Profile of Immune Cells in Axillary Lymph Nodes Predicts Disease-Free Survival in Breast Cancer

BACKGROUND: While lymph node metastasis is among the strongest predictors of disease-free and overall survival for patients with breast cancer, the immunological nature of tumor-draining lymph nodes is often ignored, and may provide additional prognostic information on clinical outcome. METHODS AND FINDINGS: We performed immunohistochemical analysis of 47 sentinel and 104 axillary (nonsentinel) nodes from 77 breast cancer patients with 5 y of follow-up to determine if alterations in CD4, CD8, and CD1a cell populations predict nodal metastasis or disease-free survival. Sentinel and axillary node CD4 and CD8 T cells were decreased in breast cancer patients compared to control nodes. CD1a dendritic cells were also diminished in sentinel and tumor-involved axillary nodes, but increased in tumor-free axillary nodes. Axillary node, but not sentinel node, CD4 T cell and dendritic cell populations were highly correlated with disease-free survival, independent of axillary metastasis. Immune profiling of ALN from a test set of 48 patients, applying CD4 T cell and CD1a dendritic cell population thresholds of CD4 ≥ 7.0% and CD1a ≥ 0.6%, determined from analysis of a learning set of 29 patients, provided significant risk stratification into favorable and unfavorable prognostic groups superior to clinicopathologic characteristics including tumor size, extent or size of nodal metastasis (CD4, p < 0.001 and CD1a, p < 0.001). Moreover, axillary node CD4 T cell and CD1a dendritic cell populations allowed more significant stratification of disease-free survival of patients with T1 (primary tumor size 2 cm or less) and T2 (5 cm or larger) tumors than all other patient characteristics. Finally, sentinel node immune profiles correlated primarily with the presence of infiltrating tumor cells, while axillary node immune profiles appeared largely independent of nodal metastases, raising the possibility that, within axillary lymph nodes, immune profile changes and nodal metastases represent independent processes. CONCLUSION: These findings demonstrate that the immune profile of tumor-draining lymph nodes is of novel biologic and clinical importance for patients with early stage breast cancer

Bioluminescent Monitoring of NIS-Mediated I Ablative Effects in MCF-7 Xenografts

Optical imaging has made it possible to monitor response to anticancer therapies in tumor xenografts. The concept of treating breast cancers with 131 I is predicated on the expression of the Na + /I − symporter (NIS) in many tumors and uptake of I in some. The pattern of 131 I radioablative effects were investigated in an MCF-7 xenograft model dually transfected with firefly luciferase and NIS genes. On Day 16 after tumor cell implantation, 3 mCi of 131 I was injected. Bioluminescent imaging using d -luciferin and a cooled charge-coupled device camera was carried out on Days 1, 2, 3, 7, 10, 16, 22, 29, and 35. Tumor bioluminescence decreased in 131 I-treated tumors after Day 3 and reached a nadir on Day 22. Conversely, bioluminescence steadily increased in controls and was 3.85-fold higher than in treated tumors on Day 22. Bioluminescence in 131 I-treated tumors increased after Day 22, corresponding to tumor regrowth. By Day 35, treated tumors were smaller and accumulated 33% less 99m TcO 4 than untreated tumors. NIS immunoreactivity was present in <50% of 131 I-treated cells compared to 85–90% of controls. In summary, a pattern of tumor regression occurring over the first three weeks after 131 I administration was observed in NIS-expressing breast cancer xenografts

Disease-free Survival Analysis of Women with Breast Cancer According to Immune profile Characteristics, Learning Set ALN Series 2, and Test Set

<p>KM curves are shown for (A) median DFS applied to the learning set ALN series 2 (<i>n</i> = 27) and test set (<i>n</i> = 48) according to size of CD4 T cell and CD1a dendritic cell populations within learning set ALN series 2 (second, randomly selected ALN per individual); (B) DFS stratified by size of CD4 T cell and CD1a dendritic cell populations within test set ALNs; and (C) DFS applied to the learning set (<i>n</i> = 29) and test set (<i>n</i> = 48) according to size of ALN CD4 T cell and ALN CD1a dendritic cell populations. Thresholds for ALN CD4 T cell and ALN CD1a dendritic cell populations were determined by ROC curves as applied to the learning set (ALN series 1). Median duration of DFS are indicated; – indicates a median DFS greater than follow-up period, 5 y. Of 29 individuals in learning set ALN series 1, 11 had recurrent disease, and of 27 individuals in learning set ALN series 2, 11 had recurrent disease. Of 48 individuals in the test set of ALNs, 22 had recurrent disease. For ALN selection from the learning set (C), a single ALN was randomly selected from series 1 or series 2 per individual. Adjusted <i>p</i>-values were determined by the permuted log-rank statistic for comparison of DFS between groups.</p

DFS Analysis of Women with Breast Cancer According to Tumor and Immune profile Characteristics, Learning Set ALN Series 1

<div><p>KM curves are shown for (A) median DFS applied to the learning set, <i>n</i> = 29, according to percent of SLN occupied by infiltrating tumor (determined by IHC), and stratified by tumor stage; (B) DFS according to size of CD4 T cell and CD1a dendritic cell populations within learning set ALN series 1 (first, arbitrarily selected ALN per individual); and (C) DFS stratified both by percent of SLN infiltrated by tumor and tumor stage, and by both axillary node CD4 T cell population and by tumor stage. A comparison of survival by all subgroups and a separate comparison of stratified T2 alone are included (* in [C]). Thresholds for percent tumor infiltration within SLN, ALN CD4 T cell, and ALN CD1a dendritic cell populations were determined by ROC curves as applied to the learning set (SLN and ALN series 1). Median duration of DFS are indicated; – indicates a median DFS greater than follow-up period, 5 y. Of 29 individuals, 11 had recurrent disease. Adjusted <i>p-</i>values were determined by the permuted log-rank statistic for comparison of disease-free survival between groups.</p> <p>TI, tumor infiltration.</p></div

Lymph Node Profile of Sentinel and Axillary Lymph Nodes

<p>Mean and standard error of CD4 and CD8 T cell, CD1a dendritic cell populations as percent of lymph node, and CD4:CD8 cell ratio are shown for (A) SLN (<i>n</i> = 29), ALN (<i>n</i> = 29), and control lymph nodes (<i>n</i> = 10); (E) tumor-involved ALNs (<i>n</i> = 9), tumor-free ALNs (<i>n</i> = 7) from patients with a positive ALND, tumor-free ALNs from patients with a negative ALND (<i>n</i> = 13), and controls (<i>n</i> = 10); (I) SLNs and ALNs stratified by disease recurrence during 5 y of follow-up (11 of 29 with recurrent disease); (L) tumor-involved ALNs stratified by disease recurrence (<i>n</i> = 9); (M) tumor-free ALNs from patients with a positive ALND stratified by disease recurrence (<i>n</i> = 7); and (N) tumor-free ALNs from patients with a negative ALND stratified by disease recurrence (<i>n</i> = 13). Representative 200× images of lymphocyte population (brown staining) and infiltrating tumor (purple staining) by IHC, including CD8 T cells in (B) SLNs, (C) ALNs, and (D) controls; (F) CD4 T cells in tumor-involved ALNs, (G) tumor-free ALNs from patients with a positive ALND, (H) tumor-free ALNs from patients with a negative ALND; and (J) CD1a dendritic cells in ALNs from patients disease-free versus (K) patients who developed recurrence.</p

Bioluminescent Monitoring of NIS-Mediated 131

Optical imaging has made it possible to monitor response to anticancer therapies in tumor xenografts. The concept of treating breast cancers with 131 I is predicated on the expression of the Na + /I − symporter (NIS) in many tumors and uptake of I in some. The pattern of 131 I radioablative effects were investigated in an MCF-7 xenograft model dually transfected with firefly luciferase and NIS genes. On Day 16 after tumor cell implantation, 3 mCi of 131 I was injected. Bioluminescent imaging using d -luciferin and a cooled charge-coupled device camera was carried out on Days 1, 2, 3, 7, 10, 16, 22, 29, and 35. Tumor bioluminescence decreased in 131 I-treated tumors after Day 3 and reached a nadir on Day 22. Conversely, bioluminescence steadily increased in controls and was 3.85-fold higher than in treated tumors on Day 22. Bioluminescence in 131 I-treated tumors increased after Day 22, corresponding to tumor regrowth. By Day 35, treated tumors were smaller and accumulated 33% less 99m TcO 4 than untreated tumors. NIS immunoreactivity was present in <50% of 131 I-treated cells compared to 85–90% of controls. In summary, a pattern of tumor regression occurring over the first three weeks after 131 I administration was observed in NIS-expressing breast cancer xenografts

Different Gene Expression Patterns in Invasive Lobular and Ductal Carcinomas of the Breast

Invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the two major histological types of breast cancer worldwide. Whereas IDC incidence has remained stable, ILC is the most rapidly increasing breast cancer phenotype in the United States and Western Europe. It is not clear whether IDC and ILC represent molecularly distinct entities and what genes might be involved in the development of these two phenotypes. We conducted comprehensive gene expression profiling studies to address these questions. Total RNA from 21 ILCs, 38 IDCs, two lymph node metastases, and three normal tissues were amplified and hybridized to ∼42,000 clone cDNA microarrays. Data were analyzed using hierarchical clustering algorithms and statistical analyses that identify differentially expressed genes (significance analysis of microarrays) and minimal subsets of genes (prediction analysis for microarrays) that succinctly distinguish ILCs and IDCs. Eleven of 21 (52%) of the ILCs (“typical” ILCs) clustered together and displayed different gene expression profiles from IDCs, whereas the other ILCs (“ductal-like” ILCs) were distributed between different IDC subtypes. Many of the differentially expressed genes between ILCs and IDCs code for proteins involved in cell adhesion/motility, lipid/fatty acid transport and metabolism, immune/defense response, and electron transport. Many genes that distinguish typical and ductal-like ILCs are involved in regulation of cell growth and immune response. Our data strongly suggest that over half the ILCs differ from IDCs not only in histological and clinical features but also in global transcription programs. The remaining ILCs closely resemble IDCs in their transcription patterns. Further studies are needed to explore the differences between ILC molecular subtypes and to determine whether they require different therapeutic strategies