have released the algorithm as a software package called ISOWN (Identification of SOmatic mutations Without matching Normal tissues).ConclusionsIn this work, we describe the development, implementation, and validation of ISOWN, an accurate algorithm for predicting somatic mutations in cancer tissues in the absence of matching normal tissues. ISOWN is available as Open Source under Apache License 2.0 from https://github.com/ikalatskaya/ISOWN