Annals of Oncology Advance Access published December 23, 2014
1 Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology F. Commo1,2,#, C. Ferté1,2,3,#, J.C. Soria2,3, S.H. Friend1, F. André2,3, J. Guinney1 1
Sage Bionetworks, Seattle, WA, USA
2
INSERM U981, Gustave Roussy, University Paris XI, Villejuif, France
3
Department of Medical Oncology, Gustave Roussy, Villejuif, France
#
These authors contributed equally to this work.
© The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: [email protected].
Downloaded from http://annonc.oxfordjournals.org/ at University of California, San Francisco on March 24, 2015
Corresponding author: Mr. Justin Guinney, Sage Bionetworks, 1100 Fairview Ave. N., mailstop M1-C108, Seattle, WA 98109, U.S.A. Phone: (+1) 206-667-2146, Email: [email protected]
2
Key Message Genomic profiling using array-based comparative genomic hybridization (aCGH) is widely used within precision medicine programs to match specific molecular alterations (amplifications or deletions) with therapeutic orientations. However, analysis of CGH profiles is a complex process whereby different analytical strategies may result in different decisions in the clinic.
line for estimation of copy-number gains and losses. Here, we review different centralization strategies and their impact on identifying molecular alterations in celllines (including the NCI60 panel where the karyotypes are known, and the CCLE panel with responses to drugs) and in 2 large precision medicine trials. In the context of these human trials, we show that centralization can have a profound impact on alteration calls, and consequently, on therapeutic recommendations.
Downloaded from http://annonc.oxfordjournals.org/ at University of California, San Francisco on March 24, 2015
A key step within CGH data processing is profile centralization, used to define a base
3
Abstract: Background: Comparative genomic hybridization (CGH) arrays are increasingly used in personalized medicine programs to identify gene copy number aberrations (CNAs) that may be used to guide clinical decisions made during molecular tumor boards. However, analytical processes such as the centralization step may in the precision medicine context. Patients and Methods: The effect of three different centralization methods: median, maximum peak, alternative peak, were evaluated on three datasets: (i) the NCI60 cell lines panel, (ii) the Cancer Cell Line Encyclopedia panel (CCLE), and (iii) the patients enrolled in prospective molecular screening trials (SAFIR-01 n=283, MOSCATO-01 n=309), and compared with karyotyping, drug sensitivity, and patient-drug matching, respectively. Results: Using the NCI60 cell lines panel, the profiles generated by the alternative peak method were significantly closer to the cell karyotypes than those generated by the other centralization strategies (p
1 Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology F. Commo1,2,#, C. Ferté1,2,3,#, J.C. Soria2,3, S.H. Friend1, F. André2,3, J. Guinney1 1
Sage Bionetworks, Seattle, WA, USA
2
INSERM U981, Gustave Roussy, University Paris XI, Villejuif, France
3
Department of Medical Oncology, Gustave Roussy, Villejuif, France
#
These authors contributed equally to this work.
© The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: [email protected].
Downloaded from http://annonc.oxfordjournals.org/ at University of California, San Francisco on March 24, 2015
Corresponding author: Mr. Justin Guinney, Sage Bionetworks, 1100 Fairview Ave. N., mailstop M1-C108, Seattle, WA 98109, U.S.A. Phone: (+1) 206-667-2146, Email: [email protected]
2
Key Message Genomic profiling using array-based comparative genomic hybridization (aCGH) is widely used within precision medicine programs to match specific molecular alterations (amplifications or deletions) with therapeutic orientations. However, analysis of CGH profiles is a complex process whereby different analytical strategies may result in different decisions in the clinic.
line for estimation of copy-number gains and losses. Here, we review different centralization strategies and their impact on identifying molecular alterations in celllines (including the NCI60 panel where the karyotypes are known, and the CCLE panel with responses to drugs) and in 2 large precision medicine trials. In the context of these human trials, we show that centralization can have a profound impact on alteration calls, and consequently, on therapeutic recommendations.
Downloaded from http://annonc.oxfordjournals.org/ at University of California, San Francisco on March 24, 2015
A key step within CGH data processing is profile centralization, used to define a base
3
Abstract: Background: Comparative genomic hybridization (CGH) arrays are increasingly used in personalized medicine programs to identify gene copy number aberrations (CNAs) that may be used to guide clinical decisions made during molecular tumor boards. However, analytical processes such as the centralization step may in the precision medicine context. Patients and Methods: The effect of three different centralization methods: median, maximum peak, alternative peak, were evaluated on three datasets: (i) the NCI60 cell lines panel, (ii) the Cancer Cell Line Encyclopedia panel (CCLE), and (iii) the patients enrolled in prospective molecular screening trials (SAFIR-01 n=283, MOSCATO-01 n=309), and compared with karyotyping, drug sensitivity, and patient-drug matching, respectively. Results: Using the NCI60 cell lines panel, the profiles generated by the alternative peak method were significantly closer to the cell karyotypes than those generated by the other centralization strategies (p
