IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0086511.html
   My bibliography  Save this article

Accurate Data Processing Improves the Reliability of Affymetrix Gene Expression Profiles from FFPE Samples

Author

Listed:
  • Maurizio Callari
  • Antonio Lembo
  • Giampaolo Bianchini
  • Valeria Musella
  • Vera Cappelletti
  • Luca Gianni
  • Maria Grazia Daidone
  • Paolo Provero

Abstract

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/.

Suggested Citation

  • Maurizio Callari & Antonio Lembo & Giampaolo Bianchini & Valeria Musella & Vera Cappelletti & Luca Gianni & Maria Grazia Daidone & Paolo Provero, 2014. "Accurate Data Processing Improves the Reliability of Affymetrix Gene Expression Profiles from FFPE Samples," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-10, January.
    • Handle: RePEc:plo:pone00:0086511
    DOI: 10.1371/journal.pone.0086511
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0086511
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0086511&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0086511?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Charles M. Perou & Therese Sørlie & Michael B. Eisen & Matt van de Rijn & Stefanie S. Jeffrey & Christian A. Rees & Jonathan R. Pollack & Douglas T. Ross & Hilde Johnsen & Lars A. Akslen & Øystein Flu, 2000. "Molecular portraits of human breast tumours," Nature, Nature, vol. 406(6797), pages 747-752, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Xi & Hoadley, Katherine A. & Hannig, Jan & Marron, J.S., 2023. "Jackstraw inference for AJIVE data integration," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    2. Egashira, Kento & Yata, Kazuyoshi & Aoshima, Makoto, 2024. "Asymptotic properties of hierarchical clustering in high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    3. María Elena Martínez & Jonathan T Unkart & Li Tao & Candyce H Kroenke & Richard Schwab & Ian Komenaka & Scarlett Lin Gomez, 2017. "Prognostic significance of marital status in breast cancer survival: A population-based study," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    4. Yishai Shimoni, 2018. "Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-15, February.
    5. Yoo-Ah Kim & Stefan Wuchty & Teresa M Przytycka, 2011. "Identifying Causal Genes and Dysregulated Pathways in Complex Diseases," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-13, March.
    6. Radhakrishnan Nagarajan & Marco Scutari, 2013. "Impact of Noise on Molecular Network Inference," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    7. R Joseph Bender & Feilim Mac Gabhann, 2013. "Expression of VEGF and Semaphorin Genes Define Subgroups of Triple Negative Breast Cancer," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-15, May.
    8. Deepak Poduval & Zuzana Sichmanova & Anne Hege Straume & Per Eystein Lønning & Stian Knappskog, 2020. "The novel microRNAs hsa-miR-nov7 and hsa-miR-nov3 are over-expressed in locally advanced breast cancer," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-23, April.
    9. Zhiguang Huo & Li Zhu & Tianzhou Ma & Hongcheng Liu & Song Han & Daiqing Liao & Jinying Zhao & George Tseng, 2020. "Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(1), pages 1-22, April.
    10. Markus Ringnér & Erik Fredlund & Jari Häkkinen & Åke Borg & Johan Staaf, 2011. "GOBO: Gene Expression-Based Outcome for Breast Cancer Online," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    11. Casey S Greene & Olga G Troyanskaya, 2012. "Chapter 2: Data-Driven View of Disease Biology," PLOS Computational Biology, Public Library of Science, vol. 8(12), pages 1-8, December.
    12. Mark Reimers, 2010. "Making Informed Choices about Microarray Data Analysis," PLOS Computational Biology, Public Library of Science, vol. 6(5), pages 1-7, May.
    13. Alan A. Arslan & Yian Zhang & Nedim Durmus & Sultan Pehlivan & Adrienne Addessi & Freya Schnabel & Yongzhao Shao & Joan Reibman, 2021. "Breast Cancer Characteristics in the Population of Survivors Participating in the World Trade Center Environmental Health Center Program 2002–2019," IJERPH, MDPI, vol. 18(14), pages 1-11, July.
    14. Sandra M. Rocha & Sílvia Socorro & Luís A. Passarinha & Cláudio J. Maia, 2022. "Comprehensive Landscape of STEAP Family Members Expression in Human Cancers: Unraveling the Potential Usefulness in Clinical Practice Using Integrated Bioinformatics Analysis," Data, MDPI, vol. 7(5), pages 1-48, May.
    15. Martin H van Vliet & Christiaan N Klijn & Lodewyk F A Wessels & Marcel J T Reinders, 2007. "Module-Based Outcome Prediction Using Breast Cancer Compendia," PLOS ONE, Public Library of Science, vol. 2(10), pages 1-10, October.
    16. Sung Gwe Ahn & Minkyung Lee & Tae Joo Jeon & Kyunghwa Han & Hak Min Lee & Seung Ah Lee & Young Hoon Ryu & Eun Ju Son & Joon Jeong, 2014. "[18F]-Fluorodeoxyglucose Positron Emission Tomography Can Contribute to Discriminate Patients with Poor Prognosis in Hormone Receptor-Positive Breast Cancer," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-7, August.
    17. Erhan Bilal & Janusz Dutkowski & Justin Guinney & In Sock Jang & Benjamin A Logsdon & Gaurav Pandey & Benjamin A Sauerwine & Yishai Shimoni & Hans Kristian Moen Vollan & Brigham H Mecham & Oscar M Rue, 2013. "Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-16, May.
    18. Silje Kjølle & Kenneth Finne & Even Birkeland & Vandana Ardawatia & Ingeborg Winge & Sura Aziz & Gøril Knutsvik & Elisabeth Wik & Joao A. Paulo & Heidrun Vethe & Dimitrios Kleftogiannis & Lars A. Aksl, 2023. "Hypoxia induced responses are reflected in the stromal proteome of breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    19. Zheqi Li & Olivia McGinn & Yang Wu & Amir Bahreini & Nolan M. Priedigkeit & Kai Ding & Sayali Onkar & Caleb Lampenfeld & Carol A. Sartorius & Lori Miller & Margaret Rosenzweig & Ofir Cohen & Nikhil Wa, 2022. "ESR1 mutant breast cancers show elevated basal cytokeratins and immune activation," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    20. Leann A. Lovejoy & Craig D. Shriver & Svasti Haricharan & Rachel E. Ellsworth, 2023. "Survival Disparities in US Black Compared to White Women with Hormone Receptor Positive-HER2 Negative Breast Cancer," IJERPH, MDPI, vol. 20(4), pages 1-15, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0086511. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.