The Cancer Genome Anatomy Project: Online Resources to Reveal the Molecular Signatures of Cancer

2002 ◽  
Vol 20 (7-8) ◽  
pp. 1038-1050 ◽  
Author(s):  
Robert L. Strausberg ◽  
Kenneth H. Buetow ◽  
Susan F. Greenhut ◽  
Lynette H. Grouse ◽  
Carl F. Schaefer
2001 ◽  
Vol 195 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Robert L. Strausberg

Oral Oncology ◽  
2003 ◽  
Vol 39 (3) ◽  
pp. 248-258 ◽  
Author(s):  
C Leethanakul ◽  
V Knezevic ◽  
V Patel ◽  
P Amornphimoltham ◽  
J Gillespie ◽  
...  

2001 ◽  
Vol 11 (11) ◽  
pp. S66-S71 ◽  
Author(s):  
Robert L Strausberg ◽  
Susan F Greenhut ◽  
Lynette H Grouse ◽  
Carl F Schaefer ◽  
Kenneth H Buetow

2018 ◽  
pp. 1-11
Author(s):  
Neeraja M. Krishnan ◽  
Mohanraj I ◽  
Janani Hariharan ◽  
Binay Panda

Purpose With large amounts of multidimensional molecular data on cancers generated and deposited into public repositories such as The Cancer Genome Atlas and International Cancer Genome Consortium, a cancer type agnostic and integrative platform will help to identify signatures with clinical relevance. We devised such a platform and showcase it by identifying a molecular signature for patients with metastatic and recurrent (MR) head and neck squamous cell carcinoma (HNSCC). Methods We devised a statistical framework accompanied by a graphical user interface–driven application, Clinical Association of Functionally Established MOlecular CHAnges ( CAFE MOCHA; https://github.com/binaypanda/CAFEMOCHA), to discover molecular signatures linked to a specific clinical attribute in a cancer type. The platform integrates mutations and indels, gene expression, DNA methylation, and copy number variations to discover a classifier first and then to predict an incoming tumor for the same by pulling defined class variables into a single framework that incorporates a coordinate geometry–based algorithm called complete specificity margin-based clustering, which ensures maximum specificity. CAFE MOCHA classifies an incoming tumor sample using either its matched normal or a built-in database of normal tissues. The application is packed and deployed using the install4j multiplatform installer. We tested CAFE MOCHA in HNSCC tumors (n = 513) followed by validation in tumors from an independent cohort (n = 18) for discovering a signature linked to distant MR. Results CAFE MOCHA identified an integrated signature, MR44, associated with distant MR HNSCC, with 80% sensitivity and 100% specificity in the discovery stage and 100% sensitivity and 100% specificity in the validation stage. Conclusion CAFE MOCHA is a cancer type and clinical attribute agnostic statistical framework to discover integrated molecular signatures.


1997 ◽  
Vol 33 (6) ◽  
pp. 801

2006 ◽  
Vol 7 (2) ◽  
pp. 97-102 ◽  
Author(s):  
Zhi Gang HUANG ◽  
Zhi Hua RAN ◽  
Wei LU ◽  
Shu Dong XIAO

2000 ◽  
Vol 16 (3) ◽  
pp. 103-106 ◽  
Author(s):  
Robert L. Strausberg ◽  
Kenneth H. Buetow ◽  
Michael R. Emmert-Buck ◽  
Richard D. Klausner

Neoplasia ◽  
1999 ◽  
Vol 1 (2) ◽  
pp. 101-106 ◽  
Author(s):  
David B. Krizman ◽  
Lukas Wagner ◽  
Alex Lash ◽  
Robert L. Strausberg ◽  
Michael R. Emmert-Buck

2000 ◽  
Vol 10 (8) ◽  
pp. 1259-1265 ◽  
Author(s):  
R. Clifford

2001 ◽  
Vol 11 ◽  
pp. S66-S71 ◽  
Author(s):  
Robert L. Strausberg ◽  
Susan F. Greenhut ◽  
Lynette H. Grouse ◽  
Carl F. Schaefer ◽  
Kenneth H. Buetow

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