Cancer genome analysis: a landscape seen from many angles

2007 ◽  
Vol 4 (4) ◽  
pp. 269-276
Author(s):  
Henrik Edgren ◽  
Maija Wolf ◽  
Olli Kallioniemi ◽  
Matthias Nees
2017 ◽  
Vol 38 (10) ◽  
pp. 1325-1335 ◽  
Author(s):  
Giuliano Crispatzu ◽  
Pranav Kulkarni ◽  
Mohammad R. Toliat ◽  
Peter Nürnberg ◽  
Marco Herling ◽  
...  

2017 ◽  
Vol 2 (20) ◽  
pp. 457 ◽  
Author(s):  
Ai Okada ◽  
Kenichi Chiba ◽  
Hiroko Tanaka ◽  
Satoru Miyano ◽  
Yuichi Shiraishi

2020 ◽  
Vol 48 (11) ◽  
pp. 965-971 ◽  
Author(s):  
Tomomi Yamaguchi ◽  
Toshiaki Akahane ◽  
Ohi Harada ◽  
Yasutaka Kato ◽  
Eriko Aimono ◽  
...  

2014 ◽  
Vol 30 (9) ◽  
pp. 1295-1296 ◽  
Author(s):  
Xin Lu ◽  
Roman K. Thomas ◽  
Martin Peifer

2017 ◽  
Author(s):  
Ai Okada ◽  
Kenichi Chiba ◽  
Hiroko Tanaka ◽  
Satoru Miyano ◽  
Yuichi Shiraishi

AbstractSummaryWe introduce paplot, the software for generating dynamic reports that are frequently necessary in the post analytical phases of cancer genome studies. The “interactive” nature of the paplot-generated reports enables users to extract much richer information than that obtained from static graphs via most conventional visualization tools.Availability and implementationThe python implementation for paplot (MIT license) is available at https://github.com/Genomon-Project/paplot. The documentation is at http://paplot-doc.readthedocs.io/en/latest/[email protected]


2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Lech Nieroda ◽  
Lukas Maas ◽  
Scott Thiebes ◽  
Ulrich Lang ◽  
Ali Sunyaev ◽  
...  

2020 ◽  
Vol 2 (Supplement_3) ◽  
pp. ii12-ii12
Author(s):  
Kazuhiro Miwa ◽  
Hiroaki Takei ◽  
Takeshi Ito ◽  
Kazutoshi Yokoyama ◽  
Hirohito Yano ◽  
...  

Abstract Objective: Our hospital has been designated as a cancer genome medical cooperation hospital, and it is our responsibility to play a central role in cancer medicine. We were one of the first local hospitals to clinically apply cancer genome analysis, and in January 2019, we started PleSsision-Rapid testing as a clinical study without patient burden. This study examines data from patients with brain tumors, subjects it to cancer genome analysis, and reports on its utility and efficacy. Method: Genome analysis was performed by PleSsision-Rapid examination for patients with brain tumors who underwent surgery between January 2019 and July 2020. Tissue DNA extracted from pathological specimens was used to perform next-generation sequencing (NGS) analysis. In the PleSsision-Rapid test, 160 genes are comprehensively analyzed, examined by genomics, and evaluated for the presence or absence of actionable and druggable mutations, and the mutation rate is determined. Results: There were 15 cases total. Histopathological diagnoses included glioblastoma (n=5), diffuse astrocytoma (n=1), metastatic brain tumor (n=4), meningioma (n=2), central nervous system primary malignant lymphoma (n=1), germinoma (n=1), and Langerhans cell histiocytosis (n=1). Of these 15 brain tumor cases, actionable mutations were detected in 80.0% of cases and druggable mutations were detected in 66.6%. The average mutation rate was 8.59±5.32 (range, 1.3 to 22.8) per patient. Conclusion: Although future improvements will be needed for cancer genome analysis in brain tumors, this strategy may be useful for the selection of molecularly targeted drugs with high antitumor efficacy. We will continue to accumulate and study such cases in the future.


2020 ◽  
Vol 17 (4) ◽  
Author(s):  
Giulia Orlando ◽  
Adam Mead

Sign in / Sign up

Export Citation Format

Share Document