Comparison of molecular profile in triple-negative inflammatory and non-inflammatory breast cancer not of mesenchymal stem-like subtype


Autoři: Yohei Funakoshi aff001;  Ying Wang aff004;  Takashi Semba aff001;  Hiroko Masuda aff001;  David Hout aff005;  Naoto T. Ueno aff001;  Xiaoping Wang aff001
Působiště autorů: Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America aff001;  Section of Translational Breast Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America aff002;  Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America aff003;  Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America aff004;  Insight Genetics, Inc., Nashville, Tennessee, United States of America aff005
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222336

Souhrn

Background

Inflammatory breast cancer (IBC) is an aggressive form of breast cancer. The triple-negative subtype of IBC (TN-IBC) is particularly aggressive. Identification of molecular differences between TN-IBC and TN-non-IBC may help clarify the unique clinical behaviors of TN-IBC. However, our previous study comparing gene expression between TN-IBC and TN-non-IBC did not identify any TN-IBC-specific molecular signature. Lehmann et al recently reported that the mesenchymal stem-like (MSL) TNBC subtype consisted of infiltrating tumor-associated stromal cells but not cancer cells. Therefore, we compared the gene expression profiles between TN-IBC and TN-non-IBC patient samples not of the MSL subtype.

Methods

We classified 88 TNBC samples from the World IBC Consortium into subtypes according to the Vanderbilt classification and Insight TNBCtype, removed samples of MSL and unstable subtype, and compared gene expression profiles between the remaining TN-IBC and TN-non-IBC samples.

Results

In the Vanderbilt analysis, we identified 75 genes significantly differentially expressed between TN-IBC and TN-non-IBC at an FDR of 0.2. In the Insight TNBCtype analysis, we identified 81 genes significantly differentially expressed between TN-IBC and TN-non-IBC at an FDR of 0.4. In both analyses, the top canonical pathway was “Fc Receptor-mediated Phagocytosis in Macrophages and Monocytes”, and the top 10 differentially regulated genes included PADI3 and MCTP1, which were up-regulated, and CDC42EP3, SSR1, RSBN1, and ZC3H13, which were downregulated.

Conclusions

Our data suggest that the activity of macrophages might be enhanced in TN-IBC compared with TN-non-IBC. Further clinical and preclinical studies are needed to determine the cross-talk between macrophages and IBC cells.

Klíčová slova:

Biology and life sciences – Genetics – Gene expression – Gene regulation – Small interfering RNAs – Cell biology – Cellular types – Animal cells – Blood cells – White blood cells – Macrophages – Monocytes – Immune cells – Cell processes – Phagocytosis – Biochemistry – Nucleic acids – RNA – Non-coding RNA – Medicine and health sciences – Immunology – Immune response – Inflammation – Oncology – Cancers and neoplasms – Breast tumors – Breast cancer – Diagnostic medicine – Signs and symptoms – Pathology and laboratory medicine


Zdroje

1. Hance KW, Anderson WF, Devesa SS, Young HA, Levine PH. Trends in inflammatory breast carcinoma incidence and survival: the surveillance, epidemiology, and end results program at the National Cancer Institute. J Natl Cancer Inst. 2005;97(13):966–75. Epub 2005/07/07. doi: 10.1093/jnci/dji172 15998949.

2. van Golen KL, Davies S, Wu ZF, Wang Y, Bucana CD, Root H, et al. A novel putative low-affinity insulin-like growth factor-binding protein, LIBC (lost in inflammatory breast cancer), and RhoC GTPase correlate with the inflammatory breast cancer phenotype. Clin Cancer Res. 1999;5(9):2511–9. Epub 1999/09/28. 10499627.

3. Alpaugh ML, Tomlinson JS, Ye Y, Barsky SH. Relationship of sialyl-Lewis(x/a) underexpression and E-cadherin overexpression in the lymphovascular embolus of inflammatory breast carcinoma. Am J Pathol. 2002;161(2):619–28. Epub 2002/08/07. doi: 10.1016/S0002-9440(10)64217-4 12163386.

4. Silvera D, Arju R, Darvishian F, Levine PH, Zolfaghari L, Goldberg J, et al. Essential role for eIF4GI overexpression in the pathogenesis of inflammatory breast cancer. Nat Cell Biol. 2009;11(7):903–8. Epub 2009/06/16. doi: 10.1038/ncb1900 19525934.

5. Wang X, Saso H, Iwamoto T, Xia W, Gong Y, Pusztai L, et al. TIG1 promotes the development and progression of inflammatory breast cancer through activation of Axl kinase. Cancer Res. 2013;73(21):6516–25. Epub 2013/09/10. doi: 10.1158/0008-5472.CAN-13-0967 24014597.

6. Wang X, Reyes ME, Zhang D, Funakoshi Y, Trape AP, Gong Y, et al. EGFR signaling promotes inflammation and cancer stem-like activity in inflammatory breast cancer. Oncotarget. 2017;8(40):67904–17. Epub 2017/10/06. doi: 10.18632/oncotarget.18958 28978083.

7. Matsuda N, Lim B, Wang X, Ueno NT. Early clinical development of epidermal growth factor receptor targeted therapy in breast cancer. Expert opinion on investigational drugs. 2017;26(4):463–79. doi: 10.1080/13543784.2017.1299707 28271910.

8. Matsuda N, Wang X, Lim B, Krishnamurthy S, Alvarez RH, Willey JS, et al. Safety and Efficacy of Panitumumab Plus Neoadjuvant Chemotherapy in Patients With Primary HER2-Negative Inflammatory Breast Cancer. JAMA oncology. 2018;4(9):1207–13. doi: 10.1001/jamaoncol.2018.1436 29879283.

9. Allen SG, Chen YC, Madden JM, Fournier CL, Altemus MA, Hiziroglu AB, et al. Macrophages Enhance Migration in Inflammatory Breast Cancer Cells via RhoC GTPase Signaling. Scientific reports. 2016;6:39190. Epub 2016/12/20. doi: 10.1038/srep39190 27991524.

10. Wolfe AR, Trenton NJ, Debeb BG, Larson R, Ruffell B, Chu K, et al. Mesenchymal stem cells and macrophages interact through IL-6 to promote inflammatory breast cancer in pre-clinical models. Oncotarget. 2016;7(50):82482–92. Epub 2016/10/21. doi: 10.18632/oncotarget.12694 27756885.

11. Chaher N, Arias-Pulido H, Terki N, Qualls C, Bouzid K, Verschraegen C, et al. Molecular and epidemiological characteristics of inflammatory breast cancer in Algerian patients. Breast Cancer Res Treat. 2012;131(2):437–44. Epub 2011/03/02. doi: 10.1007/s10549-011-1422-5 21360074.

12. Dawood S, Ueno NT, Valero V, Woodward WA, Buchholz TA, Hortobagyi GN, et al. Differences in survival among women with stage III inflammatory and noninflammatory locally advanced breast cancer appear early: a large population-based study. Cancer. 2011;117(9):1819–26. Epub 2011/04/22. doi: 10.1002/cncr.25682 21509759.

13. Li J, Gonzalez-Angulo AM, Allen PK, Yu TK, Woodward WA, Ueno NT, et al. Triple-negative subtype predicts poor overall survival and high locoregional relapse in inflammatory breast cancer. Oncologist. 2011;16(12):1675–83. Epub 2011/12/08. doi: 10.1634/theoncologist.2011-0196 22147002.

14. Zell JA, Tsang WY, Taylor TH, Mehta RS, Anton-Culver H. Prognostic impact of human epidermal growth factor-like receptor 2 and hormone receptor status in inflammatory breast cancer (IBC): analysis of 2,014 IBC patient cases from the California Cancer Registry. Breast Cancer Res. 2009;11(1):R9. Epub 2009/02/21. doi: 10.1186/bcr2225 19228416.

15. 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 Invest. 2011;121(7):2750–67. Epub 2011/06/03. doi: 10.1172/JCI45014 21633166.

16. Masuda H, Baggerly KA, Wang Y, Iwamoto T, Brewer T, Pusztai L, et al. Comparison of molecular subtype distribution in triple-negative inflammatory and non-inflammatory breast cancers. Breast Cancer Res. 2013;15(6):R112. Epub 2013/11/28. doi: 10.1186/bcr3579 24274653.

17. Lehmann BD, Jovanovic B, Chen X, Estrada MV, Johnson KN, Shyr Y, et al. Refinement of Triple-Negative Breast Cancer Molecular Subtypes: Implications for Neoadjuvant Chemotherapy Selection. PLoS One. 2016;11(6):e0157368. Epub 2016/06/17. doi: 10.1371/journal.pone.0157368 27310713.

18. Van Laere SJ, Ueno NT, Finetti P, Vermeulen P, Lucci A, Robertson FM, et al. Uncovering the molecular secrets of inflammatory breast cancer biology: an integrated analysis of three distinct affymetrix gene expression datasets. Clin Cancer Res. 2013;19(17):4685–96. Epub 2013/02/12. doi: 10.1158/1078-0432.CCR-12-2549 23396049.

19. Chen X, Li J, Gray WH, Lehmann BD, Bauer JA, Shyr Y, et al. TNBCtype: A Subtyping Tool for Triple-Negative Breast Cancer. Cancer Inform. 2012;11:147–56. Epub 2012/08/09. doi: 10.4137/CIN.S9983 22872785.

20. Ring BZ, Hout DR, Morris SW, Lawrence K, Schweitzer BL, Bailey DB, et al. Generation of an algorithm based on minimal gene sets to clinically subtype triple negative breast cancer patients. BMC Cancer. 2016;16:143. Epub 2016/02/26. doi: 10.1186/s12885-016-2198-0 26908167.

21. Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://wwwR-projectorg/. 2016.

22. Pounds S, Morris SW. Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics. 2003;19(10):1236–42. Epub 2003/07/02. doi: 10.1093/bioinformatics/btg148 12835267.

23. Harano K, Wang Y, Lim B, Seitz RS, Morris SW, Bailey DB, et al. Rates of immune cell infiltration in patients with triple-negative breast cancer by molecular subtype. PLoS One. 2018;13(10):e0204513. Epub 2018/10/13. doi: 10.1371/journal.pone.0204513 30312311 authors RSS, SWM, DBB, DRH, RLS, and BZR. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

24. Turunen S, Huhtakangas J, Nousiainen T, Valkealahti M, Melkko J, Risteli J, et al. Rheumatoid arthritis antigens homocitrulline and citrulline are generated by local myeloperoxidase and peptidyl arginine deiminases 2, 3 and 4 in rheumatoid nodule and synovial tissue. Arthritis Res Ther. 2016;18(1):239. Epub 2016/10/22. doi: 10.1186/s13075-016-1140-9 27765067.

25. Kanno T, Kawada A, Yamanouchi J, Yosida-Noro C, Yoshiki A, Shiraiwa M, et al. Human peptidylarginine deiminase type III: molecular cloning and nucleotide sequence of the cDNA, properties of the recombinant enzyme, and immunohistochemical localization in human skin. J Invest Dermatol. 2000;115(5):813–23. Epub 2000/11/09. doi: 10.1046/j.1523-1747.2000.00131.x 11069618.

26. Hirsch DS, Pirone DM, Burbelo PD. A new family of Cdc42 effector proteins, CEPs, function in fibroblast and epithelial cell shape changes. J Biol Chem. 2001;276(2):875–83. Epub 2000/10/18. doi: 10.1074/jbc.M007039200 11035016.

27. Joberty G, Perlungher RR, Macara IG. The Borgs, a new family of Cdc42 and TC10 GTPase-interacting proteins. Mol Cell Biol. 1999;19(10):6585–97. Epub 1999/09/22. doi: 10.1128/mcb.19.10.6585 10490598.

28. Farrugia AJ, Calvo F. Cdc42 regulates Cdc42EP3 function in cancer-associated fibroblasts. Small GTPases. 2017;8(1):49–57. Epub 2016/06/02. doi: 10.1080/21541248.2016.1194952 27248291.


Článek vyšel v časopise

PLOS One


2019 Číslo 9
Nejčtenější tento týden