GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    In: Cancer Cell, Elsevier BV, Vol. 41, No. 4 ( 2023-04), p. 678-692.e7
    Type of Medium: Online Resource
    ISSN: 1535-6108
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2074034-7
    detail.hit.zdb_id: 2078448-X
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 6140-6140
    Abstract: Background: Glioblastoma is the most prevalent and severe type of malignant brain tumor in adults. Although the genetic make-up initiating glioblastoma is increasingly better understood, a better understanding in the mechanisms that drive its evolution, heterogeneity and therapy resistance may reveal new directions for therapy development. To get better insights into glioblastoma evolution, we analyzed and de-convoluted transcriptomes of primary and recurrent glioblastoma resections. Material and Methods: Matching primary and secondary resections from n=185 glioblastoma patients were collected as part of EORTC Study 1542. The study was extended with tumor pairs from n=51 patients from the international GLASS study. The datasets were subjected to differential and deconvolution analysis using in-house algorithms. Results: When mapping the tumor samples into a reduced Glioblastoma Intrinsic Transcriptional Subtype space, we visualized subtype traversal, indicating that the CL subtype most often switches. As we found no more transitions from MES to other subtypes than to be expected by chance, we concluded that MES is an end-state. On average, tumor cell percentages decreased from ~67% to ~50% mostly due to an increase in TAM/microglia. Differential expression analysis was performed with correction for tumor cell percentages. While expression of most known oncogenes did not change considerably over time, marker genes of TAM/microglia, neurons and oligodendrocytes were up-regulated whereas endothelial cell markers were down-regulated over time. Furthermore, a cluster of ~30 extracellular matrix-associated genes increase significantly over time. A signature representing the gene-set was significantly associated with poor survival; high signatures were in particular associated to survival in secondary resections (P = 6.613e-06, Kaplan-Meier estimator). This suggests that the increase of extracellular matrix expression fulfils an important role in glioblastoma evolution. Conclusion: Using a large cohort, we interrogated changes in the glioblastoma transcriptome over time and found that in particular the composition of the tumor and its environment changes. The tumor cell percentage drops, suggesting more invasion or recruitment of non-malignant cells or a combination of both. This change is independent of an increase in the prognostic increase in extracellular matrix expression. Citation Format: Youri Hoogstrate, Kaspar Draaisma, Santoesha A. Ghisai, Iris de Heer, Levi van Hijfte, Wouter Coppieters, Melissa Kerkhof, Astrid Weyerbrock, Marc Sanson, Ann Hoeben, Slávka Lukacova, Giuseppe Lombardi, Sieger Leenstra, Monique Hanse, Ruth Fleischeuer, Colin Watts, Joseph McAbee, Nicos Angelopoulos, Thierry Gorlia, Vassilis Golfinopoulos, Johan M. Kros, Vincent Bours, Martin J. van den Bent, Pierre A. Robe, Pim J. French. Transcriptional evolution of glioblastoma reveals changes in bulk composition, mesenchymal sub-type as end-state, and a prognostic association with increased extracellular matrix gene expression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6140.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    Online Resource
    Online Resource
    Frontiers Media SA ; 2020
    In:  Frontiers in Cell and Developmental Biology Vol. 8 ( 2020-9-30)
    In: Frontiers in Cell and Developmental Biology, Frontiers Media SA, Vol. 8 ( 2020-9-30)
    Type of Medium: Online Resource
    ISSN: 2296-634X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2020
    detail.hit.zdb_id: 2737824-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 4
    In: iScience, Elsevier BV, Vol. 26, No. 1 ( 2023-01), p. 105760-
    Type of Medium: Online Resource
    ISSN: 2589-0042
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2927064-9
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 5
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1228-1228
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1228-1228
    Abstract: Background: Spatially resolved transcriptomics is a novel and already highly recognized method that allows RNA sequencing results to be annotated with local tissue phenotypes. The NanoString GeoMx Digital Spatial Profiling (DSP) Platform allows users to collect RNA expression data from manually selected Regions of Interest (ROIs) on FFPE tissue sections. Here, we extensively evaluated data from the DSP platform with its associated pipeline and identify significant background noise interference issues which compromise data interpretation. Alternative and more suitable workflows are presented for correct data analysis. Methods: In this study, 12 paired tumor samples were collected from six glioma patients who underwent two separate resections. For all patients, the first resection was a low grade astrocytoma (WHO grade II or III) and the second resection was a high grade astrocytoma (WHO grade IV). The DSP platform was used to collect expression data of 1,800 genes from 72 ROIs (i.e. 6 per sample). Biological replicates were made of eight tumors from four patients. Gene expression data was normalized with both standard NanoString methods and several alternative methods (e.g. DeSeq2, gamma fit correction and quantile normalization). Weighted Gene Co-expression Network analysis (WGCNA) was used for biological validation. In addition to our own study, six publicly available NanoString DSP datasets were evaluated. Results: Data distributions of all glioma samples, when exposed to standard data processing, were burdened with significant background noise interference. Notably, differences in noise interference were largest between biologically distinct tumor subgroups (i.e. between first and second glioma resections), which was confirmed in replicate experiments. The noise interference patterns were also present in all six publicly available NanoString DSP datasets which will invariably lead to incorrect interpretation of the underlying biology. To correct for noise interference, we tested several normalization methods. The relatively crude quantile normalization method provided the least biased result and showed the highest concordance with bulk RNA sequencing data. To evaluate the biological validity of our alternative approach, we used T cell counts from our tissue regions as an independent parameter, that were quantified using immune fluorescence. Unsupervised WGCNA identified gene clusters enriched for lymphocyte genes that highly correlated with T cell quantities in ROIs, confirming that alternative normalization can extract a biological signal from the DSP platform. Conclusion: The DSP Platform platform suffers from significant noise interference when using standard analysis tools that obscure its results. Here, we revised the workflow and provide an alternative normalization that adequately addresses noise interference and enables correct interpretation of gene expression data. Citation Format: Levi van Hijfte, Marjolein Geurts, Wies R. Vallentgoed, Paul H. Eilers, Peter A. Sillevis Smitt, Reno Debets, Pim J. French. Spatial transcriptomics: Data processing revisited to address noise interference [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1228.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...