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Gene expression profiling and prediction of response to hormonal neoadjuvant treatment with anastrozole in surgically resectable breast cancer

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Abstract

Aromatase inhibition (AI) is the most effective endocrine treatment for breast cancer in post-menopausal patients, but a percentage of hormone receptor-positive cancers do not benefit from such therapy: for example, about 20% of patients treated with anastrozole do not respond and it is still impossible to accurately predict sensitivity. Our main goal was to identify a robust expression signature predictive of response to neoadjuvant treatment with anastrozole in patients with ER+ breast cancer. At the same time, we addressed the question of delineating treatment effects and possible mechanisms of intrinsic resistance occurring in non-responder patients. We analyzed the transcriptome of 17 tru-cut biopsies before treatment and 13 matched surgical samples after 3 months treatment with anastrozole taken from ER+ breast tumors. Molecular profiles were related to clinical response data. Treatment with anastrozole was associated with a decreased expression of genes relating to cell proliferation and an increased expression of genes relating to inflammatory processes. There was also an enrichment of induction of T-cell anergy, positive regulation of androgen signalling, synaptic transmission and vesicle trafficking in non-responders, and of cell cycle inhibition and induction of immune response in responders. We identified an expression signature of 77 probes (54 genes) that predicted response in 100% of our cases. Five of them were able to accurately predict response on an independent dataset (P = 0.0056) of 52 ER+ breast cancers treated with letrozole. Ten fixed independent samples from the anastrozole study were also used for RT-qPCR validations. This study suggests that a relative small number of genes analysed in a pre-treatment biopsy may identify patients likely to respond to AI neoadjuvant treatment. This may have practical utility translatable to the clinics. Furthermore, it delineates novel mechanisms of intrinsic resistance to AI therapy that could be further investigated in order to explore circumventing treatments.

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Acknowledgements

We thank Dr Annalisa Lanza, from Fondazione Maugeri, for sample storing and delivery and Prof Paolo Provero (Molecular Biotechnology Centre, University of Torino) for his supervision on data analysis. The study was partially supported by an educational grant from Astra Zeneca, who had no role in study design, data collection or interpretation, or manuscript submission, for which the authors had full responsibility.

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Authors and Affiliations

  1. Cancer Genomics Laboratory, Fondazione “Edo ed Elvo Tempia Valenta”, Via Malta 3, 13900, Biella, Italy

    Maurizia Mello-Grand, Vijay Singh, Chiara Ghimenti, Maria Scatolini, Enrico Grosso & Giovanna Chiorino

  2. Unit of Senology, IRCCS Fondazione “Salvatore Maugeri”, Pavia, Italy

    Lea Regolo & Alberto Costa

  3. Unit of Medical Oncology, IRCCS Fondazione “Salvatore Maugeri”, Pavia, Italy

    Alberto Zambelli, Gian Antonio Da Prada & Vittorio Fregoni

  4. Unit of Pathology, IRCCS Fondazione “Salvatore Maugeri”, Pavia, Italy

    Laura Villani

  5. Consorzio per le valutazioni Biologiche e Farmacologiche (CVBF), Università degli Studi di Pavia e IRCCS Fondazione “Salvatore Maugeri”, Pavia, Italy

    Paola Baiardi

  6. Southern Europe New Drug Organization, Milano, Italy

    Silvia Marsoni

  7. Breast Research Group, University of Edinburgh, Edinburgh, UK

    William R. Miller

  8. Ente Ospedaliero Cantonale, Lugano, Switzerland

    Alberto Costa

Authors
  1. Maurizia Mello-Grand
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  2. Vijay Singh
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  3. Chiara Ghimenti
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  4. Maria Scatolini
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  12. Silvia Marsoni
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  13. William R. Miller
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  15. Giovanna Chiorino
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Corresponding author

Correspondence to Giovanna Chiorino.

Additional information

Maurizia Mello-Grand and Vijay Singh contributed equally to the work.

Electronic supplementary material

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Supplementary Figure 1

Heat map diagram of difference matrix (D) prepared using 1280 differentially expressed probes. R=responders, N=non-responders (PDF 41 kb)

Supplementary Figure 2

RT-qPCR results. Relative expression value box-plots of four response correlated genes (CXCL3, GSS, FBXL18 and DUSP1), using the average of RPLP0 and TBP as reference, for non-responders (white boxes) and responders (grey boxes) (PDF 1187 kb)

Supplementary material 3 (PDF 7 kb)

Supplementary material 4 (PDF 42 kb)

Supplementary material 5 (PDF 20 kb)

Supplementary material 6 (PDF 70 kb)

Supplementary material 7 (PDF 27 kb)

Supplementary material 8 (PDF 67 kb)

Supplementary material 9 (PDF 101 kb)

Supplementary material 10 (PDF 10 kb)

Supplementary material 11 (PDF 9 kb)

Supplementary material 12 (PDF 17 kb)

Supplementary material 13 (PDF 41 kb)

Supplementary material 14 (PDF 23 kb)

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Mello-Grand, M., Singh, V., Ghimenti, C. et al. Gene expression profiling and prediction of response to hormonal neoadjuvant treatment with anastrozole in surgically resectable breast cancer. Breast Cancer Res Treat 121, 399–411 (2010). https://doi.org/10.1007/s10549-010-0887-y

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  • DOI: https://doi.org/10.1007/s10549-010-0887-y

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