scholarly journals The predictive capability of immunohistochemistry and DNA sequencing for determining TP53 functional mutation status: a comparative study of 41 glioblastoma patients

Oncotarget ◽  
2019 ◽  
Vol 10 (58) ◽  
pp. 6204-6218 ◽  
Author(s):  
Aarash K. Roshandel ◽  
Christopher M. Busch ◽  
Jennifer Van Mullekom ◽  
Joshua A. Cuoco ◽  
Cara M. Rogers ◽  
...  
Keyword(s):  
Comparative Study ◽  
Dna Sequencing ◽  
Predictive Capability ◽  
Mutation Status ◽  
Functional Mutation

scholarly journals Comparative study of biofilm formation on biocidal antifouling and fouling-release coatings using next-generation DNA sequencing

Biofouling ◽  
2018 ◽  
Vol 34 (4) ◽  
pp. 464-477 ◽  
Author(s):  
Mark O. Winfield ◽  
Adrian Downer ◽  
Jennifer Longyear ◽  
Marie Dale ◽  
Gary L. A. Barker
Keyword(s):  
Comparative Study ◽  
Dna Sequencing ◽  
Biofilm Formation ◽  
Next Generation ◽  
Next Generation Dna Sequencing ◽  
Fouling Release

scholarly journals Analysis ofKRASMutations of Exon 2 Codons 12 and 13 by SNaPshot Analysis in Comparison to Common DNA Sequencing

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
Rica Zinsky ◽  
Servet Bölükbas ◽  
Holger Bartsch ◽  
Joachim Schirren ◽  
Annette Fisseler-Eckhoff
Keyword(s):  
Dna Sequencing ◽  
Mutation Detection ◽  
Nonsmall Cell Lung Cancer ◽  
Tissue Samples ◽  
Exon 2 ◽  
Mutation Status ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
Status Analysis ◽  
Formalin Fixed

Due to the call for fastKRASmutation status analysis for treatment of patients with monoclonal antibodies for metastatic colorectal cancer, sensitive, economic, and easily feasible methods are required. Under this aspect, the sensitivity and specificity of the SNaPshot analysis in comparison to the commonly used DNA sequencing was checked. We examinedKRASmutations in exon 2 codons 12 and 13 with DNA sequencing and SNaPshot analysis in 100 formalin-fixed paraffin-embedded tumor tissue samples of pancreatic carcinoma, colorectal carcinoma, and nonsmall cell lung cancer specimens of the primary tumor or metastases. 40% of these samples demonstrated mutatedKRASgenes using sequencing and SNaPshot-analysis; additional five samples (45/100) were identified only with the SNaPshot.KRASmutation detection is feasible with the reliable SNaPshot analysis method. The more frequent mutation detection by the SNaPshot analysis shows that this method has a high probability of accuracy in the detection ofKRASmutations compared to sequencing.

scholarly journals Comparative study of IDH1 mutations in gliomas by immunohistochemistry and DNA sequencing

2013 ◽  
Vol 15 (6) ◽  
pp. 718-726 ◽  
Author(s):  
S. Agarwal ◽  
M. C. Sharma ◽  
P. Jha ◽  
P. Pathak ◽  
V. Suri ◽  
...  
Keyword(s):  
Comparative Study ◽  
Dna Sequencing ◽  
Idh1 Mutations

scholarly journals Comparative study of TERT promoter mutation status within spatially, temporally and morphologically distinct components of urothelial carcinoma

2017 ◽  
Vol 72 (2) ◽  
pp. 354-356 ◽  
Author(s):  
Noah A Brown ◽  
Madelyn Lew ◽  
Helmut C Weigelin ◽  
Alon Z Weizer ◽  
Jeffrey S Montgomery ◽  
...  
Keyword(s):  
Comparative Study ◽  
Urothelial Carcinoma ◽  
Tert Promoter Mutation ◽  
Promoter Mutation ◽  
Mutation Status ◽  
Tert Promoter

scholarly journals Microfluidics for rapid detection of isocitrate dehydrogenase 1 mutation for intraoperative application

2016 ◽  
Vol 124 (6) ◽  
pp. 1611-1618 ◽  
Author(s):  
Abudumijiti Aibaidula ◽  
Wang Zhao ◽  
Jin-song Wu ◽  
Hong Chen ◽  
Zhi-feng Shi ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Isocitrate Dehydrogenase ◽  
Soft Lithography ◽  
Idh1 Mutation ◽  
Fluorescence Signal ◽  
Wild Type ◽  
Isocitrate Dehydrogenase 1 ◽  
Tissue Samples ◽  
Mutation Status ◽  
New Approach

OBJECT Conventional methods for isocitrate dehydrogenase 1 (IDH1) detection, such as DNA sequencing and immunohistochemistry, are time- and labor-consuming and cannot be applied for intraoperative analysis. To develop a new approach for rapid analysis of IDH1 mutation from tiny tumor samples, this study used microfluidics as a method for IDH1 mutation detection. METHODS Forty-seven glioma tumor samples were used; IDH1 mutation status was investigated by immunohistochemistry and DNA sequencing. The microfluidic device was fabricated from polydimethylsiloxane following standard soft lithography. The immunoanalysis was conducted in the microfluidic chip. Fluorescence images of the on-chip microcolumn taken by the charge-coupled device camera were collected as the analytical results readout. Fluorescence signals were analyzed by NIS-Elements software to gather detailed information about the IDH1 concentration in the tissue samples. RESULTS DNA sequencing identified IDH1 R132H mutation in 33 of 47 tumor samples. The fluorescence signal for IDH1-mutant samples was 5.49 ± 1.87 compared with 3.90 ± 1.33 for wild type (p = 0.005). Thus, microfluidics was capable of distinguishing IDH1-mutant tumor samples from wild-type samples. When the cutoff value was 4.11, the sensitivity of microfluidics was 87.9% and the specificity was 64.3%. CONCLUSIONS This new approach was capable of analyzing IDH1 mutation status of tiny tissue samples within 30 minutes using intraoperative microsampling. This approach might also be applied for rapid pathological diagnosis of diffuse gliomas, thus guiding personalized resection.

scholarly journals PATH-15. DEEP LEARNING ENABLED PREDICTION OF ISOCITRATE DEHYDROGENASE MUTATION STATUS FROM DIGITAL HISTOPATHOLOGY DATA IN GLIOBLASTOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii167-ii167
Author(s):  
Sied Kebir ◽  
Tobias Blau ◽  
Lazaros Lazaridis ◽  
Teresa Schmidt ◽  
Kathy Keyvani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Sequencing ◽  
Isocitrate Dehydrogenase ◽  
Area Under The Curve ◽  
Immunohistochemical Analysis ◽  
The Cancer Genome Atlas ◽  
Idh Mutation ◽  
Characteristic Analysis ◽  
Mutation Status

Abstract BACKGROUND The determination of isocitrate dehydrogenase (IDH) mutation status plays a crucial role in the diagnosis of glioblastoma. Depending on the age of the patient and the result of the immunohistochemical analysis, additional DNA sequencing may be required to determine IDH mutation status. As DNA sequencing results can occasionally take several days until available, there is a need for inexpensive and fast non-invasive methods. In this work, we investigated whether IDH mutation detection by artificial intelligence (deep learning) from digitized hematoxylin-eosin (H&E) stained sectional specimens is feasible. METHODS Patients with histologically confirmed glioblastoma from The Cancer Genome Atlas cohort were included if digitized H&E stained whole-slide scans with corresponding information on IDH status were publicly available. The total cohort was subdivided into a training, validation, and test cohort in a ratio of 44:33:23. Whole-slide scans were partitioned into tiles of fixed size and used to train a Resnet-34 convolutional neural network. The evaluation of the trained model was performed once on the test cohort using Receiver Operating Characteristic analysis and Area-Under-The-Curve (AUC) metric. To ascertain which regions of the H&E specimens were decisive for the determination of IDH status, the Grad-CAM method was used. RESULTS 124 patients were included, 29 of which were IDH mutant. The digitized H&E slides had an average size of 2.5 gigabytes per image file and approximately 1000 tiles per slide were prepared. The prediction AUC of the trained model was 0.94. The duration of IDH prediction was about 3.5 seconds per slide. The Grad-CAM evaluation confirmed that the model mainly used cellular regions to collect decision-supporting information. CONCLUSIONS This pilot study shows the promising potential of deep learning for the prediction of IDH mutation status from digitized H&E scans in glioblastoma. To confirm these data, testing this model on an independent cohort is needed.

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