Advertisement

Virchows Archiv

, Volume 475, Issue 6, pp 709–725 | Cite as

The role of Ki-67 in Asian triple negative breast cancers: a novel combinatory panel approach

  • An Sen Tan
  • Joe Poe Sheng Yeong
  • Chi Peng Timothy Lai
  • Chong Hui Clara Ong
  • Bernett Lee
  • Jeffrey Chun Tatt Lim
  • Aye Aye Thike
  • Jabed Iqbal
  • Rebecca Alexandra Dent
  • Elaine Hsuen Lim
  • Puay Hoon TanEmail author
Original Article
  • 130 Downloads

Abstract

The proliferation marker Ki-67 is frequently used to assess aggressiveness in the pathological evaluation of cancer, but its role remains uncertain in triple-negative breast cancer (TNBC). We aimed to quantify and localize Ki-67 expression in both epithelial and immune compartments in TNBC and investigate its association with clinicopathological parameters and survival outcomes. A total of 406 TNBC cases diagnosed between 2003 and 2015 at Singapore General Hospital were recruited. Using state-of-the-art, 7-colour multiplex immunofluorescence (mIF) tissue microarrays (TMAs) were stained to assess the abundance, density and spatial distribution of Ki-67-positive tumour cells and immune cells co-decorated with cytokeratin (CK) and leukocyte common antigen (CD45) respectively. Furthermore, MKI67 mRNA profiles were analysed using NanoString technology. In multivariate analysis adjusted for tumour size, histologic grade, age at diagnosis, and lymph node stage, a high Ki-67 labelling index (LI) > 0.3% was associated with improved disease-free survival (DFS; HR = 0.727; p = 0.027). High Ki-67-positive immune cell count per TMA was a favourable prognostic marker for both DFS (HR = 0.379; p = 0.00153) and overall survival (OS; HR = 0.473; p = 0.0482). The combination of high Ki-67 LI and high MKI67 expression was associated with improved DFS (HR = 0.239; p = 0.00639) and OS (HR = 0.213; p = 0.034). This study is among the first to highlight that Ki-67 is associated with favourable prognosis in an adjuvant setting in TNBC, and the mIF-based evaluation of Ki-67 expression on both tumour and immune cells represents a novel prognostic approach.

Keywords

Breast cancer Ki-67 mRNA Multiplex immunofluorescence 

Abbreviations

ALN

axillary lymph node

CD45

leukocyte common antigen

CK

cytokeratin

CRC

colorectal cancer

DFS

disease-free survival

ER

oestrogen receptor

HER2

human epidermal growth factor receptor 2

LI

labelling index

mIF

multiplex immunofluorescence

OS

overall survival

PR

progesterone receptor

TIL

tumour infiltrating lymphocytes

TMA

tissue microarray

TNBC

triple negative breast cancer

Notes

Authors’ contributions

PT and JY conceived and directed the study. PT and JY supervised the research. JL constructed TMAs, performed IHC, prepared samples for NanoString and collated data. BL performed bioinformatics analysis. AT, JY and TL performed immunohistochemical scoring, interpreted the data and performed biostatistical analysis. CO constructed TMAs, performed IHC and collated data. TP, AT, JI, RD and EL contributed to the scientific content of the study. AT, JY and TL drafted the manuscript with the assistance and final approval of all authors.

Funding

This article was funded by the A*STAR Biomedical Research Council, National Medical Research Council Stratified Medicine Programme Office (SMPO201302) awarded to Dr. PH Tan. Dr. Jabed Iqbal is a recipient of the Transition Award from the Singapore National Medical Research Council (NMRC/TA/0041/2015).

Compliance with ethical standards

Ethics approval and consent to participate

The SingHealth Centralized Institutional Review Board (CIRB) approved the authors’ request for waiver of informed consent based on ethical consideration (Ref: 2011/433/F). The SingHealth CIRB operates in accordance with the ICH/Singapore Guideline for Good Clinical Practices and with the applicable regulatory requirement(s).

Competing interests

The authors declare that they have no competing interests.

Supplementary material

428_2019_2635_MOESM1_ESM.jpg (1.1 mb)
Supplementary Figure 1 Kaplan-Meier analysis of disease-free survival, with previously reported Ki-67 LI cut-offs. (JPG 1116 kb)
428_2019_2635_MOESM2_ESM.jpg (1.1 mb)
Supplementary Figure 2 Kaplan-Meier analysis of overall survival, with previously reported Ki-67 LI cut-offs. (JPG 1126 kb)
428_2019_2635_MOESM3_ESM.jpg (478 kb)
Supplementary Figure 3 Kaplan-Meier analysis of (A) overall survival and (B) disease free survival outcomes in women with high compared with low total Ki-67 cell count (both cancer and immune cells) per high-power field in the cohort (JPG 477 kb)
428_2019_2635_MOESM4_ESM.docx (16 kb)
Supplementary Table 1 Summary of studies investigating the prognostic value of Ki-67 in TNBC. (DOCX 15 kb)
428_2019_2635_MOESM5_ESM.docx (13 kb)
Supplementary Table 2 Tumour subtypes represented in the study population. (DOCX 13 kb)
428_2019_2635_MOESM6_ESM.docx (13 kb)
Supplementary Table 3 mIF antibody details (DOCX 13 kb)
428_2019_2635_MOESM7_ESM.docx (13 kb)
Supplementary Table 4 Summary of variables and cut-offs presented (DOCX 13 kb)
428_2019_2635_MOESM8_ESM.docx (14 kb)
Supplementary Table 5 Multivariate analysis of the prognostic value of total Ki-67 cell count (both cancer and immune cells) for DFS, with various additional cut-offs. (DOCX 13 kb)
428_2019_2635_MOESM9_ESM.docx (14 kb)
Supplementary Table 6 Multivariate analysis of survival outcomes using total Ki-67 cell count (both cancer and immune cells). (DOCX 13 kb)

References

  1. 1.
    Plasilova ML, Hayse B, Killelea BK, Horowitz NR, Chagpar AB, Lannin DR (2016) Features of triple-negative breast cancer: analysis of 38,813 cases from the national cancer database. Medicine 95(35):e4614–e4614.  https://doi.org/10.1097/MD.0000000000004614 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Thike AA, Cheok PY, Jara-Lazaro AR, Tan B, Tan P, Tan PH (2009) Triple-negative breast cancer: clinicopathological characteristics and relationship with basal-like breast cancer. Mod Pathol 23:123–133.  https://doi.org/10.1038/modpathol.2009.145 CrossRefPubMedGoogle Scholar
  3. 3.
    Thike AA, Yong-Zheng Chong L, Cheok PY, Li HH, Wai-Cheong Yip G, Huat Bay B, Tse GM-K, Iqbal J, Tan PH (2013) Loss of androgen receptor expression predicts early recurrence in triple-negative and basal-like breast cancer. Mod Pathol 27:352–360.  https://doi.org/10.1038/modpathol.2013.145 CrossRefPubMedGoogle Scholar
  4. 4.
    Cheng CL, Thike AA, Tan SYJ, Chua PJ, Bay BH, Tan PH (2015) Expression of FGFR1 is an independent prognostic factor in triple-negative breast cancer. Breast Cancer Res Treat 151(1):99–111.  https://doi.org/10.1007/s10549-015-3371-x CrossRefPubMedGoogle Scholar
  5. 5.
    Matsumoto H, Koo S, Dent R, Tan PH, Iqbal J (2015) Role of inflammatory infiltrates in triple negative breast cancer. J Clin Pathol 68(7):506–510.  https://doi.org/10.1136/jclinpath-2015-202944 CrossRefPubMedGoogle Scholar
  6. 6.
    Vincent-Salomon A, Gruel N, Lucchesi C, MacGrogan G, Dendale R, Sigal-Zafrani B, Longy M, Raynal V, Pierron G, de Mascarel I, Taris C, Stoppa-Lyonnet D, Pierga J-Y, Salmon R, Sastre-Garau X, Fourquet A, Delattre O, de Cremoux P, Aurias A (2007) Identification of typical medullary breast carcinoma as a genomic sub-group of basal-like carcinomas, a heterogeneous new molecular entity. Breast Cancer Res 9(2):R24–R24.  https://doi.org/10.1186/bcr1666 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Gerdes J, Dallenbach F, Lennert K, Lemke H, Stein H (1984) Growth fractions in malignant non-Hodgkin’s lymphomas (NHL) as determined in situ with the monoclonal antibody Ki-67. Hematol Oncol 2(4):365–371CrossRefGoogle Scholar
  8. 8.
    Li LT, Jiang G, Chen Q, Zheng JN (2015) Ki67 is a promising molecular target in the diagnosis of cancer (review). Mol Med Rep 11(3):1566–1572.  https://doi.org/10.3892/mmr.2014.2914 CrossRefPubMedGoogle Scholar
  9. 9.
    Munzone E, Botteri E, Sciandivasci A, Curigliano G, Nole F, Mastropasqua M, Rotmensz N, Colleoni M, Esposito A, Adamoli L, Luini A, Goldhirsch A, Viale G (2012) Prognostic value of Ki-67 labeling index in patients with node-negative, triple-negative breast cancer. Breast Cancer Res Treat 134(1):277–282.  https://doi.org/10.1007/s10549-012-2040-6 CrossRefPubMedGoogle Scholar
  10. 10.
    Mohammed ZMA, McMillan DC, Elsberger B, Going JJ, Orange C, Mallon E, Doughty JC, Edwards J (2012) Comparison of visual and automated assessment of Ki-67 proliferative activity and their impact on outcome in primary operable invasive ductal breast cancer. Br J Cancer 106:383–388.  https://doi.org/10.1038/bjc.2011.569 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Jonat W, Arnold N (2011) Is the Ki-67 labelling index ready for clinical use? Ann Oncol 22(3):500–502.  https://doi.org/10.1093/annonc/mdq732 CrossRefPubMedGoogle Scholar
  12. 12.
    Pollack A, DeSilvio M, Khor LY, Li R, Al-Saleem TI, Hammond ME, Venkatesan V, Lawton CA, Roach M, Shipley WU, Hanks GE, Sandler HM (2004) Ki-67 staining is a strong predictor of distant metastasis and mortality for men with prostate cancer treated with radiotherapy plus androgen deprivation: radiation therapy oncology group trial 92–02. J Clin Oncol 22(11):2133–2140.  https://doi.org/10.1200/JCO.2004.09.150 CrossRefPubMedGoogle Scholar
  13. 13.
    Zhao WY, Xu J, Wang M, Zhang ZZ, Tu L, Wang CJ, Lin TL, Shen YY, Liu Q, Cao H (2014) Prognostic value of Ki67 index in gastrointestinal stromal tumors. Int J Clin Exp Pathol 7(5):2298–2304PubMedPubMedCentralGoogle Scholar
  14. 14.
    Yamaguchi T, Fujimori T, Tomita S, Ichikawa K, Mitomi H, Ohno K, Shida Y, Kato H (2013) Clinical validation of the gastrointestinal NET grading system: Ki67 index criteria of the WHO 2010 classification is appropriate to predict metastasis or recurrence. Diagn Pathol 8(1):65.  https://doi.org/10.1186/1746-1596-8-65 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Li P, Xiao ZT, Braciak TA, Ou QJ, Chen G, Oduncu FS (2016) Association between Ki67 index and clinicopathological features in colorectal cancer. Oncol Res Treat 39(11):696–702CrossRefGoogle Scholar
  16. 16.
    Fluge Ø, Gravdal K, Carlsen E, Vonen B, Kjellevold K, Refsum S, Lilleng R, Eide TJ, Halvorsen TB, Tveit KM, Otte AP, Akslen LA, Dahl O (2009) Expression of EZH2 and Ki-67 in colorectal cancer and associations with treatment response and prognosis. Br J Cancer 101(8):1282–1289.  https://doi.org/10.1038/sj.bjc.6605333 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Melling N, Kowitz CM, Simon R, Bokemeyer C, Terracciano L, Sauter G, Izbicki JR, Marx AH (2016) High Ki67 expression is an independent good prognostic marker in colorectal cancer. J Clin Pathol 69(3):209–214.  https://doi.org/10.1136/jclinpath-2015-202985 CrossRefPubMedGoogle Scholar
  18. 18.
    Dowsett M, Nielsen TO, A’Hern R, Bartlett J, Coombes RC, Cuzick J, Ellis M, Henry NL, Hugh JC, Lively T, McShane L, Paik S, Penault-Llorca F, Prudkin L, Regan M, Salter J, Sotiriou C, Smith IE, Viale G, Zujewski JA, Hayes DF (2011) Assessment of Ki67 in breast cancer: recommendations from the international Ki67 in breast cancer working group. JNCI: J Natl Cancer Instit 103(22):1656–1664.  https://doi.org/10.1093/jnci/djr393 CrossRefGoogle Scholar
  19. 19.
    Cheang MCU, Chia SK, Voduc D, Gao D, Leung S, Snider J, Watson M, Davies S, Bernard PS, Parker JS, Perou CM, Ellis MJ, Nielsen TO (2009) Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. JNCI: J Natl Cancer Instit 101(10):736–750.  https://doi.org/10.1093/jnci/djp082 CrossRefGoogle Scholar
  20. 20.
    de Azambuja E, Cardoso F, de Castro JG, Colozza M, Mano MS, Durbecq V, Sotiriou C, Larsimont D, Piccart-Gebhart MJ, Paesmans M (2007) Ki-67 as prognostic marker in early breast cancer: a meta-analysis of published studies involving 12 155 patients. Br J Cancer 96:1504–1513.  https://doi.org/10.1038/sj.bjc.6603756 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Wang W, Wu J, Zhang P, Fei X, Zong Y, Chen X, Huang O, He J-R, Chen W, Li Y, Shen K, Zhu L (2016) Prognostic and predictive value of Ki-67 in triple-negative breast cancer. Oncotarget 7(21):31079–31087.  https://doi.org/10.18632/oncotarget.9075 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Miyashita M, Ishida T, Ishida K, Tamaki K, Amari M, Watanabe M, Ohuchi N, Sasano H (2011) Histopathological subclassification of triple negative breast cancer using prognostic scoring system: five variables as candidates. Virchows Arch 458(1):65–72.  https://doi.org/10.1007/s00428-010-1009-2 CrossRefPubMedGoogle Scholar
  23. 23.
    Constantinou C, Papadopoulos S, Karyda E, Alexopoulos A, Agnanti N, Batistatou A, Harisis H (2018) Expression and clinical significance of claudin-7, PDL-1, PTEN, c-Kit, c-Met, c-Myc, ALK, CK5/6, CK17, p53, EGFR, Ki67, p63 in triple-negative breast cancer – a single centre prospective observational study. In Vivo 32(2):303–311.  https://doi.org/10.21873/invivo.11238 CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Pan Y, Yuan Y, Liu G, Wei Y (2017) P53 and Ki-67 as prognostic markers in triple-negative breast cancer patients. PLoS One 12(2):e0172324.  https://doi.org/10.1371/journal.pone.0172324 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Kashiwagi S, Yashiro M, Takashima T, Aomatsu N, Ikeda K, Ogawa Y, Ishikawa T, Hirakawa K (2011) Advantages of adjuvant chemotherapy for patients with triple-negative breast cancer at stage II: usefulness of prognostic markers E-cadherin and Ki67. Breast Cancer Res 13(6):R122.  https://doi.org/10.1186/bcr3068 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Niikura N, Masuda S, Kumaki N, Xiaoyan T, Terada M, Terao M, Iwamoto T, Oshitanai R, Morioka T, Tuda B, Okamura T, Saito Y, Suzuki Y, Tokuda Y (2014) Prognostic significance of the Ki67 scoring categories in breast cancer subgroups. Clin Breast Cancer 14(5):323–329.e323.  https://doi.org/10.1016/j.clbc.2013.12.013 CrossRefPubMedGoogle Scholar
  27. 27.
    Hao S, He ZX, Yu KD, Yang WT, Shao ZM (2016) New insights into the prognostic value of Ki-67 labeling index in patients with triple-negative breast cancer. Oncotarget 7(17):24824–24831.  https://doi.org/10.18632/oncotarget.8531 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Nishiyama Y, Nishimura R, Osako T, Okumura Y, Arima N (2012) Ki-67, p53, and clinical outcomes of patients with triple-negative breast cancer. J Clin Oncol 30(27_suppl):142–142.  https://doi.org/10.1200/jco.2012.30.27_suppl.142 CrossRefGoogle Scholar
  29. 29.
    Polley MY, Leung SC, McShane LM, Gao D, Hugh JC, Mastropasqua MG, Viale G, Zabaglo LA, Penault-Llorca F, Bartlett JM, Gown AM, Symmans WF, Piper T, Mehl E, Enos RA, Hayes DF, Dowsett M, Nielsen TO (2013) An international Ki67 reproducibility study. J Natl Cancer Inst 105(24):1897–1906.  https://doi.org/10.1093/jnci/djt306 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Curigliano G, Burstein HJ, Winer EP, Gnant M, Dubsky P, Loibl S, Colleoni M, Regan MM, Piccart-Gebhart M, Senn HJ, Thürlimann B, on behalf of the Panel Members of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast C, Panel Members of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast C, André F, Baselga J, Bergh J, Bonnefoi H, Brucker SY, Cardoso F, Carey L, Ciruelos E, Cuzick J, Denkert C, Di Leo A, Ejlertsen B, Francis P, Galimberti V, Garber J, Gulluoglu B, Goodwin P, Harbeck N, Hayes DF, Huang CS, Huober J, Khaled H, Jassem J, Jiang Z, Karlsson P, Morrow M, Orecchia R, Osborne KC, Pagani O, Partridge AH, Pritchard K, Ro J, EJT R, Sedlmayer F, Semiglazov V, Shao Z, Smith I, Toi M, Tutt A, Viale G, Watanabe T, Whelan TJ, Xu B (2017) De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017. Ann Oncol 28(8):1700–1712.  https://doi.org/10.1093/annonc/mdx308 CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Mao Y, Qu Q, Zhang Y, Liu J, Chen X, Shen K (2014) The value of tumor infiltrating lymphocytes (TILs) for predicting response to neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis. PLoS One 9(12):e115103.  https://doi.org/10.1371/journal.pone.0115103 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Loi S, Sirtaine N, Piette F, Salgado R, Viale G, Van Eenoo F, Rouas G, Francis P, Crown JP, Hitre E, de Azambuja E, Quinaux E, Di Leo A, Michiels S, Piccart MJ, Sotiriou C (2013) Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98. J Clin Oncol 31(7):860–867.  https://doi.org/10.1200/jco.2011.41.0902 CrossRefPubMedGoogle Scholar
  33. 33.
    Adams S, Gray RJ, Demaria S, Goldstein L, Perez EA, Shulman LN, Martino S, Wang M, Jones VE, Saphner TJ, Wolff AC, Wood WC, Davidson NE, Sledge GW, Sparano JA, Badve SS (2014) Prognostic value of tumor-infiltrating lymphocytes in triple-negative breast cancers from two phase III randomized adjuvant breast cancer trials: ECOG 2197 and ECOG 1199. J Clin Oncol 32(27):2959–2966.  https://doi.org/10.1200/jco.2013.55.0491 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Jang N, Kwon HJ, Park MH, Kang SH, Bae YK (2018) Prognostic value of tumor-infiltrating lymphocyte density assessed using a standardized method based on molecular subtypes and adjuvant chemotherapy in invasive breast cancer. Ann Surg Oncol 25(4):937–946.  https://doi.org/10.1245/s10434-017-6332-2 CrossRefPubMedGoogle Scholar
  35. 35.
    Loi S, Michiels S, Salgado R, Sirtaine N, Jose V, Fumagalli D, Kellokumpu-Lehtinen PL, Bono P, Kataja V, Desmedt C, Piccart MJ, Loibl S, Denkert C, Smyth MJ, Joensuu H, Sotiriou C (2014) Tumor infiltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benefit in early breast cancer: results from the FinHER trial. Ann Oncol 25(8):1544–1550.  https://doi.org/10.1093/annonc/mdu112 CrossRefPubMedGoogle Scholar
  36. 36.
    Tay TKY, Thike AA, Pathmanathan N, Jara-Lazaro AR, Iqbal J, Sng ASH, Ye HS, Lim JCT, Koh VCY, Tan JSY, Yeong JPS, Chow ZL, Li HH, Cheng CL, Tan PH (2018) Using computer assisted image analysis to determine the optimal Ki67 threshold for predicting outcome of invasive breast cancer. Oncotarget 9(14):11619–11630.  https://doi.org/10.18632/oncotarget.24398 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Lakhani S, Ellis I, Schnitt S, Tan P, Van de Vijver M (2012) World Health Organisation classification of tumors of the breast. Int Agency Res Cancer 4:142–147Google Scholar
  38. 38.
    Stack EC, Wang C, Roman KA, Hoyt CC (2014) Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of tyramide signal amplification, multispectral imaging and multiplex analysis. Methods 70(1):46–58.  https://doi.org/10.1016/j.ymeth.2014.08.016 CrossRefPubMedGoogle Scholar
  39. 39.
    Abel EJ, Bauman TM, Weiker M, Shi F, Downs TM, Jarrard DF, Huang W (2014) Analysis and validation of tissue biomarkers for renal cell carcinoma using automated high-throughput evaluation of protein expression. Hum Pathol 45(5):1092–1099CrossRefGoogle Scholar
  40. 40.
    Lovisa S, LeBleu VS, Tampe B, Sugimoto H, Vadnagara K, Carstens JL, Wu CC, Hagos Y, Burckhardt BC, Pentcheva-Hoang T, Nischal H, Allison JP, Zeisberg M, Kalluri R (2015) Epithelial-to-mesenchymal transition induces cell cycle arrest and parenchymal damage in renal fibrosis. Nat Med 21(9):998–1009CrossRefGoogle Scholar
  41. 41.
    Garnelo M, Tan A, Her Z, Yeong J, Lim CJ, Chen J, Lim KH, Weber A, Chow P, Chung A, Ooi LL, Toh HC, Heikenwalder M, Ng IO, Nardin A, Chen Q, Abastado JP, Chew V (2015) Interaction between tumour-infiltrating B cells and T cells controls the progression of hepatocellular carcinoma. Gut 15(310814):2015–310814Google Scholar
  42. 42.
    Yeong J, Thike AA, Lim JC, Lee B, Li H, Wong SC, Hue SS, Tan PH, Iqbal J (2017) Higher densities of Foxp3(+) regulatory T cells are associated with better prognosis in triple-negative breast cancer. Breast Cancer Res Treat 163(1):21–35CrossRefGoogle Scholar
  43. 43.
    Garnelo M, Tan A, Her Z, Yeong J, Lim CJ, Chen J, Lim KH, Weber A, Chow P, Chung A, Ooi LL, Toh HC, Heikenwalder M, Ng IO, Nardin A, Chen Q, Abastado JP, Chew V (2017) Interaction between tumour-infiltrating B cells and T cells controls the progression of hepatocellular carcinoma. Gut 66(2):342–351CrossRefGoogle Scholar
  44. 44.
    Lim JCT, Yeong JPS, Lim CJ, Ong CCH, Chew VSP, Ahmed SS, Tan PH, Iqbal J (In Press) An automated staining protocol for 7-colour immunofluorescence of human tissue sections for diagnostic and prognostic use. J R Coll Pathol AustGoogle Scholar
  45. 45.
    Esbona K, Inman D, Saha S, Jeffery J, Schedin P, Wilke L, Keely P (2016) COX-2 modulates mammary tumor progression in response to collagen density. Breast Cancer Res 18(1):35.  https://doi.org/10.1186/s13058-016-0695-3 CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Mlecnik B, Bindea G, Kirilovsky A, Angell HK, Obenauf AC, Tosolini M, Church SE, Maby P, Vasaturo A, Angelova M, Fredriksen T, Mauger S, Waldner M, Berger A, Speicher MR, Pages F, Valge-Archer V, Galon J (2016) The tumor microenvironment and immunoscore are critical determinants of dissemination to distant metastasis. Sci Transl Med 8(327):327ra26CrossRefGoogle Scholar
  47. 47.
    Nghiem PT, Bhatia S, Lipson EJ, Kudchadkar RR, Miller NJ, Annamalai L, Berry S, Chartash EK, Daud A, Fling SP, Friedlander PA, Kluger HM, Kohrt HE, Lundgren L, Margolin K, Mitchell A, Olencki T, Pardoll DM, Reddy SA, Shantha EM, Sharfman WH, Sharon E, Shemanski LR, Shinohara MM, Sunshine JC, Taube JM, Thompson JA, Townson SM, Yearley JH, Topalian SL, Cheever MA (2016) PD-1 blockade with pembrolizumab in advanced merkel-cell carcinoma. N Engl J Med 374(26):2542–2552.  https://doi.org/10.1056/NEJMoa1603702 CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Feng Z, Jensen SM, Messenheimer DJ, Farhad M, Neuberger M, Bifulco CB, Fox BA (2016) Multispectral imaging of T and B cells in murine spleen and tumor. J Immunol 196(9):3943–3950.  https://doi.org/10.4049/jimmunol.1502635 CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Yeong J, Lim JCT, Lee B, Li H, Chia N, Ong CCH, Lye WK, Putti TC, Dent R, Lim E, Thike AA, Tan PH, Iqbal J (2018) High densities of tumor-associated plasma cells predict improved prognosis in triple negative breast cancer. Front Immunol 9:1209.  https://doi.org/10.3389/fimmu.2018.01209 CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Yeong J, Thike AA, Lim JC, Lee B, Li H, Wong SC, Hue SS, Tan PH, Iqbal J (2017) Higher densities of Foxp3+ regulatory T cells are associated with better prognosis in triple-negative breast cancer. Breast Cancer Res Treat 23(10):017–4161Google Scholar
  51. 51.
    Fiore C, Bailey D, Conlon N, Wu X, Martin N, Fiorentino M, Finn S, Fall K, Andersson SO, Andren O, Loda M, Flavin R (2012) Utility of multispectral imaging in automated quantitative scoring of immunohistochemistry. J Clin Pathol 65(6):496–502CrossRefGoogle Scholar
  52. 52.
    Feng Z, Bethmann D, Kappler M, Ballesteros-Merino C, Eckert A, Bell RB, Cheng A, Bui T, Leidner R, Urba WJ, Johnson K, Hoyt C, Bifulco CB, Bukur J, Wickenhauser C, Seliger B, Fox BA (2017) Multiparametric immune profiling in HPV– oral squamous cell cancer. JCI Insight 2(14).  https://doi.org/10.1172/jci.insight.93652
  53. 53.
    RStudio: integrated development environment for R (2015) RStudio, Inc, BostonGoogle Scholar
  54. 54.
    R: a language and environment for statistical computing (2016). R Foundation for statistical computing, ViennaGoogle Scholar
  55. 55.
    Walker A (2015) Openxlsx: read, write and edit XLSX files. R package version 3.0.0Google Scholar
  56. 56.
    Wickham H (2009) ggplot2: elegant graphics for data analysis. Springer-Verlag, New York.  https://doi.org/10.1007/978-0-387-98141-3 CrossRefGoogle Scholar
  57. 57.
    Wickham H (2011) The split-apply-combine strategy for data analysis. J Stat Softw 40Google Scholar
  58. 58.
    Wickham H (2016) tidyr: easily tidy data with ‘spread()’ and ‘gather()’ functions. R package version 0.6.0Google Scholar
  59. 59.
    Wickham H (2016) stringr: simple, consistent wrappers for common string operations. R package version 1.1.0Google Scholar
  60. 60.
    Wickham H, Francois R (2016) dplyr: a grammar of data manipulation. R package version 0.5.0Google Scholar
  61. 61.
    Kassambara A, Kosinski M (2016) survminer: drawing survival curves using ‘ggplot2’. R package version 0.2.4Google Scholar
  62. 62.
    Pereira B, Chin S-F, Rueda OM, Vollan H-KM, Provenzano E, Bardwell HA, Pugh M, Jones L, Russell R, Sammut S-J, Tsui DWY, Liu B, Dawson S-J, Abraham J, Northen H, Peden JF, Mukherjee A, Turashvili G, Green AR, McKinney S, Oloumi A, Shah S, Rosenfeld N, Murphy L, Bentley DR, Ellis IO, Purushotham A, Pinder SE, Børresen-Dale A-L, Earl HM, Pharoah PD, Ross MT, Aparicio S, Caldas C (2016) The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes. Nat Commun 7:11479.  https://doi.org/10.1038/ncomms11479 https://www.nature.com/articles/ncomms11479#supplementary-information CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    The Cancer Genome Atlas N (2012) Comprehensive molecular portraits of human breast tumours. Nature 490:61–70.  https://doi.org/10.1038/nature11412 https://www.nature.com/articles/nature11412#supplementary-information CrossRefGoogle Scholar
  64. 64.
    Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, Cerami E, Sander C, Schultz N (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science Signaling 6(269):pl1–pl1.  https://doi.org/10.1126/scisignal.2004088. Accessed 16 Apr 2017
  65. 65.
    Lausen B, Schumacher M (1992) Maximally selected rank statistics. Biometrics 48(1):73–85.  https://doi.org/10.2307/2532740. Accessed 16 Apr 2017
  66. 66.
    Reisenbichler ES, Horton D, Rasco M, Andea A, Hameed O (2012) Evaluation of dual immunohistochemistry and chromogenic in situ hybridization for HER2 on a single section. Am J Clin Pathol 137(1):102–110.  https://doi.org/10.1309/AJCPLNHINN9O6YSF CrossRefPubMedGoogle Scholar
  67. 67.
    Yaziji H, Goldstein LC, Barry TS et al (2004) Her-2 testing in breast cancer using parallel tissue-based methods. JAMA 291(16):1972–1977.  https://doi.org/10.1001/jama.291.16.1972 CrossRefPubMedGoogle Scholar
  68. 68.
    Bilous M, Dowsett M, Hanna W, Isola J, Lebeau A, Moreno A, Penault-Llorca F, Rüschoff J, Tomasic G, van de Vijver M (2003) Current perspectives on HER2 testing: a review of national testing guidelines. Mod Pathol 16:173–182.  https://doi.org/10.1097/01.MP.0000052102.90815.82 CrossRefPubMedGoogle Scholar
  69. 69.
    Couturier J, Vincent-Salomon A, Nicolas A, Beuzeboc P, Mouret E, Zafrani B, Sastre-Garau X (2000) Strong correlation between results of fluorescent in situ hybridization and immunohistochemistry for the assessment of the ERBB2 (HER-2/neu) gene status in breast carcinoma. Mod Pathol 13:1238–1243.  https://doi.org/10.1038/modpathol.3880228 CrossRefPubMedGoogle Scholar
  70. 70.
    Kakar S, Puangsuvan N, Stevens JM, Serenas R, Mangan G, Sahai S, Mihalov ML (2000) HER-2/neu assessment in breast cancer by immunohistochemistry and fluorescence in situ hybridization: comparison of results and correlation with survival. Mol Diagn 5(3):199–207.  https://doi.org/10.1007/BF03262077 CrossRefPubMedGoogle Scholar
  71. 71.
    Lebeau A, Deimling D, Kaltz C, Sendelhofert A, Iff A, Luthardt B, Untch M, Löhrs U (2001) HER-2/neu analysis in archival tissue samples of human breast cancer: comparison of immunohistochemistry and fluorescence in situ hybridization. J Clin Oncol 19(2):354–363.  https://doi.org/10.1200/JCO.2001.19.2.354 CrossRefPubMedGoogle Scholar
  72. 72.
    Ridolfi RL, Jamehdor MR, Arber JM (2000) HER-2/neu testing in breast carcinoma: a combined immunohistochemical and fluorescence in situ hybridization approach. Mod Pathol 13:866–873.  https://doi.org/10.1038/modpathol.3880154 CrossRefPubMedGoogle Scholar
  73. 73.
    Hanna WM, Kahn HJ, Pienkowska M, Blondal J, Seth A, Marks A (2001) Defining a test for HER-2/neu evaluation in breast cancer in the diagnostic setting. Mod Pathol 14:677–685.  https://doi.org/10.1038/modpathol.3880372 CrossRefPubMedGoogle Scholar
  74. 74.
    Duchrow M, Ziemann T, Windhövel U, Bruch HP, Broll R (2003) Colorectal carcinomas with high MIB-1 labelling indices but low pKi67 mRNA levels correlate with better prognostic outcome. Histopathology 42(6):566–574.  https://doi.org/10.1046/j.1365-2559.2003.01613.x CrossRefPubMedGoogle Scholar
  75. 75.
    Bertucci F, Finetti P, Roche H, Le Doussal JM, Marisa L, Martin AL, Lacroix-Triki M, Blanc-Fournier C, Jacquemier J, Peyro-Saint-Paul H, Viens P, Sotiriou C, Birnbaum D, Penault-Llorca F (2013) Comparison of the prognostic value of genomic grade index, Ki67 expression and mitotic activity index in early node-positive breast cancer patients. Ann Oncol 24(3):625–632.  https://doi.org/10.1093/annonc/mds510 CrossRefPubMedGoogle Scholar
  76. 76.
    Prihantono P, Hatta M, Binekada C, Sampepajung D, Haryasena H, Nelwan B, Asadul Islam A, Nilawati Usman A (2017) Ki-67 expression by immunohistochemistry and quantitative real-time polymerase chain reaction as predictor of clinical response to neoadjuvant chemotherapy in locally advanced breast cancer. J Oncol 2017:6209849–6209848.  https://doi.org/10.1155/2017/6209849 CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Petit T, Wilt M, Velten M, Millon R, Rodier JF, Borel C, Mors R, Haegele P, Eber M, Ghnassia JP (2004) Comparative value of tumour grade, hormonal receptors, Ki-67, HER-2 and topoisomerase II alpha status as predictive markers in breast cancer patients treated with neoadjuvant anthracycline-based chemotherapy. Eur J Cancer 40(2):205–211CrossRefGoogle Scholar
  78. 78.
    Keam B, Im SA, Kim HJ, Oh DY, Kim JH, Lee SH, Chie EK, Han W, Kim DW, Moon WK, Kim TY, Park IA, Noh DY, Heo DS, Ha SW, Bang YJ (2007) Prognostic impact of clinicopathologic parameters in stage II/III breast cancer treated with neoadjuvant docetaxel and doxorubicin chemotherapy: paradoxical features of the triple negative breast cancer. BMC Cancer 7(1):203.  https://doi.org/10.1186/1471-2407-7-203 CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Pohl G, Rudas M, Taucher S, Stranzl T, Steger GG, Jakesz R, Pirker R, Filipits M (2003) Expression of cell cycle regulatory proteins in breast carcinomas before and after preoperative chemotherapy. Breast Cancer Res Treat 78(1):97–103CrossRefGoogle Scholar
  80. 80.
    Jones RL, Salter J, A’Hern R, Nerurkar A, Parton M, Reis-Filho JS, Smith IE, Dowsett M (2010) Relationship between oestrogen receptor status and proliferation in predicting response and long-term outcome to neoadjuvant chemotherapy for breast cancer. Breast Cancer Res Treat 119(2):315–323.  https://doi.org/10.1007/s10549-009-0329-x CrossRefPubMedGoogle Scholar
  81. 81.
    Brown RW, Allred CD, Clark GM, Osborne CK, Hilsenbeck SG (1996) Prognostic value of Ki-67 compared to S-phase fraction in axillary node-negative breast cancer. Clin Cancer Res 2(3):585–592PubMedGoogle Scholar
  82. 82.
    Weikel W, Brumm C, Wilkens C, Beck T, Knapstein PG (1995) Growth fractions (Ki-67) in primary breast cancers, with particular reference to node-negative tumors. Cancer Detect Prev 19(5):446–450PubMedGoogle Scholar
  83. 83.
    Rhee J, Han S-W, Oh D-Y, Kim JH, Im S-A, Han W, Ae Park I, Noh D-Y, Bang Y-J, Kim T-Y (2008) The clinicopathologic characteristics and prognostic significance of triple-negativity in node-negative breast cancer. BMC Cancer 8(1):307.  https://doi.org/10.1186/1471-2407-8-307 CrossRefPubMedPubMedCentralGoogle Scholar
  84. 84.
    Bauer KR, Brown M, Cress RD, Parise CA, Caggiano V (2007) Descriptive analysis of estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2-negative invasive breast cancer, the so-called triple-negative phenotype: a population-based study from the California cancer registry. Cancer 109(9):1721–1728.  https://doi.org/10.1002/cncr.22618 CrossRefGoogle Scholar
  85. 85.
    Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, Lickley LA, Rawlinson E, Sun P, Narod SA (2007) Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res 13(15 Pt 1):4429–4434.  https://doi.org/10.1158/1078-0432.ccr-06-3045 CrossRefPubMedGoogle Scholar
  86. 86.
    Haffty BG, Yang Q, Reiss M, Kearney T, Higgins SA, Weidhaas J, Harris L, Hait W, Toppmeyer D (2006) Locoregional relapse and distant metastasis in conservatively managed triple negative early-stage breast cancer. J Clin Oncol 24(36):5652–5657.  https://doi.org/10.1200/jco.2006.06.5664 CrossRefPubMedGoogle Scholar
  87. 87.
    Carey LA, Perou CM, Livasy CA, Dressler LG, Cowan D, Conway K, Karaca G, Troester MA, Tse CK, Edmiston S, Deming SL, Geradts J, Cheang MC, Nielsen TO, Moorman PG, Earp HS, Millikan RC (2006) Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 295(21):2492–2502.  https://doi.org/10.1001/jama.295.21.2492 CrossRefPubMedGoogle Scholar
  88. 88.
    Sirohi B, Arnedos M, Popat S, Ashley S, Nerurkar A, Walsh G, Johnston S, Smith IE (2008) Platinum-based chemotherapy in triple-negative breast cancer. Ann Oncol 19(11):1847–1852.  https://doi.org/10.1093/annonc/mdn395 CrossRefPubMedGoogle Scholar
  89. 89.
    Uhm JE, Park YH, Yi SY, Cho EY, Choi YL, Lee SJ, Park MJ, Lee S-H, Jun HJ, Ahn JS, Kang WK, Park K, Im Y-H (2008) Treatment outcomes and clinicopathologic characteristics of triple-negative breast cancer patients who received platinum-containing chemotherapy. Int J Cancer 124(6):1457–1462.  https://doi.org/10.1002/ijc.24090 CrossRefGoogle Scholar
  90. 90.
    Carey LA, Dees EC, Sawyer L, Gatti L, Moore DT, Collichio F, Ollila DW, Sartor CI, Graham ML, Perou CM (2007) The triple negative paradox: primary tumor chemosensitivity of breast cancer subtypes. Clin Cancer Res 13(8):2329–2334CrossRefGoogle Scholar
  91. 91.
    Dan-Dan Xiong X-GL, He R-Q, Pan D-H, Luo Y-H, Dang Y-W, Luo D-Z, Chen G, Peng Z-G, Gan T-Q (2017) Ki67/MIB-1 predicts better prognoses in colorectal cancer patients received both surgery and adjuvant radio-chemotherapy: a meta-analysis of 30 studies. Int J Clin Exp Med 10(2):1788–1804Google Scholar
  92. 92.
    Tseng L-M, Chiu J-H, Liu C-Y, Tsai Y-F, Wang Y-L, Yang C-W, Shyr Y-M (2017) A comparison of the molecular subtypes of triple-negative breast cancer among non-Asian and Taiwanese women. Breast Cancer Res Treat 163(2):241–254.  https://doi.org/10.1007/s10549-017-4195-7 CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Anders CK, Deal AM, Miller CR, Khorram C, Meng H, Burrows E, Livasy C, Fritchie K, Ewend MG, Perou CM, Carey LA (2010) The prognostic contribution of clinical breast cancer subtype, age, and race among patients with breast cancer brain metastases. Cancer 117(8):1602–1611.  https://doi.org/10.1002/cncr.25746 CrossRefPubMedPubMedCentralGoogle Scholar
  94. 94.
    Varga Z, Diebold J, Dommann-Scherrer C, Frick H, Kaup D, Noske A, Obermann E, Ohlschlegel C, Padberg B, Rakozy C, Sancho Oliver S, Schobinger-Clement S, Schreiber-Facklam H, Singer G, Tapia C, Wagner U, Mastropasqua MG, Viale G, Lehr HA (2012) How reliable is Ki-67 immunohistochemistry in grade 2 breast carcinomas? A QA study of the Swiss Working Group of Breast- and Gynecopathologists. PLoS One 7(5):e37379.  https://doi.org/10.1371/journal.pone.0037379 CrossRefPubMedPubMedCentralGoogle Scholar
  95. 95.
    Erdem O, Dursun A, Coskun U, Gunel N (2005) The prognostic value of p53 and c-erbB-2 expression, proliferative activity and angiogenesis in node-negative breast carcinoma. Tumori 91(1):46–52CrossRefGoogle Scholar
  96. 96.
    Gonzalez MA, Pinder SE, Callagy G, Vowler SL, Morris LS, Bird K, Bell JA, Laskey RA, Coleman N (2003) Minichromosome maintenance protein 2 is a strong independent prognostic marker in breast cancer. J Clin Oncol 21(23):4306–4313.  https://doi.org/10.1200/jco.2003.04.121 CrossRefPubMedGoogle Scholar
  97. 97.
    Dowsett M, Nielsen TO, A’Hern R, Bartlett J, Coombes RC, Cuzick J, Ellis M, Henry NL, Hugh JC, Lively T, McShane L, Paik S, Penault-Llorca F, Prudkin L, Regan M, Salter J, Sotiriou C, Smith IE, Viale G, Zujewski JA, Hayes DF (2011) Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. J Natl Cancer Inst 103(22):1656–1664.  https://doi.org/10.1093/jnci/djr393 CrossRefPubMedPubMedCentralGoogle Scholar
  98. 98.
    Stalhammar G, Fuentes Martinez N, Lippert M, Tobin NP, Molholm I, Kis L, Rosin G, Rantalainen M, Pedersen L, Bergh J, Grunkin M, Hartman J (2016) Digital image analysis outperforms manual biomarker assessment in breast cancer. Mod Pathol 29(4):318–329.  https://doi.org/10.1038/modpathol.2016.34 CrossRefPubMedGoogle Scholar
  99. 99.
    Teshome M, Hunt KK (2014) Neoadjuvant therapy in the treatment of breast cancer. Surg Oncol Clin N Am 23(3):505–523.  https://doi.org/10.1016/j.soc.2014.03.006 CrossRefPubMedPubMedCentralGoogle Scholar
  100. 100.
    Thompson AM, Moulder-Thompson SL (2012) Neoadjuvant treatment of breast cancer. Ann Oncol 23(suppl_10):x231–x236.  https://doi.org/10.1093/annonc/mds324 CrossRefPubMedPubMedCentralGoogle Scholar
  101. 101.
    Fasching PA, Heusinger K, Haeberle L, Niklos M, Hein A, Bayer CM, Rauh C, Schulz-Wendtland R, Bani MR, Schrauder M, Kahmann L, Lux MP, Strehl JD, Hartmann A, Dimmler A, Beckmann MW, Wachter DL (2011) Ki67, chemotherapy response, and prognosis in breast cancer patients receiving neoadjuvant treatment. BMC Cancer 11:486.  https://doi.org/10.1186/1471-2407-11-486 CrossRefPubMedPubMedCentralGoogle Scholar
  102. 102.
    Kobierzycki C, Pula B, Wojnar A, Podhorska-Okolow M, Dziegiel P (2012) Tissue microarray technique in evaluation of proliferative activity in invasive ductal breast cancer. Anticancer Res 32(3):773–777PubMedGoogle Scholar
  103. 103.
    Ruiz C, Seibt S, Al Kuraya K, Siraj AK, Mirlacher M, Schraml P, Maurer R, Spichtin H, Torhorst J, Popovska S, Simon R, Sauter G (2006) Tissue microarrays for comparing molecular features with proliferation activity in breast cancer. Int J Cancer 118(9):2190–2194.  https://doi.org/10.1002/ijc.21581 CrossRefPubMedGoogle Scholar
  104. 104.
    Muftah AA, Aleskandarany MA, Al-Kaabi MM, Sonbul SN, Diez-Rodriguez M, Nolan CC, Caldas C, Ellis IO, Rakha EA, Green AR (2017) Ki67 expression in invasive breast cancer: the use of tissue microarrays compared with whole tissue sections. Breast Cancer Res Treat 164(2):341–348.  https://doi.org/10.1007/s10549-017-4270-0 CrossRefPubMedPubMedCentralGoogle Scholar
  105. 105.
    Batistatou A, Televantou D, Bobos M, Eleftheraki AG, Kouvaras E, Chrisafi S, Koukoulis GK, Malamou-Mitsi V, Fountzilas G (2013) Evaluation of current prognostic and predictive markers in breast cancer: a validation study of tissue microarrays. Anticancer Res 33(5):2139–2145PubMedGoogle Scholar
  106. 106.
    Thomson TA, Zhou C, Chu C, Knight B (2009) Tissue microarray for routine analysis of breast biomarkers in the clinical laboratory. Am J Clin Pathol 132(6):899–905.  https://doi.org/10.1309/ajcpw37qgecdycdo CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • An Sen Tan
    • 1
  • Joe Poe Sheng Yeong
    • 2
    • 3
  • Chi Peng Timothy Lai
    • 1
  • Chong Hui Clara Ong
    • 2
  • Bernett Lee
    • 3
  • Jeffrey Chun Tatt Lim
    • 2
  • Aye Aye Thike
    • 2
  • Jabed Iqbal
    • 2
  • Rebecca Alexandra Dent
    • 4
  • Elaine Hsuen Lim
    • 4
  • Puay Hoon Tan
    • 2
    Email author
  1. 1.Lee Kong Chian School of MedicineNanyang Technological UniversitySingaporeSingapore
  2. 2.Division of PathologySingapore General HospitalSingaporeSingapore
  3. 3.Singapore Immunology Network (SIgN), Agency of ScienceTechnology and Research (A*STAR)SingaporeSingapore
  4. 4.National Cancer Centre SingaporeSingaporeSingapore

Personalised recommendations