Increased tumor-associated macrophages (TAMs) are reported to be associated with poor prognosis in classic Hodgkin lymphoma (CHL). We investigated the prognostic significance of TAMs in the E2496 Intergroup trial, a multicenter phase 3 randomized controlled trial comparing ABVD and Stanford V chemotherapy in locally extensive and advanced stage CHL. Tissue microarrays were constructed from formalin-fixed, paraffin-embedded tumor tissue and included 287 patients. Patients were randomly assigned into training (n = 143) and validation (n = 144) cohorts. Immunohistochemistry for CD68 and CD163, and in situ hybridization for EBV-encoded RNA were performed. CD68 and CD163 IHC were analyzed by computer image analysis; optimum thresholds for overall survival (OS) were determined in the training cohort and tested in the independent validation cohort. Increased CD68 and CD163 expression was significantly associated with inferior failure-free survival and OS in the validation cohort. Increased CD68 and CD163 expression was associated with increased age, EBV-encoded RNA positivity, and mixed cellularity subtype of CHL. Multivariate analysis in the validation cohort showed increased CD68 or CD163 expression to be significant independent predictors of inferior failure-free survival and OS. We demonstrate the prognostic significance of TAMs in locally extensive and advanced-stage CHL in a multicenter phase 3 randomized controlled clinical trial.

Despite advances in the treatment of classic Hodgkin lymphoma (CHL), current therapies fail to cure 10%-15% of patients, and a similar proportion of patients may be overtreated, resulting in both short-term and long-term treatment-related complications. The International Prognostic Factors Project Score (IPS) is the current gold standard used to risk-stratify patients with advanced-stage CHL, but its power to identify patients in whom treatment is likely to fail in the modern treatment era has weakened.1–3  Robust biomarkers are thus needed to better risk-stratify patients at diagnosis.

In CHL, the malignant Hodgkin-Reed-Sternberg (HRS) cells are greatly outnumbered by non-neoplastic cells in the tumor microenvironment, including macrophages, T cells, B cells, eosinophils, mast cells, and other stromal elements. Manipulation of the microenvironment by HRS cells through expression of a variety of cytokines and chemokines is thought to be the driving force for an abnormal immune response, perpetuated by additional factors secreted by recruited reactive cells in the microenvironment.4  Tumor-associated macrophages (TAMs) were shown to be associated with inferior outcomes in CHL.5  Steidl et al showed a macrophage gene expression signature to be associated with primary treatment failure in CHL and subsequently showed, using an independent validation cohort, that increased CD68 IHC expression was associated with inferior outcomes, including outcome after salvage treatment with autologous stem cell transplantation.6  Since then, most,7–18  but not all,12,18–20  subsequent studies have confirmed the inferior prognostic significance of TAMs in CHL using CD68 and/or CD163 IHC. In addition, early interim positron emission tomography analysis after 2 courses of chemotherapy has prognostic value in advanced-stage CHL, and increased CD68 IHC expression was recently shown to be associated with a higher rate of early positron emission tomography positivity.8  However, there has been variability in suggested threshold values for CD68 and CD163 IHC expression in the literature. This variability may reflect differences in IHC quantitation methodology between studies, the use of manual visual scoring techniques, and lack of subsequent validation of thresholds in their respective studies. In addition, studies thus far represent retrospective single institution experiences.

We address these current issues in our study by investigating the prognostic significance of TAMs using CD68 and CD163 IHC in the E2496 Intergroup trial, a large multicenter phase 3 randomized controlled clinical trial comparing ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) and Stanford V (doxorubicin, vinblastine, bleomycin, vincristine, mechloroethamine, etoposide, and prednisone) chemotherapy. We use an objective method of quantitating CD68 and CD163 IHC expression with computer image analysis (Aperio Technologies) and establish optimum thresholds for CD68 and CD163 IHC expression using software X-tile (Version 3.6.1), which is based on the maximal χ2 value of the log-rank test for overall survival (OS) in a training cohort. These thresholds are then tested in a separate independent validation cohort.

Patients and samples

A total of 287 patients diagnosed with CHL according to the World Health Organization 2008 classification21  and with tissue available were included in this study, conducted in accordance with the Declaration of Helsinki. This represents a subset of the main clinical trial based on the availability of diagnostic paraffin blocks following central pathology review and patient consent for correlative studies (supplemental Table 1, available on the Blood Web site; see the Supplemental Materials link at the top of the online article). These patients had locally extensive and advanced-stage CHL (stage 1 or 2 with bulky mediastinal disease, stage 3 and 4) enrolled in the E2496 ECOG/SWOG/NCIC/CALGB Intergroup trial, a phase 3 randomized controlled trial comparing ABVD and Stanford V chemotherapy treatment. All patients had complete data for CD68 and CD163 IHC, and in situ hybridization for EBV-encoded RNA (EBER ISH). The statistical software X-tile (Version 3.6.1)22  was used to randomly assign patients into training (n = 143) and validation (n = 144) cohorts. All participating sites received local institutional review board approval.

IHC

Tissue microarrays were constructed using duplicate 1.5-mm-diameter cores of formalin-fixed, paraffin-embedded tumor tissue. IHC performed on the tissue microarrays included CD68 (clone KP1, Dako North America; dilution 1:2000), CD163 (clone 10D6, Novocastra; dilution 1:100), and CD30 (clone BerH2, Dako North America; dilution 1:30). IHC stains were performed on a fully automated stainer (Ventana Benchmark XT) using a multimer detection kit (UltraView Universal DAB).

EBER ISH

EBER ISH was performed using the INFORM EBER probe (Ventana). Slides were also stained on an automated stainer (Ventana Benchmark XT) using the Ventana ISH/iView Blue detection kit. A known positive control was used. Nuclear staining in HRS cells was considered positive.

Immunohistochemistry scoring

CD68 and CD163 IHCs were analyzed by computer image analysis (Aperio Technologies) and pathologist scoring (visual, K.L.T.). Immunostained slides were scanned by Aperio ScanScope XT at 20× magnification. CD68 and CD163 IHCs were analyzed using the Positive Pixel Count algorithm with the Aperio ImageScope (Version 11) viewer. Every core of tissue on the TMA was checked by a pathologist (K.L.T.) to ensure that computer image analysis was performed correctly. Aperio was able to analyze tissue cores in their entirety. Only areas containing tumor were analyzed for IHC expression. Areas without tumor (eg, fibrosis, medium to large blood vessels, residual reactive lymph node) and areas with necrosis or significant artifact (eg, tissue folding and crush artifact) were deselected and excluded from analysis. Cores lacking CD30+ HRS cells were also excluded from analysis. For the positive pixel count algorithm, hue value of 0.1 and hue width of 0.5 were used, and any intensity of staining was considered positive. A color saturation threshold of 0.1 was used for most cores. The color saturation threshold was rarely increased to 0.15 in cases with nonspecific background staining, to minimize analysis of nonspecific background staining. The number of positive pixels was divided by the total number of pixels (negative and positive) in the analyzed area, and multiplied by 100, to derive the percentage of positive pixels. Scores from both cores of the same patient were averaged when possible (Figure 1).

Figure 1

CD68 and CD163 IHC expression and accompanying computer image analysis (original magnification ×10). (A) CD68low, 4.2%. (B) CD68high, 19.5%. (C) CD163low, 1.8%. (D) CD163high, 26.9%.

Figure 1

CD68 and CD163 IHC expression and accompanying computer image analysis (original magnification ×10). (A) CD68low, 4.2%. (B) CD68high, 19.5%. (C) CD163low, 1.8%. (D) CD163high, 26.9%.

Close modal

Visual scoring was performed by estimating relative percentages of CD68+ and CD163+ cells in relation to overall cellularity in both tissue cores from the same patient where possible; scores were recorded in 10% increments. Visual and Aperio scores showed excellent correlation (supplemental Figure 1).

Statistical analysis

Failure free survival (FFS) was defined as the time from randomization to treatment arm until progression, relapse, or death from any cause. OS was defined as the date of randomization to treatment arm to death from any cause. Correlation between variables was analyzed by Pearson correlation coefficient (R). Differences in variables between groups were analyzed by Pearson χ2 test, Student t test, and ANOVA. Survival estimates were calculated using the Kaplan-Meier method with differences assessed using the log-rank test. The Cox proportional hazards regression model was used for multivariate analysis. Statistical analyses were performed using SPSS software (Version 14.0). Two-sided P < .05 was considered statistically significant.

The statistical software X-tile (Version 3.6.1)22  was used to randomly assign patients into training and validation cohorts, as mentioned previously. X-tile was also used to determine the thresholds for CD68 and CD163 IHC expression, by selecting the maximal χ2 values of the log-rank test for OS between 2 groups, designated as low and high risk. These thresholds were then carried forward and tested in the independent validation cohort.

Patient characteristics

There were no significant differences between the subset of cases available for correlative studies (n = 287) and those not available from the total clinical trial cohort (n = 507, giving a total of 794 patients; supplemental Table 1), suggesting that these cases were representative of the entire patient population. There were also no significant differences in patient characteristics between training and validation cohorts (Table 1).

Table 1

Comparison of patient characteristics in training and validation cohorts

Training
Validation
P
n%n%
Total 143  144   
Age (≥ 45 y)      
    No 118 83 122 85 .61 
    Yes 25 17 22 15  
Sex      
    Female 63 44 63 44 .96 
    Male 80 56 81 56  
Stage 4 disease      
    No 103 72 113 78 .21 
    Yes 40 28 31 22  
Albumin (< 4 g/dL)*      
    No 45 31 42 29 .70 
    Yes 94 66 97 67  
    Unknown  
Hemoglobin (< 10.5 g/dL)*      
    No 117 82 109 76 .15 
    Yes 18 13 27 19  
    Unknown  
WBC count (≥ 15 000/mm3)*      
    No 114 80 112 78 .85 
    Yes 20 14 21 15  
    Unknown 11  
Lymphocyte count (< 600/mm3 or < 8%)*      
    No 126 88 125 87 .81 
    Yes 10  
    Unknown  
IPS (≥ 3)*      
    No 99 69 90 63 .26 
    Yes 44 31 53 37  
    Unknown  
Histologic subtype*      
    Nodular sclerosis 115 80 108 75 .61 
    Mixed cellularity 18 13 20 14  
    Lymphocyte rich  
    Lymphocyte depleted  
    Unclassified 10  
Treatment received      
    ABVD 74 52 70 49 .60 
    Stanford V 69 48 74 51  
CD68 IHC expression      
    ≤ 12.7% (CD68low89 62 89 62 .94 
    > 12.7% (CD68high54 38 55 38  
CD163 IHC expression      
    ≤ 16.8% (CD163low90 63 78 54 .13 
    > 16.8% (CD163high53 37 66 46  
EBER ISH      
    Positive 25 17 24 17 .85 
    Negative 118 83 120 83  
Training
Validation
P
n%n%
Total 143  144   
Age (≥ 45 y)      
    No 118 83 122 85 .61 
    Yes 25 17 22 15  
Sex      
    Female 63 44 63 44 .96 
    Male 80 56 81 56  
Stage 4 disease      
    No 103 72 113 78 .21 
    Yes 40 28 31 22  
Albumin (< 4 g/dL)*      
    No 45 31 42 29 .70 
    Yes 94 66 97 67  
    Unknown  
Hemoglobin (< 10.5 g/dL)*      
    No 117 82 109 76 .15 
    Yes 18 13 27 19  
    Unknown  
WBC count (≥ 15 000/mm3)*      
    No 114 80 112 78 .85 
    Yes 20 14 21 15  
    Unknown 11  
Lymphocyte count (< 600/mm3 or < 8%)*      
    No 126 88 125 87 .81 
    Yes 10  
    Unknown  
IPS (≥ 3)*      
    No 99 69 90 63 .26 
    Yes 44 31 53 37  
    Unknown  
Histologic subtype*      
    Nodular sclerosis 115 80 108 75 .61 
    Mixed cellularity 18 13 20 14  
    Lymphocyte rich  
    Lymphocyte depleted  
    Unclassified 10  
Treatment received      
    ABVD 74 52 70 49 .60 
    Stanford V 69 48 74 51  
CD68 IHC expression      
    ≤ 12.7% (CD68low89 62 89 62 .94 
    > 12.7% (CD68high54 38 55 38  
CD163 IHC expression      
    ≤ 16.8% (CD163low90 63 78 54 .13 
    > 16.8% (CD163high53 37 66 46  
EBER ISH      
    Positive 25 17 24 17 .85 
    Negative 118 83 120 83  

P values are for comparing training with validation cohorts.

*

Pearson χ2 test was performed with unknown or unclassified cases excluded.

CD68 expression

Using the optimum threshold of 12.7% obtained with X-tile, 89 patients had low CD68 expression (≤ 12.7%, CD68low) and 54 patients had high CD68 expression (> 12.7%, CD68high) in the training cohort. Carrying this threshold forward, the validation cohort consisted of 89 CD68low and 55 CD68high patients. There were no significant differences in CD68 expression (P = .91) or in proportions of CD68low and CD68high patients between training and validation cohorts (P = .94).

In the training cohort, CD68high patients had inferior outcomes, with the 5-year FFS rate being 50% versus 81% and 5-year OS rate being 76% versus 98%. In the validation cohort, CD68high patients also had significantly inferior outcomes, with the 5-year FFS rate being 64% versus 78% (P = .04) and 5-year OS rate being 81% versus 94% (P < .01; Figure 2).

Figure 2

Survival analysis based on macrophage content. (A) CD68 and (B) CD163 IHC expression and survival in training and validation cohorts.

Figure 2

Survival analysis based on macrophage content. (A) CD68 and (B) CD163 IHC expression and survival in training and validation cohorts.

Close modal

CD163 expression

Using the optimum threshold of 16.8% obtained with X-tile, 90 patients had low CD163 expression (≤ 16.8%, CD163low) and 53 patients had high CD163 expression (> 16.8%, CD163high) in the training cohort. Carrying this threshold forward, the validation cohort consisted of 78 CD163low and 66 CD163high patients. There were no significant differences in CD163 expression (P = .48) and proportions of CD163low and CD163high patients between training and validation cohorts (P = .13).

In the training cohort, CD163high patients had inferior outcomes, with the 5-year FFS rate being 56% versus 78% and the 5-year OS rate being 79% versus 96%. In the validation cohort, CD163high patients also had significantly inferior outcomes with the 5-year FFS rate being 63% versus 82% (P < .01) and 5-year OS rate being 81% versus 96% (P < .01; Figure 2).

Correlation of increased CD68 and CD163 expression with clinical and pathologic characteristics

When considering the entire cohort, patients with increased CD68 expression (CD68high) were significantly older (P < .01) and had increased proportions of mixed cellularity subtype of CHL (P < .01) and EBER+ cases (P < .01). Similarly, CD163high patients were also significantly older (P = .04) and had increased proportions of mixed cellularity subtype of CHL (P < .01) and EBER+ cases (P < .01; Table 2).

Table 2

Comparison of patient characteristics with CD68 and CD163 IHC expression in the entire cohort

CD68low
CD68high
PCD163low
CD163high
P
n%n%n%n%
Total 178  109   168  119   
Age (≥ 45 y)           
    No 157 88 83 76 < .01 147 88 93 78 .04 
    Yes 21 12 26 24  21 13 26 22  
Sex           
    Female 82 46 44 40 .35 76 45 50 42 .59 
    Male 96 54 65 60  92 55 69 58  
Stage 4 disease           
    No 132 74 84 77 .58 129 77 87 73 .48 
    Yes 46 26 25 23  39 23 32 27  
Albumin (< 4 g/dL)*           
    No 56 31 31 28 .62 45 27 42 35 .11 
    Yes 117 66 74 68  118 70 73 61  
    Unknown   
Hemoglobin (< 10.5 g/dL)*           
    No 140 79 86 79 .97 136 81 90 76 .39 
    Yes 28 16 17 16  24 14 21 18  
    Unknown 10   
WBC count (≥ 15 000/mm3)*           
    No 135 76 91 83 .10 129 77 97 82 .18 
    Yes 30 17 11 10  28 17 13 11  
    Unknown 13  11  
Lymphocyte count (< 600/mm3 or < 8%)*           
    No 157 88 94 86 .19 148 88 103 87 .32 
    Yes 10  10  
    Unknown 12  11  
IPS (≥ 3)*           
    No 120 67 69 63 .54 114 68 75 63 .45 
    Yes 58 33 39 36  54 32 43 36  
    Unknown   
Histologic subtype*           
    Nodular sclerosis 152 85 71 65 < .01 141 84 82 69 < .01 
    Mixed cellularity 11 27 25  13 25 21  
    Lymphocyte rich   
    Lymphocyte depleted   
    Unclassified 10   
Treatment received           
    ABVD 88 49 56 51 .75 82 49 62 52 .58 
    Stanford V 90 51 53 49  86 51 57 48  
EBER ISH           
    Positive 15 34 31 < .01 20 12 29 24 < .01 
    Negative 163 92 75 69  148 88 90 76  
CD68low
CD68high
PCD163low
CD163high
P
n%n%n%n%
Total 178  109   168  119   
Age (≥ 45 y)           
    No 157 88 83 76 < .01 147 88 93 78 .04 
    Yes 21 12 26 24  21 13 26 22  
Sex           
    Female 82 46 44 40 .35 76 45 50 42 .59 
    Male 96 54 65 60  92 55 69 58  
Stage 4 disease           
    No 132 74 84 77 .58 129 77 87 73 .48 
    Yes 46 26 25 23  39 23 32 27  
Albumin (< 4 g/dL)*           
    No 56 31 31 28 .62 45 27 42 35 .11 
    Yes 117 66 74 68  118 70 73 61  
    Unknown   
Hemoglobin (< 10.5 g/dL)*           
    No 140 79 86 79 .97 136 81 90 76 .39 
    Yes 28 16 17 16  24 14 21 18  
    Unknown 10   
WBC count (≥ 15 000/mm3)*           
    No 135 76 91 83 .10 129 77 97 82 .18 
    Yes 30 17 11 10  28 17 13 11  
    Unknown 13  11  
Lymphocyte count (< 600/mm3 or < 8%)*           
    No 157 88 94 86 .19 148 88 103 87 .32 
    Yes 10  10  
    Unknown 12  11  
IPS (≥ 3)*           
    No 120 67 69 63 .54 114 68 75 63 .45 
    Yes 58 33 39 36  54 32 43 36  
    Unknown   
Histologic subtype*           
    Nodular sclerosis 152 85 71 65 < .01 141 84 82 69 < .01 
    Mixed cellularity 11 27 25  13 25 21  
    Lymphocyte rich   
    Lymphocyte depleted   
    Unclassified 10   
Treatment received           
    ABVD 88 49 56 51 .75 82 49 62 52 .58 
    Stanford V 90 51 53 49  86 51 57 48  
EBER ISH           
    Positive 15 34 31 < .01 20 12 29 24 < .01 
    Negative 163 92 75 69  148 88 90 76  

P values compare CD68low with CD68high, and CD163low with CD163high patients.

*

Pearson χ2 test was performed with unknown or unclassified cases excluded.

Both CD68high and CD163high were significantly associated with inferior outcomes in patients treated with either ABVD (CD68: FFS, P < .01; OS, P < .01; CD163: FFS, P = .03; OS, P = .04) or Stanford V chemotherapy (CD68: FFS, P < .01; OS, P = .02; CD163: FFS, P < .01; OS, P < .01; supplemental Figure 2).

EBER+ cases showed significantly higher CD68 and CD163 expression than EBER cases (P < .01; Table 3). In addition, EBER+ patients were significantly older (P = .01), more often male (P = .02), with lower white blood cell counts (P = .02), IPS ≥ 3 (P = .04), and increased proportions of mixed cellularity subtype of CHL (P < .01), CD68high (P < .01), and CD163high (P < .01; supplemental Table 2). It was thus not surprising to observe significantly higher CD68 and CD163 expression in mixed cellularity subtype of CHL, compared with nodular sclerosis and lymphocyte rich subtypes (P < .01; Table 3).

Table 3

Comparison of CD68 and CD163 IHC expression in training and validation cohorts, in EBER+ and EBER cases, and selected subtypes of CHL in the entire cohort

nCD68 expression
CD163 expression
MedianRangePMedianRangeP
Training 143 10.6 2.7-57.4 .91 13.0 0.4-79.5 .48 
Validation 144 10.5 2.4-51.8  13.5 0.4-79.1  
EBER+ 49 15.7 3.1-57.4 < .01 29.9 1.5-79.5 < .01 
EBER 238 9.6 2.4-37.2  11.1 0.4-79.1  
Nodular sclerosis 223 9.6 2.7-51.8 < .01 10.9 0.4-79.1 < .01 
Mixed cellularity 38 16.1 2.8-57.4  28.9 0.4-79.5  
Lymphocyte rich 7.1 2.8-26.7  8.9 1.4-41.4  
nCD68 expression
CD163 expression
MedianRangePMedianRangeP
Training 143 10.6 2.7-57.4 .91 13.0 0.4-79.5 .48 
Validation 144 10.5 2.4-51.8  13.5 0.4-79.1  
EBER+ 49 15.7 3.1-57.4 < .01 29.9 1.5-79.5 < .01 
EBER 238 9.6 2.4-37.2  11.1 0.4-79.1  
Nodular sclerosis 223 9.6 2.7-51.8 < .01 10.9 0.4-79.1 < .01 
Mixed cellularity 38 16.1 2.8-57.4  28.9 0.4-79.5  
Lymphocyte rich 7.1 2.8-26.7  8.9 1.4-41.4  

No significant differences in outcome were seen between EBER+ and EBER patients (FFS, P = .66; OS, P = .44). However, CD163high was significantly associated with inferior outcomes in both EBER+ (FFS, P < .01; OS, P = .02) and EBER (FFS, P = .01; OS, P < .01) patients. CD68high was significantly associated with inferior outcomes in EBER cases (FFS, P < .01; OS, P < .01) but not EBER+ cases (FFS, P = .34; OS, P = .33; supplemental Figure 3).

Increased CD68 or CD163 expression is a significant independent predictor of inferior outcome

Univariate and multivariate analyses were performed on the validation cohort. On univariate analysis, stage 4 disease, low lymphocyte count, and increased CD68 and CD163 expression were significantly associated with inferior FFS. Increased age and increased CD68 and CD163 expression were significantly associated with inferior OS (Table 4).

Table 4

Univariate analysis in the validation cohort

Factor5-y FFS, %P5-y OS, %P
Age (≥ 45 y)     
    No 74 .49 91 < .01 
    Yes 69  77  
Sex     
    Female 77 .39 90 .95 
    Male 70  88  
Stage 4 disease     
    No 77 .01 91 .12 
    Yes 57  84  
Albumin (< 4 g/dL)     
    No 74 .96 87 .9 
    Yes 73  89  
Hemoglobin (< 10.5 g/dL)     
    No 77 .17 91 .08 
    Yes 58  76  
WBC count (≥ 15 000/mm3)     
    No 73 .85 89 .67 
    Yes 76  86  
Lymphocyte count (< 600/mm3 or < 8%)     
    No 77 .02 89 .48 
    Yes 25  78  
IPS (≥ 3)     
    No 74 .65 91 .34 
    Yes 70  86  
CD68     
    ≤ 12.7% (CD68low78 .04 94 < .01 
    > 12.7% (CD68high64  81  
CD163     
    ≤ 16.8% (CD163low82 < .01 96 < .01 
    > 16.8% (CD163high63  81  
EBER     
    Negative 75 .24 90 .43 
    Positive 62  83  
Factor5-y FFS, %P5-y OS, %P
Age (≥ 45 y)     
    No 74 .49 91 < .01 
    Yes 69  77  
Sex     
    Female 77 .39 90 .95 
    Male 70  88  
Stage 4 disease     
    No 77 .01 91 .12 
    Yes 57  84  
Albumin (< 4 g/dL)     
    No 74 .96 87 .9 
    Yes 73  89  
Hemoglobin (< 10.5 g/dL)     
    No 77 .17 91 .08 
    Yes 58  76  
WBC count (≥ 15 000/mm3)     
    No 73 .85 89 .67 
    Yes 76  86  
Lymphocyte count (< 600/mm3 or < 8%)     
    No 77 .02 89 .48 
    Yes 25  78  
IPS (≥ 3)     
    No 74 .65 91 .34 
    Yes 70  86  
CD68     
    ≤ 12.7% (CD68low78 .04 94 < .01 
    > 12.7% (CD68high64  81  
CD163     
    ≤ 16.8% (CD163low82 < .01 96 < .01 
    > 16.8% (CD163high63  81  
EBER     
    Negative 75 .24 90 .43 
    Positive 62  83  

To determine whether CD68 or CD163 was independently associated with outcomes, respectively, 2 separate multivariate analyses were performed, including the factors significantly associated with FFS or OS in univariate analysis. These analyses demonstrated that increased CD68 or CD163 expression was a significant independent predictor of inferior FFS and OS (Table 5).

Table 5

Multivariate analyses in the validation cohort

FactorHR95% CIP
FFS    
    Lymphocyte count (< 600/mm3 or < 8%) 2.1 0.8-5.8 .14 
    Stage 4 disease 2.2 1.0-4.7 .04 
    CD68high 2.1 1.1-4.2 .04 
    Lymphocyte count (< 600/mm3 or < 8%) 2.1 0.8-5.9 .14 
    Stage 4 disease 1.8 0.8-3.9 .13 
    CD163high 2.5 1.2-5.3 .02 
OS    
    Age (≥ 45 y) 2.5 0.9-7.1 .08 
    CD68high 3.5 1.2-10.2 .02 
    Age (≥ 45 y) 3.4 1.3-9.2 .02 
    CD163high 3.9 1.3-11.9 .02 
FactorHR95% CIP
FFS    
    Lymphocyte count (< 600/mm3 or < 8%) 2.1 0.8-5.8 .14 
    Stage 4 disease 2.2 1.0-4.7 .04 
    CD68high 2.1 1.1-4.2 .04 
    Lymphocyte count (< 600/mm3 or < 8%) 2.1 0.8-5.9 .14 
    Stage 4 disease 1.8 0.8-3.9 .13 
    CD163high 2.5 1.2-5.3 .02 
OS    
    Age (≥ 45 y) 2.5 0.9-7.1 .08 
    CD68high 3.5 1.2-10.2 .02 
    Age (≥ 45 y) 3.4 1.3-9.2 .02 
    CD163high 3.9 1.3-11.9 .02 

HR indicates hazard ratio; and CI, confidence interval.

We confirm the prognostic significance of TAMs in CHL in the E2496 ECOG/SWOG/NCIC/CALGB Intergroup trial, a multicenter phase 3 randomized controlled trial comparing ABVD and Stanford V chemotherapy. Increased CD68 and CD163 IHC expression was significantly associated with inferior outcomes in locally extensive and advanced-stage CHL. Multivariate analysis in the validation cohort showed CD68 or CD163 expression to be significant independent predictors of FFS and OS. All previous studies on TAMs and outcome in CHL have been based on retrospective single institution experiences. This work represents the first confirmation of the prognostic role of TAMs in CHL in a multicenter randomized controlled clinical trial setting.

Although most studies in the literature demonstrate an association between increased TAMs and inferior outcome in CHL, there has been variability in suggested threshold values for CD68 and/or CD163 IHC expression. This probably relates to differences in IHC quantitation methodology, with most using manual visual scoring techniques, and a lack of subsequent validation of thresholds in these studies. The lack of reproducibility and inconsistency of manual or visual IHC scoring has been identified as a potential pitfall regarding the use of IHC biomarkers in routine clinical practice.5  Azambuja et al showed poor interobserver agreement for low scores with CD68 IHC.20  These issues are potentially overcome through use of computer image analysis to produce greater objectivity in scoring. Kamper et al used computer-assisted stereologic analysis and point grid counting methodology in assessing CD68 and CD163 IHC, and showed increased CD68 and CD163 IHC expression to be associated with inferior outcome in CHL.16  However, they reported this method to be labor intensive.16  We used Aperio Technologies for computer image analysis to assess CD68 and CD163 IHC. In addition, we attempted to produce robust thresholds for CD68 and CD163 IHC expression by developing optimum thresholds based on the maximal χ2 values of the log-rank test for OS in a training cohort, and then testing these thresholds in a separate independent validation cohort. Aperio was able to analyze tissue cores in their entirety, with scores averaged from both cores of the same patient to provide a more representative score for each case, within the limitations of a tissue microarray. In addition, Aperio is a user friendly system and shows potential for application on whole tissue sections. Compared with CD68, CD163 appeared to show an overall crisper and stronger intensity of staining and a cleaner background in our hands, making it more ideal for analysis by Aperio's Positive Pixel Count algorithm. This experience with the quality of CD163 IHC is in agreement with other investigators.19,20  In addition, the KP1 clone for CD68 has been reported to be a less specific marker for macrophages, as it is also known to react with myeloid and fibroblastic cells,23  whereas clone 10D6 for CD163 has been reported to be more specific than both KP1 and PGM1 clones for CD68 in identifying macrophages.24  For these reasons, CD163 may be a better marker for identifying TAMs than CD68.

Expression of CD163 is restricted to cells of the monocyte/macrophage lineage and is reported to be a more specific marker for alternatively activated (M2) macrophages in the M1/M2 macrophage polarization model.24–27  We showed a correlation between CD68 and CD163 expression, suggesting that TAMs in CHL show features described for M2 macrophages, including promoting tumor growth and angiogenesis, tissue remodeling, and suppression of adaptive immune responses, contributing to immune evasion by tumor cells, and thus associated with poor prognosis.28–32  However, the macrophage polarization model based on in vitro experiments is probably an oversimplification of macrophages in vivo, which probably display a spectrum of activated phenotypes and with the ability to switch from one functional phenotype to another in response to varied local microenvironmental signals.30,33,34 

We also showed increased CD68 and CD163 expression to be associated with increased age, EBER positivity, and mixed cellularity subtype of CHL.16,20,35  Indeed, a gene expression profiling study showed overexpression of genes associated with either histiocytes or T cells, including CD68 and CD163, in EBV+ CHL compared with EBV CHL.36  Despite an association with increased CD68 and CD163 expression in EBER+ cases, no survival differences were seen with regards to EBER status in our study. However, TAMs remained predictive of inferior outcomes in both EBER+ and EBER cases, suggesting that there are probably other mechanisms responsible for the recruitment and the poor prognostic impact of increased TAMs in the CHL microenvironment. CD163 may be a better marker than CD68 in predicting inferior outcomes in EBER+ cases; however, numbers of EBER+ patients in our study were small, and this requires further study. The precise biologic mechanisms underlying TAMs and the relationship between TAMs with EBV and tumor cells are currently not well understood, and further functional studies are required.

In conclusion, we confirm the prognostic significance of TAMs using CD68 and CD163 IHC in CHL in the E2496 Intergroup trial, a multicenter phase 3 randomized controlled clinical trial comparing ABVD and Stanford V chemotherapy. We demonstrate an objective method of quantitating CD68 and CD163 IHC expression using computer image analysis (Aperio Technologies) and established robust thresholds for CD68 and CD163 IHC expression that is trained and validated in independent cohorts. Our findings, in conjunction with other previous studies, firmly establish TAMs to be important in the CHL tumor microenvironment. Further functional studies are required to determine the precise biologic mechanisms associated with increased numbers of TAMs in the tumor microenvironment and the relationship with EBV in CHL. Evaluation of TAMs should be considered in prospective clinical trials, and patients with increased TAMs may benefit from more intensive chemotherapy or novel agents designed to disrupt the crosstalk between HRS cells and benign macrophages.

The online version of this article contains a data supplement.

The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

The authors thank Lynda Bell, Sylvia Lee, and Julie Lorette at the Center for Translational and Applied Genomics for technical support.

This work was coordinated by the Eastern Cooperative Oncology Group (Dr Robert L. Comis, Chair) and supported in part by Public Health Service (grants CA21115, CA23318, CA66636, CA17145, CA11083, CA32102, CA38926, CA77202, CA21076, and CA77470), the National Cancer Institute, National Institutes of Health, and the Department of Health and Human Services. Biospecimens were provided by the ECOG Pathology Coordinating Office and Reference Laboratory. K.L.T and D.W.S were supported by the Terry Fox Foundation Strategic Health Research Training Program in Cancer Research at Canadian Institutes of Health Research (postdoctoral fellowships grant TGT-53912). C.S. was supported by postdoctoral fellowships of the Cancer Research Society (Steven E. Drabin Fellowship) and the Michael Smith Foundation for Health Research.

The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute.

National Institutes of Health

Contribution: K.L.T. performed the research, analyzed data, and prepared the manuscript; D.W.S. analyzed data; F.H., B.S.K., R.I.F., N.L.B., R.H.A., R.B., L.M.R., J.M.C., C.S., L.I.G., and S.J.H. contributed data and materials; and R.D.G. designed the research, analyzed data, and prepared the manuscript.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Correspondence: Randy D. Gascoyne, British Columbia Cancer Agency & British Columbia Cancer Research Centre, 675 W 10th Ave, Room 5-113, Vancouver, BC, V5Z 1L3, Canada; e-mail: [email protected].

1
Moccia
 
AM
Donaldson
 
J
Chhanabhai
 
M
et al. 
The International Prognostic Factor Project Score (IPS) in advanced stage Hodgkin lymphoma has limited utility in patients treated in the modern era.
J Clin Oncol
2012
, vol. 
30
 
27
(pg. 
3383
-
3388
)
2
Guisado-Vasco
 
P
Arranz-Saez
 
R
Canales
 
M
et al. 
Stage IV and age over 45 years are the only prognostic factors of the International Prognostic Score for the outcome of advanced Hodgkin lymphoma in the Spanish Hodgkin Lymphoma Study Group series.
Leuk Lymphoma
2012
, vol. 
53
 
5
(pg. 
812
-
819
)
3
Hasenclever
 
D
Diehl
 
V
A prognostic score for advanced Hodgkin's disease: International Prognostic Factors Project on Advanced Hodgkin's Disease.
N Engl J Med
1998
, vol. 
339
 
21
(pg. 
1506
-
1514
)
4
Steidl
 
C
Connors
 
JM
Gascoyne
 
RD
Molecular pathogenesis of Hodgkin's lymphoma: increasing evidence of the importance of the microenvironment.
J Clin Oncol
2011
, vol. 
29
 
14
(pg. 
1812
-
1826
)
5
Steidl
 
C
Farinha
 
P
Gascoyne
 
RD
Macrophages predict treatment outcome in Hodgkin's lymphoma.
Haematologica
2011
, vol. 
96
 
2
(pg. 
186
-
189
)
6
Steidl
 
C
Lee
 
T
Shah
 
SP
et al. 
Tumor-associated macrophages and survival in classic Hodgkin's lymphoma.
N Engl J Med
2010
, vol. 
362
 
10
(pg. 
875
-
885
)
7
Casulo
 
C
Arcila
 
M
Teruya-Feldstein
 
J
Maragulia
 
J
Moskowitz
 
CH
Tumor-associated macrophages are predictive of survival in relapsed and refractory Hodgkin lymphoma [abstract].
Blood (ASH Annual Meeting Abstracts)
2011
, vol. 
118
 
21
pg. 
2630
 
8
Touati
 
M
Delage-Corre
 
M
Monteil
 
J
et al. 
CD68+ tumor-associated macrophages predict unfavorable treatment outcomes in classic Hodgkin's lymphoma in correlation with early FDG-PET assessment results [abstract].
Blood (ASH Annual Meeting Abstracts)
2011
, vol. 
118
 
21
pg. 
1558
 
9
Farinha
 
P
Rodrigues
 
S
Fernandes
 
T
et al. 
Lymphoma associated macrophages predict survival in uniformly treated patients with classical Hodgkin lymphoma [abstract].
Mod Pathol
2011
, vol. 
24
 
1 Suppl
pg. 
1257
 
10
Greaves
 
P
Clear
 
A
Coutinho
 
R
et al. 
Expression of FOXP3, CD68 and CD20 at diagnosis in the microenvironment of classical Hodgkin lymphoma is predictive of outcome [prepublished online ahead of print October 8, 2012].
J Clin Oncol
 
11
Deau
 
B
Bachy
 
E
Ribrag
 
V
et al. 
Macrophages and mast cells infiltration are biomarkers of primary refractory Hodgkin's lymphoma [abstract].
Blood (ASH Annual Meeting Abstracts)
2010
, vol. 
116
 
21
pg. 
3881
 
12
Sánchez-Espiridión
 
B
Martin-Moreno
 
AM
Montalban
 
C
et al. 
Immunohistochemical markers for tumor associated macrophages and survival in advanced classical Hodgkin lymphoma.
Haematologica
2012
, vol. 
97
 
7
(pg. 
1080
-
1084
)
13
Tzankov
 
A
Matter
 
MS
Dirnhofer
 
S
Refined prognostic role of CD68-positive tumor macrophages in the context of the cellular micromilieu of classical Hodgkin lymphoma.
Pathobiology
2010
, vol. 
77
 
6
(pg. 
301
-
308
)
14
Jakovic
 
LR
Mihaljevic
 
BS
Perunicic Jovanovic
 
MD
Bogdanovic
 
AD
Andjelic
 
BM
Bumbasirevic
 
VZ
The prognostic relevance of tumor associated macrophages in advanced stage classical Hodgkin lymphoma.
Leuk Lymphoma
2011
, vol. 
52
 
10
(pg. 
1913
-
1919
)
15
Yoon
 
DH
Koh
 
YW
Kang
 
HJ
et al. 
CD68 and CD163 as prognostic factors for Korean patients with Hodgkin lymphoma.
Eur J Haematol
2012
, vol. 
88
 
4
(pg. 
292
-
305
)
16
Kamper
 
P
Bendix
 
K
Hamilton-Dutoit
 
S
Honore
 
B
Nyengaard
 
JR
d'Amore
 
F
Tumor-infiltrating macrophages correlate with adverse prognosis and Epstein-Barr virus status in classical Hodgkin's lymphoma.
Haematologica
2011
, vol. 
96
 
2
(pg. 
269
-
276
)
17
Zaki
 
MA
Wada
 
N
Ikeda
 
J
et al. 
Prognostic implication of types of tumor-associated macrophages in Hodgkin lymphoma.
Virchows Arch
2011
, vol. 
459
 
4
(pg. 
361
-
366
)
18
Agostinelli
 
C
Gallamini
 
A
Rigacci
 
L
et al. 
The prognostic value of biologic markers in classical Hodgkin lymphoma (CHL) patients [abstract].
Ann Oncol
2011
, vol. 
22
 
Suppl 4
(pg. 
153
-
154
)
19
Harris
 
JA
Jain
 
S
Ren
 
Q
Zarineh
 
A
Liu
 
C
Ibrahim
 
S
CD163 versus CD68 in tumor associated macrophages of classical Hodgkin lymphoma.
Diagn Pathol
2012
, vol. 
7
 pg. 
12
 
20
Azambuja
 
D
Natkunam
 
Y
Biasoli
 
I
et al. 
Lack of association of tumor-associated macrophages with clinical outcome in patients with classical Hodgkin's lymphoma.
Ann Oncol
2012
, vol. 
23
 
3
(pg. 
736
-
742
)
21
Swerdlow
 
SH
Harris
 
NL
Jaffe
 
ES
et al. 
WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues
2008
Lyon, France
IARC
22
Camp
 
RL
Dolled-Filhart
 
M
Rimm
 
DL
X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization.
Clin Cancer Res
2004
, vol. 
10
 
21
(pg. 
7252
-
7259
)
23
Kunisch
 
E
Fuhrmann
 
R
Roth
 
A
Winter
 
R
Lungershausen
 
W
Kinne
 
RW
Macrophage specificity of three anti-CD68 monoclonal antibodies (KP1, EBM11, and PGM1) widely used for immunohistochemistry and flow cytometry.
Ann Rheum Dis
2004
, vol. 
63
 
7
(pg. 
774
-
784
)
24
Lau
 
SK
Chu
 
PG
Weiss
 
LM
CD163: a specific marker of macrophages in paraffin-embedded tissue samples.
Am J Clin Pathol
2004
, vol. 
122
 
5
(pg. 
794
-
801
)
25
Heusinkveld
 
M
van der Burg
 
SH
Identification and manipulation of tumor associated macrophages in human cancers.
J Transl Med
2011
, vol. 
9
 pg. 
216
 
26
Ambarus
 
CA
Krausz
 
S
van Eijk
 
M
et al. 
Systematic validation of specific phenotypic markers for in vitro polarized human macrophages.
J Immunol Methods
2012
, vol. 
375
 
2
(pg. 
196
-
206
)
27
Van Gorp
 
H
Delputte
 
PL
Nauwynck
 
HJ
Scavenger receptor CD163, a Jack-of-all-trades and potential target for cell-directed therapy.
Mol Immunol
2010
, vol. 
47
 
8
(pg. 
1650
-
1660
)
28
Lawrence
 
T
Natoli
 
G
Transcriptional regulation of macrophage polarization: enabling diversity with identity.
Nat Rev Immunol
2011
, vol. 
11
 
11
(pg. 
750
-
761
)
29
Mantovani
 
A
Germano
 
G
Marchesi
 
F
Locatelli
 
M
Biswas
 
SK
Cancer-promoting tumor-associated macrophages: new vistas and open questions.
Eur J Immunol
2011
, vol. 
41
 
9
(pg. 
2522
-
2525
)
30
Murray
 
PJ
Wynn
 
TA
Protective and pathogenic functions of macrophage subsets.
Nat Rev Immunol
2011
, vol. 
11
 
11
(pg. 
723
-
737
)
31
Allavena
 
P
Mantovani
 
A
Immunology in the clinic review series; focus on cancer: tumour-associated macrophages: undisputed stars of the inflammatory tumour microenvironment.
Clin Exp Immunol
2012
, vol. 
167
 
2
(pg. 
195
-
205
)
32
Fukuda
 
K
Kobayashi
 
A
Watabe
 
K
The role of tumor-associated macrophage in tumor progression.
Front Biosci (Schol Ed)
2012
, vol. 
4
 
Suppl 4
(pg. 
787
-
798
)
33
Mosser
 
DM
Edwards
 
JP
Exploring the full spectrum of macrophage activation.
Nat Rev Immunol
2008
, vol. 
8
 
12
(pg. 
958
-
969
)
34
Stables
 
MJ
Shah
 
S
Camon
 
EB
et al. 
Transcriptomic analyses of murine resolution-phase macrophages.
Blood
2011
, vol. 
118
 
26
(pg. 
e192
-
e208
)
35
Hohaus
 
S
Santangelo
 
R
Giachelia
 
M
et al. 
The viral load of Epstein-Barr virus (EBV) DNA in peripheral blood predicts for biological and clinical characteristics in Hodgkin lymphoma.
Clin Cancer Res
2011
, vol. 
17
 
9
(pg. 
2885
-
2892
)
36
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
, vol. 
113
 
12
(pg. 
2765
-
3775
)
Sign in via your Institution