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Dendritic Cell Based Tumor Vaccination in Prostate and Renal Cell Cancer: A Systematic Review and Meta-Analysis

  • Andreas Draube ,

    Contributed equally to this work with: Andreas Draube, Nela Klein-González

    Affiliation Laboratory for Tumor and Transplantation Immunology, Department I of Internal Medicine, University Hospital of Cologne, Cologne, Germany

  • Nela Klein-González ,

    Contributed equally to this work with: Andreas Draube, Nela Klein-González

    Affiliations Laboratory for Tumor and Transplantation Immunology, Department I of Internal Medicine, University Hospital of Cologne, Cologne, Germany, Center for Molecular Medicine (CMMC), University of Cologne, Cologne, Germany

  • Stefanie Mattheus,

    Affiliation Laboratory for Tumor and Transplantation Immunology, Department I of Internal Medicine, University Hospital of Cologne, Cologne, Germany

  • Corinne Brillant,

    Affiliation Cochrane Hematological Malignancies Group, Department I of Internal Medicine, University Hospital of Cologne, Cologne, Germany

  • Martin Hellmich,

    Affiliation Institute of Medical Statistics, Informatics and Epidemiology (IMSIE), University of Cologne, Cologne, Germany

  • Andreas Engert,

    Affiliation Cochrane Hematological Malignancies Group, Department I of Internal Medicine, University Hospital of Cologne, Cologne, Germany

  • Michael von Bergwelt-Baildon

    Affiliation Laboratory for Tumor and Transplantation Immunology, Department I of Internal Medicine, University Hospital of Cologne, Cologne, Germany

Dendritic Cell Based Tumor Vaccination in Prostate and Renal Cell Cancer: A Systematic Review and Meta-Analysis

  • Andreas Draube, 
  • Nela Klein-González, 
  • Stefanie Mattheus, 
  • Corinne Brillant, 
  • Martin Hellmich, 
  • Andreas Engert, 
  • Michael von Bergwelt-Baildon



More than 200 clinical trials have been performed using dendritic cells (DC) as cellular adjuvants in cancer. Yet the key question whether there is a link between immune and clinical response remains unanswered. Prostate and renal cell cancer (RCC) have been extensively studied for DC-based immunotherapeutic interventions and were therefore chosen to address the above question by means of a systematic review and meta-analysis.

Methodology/Principal Findings

Data was obtained after a systematic literature search from clinical trials that enrolled at least 6 patients. Individual patient data meta-analysis was performed by means of conditional logistic regression grouped by study. Twenty nine trials involving a total of 906 patients were identified in prostate cancer (17) and RCC (12). Objective response rates were 7.7% in prostate cancer and 12.7% in RCC. The combined percentages of objective responses and stable diseases (SD) amounted to a clinical benefit rate (CBR) of 54% in prostate cancer and 48% in RCC. Meta-analysis of individual patient data (n = 403) revealed the cellular immune response to have a significant influence on CBR, both in prostate cancer (OR 10.6, 95% CI 2.5–44.1) and in RCC (OR 8.4, 95% CI 1.3–53.0). Furthermore, DC dose was found to have a significant influence on CBR in both entities. Finally, for the larger cohort of prostate cancer patients, an influence of DC maturity and DC subtype (density enriched versus monocyte derived DC) as well as access to draining lymph nodes on clinical outcome could be demonstrated.


As a ‘proof of principle’ a statistically significant effect of DC-mediated cellular immune response and of DC dose on CBR could be demonstrated. Further findings concerning vaccine composition, quality control, and the effect of DC maturation status are relevant for the immunological development of DC-based vaccines.


For more than a century it has been hypothesized that the immune system can be redirected to target malignant cells and thus cure cancer [1], [2]. This concept is based on the notion of cancer immunosurveillance [3]. In accordance to this concept it was shown that in patients with colon cancer the density of infiltrating, antigen-experienced T cells correlated better with clinical outcome than classical histopathological staging [4]. Furthermore, it could be demonstrated that people with a high degree of natural cytotoxicity had a lower risk to develop cancer [5].

Dendritic cells (DC) [6] play a crucial role in the induction of antigen-specific T-cell responses and are therefore the most frequently used cellular adjuvant in clinical trials. However, the heterogeneity of vaccination algorithms, non-standardized cellular products and the lack of established criteria for clinical and immune responses has made it impossible to draw valid conclusions from single clinical trials [7][10]. The key questions about whether induction of immunity is linked to clinical response and whether a dose-response relationship exists remain unanswered. Prostate and renal cell cancer (RCC) are regularly infiltrated by antigen-specific immune cells and are considered susceptible to immunotherapy [11][13]. DC vaccination has been extensively studied in these cancers [14][18]. Additionally, the clinical response criteria used for these tumor entities are complementary: Conventional RECIST/WHO criteria in RCC and combination of radiographic criteria with biochemical markers in prostate cancer. For these reasons, they were identified as an ideal model for a systematic review of DC-based tumor vaccines. The aim of this study was to analyze the studies identified in the literature search concerning vaccination strategies, quality control, and induction of immune and clinical response. Furthermore, using individual-patient data the hypothesis was tested whether induction of immunity indeed leads to disease control. Additionally, immunologic properties with potential influence on vaccination success (e.g. maturation status, access to draining lymph nodes) were analyzed.


Literature search, inclusion and exclusion criteria

A highly sensitive search string for Medline-Ovid was developed in which keywords related to ‘cancer’, ‘dendritic cells’, and ‘clinical trial’ were combined (Text S1). The Medline database search covered the period from January 1987 to September 2008, and was recently up-dated covering the period until December 2010. The search was performed in accordance to the PRISMA statement [19] which is focused on meta-analysis of randomized trials, but can be used as a general basis for systematic reviews.

Clinical trials using DC in prostate and RCC, that enrolled at least 6 patients, published between January 2000 and December 2010, and in English language were included. Studies in which different tumor entities were treated, follow-up studies, and trials using allogeneic DC were excluded.

Study selection and data extraction

Two reviewers (NKG and SM) independently screened the articles identified in the literature search. Disagreements between the reviewers (about 15% of the cases) were resolved by a consensus involving two other reviewers (AD, MBB). Data were collected both at trial and individual patient level in an Access database for: DC subtype, phenotype and purity, antigen delivery, route of vaccination, dose (number of DC per vaccination * number of vaccinations), adjuvants given, toxicity, immune monitoring, and clinical response. Additionally, the following individual patient information was recorded: age, gender, staging, pretreatment, and concomitant therapy. Although information given on phenotype regarding the maturation status of DC was documented, it was the maturation status indicated by the authors that was assumed for the meta-analysis. A cellular or humoral immune response was considered positive when at least one method led to a positive result for the specific antigen, if had not already been detected before vaccination. For clinical outcome, complete response (CR), partial response (PR), mixed response (MR), stable disease (SD), and progressive disease (PD) were documented. The term clinical benefit rate (CBR) was used representing the rate of patients who were either in CR, PR, MR, or SD [20].

Statistical methods

Descriptive analyses were performed both at study data level and at individual patient data level. Prostate and RCC trials were analyzed separately. Available individual patient data was used for meta-analysis. Variables were dichotomized: 1) DC subtype: mature monocyte-derived DC (mat-moDC) vs. immature monocyte-derived DC (imm-moDC); 2) route: intravenous (i.v.) vs. intradermal (i.d.)/intranodal (i.n.)/intralymphatic (i.l)/subcutaneous (s.c.); 3) total DC dose for prostate cancer: ≥86.4×106 (median) vs. <86.4×106; 4) total DC dose for RCC: ≥38.7×106 (median) vs. <38.7×106; 5) age ≥65 vs. <65; 6) clinical response: PD vs. CR, PR, MR, SD; 7) cellular and 8) humoral immune response: positive vs. negative.

Influences on clinical outcome of cellular immune response, humoral immune response, total DC dose, and age were assessed by means of conditional logistic regression grouped by study, thus adjusting varying vaccination protocols or response criteria. Odds ratios (OR) with 95% confidence intervals were calculated. Additionally, the influence of the continuous variable “logarithm of total DC dose (logdose)” on clinical outcome was assessed. Forest plots were supplemented with the overall Mantel-Haenszel estimate (fixed effect).

Data on variables constant within trials, i.e. DC subtype and route of vaccination, were pooled and analyzed by Pearson's chi-square test (unstratified). P-values smaller than 0.05 were considered to indicate statistical significance though no correction for multiple testing was applied. Statistical analyses were performed in IBM SPSS Statistics 19.0 (IBM SPSS Statistics Inc., Chicago, IL), Stata 11.1 (StataCorp, College Station, TX) or Comprehensive Meta Analysis 2.2 (Biostat, Englewood, NJ).


Identification of studies

Of 268 articles identified as reports about clinical trials, 41 were conducted in patients with prostate cancer or RCC. Among these, 12 were excluded for the following reasons: follow-up studies (4) [21][24]; use of allogeneic DC (1) [25]; inclusion of two tumor entities (1) [26]; <6 patients (3) [27][29]; not written in English (1) [30]; lack of information about clinical outcome (1) [31]; and follow-up reporting of the same study (1) [32], [33]. One study included both prostate and RCC patients; however, only prostate cancer patients were included because the number of patients with RCC was only five [34]. Figure 1 displays the search process.

Figure 1. Flow diagram showing record identification, record screening, full text article eligibility and study inclusion process.

Twenty nine studies in prostate (17) and RCC (12) met all inclusion criteria and were subject of this meta-analysis [33][61]. In total, the studies included 906 patients (prostate cancer 720, RCC 186). Apart from three randomized phase III studies in prostate cancer [33], [40], [42], all trials were phase I/II clinical studies. The histological RCC subtype was specified in 10 of 12 studies and was either clear cell in all patients [51], [52], [54], [59], [61] or clear cell in the vast majority of patients with few exceptions (in all studies <15%: papillary, clear cell/sarcomatoid or chromophilic) [50], [53], [55], [57], [58]. The median patient number within the phase I/II studies was 14 for prostate cancer (range 6–31) and 12 for RCC (range 8–35) (Table 1).

Table 1. Clinical trials with DC based tumor vaccination in prostate cancer and RCC.

Patient characteristics

In prostate cancer, 662 patients had metastatic disease, 51 biochemical relapse, and 7 local recurrence. All RCC patients had metastatic disease (Table S1).

87% (458/526 patients with data available) of prostate cancer patients had prior surgery or radiotherapy, 17% (109/646) prior chemotherapy, and 96% (632/660) prior hormone therapy or castration. In one prostate cancer trial all patients received IFN-γ simultaneously [41].

92% (142/154 patients with data available) of RCC patients had prior surgery or radiotherapy, 17% (24/143) prior chemotherapy and 36% (54/151) prior immunotherapy. In 36% (67/182) of RCC patients systemic IL-2 [50], [57], [60] or IL2/IFN-α [58] was simultaneously applied to the DC vaccine. In one study, IL2 was given immediately after the last vaccination [61]. In two studies, 3 and 6 patients, respectively, received IL-2, IL-2/IFN-γ or IL2/IFN-γ/5-FU after the end of the vaccination period [56], [59].


Prostate Cancer.

Imm-moDC were used in 5 trials (90 patients) [34], [35], [39], [44], [45], whereas mat-moDC were used in 6 trials (77 patients) [38], [41], [43], [47][49]. In 3 phase I/II studies (65 patients) [36], [37], [46] and in the 3 phase III studies (488 patients) [33], [40], [42] density-enriched DC were used. Numerous strategies for antigen delivery were employed (Table 1): Peptide pulsing was preferred in 6 studies [38], [41], [44], [45], [48], [49]; loading with whole protein in 7 studies [33], [35][37], [40], [42], [46]; loading with tumor lysates in one study [34]; and RNA transfection or coincubation in 3 studies [39], [43], [47]. In 11 studies (87% of all prostate cancer patients), vaccination was intravenously applied or an intravenous and a non-intravenous route (i.d., s.c., i.l., i.n.) were combined. In 6 studies (13% of all patients) a non-intravenous route was preferred. Details are compiled in Table 1.

Cell numbers applied per vaccination and numbers of vaccination are given in Table S2.


Imm-moDC were used in 3 studies (34 patients) [53], [57], [59], whereas mat-moDC in 9 studies (152 patients) [50][52], [54][56], [58], [60], [61]. Peptide pulsing was chosen in two studies [51], [61]; loading with tumor lysates in 6 studies [53][58]; RNA coincubation in two studies [52], [59]; and cell fusion with autologous tumor cells in another study [60]. In one study DC were either loaded with peptides or tumor lysate [50]. In 10 studies a non-intravenous route was chosen (84% of all RCC patients), whereas in two studies (16% of patients) an intravenous or a combined vaccination was preferred (Table 1).

Comparison of vaccines between prostate cancer and RCC trials.

In prostate cancer trials an intravenous route or a combination of different routes was the strategy most frequently preferred, whereas a non-intravenous route was chosen in the majority of RCC trials. In regard to the total patient number most prostate cancer patients received density-enriched DC, which was due to the large randomized trials, whereas most RCC patients received mat-moDC. In prostate cancer trials, pulsing with defined peptides or proteins was the preferred antigen delivery strategy, whereas in RCC it was pulsing with tumor cell lysates.

In regard to individual patient data available for the meta-analysis compared to the study data level described above, it is important to mention that for prostate cancer the percentages of patients receiving mat-moDC, imm-moDC, and density-enriched DC were more evenly distributed with 33%, 39%, and 28%, respectively. The ratio of intravenous - non-intravenous route was also more evenly distributed: 52% vs. 48% (compare to Table 1 and Table S5). In contrast, for RCC individual-patient data available, the uneven distribution regarding the route of vaccination and the DC maturation status was the same compared to the study data level described above.

Quality controls

To determine quality control of the DC-based vaccines, criteria proposed by Figdor and colleagues were followed concerning phenotype and purity [62]. The authors suggested six surface antigens for mat-moDC to be documented (CD83, CD80, CD86, MHC-I, MHC-II, CCR7) and 7 for imm-moDC (CD14, CD83, CD80, CD86, MHC-I, MHC-II, CCR5). No trial using moDC reported all antigens proposed. Nevertheless, in prostate cancer two studies reported 5 of 6 and 6 of 7; in RCC four studies reported 5 of 6 and 6 of 7 proposed antigens for mat-moDC and imm-moDC, respectively [39], [49][51], [55], [59]. On the other hand, two prostate cancer studies and three RCC studies failed to report the phenotype [34], [44], [53], [56], [60]. The majority of the studies provided information for the following surrogate markers: CD14 (prostate cancer 8/17 studies, RCC 7/12 studies), HLA-DR (prostate cancer 11/17, RCC 7/12), CD86 (prostate cancer 11/17, RCC 8/12), and CD83 (prostate cancer 9/17, RCC 9/12).

Information about purity of DC vaccine was available only in 10 studies, (prostate cancer: 8/17, RCC: 2/12). Purity above 80% proposed by Figdor and co-workers was indicated only in four trials (prostate cancer: 3/17 and RCC: 1/12) [35], [41], [49], [55].

Comparing RCC with prostate cancer revealed that information on DC phenotype was slightly more detailed in RCC trials, whereas the information on DC purity was better in prostate cancer trials.

Treatment related toxicity

DC vaccination was safe in both entities: only mild side effects were described. Most adverse effects were local reactions at the injection site, fever and flu-like symptoms. Less common were myalgias, fatigue, bone or articular pains. Few toxicities were detected above grade 2 or were linked to additional cytokine therapy (e.g. [58]). A detailed overview is provided in Table S3.

Immune response assessment

The cellular immune response was determined in all studies except for two [40], [44]. At least one of the functional tests proposed by Figdor – ELISPOT, cytotoxicity, or cytokine production after antigen-specific stimulation [62] – was performed in the majority of the studies (prostate cancer 10/17; RCC trials 10/12). Humoral immune response using antigen-specific ELISA was reported in 6 of 17 prostate cancer and in 2 of 12 RCC trials.

Analysis of study data level (Table 1) revealed that DC vaccination led to an antigen-specific cellular immune response in 77% of patients with prostate cancer (196/256 patients tested) and in 61% of patients with RCC (63/104 patients tested) (Fig. 2A). A specific humoral immune response was detected in 55% of prostate cancer patients (137/251 patients tested), whereas no humoral response was observed in the few RCC patients tested (0/18 patients tested). Complete information about immune assessment is summarized in Table S4.

Figure 2. Immune and clinical responses in prostate and renal cell cancer (RCC) patients.

(A) Induction of tumor antigen specific cellular or humoral immune responses. An immune response was considered positive when at least one of the conducted assays was positive. (B) Analysis of objective response (black columns) and clinical benefit rate (CBR; grey columns). CR, complete response; PR, partial response; MR, mixed response; SD, stable disease.

Clinical response

Lack of radiologically measurable disease is a frequent problem in prostate cancer trials. The Prostate-Specific Antigen Working Group of the NCI has therefore incorporated PSA levels into remission criteria for metastatic disease [63]. Most studies combined both PSA levels and radiological response [36][38], [41], [43], [44], [48]. In 12 of 17 trials ‘progressive disease’ as prerequisite before study entry was explicitly required, whereas in the other 5 trials the study entry was not limited, it was allowed for all patients with metastatic or hormone refractory disease (Table S1). Six trials could not be assessed for clinical response since: 1) clinical response was not reported [45]; 2) only reports on PSA doubling times [39]; 3) median time to progression (TTP) [33], [40], [42]; or 4) median PSA courses of vaccination groups [47] were documented.

In RCC trials response criteria were in accordance to RECIST or WHO criteria; however, these were explicitly mentioned only in 5 trials [16], [55], [56], [58], [61]. Eight studies only included patients with documented progressive or new metastatic disease before nephrectomy, in one study 85% of the patients had progressive disease, and in three studies enrollment was allowed to all patients with metastatic disease. In one trial clinical response was not reported [52]. Table S1 contains information about the trials clinical response evaluation.

Overall, the clinical response was documented in 181 prostate cancer patients. 7.7% of the patients had an objective response (CR+PR+MR). Additionally, 83 SD were observed representing a clinical benefit rate (CBR) of 54% (97/181) (Fig. 2B). On the other hand, in a total of 166 RCC patients investigated, objective response rate was 12.7%. SD was found in 58 patients, resulting in a CBR of 48% (79/166) (Fig. 2B; Table S1).


Individual data was available from 231 prostate cancer and 172 RCC patients (Table S5). Different variables were tested for their influence on CBR in which stable diseases (SD) were included. Separated stratified analyses for the influence only on CR+PR were not possible due to their low number. Influence of the following variables on CBR was assessed by conditional logistic regression grouped by study: 1) cellular immune response, 2) total DC dose, 3) humoral immune response, 4) age, and 5) gender of RCC patients (Table 2). Additionally, the continuous variable DC logdose was assessed. DC subtype and route of vaccination were constant within most of the trials and therefore tested by means of unstratified chi-square tests (Table 3).

Table 2. Meta-analysis of influence on clinical benefit rate - conditional logistic regression grouped by study.

Table 3. Meta-analysis of influence on clinical benefit rate – chi-square analysis of pooled individual data across studies.

The most relevant finding is that the cellular immune response was found to have a significant influence on CBR, both in prostate cancer (OR 10.6, 95% CI 2.5–44.1) and in RCC (OR 8.4, 95% CI 1.3–53.0) (Fig. 3).

Figure 3. Meta-analysis of the odds ratio (OR) of cellular immune response and clinical benefit rate.

Conditional logistic regression grouped by study using individual patient data revealed a statistically significant association between clinical benefit rate and induction of tumor antigen specific cellular immune response for prostate cancer and renal cell cancer (RCC) patients. Horizontal lines denote 95% confidence intervals.

For assessment of the influence of total DC dose, the median between the trials served as threshold. Strikingly, a significant OR was identified for higher DC doses both in prostate cancer and RCC trials (prostate cancer: OR 4.8, 95% CI 1.2–18.9; RCC: OR 7.0, 95% CI 1.9–25.0). To visualize stratified analyses by Forest plots for the influence on clinical outcome of cellular immune response and total DC dose, additional classical Mantel-Haenszel analyses were conducted. Although compared to conditional logistic regression fewer studies and lower patient numbers could be included in these analyses, similar results, which were statistically significant, were obtained (Figure S1).

To further study an association between increasing total DC dose and clinical benefit, the non-dichotomised logarithmic total DC dose (logdose) was assessed by conditional logistic regression. Interestingly, for prostate cancer no continuous effect of increasing DC dose could be observed (OR 1.3, 95% CI 0.6–2.9), but there was a statistical significant effect in RCC trials (OR 4.4; 95% CI 2.3–8.5).

Specific humoral immune responses were determined only in 12 RCC patients. Thus, the analysis was not feasible for RCC. In prostate cancer patients no statistically significant association of humoral response and CBR was detected (OR 0.8, 95% CI 0.1–4.5). There was no significant association between the variables age in both tumor entities and gender in RCC and CBR.

Chi-square tests across the studies revealed a significant positive influence of DC maturation status on the CBR in prostate cancer patients (mat-moDC vs. imm-moDC, OR 2.8, 95% CI 1.4–6.9). Interestingly, vaccination routes with access to draining lymph nodes (i.d., i.n., i.l., s.c) were found to result in better clinical response rates in comparison to intravenous (i.v.) injections in both tumor entities. Nevertheless, the OR was statistically significant only in prostate cancer patients (OR 3.2, 95% CI 1.1–9.0). Strikingly, comparing monocyte-derived DC with density-enriched DC in prostate cancer trials revealed an advantage for density enriched DC over moDC (OR 0.2, 95% CI 0.1–0.4). Even if the comparison was restricted to mat-moDC, the analysis led to the same finding (OR 0.3, 95% CI 0.1–0.7).

To further investigate the influence of DC subtype on CBR, additional conditional logistic regression tests were performed analyzing the variable DC logdose within the subgroups of patients treated with either mature or immature moDC. In prostate cancer, for both subgroups, this analysis did not change the statistically not significant result described above. In contrast, in RCC, an influence of the increasing DC dose was found only in the group of patients treated with mat-moDC (OR 4.3, 95% CI 2.09–7.79) but not in the group of patients treated with imm-moDC.


A growing body of knowledge about tumor immunosurveillance – and loss thereof – has contributed to the refinement of anti-tumor immunotherapy [64]. Yet, despite high expectations in DC-based vaccination trials, thus far clinical responses have been disappointing [20], [65], [66]. Lack of efficacy can be explained by well-defined tumor escape mechanisms [67][69], which are currently being addressed by combining DC vaccination with other approaches, such as CTLA4 [70] or CD25 blockade [52]. However it is also due to the design of clinical trials conducted so far: Most are small phase I/II studies with conventional primary end points safety or feasibility. The objective of this systematic review and meta-analysis was to determine: first, whether an association between immune and clinical response could be detected; and second, which factors were associated with better clinical outcome.

Interestingly, the total objective clinical response rates (7.7% for prostate cancer and 12.7% for RCC) were quite similar compared to DC-based vaccination trials in other tumor entities [20], [65], [71]. When including SD the clinical benefit rate (CBR) was 54% for prostate cancer and 48% for RCC patients, which is consistent with previous reports [14], [16].

Results of meta-analysis of non-randomized phase I/II trials have to be interpreted with caution. For example, the variables dose or DC subtype represent potential confounders to each other. Moreover, definitions of clinical endpoints and immune monitoring are not always consistent among the trials (Tables S1 and S4). Nevertheless, for the key variables (cellular immune response and dose) we could perform conditional logistic regression grouped by study, thus adjusting varying vaccination protocols or response criteria.

Due to the low number of patients with objective responses we had to focus on CBR (CR+PR+MR+SD). However, this clinical endpoint also addresses fundamental aspects of oncology: There is an increasing debate on ideal clinical response criteria in active immunotherapy approaches [72]. Thus far, most trials using DC-vaccines have been conducted in the palliative setting of metastatic or locally advanced disease. For these patients reducing symptoms and prolonging survival time with acceptable side effects are the primary clinical treatment objectives. Thus, overall survival (OS) and time to progression (TTP) would be the ideal endpoints in this situation. This identical challenge has been identified for so called ‘targeted drugs’ [73]. A significant prolongation of median OS was first found in a randomized DC-based trial using density-enriched DC conducted in metastatic prostate cancer [33], which is included in this meta-analysis. It has recently been confirmed by another phase III trial [42] and has led to the FDA approval of Sipuleucel-T in hormone-refractory prostate cancer. When the endpoints OS or TTP are not available, terms of disease stabilization or clinical benefit rate might help evaluation of therapeutic success [20], [74]. However, as shown in our analysis the differences in SD definitions remain problematic (Table S1). Additionally, in nearly one third of the trials included in this study, patients without documented progressive disease at study entry were included. Thus, using CBR as a clinical endpoint will need concise and consistent definitions of SD.

Despite the aforementioned limitations this meta-analysis provides new insights into the field of cancer vaccination. First, we found an association between specific cellular immune response and clinical outcome, both in prostate cancer and RCC. Thus far, this has been reported only within single trials in prostate cancer [22], [37], [43], [46], [48] and RCC [61]. Importantly, this association provides a proof of concept for DC-based vaccines. As analyzed in detail, RCC and prostate cancer trials mostly differ in the chosen clinical response criteria and vaccine strategies. For the latter this was particularly observed in the preferred route of vaccination, DC subtype, and different strategies for antigen delivery. Thus, the observed link between induction of cellular immune response and clinical benefit identified by stratified analyses is of great importance due to the fact that is was identified independent of the chosen vaccination strategy and it was, independently of each other, found in both tumor entities.

The second finding relates to the dose of DC. We found a positive influence of higher DC doses on CBR in both tumor entities. The need of a sufficient amount of vaccinated DC to enable activation of effector cells has already been suggested not only in murine models [75], but also in clinical trials [76]. For prostate cancer, Small described an association between DC dose and TTP but failed to show statistical significance [46]. Interestingly, in prostate cancer a positive influence was not found for continuous increasing doses. It seems that a high threshold exists, which has to be exceeded in order to achieve a better clinical response. In RCC also continuous increasing doses had a positive influence on CBR. This difference found between the tumor entities is difficult to interpret, but might be explained by different dose distributions. In addition, it is relevant that in RCC an influence of the increasing DC dose was only found in the group of patients treated with mat-moDC, but not in the group of patients treated with imm-moDC. This finding is of great interest because it further underlines the importance of maturation status as discussed below.

Specific humoral immune response was shown to have no influence on CBR in prostate cancer. However, the number of patients included in this stratified analysis was very low (Table 2). Nevertheless, a chi-square test across the studies that allowed inclusion of a higher number of patients (63) also failed to show a significant association between humoral response and CBR (data not shown).

Our analysis revealed that in prostate cancer patients vaccination of mat- moDC was associated with a higher CBR than vaccination with imm-moDC. For this analysis only monocyte-derived DC were included as density-enriched DC might have a different biology and few information on their maturation status exist. It has been suggested that imm-moDC bear the risk of inducing tolerance [67], [77][79]. Indeed, Dhodapkar and colleagues observed tolerizing effects of immature DC to influenza matrix peptide in healthy individuals [80]. In melanoma patients it has been demonstrated that maturation status affects not only lymph-node homing [81], but also the induction of T-cell and thus clinical responses [82], [83]. However, no statistically significant association of maturation status and clinical response has been reported so far. Our finding adds weight to experimental evidence of a tolerizing effect of imm-moDC. This observation could however not be reproduced in RCC – likely due to an inhomogeneous distribution of mat-moDC and imm-moDC within the RCC trials.

Interestingly, comparing the influence on clinical outcome of density-enriched DC with that of monocyte-derived DC in prostate cancer revealed an advantage of density-enriched DC over moDC, even if the comparison was restricted to mat-moDC. This finding might further underscore the FDA approval of the cellular therapeutic Sipuleucel-T [42]. Nevertheless, the limitations of pooled chi-square analyses have to be kept in mind especially for this comparison: using this method there is no adjusting for potential systematic differences between the trials using either DC type. For instance, in trials using density-enriched DC, total DC dose was considerably higher than in trials using monocyte-derived DC. It is only a randomized trial that would allow reliable conclusions about the superiority of either DC type.

The optimal route of vaccination is still subject to debate. DC enter lymph nodes in afferent lymph vessels [84], [85]. Mature DC lack CD62L, a homing receptor necessary to enter lymph nodes across high endothelial venules from the blood [84], [86]. For these reasons a route of vaccination allowing access to draining lymph nodes (i.d., i.l., s.c., i.n) should be superior to an intravenous route. Indeed, murine studies have established that intravenously injected DC accumulate in the spleen and non-lymphoid tissues, but not in the lymph nodes [87]. Our analysis revealed a positive influence on CBR of a non-intravenous route (i.d., i.l., s.c., i.n) compared with an intravenous route for moDC in both cancers, reaching statistical significance only in prostate cancer. This finding confirms observations in single trials that have compared different routes [88]. The statistically not significant result for RCC might be explained by the inhomogeneous distribution of intravenous and non-intravenous routes in RCC trials.

This systematic review indicates that vaccine quality controls clearly need improvement. Notably, only 10 out of 29 trials reported the DC purity, and of these, only in four trials purity was above 80% as propounded by Figdor [62]. Moreover, five clinical trials reported no information about DC phenotype at all. One of the prerequisites for future randomized clinical trials using DC should be a definitive consensus about the vaccine quality controls. Information about toxicity was provided in most of the trials and our analysis further confirms that safety is no general issue in DC-based immunotherapy anymore.

Taken together, our meta-analysis demonstrated an association between specific cellular immune response and clinical benefit, both in prostate cancer and RCC trials, thus implying a proof of concept for DC vaccination. Moreover, this study provides evidence for previous assumptions: Relevance of dose, mature phenotype, and lymph-node access. However, the systematic review also revealed a strong heterogeneity regarding DC purity and dose, DC subtype, antigen delivery, route of vaccination, and quality controls. Therefore, we conclude that DC-based immunotherapy clearly warrants further investigation in phase III randomized trials. It is essential that future trials fulfill quality criteria, e.g. as postulated by Figdor [62] and others [9], [72]. It is important not only to standardize the vaccine itself, but also the immune monitoring and the criteria to assess clinical responses [89]. Only if DC are generated and validated like a drug, can they finally be made available to cancer patients as a standard option.

Supporting Information

Figure S1.

Forest plots of Mantel-Haenszel odds ratio estimates. Meta-analysis of the odds ratio of cellular immune response (A, B) or total DC dose (C, D) and clinical benefit rate for prostate cancer and RCC trials are displayed. The size of the squares is proportional to the sample size. Horizontal lines denote 95% confidence intervals for single studies, the diamond the 95% confidence interval for the overall Mantel-Haenszel estimate (fixed effect).



Table S1.

Additional information about included patients, clinical response criteria and clinical responses.



Table S2.

Additional information about number of DC per vaccination and number of vaccinations.



Table S3.

Additional information about toxicity criteria and observed toxicity.



Table S4.

Detailed information about humoral and cellular immune response testing within the trials.



Table S5.

Additional information about data extraction for individual patient data level analyses.



Text S1.

Search string of the Medline Ovid search.




We thank Frauke Naumann (formerly Cochrane Hematological Malignancies Group, Cologne, Germany) for the assistance in developing the search string and performing the literature search. We also thank Andrej Wöhrmann (Medeora©) for the assistance in developing the database.

Author Contributions

Conceived and designed the experiments: AD NKG MB. Performed the experiments: AD NKG SM CB MH MB. Analyzed the data: AD NKG SM CB MH AE MB. Wrote the paper: AD NKG SM CB MH AE MB.


  1. 1. Coley WB (1991) The treatment of malignant tumors by repeated inoculations of erysipelas. With a report of ten original cases. 1893. Clin Orthop Relat Res 3–11.
  2. 2. Ehrlich P (1909) Ueber den jetzigen Stand der Karzinomforschung. Ned Tijdschr Geneeskd 5: 273–290.
  3. 3. Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD (2002) Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol 3: 991–998.
  4. 4. Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, et al. (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313: 1960–1964.
  5. 5. Imai K, Matsuyama S, Miyake S, Suga K, Nakachi K (2000) Natural cytotoxic activity of peripheral-blood lymphocytes and cancer incidence: an 11-year follow-up study of a general population. Lancet 356: 1795–1799.
  6. 6. Steinman RM, Cohn ZA (1973) Identification of a novel cell type in peripheral lymphoid organs of mice. I. Morphology, quantitation, tissue distribution. J Exp Med 137: 1142–1162.
  7. 7. Cranmer LD, Trevor KT, Hersh EM (2004) Clinical applications of dendritic cell vaccination in the treatment of cancer. Cancer Immunol Immunother 53: 275–306.
  8. 8. Osada T, Clay TM, Woo CY, Morse MA, Lyerly HK (2006) Dendritic cell-based immunotherapy. Int Rev Immunol 25: 377–413.
  9. 9. Tuyaerts S, Aerts JL, Corthals J, Neyns B, Heirman C, et al. (2007) Current approaches in dendritic cell generation and future implications for cancer immunotherapy. Cancer Immunol Immunother 56: 1513–1537.
  10. 10. Copier J, Dalgleish AG, Britten CM, Finke LH, Gaudernack G, et al. (2009) Improving the efficacy of cancer immunotherapy. Eur J Cancer 45: 1424–1431.
  11. 11. Verra N, de Jong D, Bex A, Batchelor D, Dellemijn T, et al. (2005) Infiltration of activated dendritic cells and T cells in renal cell carcinoma following combined cytokine immunotherapy. Eur Urol 48: 527–533.
  12. 12. Jocham D, Richter A, Hoffmann L, Iwig K, Fahlenkamp D, et al. (2004) Adjuvant autologous renal tumour cell vaccine and risk of tumour progression in patients with renal-cell carcinoma after radical nephrectomy: phase III, randomised controlled trial. Lancet 363: 594–599.
  13. 13. Asemissen AM, Brossart P (2009) Vaccination strategies in patients with renal cell carcinoma. Cancer Immunol Immunother 58: 1169–1174.
  14. 14. Thomas-Kaskel AK, Waller CF, Schultze-Seemann W, Veelken H (2007) Immunotherapy with dendritic cells for prostate cancer. Int J Cancer 121: 467–473.
  15. 15. Kiessling A, Fussel S, Wehner R, Bachmann M, Wirth MP, et al. (2008) Advances in specific immunotherapy for prostate cancer. Eur Urol 53: 694–708.
  16. 16. Berntsen A, Geertsen PF, Svane IM (2006) Therapeutic dendritic cell vaccination of patients with renal cell carcinoma. Eur Urol 50: 34–43.
  17. 17. Jahnisch H, Fussel S, Kiessling A, Wehner R, Zastrow S, et al. (2010) Dendritic cell-based immunotherapy for prostate cancer. Clin Dev Immunol 2010: 517493. 517493.
  18. 18. Van Poppel H, Joniau S, Van Gool SW (2009) Vaccine therapy in patients with renal cell carcinoma. Eur Urol 55: 1333–1342.
  19. 19. Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6: e1000097.
  20. 20. Nagorsen D, Thiel E (2006) Clinical and immunologic responses to active specific cancer vaccines in human colorectal cancer. Clin Cancer Res 12: 3064–3069.
  21. 21. Fong L, Brockstedt D, Benike C, Wu L, Engleman EG (2001) Dendritic cells injected via different routes induce immunity in cancer patients. J Immunol 166: 4254–4259.
  22. 22. Lodge PA, Jones LA, Bader RA, Murphy GP, Salgaller ML (2000) Dendritic cell-based immunotherapy of prostate cancer: immune monitoring of a phase II clinical trial. Cancer Res 60: 829–833.
  23. 23. Murphy GP, Snow P, Simmons SJ, Tjoa BA, Rogers MK, et al. (2000) Use of artificial neural networks in evaluating prognostic factors determining the response to dendritic cells pulsed with PSMA peptides in prostate cancer patients. Prostate 42: 67–72.
  24. 24. Kraemer M, Hauser S, Schmidt-Wolf IG (2010) Long-term survival of patients with metastatic renal cell carcinoma treated with pulsed dendritic cells. Anticancer Res 30: 2081–2086.
  25. 25. Holtl L, Ramoner R, Zelle-Rieser C, Gander H, Putz T, et al. (2005) Allogeneic dendritic cell vaccination against metastatic renal cell carcinoma with or without cyclophosphamide. Cancer Immunol Immunother 54: 663–670.
  26. 26. Avigan D, Vasir B, Gong J, Borges V, Wu Z, et al. (2004) Fusion cell vaccination of patients with metastatic breast and renal cancer induces immunological and clinical responses. Clin Cancer Res 10: 4699–4708.
  27. 27. Arroyo JC, Gabilondo F, Llorente L, Meraz-Rios MA, Sanchez-Torres C (2004) Immune response induced in vitro by CD16- and CD16+ monocyte-derived dendritic cells in patients with metastatic renal cell carcinoma treated with dendritic cell vaccines. J Clin Immunol 24: 86–96.
  28. 28. Matsumoto A, Haraguchi K, Takahashi T, Azuma T, Kanda Y, et al. (2007) Immunotherapy against metastatic renal cell carcinoma with mature dendritic cells. Int J Urol 14: 277–283.
  29. 29. Lemoine FM, Cherai M, Giverne C, Dimitri D, Rosenzwajg M, et al. (2009) Massive expansion of regulatory T-cells following interleukin 2 treatment during a phase I-II dendritic cell-based immunotherapy of metastatic renal cancer. Int J Oncol 35: 569–581.
  30. 30. Wang H, Zhou FJ, Wang QJ, Qin ZK, Huang LX, et al. (2006) [Efficacy of autologous renal tumor cell lysate-loaded dendritic cell vaccine in combination with cytokine-induced killer cells on advanced renal cell carcinoma–a report of ten cases]. Ai Zheng 25: 625–630.
  31. 31. Putz T, Ramoner R, Gander H, Rahm A, Bartsch G, et al. (2004) Monitoring of CD4+ and CD8+ T-cell responses after dendritic cell-based immunotherapy using CFSE dye dilution analysis. J Clin Immunol 24: 653–663.
  32. 32. Lee D (2003) Autologous dendritic cells pulsed with prostatic acid phosphatase (APC8015) for patients with hormone-refractory prostate cancer with a Gleason score <or = 7. Clin Prostate Cancer 2: 81–83.
  33. 33. Small EJ, Schellhammer PF, Higano CS, Redfern CH, Nemunaitis JJ, et al. (2006) Placebo-controlled phase III trial of immunologic therapy with sipuleucel-T (APC8015) in patients with metastatic, asymptomatic hormone refractory prostate cancer. J Clin Oncol 24: 3089–3094.
  34. 34. Pandha HS, John RJ, Hutchinson J, James N, Whelan M, et al. (2004) Dendritic cell immunotherapy for urological cancers using cryopreserved allogeneic tumour lysate-pulsed cells: a phase I/II study. BJU International 94: 412–418.
  35. 35. Barrou B, Benoit G, Ouldkaci M, Cussenot O, Salcedo M, et al. (2004) Vaccination of prostatectomized prostate cancer patients in biochemical relapse, with autologous dendritic cells pulsed with recombinant human PSA. Cancer Immunol Immunother 53: 453–460.
  36. 36. Burch PA, Breen JK, Buckner JC, Gastineau DA, Kaur JA, et al. (2000) Priming tissue-specific cellular immunity in a phase I trial of autologous dendritic cells for prostate cancer. Clin Cancer Res 6: 2175–2182.
  37. 37. Fong L, Brockstedt D, Benike C, Breen JK, Strang G, et al. (2001) Dendritic cell-based xenoantigen vaccination for prostate cancer immunotherapy. J Immunol 167: 7150–7156.
  38. 38. Fuessel S, Meye A, Schmitz M, Zastrow S, Linne C, et al. (2006) Vaccination of hormone-refractory prostate cancer patients with peptide cocktail-loaded dendritic cells: results of a phase I clinical trial. Prostate 66: 811–821.
  39. 39. Heiser A, Coleman D, Dannull J, Yancey D, Maurice MA, et al. (2002) Autologous dendritic cells transfected with prostate-specific antigen RNA stimulate CTL responses against metastatic prostate tumors. J Clin Invest 109: 409–417.
  40. 40. Higano CS, Schellhammer PF, Small EJ, Burch PA, Nemunaitis J, et al. (2009) Integrated data from 2 randomized, double-blind, placebo-controlled, phase 3 trials of active cellular immunotherapy with sipuleucel-T in advanced prostate cancer. Cancer 115: 3670–3679.
  41. 41. Hildenbrand B, Sauer B, Kalis O, Stoll C, Freudenberg MA, et al. (2007) Immunotherapy of patients with hormone-refractory prostate carcinoma pre-treated with interferon-gamma and vaccinated with autologous PSA-peptide loaded dendritic cells–a pilot study. Prostate 67: 500–508.
  42. 42. Kantoff PW, Higano CS, Shore ND, Berger ER, Small EJ, et al. (2010) Sipuleucel-T immunotherapy for castration-resistant prostate cancer. N Engl J Med 363: 411–422.
  43. 43. Mu LJ, Kyte JA, Kvalheim G, Aamdal S, Dueland S, et al. (2005) Immunotherapy with allotumour mRNA-transfected dendritic cells in androgen-resistant prostate cancer patients. Br J Cancer 93: 749–756.
  44. 44. Murphy GP, Tjoa BA, Simmons SJ, Rogers MK, Kenny GM, et al. (2000) Higher-dose and less frequent dendritic cell infusions with PSMA peptides in hormone-refractory metastatic prostate cancer patients. Prostate 43: 59–62.
  45. 45. Perambakam S, Hallmeyer S, Reddy S, Mahmud N, Bressler L, et al. (2006) Induction of specific T cell immunity in patients with prostate cancer by vaccination with PSA146-154 peptide. Cancer Immunol Immunother 55: 1033–1042.
  46. 46. Small EJ, Fratesi P, Reese DM, Strang G, Laus R, et al. (2000) Immunotherapy of hormone-refractory prostate cancer with antigen-loaded dendritic cells. J Clin Oncol 18: 3894–3903.
  47. 47. Su Z, Dannull J, Yang BK, Dahm P, Coleman D, et al. (2005) Telomerase mRNA-transfected dendritic cells stimulate antigen-specific CD8+ and CD4+ T cell responses in patients with metastatic prostate cancer. J Immunol 174: 3798–3807.
  48. 48. Thomas-Kaskel AK, Zeiser R, Jochim R, Robbel C, Schultze-Seemann W, et al. (2006) Vaccination of advanced prostate cancer patients with PSCA and PSA peptide-loaded dendritic cells induces DTH responses that correlate with superior overall survival. Int J Cancer 119: 2428–2434.
  49. 49. Waeckerle-Men Y, Uetz-von Allmen E, Fopp M, von Moos R, Bohme C, et al. (2006) Dendritic cell-based multi-epitope immunotherapy of hormone-refractory prostate carcinoma. Cancer Immunol Immunother 55: 1524–1533.
  50. 50. Berntsen A, Trepiakas R, Wenandy L, Geertsen PF, thor Straten P, et al. (2008) Therapeutic dendritic cell vaccination of patients with metastatic renal cell carcinoma: a clinical phase 1/2 trial. J Immunother 31: 771–780.
  51. 51. Bleumer I, Tiemessen DM, Oosterwijk-Wakka JC, Voller MC, De Weijer K, et al. (2007) Preliminary analysis of patients with progressive renal cell carcinoma vaccinated with CA9-peptide-pulsed mature dendritic cells. J Immunother 30: 116–122.
  52. 52. Dannull J, Su Z, Rizzieri D, Yang BK, Coleman D, et al. (2005) Enhancement of vaccine-mediated antitumor immunity in cancer patients after depletion of regulatory T cells. J Clin Invest 115: 3623–3633.
  53. 53. Gitlitz BJ, Belldegrun AS, Zisman A, Chao DH, Pantuck AJ, et al. (2003) A pilot trial of tumor lysate-loaded dendritic cells for the treatment of metastatic renal cell carcinoma. J Immunother 26: 412–419.
  54. 54. Holtl L, Zelle-Rieser C, Gander H, Papesh C, Ramoner R, et al. (2002) Immunotherapy of metastatic renal cell carcinoma with tumor lysate-pulsed autologous dendritic cells. Clin Cancer Res 8: 3369–3376.
  55. 55. Kim JH, Lee Y, Bae YS, Kim WS, Kim K, et al. (2007) Phase I/II study of immunotherapy using autologous tumor lysate-pulsed dendritic cells in patients with metastatic renal cell carcinoma. Clin Immunol 125: 257–267.
  56. 56. Marten A, Flieger D, Renoth S, Weineck S, Albers P, et al. (2002) Therapeutic vaccination against metastatic renal cell carcinoma by autologous dendritic cells: preclinical results and outcome of a first clinical phase I/II trial. Cancer Immunol Immunother 51: 637–644.
  57. 57. Oosterwijk-Wakka JC, Tiemessen DM, Bleumer I, de Vries IJ, Jongmans W, et al. (2002) Vaccination of patients with metastatic renal cell carcinoma with autologous dendritic cells pulsed with autologous tumor antigens in combination with interleukin-2: a phase 1 study. J Immunother 25: 500–508.
  58. 58. Schwaab T, Schwarzer A, Wolf B, Crocenzi TS, Seigne JD, et al. (2009) Clinical and immunologic effects of intranodal autologous tumor lysate-dendritic cell vaccine with Aldesleukin (Interleukin 2) and IFN-{alpha}2a therapy in metastatic renal cell carcinoma patients. Clin Cancer Res 15: 4986–4992.
  59. 59. Su Z, Dannull J, Heiser A, Yancey D, Pruitt S, et al. (2003) Immunological and clinical responses in metastatic renal cancer patients vaccinated with tumor RNA-transfected dendritic cells. Cancer Res 63: 2127–2133.
  60. 60. Wei YC, Sticca RP, Li J, Holmes LM, Burgin KE, et al. (2007) Combined treatment of dendritoma vaccine and low-dose interleukin-2 in stage IV renal cell carcinoma patients induced clinical response: A pilot study. Oncol Rep 18: 665–671.
  61. 61. Wierecky J, Muller MR, Wirths S, Halder-Oehler E, Dorfel D, et al. (2006) Immunologic and clinical responses after vaccinations with peptide-pulsed dendritic cells in metastatic renal cancer patients. Cancer Res 66: 5910–5918.
  62. 62. Figdor CG, de Vries IJ, Lesterhuis WJ, Melief CJ (2004) Dendritic cell immunotherapy: mapping the way. Nat Med 10: 475–480.
  63. 63. Bubley GJ, Carducci M, Dahut W, Dawson N, Daliani D, et al. (1999) Eligibility and response guidelines for phase II clinical trials in androgen-independent prostate cancer: recommendations from the Prostate-Specific Antigen Working Group. J Clin Oncol 17: 3461–3467.
  64. 64. Finn OJ (2008) Cancer immunology. N Engl J Med 358: 2704–2715.
  65. 65. Rosenberg S, Yang J, Restifo N (2004) Cancer immunotherapy: moving beyond current vaccines. Nat Med 10: 909–915.
  66. 66. Steinman RM, Banchereau J (2007) Taking dendritic cells into medicine. Nature 449: 419–426.
  67. 67. Steinman RM, Hawiger D, Nussenzweig MC (2003) Tolerogenic dendritic cells. Annu Rev Immunol 21: 685–711.
  68. 68. Zou W (2006) Regulatory T cells, tumour immunity and immunotherapy. Nat Rev Immunol 6: 295–307.
  69. 69. Gabrilovich D (2004) Mechanisms and functional significance of tumour-induced dendritic-cell defects. Nat Rev Immunol 4: 941–952.
  70. 70. Ribas A, Comin-Anduix B, Chmielowski B, Jalil J, de la Rocha P, et al. (2009) Dendritic cell vaccination combined with CTLA4 blockade in patients with metastatic melanoma. Clin Cancer Res 15: 6267–6276.
  71. 71. Engell-Noerregaard L, Hansen TH, Andersen MH, Thor Straten P, Svane IM (2009) Review of clinical studies on dendritic cell-based vaccination of patients with malignant melanoma: assessment of correlation between clinical response and vaccine parameters. Cancer Immunol Immunother 58: 1–14.
  72. 72. Hoos A, Parmiani G, Hege K, Sznol M, Loibner H, et al. (2007) A clinical development paradigm for cancer vaccines and related biologics. J Immunother 30: 1–15.
  73. 73. Zhao B, Schwartz LH, Larson SM (2009) Imaging surrogates of tumor response to therapy: anatomic and functional biomarkers. J Nucl Med 50: 239–249.
  74. 74. Schlom J, Gulley JL, Arlen PM (2008) Paradigm shifts in cancer vaccine therapy. Exp Biol Med (Maywood) 233: 522–534.
  75. 75. MartIn-Fontecha A, Sebastiani S, Hopken UE, Uguccioni M, Lipp M, et al. (2003) Regulation of dendritic cell migration to the draining lymph node: impact on T lymphocyte traffic and priming. J Exp Med 198: 615–621.
  76. 76. Verdijk P, Aarntzen EH, Lesterhuis WJ, Boullart AC, Kok E, et al. (2009) Limited amounts of dendritic cells migrate into the T-cell area of lymph nodes but have high immune activating potential in melanoma patients. Clin Cancer Res 15: 2531–2540.
  77. 77. Banchereau J, Steinman RM (1998) Dendritic cells and the control of immunity. Nature 392: 245–252.
  78. 78. Steinman RM, Nussenzweig MC (2002) Avoiding horror autotoxicus: the importance of dendritic cells in peripheral T cell tolerance. Proc Natl Acad Sci U S A 99: 351–358.
  79. 79. Wakkach A, Fournier N, Brun V, Breittmayer JP, Cottrez F, et al. (2003) Characterization of dendritic cells that induce tolerance and T regulatory 1 cell differentiation in vivo. Immunity 18: 605–617.
  80. 80. Dhodapkar MV, Steinman RM, Krasovsky J, Munz C, Bhardwaj N (2001) Antigen-specific inhibition of effector T cell function in humans after injection of immature dendritic cells. J Exp Med 193: 233–238.
  81. 81. De Vries IJ, Krooshoop DJ, Scharenborg NM, Lesterhuis WJ, Diepstra JH, et al. (2003) Effective migration of antigen-pulsed dendritic cells to lymph nodes in melanoma patients is determined by their maturation state. Cancer Res 63: 12–17.
  82. 82. Jonuleit H, Giesecke-Tuettenberg A, Tuting T, Thurner-Schuler B, Stuge TB, et al. (2001) A comparison of two types of dendritic cell as adjuvants for the induction of melanoma-specific T-cell responses in humans following intranodal injection. Int J Cancer 93: 243–251.
  83. 83. de Vries IJ, Lesterhuis WJ, Scharenborg NM, Engelen LP, Ruiter DJ, et al. (2003) Maturation of dendritic cells is a prerequisite for inducing immune responses in advanced melanoma patients. Clin Cancer Res 9: 5091–5100.
  84. 84. von Andrian UH, Memper TR (2003) Homing and cellular traffic in lymph nodes. Nat Rev Immunol 3: 867–878.
  85. 85. Miyasaka M, Tanaka T (2004) Lymphocyte trafficking across high endothelial venules: dogmas and enigmas. Nat Rev Immunol 4: 360–370.
  86. 86. von Bergwelt-Baildon M, Shimabukuro-Vornhagen A, Popov A, Klein-Gonzalez N, Fiore F, et al. (2006) CD40-activated B cells express full lymph node homing triad and induce T-cell chemotaxis: potential as cellular adjuvants. Blood 107: 2786–2789.
  87. 87. Robert C, Klein C, Cheng G, Kogan A, Mulligan RC, et al. (2003) Gene therapy to target dendritic cells from blood to lymph nodes. Gene Ther 10: 1479–1486.
  88. 88. Butterfield LH, Ribas A, Dissette VB, Amarnani SN, Vu HT, et al. (2003) Determinant spreading associated with clinical response in dendritic cell-based immunotherapy for malignant melanoma. Clin Cancer Res 9: 998–1008.
  89. 89. Britten CM, Janetzki S, van der Burg SH, Gouttefangeas C, Hoos A (2008) Toward the harmonization of immune monitoring in clinical trials: quo vadis? Cancer Immunol Immunother 57: 285–288.