Ferritin Heavy Chain in Triple Negative Breast Cancer: A Favorable Prognostic Marker that Relates to a Cluster of Differentiation 8 Positive (CD8+) Effector T-cell Response
2014; Elsevier BV; Volume: 13; Issue: 7 Linguagem: Inglês
10.1074/mcp.m113.037176
ISSN1535-9484
AutoresNing Qing Liu, Tommaso De Marchi, A. Mieke Timmermans, Robin Beekhof, Anita Trapman-Jansen, Renée Foekens, Maxime P. Look, Carolien H. M. van Deurzen, Paul N. Span, Fred C.G.J. Sweep, Julie Benedicte Brask, Vera Timmermans‐Wielenga, Reno Debets, John W.M. Martens, John A. Foekens, Arzu Umar,
Tópico(s)Epigenetics and DNA Methylation
ResumoFerritin heavy chain (FTH1) is a 21-kDa subunit of the ferritin complex, known for its role in iron metabolism, and which has recently been identified as a favorable prognostic protein for triple negative breast cancer (TNBC) patients. Currently, it is not well understood how FTH1 contributes to an anti-tumor response. Here, we explored whether expression and cellular compartmentalization of FTH1 correlates to an effective immune response in TNBC patients. Analysis of the tumor tissue transcriptome, complemented with in silico pathway analysis, revealed that FTH1 was an integral part of an immunomodulatory network of cytokine signaling, adaptive immunity, and cell death. These findings were confirmed using mass spectrometry (MS)-derived proteomic data, and immunohistochemical staining of tissue microarrays. We observed that FTH1 is localized in both the cytoplasm and/or nucleus of cancer cells. However, high cytoplasmic (c) FTH1 was associated with favorable prognosis (Log-rank p = 0.001), whereas nuclear (n) FTH1 staining was associated with adverse prognosis (Log-rank p = 0.019). cFTH1 staining significantly correlated with total FTH1 expression in TNBC tissue samples, as measured by MS analysis (Rs = 0.473, p = 0.0007), but nFTH1 staining did not (Rs = 0.197, p = 0.1801). Notably, IFN γ-producing CD8+ effector T cells, but not CD4+ T cells, were preferentially enriched in tumors with high expression of cFTH1 (p = 0.02). Collectively, our data provide evidence toward new immune regulatory properties of FTH1 in TNBC, which may facilitate development of novel therapeutic targets. Ferritin heavy chain (FTH1) is a 21-kDa subunit of the ferritin complex, known for its role in iron metabolism, and which has recently been identified as a favorable prognostic protein for triple negative breast cancer (TNBC) patients. Currently, it is not well understood how FTH1 contributes to an anti-tumor response. Here, we explored whether expression and cellular compartmentalization of FTH1 correlates to an effective immune response in TNBC patients. Analysis of the tumor tissue transcriptome, complemented with in silico pathway analysis, revealed that FTH1 was an integral part of an immunomodulatory network of cytokine signaling, adaptive immunity, and cell death. These findings were confirmed using mass spectrometry (MS)-derived proteomic data, and immunohistochemical staining of tissue microarrays. We observed that FTH1 is localized in both the cytoplasm and/or nucleus of cancer cells. However, high cytoplasmic (c) FTH1 was associated with favorable prognosis (Log-rank p = 0.001), whereas nuclear (n) FTH1 staining was associated with adverse prognosis (Log-rank p = 0.019). cFTH1 staining significantly correlated with total FTH1 expression in TNBC tissue samples, as measured by MS analysis (Rs = 0.473, p = 0.0007), but nFTH1 staining did not (Rs = 0.197, p = 0.1801). Notably, IFN γ-producing CD8+ effector T cells, but not CD4+ T cells, were preferentially enriched in tumors with high expression of cFTH1 (p = 0.02). Collectively, our data provide evidence toward new immune regulatory properties of FTH1 in TNBC, which may facilitate development of novel therapeutic targets. Triple negative breast cancer (TNBC) 1The abbreviations used are: TNBC, triple negative breast cancer; nLC-MS/MS, nano-scale liquid chromatography hyphenated with high resolution tandem mass spectrometry; FTH1, ferritin heavy chain; ROS, reactive oxygen species; IHC, immunohistochemistry/immunohistochemical; MSigDB, molecular signatures database; FDR, false discovery rate; IFN γ, interferon gamma; L1, recycling of the cell adhesion molecular L1; TMA, tissue microarray; cFTH1, cytoplasmic FTH1; nFTH1, nuclear FTH1; HR, hazard ratios; CI, confidence interval; FTL, ferritin light chain; M, mesenchymal subtype; IM, immunomodulatory subtype; MHC, I/II major histocompatibility complex class I and class II; HSP, heat shock proteins; TAP, antigen peptide transporters; Treg, regulatory T cell. 1The abbreviations used are: TNBC, triple negative breast cancer; nLC-MS/MS, nano-scale liquid chromatography hyphenated with high resolution tandem mass spectrometry; FTH1, ferritin heavy chain; ROS, reactive oxygen species; IHC, immunohistochemistry/immunohistochemical; MSigDB, molecular signatures database; FDR, false discovery rate; IFN γ, interferon gamma; L1, recycling of the cell adhesion molecular L1; TMA, tissue microarray; cFTH1, cytoplasmic FTH1; nFTH1, nuclear FTH1; HR, hazard ratios; CI, confidence interval; FTL, ferritin light chain; M, mesenchymal subtype; IM, immunomodulatory subtype; MHC, I/II major histocompatibility complex class I and class II; HSP, heat shock proteins; TAP, antigen peptide transporters; Treg, regulatory T cell. is a specific subtype of breast cancer, which lacks expression of estrogen receptor α (ESR1), progesterone receptor (PGR) and human epidermal growth factor receptor 2 (ERBB2). The incidence of TNBC accounts for ∼15% of all the breast cancer cases. It represents one of the most aggressive breast cancer subtypes, of which ∼30% of patients develop distant metastasis within 5 years after surgical removal of primary tumors (1.Dent R. Trudeau M. Pritchard K.I. Hanna W.M. Kahn H.K. Sawka C.A. Lickley L.A. Rawlinson E. Sun P. Narod S.A. Triple-negative breast cancer: clinical features and patterns of recurrence.Clin. Cancer Res. 2007; 13: 4429-4434Crossref PubMed Scopus (3292) Google Scholar). To date, no targeted therapy is available for TNBC patients. Therefore, the majority of TNBC patients is recommended to receive standard adjuvant chemotherapy, even when this is not beneficial to these patients. To reduce unnecessary administration of adjuvant chemotherapy, multiple prognostic signatures have been developed using gene expression (2.Rody A. Karn T. Liedtke C. Pusztai L. Ruckhaeberle E. Hanker L. Gaetje R. Solbach C. Ahr A. Metzler D. Schmidt M. Müller V. 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Comparative proteome analysis revealing an 11-protein signature for aggressive triple negative breast cancer.J. Natl. Cancer Inst. 2014; 106: djt376Crossref PubMed Scopus (43) Google Scholar). These signatures are not only of clinical importance, but also implicate the underlying mechanisms of TNBC disease progression. Modern mass spectrometry (MS) techniques facilitate clinical proteomic research as well as functional biochemical research. On one hand, hundreds of protein biomarkers can be identified for various diseases in a high throughput manner, which largely accelerates discovery of prognostic and predictive markers for clinical use (6.Warmoes M. Jaspers J.E. Pham T.V. Piersma S.R. Oudgenoeg G. Massink M.P.G. Waisfisz Q. Rottenberg S. Boven E. Jonkers J. Jimenez C.R. Proteomics of mouse BRCA1-deficient mammary tumors identifies DNA repair proteins with potential diagnostic and prognostic value in human breast cancer.Mol. Cell. 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More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS.J. Proteome Res. 2011; 10: 1785-1793Crossref PubMed Scopus (476) Google Scholar). Therefore, interpretation of proteomic data needs to take into account analysis at the pathway level. A combination of genomic and proteomic approaches circumvents drawbacks of either technique and provides more confident biological insight into disease phenotypes. This approach uses genomic data to present global pathological events, and proteomic data to confirm changes with respect to potentially causative proteins. Various molecular signatures for TNBC implicate positive immune regulation as a favorable prognostic feature in patients (2.Rody A. Karn T. Liedtke C. Pusztai L. Ruckhaeberle E. Hanker L. Gaetje R. Solbach C. Ahr A. Metzler D. Schmidt M. Müller V. Holtrich U. Kaufmann M. A clinically relevant gene signature in triple negative and basal-like breast cancer.Breast Cancer Res. 2011; 13: R97Crossref PubMed Scopus (245) Google Scholar, 3.Hallett R.M. Dvorkin-Gheva A. Bane A. Hassell J.A. A Gene Signature for Predicting Outcome in Patients with Basal-like Breast Cancer.Sci. Rep. 2012; 2: 227Crossref PubMed Scopus (56) Google Scholar, 4.Yau C. Esserman L. Moore D.H. Waldman F. Sninsky J. Benz C.C. A multigene predictor of metastatic outcome in early stage hormone receptor-negative and triple-negative breast cancer.Breast Cancer Res. 2010; 12: R85Crossref PubMed Scopus (159) Google Scholar). This observation could be important to understand disease progression of TNBC, and also suggest that some of the immune regulatory proteins may serve as potential therapeutic targets for TNBC patients. Using nano-scale liquid chromatography hyphenated with high resolution tandem MS (nLC-MS/MS) technique, we previously identified an 11-protein signature that predicts prognosis of TNBC patients (5.Liu N.Q. Stingl C. Look M.P. Smid M. Braakman R.B.H. De Marchi T. Sieuwerts A.M. Span P.N. Sweep F.C. G.J. Linderholm B.K. Mangia A. Paradiso A. Dirix L.Y. Van Laere S.J. Luider T.M. Martens J.W.M. Foekens J.A. Umar A. Comparative proteome analysis revealing an 11-protein signature for aggressive triple negative breast cancer.J. Natl. Cancer Inst. 2014; 106: djt376Crossref PubMed Scopus (43) Google Scholar). Ferritin heavy chain (FTH1), one of the discriminatory proteins in this signature, has been suggested to display immunomodulatory functions. In order to better understand the role of immune-modulation in TNBC disease progression, potentially enhancing an effective anti-tumor immune response, we further investigated FTH1 function. FTH1 is a 21 kDa subunit of the ferritin complex. The ferritin complex captures intracellular ferrous iron (Fe2+) and converts it into ferric iron (Fe3+) by the ferroxidase activity of FTH1, which potentially reduces DNA damage caused by Fe2+ induced reactive oxygen species (ROS) and protects cancer cells from cell death (12.Knovich M.A. Storey J.A. Coffman L.G. Torti S.V. Torti F.M. Ferritin for the clinician.Blood Rev. 2009; 23: 95-104Crossref PubMed Scopus (353) Google Scholar, 13.Shpyleva S.I. Tryndyak V.P. Kovalchuk O. Starlard-Davenport A. Chekhun V.F. Beland F. a Pogribny I.P. Role of ferritin alterations in human breast cancer cells.Breast Cancer Res. Treat. 2011; 126: 63-71Crossref PubMed Scopus (140) Google Scholar). FTH1 interacts with some important pathways related to TNBC, such as the NK-κB pathway (14.Pham C.G. Bubici C. Zazzeroni F. Papa S. Jones J. Alvarez K. Jayawardena S. De Smaele E. Cong R. Beaumont C. Torti F.M. Torti S.V. Franzoso G. Ferritin heavy chain upregulation by NF-kappaB inhibits TNFalpha-induced apoptosis by suppressing reactive oxygen species.Cell. 2004; 119: 529-542Abstract Full Text Full Text PDF PubMed Scopus (530) Google Scholar, 15.Kiessling M.K. Klemke C.D. Kaminski M.M. Galani I.E. Krammer P.H. Gülow K. Inhibition of constitutively activated nuclear factor-kappaB induces reactive oxygen species- and iron-dependent cell death in cutaneous T-cell lymphoma.Cancer Res. 2009; 69: 2365-2374Crossref PubMed Scopus (96) Google Scholar) and apoptosis pathways (16.Liu F. Du Z.-Y. He J.-L. Liu X.-Q. Yu Q.-B. Wang Y.-X. FTH1 binds to Daxx and inhibits Daxx-mediated cell apoptosis.Mol. Biol. Rep. 2012; 39: 873-879Crossref PubMed Scopus (20) Google Scholar), which indicates the importance of this protein in TNBC progression. Also, FTH1 has been suggested as an immunomodulatory protein in various other cancer types including melanoma (17.Gray C.P. Arosio P. Hersey P. Association of Increased Levels of Heavy-Chain Ferritin with Increased CD4 + CD25 + Regulatory T-Cell Levels in Patients with Melanoma Association of Increased Levels of Heavy-Chain Ferritin with Patients with Melanoma 1.Clin. Cancer Res. 2003; 9: 2551-2559PubMed Google Scholar) and non-TNBC (18.Halpern M. Zahalka M.A. Traub L. Moroz C. Antibodies to Placental Immunoregulatory Ferritin with Transfer of Polyclonal Lymphocytes Arrest MCF-7 Human Breast Cancer Growth in a Nude Mouse Model.Neoplasia. 2007; 9: 487-494Crossref PubMed Scopus (11) Google Scholar). Interestingly, FTH1 expression is up-regulated in TNBC cell lines, especially in the chromatin-bound nuclear fraction of MDA-MB-231 (TNBC) in contrast to MCF-7 (non-TNBC) cell lines (13.Shpyleva S.I. Tryndyak V.P. Kovalchuk O. Starlard-Davenport A. Chekhun V.F. Beland F. a Pogribny I.P. Role of ferritin alterations in human breast cancer cells.Breast Cancer Res. Treat. 2011; 126: 63-71Crossref PubMed Scopus (140) Google Scholar). Subcellular localization of this protein may be potentially important for TNBC disease progression. In the present study, we used a combined transcriptomic and proteomic approach together with immunohistochemistry (IHC) to investigate the function and subcellular localization of FTH1 in TNBC. Our data reveals a clear relationship between cytoplasmic localization of FTH1 and enhanced numbers of CD8+ but not CD4+ tumor-infiltrating T cells, which in turn is associated with less aggressive TNBC. Transcriptomic and proteomic data sets used in this study were previously published and can be accessed from public databases. The gene expression profiling data of 63 TNBC samples were extracted from publically available data set (Accession number: GSE2034, GSE5327) (19.Wang Y. Klijn J.G.M. Zhang Y. Sieuwerts A.M. Look M.P. Yang F. Talantov D. Timmermans M. Meijer-van Gelder M.E. Yu J. Jatkoe T. Berns E.M. J.J. Atkins D. Foekens J.A. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.Lancet. 2005; 365: 671-679Abstract Full Text Full Text PDF PubMed Scopus (1991) Google Scholar, 20.Minn A.J. Gupta G.P. Padua D. Bos P. Nguyen D.X. Nuyten D. Kreike B. Zhang Y. Wang Y. Ishwaran H. Foekens J.A. Van de Vijver M. Massagué J. Lung metastasis genes couple breast tumor size and metastatic spread.Proc. Natl. Acad. Sci. U.S.A. 2007; 104: 6740-6745Crossref PubMed Scopus (307) Google Scholar). The global nLC-MS/MS data of 126 TNBC samples are available in ProteomeXchange database (Accession number: PXD000260) (5.Liu N.Q. Stingl C. Look M.P. Smid M. Braakman R.B.H. De Marchi T. Sieuwerts A.M. Span P.N. Sweep F.C. G.J. Linderholm B.K. Mangia A. Paradiso A. Dirix L.Y. Van Laere S.J. Luider T.M. Martens J.W.M. Foekens J.A. Umar A. Comparative proteome analysis revealing an 11-protein signature for aggressive triple negative breast cancer.J. Natl. Cancer Inst. 2014; 106: djt376Crossref PubMed Scopus (43) Google Scholar). A total of 47 samples were measured by both gene expression and nLC-MS/MS profiling. Gene set enrichment analysis (GSEA) (21.Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc. Natl. Acad. Sci. U.S.A. 2005; 102: 15545-15550Crossref PubMed Scopus (26549) Google Scholar) was performed on gene expression data of all the 63 samples searched against TNBC subtype database (supplemental Table S1) constructed based on previously reported findings (22.Lehmann B.D. Bauer J.A. Chen X. Sanders M.E. Chakravarthy A.B. Shyr Y. Pietenpol J.A. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies.J. Clin. Invest. 2011; 121: 2750-2767Crossref PubMed Scopus (3441) Google Scholar) and the Molecular Signatures Database (MSigDB) (version 3.1) (23.Liberzon A. Subramanian A. Pinchback R. Thorvaldsdóttir H. Tamayo P. Mesirov J.P. Molecular signatures database (MSigDB) 3.0.Bioinformatics. 2011; 27: 1739-1740Crossref PubMed Scopus (2706) Google Scholar). False discovery rates (FDRs) of enriched pathways were estimated based on 1,000 time permutation on defined phenotypes with fixed 149 seeds. Multiple probes assigned to the same gene were collapsed into single gene expression using the probes with the highest expression value for every tested sample. Genes were ranked using Student's t test when gene sets were compared between good and poor prognostic samples (dichotomized phenotype), and using Pearson correlation when gene sets were associated with expression of FTH1 (continuous phenotype). In accordance with previous findings (22.Lehmann B.D. Bauer J.A. Chen X. Sanders M.E. Chakravarthy A.B. Shyr Y. Pietenpol J.A. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies.J. Clin. Invest. 2011; 121: 2750-2767Crossref PubMed Scopus (3441) Google Scholar), a less stringent cutoff of FDR<0.60 was used to enrich for biological pathways because of the batch effects of the two data set and intrinsic heterogeneity of TNBC. We visualized the results of Gene Set Enrichment Analysis (21.Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc. Natl. Acad. Sci. U.S.A. 2005; 102: 15545-15550Crossref PubMed Scopus (26549) Google Scholar) from the gene expression data analyzed using EnrichmentMap software (24.Merico D. Isserlin R. Stueker O. Emili A. Bader G.D. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation.PloS One. 2010; 5: e13984Crossref PubMed Scopus (1312) Google Scholar). Permissive p value cutoff of 0.05 and FDR cutoff of 0.25 were applied with MSigDB (version 3.1) (23.Liberzon A. Subramanian A. Pinchback R. Thorvaldsdóttir H. Tamayo P. Mesirov J.P. Molecular signatures database (MSigDB) 3.0.Bioinformatics. 2011; 27: 1739-1740Crossref PubMed Scopus (2706) Google Scholar) for the visualization, according to the online user guide (http://baderlab.org/Software/EnrichmentMap/UserManual). To visualize gene and protein interaction map, we selected genes and proteins of which the expression significantly correlate with expression of FTH1 with a p value cutoff of 0.01 (Spearman's rank correlation) in transcriptomic and proteomic data sets, respectively. Subsequently, the gene list with corresponding correlation coefficients were submitted to IPA Knowledge Base using Ingenuity System (version 18030641), whereas protein interaction maps were visualized via STRING (version 9.1) (25.Franceschini A. Szklarczyk D. Frankild S. Kuhn M. Simonovic M. Roth A. Lin J. Minguez P. Bork P. Von Mering C. Jensen L.J. STRING v9.1: protein-protein interaction networks, with increased coverage and integration.Nucleic Acids Res. 2013; 41: D808-D815Crossref PubMed Scopus (3290) Google Scholar). Correlation heat maps were created with an unsupervised hierarchical method with Euclidean distance and complete linkage using Cluster (version 3.0) (26.Eisen M.B. Spellman P.T. Brown P.O. Botstein D. Cluster analysis and display of genome-wide expression patterns.Proc. Natl. Acad. Sci. U.S.A. 1998; 95: 14863-14868Crossref PubMed Scopus (13204) Google Scholar). nLC-MS/MS data set normalized by "Combat" algorithm was used for clustering analysis. Proteins of "IFN γ pathway" (interferon gamma), "L1 pathway" (recycling of the cell adhesion molecular L1), "antigen presentation pathway," and "apoptosis pathway" were clustered using an unsupervised hierarchical method with Euclidean distance and complete linkage. Based on heat map patterns, three clusters were defined for each pathway with high, medium or low expression values. These clusters were stratified with use of survival analyses in order to evaluate the association between pathways and patient prognosis. A tissue microarray (TMA) was constructed from a cohort of 412 primary TNBC tissues from local tissue bank. Triple negativity of formalin-fixed paraffin-embedded tissues was confirmed by IHC staining of ESR1, PGR and ERBB2. When IHC staining of ERBB2 protein was scored as 2+, fluorescence in situ hybridization (Dako Denmark A/S, Glostrup, Denmark) was used to assess amplification of the corresponding gene. For inclusion into the final analysis, we followed the same criteria as described in our previous study (5.Liu N.Q. Stingl C. Look M.P. Smid M. Braakman R.B.H. De Marchi T. Sieuwerts A.M. Span P.N. Sweep F.C. G.J. Linderholm B.K. Mangia A. Paradiso A. Dirix L.Y. Van Laere S.J. Luider T.M. Martens J.W.M. Foekens J.A. Umar A. Comparative proteome analysis revealing an 11-protein signature for aggressive triple negative breast cancer.J. Natl. Cancer Inst. 2014; 106: djt376Crossref PubMed Scopus (43) Google Scholar): All tissues were obtained from patients with negative lymph-node status, who did not receive systemic adjuvant therapy (n = 191). Twenty-six patients who had no distant metastasis were excluded because of insufficient time of clinical follow-up ( 7015 (10.2%)Menopausal statusPremenopausal75 (51.0%)Postmenopausal72 (49.0%)Tumor size (cm)Mean (S.D.)2.5 (1.2)pT1, ≤ 2 cm62 (42.2%)pT2+pT3, >2 cm77 (52.4%)Unknown8 (5.4%)GradeGrade 11 (0.7%)Grade 222 (15.0%)Grade 3121 (82.3%)Unknown3 (2.0%)Metastases within 5 yearsYes39 (26.5%)No108 (73.5%)a These 147 samples consist of samples (n = 48) overlapping with our LC-MS data set (5.Liu N.Q. Stingl C. Look M.P. Smid M. Braakman R.B.H. De Marchi T. Sieuwerts A.M. Span P.N. Sweep F.C. G.J. Linderholm B.K. Mangia A. Paradiso A. Dirix L.Y. Van Laere S.J. Luider T.M. Martens J.W.M. Foekens J.A. Umar A. Comparative proteome analysis revealing an 11-protein signature for aggressive triple negative breast cancer.J. Natl. Cancer Inst. 2014; 106: djt376Crossref PubMed Scopus (43) Google Scholar). Open table in a new tab This study was approved by the Medical Ethics Committee of the Erasmus Medical Center Rotterdam, The Netherlands (MEC 02.953) and was performed in accordance to the Code of Conduct of the Federation of Medical Scientific Societies in The Netherlands, and wherever possible we adhered to the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) (27.McShane L.M. Altman D.G. Sauerbrei W. Taube S.E. Gion M. Clark G.M. Reporting recommendations for tumor marker prognostic studies (REMARK).J. Natl. Cancer Inst. 2005; 97: 1180-1184Crossref PubMed Scopus (1140) Google Scholar). IHC staining was performed with the above-described TMA of TNBC tissues. All tumor tissues on this TMA were evaluated by a dedicated pathologist (C.H.M.v.D.) to assess histologic tumor subtype and grade according to Bloom and Richardson (28.Elston C.W. Ellis I.O. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up.Histopathology. 1991; 19: 403-410Crossref PubMed Scopus (4785) Google Scholar). Each tissue in the TMA was prepared in biological triplicates from three invasive tumor regions based on hematoxylin and eosin staining of whole tissue sections. Tissue sections of 4 μm were incubated for 1 h at room temperature with anti-FTH1 antibody at a dilution of 1:100 against residues near the C terminus of the protein (rabbit-anti-human monoclonal antibody, clone name: EPR3005Y, GeneTex Inc., Irvine, CA, USA). Antigen retrieval was performed prior to antibody incubation by heating the slides for 40 min at 95 °C and washing with Dako retrieval solution (pH = 6) (DakoCytomation, Carpinteria, CA, USA) when the slides were cooled down to room temperature. Staining was visualized by anti-mouse EnVision+® System-HRP (DAB) (DakoCytomation). Cytoplasmic (c) and nuclear (n) FTH1 stainings were separately scored as percentages of positive invasive tumor cells (6 categories: ≤1%, 2–10%, 11–25%, 26–50%, 51–75%, >75%) by two independent observers. Average scores of biological triplicates were recorded for statistical analysis. Next, tissue sections were prepared from 30 samples that represent different staining patterns of FTH1 on our TMA. Each of these sections was stained with anti-FTH1, -CD3, -CD4 and -CD8 antibodies. Anti-CD3 (rabbit-anti-human polyclonal antibody) and anti-CD8 (mouse-anti-human monoclonal antibody, clone name: C8/C144B) were purchased from Dako Denmark A/S (Glostrup, Denmark). Ready-to-use anti-CD4 antibody was purchased from Ventana (rabbit-anti-human monoclonal antibody, clone name: SP35, Ventana medical systems Inc., Tucson, AZ, USA). IHC staining of CD8 was performed with anti-CD8 antibody at a dilution of 1:100 and washed in a retrieval solution at pH = 9 for 40 min at room temperature. The rest of the protocol was the same as described for FTH1 staining. Slides were incubated with anti-CD3 antibody at a dilution of 1:150 for 32 min, and with ready-to-use anti-CD4 antibody for 16 min at room temperature. Antigen retrieval prior to CD3 and CD4 stainings was performed by heating slides for 64 min at 97 °C and washing with Tris-EDTA solution (pH = 8.4) when the slides were cooled down to room temperature. Staining was visualized by discoveryTM universal secondary antibody (Ventana, Ventana medical systems Inc., Tucson, AZ, USA). FTH1 staining on whole tissue sections was scored following the same method as used for TMA. CD3 was scored in density of positive staining cells, and CD4 and CD8 were scored as CD4/CD8 ratio. The density of CD4 and CD8 markers was calculated using the formula: density of CD4+ and CD8+ T-lymphocytes = density of CD3+ T-lymphocytes × percentage of CD4+ and CD8+ T-lymphocytes. All slides were scored by two independent observers, of whom one was a pathologist (C.H.M.v.D.). Univariate and multivariate Cox regression analyses were performed using Stata software (version 12.0). Hazard ratios (HR) and the corresponding 95% confidenc
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