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Spatial organization of dendritic cells within tumor draining lymph nodes impacts clinical outcome in breast cancer patients.

Chang AY, Bhattacharya N, Mu J, Setiadi AF, Carcamo-Cavazos V, Lee GH, Simons DL, Yadegarynia S, Hemati K, Kapelner A, Ming Z, Krag DN, Schwartz EJ, Chen DZ, Lee PP - J Transl Med (2013)

Bottom Line: Degree of clustering of DCs (in terms of spatial proximity of the cells to each other) was reduced in TDLNs compared to HLNs.The average number of T cells within a standardized radius of a clustered DC was increased compared to that of an unclustered DC, suggesting that DC clustering was associated with T cell interaction.Furthermore, the number of T cells within the radius of a clustered DC was reduced in tumor-positive TDLNs compared to HLNs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Medicine, Stanford University, 269 Campus Drive, 94305 Stanford, CA, USA. plee@coh.org.

ABSTRACT

Background: Dendritic cells (DCs) are important mediators of anti-tumor immune responses. We hypothesized that an in-depth analysis of dendritic cells and their spatial relationships to each other as well as to other immune cells within tumor draining lymph nodes (TDLNs) could provide a better understanding of immune function and dysregulation in cancer.

Methods: We analyzed immune cells within TDLNs from 59 breast cancer patients with at least 5 years of clinical follow-up using immunohistochemical staining with a novel quantitative image analysis system. We developed algorithms to analyze spatial distribution patterns of immune cells in cancer versus healthy intra-mammary lymph nodes (HLNs) to derive information about possible mechanisms underlying immune-dysregulation in breast cancer. We used the non-parametric Mann-Whitney test for inter-group comparisons, Wilcoxon Matched-Pairs Signed Ranks test for intra-group comparisons and log-rank (Mantel-Cox) test for Kaplan Maier analyses.

Results: Degree of clustering of DCs (in terms of spatial proximity of the cells to each other) was reduced in TDLNs compared to HLNs. While there were more numerous DC clusters in TDLNs compared to HLNs,DC clusters within TDLNs tended to have fewer member DCs and also consisted of fewer cells displaying the DC maturity marker CD83. The average number of T cells within a standardized radius of a clustered DC was increased compared to that of an unclustered DC, suggesting that DC clustering was associated with T cell interaction. Furthermore, the number of T cells within the radius of a clustered DC was reduced in tumor-positive TDLNs compared to HLNs. Importantly, clinical outcome analysis revealed that DC clustering in tumor-positive TDLNs correlated with the duration of disease-free survival in breast cancer patients.

Conclusions: These findings are the first to describe the spatial organization of DCs within TDLNs and their association with survival outcome. In addition, we characterized specific changes in number, size, maturity, and T cell co-localization of such clusters. Strategies to enhance DC function in-vivo, including maturation and clustering, may provide additional tools for developing more efficacious DC cancer vaccines.

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High-resolution IHC Images of DC Clusters in two staining panels. Immunohistochemistry stained images of DC clusters from healthy, NSLN- and NSLN+ patient samples, serially stained in both a DC maturity assessment panel and a T cell colocalization panel. Stains for the maturity panel include: Red (CD1a, Immature DCs), Brown (CD83, Mature DCs), Blue (Hematoxylin, Non-DC cells). Stains for the T cell colocalization panel include: Magenta (CD1a, Immature DCs), Dark Blue (CD3, T cells), Brown (CD20, B cells), Light Blue (Hematoxylin, other cells). The dark purple cells in both panels are pan-cytokeratin-stained tumor cells. All images were taken at 400× resolution.
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Figure 2: High-resolution IHC Images of DC Clusters in two staining panels. Immunohistochemistry stained images of DC clusters from healthy, NSLN- and NSLN+ patient samples, serially stained in both a DC maturity assessment panel and a T cell colocalization panel. Stains for the maturity panel include: Red (CD1a, Immature DCs), Brown (CD83, Mature DCs), Blue (Hematoxylin, Non-DC cells). Stains for the T cell colocalization panel include: Magenta (CD1a, Immature DCs), Dark Blue (CD3, T cells), Brown (CD20, B cells), Light Blue (Hematoxylin, other cells). The dark purple cells in both panels are pan-cytokeratin-stained tumor cells. All images were taken at 400× resolution.

Mentions: To determine how DCs in TDLNs may differ from those of HLNs, we assessed alterations in DC number and maturity between these groups. First, all stained slides were microscopically examined, and it was observed that semi-quantitatively, HLNs had a greater number of DCs in each section than NSLN- and NSLN+. The pattern was seen between HLNs and the TDLNs of breast cancer patients who remained disease-free or relapsed during a five-year follow-up period post-treatment. HLN DCs appeared to form larger, more densely populated “clusters” than their TDLN counterparts (Figures 1 and 2). Given these qualitative differences, immune cell numbers and spatial distributions were analyzed quantitatively using a novel quantitative, spatial analysis imaging approach [21]. Since there are variations in size and cell number among lymph nodes, we used DCs as a percentage of total cells in a node (calculated as a sum of all T cells, DCs, and other hematoxylin-stained cells identified and counted by GemIdent) to account for this. Total DCs were counted as all cells in a node that expressed CD1a and/or CD83 (to account for both immature and mature DC populations). We found that DCs were significantly decreased in both NSLN- (median value: 0.38%, p<0.01) as well as NSLN+ (0.29%, p<0.01) compared to HLNs (1.06%, Figure 3A). The percentage of mature DCs in both NSLN- and NSLN+ (defined as the percentage of total DCs in each node that was CD83-expressing) was also significantly reduced compared to healthy controls (median value: 37.35%, p<0.001 and 37.89%, p<0.001 respectively vs. 89.00%) (Figure 3B). Moving beyond numbers of individual cell types, we hypothesized that spatial organization of DCs may also be altered. We quantified the clustering behavior of DCs using the DBC algorithm we developed. As with total overall DC number, we took into account variation in lymph node size and evaluated the degree of clustering in each node as the percentage of total DCs that were classified as in a cluster by our algorithm (Figure 3C). We observed a stepwise reduction in DC clustering, as a trend (p=0.07) towards decreased clustering in DCs from NSLN- (88.89%) compared to HLNs (93.05%), a significant difference in NSLN+ (69.99%) versus healthy nodes (p<0.01), and a significant reduction in NSLN+ versus NSLN- (p<0.01). Importantly, DC clustering was not merely a reflection of the percentage of DCs in a lymph node, as there was a significant reduction in DC clustering in NSLN+ nodes even though the percentage of DCs between NSLN+ and NSLN- nodes were similar (Figure 3A).


Spatial organization of dendritic cells within tumor draining lymph nodes impacts clinical outcome in breast cancer patients.

Chang AY, Bhattacharya N, Mu J, Setiadi AF, Carcamo-Cavazos V, Lee GH, Simons DL, Yadegarynia S, Hemati K, Kapelner A, Ming Z, Krag DN, Schwartz EJ, Chen DZ, Lee PP - J Transl Med (2013)

High-resolution IHC Images of DC Clusters in two staining panels. Immunohistochemistry stained images of DC clusters from healthy, NSLN- and NSLN+ patient samples, serially stained in both a DC maturity assessment panel and a T cell colocalization panel. Stains for the maturity panel include: Red (CD1a, Immature DCs), Brown (CD83, Mature DCs), Blue (Hematoxylin, Non-DC cells). Stains for the T cell colocalization panel include: Magenta (CD1a, Immature DCs), Dark Blue (CD3, T cells), Brown (CD20, B cells), Light Blue (Hematoxylin, other cells). The dark purple cells in both panels are pan-cytokeratin-stained tumor cells. All images were taken at 400× resolution.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC3852260&req=5

Figure 2: High-resolution IHC Images of DC Clusters in two staining panels. Immunohistochemistry stained images of DC clusters from healthy, NSLN- and NSLN+ patient samples, serially stained in both a DC maturity assessment panel and a T cell colocalization panel. Stains for the maturity panel include: Red (CD1a, Immature DCs), Brown (CD83, Mature DCs), Blue (Hematoxylin, Non-DC cells). Stains for the T cell colocalization panel include: Magenta (CD1a, Immature DCs), Dark Blue (CD3, T cells), Brown (CD20, B cells), Light Blue (Hematoxylin, other cells). The dark purple cells in both panels are pan-cytokeratin-stained tumor cells. All images were taken at 400× resolution.
Mentions: To determine how DCs in TDLNs may differ from those of HLNs, we assessed alterations in DC number and maturity between these groups. First, all stained slides were microscopically examined, and it was observed that semi-quantitatively, HLNs had a greater number of DCs in each section than NSLN- and NSLN+. The pattern was seen between HLNs and the TDLNs of breast cancer patients who remained disease-free or relapsed during a five-year follow-up period post-treatment. HLN DCs appeared to form larger, more densely populated “clusters” than their TDLN counterparts (Figures 1 and 2). Given these qualitative differences, immune cell numbers and spatial distributions were analyzed quantitatively using a novel quantitative, spatial analysis imaging approach [21]. Since there are variations in size and cell number among lymph nodes, we used DCs as a percentage of total cells in a node (calculated as a sum of all T cells, DCs, and other hematoxylin-stained cells identified and counted by GemIdent) to account for this. Total DCs were counted as all cells in a node that expressed CD1a and/or CD83 (to account for both immature and mature DC populations). We found that DCs were significantly decreased in both NSLN- (median value: 0.38%, p<0.01) as well as NSLN+ (0.29%, p<0.01) compared to HLNs (1.06%, Figure 3A). The percentage of mature DCs in both NSLN- and NSLN+ (defined as the percentage of total DCs in each node that was CD83-expressing) was also significantly reduced compared to healthy controls (median value: 37.35%, p<0.001 and 37.89%, p<0.001 respectively vs. 89.00%) (Figure 3B). Moving beyond numbers of individual cell types, we hypothesized that spatial organization of DCs may also be altered. We quantified the clustering behavior of DCs using the DBC algorithm we developed. As with total overall DC number, we took into account variation in lymph node size and evaluated the degree of clustering in each node as the percentage of total DCs that were classified as in a cluster by our algorithm (Figure 3C). We observed a stepwise reduction in DC clustering, as a trend (p=0.07) towards decreased clustering in DCs from NSLN- (88.89%) compared to HLNs (93.05%), a significant difference in NSLN+ (69.99%) versus healthy nodes (p<0.01), and a significant reduction in NSLN+ versus NSLN- (p<0.01). Importantly, DC clustering was not merely a reflection of the percentage of DCs in a lymph node, as there was a significant reduction in DC clustering in NSLN+ nodes even though the percentage of DCs between NSLN+ and NSLN- nodes were similar (Figure 3A).

Bottom Line: Degree of clustering of DCs (in terms of spatial proximity of the cells to each other) was reduced in TDLNs compared to HLNs.The average number of T cells within a standardized radius of a clustered DC was increased compared to that of an unclustered DC, suggesting that DC clustering was associated with T cell interaction.Furthermore, the number of T cells within the radius of a clustered DC was reduced in tumor-positive TDLNs compared to HLNs.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Medicine, Stanford University, 269 Campus Drive, 94305 Stanford, CA, USA. plee@coh.org.

ABSTRACT

Background: Dendritic cells (DCs) are important mediators of anti-tumor immune responses. We hypothesized that an in-depth analysis of dendritic cells and their spatial relationships to each other as well as to other immune cells within tumor draining lymph nodes (TDLNs) could provide a better understanding of immune function and dysregulation in cancer.

Methods: We analyzed immune cells within TDLNs from 59 breast cancer patients with at least 5 years of clinical follow-up using immunohistochemical staining with a novel quantitative image analysis system. We developed algorithms to analyze spatial distribution patterns of immune cells in cancer versus healthy intra-mammary lymph nodes (HLNs) to derive information about possible mechanisms underlying immune-dysregulation in breast cancer. We used the non-parametric Mann-Whitney test for inter-group comparisons, Wilcoxon Matched-Pairs Signed Ranks test for intra-group comparisons and log-rank (Mantel-Cox) test for Kaplan Maier analyses.

Results: Degree of clustering of DCs (in terms of spatial proximity of the cells to each other) was reduced in TDLNs compared to HLNs. While there were more numerous DC clusters in TDLNs compared to HLNs,DC clusters within TDLNs tended to have fewer member DCs and also consisted of fewer cells displaying the DC maturity marker CD83. The average number of T cells within a standardized radius of a clustered DC was increased compared to that of an unclustered DC, suggesting that DC clustering was associated with T cell interaction. Furthermore, the number of T cells within the radius of a clustered DC was reduced in tumor-positive TDLNs compared to HLNs. Importantly, clinical outcome analysis revealed that DC clustering in tumor-positive TDLNs correlated with the duration of disease-free survival in breast cancer patients.

Conclusions: These findings are the first to describe the spatial organization of DCs within TDLNs and their association with survival outcome. In addition, we characterized specific changes in number, size, maturity, and T cell co-localization of such clusters. Strategies to enhance DC function in-vivo, including maturation and clustering, may provide additional tools for developing more efficacious DC cancer vaccines.

Show MeSH
Related in: MedlinePlus