6 research outputs found

    Identification of Novel SNPs in Glioblastoma Using Targeted Resequencing

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    High-throughput sequencing opens avenues to find genetic variations that may be indicative of an increased risk for certain diseases. Linking these genomic data to other “omics” approaches bears the potential to deepen our understanding of pathogenic processes at the molecular level. To detect novel single nucleotide polymorphisms (SNPs) for glioblastoma multiforme (GBM), we used a combination of specific target selection and next generation sequencing (NGS). We generated a microarray covering the exonic regions of 132 GBM associated genes to enrich target sequences in two GBM tissues and corresponding leukocytes of the patients. Enriched target genes were sequenced with Illumina and the resulting reads were mapped to the human genome. With this approach we identified over 6000 SNPs, including over 1300 SNPs located in the targeted genes. Integrating the genome-wide association study (GWAS) catalog and known disease associated SNPs, we found that several of the detected SNPs were previously associated with smoking behavior, body mass index, breast cancer and high-grade glioma. Particularly, the breast cancer associated allele of rs660118 SNP in the gene SART1 showed a near doubled frequency in glioblastoma patients, as verified in an independent control cohort by Sanger sequencing. In addition, we identified SNPs in 20 of 21 GBM associated antigens providing further evidence that genetic variations are significantly associated with the immunogenicity of antigens

    Cancer cell classification with coherent diffraction imaging using an extreme ultraviolet radiation source

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    In cancer treatment, it is highly desirable to classify single cancer cells in real time. The standard method is polymerase chain reaction requiring a substantial amount of resources and time. Here, we present an innovative approach for rapidly classifying different cell types: we measure the diffraction pattern of a single cell illuminated with coherent extreme ultraviolet (XUV) laser-generated radiation. These patterns allow distinguishing different breast cancer cell types in a subsequent step. Moreover, the morphology of the object can be retrieved from the diffraction pattern with submicron resolution. In a proof-of-principle experiment, we prepared single MCF7 and SKBR3 breast cancer cells on gold-coated silica slides. The output of a laser-driven XUV light source is focused onto a single unstained and unlabeled cancer cell. With the resulting diffraction pattern, we could clearly identify the different cell types. With an improved setup, it will not only be feasible to classify circulating tumor cells with a high throughput, but also to identify smaller objects such as bacteria or even viruses

    Cancer cell classification with coherent diffraction imaging using an extreme ultraviolet radiation source

    No full text
    In cancer treatment, it is highly desirable to classify single cancer cells in real time. The standard method is polymerase chain reaction requiring a substantial amount of resources and time. Here, we present an innovative approach for rapidly classifying different cell types: we measure the diffraction pattern of a single cell illuminated with coherent extreme ultraviolet (XUV) laser-generated radiation. These patterns allow distinguishing different breast cancer cell types in a subsequent step. Moreover, the morphology of the object can be retrieved from the diffraction pattern with submicron resolution. In a proof-of-principle experiment, we prepared single MCF7 and SKBR3 breast cancer cells on gold-coated silica slides. The output of a laser-driven XUV light source is focused onto a single unstained and unlabeled cancer cell. With the resulting diffraction pattern, we could clearly identify the different cell types. With an improved setup, it will not only be feasible to classify circulating tumor cells with a high throughput, but also to identify smaller objects such as bacteria or even viruses

    Filtration based assessment of CTCs and CellSearch® based assessment are both powerful predictors of prognosis for metastatic breast cancer patients

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    Abstract Background The assessment of circulating tumor cells (CTCs) has been shown to enable monitoring of treatment response and early detection of metastatic breast cancer (MBC) recurrence. The aim of this study was to compare a well-established CTC detection method based on immunomagnetic isolation with a new, filtration-based platform. Methods In this prospective study, two 7.5 ml blood draws were obtained from 60 MBC patients and CTC enumeration was assessed using both the CellSearch® and the newly developed filtration-based platform. We analyzed the correlation of CTC-positivity between both methods and their ability to predict prognosis. Overall survival (OS) was calculated and Kaplan-Meier curves were estimated with thresholds of ≥1 and ≥5 detected CTCs. Results The CTC positivity rate of the CellSearch® system was 56.7% and of the filtration-based platform 66.7%. There was a high correlation of CTC enumeration obtained with both methods. The OS for patients without detected CTCs, regardless of the method used, was significantly higher compared to patients with one or more CTCs (p < 0.001). The median OS of patients with no CTCs vs. ≥ 1 CTC assessed by CellSearch® was 1.83 years (95% CI: 1.63–2.02) vs. 0.74 years (95% CI: 0.51–1.52). If CTCs were detected by the filtration-based method the median OS times were 1.88 years (95% CI: 1.74–2.03) vs. 0.59 years (95% CI: 0.38–0.80). Conclusions The newly established EpCAM independently filtration-based system is a suitable method to determine CTC counts for MBC patients. Our study confirms CTCs as being strong predictors of prognosis in our population of MBC patients
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