Purpose: Although the central role of the immune system for tumor prognosis is generally accepted, a single robust marker is not yet available.

Experimental Design: On the basis of receiver operating characteristic analyses, robust markers were identified from a 60-gene B cell–derived metagene and analyzed in gene expression profiles of 1,810 breast cancer; 1,056 non–small cell lung carcinoma (NSCLC); 513 colorectal; and 426 ovarian cancer patients. Protein and RNA levels were examined in paraffin-embedded tissue of 330 breast cancer patients. The cell types were identified with immunohistochemical costaining and confocal fluorescence microscopy.

Results: We identified immunoglobulin κ C (IGKC) which as a single marker is similarly predictive and prognostic as the entire B-cell metagene. IGKC was consistently associated with metastasis-free survival across different molecular subtypes in node-negative breast cancer (n = 965) and predicted response to anthracycline-based neoadjuvant chemotherapy (n = 845; P < 0.001). In addition, IGKC gene expression was prognostic in NSCLC and colorectal cancer. No association was observed in ovarian cancer. IGKC protein expression was significantly associated with survival in paraffin-embedded tissues of 330 breast cancer patients. Tumor-infiltrating plasma cells were identified as the source of IGKC expression.

Conclusion: Our findings provide IGKC as a novel diagnostic marker for risk stratification in human cancer and support concepts to exploit the humoral immune response for anticancer therapy. It could be validated in several independent cohorts and carried out similarly well in RNA from fresh frozen as well as from paraffin tissue and on protein level by immunostaining. Clin Cancer Res; 18(9); 2695–703. ©2012 AACR.

See commentary by Whiteside and Ferrone, p. 2417

Translational Relevance

This study reports that immunoglobulin κ C (IGKC) RNA levels robustly define prognosis in a comprehensive analysis of available breast cancer data sets and predict response to neoadjuvant anthracycline–based therapy. In addition, IGKC maintains its prognostic relevance also in non–small cell lung and colorectal cancer, suggesting a global mechanism in the biology of adenocarcinomas. Using real-time PCR and immunohistochemistry in formalin-fixed, paraffin-embedded (FFPE) tissue, we could validate the prognostic impact of IGKC. Furthermore, using confocal microscopy, we identified tumor-infiltrating plasmablasts and plasma cells as the source of IGKC expression. This study has major clinical implications: (i) IGKC as a prognostic and predictive marker that lends itself to systematic testing in FFPE tissue samples allows an improved prediction of prognosis and response to chemotherapy, and (ii) the protective effects of this naturally occurring humoral immune response support the concept of immunotherapy.

It has become evident that the immune response in the tumor environment plays a pivotal role in all stages of carcinogenesis and in different context may promote or inhibit tumor progression (1–3). In human cancer, this concept is supported by the observation that the presence of specific immune cells can be linked to different clinical outcomes. For instance, high numbers of T lymphocytes were associated with good prognosis in many human cancer types (4, 5). Also, immune modulatory chemokines were related to the natural course of cancer as well as to response to therapy (6, 7).

With introduction of high resolution gene expression arrays, it has become evident that a lot of prognostic gene signatures consist of immune markers (8–11). Particularly in breast cancer, several prognostic and predictive gene signatures reflect the individual immune response, independent from traditional markers like hormone receptor status or Ki-67 proliferation index (12–14). To systematically distinguish between T and B cell–related effects on the natural course of breast cancer, we previously showed that the humoral immune system, as summarized in a 60-gene B-cell signature, had a strong protective impact on metastasis-free survival (MFI) in node-negative breast cancer patients (15). However, analysis of a 60-gene signature by real-time PCR (RT-PCR) is costly and relatively labor intensive. To improve the clinical applicability, we studied whether the influence of the B-cell metagene on prognosis can be narrowed down to a single gene. We report that the prognostic information provided by both mRNA and protein levels of immunoglobulin κ C (IGKC) was comparable with that of the 60-gene B-cell metagene. Next, we identified the cellular source of IGKC and evaluated this host-dependent signature in other common cancer types. Finally, to translate the findings to robust analytic tools for clinical diagnostics on routinely archived tissue, immunohistochemistry was applied.

Patients

Our analysis includes gene array data from 1,810 breast cancer patients (965 node negative, without chemotherapy and 845 with anthracycline-based chemotherapy), 1,056 non–small cell lung carcinoma (NSCLC), 513 colorectal, and 426 ovarian cancer patients. In addition, paraffin-embedded tissue blocks of 330 node-negative breast cancer patients were analyzed (detailed description, Supplementary Methods).

Gene expression analysis and immunostaining

HG-U133A arrays were used to analyze Uppsala lung cancer (n = 196) cohorts (Supplementary Table S1). All other gene array data are publicly available (Supplementary Methods). IGKC mRNA levels in formalin-fixed, paraffin-embedded (FFPE) tissue were quantified by quantitative RT-PCR (qRT-PCR). For both immunohistochemistry and confocal-fluorescence microscopy, antibodies against MUM1/IRF4, CD20, pan-cytokeratin, or immunoglobulin G (IgG) and IGKC were used as previously described (details, Supplementary Methods).

Statistical analysis

Survival was analyzed by univariate and multivariate Cox models and visualized by Kaplan–Meier plots. The Brier score was used to evaluate the ability to predict survival. Meta-analyses were conducted by fixed and random effect models and visualized with forest plots (details, Supplementary Methods).

IGKC is a representative marker of the B-cell gene signature

To condense the previously described breast cancer B-cell signature (15) that consists of 60 genes, we analyzed microarray data from our own breast cancer cohort (Mainz) and 2 independent cohorts [Rotterdam (19); Transbig (16, 17)]. The bioinformatic strategy was based on the optimal combination of 2 criteria (Fig. 1): (i) the best average correlation of each of the 60 genes with all other members of the B-cell metagene as a measure of representativeness, and (ii) the largest area under the receiver operating characteristic curve, as a measure of the ability of each individual gene to discriminate between patients with and without metastasis during a 5-year follow-up period. Using these 2 criteria, IGKC was identified as one of the genes with the best average correlation and largest area under the curve (AUC; Fig. 1) and also showed a wide dynamic range with a unimodal distribution (Supplementary Fig. S1). The results obtained by microarrays were confirmed with qRT-PCR in archived FFPE tissue from the Mainz cohort. IGKC mRNA levels determined by qRT-PCR, correlated very well with the levels measured by gene array in fresh-frozen samples of the same tumors (Fig. 2A) and similarly, IGKC mRNA levels in paraffin-embedded tissue showed a significant association with MFI both in univariate and multivariate analyses (Table 3; Kaplan–Meier plot: Fig. 2B).

Figure 1.

IGKC is representative for the B-cell metagene. Each spot represents one of the 60 genes of the B-cell metagene. IGKC is indicated by the red color (and additionally by an arrow). Average correlation is the mean of all absolute Pearson correlations of an individual gene with all other members of the B-cell metagene. AUC is a measure for the ability of the corresponding gene to discriminate between patients with and without metastasis. High values indicate better prognosis.

Figure 1.

IGKC is representative for the B-cell metagene. Each spot represents one of the 60 genes of the B-cell metagene. IGKC is indicated by the red color (and additionally by an arrow). Average correlation is the mean of all absolute Pearson correlations of an individual gene with all other members of the B-cell metagene. AUC is a measure for the ability of the corresponding gene to discriminate between patients with and without metastasis. High values indicate better prognosis.

Close modal
Figure 2.

Confirmation in paraffin embedded tissue. A, RNA levels determined by gene array in fresh-frozen tumor tissue of node-negative breast cancer patients correlate with RNA levels of the same tumors determined by qRT-PCR in FFPE tissue. B, Kaplan–Meier plot for IGKC RNA levels in paraffin-embedded tissue (n = 330).

Figure 2.

Confirmation in paraffin embedded tissue. A, RNA levels determined by gene array in fresh-frozen tumor tissue of node-negative breast cancer patients correlate with RNA levels of the same tumors determined by qRT-PCR in FFPE tissue. B, Kaplan–Meier plot for IGKC RNA levels in paraffin-embedded tissue (n = 330).

Close modal
Table 1.

IGKC is associated with MFI in 3 independent cohorts of systemically untreated node-negative breast cancer (combined Mainz, Rotterdam, and Transbig cohorts, n = 766): univariate Cox analysis

Mainz cohort (n = 200)Rotterdam cohort (n = 286)Transbig cohort (n = 280)Combined cohorts (n = 766)
IGKCa 
P 0.052 <0.001 0.060 <0.001 
HR (95% CI) 0.81 (0.65—1.00) 0.80 (0.71–0.90) 0.85 (0.72–1.01) 0.79 (0.72–0.86) 
Mainz cohort (n = 200)Rotterdam cohort (n = 286)Transbig cohort (n = 280)Combined cohorts (n = 766)
IGKCa 
P 0.052 <0.001 0.060 <0.001 
HR (95% CI) 0.81 (0.65—1.00) 0.80 (0.71–0.90) 0.85 (0.72–1.01) 0.79 (0.72–0.86) 

aIGKC was analyzed as a continuous variable.

Table 2.

Multivariate Cox analysis adjusted to established clinical factors (combined Mainz and Transbig cohorts, n = 480)

PHR (95% CI)
Age (<50 vs. ≥50 y) 0.791 1.14 (0.74–1.73) 
pT stage (≤2 vs. >2 cm) 0.012 1.78 (1.13–2.78) 
Histologic grade (grade 1 and 2 vs. grade 3) 0.001 2.27 (1.41–3.65) 
ER and PR (negative vs. positive) 0.964 1.01 (0.61–1.67) 
HER2 status (negative vs. positive) 0.231 1.42 (0.79–2.53) 
IGKC (continuous variable) 0.005 0.81 (0.70–0.93) 
PHR (95% CI)
Age (<50 vs. ≥50 y) 0.791 1.14 (0.74–1.73) 
pT stage (≤2 vs. >2 cm) 0.012 1.78 (1.13–2.78) 
Histologic grade (grade 1 and 2 vs. grade 3) 0.001 2.27 (1.41–3.65) 
ER and PR (negative vs. positive) 0.964 1.01 (0.61–1.67) 
HER2 status (negative vs. positive) 0.231 1.42 (0.79–2.53) 
IGKC (continuous variable) 0.005 0.81 (0.70–0.93) 
Table 3.

Prognostic relevance of IGKC determined by qRT-PCR in paraffin-embedded tumor tissue of patients (n = 330) with node-negative breast cancer

PHR (95% CI)
Univariate Cox analysis of MFI 
IGKC (continuous variable) 0.004 0.882 (0.809–0.960) 
Multivariate Cox analysis of MFI adjusted to established clinical factors 
Age (<50 vs. >50 y) 0.307 0.944 (0.593–1.501) 
pT stage (<2 vs. >2cm) 0.880 0 964 (0.601–1.547) 
Histologic grade (grade 3 vs. Grades 1 and 2) <0.001 3.853 (2.386–6.238) 
ER and PR (negative vs. positive) 0.136 1.533 (0.874–2.690) 
ERBB2 status (positive vs. negative) 0.405 1.277 (0.718–2.270) 
IGKC (continuous variable) 0.001 0.871 (0.805–0.944) 
PHR (95% CI)
Univariate Cox analysis of MFI 
IGKC (continuous variable) 0.004 0.882 (0.809–0.960) 
Multivariate Cox analysis of MFI adjusted to established clinical factors 
Age (<50 vs. >50 y) 0.307 0.944 (0.593–1.501) 
pT stage (<2 vs. >2cm) 0.880 0 964 (0.601–1.547) 
Histologic grade (grade 3 vs. Grades 1 and 2) <0.001 3.853 (2.386–6.238) 
ER and PR (negative vs. positive) 0.136 1.533 (0.874–2.690) 
ERBB2 status (positive vs. negative) 0.405 1.277 (0.718–2.270) 
IGKC (continuous variable) 0.001 0.871 (0.805–0.944) 

IGKC is associated with better prognosis in breast cancer

To further validate the prognostic impact of IGKC, we analyzed mRNA expression as a single marker in 5 publicly accessible gene array data sets of node-negative breast cancer patients who did not receive chemotherapy: the Mainz (15), Rotterdam (19), Transbig (16, 17), Yu (18), and NKI (20, 21) cohorts. The meta-analysis revealed a highly significant association of IGKC RNA levels with better prognosis (P < 0.0001, Fig. 3). The expression of IGKC was further analyzed in the 3 molecularly and biologically different subtypes of breast cancer (14): (i) estrogen receptor (ER) status positive and HER2 status negative, (ii) ER status negative, and (iii) HER2 status positive and ER status positive or negative carcinomas. High IGKC expression correlated with good prognosis in all subgroups with a particularly strong association in the HER2-positive subgroup (Fig. 3). The univariate (Table 1) and multivariate Cox regression models (Table 2) adjusted to established clinical factors (Supplementary Fig. S2) confirmed the association of IGKC with MFI (Table 1, Supplementary Table S2), disease-free survival (Supplementary Table S3A) and overall survival (OS; Supplementary Table S3B). For further illustration, IGKC gene expression was dichotomized at the median, and Kaplan–Meier curves were plotted (Supplementary Fig. S3). IGKC correlated with recently published biological signatures (15), the B-cell metagene, and to a lesser degree, with the T-cell metagene (15). In addition, there was a weak inverse correlation with the ER metagene but not with the proliferation metagene (Supplementary Fig. S4 and S5). Brier score analysis showed that IGKC alone has a similar predictive power as the complete 60-gene–based B-cell signature (Supplementary Fig. S6).

Figure 3.

IGKC is associated with better prognosis in node-negative breast cancer and NSCLC. The meta-analysis shows the influence of IGKC on metastasis-free survival in 5 cohorts (Mainz, Rotterdam, Transbig, NKI, and Yu). Data are given for all patients (A) as well as for the 3 breast cancer subgroups according to Desmedt (14), namely ER positive and HER2 negative (B), ER negative and HER2 negative (C), as well as HER2 positive and ER positive or negative (D) patients. In all cohorts, high IGKC is associated with a trend toward better prognosis resulting in a highly significant association in the meta-analysis using the fixed as well as the random effects models. The Yu cohort contains only ER-negative patients and only 1 event in the HER2-positive group and was excluded from the analyses in B and D. IGKC expression is also prognostic in NSCLC either in the univariate Cox model (E) or in the model adjusted to the proliferation marker UBE2C. n, number of patients; P: P value of the fixed effect model.

Figure 3.

IGKC is associated with better prognosis in node-negative breast cancer and NSCLC. The meta-analysis shows the influence of IGKC on metastasis-free survival in 5 cohorts (Mainz, Rotterdam, Transbig, NKI, and Yu). Data are given for all patients (A) as well as for the 3 breast cancer subgroups according to Desmedt (14), namely ER positive and HER2 negative (B), ER negative and HER2 negative (C), as well as HER2 positive and ER positive or negative (D) patients. In all cohorts, high IGKC is associated with a trend toward better prognosis resulting in a highly significant association in the meta-analysis using the fixed as well as the random effects models. The Yu cohort contains only ER-negative patients and only 1 event in the HER2-positive group and was excluded from the analyses in B and D. IGKC expression is also prognostic in NSCLC either in the univariate Cox model (E) or in the model adjusted to the proliferation marker UBE2C. n, number of patients; P: P value of the fixed effect model.

Close modal

IGKC predicts response to anthracycline-based chemotherapy

In addition to the prediction of survival, IGKC expression levels were evaluated with regard to response to cytostatic drugs. We selected all published gene array data of breast cancer patients who had received anthracycline-based neoadjuvant therapy (Fig. 4). High IGKC expression was associated with complete response (CR) in a meta-analysis that included 7 cohorts (n = 845; P < 0.0001, Fig.4). Analysis of the subgroups according to Desmedt (14) showed that IGKC is predictive for response in the ER/HER2 and in the HER2+ subgroups but not in the ER+/HER2 subgroups. In particular, the association with CR was pronounced for the ER-negative patients (P < 0.0001, Supplementary Fig. S7A). In multivariate analyses, the association of IGKC with CR was independent of progesterone receptor (PR), HER2, proliferation status (represented by ubiquitin-conjugating enzyme E2C; UBE2C), and type of chemotherapy (anthracycline-based chemotherapy with or without additional taxane; Supplementary Fig. S7C–S7G). Again, a comparison of the ability to predict response to chemotherapy based on logistic regression models yielded similar AUC values for IGKC and the 60-gene B-cell signature (Supplementary Fig. S8). In conclusion, IGKC showed strong correlation with survival, but also predicts chemosensitivity in ER-negative patients in the neoadjuvant setting.

Figure 4.

IGKC is associated with better response (CR) in breast cancer patients treated with anthracycline-based neoadjuvant chemotherapy. A meta-analysis of all available cohorts treated with anthracycline-based neoadjuvant chemotherapy was carried out. Data are given for all patients (A) as well as for the 3 breast cancer subgroups according to Desmedt4. IGKC is associated with better response to chemotherapy in the total group (A), the Desmedt subgroups shown in C and D but not in the ER-positive subgroup (B). n, number of patients.

Figure 4.

IGKC is associated with better response (CR) in breast cancer patients treated with anthracycline-based neoadjuvant chemotherapy. A meta-analysis of all available cohorts treated with anthracycline-based neoadjuvant chemotherapy was carried out. Data are given for all patients (A) as well as for the 3 breast cancer subgroups according to Desmedt4. IGKC is associated with better response to chemotherapy in the total group (A), the Desmedt subgroups shown in C and D but not in the ER-positive subgroup (B). n, number of patients.

Close modal

IGKC is also prognostic in NSCLC and colorectal cancer

Because the immune response represents a general mechanism in tumor biology, we analyzed the prognostic impact of IGKC expression in lung, colorectal, and ovarian carcinomas (Supplementary Fig. S9). For lung cancer, we evaluated a novel cohort of 196 NSCLC patients from Uppsala. Both the B-cell metagene as well as single IGKC mRNA expression were significantly associated with longer survival in the univariate (P < 0.001) and multivariate Cox regression model (P = 0.032) adjusted to established clinical factors (Supplementary Fig. S9). Interestingly, Kaplan–Meier analysis in the subgroups revealed that this prognostic relevance was restricted to lung adenocarcinoma and was not seen in squamous lung carcinomas (Supplementary Fig. S9A and S9B), possibly because of the smaller sample size (n = 66). To further validate these results, we conducted a meta-analysis of publicly available Affymetrix data sets, including a total of 1,056 lung carcinomas (Fig. 3E and F). Both the univariate (P = 0.011; Fig. 3E) and the bivariate meta-analysis, adjusted to the proliferation marker ubiquinin-conjugating enzyme 2C UBE2C (P = 0.015; Fig. 3F), showed a significant association of IGKC with long-term overall survival.

Furthermore, we confirmed a significant association between IGKC and relapse-free survival in a meta-analysis of gene expression data of 513 patients with adenocarcinoma of the colorectum (Supplementary Fig. S9D). For overall survival, the association did not show significance (Supplementary Fig. S9E). No association was seen in a meta-analysis of 426 patients with ovarian cancer (Supplementary Fig. S9F).

IGKC protein expression in archived breast cancer tissue

Valuable biomarkers should be applicable for routine diagnostics. A major obstacle for gene expression studies is the limited availability of fresh tumor tissue in clinical practice. Indeed, most prognostic markers in breast cancer, for example, ER, PR, HER2, and Ki-67, are routinely determined by immunohistochemistry. Therefore, we tested a monoclonal antibody against IGKC in FFPE tumor samples from the Mainz breast cancer cohort and found that IGKC was expressed in lymphoid cells in the tumor stroma of breast cancer (Fig. 5A). Immunostaining intensities correlated with IGKC RNA levels isolated from the tissue slides (P = 0.014; Jonckhere-terpstra test comparing staining intensity groups 0 vs. 1+ vs. 2+/3+) as well as with MFI (Fig. 5B).

Figure 5.

IGKC immunohistochemistry in paraffin-embedded tissue. A, IGKC immunostaining intensities 3+, 2+, 1+, and 0 in representative sections of ductal breast carcinomas; note the expression within the desmoplastic stroma in-between tumor cell nests; magnification: ×400. B, prognostic relevance of IGKC immunostaining intensity in paraffin-embedded tumor tissue of the “Mainz node-negative paraffin cohort.” C, coexpression by immunofluorescence of IGKC with MUM1/IRF4, a marker for plasmablasts and plasma cells; IgG coexpression illustrates the isotype switch; B-cell marker CD20; epithelial cell marker cytokeratin decorates tumor cells. IGKC is visualized by green fluorescence, whereas MUM1/IRF4, IgG, CD20, and cytokeratin emit a red signal. Scale bars, 50 and 5 μm in the overviews and close-ups, respectively. The corresponding positive and negative controls are shown in Supplementary Fig. S10.

Figure 5.

IGKC immunohistochemistry in paraffin-embedded tissue. A, IGKC immunostaining intensities 3+, 2+, 1+, and 0 in representative sections of ductal breast carcinomas; note the expression within the desmoplastic stroma in-between tumor cell nests; magnification: ×400. B, prognostic relevance of IGKC immunostaining intensity in paraffin-embedded tumor tissue of the “Mainz node-negative paraffin cohort.” C, coexpression by immunofluorescence of IGKC with MUM1/IRF4, a marker for plasmablasts and plasma cells; IgG coexpression illustrates the isotype switch; B-cell marker CD20; epithelial cell marker cytokeratin decorates tumor cells. IGKC is visualized by green fluorescence, whereas MUM1/IRF4, IgG, CD20, and cytokeratin emit a red signal. Scale bars, 50 and 5 μm in the overviews and close-ups, respectively. The corresponding positive and negative controls are shown in Supplementary Fig. S10.

Close modal

IGKC is expressed in tumor-infiltrating plasma cells

Finally, to identify the cell type that was responsible for IGKC expression, we carried out costaining with antibodies against IGKC and either CD20 (a B-lymphocyte marker expressed in mature B cells but not on plasma cells), pan-cytokeratin (a marker for epithelial cells), or MUM1/IRF4 (a marker for activated B cells, plasmablasts, and plasma cells). No colocalization between IGKC and CD20, or IGKC and cytokeratin was observed (Fig. 5C). However, more than 90% of all cells that stained positive for nuclear MUM1/IRF4 were also positive for cytoplasmic IGKC (Fig. 5C). In addition, costaining with anti-human IgG showed that IGKC is only expressed in IgG-positive cells. Collectively, our results indicate that IGKC is expressed in mature plasma cells.

Here, we describe a B cell–related gene signature, best represented by IGKC, as a strong prognostic marker in human breast, lung, and colorectal adenocarcinomas. Tumor-infiltrating plasma cells were identified to be the source of IGKC expression, which supports the concept that the adaptive humoral immune response is responsible for this host-dependent protective effect.

Numerous studies have shown the association of infiltrating immune cells and prognosis and response to therapy in different cancer types. However, most often the clinical relevance was ascribed to the T-cell lineage, with predominance of CD8+, and CD45RO+ T lymphocytes in colorectal, lung, and ovarian cancer (22–25).

The immune infiltrates in breast cancer have been characterized recently. Our own group systematically investigates the role of B and T cells, as typified by their respective metagenes, in the natural course of medically untreated node-negative breast cancer (15). Our description of a strong and independent prognostic impact of the humoral immune system in rapidly proliferating node-negative breast cancer was now confirmed by Bianchini and colleagues (26). Our study presents a consequent extension of the previous work with focus of B-cell lineage and a systematic implementation of solid biostatistics; therewith, we were able to condense the 60-gene B-cell signature to IGKC as a single gene. In addition to the prognostic impact, IGKC expression predicts also response to neoadjuvant chemotherapy in breast cancer. This further substantiates the hypothesis that chemotherapy does not only exert a direct cytotoxic effect but at the same time enhances the antitumor immune response (27, 28).

The robust reproduction of IGKC's clinical relevance in other cancer types represents in general one of the sparse exception that gene signatures are compatible between different cancer types. Mainly proliferation-related signatures have been shown to be transferable (29). Likewise the immunohistochemical analysis of the proliferation marker Ki-67 is of clinical importance in a variety of cancer entities. (30). In near analogy, the B-cell metagene reflects a general beneficial biological mechanism, which can easily be measured by IGKC protein staining. The validation of the gene expression findings in 330 node-negative FFPE tumors by immunohistochemistry was therefore of particular importance because fresh-frozen tissue is logistically demanding to obtain on a routine basis and often only small biopsies are available. Thus, an antibody-based detection of IGKC is applicable in routine cancer diagnostics.

Our finding that IGKC in tumors arises from plasma cells contradicts the provocative assumption that tumor cells are capable of producing immunoglobulins to promote growth and survival (31). Rather, it supports a previous report that breast cancer specimens typically have tumor infiltration of IgG-positive plasma cells (32). Similarly, another study of Wang and colleagues described that the majority of tumor-infiltrating plasma cells in invasive-ductal breast carcinomas was of IgG isotype suggesting that a tumor-derived antigen response may lead to the maturation of systemic B cells (33). In accordance, in our study, costaining for IGKC and IgG confirmed increased heavy class isotype switch to IgG. This antigen-dependent switch from immunoglobulin M and immunoglobulin D to IgG1 production is a well-known feature of B-cell maturation (34) and plasma cell differentiation (35) after antigen encounter. Notably, several reports have characterized oligoclonal expansion of B cells in breast cancer (36–40). But none of these groups have yet shown a robust clinical impact of these intriguing findings.

Interestingly, the impact of the peritumoral immune system could be shown in other tumor entities, that is, in NSCLC and colorectal cancer, but not in ovarian cancer. We speculate that this may be explained by distinct growth pattern in different organs and subsequent different immunogenic properties. The biological roles of the IGKC signature have to be addressed in further studies. Nevertheless, the strong prognostic impact shared by breast, lung, and colorectal adenocarcinomas represents, to the best of our knowledge, the first robust comprehensive biomarker predicting the response of the immune system in a variety of cancer types.

We have to acknowledge the retrospective nature of our study, but currently prospective analyses of breast cancer without adjuvant treatment are not feasible considering current treatment recommendations (41). Also, a detailed evaluation of additional malignant tumor types is difficult because of limited clinical and pathologic data in the published expression array data sets. It should be considered that not only κ but also λ light chain–associated probe sets are among the top genes indicating an antitumor response (Supplementary Fig. S11). However, IGKC combines the advantages of not only belonging to the top genes indicating a favorable prognosis but also offers the possibility that RNA from paraffin tissue can be used, and the results could be validated by immunostaining with commercially available antibodies.

The novelty of our study is (i) the translation of our B-cell metagene approach (15) to other tumor types, (ii) the validation by independent methods, and (iii) the establishment of IGKC as a biomarker for clinical diagnostics on FFPE tissues. In conclusion, our findings strongly support the emerging role of the immune system as a clinically relevant hallmark of cancer biology (42).

No potential conflicts of interest were disclosed.

The authors thank Ms. Seehase for her most competent support with data processing and statistical analyses and Mrs. Holzer as well as Mrs. Pfeffer for excellent technical assistance. Dr. Simon Ekman, Michael Bergkvist, and Kristina Lamberg helped to establish the Uppsala lung cancer cohort. The authors also thank Dr. Friedrich Kommoss for helpful discussion and Ms. Susanne Lindemann for valuable bibliographic support.

The study was supported by the Federal Ministry of Education and Research (BMBF, NGFN project Oncoprofile), the Swedish Cancer foundation, the Uppsala Lions Cancer foundation, and by the German Research Council (DFG, contract numbers RA 870/4-1 and RA 870/5-1).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
LaMarco
K
. 
Dynamic duo: cancer drugs and the immune system
.
Sci Transl Med
2009
1
:
12ec44
.
2.
Ostrand-Rosenberg
S
. 
Immune surveillance: a balance between protumor and antitumor immunity
.
Curr Opin Genet Dev
2008
;
18
:
11
8
.
3.
Butler
MO
,
Friedlander
P
,
Milstein
MI
,
Mooney
MM
,
Metzler
G
,
Murray
AP
, et al
Establishment of antitumor memory in humans using in vitro-educated CD8+ T cells
.
Sci Transl Med
2011
;
3
:
80ra34
.
4.
Martinet
L
,
Garrido
I
,
Filleron
T
,
Le Guellec
S
,
Bellard
E
,
Fournie
JJ
, et al
Human solid tumors contain high endothelial venules: association with T- and B-lymphocyte infiltration and favorable prognosis in breast cancer
.
Cancer Res
2011
;
71
:
5678
87
.
5.
Pagès
F
,
Galon
J
,
Dieu-Nosjean
MC
,
Tartour
E
,
Sautès-Fridman
C
,
Fridman
WH
. 
Immune infiltration in human tumors: a prognostic factor that should not be ignored
.
Oncogene
2010
;
29
:
1093
102
.
6.
Wang
X
,
Lu
L
,
Jiang
S
. 
Regulatory T cells: customizing for the clinic
.
Sci Transl Med
2011
;
3
:
83ps19
.
7.
Halama
N
,
Michel
S
,
Kloor
M
,
Zoernig
I
,
Benner
A
,
Spille
A
, et al
Localization and density of immune cells in the invasive margin of human colorectal cancer liver metastases are prognostic for response to chemotherapy
.
Cancer Res
2011
;
71
:
5670
7
.
8.
Roepman
P
,
Jassem
J
,
Smit
EF
,
Muley
T
,
Niklinski
J
,
van de Velde
T
, et al
An immune response enriched 72-gene prognostic profile for early-stage non-small-cell lung cancer
.
Clin Cancer Res
2009
;
15
:
284
90
.
9.
Gao
Q
,
Wang
XY
,
Qiu
SJ
,
Zhou
J
,
Shi
YH
,
Zhang
BH
, et al
Tumor stroma, reaction-related gene signature predicts clinical outcome in human hepatocellular carcinoma
.
Cancer Sci
2011
;
102
:
1522
31
.
10.
Finak
G
,
Bertos
N
,
Pepin
F
,
Sadekova
S
,
Souleimanova
M
,
Zhao
H
, et al
Stromal gene expression predicts clinical outcome in breast cancer
.
Nat Med
2008
;
14
:
518
27
.
11.
Reis-Filho
JS
,
Weigelt
B
,
Fumagalli
D
,
Sotiriou
C
. 
Molecular profiling: moving away from tumor philately
.
Sci Transl Med
2010
;
2
:
47ps43
.
12.
Teschendorff
AE
,
Miremadi
A
,
Pinder
SE
,
Ellis
IO
,
Caldas
C
. 
An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer
.
Genome Biol
2007
;
8
:
R157
.
13.
Alexe
G
,
Dalgin
GS
,
Scanfeld
D
,
Tamayo
P
,
Mesirov
JP
,
DeLisi
C
, et al
High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates
.
Cancer Res
2007
;
67
:
10669
76
.
14.
Desmedt
C
,
Haibe-Kains
B
,
Wirapati
P
,
Buyse
M
,
Larsimont
D
,
Bontempi
G
, et al
Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes
.
Clin Cancer Res
2008
;
14
:
5158
65
.
15.
Schmidt
M
,
Böhm
D
,
Von Törne
C
,
Steiner
E
,
Puhl
A
,
Pilch
H
, et al
The humoral immune system has a key prognostic impact in node-negative breast cancer
.
Cancer Res
2008
;
68
:
5405
13
.
16.
Desmedt
C
,
Piette
F
,
Loi
S
,
Wang
Y
,
Lallemand
F
,
Haibe-Kains
B
, et al
Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series
.
Clin Cancer Res
2007
;
13
:
3207
14
.
17.
Loi
S
,
Haibe-Kains
B
,
Desmedt
C
,
Lallemand
F
,
Tutt
AM
,
Gillet
C
, et al
Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade
.
J Clin Oncol
2007
;
25
:
1239
46
.
18.
Yu
JX
,
Sieuwerts
AM
,
Zhang
Y
,
Martens
JW
,
Smid
M
,
Klijn
JG
, et al
Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer
.
BMC Cancer
2007
;
7
:
182
.
19.
Wang
Y
,
Klijn
JGM
,
Zhang
Y
,
Sieuwerts
AM
,
Look
MP
,
Yang
F
, et al
Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer
.
Lancet
2005
;
365
:
671
9
.
20.
van't Veer
LJ
,
Dai
H
,
van de Vijver
MJ
,
He
YD
,
Hart
AA
,
Mao
M
, et al
Gene expression profiling predicts clinical outcome of breast cancer
.
Nature
2002
;
415
:
530
6
.
21.
van de
Vijver
MJ
,
He
YD
,
van't Veer
LJ
,
Dai
H
,
Hart
AA
,
Voskuil
DW
, et al
A gene-expression signature as a predictor of survival in breast cancer
.
N Engl J Med
2002
;
347
:
1999
2009
.
22.
Mantovani
A
,
Romero
P
,
Palucka
AK
,
Marincola
FM
. 
Tumour immunity: effector response to tumour and role of the microenvironment
.
Lancet
2008
;
371
:
771
83
.
23.
Pagès
F
,
Berger
A
,
Camus
M
,
Sanchez-Cabo
F
,
Costes
A
,
Molidor
R
, et al
Effector memory T cells, early metastasis, and survival in colorectal cancer
.
N Engl J Med
2005
;
353
:
2654
66
.
24.
Shimizu
K
,
Nakata
M
,
Hirami
Y
,
Yukawa
T
,
Maeda
A
,
Tanemoto
K
. 
Tumor-infiltrating Foxp3+ regulatory T cells are correlated with cyclooxygenase-2 expression and are associated with recurrence in resected non-small cell lung cancer
.
J Thorac Oncol
2010
;
5
:
585
90
.
25.
Stumpf
M
,
Hasenburg
A
,
Riener
MO
,
Jütting
U
,
Wang
C
,
Shen
Y
, et al
Intraepithelial CD8-positive T lymphocytes predict survival for patients with serous stage III ovarian carcinomas: relevance of clonal selection of T lymphocytes
.
Br J Cancer
2009
;
101
:
1513
21
.
26.
Bianchini
G
,
Qi
Y
,
Alvarez
RH
,
Iwamoto
T
,
Coutant
C
,
Ibrahim
NK
, et al
Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers
.
J Clin Oncol
2010
;
28
:
4316
23
.
27.
Ménard
C
,
Martin
F
,
Apetoh
L
,
Bouyer
F
,
Ghiringhelli
F
. 
Cancer chemotherapy: not only a direct cytotoxic effect, but also an adjuvant for antitumor immunity
.
Cancer Immunol Immunother
2008
;
57
:
1579
87
.
28.
Zitvogel
L
,
Apetoh
L
,
Ghiringhelli
F
,
André
F
,
Tesniere
A
,
Kroemer
G
. 
The anticancer immune response: indispensable for therapeutic success?
J Clin Invest
2008
;
118
:
1991
2001
.
29.
Starmans
MH
,
Krishnapuram
B
,
Steck
H
,
Horlings
H
,
Nuyten
DS
,
van de Vijver
MJ
, et al
Robust prognostic value of a knowledge-based proliferation signature across large patient microarray studies spanning different cancer types
.
Br J Cancer
2008
;
99
:
1884
90
.
30.
Jalava
P
,
Kuopio
T
,
Juntti-Patinen
L
,
Kotkansalo
T
,
Kronqvist
P
,
Collan
Y
. 
Ki67 immunohistochemistry: a valuable marker in prognostication but with a risk of misclassification: proliferation subgroups formed based on Ki67 immunoreactivity and standardized mitotic index
.
Histopathology
2006
;
48
:
674
82
.
31.
Qiu
X
,
Zhu
X
,
Zhang
L
,
Mao
Y
,
Zhang
J
,
Hao
P
, et al
Human epithelial cancers secrete immunoglobulin g with unidentified specificity to promote growth and survival of tumor cells
.
Cancer Res
2003
;
63
:
6488
95
.
32.
Ito
T
,
Saga
S
,
Nagayoshi
S
,
Imai
M
,
Aoyama
A
,
Yokoi
T
, et al
Class distribution of immunoglobulin-containing plasma cells in the stroma of medullary carcinoma of breast
.
Breast Cancer Res Treat
1986
;
7
:
97
103
.
33.
Wang
Y
,
Ylera
F
,
Boston
M
,
Kang
SG
,
Kutok
JL
,
Klein-Szanto
AJ
, et al
Focused antibody response in plasma cell-infiltrated non-medullary (NOS) breast cancers
.
Breast Cancer Res Treat
2007
;
104
:
129
44
.
34.
Coffman
RL
,
Cohn
M
. 
The class of surface immunoglobulin on virgin and memory B lymphocytes
.
J Immunol
1977
;
118
:
1806
15
.
35.
Kuritani
T
,
Cooper
MD
. 
Human B-cell differentiation. I. Analysis of immunoglobulin heavy chain switching using monoclonal anti-immunoglobulin M, G, and A antibodies and pokeweed mitogen-induced plasma cell differentiation
.
J Exp Med
1982
;
155
:
839
51
.
36.
Coronella
JA
,
Telleman
P
,
Kingsbury
GA
,
Truong
TD
,
Hays
S
,
Junghans
RP
. 
Evidence for an antigen-driven humoral immune response in medullary ductal breast cancer
.
Cancer Res
2001
;
61
:
7889
99
.
37.
Hansen
MH
,
Nielsen
H
,
Ditzel
HJ
. 
The tumor-infiltrating B cell response in medullary breast cancer is oligoclonal and directed against the autoantigen actin exposed on the surface of apoptotic cancer cells
.
Proc Natl Acad Sci U S A
2001
;
98
:
12659
64
.
38.
Coronella
JA
,
Spier
C
,
Welch
M
,
Trevor
KT
,
Stopeck
AT
,
Villar
H
, et al
Antigen-driven oligoclonal expansion of tumor-infiltrating B cells in infiltrating ductal carcinoma of the breast
.
J Immunol
2002
;
169
:
1829
36
.
39.
Nzula
S
,
Going
JJ
,
Stott
DI
. 
Antigen-driven clonal proliferation, somatic hypermutation, and selection of B lymphocytes infiltrating human ductal breast carcinomas
.
Cancer Res
2003
;
63
:
3275
80
.
40.
Kotlan
B
,
Simsa
P
,
Teillaud
J
,
Fridman
WH
,
Toth
J
,
McKnight
M
, et al
Novel ganglioside antigen identified by B cells in human medullary breast carcinomas: the proof of principle concerning the tumor-infiltrating B lymphocytes
.
J Immunol
2005
;
175
:
2278
85
.
41.
Goldhirsch
A
,
Wood
WC
,
Coates
AS
,
Gelber
RD
,
Thürlimann
B
,
Senn
H
. 
Strategies for subtypes - dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011
.
Ann Oncol
2011
;
22
:
1736
47
.
42.
Hanahan
D
,
Weinberg
RA
. 
Hallmarks of cancer: the next generation
.
Cell
2011
;
144
:
646
74
.