Original Article
Tumor-Associated Macrophages and Survival in Classic Hodgkin's Lymphoma
N Engl J Med 2010; 362:875-885March 11, 2010
- Abstract
Background
Despite advances in treatments for Hodgkin's lymphoma, about 20% of patients still die from progressive disease. Current prognostic models predict the outcome of treatment with imperfect accuracy, and clinically relevant biomarkers have not been established to improve on the International Prognostic Score.
Methods
Using gene-expression profiling, we analyzed 130 frozen samples obtained from patients with classic Hodgkin's lymphoma during diagnostic lymph-node biopsy to determine which cellular signatures were correlated with treatment outcome. We confirmed our findings in an independent cohort of 166 patients, using immunohistochemical analysis.
Results
Gene-expression profiling identified a gene signature of tumor-associated macrophages that was significantly associated with primary treatment failure (P=0.02). In an independent cohort of patients, we found that an increased number of CD68+ macrophages was correlated with a shortened progression-free survival (P=0.03) and with an increased likelihood of relapse after autologous hematopoietic stem-cell transplantation (P=0.008), resulting in shortened disease-specific survival (P=0.003). In multivariate analysis, this adverse prognostic factor outperformed the International Prognostic Score for disease-specific survival (P=0.003 vs. P=0.03). The absence of an elevated number of CD68+ cells in patients with limited-stage disease defined a subgroup of patients with a long-term disease-specific survival of 100% with the use of current treatment strategies.
Conclusions
An increased number of tumor-associated macrophages was strongly associated with shortened survival in patients with classic Hodgkin's lymphoma and provides a new biomarker for risk stratification.
Media in This Article
Figure 1
Hierarchical Clustering of Gene-Expression Profiles in Hodgkin's Lymphoma, True and False Positive and Negative Rates for Three Models of Outcome Prediction, and the Importance of Individual Genes for Outcome Prediction.Figure 2
Representative Immunohistochemical Analyses for CD68 in Lymph-Node Biopsy Samples from Two Patients.Article Activity
- Article
Current therapies do not cure at least 20% of patients with classic Hodgkin's lymphoma, and a similar proportion of patients are overtreated.1 It remains a challenge to identify patients whose disease will not be eradicated by standard therapies. Currently, most patients receive at least four cycles of polychemotherapy and, if indicated, radiotherapy.2 Autologous hematopoietic stem-cell transplantation can rescue about 50% of patients in whom primary therapy has failed.
Initial clinical decisions and risk stratification for patients with Hodgkin's lymphoma are largely based on clinical variables that distinguish those who are at high risk from those at standard risk. Classic Hodgkin's lymphoma, which constitutes about 95% of all cases of Hodgkin's lymphoma and is distinguished by the presence of Reed–Sternberg cells with characteristic features, is usually managed on the basis of Ann Arbor staging as follows: limited disease (stages I and IIA without constitutional symptoms) or advanced disease (stages IB and IIB with bulky disease [largest deposit, ≥10 cm in diameter] and stages III and IV either A or B [with or without constitutional symptoms]).3 The International Prognostic Score (on a scale of 0 to 7, with higher scores indicating increased risk) is the standard that is used for risk stratification of advanced-stage Hodgkin's lymphoma,4 but it does not apply to limited stages, and none of the published prognostic-factor systems can reliably identify patients in whom treatment is likely to fail. Moreover, neither the International Prognostic Score nor its individual clinical components are suitable for accurately predicting the outcome of autologous hematopoietic stem-cell transplantation in patients with Hodgkin's lymphoma. For these reasons, reliable biomarkers for predicting long-term survival at diagnosis are needed for such patients.
In Hodgkin's lymphoma, unlike most other cancers, the malignant Reed–Sternberg cells are outnumbered by non-neoplastic cells in the microenvironment of the tumor. The frequency and distribution of these cellular components and Reed–Sternberg cells vary considerably among individual patients and among subtypes of Hodgkin's lymphoma.5 Several studies have focused on the prediction of outcomes by means of markers expressed predominantly by Reed–Sternberg cells6-9 or the microenvironment.10-13 However, most of these markers require validation in independent cohorts.
Methods
Samples from Patients
For gene-expression profiling, we selected fresh-frozen lymph-node specimens, which had been obtained at the time of diagnosis from 130 patients with Hodgkin's lymphoma, from the tissue archives at the British Columbia Cancer Agency (BCCA) and the University of Nebraska Medical Center. The criteria that we used included a primary diagnosis of classic Hodgkin's lymphoma after central review, representative lymph-node tissue (at least 1 cm2 in tissue sections), negative status for human immunodeficiency virus infection, and first-line treatment with ABVD chemotherapy (doxorubicin, bleomycin, vinblastine, and dacarbazine) or an ABVD-like regimen and, if indicated, radiation therapy (including wide-field radiation for patients with limited-stage disease).
Primary treatment was defined as a failure if the lymphoma had progressed at any time after the initiation of therapy; treatment success was defined as the absence of progression or relapse. The median follow-up time for living patients in the treatment-success group was 3.9 years (range, 0.5 to 21.0). The gene-expression cohort was stratified according to treatment outcome (failure or success) in order to analyze differences between the two outcomes (see Figure S1 in the Supplementary Appendix, available with the full text of this article at NEJM.org). Advanced-stage disease was defined with the use of Ann Arbor staging criteria.3
On the basis of the availability of formalin-fixed, paraffin-embedded diagnostic lymph-node specimens, we also selected samples from 166 independent cases of classic Hodgkin's lymphoma for immunohistochemical testing on a tissue microarray. To increase the statistical power for observations linked to progression-free and disease-specific survival, this cohort was enriched for all available cases of treatment failure that were identified through the Centre for Lymphoid Cancer database at the BCCA. The number of cases of treatment failure was roughly matched to the number of cases of treatment success. For the independent cohort, we also recorded the outcome of secondary therapy delivered with curative intent in 61 patients; these secondary therapies included autologous stem-cell transplantation in 55 patients, CVPP chemotherapy (cyclophosphamide, vinblastine, procarbazine, and prednisone) plus involved-field radiation in 5 patients, and GDP chemotherapy (gemcitabine, dexamethasone, and cisplatin) plus extended-field radiation in 1 patient. In this cohort, the median follow-up time was 4.0 years (range, 0.5 to 20.8).
The study was approved by institutional review boards at the University of British Columbia–BCCA and the University of Nebraska Medical Center. Written informed consent was obtained from all patients in accordance with the principles of the Declaration of Helsinki.
Gene-Expression Analysis
Total RNA was extracted from multiple (10 to 20) 20-μm freshly cut tissue sections after mechanical homogenization. Expression profiles were obtained with the use of GeneChip Human Genome U133 Plus 2.0 arrays (Affymetrix). (Data are available at www.ncbi.nlm.nih.gov/geo/query/acc.cgi [accession number, GSE17920].) RNA preparation, array hybridization, and washing were performed according to routine protocol with modifications. All 130 microarrays met homogeneous criteria for quality control (for details on data processing and statistical analysis of array data, see the Supplementary Appendix).
Immunohistochemical Analysis
To confirm the findings of the gene-expression analysis, we performed immunohistochemical analysis on a tissue microarray that was constructed from duplicate 1.5-mm cores of 166 independent samples enriched for cases of treatment failure. Markers that were used included CD3, CD20, CD30, CD68, and MMP11. Immunohistochemical scoring ranged from 1 to 3 for CD68, from 1 to 4 for CD20, and from 0 to 3 for MMP11, with higher scores indicating a greater proportion of positive cells (for details, see the Supplementary Appendix). Scores were stratified as high (3 or 4) or low (1 or 2) for CD20 staining.
Predictive Models
In brief, we used the gene-expression data from the 130 patients for whom all prognostic factors of the International Prognostic Score were recorded and constructed a multidimensional classifier on the basis of feature selection, using sparse multinomial logistic regression (SMLR) and leave-one-out cross-validation.14 Our aim was to build a robust discriminative model that was predictive of treatment failure in addition to identifying a small set of features (genes) that could be used as the basis for separating these data into the respective outcome groups. We determined the relative importance of variables by means of a decision-tree–based algorithm (random forest),15 using the SMLR selected features. The relative importance of variables was defined with the use of a standard method, which is based on randomization of the variable values and measurement of the resultant decline in the accuracy of the model. We measured classification accuracy using a receiver-operating-characteristic (ROC) curve and the area under the curve (AUC).
Treatment failure was assigned as the positive class, and treatment success as the negative class. Thus, an accurate prediction of failure was a true positive result, and an inaccurate prediction of failure was a false positive result; an accurate prediction of success was a true negative result, and an inaccurate prediction of success was a false negative result. (For additional details on the predictive models, see the Supplementary Appendix.)
Statistical Analysis
Group comparisons were performed by means of the chi-square test and Student's t-test. For time-to-event analyses, we used two primary end points: progression-free survival (based on the time from initial diagnosis to progression at any time, relapse from complete response, or initiation of new, previously unplanned treatment) and disease-specific survival (based on the time from initial diagnosis to death from lymphoma or its treatment, with data for patients who died of unrelated causes censored at the time of death). Cox proportional-hazards models and time-to-event analyses with the use of the Kaplan–Meier method were performed with SPSS software, version 11.0. Two-sided P values of less than 0.05 were considered to indicate statistical significance.
Results
Gene-Expression Analysis
Unsupervised hierarchical clustering of the gene-expression results did not identify clusters that were significantly associated with the outcome of treatment or any other reported clinical variable (data not shown). However, using Globaltest, with which we tested all prefiltered genes, we found a significant correlation between the gene-expression profile and the outcome of first-line treatment (P=0.02) (Table S1 in the Supplementary Appendix).
Table 1Table 1
Demographic and Clinical Characteristics of the Two Cohorts of Patients. shows the clinical characteristics of the gene-expression cohort. On the basis of supervised analyses with the data set stratified according to the failure or success of primary treatment (Figure S1 in the Supplementary Appendix), we identified 271 differentially expressed genes in the two outcome groups (Tables S2 and S3 in the Supplementary Appendix). Unsupervised hierarchical clustering of all 130 expression profiles with the use of these differentially expressed genes identified two clusters, one of which was associated with treatment success (cluster A) and the other with both success and failure (cluster B) (Figure 1AFigure 1Hierarchical Clustering of Gene-Expression Profiles in Hodgkin's Lymphoma, True and False Positive and Negative Rates for Three Models of Outcome Prediction, and the Importance of Individual Genes for Outcome Prediction.). In the treatment-failure group, pathway analyses identified the functions of cell-mediated immune response, cell-to-cell signaling and interaction, and up-regulation of pathway genes involved in interleukin-12 signaling and production in macrophages and apoptosis (Table S4 in the Supplementary Appendix). Down-regulated pathways in the treatment-failure group included genes involved in CTLA4 signaling in cytotoxic T lymphocytes and G-protein–coupled signaling.These results prompted an investigation of the association between microenvironment gene signatures and outcome. Using Globaltest, we performed associative testing to identify previously described cellular and pathway gene signatures that were differentially expressed in the two outcome groups.16 In the treatment-failure group, there was overexpression of gene signatures of tumor-associated macrophages (P=0.02)17 and monocytes (P=0.01),18 findings that are in agreement with the reported overexpression of macrophage-signaling–associated genes according to pathway analysis (Table S1 and Figure S2 in the Supplementary Appendix). There was also overexpression of gene signatures for angiogenic cells (P=0.04),19 adipocytes (P=0.01),20 and Reed–Sternberg cells (P=0.047)21 and underexpression of a signature for germinal center B cells (P=0.01)22 in the treatment-failure group (Table S1 in the Supplementary Appendix). Furthermore, previously described genes that are associated with an unfavorable outcome in Hodgkin's lymphoma,23 such as lysozyme (LYZ) and cathepsin L1 (CTSL1), were overexpressed in the treatment-failure group (P=0.04).
To test the overall power of expression profiles for outcome prediction, we constructed a classifier by means of sparse multinomial logistic regression.14 This algorithm identified 86 nonredundant annotated genes (Table S5 in the Supplementary Appendix), age, and Ann Arbor stage by a cross-validation approach. Figure 1C shows the 30 features with the highest discriminative power, as determined by a random-forest algorithm.15 Among the 27 individual genes with discriminative power exceeding that of the best clinical variable (age) was MMP11 (probe set 235908_at), which was overexpressed in the treatment-failure group (P=0.03 after adjustment for the false discovery rate). We selected this gene for further immunohistochemical testing, since previous studies have shown that the gene family of matrix metallopeptidases are overexpressed in patients in whom treatment has failed24 and that MMP11 in particular is expressed by tumor-associated macrophages.25
We compared the three data sources for feature selection and settled on a gene-expression profiling model for gene-expression probe sets only, a clinical model based on the International Prognostic Score only, and a combination model including both features. This comparison showed that the AUC value was highest for the gene-expression model, as compared with the clinical and combination models (0.837 vs. 0.625 and 0.821, respectively) (Figure 1B). The differences in accuracy among the models were most prominent at low false negative rates, with the gene-expression model and the combination model yielding higher true negative rates than the clinical model.
Immunohistochemical Analysis
Our gene-expression study and previous studies by other investigators suggested that a predominance of tumor-infiltrating macrophages, a lack of small B cells, and overexpression of matrix metallopeptidases were correlated with the failure of primary treatment. For these reasons, we selected the markers CD68 (macrophages), CD20 (B cells), and MMP11 for immunohistochemical analysis of a tissue array containing samples from lymph-node biopsies in 166 patients with classic Hodgkin's lymphoma (who were unrelated to the patients in the gene-expression analysis), including 79 patients in whom treatment had failed (Table 1). Of these markers, CD68 and CD20 antibodies are routinely used in the diagnosis of lymphoma. The tissue microarray was also stained for CD30 (a marker for Reed–Sternberg cells) and CD3 (a marker for T cells), but neither the number of CD30+ cells nor the number of CD3+ cells was correlated with outcome (data not shown).
Of these immunohistochemical markers, CD68 stood out because of its significant correlation with primary and secondary treatment outcomes (Figure 2Figure 2
Representative Immunohistochemical Analyses for CD68 in Lymph-Node Biopsy Samples from Two Patients.). Using univariate analysis, we found a significant correlation between the number of CD68+ tumor-infiltrating macrophages and shortened progression-free survival (P=0.03) (Table 2Table 2
Progression-free and Disease-Specific Survival in a Validation Cohort of 166 Patients.). Patients with a high number of CD68+ cells (an immunohistochemical score of 3) had a median progression-free survival of 2.7 years, whereas during an observation period of 16.4 years, the median survival was not reached in patients with a score of 1 (Figure 3AFigure 3
Survival in a Validation Cohort of 166 Patients, According to the Number of Infiltrating CD68+ Macrophages in Pretreatment Lymph-Node Biopsy Specimens.). In a multivariate Cox regression model that included factors with respect to the International Prognostic Score and immunohistochemical scores for CD68, CD20, and MMP11, CD68 was not an independent factor for an association with progression-free survival. In contrast, an increased number of CD68+ macrophages correlated with disease-specific survival in both univariate and multivariate analysis (P=0.003 for both comparisons) and outperformed the International Prognostic Score (P=0.03) (Table 2).The 10-year disease-specific survival rate was significantly lower among patients with a CD68 immunohistochemical score of 3 (59.6%) than among those with a score of 2 (67.4%) or 1 (88.6%) (P=0.003 for all comparisons) (Figure 3B). The number of tumor-infiltrating macrophages was also correlated with the outcome after secondary treatment. Secondary treatment that was administered with curative intent failed in only 12.5% of patients with a CD68 immunohistochemical score of 1, as compared with failure in 51.7% of those with a score of 2 and in 62.5% of those with a score of 3 (P=0.009 for all comparisons). In particular, there was a significant correlation between the failure of autologous hematopoietic stem-cell transplantation and a CD68 score of more than 1 (P=0.008). A high-risk International Prognostic Score (>3)4 was not significantly associated with the number of CD68+ macrophages in diagnostic biopsy samples (8.7% for a score of 1, as compared with 18.1% for a score of 2 and 22.9% for a score of 3; P=0.17 for all comparisons). When the analysis was restricted to limited-stage disease, a CD68 score of 1 was associated with a long-term disease-specific survival rate of 100% (P=0.04) (Figure 3C).
In agreement with the findings of the gene-expression study, MMP11 immunohistochemical staining showed a significant correlation with progression-free survival in both univariate analysis (P=0.008) and multivariate analysis (P=0.009), although not with disease-specific survival (Table 2). Among the cells with positive staining were Reed–Sternberg cells, macrophages, and endothelial cells (data not shown).
Using univariate analysis, we also found that an increased number of CD20+ small B cells (immunohistochemical score, >2) was significantly associated with prolonged progression-free survival (P=0.02) and disease-specific survival (P=0.02) (Table 2). However, the number of CD20+ small B cells was not an independent predictor of survival and was strongly correlated with advanced-stage disease (P=0.002). Neither the number of CD20+ Reed–Sternberg cells nor the presence or absence of primary or secondary lymphoid follicles was associated with the outcome.
Discussion
We found that the overexpression of a macrophage signature in expression-profile studies of diagnostic lymph-node specimens obtained from patients with classic Hodgkin's lymphoma was associated with the failure of primary treatment. Using immunohistochemical analysis, we also found that an increased number of CD68+ cells (a marker of benign macrophages) in the diagnostic sample was associated with a poorer outcome in an independent set of samples from 166 patients. Multivariate analysis revealed that the number of CD68+ cells was also associated with the outcome of secondary treatment, independently of the International Prognostic Score.
Three previous studies that have used expression profiles of the microenvironment in Hodgkin's lymphoma have been reported.23,24,26 Of these, one identified a gene signature of macrophages but did not show the clinical value of tumor-associated macrophages assessed by means of a common immunohistochemical marker.23 The association between the number of macrophages and treatment outcome has also been studied in other B-cell cancers.19,27,28 Differences in survival among patients with various lymphoma subtypes, which are linked to macrophage content, might be explained by the variable presence of macrophages with M1 or M2 differentiation in biopsy samples, indicating distinct biologic features of the tumors.29 However, in these lymphoma subtypes, including Hodgkin's lymphoma, the functional link between macrophage numbers and the contribution of these cells to the treatment outcome remains unclear.23,30 Our gene-expression classifier for the outcome of primary treatment outcome revealed MMP11, a gene that has been found by other investigators to be expressed in tumor-associated macrophages involved in remodeling of apoptotic lymphatic tissue.25 Using immunohistochemical analysis, we were able to confirm the correlation between the number of MMP11+ cells and progression-free survival in an independent cohort of patients. However, MMP11 stained many different cell types, including macrophages, and thus did not allow us to identify the particular cells that were responsible for the production of the protein.
Our findings also validate the recent report of a correlation between an increased number of small B cells and a favorable outcome.26 The recently described correlation between the number of CD20-positive B cells and survival in patients with classic Hodgkin's lymphoma31 needs to be reassessed in the context of clinical studies showing successful treatment with the addition of rituximab to standard chemotherapy.32,33
We report the clinical value of a single marker, CD68, in the identification of tumor-associated macrophages by immunohistochemical analysis, an analytic method that can be easily incorporated into a routine diagnostic approach. The use of such markers in combination with well-established clinical risk factors could improve on the predictive value of a single biomarker used alone.
We focused on tumor-associated macrophages because of the strong signal from the gene-expression data and the recently renewed interest in these non-neoplastic cells as major contributors to the biologic features of lymphoma and outcome prediction.19 The value of assessing the number of tumor-associated macrophages as a biomarker is highlighted by the association between these cells and the outcome after secondary therapy with autologous stem-cell transplantation, a widely used treatment option. Accurate prediction of the outcome after secondary treatments with curative intent would provide better risk stratification for these therapeutic options. Clinical predictors of the outcome after autologous stem-cell transplantation have been of limited value.34 In addition, our finding that in the tissue-microarray cohort, none of the patients with limited-stage disease and a low number of macrophages died is encouraging, since the applicability of the International Prognostic Score is restricted to advanced-stage disease. Thus, the CD68+ macrophage content represents a biomarker with clinical applicability in all stages of classic Hodgkin's lymphoma, both at the time of diagnosis and at the time of relapse.
In summary, our study showed the value of enumerating CD68+ macrophages in diagnostic lymph-node samples for prediction of the outcome after primary treatment and secondary treatment (in particular, autologous stem-cell transplantation). The absence of an increased number of CD68+ cells in patients with limited-stage disease defines a subgroup of patients for whom the rate of long-term disease-specific survival is 100% with the use of available treatments.
Presented in part at the Seventh International Symposium on Hodgkin Lymphoma, Cologne, Germany, November 5, 2007, and the 50th American Society of Hematology Annual Meeting, San Francisco, December 8, 2008.
Supported by grants from Deutsche Forschungsgemeinschaft, the Cancer Research Society, and the Lymphoma Research Foundation (to Dr. Steidl), the Michael Smith Foundation for Health Research (to Drs. Steidl and Shah), Roche Molecular Systems, the Canadian Institutes of Health Research (178536, to Dr. Gascoyne), the National Cancer Institute (UO1-CA114778-01, to Dr. Chan), and the Intramural Research Program of the National Cancer Institute (NCI) and by an NCI Strategic Partnering to Evaluate Cancer Signatures (SPECS) grant (UO1-CA 114778) to the Lymphoma/Leukemia Molecular Profiling Project consortium.
Dr. Gascoyne reports receiving consulting fees from Genentech, Roche Canada, and Eli Lilly and research support from Roche Canada.
No other potential conflict of interest relevant to this article was reported.
We thank Adele Telenius, Bruce Woolcock, Lorraine May, and the Center for Translational and Applied Genomics for their technical support.
Source Information
The authors' affiliations are listed in the Appendix.
Address reprint requests to Dr. Gascoyne at the Department of Pathology and Advanced Therapeutics, British Columbia Cancer Agency, 675 W. 10th Ave., Rm. 5-113, Vancouver, BC V5Z 1L3, Canada, or at rgascoyn@bccancer.bc.ca.
Appendix
The authors' affiliations are as follows: the Department of Pathology and Laboratory Medicine (C.S., T.L., S.P.S., P.F., G.H., R.D.G.), the Genome Sciences Centre (T.N., A.D., S.J.J.), and the Division of Medical Oncology (J.M.C.), British Columbia Cancer Agency, and the Department of Computer Science (S.P.S.), University of British Columbia, Vancouver, BC, Canada; the Department of Pathology, University of Nebraska Medical Center, Omaha (J.I., D.D.W., M.A.B., W.C.C.); the Department of Pathology, University of Würzburg, Würzburg, Germany (A.R., H.-K.M.-H.); the Department of Pathology, University of Arizona, Tucson (L.M.R.); the Hospital Clinic, University of Barcelona, Barcelona (E.C.); the Department of Pathology, Radium Hospital, Oslo (J.D.); the Department of Pathology, Oregon Health Sciences Center, Portland (R.M.B.); the Department of Pathology and Laboratory Medicine, Cleveland Clinic, and the Department of Molecular Pathology, Cleveland Clinic Lerner College of Medicine, Cleveland (J.R.C., R.R.T.); and the Laboratory of Pathology (E.S.J.) and the Metabolism Branch (G.L., L.M.S.), Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD.
- References
References
1
Bjorkholm M ,Axdorph U ,Grimfors G , et al. Fixed versus response-adapted MOPP/ABVD chemotherapy in Hodgkin's disease: a prospective randomized trial. Ann Oncol 1995;6:895-899
Web of Science | Medline2
Canellos GP ,Anderson JR ,Propert KJ , et al. Chemotherapy of advanced Hodgkin's disease with MOPP, ABVD, or MOPP alternating with ABVD. N Engl J Med 1992;327:1478-1484
Full Text | Web of Science | Medline3
Diehl V, Stein H, Hummel M, Zollinger R, Connors JM. Hodgkin's lymphoma: biology and treatment strategies for primary, refractory, and relapsed disease. Hematology Am Soc Hematol Educ Program 2003:225-47.
4
Hasenclever D ,Diehl V ,Armitage JO , et al. A prognostic score for advanced Hodgkin's disease. N Engl J Med 1998;339:1506-1514
Full Text | Web of Science | Medline5
Swerdlow SH, Campo E, Harris NL, et al., eds. WHO classification of tumours of haematopoietic and lymphoid tissues. 4th ed. Lyon, France: International Agency for Research on Cancer, 2008.
6
Sup SJ ,Alemany CA ,Pohlman B , et al. Expression of bcl-2 in classical Hodgkin's lymphoma: an independent predictor of poor outcome. J Clin Oncol 2005;23:3773-3779
CrossRef | Web of Science | Medline7
Doussis-Anagnostopoulou IA ,Vassilakopoulos TP ,Thymara I , et al. Topoisomerase IIalpha expression as an independent prognostic factor in Hodgkin's lymphoma. Clin Cancer Res 2008;14:1759-1766
CrossRef | Web of Science | Medline8
Natkunam Y ,Lossos IS ,Taidi B , et al. Expression of the human germinal center-associated lymphoma (HGAL) protein, a new marker of germinal center B-cell derivation. Blood 2005;105:3979-3986
CrossRef | Web of Science | Medline9
Diepstra A ,van Imhoff GW ,Karim-Kos HE , et al. HLA class II expression by Hodgkin Reed-Sternberg cells is an independent prognostic factor in classical Hodgkin's lymphoma. J Clin Oncol 2007;25:3101-3108
CrossRef | Web of Science | Medline10
Alvaro T ,Lejeune M ,Salvado MT , et al. Outcome in Hodgkin's lymphoma can be predicted from the presence of accompanying cytotoxic and regulatory T cells. Clin Cancer Res 2005;11:1467-1473
CrossRef | Web of Science | Medline11
Alvaro-Naranjo T ,Lejeune M ,Salvado-Usach MT , et al. Tumor-infiltrating cells as a prognostic factor in Hodgkin's lymphoma: a quantitative tissue microarray study in a large retrospective cohort of 267 patients. Leuk Lymphoma 2005;46:1581-1591
CrossRef | Web of Science | Medline12
Kelley TW ,Pohlman B ,Elson P ,Hsi ED . The ratio of FOXP3+ regulatory T cells to granzyme B+ cytotoxic T/NK cells predicts prognosis in classical Hodgkin lymphoma and is independent of bcl-2 and MAL expression. Am J Clin Pathol 2007;128:958-965
CrossRef | Web of Science | Medline13
Tzankov A ,Meier C ,Hirschmann P ,Went P ,Pileri SA ,Dirnhofer S . Correlation of high numbers of intratumoral FOXP3+ regulatory T cells with improved survival in germinal center-like diffuse large B-cell lymphoma, follicular lymphoma and classical Hodgkin's lymphoma. Haematologica 2008;93:193-200
CrossRef | Web of Science | Medline14
Krishnapuram B ,Carin L ,Figueiredo MA ,Hartemink AJ . Sparse multinomial logistic regression: fast algorithms and generalization bounds. IEEE Trans Pattern Anal Mach Intell 2005;27:957-968
CrossRef | Web of Science | Medline15
Breiman L . Random forests. Mach Learn 2001;45:5-32
CrossRef | Web of Science16
Shaffer AL ,Wright G ,Yang L , et al. A library of gene expression signatures to illuminate normal and pathological lymphoid biology. Immunol Rev 2006;210:67-85
CrossRef | Web of Science | Medline17
Duff MD ,Mestre J ,Maddali S ,Yan ZP ,Stapleton P ,Daly JM . Analysis of gene expression in the tumor-associated macrophage. J Surg Res 2007;142:119-128
CrossRef | Web of Science | Medline18
Su AI ,Wiltshire T ,Batalov S , et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A 2004;101:6062-6067
CrossRef | Web of Science | Medline19
Lenz G ,Wright G ,Dave SS , et al. Stromal gene signatures in large-B-cell lymphomas. N Engl J Med 2008;359:2313-2323
Full Text | Web of Science | Medline20
Urs S ,Smith C ,Campbell B , et al. Gene expression profiling in human preadipocytes and adipocytes by microarray analysis. J Nutr 2004;134:762-770
Web of Science | Medline21
Karube K ,Ohshima K ,Suzumiya J ,Kawano R ,Kikuchi M ,Harada M . Gene expression profile of cytokines and chemokines in microdissected primary Hodgkin and Reed-Sternberg (HRS) cells: high expression of interleukin-11 receptor alpha. Ann Oncol 2006;17:110-116
CrossRef | Web of Science | Medline22
Dave SS ,Fu K ,Wright GW , et al. Molecular diagnosis of Burkitt's lymphoma. N Engl J Med 2006;354:2431-2442
Full Text | Web of Science | Medline23
Sanchez-Aguilera A ,Montalban C ,de la Cueva P , et al. Tumor microenvironment and mitotic checkpoint are key factors in the outcome of classic Hodgkin lymphoma. Blood 2006;108:662-668
CrossRef | Web of Science | Medline24
Devilard E ,Bertucci F ,Trempat P , et al. Gene expression profiling defines molecular subtypes of classical Hodgkin's disease. Oncogene 2002;21:3095-3102
CrossRef | Web of Science | Medline25
Odaka C ,Izumiyama S . Expression of stromelysin-3 (matrix metalloproteinase-11) in macrophages of murine thymus following thymocyte apoptosis. Cell Immunol 2005;235:21-28
CrossRef | Web of Science | Medline26
Chetaille B ,Bertucci F ,Finetti P , et al. Molecular profiling of classical Hodgkin lymphoma tissues uncovers variations in the tumor microenvironment and correlations with EBV infection and outcome. Blood 2009;113:2765-3775
CrossRef | Web of Science | Medline27
Dave SS ,Wright G ,Tan B , et al. Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N Engl J Med 2004;351:2159-2169
Full Text | Web of Science | Medline28
Farinha P ,Masoudi H ,Skinnider BF , et al. Analysis of multiple biomarkers shows that lymphoma-associated macrophage (LAM) content is an independent predictor of survival in follicular lymphoma (FL). Blood 2005;106:2169-2174
CrossRef | Web of Science | Medline29
Mantovani A ,Sozzani S ,Locati M ,Allavena P ,Sica A . Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes. Trends Immunol 2002;23:549-555
CrossRef | Web of Science | Medline30
Kuppers R . The biology of Hodgkin's lymphoma. Nat Rev Cancer 2009;9:15-27
CrossRef | Web of Science | Medline31
Jones RJ ,Gocke CD ,Kasamon YL , et al. Circulating clonotypic B cells in classic Hodgkin lymphoma. Blood 2009;113:5920-5926
CrossRef | Web of Science | Medline32
Younes A ,Romaguera J ,Hagemeister F , et al. A pilot study of rituximab in patients with recurrent, classic Hodgkin disease. Cancer 2003;98:310-314
CrossRef | Web of Science | Medline33
Falchi L ,Capello D ,Palumbo B , et al. A case of nodular sclerosis Hodgkin's lymphoma repeatedly relapsing in the context of composite plasma cell-hyaline vascular Castleman's disease: successful response to rituximab and radiotherapy. Eur J Haematol 2007;79:455-461
CrossRef | Web of Science | Medline34
Perz JB ,Giles C ,Szydlo R , et al. LACE-conditioned autologous stem cell transplantation for relapsed or refractory Hodgkin's lymphoma: treatment outcome and risk factor analysis in 67 patients from a single centre. Bone Marrow Transplant 2007;39:41-47
CrossRef | Web of Science | Medline
- Citing Articles (99)
Citing Articles
1
P. Allavena, A. Mantovani. (2012) Immunology in the clinic review series; focus on cancer: tumour-associated macrophages: undisputed stars of the inflammatory tumour microenvironment. Clinical & Experimental Immunology 167:2, 195-205
CrossRef2
Donatella Aldinucci, Annunziata Gloghini, Antonio Pinto, Alfonso Colombatti, Antonino Carbone. (2012) The role of CD40/CD40L and interferon regulatory factor 4 in Hodgkin lymphoma microenvironment. Leukemia & Lymphoma 53:2, 195-201
CrossRef3
Mark Roschewski, Wyndham H. Wilson. (2012) EBV-associated lymphomas in adults. Best Practice & Research Clinical Haematology
CrossRef4
Naoki Wada, Mona A A Zaki, Masaharu Kohara, Hiroyasu Ogawa, Haruo Sugiyama, Shosaku Nomura, Itaru Matsumura, Masayuki Hino, Yuzuru Kanakura, Hiroshi Inagaki, Eiichi Morii, Katsuyuki Aozasa. (2012) Diffuse large B cell lymphoma with an interfollicular pattern of proliferation shows a favourable prognosis: a study of the Osaka Lymphoma Study Group. Histopathologyno-no
CrossRef5
Long V. Nguyen, Robert Vanner, Peter Dirks, Connie J. Eaves. (2012) Cancer stem cells: an evolving concept. Nature Reviews Cancer
CrossRef6
Y. Lee, M. Chittezhath, V. Andre, H. Zhao, M. Poidinger, A. Biondi, G. D'Amico, S. K. Biswas. (2012) Protumoral role of monocytes in human B-cell precursor acute lymphoblastic leukemia: involvement of the chemokine CXCL10. Blood 119:1, 227-237
CrossRef7
Ryan A. Wilcox, Kay Ristow, Thomas M. Habermann, David J. Inwards, Ivana N. M. Micallef, Patrick B. Johnston, Joseph P. Colgan, Grzegorz S. Nowakowski, Stephen M. Ansell, Thomas E. Witzig, Svetomir N. Markovic, Luis Porrata. (2012) The absolute monocyte count is associated with overall survival in patients newly diagnosed with follicular lymphoma. Leukemia & Lymphoma1-6
CrossRef8
Giampietro Gasparini, Francesco Torino, Takayuki Ueno, Stefano Cascinu, Teresa Troiani, Alberto Ballestrero, Rossana Berardi, Junichi Shishido, Akihiko Yoshizawa, Yukiko Mori, Satoshi Nagayama, Paola Morosini, Masakazu Toi. (2012) A phase II study of neoadjuvant bevacizumab plus capecitabine and concomitant radiotherapy in patients with locally advanced rectal cancer. Angiogenesis
CrossRef9
Naoki Wada, Mona A A Zaki, Yumiko Hori, Koji Hashimoto, Machiko Tsukaguchi, Yoichi Tatsumi, Jun Ishikawa, Nobuhiko Tominaga, Hiroto Sakoda, Hironori Take, Mitsuru Tsudo, Maki Kuwayama, Eiichi Morii, Katsuyuki Aozasa. (2012) Tumour-associated macrophages in diffuse large B-cell lymphoma: a study of the Osaka Lymphoma Study Group. Histopathology 60:2, 313-319
CrossRef10
Bosko Andjelic, Biljana Mihaljevic, Milena Todorovic, Jelena Bila, Ljubomir Jakovic, Maja Perunicic Jovanovic. (2012) The Number of Lymphoma-associated Macrophages in Tumor Tissue Is an Independent Prognostic Factor in Patients With Follicular Lymphoma. Applied Immunohistochemistry & Molecular Morphology 20:1, 41-46
CrossRef11
Junko Okabe-Kado, Takashi Kasukabe, Yasuhiko Kaneko. (2012) Extracellular NM23 Protein as a Therapeutic Target for Hematologic Malignancies. Advances in Hematology 2012, 1-10
CrossRef12
Qi-chun Cai, Hong Liao, Su-xia Lin, Yi Xia, Xiao-xaio Wang, Yan Gao, Ze-xiao Lin, Jia-bin Lu, Hui-qiang Huang. (2011) High expression of tumor-infiltrating macrophages correlates with poor prognosis in patients with diffuse large B-cell lymphoma. Medical Oncology
CrossRef13
Edmund J. Keliher, Jeongsoo Yoo, Matthias Nahrendorf, Jason S. Lewis, Brett Marinelli, Andita Newton, Mikael J. Pittet, Ralph Weissleder. (2011) 89 Zr-Labeled Dextran Nanoparticles Allow in Vivo Macrophage Imaging. Bioconjugate Chemistry 22:12, 2383-2389
CrossRef14
P Guisado-Vasco, R Arranz, M Canales, A Cánovas, JG Laraña, R García-Sanz, A Lopez, JL López, M Llanos, JM Moraleda, J Rodriguez, C Rayón, P Sabin, A Salar, A Marín, M Morente, P Sánchez-Godoy, JF Tomás, A Muriel, V Abraira, MA Piris, JF Garcia, C Montalban. (2011) Stage IV and age over 45 years are the only prognostic factors of the International Prognostic Score (IPS) for the outcome of advanced Hodgkin Lymphoma in the Spanish Hodgkin Lymphoma Study Group series. Leukemia & Lymphoma1-18
CrossRef15
Kara M. Kelly. (2011) Management of children with high-risk Hodgkin lymphoma. British Journal of Haematologyno-no
CrossRef16
Soyoung Lee, Clemens A. Schmitt, Maurice Reimann. (2011) The Myc/macrophage tango: Oncogene-induced senescence, Myc style. Seminars in Cancer Biology 21:6, 377-384
CrossRef17
Ranjana H. Advani, Richard T. Hoppe, Lauren S. Maeda, David M. Baer, Joseph Mason, Saul A. Rosenberg, Sandra J. Horning. (2011) Stage I-IIA Non-Bulky Hodgkin's Lymphoma. Is Further Distinction Based on Prognostic Factors Useful? The Stanford Experience. International Journal of Radiation Oncology*Biology*Physics 81:5, 1374-1379
CrossRef18
R. Advani. (2011) Optimal Therapy of Advanced Hodgkin Lymphoma. Hematology 2011:1, 310-316
CrossRef19
T. A. Fehniger, S. Larson, K. Trinkaus, M. J. Siegel, A. F. Cashen, K. A. Blum, T. S. Fenske, D. D. Hurd, A. Goy, S. E. Schneider, C. R. Keppel, N. D. Wagner-Johnston, K. R. Carson, N. L. Bartlett. (2011) A phase 2 multicenter study of lenalidomide in relapsed or refractory classical Hodgkin lymphoma. Blood 118:19, 5119-5125
CrossRef20
Sara M Weis, David A Cheresh. (2011) Tumor angiogenesis: molecular pathways and therapeutic targets. Nature Medicine 17:11, 1359-1370
CrossRef21
Ryan A. Wilcox. (2011) Cutaneous T-cell lymphoma: 2011 update on diagnosis, risk-stratification, and management. American Journal of Hematology 86:11, 928-948
CrossRef22
Dok Hyun Yoon, Young Wha Koh, Hyo Jeong Kang, Shin Kim, Chan-Sik Park, Sang-wook Lee, Cheolwon Suh, Jooryung Huh. (2011) CD68 and CD163 as prognostic factors for Korean patients with Hodgkin lymphoma. European Journal of Haematologyno-no
CrossRef23
Haorile Chagan-Yasutan, Kunihiro Tsukasaki, Yayoi Takahashi, Shigeru Oguma, Hideo Harigae, Naoto Ishii, Jing Zhang, Manabu Fukumoto, Toshio Hattori. (2011) Involvement of osteopontin and its signaling molecule CD44 in clinicopathological features of adult T cell leukemia. Leukemia Research 35:11, 1484-1490
CrossRef24
Rita A. Mukhtar, Amy P. Moore, Onouwem Nseyo, Frederick L. Baehner, Alfred Au, Dan H. Moore, Patrick Twomey, Michael J. Campbell, Laura J. Esserman. (2011) Elevated PCNA+ tumor-associated macrophages in breast cancer are associated with early recurrence and non-Caucasian ethnicity. Breast Cancer Research and Treatment 130:2, 635-644
CrossRef25
Peter J. Murray, Thomas A. Wynn. (2011) Protective and pathogenic functions of macrophage subsets. Nature Reviews Immunology 11:11, 723-737
CrossRef26
Florian Leuschner, Partha Dutta, Rostic Gorbatov, Tatiana I Novobrantseva, Jessica S Donahoe, Gabriel Courties, Kang Mi Lee, James I Kim, James F Markmann, Brett Marinelli, Peter Panizzi, Won Woo Lee, Yoshiko Iwamoto, Stuart Milstein, Hila Epstein-Barash, William Cantley, Jamie Wong, Virna Cortez-Retamozo, Andita Newton, Kevin Love, Peter Libby, Mikael J Pittet, Filip K Swirski, Victor Koteliansky, Robert Langer, Ralph Weissleder, Daniel G Anderson, Matthias Nahrendorf. (2011) Therapeutic siRNA silencing in inflammatory monocytes in mice. Nature Biotechnology 29:11, 1005-1010
CrossRef27
Mona A. A. Zaki, Naoki Wada, Junichiro Ikeda, Hirohiko Shibayama, Koji Hashimoto, Tamotsu Yamagami, Yoichi Tatsumi, Machiko Tsukaguchi, Hironori Take, Mitsuru Tsudo, Eiichi Morii, Katsuyuki Aozasa. (2011) Prognostic implication of types of tumor-associated macrophages in Hodgkin lymphoma. Virchows Archiv 459:4, 361-366
CrossRef28
Ljubomir R. Jakovic, Biljana S. Mihaljevic, Maja D. Perunicic Jovanovic, Andrija D. Bogdanovic, Bosko M. Andjelic, Vladimir Z. Bumbasirevic. (2011) The prognostic relevance of tumor associated macrophages in advanced stage classical Hodgkin lymphoma. Leukemia & Lymphoma 52:10, 1913-1919
CrossRef29
Chiara Porta, Elena Riboldi, Maria Grazia Totaro, Laura Strauss, Antonio Sica, Alberto Mantovani. (2011) Macrophages in cancer and infectious diseases: the ‘good’ and the ‘bad’. Immunotherapy 3:10, 1185-1202
CrossRef30
Antonio Sica, Chiara Porta, Sara Morlacchi, Stefania Banfi, Laura Strauss, Monica Rimoldi, Maria Grazia Totaro, Elena Riboldi. (2011) Origin and Functions of Tumor-Associated Myeloid Cells (TAMCs). Cancer Microenvironment
CrossRef31
Alberto Mantovani, Giovanni Germano, Federica Marchesi, Marco Locatelli, Subhra K. Biswas. (2011) Cancer-promoting tumor-associated macrophages: New vistas and open questions. European Journal of Immunology 41:9, 2522-2525
CrossRef32
Mikael J. Pittet, Filip K. Swirski. (2011) Monocytes link atherosclerosis and cancer. European Journal of Immunology 41:9, 2519-2522
CrossRef33
S M Pyonteck, B B Gadea, H-W Wang, V Gocheva, K E Hunter, L H Tang, J A Joyce. (2011) Deficiency of the macrophage growth factor CSF-1 disrupts pancreatic neuroendocrine tumor development. Oncogene
CrossRef34
A. A. Alizadeh, A. J. Gentles, A. J. Alencar, C. L. Liu, H. E. Kohrt, R. Houot, M. J. Goldstein, S. Zhao, Y. Natkunam, R. H. Advani, R. D. Gascoyne, J. Briones, R. J. Tibshirani, J. H. Myklebust, S. K. Plevritis, I. S. Lossos, R. Levy. (2011) Prediction of survival in diffuse large B-cell lymphoma based on the expression of 2 genes reflecting tumor and microenvironment. Blood 118:5, 1350-1358
CrossRef35
Leonid S. Metelitsa. (2011) Anti-tumor potential of type-I NKT cells against CD1d-positive and CD1d-negative tumors in humans. Clinical Immunology 140:2, 119-129
CrossRef36
Xuesong Wu, Ryan E Sells, Sam T Hwang. (2011) Upregulation of Inflammatory Cytokines and Oncogenic Signal Pathways Preceding Tumor Formation in a Murine Model of T-Cell Lymphoma in Skin. Journal of Investigative Dermatology 131:8, 1727-1734
CrossRef37
Marco Erreni, Alberto Mantovani, Paola Allavena. (2011) Tumor-associated Macrophages (TAM) and Inflammation in Colorectal Cancer. Cancer Microenvironment 4:2, 141-154
CrossRef38
Jaime Rodriguez-Canales, Franziska C. Eberle, Elaine S. Jaffe, Michael R. Emmert-Buck. (2011) Why is it crucial to reintegrate pathology into cancer research?. BioEssays 33:7, 490-498
CrossRef39
Astrid Schmieder, Kai Schledzewski, Julia Michel, Jan P. Tuckermann, Lydia Tome, Carsten Sticht, Cleopatra Gkaniatsou, Jan P. Nicolay, Alexandra Demory, Jörg Faulhaber, Julia Kzhyshkowska, Cyrill Géraud, Sergij Goerdt. (2011) Synergistic activation by p38MAPK and glucocorticoid signaling mediates induction of M2-like tumor-associated macrophages expressing the novel CD20 homolog MS4A8A. International Journal of Cancer 129:1, 122-132
CrossRef40
Maria Kowalska, Joanna Tajer, Magdalena Chechlinska, Malgorzata Fuksiewicz, Beata Kotowicz, Janina Kaminska, Jan Walewski. (2011) Serum macrophage colony-stimulating factor (M-CSF) in patients with Hodgkin lymphoma. Medical Oncology
CrossRef41
F. Munari, S. Lonardi, M. A. Cassatella, C. Doglioni, M. G. Cangi, A. Amedei, F. Facchetti, Y. Eishi, M. Rugge, M. Fassan, M. de Bernard, M. M. D'Elios, W. Vermi. (2011) Tumor-associated macrophages as major source of APRIL in gastric MALT lymphoma. Blood 117:24, 6612-6616
CrossRef42
P. Kamper, M. Ludvigsen, K. Bendix, S. Hamilton-Dutoit, G. A. Rabinovich, M. B. Moller, J. R. Nyengaard, B. Honore, F. d'Amore. (2011) Proteomic analysis identifies galectin-1 as a predictive biomarker for relapsed/refractory disease in classical Hodgkin lymphoma. Blood 117:24, 6638-6649
CrossRef43
Neta Erez, Lisa M. Coussens. (2011) Leukocytes as paracrine regulators of metastasis and determinants of organ-specific colonization. International Journal of Cancer 128:11, 2536-2544
CrossRef44
Julia S Pettersen, Judilyn Fuentes-Duculan, Mayte Suárez-Fariñas, Katherine C Pierson, Alexander Pitts-Kiefer, Linda Fan, Daniel A Belkin, Claire Q F Wang, Shivaprasad Bhuvanendran, Leanne M Johnson-Huang, Mark J Bluth, James G Krueger, Michelle A Lowes, John A Carucci. (2011) Tumor-Associated Macrophages in the Cutaneous SCC Microenvironment Are Heterogeneously Activated. Journal of Investigative Dermatology 131:6, 1322-1330
CrossRef45
R. D. Gascoyne, C. Steidl. (2011) VII. The role of the microenvironment in lymphoid cancers. Annals of Oncology 22:Supplement 4, iv47-iv50
CrossRef46
Leena C. Hertzen, H. Joensuu, T. Haahtela. (2011) Microbial deprivation, inflammation and cancer. Cancer and Metastasis Reviews 30:2, 211-223
CrossRef47
Frank Vari, Maher K Gandhi. (2011) Broad-spectrum immunosuppression by classless monocytes in non-Hodgkin’s lymphoma. Immunotherapy 3:6, 723-726
CrossRef48
Lauren S. Maeda, Mark Lee, Ranjana H Advani. (2011) Current concepts and controversies in the management of early stage Hodgkin lymphoma. Leukemia & Lymphoma 52:6, 962-971
CrossRef49
D. Azambuja, Y. Natkunam, I. Biasoli, I. S. Lossos, M. W. Anderson, J. C. Morais, N. Spector. (2011) Lack of association of tumor-associated macrophages with clinical outcome in patients with classical Hodgkin's lymphoma. Annals of Oncology
CrossRef50
M. Dietel, C. Denkert, K. Hauptmann, I. Anagnostopoulos, K. Jöhrens. (2011) Konzept der prädiktiven Molekularpathologie. Der Onkologe 17:5, 437-452
CrossRef51
Matthew D. Vesely, Michael H. Kershaw, Robert D. Schreiber, Mark J. Smyth. (2011) Natural Innate and Adaptive Immunity to Cancer. Annual Review of Immunology 29:1, 235-271
CrossRef52
Mario Leonardo Squadrito, Michele De Palma. (2011) Macrophage regulation of tumor angiogenesis: Implications for cancer therapy. Molecular Aspects of Medicine 32:2, 123-145
CrossRef53
Raffaella Bonecchi, Massimo Locati, Alberto Mantovani. (2011) Chemokines and Cancer: A Fatal Attraction. Cancer Cell 19:4, 434-435
CrossRef54
Christian Steidl, Sohrab P. Shah, Bruce W. Woolcock, Lixin Rui, Masahiro Kawahara, Pedro Farinha, Nathalie A. Johnson, Yongjun Zhao, Adele Telenius, Susana Ben Neriah, Andrew McPherson, Barbara Meissner, Ujunwa C. Okoye, Arjan Diepstra, Anke van den Berg, Mark Sun, Gillian Leung, Steven J. Jones, Joseph M. Connors, David G. Huntsman, Kerry J. Savage, Lisa M. Rimsza, Douglas E. Horsman, Louis M. Staudt, Ulrich Steidl, Marco A. Marra, Randy D. Gascoyne. (2011) MHC class II transactivator CIITA is a recurrent gene fusion partner in lymphoid cancers. Nature 471:7338, 377-381
CrossRef55
Nicolas André, Laetitia Padovani, Eddy Pasquier. (2011) Metronomic scheduling of anticancer treatment: the next generation of multitarget therapy?. Future Oncology 7:3, 385-394
CrossRef56
Alberto Mantovani. (2011) B cells and macrophages in cancer: yin and yang. Nature Medicine 17:3, 285-286
CrossRef57
A. Rinaldi, M. Mian, E. Chigrinova, L. Arcaini, G. Bhagat, U. Novak, P. M. V. Rancoita, C. P. De Campos, F. Forconi, R. D. Gascoyne, F. Facchetti, M. Ponzoni, S. Govi, A. J. M. Ferreri, M. Mollejo, M. A. Piris, L. Baldini, J. Soulier, C. Thieblemont, V. Canzonieri, V. Gattei, R. Marasca, S. Franceschetti, G. Gaidano, A. Tucci, S. Uccella, M. G. Tibiletti, S. Dirnhofer, C. Tripodo, C. Doglioni, R. Dalla Favera, F. Cavalli, E. Zucca, I. Kwee, F. Bertoni. (2011) Genome-wide DNA profiling of marginal zone lymphomas identifies subtype-specific lesions with an impact on the clinical outcome. Blood 117:5, 1595-1604
CrossRef58
Paweł Basta, Krystyna Galazka, Pawel Mach, Wojciech Jozwicki, Malgorzata Walentowicz, Lukasz Wicherek. (2011) The Immunohistochemical Analysis of RCAS1, HLA-G, and B7H4-Positive Macrophages in Partial and Complete Hydatidiform Mole in Both Applied Therapeutic Surgery and Surgery Followed by Chemotherapy. American Journal of Reproductive Immunology 65:2, 164-172
CrossRef59
Paolo Corradini, Barbara Sarina, Lucia Farina. (2011) Allogeneic transplantation for Hodgkin’s lymphoma. British Journal of Haematology 152:3, 261-272
CrossRef60
Vincent T. DeVita, George P. Canellos. (2011) Hematology in 2010: New therapies and standard of care in oncology. Nature Reviews Clinical Oncology 8:2, 67-68
CrossRef61
Juan M. Melero-Martin, Andrew C. Dudley. (2011) Concise Review: Vascular Stem Cells and Tumor Angiogenesis. STEM CELLS 29:2, 163-168
CrossRef62
Guido Ricardo Gonzalez-Fontal, Joaquin D. Rosales, Roberto Jaramillo, Andres F. Henao-Martinez. (2011) Primary Extranodal, Extralymphatic Hodgkin Lymphoma of the Mandible. Case Reports in Medicine 2011, 1-4
CrossRef63
Eric D. Hsi, Tamar Tadmor. (2011) Microenvironment in peripheral T cell lymphomas: macrophages and angiogenesis as targets. Leukemia & Lymphoma 52:1, 3-4
CrossRef64
Enrico Derenzini, Anas Younes. (2011) Predicting treatment outcome in classical Hodgkin lymphoma: genomic advances. Genome Medicine 3:4, 26
CrossRef65
Yvette L. Kasamon. (2011) Prognostication and Risk-Adapted Therapy of Hodgkin's Lymphoma Using Positron Emission Tomography. Advances in Hematology 2011, 1-12
CrossRef66
Evgeny Klyuchnikov, Ulrike Bacher, Nicolaus Kröger, Ilya Kazantsev, Tatjana Zabelina, Francis Ayuk, Axel Rolf Zander. (2011) The Role of Allogeneic Stem Cell Transplantation in Relapsed/Refractory Hodgkin's Lymphoma Patients. Advances in Hematology 2011, 1-9
CrossRef67
Jeong Hyeon Lee, Yoon Jin Kwak, Chul Hwan Kim, Insun Kim. (2011) Association of CD57+ Natural Killer Cells with Better Overall Survival in DLBCL Patients. The Korean Journal of Pathology 45:4, 361
CrossRef68
Claudio Fozza, Maurizio Longinotti. (2011) T-Cell Traffic Jam in Hodgkin's Lymphoma: Pathogenetic and Therapeutic Implications. Advances in Hematology 2011, 1-8
CrossRef69
Donatella Aldinucci, Marta Celegato, Cinzia Borghese, Alfonso Colombatti, Antonino Carbone. (2011) IRF4 silencing inhibits Hodgkin lymphoma cell proliferation, survival and CCL5 secretion. British Journal of Haematology 152:2, 182-190
CrossRef70
François Bertucci, Bruno Chetaille, Luc Xerri. (2011) Gene Expression Profiling for In Silico Microdissection of Hodgkin's Lymphoma Microenvironment and Identification of Prognostic Features. Advances in Hematology 2011, 1-8
CrossRef71
S. Montes-Moreno. (2011) Hodgkin's Lymphomas: A Tumor Recognized by Its Microenvironment. Advances in Hematology 2011, 1-6
CrossRef72
Mário Henrique M. Barros, Gabriela Vera-Lozada, Fernando A. Soares, Gerald Niedobitek, Rocio Hassan. (2011) Tumor microenvironment composition in pediatric classical Hodgkin lymphoma is modulated by age and Epstein-Barr virus infection. International Journal of Cancern/a-n/a
CrossRef73
Rita A Mukhtar, Onouwem Nseyo, Michael J Campbell, Laura J Esserman. (2011) Tumor-associated macrophages in breast cancer as potential biomarkers for new treatments and diagnostics. Expert Review of Molecular Diagnostics 11:1, 91-100
CrossRef74
Diponkar Banerjee. (2011) Recent Advances in the Pathobiology of Hodgkin's Lymphoma: Potential Impact on Diagnostic, Predictive, and Therapeutic Strategies. Advances in Hematology 2011, 1-19
CrossRef75
Pier Paolo Piccaluga, Claudio Agostinelli, Anna Gazzola, Claudio Tripodo, Francesco Bacci, Elena Sabattini, Maria Teresa Sista, Claudia Mannu, Maria Rosaria Sapienza, Maura Rossi, Maria Antonella Laginestra, Carlo A. Sagramoso-Sacchetti, Simona Righi, Stefano A. Pileri. (2011) Pathobiology of Hodgkin Lymphoma. Advances in Hematology 2011, 1-18
CrossRef76
Mikhail Roshal, Brent L. Wood, Jonathan R. Fromm. (2011) Flow Cytometric Detection of the Classical Hodgkin Lymphoma: Clinical and Research Applications. Advances in Hematology 2011, 1-9
CrossRef77
Marc Baay, Anja Brouwer, Patrick Pauwels, Marc Peeters, Filip Lardon. (2011) Tumor Cells and Tumor-Associated Macrophages: Secreted Proteins as Potential Targets for Therapy. Clinical and Developmental Immunology 2011, 1-12
CrossRef78
T. Lwin, J. Lin, Y. S. Choi, X. Zhang, L. C. Moscinski, K. L. Wright, E. M. Sotomayor, W. S. Dalton, J. Tao. (2010) Follicular dendritic cell-dependent drug resistance of non-Hodgkin lymphoma involves cell adhesion-mediated Bim down-regulation through induction of microRNA-181a. Blood 116:24, 5228-5236
CrossRef79
Stephen L. Shiao, Lisa M. Coussens. (2010) The Tumor-Immune Microenvironment and Response to Radiation Therapy. Journal of Mammary Gland Biology and Neoplasia 15:4, 411-421
CrossRef80
K. A. Blum. (2010) Upcoming Diagnostic and Therapeutic Developments in Classical Hodgkin's Lymphoma. Hematology 2010:1, 93-100
CrossRef81
S. S. Dave. (2010) Host Factors for Risk and Survival in Lymphoma. Hematology 2010:1, 255-258
CrossRef82
Jonathan Sive, David Linch. 2010. Hodgkin Lymphoma. , 639-654.
CrossRef83
V. Coelho, S. Krysov, A. M. Ghaemmaghami, M. Emara, K. N. Potter, P. Johnson, G. Packham, L. Martinez-Pomares, F. K. Stevenson. (2010) Glycosylation of surface Ig creates a functional bridge between human follicular lymphoma and microenvironmental lectins. Proceedings of the National Academy of Sciences 107:43, 18587-18592
CrossRef84
J. Han Krieken. (2010) New developments in the pathology of malignant lymphoma: a review of the literature published from April 2010–July 2010. Journal of Hematopathology 3:2-3, 117-126
CrossRef85
Andreas Josting. (2010) Prognostic factors in Hodgkin lymphoma. Expert Review of Hematology 3:5, 583-592
CrossRef86
Subhra K Biswas, Alberto Mantovani. (2010) Macrophage plasticity and interaction with lymphocyte subsets: cancer as a paradigm. Nature Immunology 11:10, 889-896
CrossRef87
Hubert E. Blum. (2010) Individualized medicine 2010. Journal of Cellular and Molecular Medicine 14:9, 2257-2263
CrossRef88
B. Sanchez-Espiridion, C. Montalban, A. Lopez, J. Menarguez, P. Sabin, C. Ruiz-Marcellan, A. Lopez, R. Ramos, J. Rodriguez, A. Canovas, C. Camarero, M. Canales, J. Alves, R. Arranz, A. Acevedo, A. Salar, S. Serrano, A. Bas, J. M. Moraleda, P. Sanchez-Godoy, F. Burgos, C. Rayon, M. F. Fresno, J. G. Larana, M. Garcia-Cosio, C. Santonja, J. L. Lopez, M. Llanos, M. Mollejo, J. Gonzalez-Carrero, A. Marin, J. Forteza, R. Garcia-Sanz, J. F. Tomas, M. M. Morente, M. A. Piris, J. F. Garcia, . (2010) A molecular risk score based on 4 functional pathways for advanced classical Hodgkin lymphoma. Blood 116:8, e12-e17
CrossRef89
John Gribben, Andreas Rosenwald, Randy Gascoyne, Georg Lenz. (2010) Targeting the microenvironment. Leukemia & Lymphoma 51:S1, 34-40
CrossRef90
Alberto Fabbri, Alessandro Gozzetti, Francesco Forconi, Francesco Lauria. (2010) Predicting the clinical course of Hodgkin lymphoma. Nature Reviews Clinical Oncology 7:8,
CrossRef91
Christiana Ruhrberg, Michele De Palma. (2010) A double agent in cancer: Deciphering macrophage roles in human tumors. Nature Medicine 16:8, 861-862
CrossRef92
Richard A. Flavell, Shomyseh Sanjabi, Stephen H. Wrzesinski, Paula Licona-Limón. (2010) The polarization of immune cells in the tumour environment by TGFβ. Nature Reviews Immunology 10:8, 554-567
CrossRef93
Kelley V. Foyil, Nancy L. Bartlett. (2010) Anti-CD30 Antibodies for Hodgkin Lymphoma. Current Hematologic Malignancy Reports 5:3, 140-147
CrossRef94
(2010) Macrophages in Hodgkin's Lymphoma. New England Journal of Medicine 362:22, 2135-2136
Full Text95
Volker Diehl. (2010) Hematology: Are macrophages the bad guys in Hodgkin lymphoma?. Nature Reviews Clinical Oncology 7:6, 301-302
CrossRef96
Graziella Solinas, Federica Marchesi, Cecilia Garlanda, Alberto Mantovani, Paola Allavena. (2010) Inflammation-mediated promotion of invasion and metastasis. Cancer and Metastasis Reviews 29:2, 243-248
CrossRef97
DeVita, Vincent T. Jr., Costa, José, . (2010) Toward a Personalized Treatment of Hodgkin's Disease. New England Journal of Medicine 362:10, 942-943
Full Text98
Alexandar Tzankov, Matthias S. Matter, Stephan Dirnhofer. (2010) Refined Prognostic Role of CD68-Positive Tumor Macrophages in the Context of the Cellular Micromilieu of Classical Hodgkin Lymphoma. Pathobiology 77:6, 301-308
CrossRef99
P. Allavena, M. Chieppa, G. Bianchi, G. Solinas, M. Fabbri, G. Laskarin, A. Mantovani. (2010) Engagement of the Mannose Receptor by Tumoral Mucins Activates an Immune Suppressive Phenotype in Human Tumor-Associated Macrophages. Clinical and Developmental Immunology 2010, 1-10
CrossRef
- Letters
