Accurate data processing improves the reliability of Affymetrix gene expression profiles from FFPE samples.
Formalin fixed paraffin-embedded (FFPE) tumor specimens are the conventionally archived material in clinical practice, representing an invaluable tissue source for biomarkers development, validation and routine implementation. For many prospective clinical trials, this material has been collected allowing for a prospective-retrospective study design which represents a successful strategy to define clinical utility for candidate markers. Gene expression data can be obtained even from FFPE specimens with the broadly used Affymetrix HG-U133 Plus 2.0 microarray platform. Nevertheless, important major discrepancies remain in expression data obtained from FFPE compared to fresh-frozen samples, prompting the need for appropriate data processing which could help to obtain more consistent results in downstream analyses. In a publicly available dataset of matched frozen and FFPE expression data, the performances of different normalization methods and specifically designed Chip Description Files (CDFs) were compared. The use of an alternative CDFs together with fRMA normalization significantly improved frozen-FFPE sample correlations, frozen-FFPE probeset correlations and agreement of differential analysis between different tumor subtypes. The relevance of our optimized data processing was assessed and validated using two independent datasets. In this study we demonstrated that an appropriate data processing can significantly improve the reliability of gene expression data derived from FFPE tissues using the standard Affymetrix platform. Tools for the implementation of our data processing algorithm are made publicly available at http://www.biocut.unito.it/cdf-ffpe/.
Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Maurizio
M
Callari
Department of Molecular Biotechnology and Life Sciences, University of Turin, Turin, Italy.
Antonio
A
Lembo
Department of Medical Oncology, Ospedale San Raffaele, Milan, Italy.
Giampaolo
G
Bianchini
Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Valeria
V
Musella
Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Vera
V
Cappelletti
Department of Medical Oncology, Ospedale San Raffaele, Milan, Italy.
Luca
L
Gianni
Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Maria Grazia
MG
Daidone
Department of Molecular Biotechnology and Life Sciences, University of Turin, Turin, Italy ; Center for Translational Genomics and Bioinformatics, Ospedale San Raffaele, Milan, Italy.
Paolo
P
Provero
D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology; D016208:Databases, Factual; D005260:Female; D005557:Formaldehyde; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000295:instrumentation / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D016393:Lymphoma, B-Cell / Q000235:genetics* / Q000473:pathology; D016612:Paraffin Embedding; D046888:Tissue Array Analysis / Q000706:statistics & numerical data*; D016707:Tissue Fixation
D005557:Formaldehyde