Open Access Highly Accessed Research article

PAM50 Breast Cancer Subtyping by RT-qPCR and Concordance with Standard Clinical Molecular Markers

Roy RL Bastien1, Álvaro Rodríguez-Lescure2, Mark TW Ebbert1, Aleix Prat34, Blanca Munárriz5, Leslie Rowe1, Patricia Miller1, Manuel Ruiz-Borrego6, Daniel Anderson1, Bradley Lyons1, Isabel Álvarez7, Tracy Dowell1, David Wall1, Miguel Ángel Seguí8, Lee Barley1, Kenneth M Boucher9, Emilio Alba10, Lisa Pappas9, Carole A Davis11, Ignacio Aranda12, Christiane Fauron1, Inge J Stijleman11, José Palacios13, Antonio Antón14, Eva Carrasco15, Rosalía Caballero15, Matthew J Ellis16, Torsten O Nielsen17, Charles M Perou3, Mark Astill1, Philip S Bernard11119* and Miguel Martín18

Author Affiliations

1 The ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT, USA

2 Department of Medical Oncology, Hospital Universitario de Elche, Elche, Spain

3 Lineberger Comprehensive Cancer Center and Department of Genetics and Department of Pathology & Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

4 Department of Medicine, Universitat Autónoma de Barcelona, Barcelona, Spain

5 Department of Medical Oncology, Hospital Universitario La Fe, Valencia, Spain

6 Department of Medical Oncology, Hospital Universitario Virgen del Rocío, Sevilla, Spain

7 Department of Medical Oncology, Hospital de Donostia, San Sebastián, Spain

8 Department of Medical Oncology, Corporatiò Sanitaria Parc Taulí, Sabadell, Spain

9 Department of Oncological Sciences, Huntsman Cancer Institute, Salt Lake City, UT, USA

10 Department of Medical Oncology, Hospital Universitario Virgen de la Victoria, Málaga, Spain

11 Department of Pathology, University of Utah Health Sciences Center/Huntsman Cancer Institute, Salt Lake City, UT, USA

12 Department of Pathology, Hospital General Universitario de Alicante, Alicante, Spain

13 Department of Pathology, Hospital Virgen del Rocio, Sevilla, Spain

14 Department of Medical Oncology, Hospital Universitario Miguel Servet, Zaragoza, Spain

15 Spanish Breast Cancer Research Group, GEICAM, Madrid, Spain

16 Department of Oncology, Washington University, St. Louis, MO, USA

17 Department of Anatomical Pathology, University of British Columbia, Vancouver, Canada

18 Department of Medical Oncology, Hospital General Universitario Gregorio Marañón, Universidad Complutense, Madrid, Spain

19 Huntsman Cancer Institute, 2000 Circle of Hope, Salt Lake City, UT, 84112, USA

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BMC Medical Genomics 2012, 5:44  doi:10.1186/1755-8794-5-44

Published: 4 October 2012

Additional files

Additional file 1:

Clinical-pathological information associated with training set subtypes. Clinical-pathological information associated with the 171 samples included in the training set. (XLSX 23 kb)

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Additional file 2:

Clinical-pathological information and PAM50 data associated with GEICAM/9906 test set. Clinical-pathological information and PAM50 RT-qPCR results associated with the 814 samples included in the GEICAM9906 test set. (XLS 1106 kb)

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Additional file 3:

Additional materials and methods. Methods for plate manufacturing, PCR, calculation of log-expression ratios, PCR-efficiency, limits of detection, and limits of quantification are described (Additional files 8 and 9). (DOCX 213 kb)

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Additional file 4:

10-fold cross validation of training set. Each gene was measured in triplicate per RT-qPCR run on the Roche LC480 and 2 runs were performed for each of the 17 dilutions. The prototype samples identified by SigClust were split into 10 groups and nine of the 10 groups were used to calculate new centroids for each of the 5 possible subtype assignments. (XLSX 12 kb)

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Additional file 5:

Interference in subtype call and single/meta-gene scores from normal contamination. Interference by normal cell contamination of subtype call and single and meta-gene classes is shown. The changes in subtype classification occurred in a systematic fashion with all subtypes switching to a classification of Normal/Insufficient, with the exception of Luminal B, which switched to Luminal A. (XLSX 10 kb)

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Additional file 6:

Single and meta-gene cutoffs. Data from single and meta-gene expression score over the GEICAM 9906 samples are plotted on the 1–10 scale. The cut-points between high, intermediate, and low classes were individually derived from the training set. Samples are color-coded according to immunohistochemistry positivity (red) or negativity (blue), except in the case of the training set proliferation score where samples are colored by high, intermediate or low proliferation class. Luminal score samples are colored as being ER+/PR+, ER + or PR + (positive, red), and ER-/PR- (negative, blue). (PDF 1524 kb)

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Additional file 7:

Hierarchical clustering for GEICAM 9906. A comparison of unsupervised hierarchical clustering with supervised subtype assignment and single marker scores for GEICAM 9906. (PDF 1616 kb)

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Additional file 8:

PCR Efficiency, limits of detection, and limits of quantification. Supplemental table listing the efficiency of PCR, limits of detection, and limits of quantification for the 50 classifier and 5 housekeeper genes of the PAM50. Data are from 34 runs across 17 dilutions from a mixture of 8 breast cancer cell lines.

Format: XLSX Size: 11KB Download file

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Additional file 9:

Reproducibility of PAM50 gene measurements.Within plate, within plate batch and across plate batch coefficient of variation for the 50 classifier and 5 housekeeper genes of the PAM50 were calculated using cell lines and a tumor samples.

Format: XLSX Size: 11KB Download file

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