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Comprehensive profiling of biological processes reveals two major prognostic subtypes in breast cancer

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Tumor Biology

Abstract

Heterogeneity is the major obstacle to breast cancer target therapy. Classification of breast cancer with significant biological process may reduce the influence of heterogeneity of intrinsic tumor. We used survival analysis to filter 95 gene sets and classify 638 breast cancer samples into two subtypes based on those gene sets associated with prognosis. Clinical outcome of two subtypes were evaluated with disease-free survival, distant metastasis-free survival, and overall survival levels in three databases and ER+, PR+ HER2+, and TNBC groups. We established a novel classification with 95 prognostic gene sets. In the training and validation cohorts, the subtype 1 was characterized by significant gene sets associated with regulation of metabolic process and enzyme activity and predicted obviously improved clinical outcome than subtype 2, which was enriched by tumor cell division, mitosis, and cell cycle-related gene sets (P < 0.05). When evaluated prognostic impact of subtypes in ER+, PR+ HER2+, and TNBC groups, we found that patients in subtype 1 showed better prognosis in ER+ and PR+ groups (P < 0.05) but had no difference from prognosis of subtype 2 in HER2+ and TNBC groups. These findings may have implications in understanding of breast cancer and filtering effective therapeutic strategies for targeted therapy.

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References

  1. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–52.

    Article  CAS  PubMed  Google Scholar 

  2. Prat A, Perou CM. Deconstructing the molecular portraits of breast cancer. Mol Oncol. 2011;5(1):5–23.

    Article  CAS  PubMed  Google Scholar 

  3. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol: Off J Am Soc Clin Oncol. 2009;27(8):1160–7.

    Article  Google Scholar 

  4. Nielsen TO, Hsu FD, Jensen K, Cheang M, Karaca G, Hu Z, et al. Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma. Clin Cancer Res: Off J Am Assoc Cancer Res. 2004;10(16):5367–74.

    Article  CAS  Google Scholar 

  5. Hugh J, Hanson J, Cheang MC, Nielsen TO, Perou CM, Dumontet C, et al. Breast cancer subtypes and response to docetaxel in node-positive breast cancer: use of an immunohistochemical definition in the BCIRG 001 trial. J Clin Oncol: Off J Am Soc Clin Oncol. 2009;27(8):1168–76.

    Article  CAS  Google Scholar 

  6. Singletary SE, Allred C, Ashley P, Bassett LW, Berry D, Bland KI, et al. Revision of the American Joint Committee on cancer staging system for breast cancer. J Clin Oncol: Off J Am Soc Clin Oncol. 2002;20(17):3628–36.

    Article  Google Scholar 

  7. Cianfrocca M, Goldstein LJ. Prognostic and predictive factors in early-stage breast cancer. Oncologist. 2004;9(6):606–16.

    Article  PubMed  Google Scholar 

  8. Ludwig JA, Weinstein JN. Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer. 2005;5(11):845–56.

    Article  CAS  PubMed  Google Scholar 

  9. Gradishar WJ, Anderson BO, Balassanian R, Blair SL, Burstein HJ, Cyr A, et al. Breast cancer version 2.2015. J Natl Compr Canc Netw: JNCCN. 2015;13(4):448–75.

    CAS  PubMed  Google Scholar 

  10. Kurebayashi J, Moriya T, Ishida T, Hirakawa H, Kurosumi M, Akiyama F, et al. The prevalence of intrinsic subtypes and prognosis in breast cancer patients of different races. Breast. 2007;16 Suppl 2:S72–7.

    Article  PubMed  Google Scholar 

  11. Puig-Vives M, Sanchez MJ, Sanchez-Cantalejo J, Torrella-Ramos A, Martos C, Ardanaz E, et al. Distribution and prognosis of molecular breast cancer subtypes defined by immunohistochemical biomarkers in a Spanish population-based study. Gynecol Oncol. 2013;130(3):609–14.

    Article  CAS  PubMed  Google Scholar 

  12. Falck AK, Ferno M, Bendahl PO, Ryden L. St Gallen molecular subtypes in primary breast cancer and matched lymph node metastases—aspects on distribution and prognosis for patients with luminal A tumours: results from a prospective randomised trial. BMC Cancer. 2013;13:558.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zhao J, Liu H, Wang M, Gu L, Guo X, Gu F, et al. Characteristics and prognosis for molecular breast cancer subtypes in Chinese women. J Surg Oncol. 2009;100(2):89–94.

    Article  PubMed  Google Scholar 

  14. Carey LA, Rugo HS, Marcom PK, Mayer EL, Esteva FJ, Ma CX, et al. TBCRC 001: randomized phase II study of cetuximab in combination with carboplatin in stage IV triple-negative breast cancer. J Clin Oncol: Off Am Soc Clin Oncol. 2012;30(21):2615–23.

    Article  CAS  Google Scholar 

  15. Paoletti C, Li Y, Muniz MC, Kidwell KM, Aung K, Thomas DG, et al. Significance of circulating tumor cells in metastatic triple negative breast cancer patients within a randomized, phase II trial: TBCRC 019. Clin Cancer Res: Off J Am Assoc Cancer Res. 2015;21:2771–9.

    Article  CAS  Google Scholar 

  16. Baselga J, Gomez P, Greil R, Braga S, Climent MA, Wardley AM, et al. Randomized phase II study of the anti-epidermal growth factor receptor monoclonal antibody cetuximab with cisplatin versus cisplatin alone in patients with metastatic triple-negative breast cancer. J Clin Oncol: Off J Am Soc Clin Oncol. 2013;31(20):2586–92.

    Article  CAS  Google Scholar 

  17. Silver DP, Richardson AL, Eklund AC, Wang ZC, Szallasi Z, Li Q, et al. Efficacy of neoadjuvant cisplatin in triple-negative breast cancer. J Clin Oncol: Off J Am Soc Clin Oncol. 2010;28(7):1145–53.

    Article  CAS  Google Scholar 

  18. Desmedt C, Piette F, Loi S, Wang Y, Lallemand F, Haibe-Kains B, et al. Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin Cancer Res: Off J Am Assoc Cancer Res. 2007;13(11):3207–14.

    Article  CAS  Google Scholar 

  19. Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 2006;98(4):262–72.

    Article  CAS  PubMed  Google Scholar 

  20. Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, et al. An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci U S A. 2005;102(38):13550–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Liberzon A. A description of the molecular signatures database (MSigDB) web site. Methods Mol Biol. 2014;1150:153–60.

    Article  CAS  PubMed  Google Scholar 

  22. Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 2013;14:7.

    Article  Google Scholar 

  23. Metzger-Filho O, Tutt A, de Azambuja E, Saini KS, Viale G, Loi S, et al. Dissecting the heterogeneity of triple-negative breast cancer. J Clin Oncol: Off J Am Soc Clin Oncol. 2012;30(15):1879–87.

    Article  CAS  Google Scholar 

  24. Blows FM, Driver KE, Schmidt MK, Broeks A, van Leeuwen FE, Wesseling J, et al. Subtyping of breast cancer by immunohistochemistry to investigate a relationship between subtype and short and long term survival: a collaborative analysis of data for 10,159 cases from 12 studies. PLoS Med. 2010;7(5), e1000279.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Investig. 2011;121(7):2750–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Shah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature. 2012;486(7403):395–9.

    CAS  PubMed  Google Scholar 

  27. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98(19):10869–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Cancer Genome Atlas N. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70.

    Article  CAS  Google Scholar 

  29. Andre F, Job B, Dessen P, Tordai A, Michiels S, Liedtke C, et al. Molecular characterization of breast cancer with high-resolution oligonucleotide comparative genomic hybridization array. Clin Cancer Res: Off J Am Assoc Cancer Res. 2009;15(2):441–51.

    Article  CAS  Google Scholar 

  30. Gewinner C, Wang ZC, Richardson A, Teruya-Feldstein J, Etemadmoghadam D, Bowtell D, et al. Evidence that inositol polyphosphate 4-phosphatase type II is a tumor suppressor that inhibits PI3K signaling. Cancer Cell. 2009;16(2):115–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lehmann BD, Pietenpol JA. Identification and use of biomarkers in treatment strategies for triple-negative breast cancer subtypes. J Pathol. 2014;232(2):142–50.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (81402172) and The Jiangsu Province Natural Science Foundation of China (BK20130074)

Authors’ contributions

Fei Chen, Sheng Gao, and Hong Yin designed the study. Fengliang Wang, Jingjing Ma, Min Zhang, Mingming Lv, and Qian Zhou analyzed the data. Ziyi Fu, Cheng Lu, and Hong Yin conducted the statistical analysis. Hong Yin wrote the manuscript. All authors read and approved the final manuscript.

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    Authors and Affiliations

    1. Nanjing Maternity and Child Health Care Hospital, Affiliated Nanjing Medical University, Nanjing, 210004, China

      Fei Chen, Sheng Gao, Fengliang Wang, Jingjing Ma, Min Zhang, Mingming Lv, Qian Zhou, Ziyi Fu, Cheng Lu & Hong Yin

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    1. Fei Chen
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    Correspondence to Hong Yin.

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    Fei Chen and Sheng Gao contributed equally to this work.

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    Chen, F., Gao, S., Wang, F. et al. Comprehensive profiling of biological processes reveals two major prognostic subtypes in breast cancer. Tumor Biol. 37, 3365–3370 (2016). https://doi.org/10.1007/s13277-015-4173-9

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