Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers

New Strategies in Breast Cancer: Immunotherapy

More than 70% of breast cancers contain lymphocytic infiltration in the stroma, and preclinical studies suggest that immunoediting and partial control of cancer progression by the local immune microenvironment operate in most breast cancers. Consistent with this hypothesis, a large number of studies demonstrated a favorable prognostic and chemotherapy response predictive role for immune infiltration in breast cancer. The evidence is particularly strong for triple-negative and HER2-positive cancers. The development of clinically effective immune checkpoint inhibitors now provides an opportunity to test the therapeutic potential of augmenting the local antitumor immune response. Several phase I clinical trials using single-agent anti-PD-1 and anti-PD-L1 antibodies demonstrated objective tumor response rates, with remarkably durable responses, in heavily pretreated, metastatic, triple-negative cancers and somewhat lower responses in estrogen receptor-positive cancers. Currently, close to 50 ongoing, or soon to open, clinical trials evaluate the role of this new treatment modality in breast cancer. Clin Cancer Res; 22(9); 2105-10. ©2016 AACR.

Computational pathology: Exploring the spatial dimension of tumor ecology

Tumors are evolving ecosystems where cancer subclones and the microenvironment interact. This is analogous to interaction dynamics between species in their natural habitats, which is a prime area of study in ecology. Spatial statistics are frequently used in ecological studies to infer complex relations including predator-prey, resource dependency and co-evolution. Recently, the emerging field of computational pathology has enabled high-throughput spatial analysis by using image processing to identify different cell types and their locations within histological tumor samples. We discuss how these data may be analyzed with spatial statistics used in ecology to reveal patterns and advance our understanding of ecological interactions occurring among cancer cells and their microenvironment.

Relative Prognostic and Predictive Value of Gene Signature and Histologic Grade in Estrogen Receptor-Positive, HER2-Negative Breast Cancer

BACKGROUND

In estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer, first-generation genomic signatures serve predominately as prognostic biomarkers and secondarily as predictors of response to chemotherapy. We compared both the prognostic and predictive value of histologic grades and genomic markers.

METHODS

We retrieved publicly available cDNA microarray data from 1373 primary ER(+)/HER2(-) breast cancers and developed a genomic signature simulated from Recurrence Online (http://www.recurrenceonline.com/) to calculate the recurrence score and risk using predefined sets of genes in the cDNA microarray. We then compared the prognostic and predictive information provided by histologic grade and genomic signature.

RESULTS

Based on genomic signatures, 55%, 28%, and 17% of breast cancers were classified as low, intermediate, and high risk, respectively, whereas the histologic grades were I, II, and III in 22%, 59%, and 19% of breast cancers, respectively. Univariate analysis in the untreated cohort revealed that both histologic grade (overall P = .007) and genomic signature (P < .001) could predict prognosis. Results were similar using the genomic signature, with pathologic complete response rates of 4.6%, 5.7%, and 16.5% for low-, intermediate-, and high-risk cancers, respectively. Neither biomarker was statistically significant in multivariate analysis for predictive response to neoadjuvant chemotherapy (NAC).

CONCLUSION

Genomic signature was better at identifying low-risk cases compared to histologic grade alone, but both markers had similar predictive values for NAC response. Better predictive biomarkers for NAC response are still needed.

Subtype-Specific Metagene-Based Prediction of Outcome after Neoadjuvant and Adjuvant Treatment in Breast Cancer

PURPOSE

In spite of improvements of average benefit from adjuvant/neoadjuvant treatments, there are still individual patients with early breast cancer at high risk of relapse. We explored the association with outcome of robust gene cluster-based metagenes linked to proliferation, ER-related genes, and immune response to identify those high-risk patients.

EXPERIMENTAL DESIGN

A total of 3,847 publicly available gene-expression profiles were analyzed (untreated, N = 826; tamoxifen-treated, N = 685; chemotherapy-treated, N = 1,150). Genes poorly performing in formalin-fixed samples were removed. Outcomes of interest were pathologic-complete response (pCR) and distant metastasis-free survival (DMFS). In ER(+)HER2(-), the proliferation and ER-related metagenes were combined to define three risk groups. In HER2(+) and ER(-)HER2(-) risk groups were defined by tertiles of an immune-related metagene.

RESULTS

The high-proliferation/low-ER group of ER(+)HER2(-) breast cancer had significantly higher pCR rate [OR, 5.01 (1.76-17.99), P = 0.005], but poorer outcome [HR = 3.73 (1.63-8.51), P = 0.0018] than the low-proliferation/high-ER. A similar association with outcome applied to patients with residual disease (RD) after neoadjuvant chemotherapy (P = 0.01). In ER(-)HER2(-) and HER2(+) breast cancer, immune metagene in the high tertile was linked to higher pCR [33.7% vs. 11.6% in high and low tertile, respectively; OR, 3.87 (1.79-8.95); P = 0.0009]. In ER(-)HER2(-), after adjuvant/neoadjuvant chemotherapy, 5-year DMFS was 85.4% for high-tertile immune metagene, and 43.9% for low tertile. The outcome association was similar in patients with RD (P = 0.0055). In HER2(+) breast cancer treated with chemotherapy the association with risk of relapse was not significant.

CONCLUSIONS

We developed metagene-based predictors able to define low and high risk of relapse after adjuvant/neoadjuvant therapy. High-risk patients so defined should be preferably considered for trials with investigational agents.

An overview of triple-negative breast cancer

PURPOSE

Triple-negative breast cancer (TNBC) is a heterogeneous group of tumors comprising various breast cancers simply defined by the absence of estrogen receptor, progesterone receptor and overexpression of human epidermal growth factor receptor 2 gene. In this review, we discuss the epidemiology, risk factors, clinical characteristics and prognostic variables of TNBC, and present the summary of recommended treatment strategies and all other available treatment options.

METHODS

We performed a systematic literature search using Medline and selected those articles which seemed relevant for this review. In addition, the ClinicalTrials.gov was also scanned for ongoing trials.

RESULTS

TNBC accounts for 10-20 % of all invasive breast cancers and has been found to be associated with African-American race, younger age, higher grade and mitotic index, and more advanced stage at diagnosis. Locoregional treatment is similar to other invasive breast cancer subtypes and involves surgery-mastectomy with or without adjuvant radiotherapy or breast conservation followed by adjuvant radiotherapy. Due to lack of drug-targetable receptors, chemotherapy is the only recommended systemic treatment to improve disease outcome. TNBC is sensitive to chemotherapy as demonstrated by high pathological complete response rates achieved after neoadjuvant chemotherapy, and this approach also allows for breast-conserving surgery. The peak risk of relapse is at 3 years after surgery, thereafter recurrence risk rapidly decreases. Survival after metastatic relapse is shorter as compared to other breast cancer subtypes, treatment options are few and response rates are poor and lack durability. Important molecular characteristics have now been identified that can subdivide this group of breast cancers further and can provide alternative systemic therapies.

CONCLUSIONS

To improve therapeutic outcome of TNBC, reliable predictive biomarkers and newer drugs against the known molecular pathways are required.

Biological network-driven gene selection identifies a stromal immune module as a key determinant of triple-negative breast carcinoma prognosis

Triple-negative breast cancer (TNBC) is a heterogeneous group of aggressive breast cancers for which no targeted treatment is available. Robust tools for TNBC classification are required, to improve the prediction of prognosis and to develop novel therapeutic interventions. We analyzed 3,247 primary human breast cancer samples from 21 publicly available datasets, using a five-step method: (1) selection of TNBC samples by bimodal filtering on ER-HER2 and PR, (2) normalization of the selected TNBC samples, (3) selection of the most variant genes, (4) identification of gene clusters and biological gene selection within gene clusters on the basis of String© database connections and gene-expression correlations, (5) summarization of each gene cluster in a metagene. We then assessed the ability of these metagenes to predict prognosis, on an external public dataset (METABRIC). Our analysis of gene expression (GE) in 557 TNBCs from 21 public datasets identified a six-metagene signature (167 genes) in which the metagenes were enriched in different gene ontologies. The gene clusters were named as follows: Immunity1, Immunity2, Proliferation/DNA damage, AR-like, Matrix/Invasion1 and Matrix2 clusters respectively. This signature was particularly robust for the identification of TNBC subtypes across many datasets (n = 1,125 samples), despite technology differences (Affymetrix© A, Plus2 and Illumina©). Weak Immunity two metagene expression was associated with a poor prognosis (disease-specific survival; HR = 2.68 [1.59-4.52], p = 0.0002). The six-metagene signature (167 genes) was validated over 1,125 TNBC samples. The Immunity two metagene had strong prognostic value. These findings open up interesting possibilities for the development of new therapeutic interventions.

Molecular Features of Triple Negative Breast Cancer: Microarray Evidence and Further Integrated Analysis

Purpose

Breast cancer is a heterogeneous disease usually including four molecular subtypes such as luminal A, luminal B, HER2-enriched, and triple-negative breast cancer (TNBC). TNBC is more aggressive than other breast cancer subtypes. Despite major advances in ER-positive or HER2-amplified breast cancer, there is no targeted agent currently available for TNBC, so it is urgent to identify new potential therapeutic targets for TNBC.

Methods

We first used microarray analysis to compare gene expression profiling between TNBC and non-TNBC. Furthermore an integrated analysis was conducted based on our own and published data, leading to more robust, reproducible and accurate predictions. Additionally, we performed qRT-PCR in breast cancer cell lines to verify the findings in integrated analysis.

Results

After searching Gene Expression Omnibus database (GEO), two microarray studies were obtained according to the inclusion criteria. The integrated analysis was conducted, including 30 samples of TNBC and 77 samples of non-TNBC. 556 genes were found to be consistently differentially expressed (344 up-regulated genes and 212 down-regulated genes in TNBC). Functional annotation for these differentially expressed genes (DEGs) showed that the most significantly enriched Gene Ontology (GO) term for molecular functions was protein binding (GO: 0005515, P = 6.09E-21), while that for biological processes was signal transduction (GO: 0007165, P = 9.46E-08), and that for cellular component was cytoplasm (GO: 0005737, P = 2.09E-21). The most significant pathway was Pathways in cancer (P = 6.54E-05) based on Kyoto Encyclopedia of Genes and Genomes (KEGG). DUSP1 (Degree = 21), MYEOV2 (Degree = 15) and UQCRQ (Degree = 14) were identified as the significant hub proteins in the protein-protein interaction (PPI) network. Five genes were selected to perform qRT-PCR in seven breast cancer cell lines, and qRT-PCR results showed that the expression pattern of selected genes in TNBC lines and non-TNBC lines was nearly consistent with that in the integrated analysis.

Conclusion

This study may help to understand the pathogenesis of different breast cancer subtypes, contributing to the successful identification of therapeutic targets for TNBC.

Extensive rewiring of epithelial-stromal co-expression networks in breast cancer

BACKGROUND

Epithelial-stromal crosstalk plays a critical role in invasive breast cancer pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis.

RESULTS

We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive rewiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, and estrogen receptor-positive and estrogen receptor-negative invasive breast cancer, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in invasive breast cancer mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas.

CONCLUSIONS

Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially localized transcriptomic data. The analysis provides new biological insights into the rewiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer.

miR-30e* is an independent subtype-specific prognostic marker in breast cancer

Background:

Breast cancer clinical outcome is affected by tumor molecular features, and the identification of subtype-specific prognostic biomarkers is relevant for breast cancer translational research. Gene expression signatures proved to be able to complement prognostic information provided by classical clinico-pathological features. Recently, microRNAs (miRNAs) have been causally linked to tumorigenesis and cancer progression and have been associated with patient outcome, also in breast cancer.

Methods:

MicroRNAs associated with the development of distant metastasis were identified in a cohort of 92 ESR1+/ERBB2− lymph node-negative breast cancers from patients not receiving adjuvant treatment. Results were confirmed and further investigated in a total of 1246 miRNA and gene expression profiles of the Molecular Taxonomy of Breast Cancer International Consortium data set. Moderated t-test, univariable and multivariable Cox regression models were used for statistical analyses.

Results:

miR-30e* was identified as independent protective prognostic factor in lymph node-negative untreated patients with ESR1+/ERBB2− tumours and retained a significant association with a good prognosis in treated patients with the same tumor subtype as well as in the ERBB2+ subtype, but not in ESR1−/ERBB2− tumours.

Conclusions:

We highlighted a relevant and subtype-specific role in breast cancer for miR-30e* and demonstrated that adding miRNA markers to gene signatures and clinico-pathological features can help for a better prognostication.

Is the future of personalized therapy in triple-negative breast cancer based on molecular subtype?

Significant research has been conducted to better understand the extensive, heterogeneous molecular features of triple-negative breast cancer (TNBC). We reviewed published TNBC molecular classifications to identify major groupings that have potential for clinical trial development. With the ultimate aim to streamline translational medicine, we linked these categories of TNBC according to their gene-expression signatures, biological function, and clinical outcome. To this end, we define five potential clinically actionable groupings of TNBC: 1) basal-like TNBC with DNA-repair deficiency or growth factor pathways; 2) mesenchymal-like TNBC with epithelial-to-mesenchymal transition and cancer stem cell features; 3) immune-associated TNBC; 4) luminal/apocrine TNBC with androgen-receptor overexpression; and 5) HER2-enriched TNBC. For each defined subtype, we highlight the major biological pathways and discuss potential targeted therapies in TNBC that might abrogate disease progression. However, many of these potential targets need clinical validation by clinical trials. We have yet to know how we can enrich the targets by molecular classifications.

Use of formalin-fixed paraffin-embedded samples for gene expression studies in breast cancer patients

To obtain gene expression profiles from samples collected in clinical trials, we conducted a pilot study to assess feasibility and estimate sample attrition rates when profiling formalin-fixed, paraffin-embedded specimens. Ten matched fresh-frozen and fixed breast cancer samples were profiled using the Illumina HT-12 and Ref-8 chips, respectively. The profiles obtained with Ref 8, were neither technically nor biologically reliable since they failed to yield the expected separation between estrogen receptor positive and negative samples. With the use of Affymetrix HG-U133 2.0 Plus chips on fixed samples and a quantitative polymerase chain reaction -based sample pre-assessment step, results were satisfactory in terms of biological reliability, despite the low number of present calls (M = 21%±5). Compared with the Illumina DASL WG platform, Affymetrix data showed a wider interquartile range (1.32 vs 0.57, P<2.2 E-16,) and larger fold changes. The Affymetrix chips were used to run a pilot study on 60 fixed breast cancers. By including in the workflow the sample pre-assessment steps, 96% of the samples predicted to give good results (44/46), were in fact rated as satisfactory from the point of view of technical and biological meaningfulness. Our gene expression profiles showed strong agreement with immunohistochemistry data, were able to reproduce breast cancer molecular subtypes, and allowed the validation of an estrogen receptor status classifier derived in frozen samples. The approach is therefore suitable to profile formalin-fixed paraffin-embedded samples collected in clinical trials, provided that quality controls are run both before (sample pre-assessment) and after hybridization on the array.

Prognostic and predictive value of PDL1 expression in breast cancer

Expression of programmed cell death receptor ligand 1 (PDL1) has been scarcely studied in breast cancer. Recently PD1/PDL1-inhibitors have shown promising results in different carcinomas with correlation between PDL1 tumor expression and responses. We retrospectively analyzed PDL1 mRNA expression in 45 breast cancer cell lines and 5,454 breast cancers profiled using DNA microarrays. Compared to normal breast samples, PDL1 expression was upregulated in 20% of clinical samples and 38% of basal tumors. High expression was associated with poor-prognosis features (large tumor size, high grade, ER-negative, PR-negative, ERBB2-positive status, high proliferation, basal and ERBB2-enriched subtypes). PDL1 upregulation was associated with biological signs of strong cytotoxic local immune response. PDL1 upregulation was not associated with survival in the whole population, but was associated with better metastasis-free and overall specific survivals in basal tumors, independently of clinicopathological features. Pathological complete response after neoadjuvant chemotherapy was higher in case of PDL1 upregulation (50% versus 21%). In conclusion, PDL1 upregulation, more frequent in basal breast cancers, was associated with increased T-cell cytotoxic immune response. In this aggressive subtype, upregulation was associated with better survival and response to chemotherapy. Reactivation of dormant tumor-infiltrating lymphocytes by PDL1-inhibitors could represent promising strategy in PDL1-upregulated basal breast cancer.

Multigene prognostic tests in breast cancer: past, present, future

There is growing consensus that multigene prognostic tests provide useful complementary information to tumor size and grade in estrogen receptor (ER)-positive breast cancers. The tests primarily rely on quantification of ER and proliferation-related genes and combine these into multivariate prediction models. Since ER-negative cancers tend to have higher proliferation rates, the prognostic value of current multigene tests in these cancers is limited. First-generation prognostic signatures (Oncotype DX, MammaPrint, Genomic Grade Index) are substantially more accurate to predict recurrence within the first 5 years than in later years. This has become a limitation with the availability of effective extended adjuvant endocrine therapies. Newer tests (Prosigna, EndoPredict, Breast Cancer Index) appear to possess better prognostic value for late recurrences while also remaining predictive of early relapse. Some clinical prediction problems are more difficult to solve than others: there are no clinically useful prognostic signatures for ER-negative cancers, and drug-specific treatment response predictors also remain elusive. Emerging areas of research involve the development of immune gene signatures that carry modest but significant prognostic value independent of proliferation and ER status and represent candidate predictive markers for immune-targeted therapies. Overall metrics of tumor heterogeneity and genome integrity (for example, homologue recombination deficiency score) are emerging as potential new predictive markers for platinum agents. The recent expansion of high-throughput technology platforms including low-cost sequencing of circulating and tumor-derived DNA and RNA and rapid reliable quantification of microRNA offers new opportunities to build extended prediction models across multiplatform data.

Genomic analysis reveals that immune function genes are strongly linked to clinical outcome in the North Central Cancer Treatment Group n9831 Adjuvant Trastuzumab Trial

PURPOSE

To develop a genomic signature that predicts benefit from trastuzumab in human epidermal growth factor receptor 2-positive breast cancer.

PATIENTS AND METHODS

DASL technology was used to quantify mRNA in samples from 1,282 patients enrolled onto the Combination Chemotherapy With or Without Trastuzumab in Treating Women With Breast Cancer (North Central Cancer Treatment Group N9831 [NCCTG-N9831]) adjuvant trastuzumab trial. Cox proportional hazard ratios (HRs), adjusted for significant clinicopathologic risk factors, were used to determine the association of each gene with relapse-free survival (RFS) for 433 patients who received chemotherapy alone (arm A) and 849 patients who received chemotherapy plus trastuzumab (arms B and C). Network and pathway analyses were used to identify key biologic processes linked to RFS. The signature was built by using a voting scheme.

RESULTS

Network and functional ontology analyses suggested that increased RFS was linked to a subset of immune function genes. A voting scheme model was used to define immune gene enrichment based on the expression of any nine or more of 14 immune function genes at or above the 0.40 quantile for the population. This model was used to identify immune gene-enriched tumors in arm A and arms B and C. Immune gene enrichment was linked to increased RFS in arms B and C (HR, 0.35; 95% CI, 0.22 to 0.55; P < .001), whereas arm B and C patients who did not exhibit immune gene enrichment did not benefit from trastuzumab (HR, 0.89; 95% CI, 0.62 to 1.28; P = .53). Enriched immune function gene expression as defined by our predictive signature was not associated with increased RFS in arm A (HR, 0.90; 95% CI, 0.60 to 1.37; P = .64).

CONCLUSION

Increased expression of a subset of immune function genes may provide a means of predicting benefit from adjuvant trastuzumab.

The intracrinology of breast cancer

The importance of intracrinology, or in situ production of steroids from circulating precursors, in breast cancer has been firmly established in estrogen actions on postmenopausal patients. Expression levels of various steroid synthesizing and/or metabolizing enzymes have been examined in human breast cancer tissues by a number of groups. The enzymes examined include those capable of converting circulating DHEA-S to sex steroids (STS and 3βHSDΔ4-5 isomerase), the group of enzymes that modulate the strength of both androgens and estrogens (17βHSD family) as well as the androgenic 5αR enzymes and the estrogenic aromatase enzyme. In addition to these DHEA-related metabolism pathways, other intracrine pathways involving progesterone and cholesterol have also been examined. Some risk factors of breast cancer development, including obesity, have also been postulated to interact with steroid metabolising pathways. In this review, we aimed to summarise the current state of knowledge regarding intracrine metabolism including expression levels of various enzymes and receptors, focusing particularly upon the importance of the production of biologically potent steroids from circulating sulfated precursors such as DHEA-S. In addition, we attempted to summarise the factors, both steroidal and non-steroidal, involved in the regulation of these enzymes and propose future directions for research in this particular field. The concept of intracrinology was first proposed over 20 years ago but there still remain many unanswered questions which could open new horizons for the understanding of intracrine metabolism in the breast. This article is part of a Special Issue entitled 'Essential role of DHEA'.

Combining functional genomics strategies identifies modular heterogeneity of breast cancer intrinsic subtypes

Background

The discovery of breast cancer subtypes and subsequent development of treatments aimed at them has allowed for a great reduction in the mortality of breast cancer. But despite this progress, tumors with similar characteristics that belong to the same subtype continue to respond differently to the same treatment. Five subtypes of breast cancer, namely intrinsic subtypes, have been characterized to date based on their gene expression profiles. Among other characteristics, subtypes vary in their degree of intra-subtype heterogeneity. It is not clear, however, whether this heterogeneity is shared across all tumor traits. It is also unclear whether individual traits can be highly heterogeneous among a majority of homogeneous traits.

Results

We employ network theory to uncover gene modules and accordingly consider them as tumor traits, which capture shared biological processes among the subtypes. We then use the β-diversity metric from ecology to quantify the heterogeneity in these gene modules. In doing so, we show that breast cancer heterogeneity is contained in gene modules and that this modular heterogeneity increases monotonically across the subtypes. We identify a core of two modules that are shared among all subtypes which contain nucleosome assembly and mammary morphogenesis genes, and a number of modules that are specific to subtypes. This modular heterogeneity, which increases with global heterogeneity, relates to tumor aggressiveness. Indeed, we observe that Luminal A, the most treatable of subtypes, has the lowest modular heterogeneity whereas the Basal-like subtype, which is among the hardest to treat, has the highest. Furthermore, our analysis shows that a higher degree of global heterogeneity does not imply higher heterogeneity for all modules, as Luminal B shows the highest heterogeneity for core modules.

Conclusions

Overall, modular heterogeneity provides a framework with which to dissect cancer heterogeneity and better understand its underpinnings, thereby ultimately advancing our knowledge towards a more effective personalized cancer therapy.

Electronic supplementary material

The online version of this article (doi:10.1186/1756-0381-7-27) contains supplementary material, which is available to authorized users.

Tumor-infiltrating immune cell profiles and their change after neoadjuvant chemotherapy predict response and prognosis of breast cancer

Introduction

Tumor microenvironment immunity is associated with breast cancer outcome. A high lymphocytic infiltration has been associated with response to neoadjuvant chemotherapy, but the contribution to response and prognosis of immune cell subpopulations profiles in both pre-treated and post-treatment residual tumor is still unclear.

Methods

We analyzed pre- and post-treatment tumor-infiltrating immune cells (CD3, CD4, CD8, CD20, CD68, Foxp3) by immunohistochemistry in a series of 121 breast cancer patients homogeneously treated with neoadjuvant chemotherapy. Immune cell profiles were analyzed and correlated with response and survival.

Results

We identified three tumor-infiltrating immune cell profiles, which were able to predict pathological complete response (pCR) to neoadjuvant chemotherapy (cluster B: 58%, versus clusters A and C: 7%). A higher infiltration by CD4 lymphocytes was the main factor explaining the occurrence of pCR, and this association was validated in six public genomic datasets. A higher chemotherapy effect on lymphocytic infiltration, including an inversion of CD4/CD8 ratio, was associated with pCR and with better prognosis. Analysis of the immune infiltrate in post-chemotherapy residual tumor identified a profile (cluster Y), mainly characterized by high CD3 and CD68 infiltration, with a worse disease free survival.

Conclusions

Breast cancer immune cell subpopulation profiles, determined by immunohistochemistry-based computerized analysis, identify groups of patients characterized by high response (in the pre-treatment setting) and poor prognosis (in the post-treatment setting). Further understanding of the mechanisms underlying the distribution of immune cells and their changes after chemotherapy may contribute to the development of new immune-targeted therapies for breast cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-014-0488-5) contains supplementary material, which is available to authorized users.

Dual roles for immune metagenes in breast cancer prognosis and therapy prediction

BACKGROUND

Neoadjuvant chemotherapy for breast cancer leads to considerable variability in clinical responses, with only 10 to 20% of cases achieving complete pathologic responses (pCR). Biological and clinical factors that determine the extent of pCR are incompletely understood. Mounting evidence indicates that the patient's immune system contributes to tumor regression and can be modulated by therapies. The cell types most frequently observed with this association are effector tumor infiltrating lymphocytes (TILs), such as cytotoxic T cells, natural killer cells and B cells. We and others have shown that the relative abundance of TILs in breast cancer can be quantified by intratumoral transcript levels of coordinately expressed, immune cell-specific genes. Through expression microarray analysis, we recently discovered three immune gene signatures, or metagenes, that appear to reflect the relative abundance of distinct tumor-infiltrating leukocyte populations. The B/P (B cell/plasma cell), T/NK (T cell/natural killer cell) and M/D (monocyte/dendritic cell) immune metagenes were significantly associated with distant metastasis-free survival of patients with highly proliferative cancer of the basal-like, HER2-enriched and luminal B intrinsic subtypes.

METHODS

Given the histopathological evidence that TIL abundance is predictive of neoadjuvant treatment efficacy, we evaluated the therapy-predictive potential of the prognostic immune metagenes. We hypothesized that pre-chemotherapy immune gene signatures would be significantly predictive of tumor response. In a multi-institutional, meta-cohort analysis of 701 breast cancer patients receiving neoadjuvant chemotherapy, gene expression profiles of tumor biopsies were investigated by logistic regression to determine the existence of therapy-predictive interactions between the immune metagenes, tumor proliferative capacity, and intrinsic subtypes.

RESULTS

By univariate analysis, the B/P, T/NK and M/D metagenes were all significantly and positively associated with favorable pathologic responses. In multivariate analyses, proliferative capacity and intrinsic subtype altered the significance of the immune metagenes in different ways, with the M/D and B/P metagenes achieving the greatest overall significance after adjustment for other variables.

CONCLUSIONS

Gene expression signatures of infiltrating immune cells carry both prognostic and therapy-predictive value that is impacted by tumor proliferative capacity and intrinsic subtype. Anti-tumor functions of plasma B cells and myeloid-derived antigen-presenting cells may explain more variability in pathologic response to neoadjuvant chemotherapy than previously recognized.

Claudin-low breast cancers: clinical, pathological, molecular and prognostic characterization

BACKGROUND

The lastly identified claudin-low (CL) subtype of breast cancer (BC) remains poorly described as compared to the other molecular subtypes. We provide a comprehensive characterization of the largest series of CL samples reported so far.

METHODS

From a data set of 5447 invasive BC profiled using DNA microarrays, we identified 673 CL samples (12,4%) that we describe comparatively to the other molecular subtypes at several levels: clinicopathological, genomic, transcriptional, survival, and response to chemotherapy.

RESULTS

CL samples display profiles different from other subtypes. For example, they differ from basal tumors regarding the hormone receptor status, with a lower frequency of triple negative (TN) tumors (52% vs 76% for basal cases). Like basal tumors, they show high genomic instability with many gains and losses. At the transcriptional level, CL tumors are the most undifferentiated tumors along the mammary epithelial hierarchy. Compared to basal tumors, they show enrichment for epithelial-to-mesenchymal transition markers, immune response genes, and cancer stem cell-like features, and higher activity of estrogen receptor (ER), progesterone receptor (PR), EGFR, SRC and TGFβ pathways, but lower activity of MYC and PI3K pathways. The 5-year disease-free survival of CL cases (67%) and the rate of pathological complete response (pCR) to primary chemotherapy (32%) are close to those of poor-prognosis and good responder subtypes (basal and ERBB2-enriched). However, the prognostic features of CL tumors are closer to those observed in the whole BC series and in the luminal A subtype, including proliferation-related gene expression signatures (GES). Immunity-related GES valuable in basal breast cancers are not significant in CL tumors. By contrast, the GES predictive for pCR in CL tumors resemble more to those of basal and HER2-enriched tumors than to those of luminal A tumors.

CONCLUSIONS

Many differences exist between CL and the other subtypes, notably basal. An unexpected finding concerns the relatively high numbers of ER-positive and non-TN tumors within CL subtype, suggesting a larger heterogeneity than in basal and luminal A subtypes.

High-throughput gene expression and mutation profiling: current methods and future perspectives

Following the completion of the human genome sequence at the beginning of the new millennium, a series of high-throughput methods have changed cancer research. Using these techniques, global analysis such as expression profiling could be carried out on a genomic scale. In breast cancer they led to the classification of the intrinsic subtypes, and the development of several prognostic and predictive 'genomic tests' for patient stratification. During the last 2 years we have faced a similar dramatic revolution with the introduction of next generation sequencing (NGS). These techniques allow sequencing of the complete human exome or whole genome with a cost reduction in the order of 10,000-100,000 fold. Consequently, the number of known cancer genome sequences exploded with more than 6,000 samples, published between 2011 and 2013. These studies have led to important and surprising discoveries both for basic cancer research and clinical applications. They relate to understanding the development of cancer as well as the heterogeneity of the disease, and how to use this information to guide the development and application of therapies. Although it is foreseeable that the sequencing surveys of neoplasms will soon conclude, their introduction into clinical practice is just beginning.

Breast cancer classification: linking molecular mechanisms to disease prognosis

Breast cancer was traditionally perceived as a single disease; however, recent advances in gene expression and genomic profiling have revealed that breast cancer is in fact a collection of diseases exhibiting distinct anatomical features, responses to treatment and survival outcomes. Consequently, a number of schemes have been proposed for subtyping of breast cancer to bring out the biological and clinically relevant characteristics of the subtypes. Although some of these schemes capture underlying molecular differences, others predict variations in response to treatment and survival patterns. However, despite this diversity in the approaches, it is clear that molecular mechanisms drive clinical outcomes, and therefore an effective scheme should integrate molecular as well as clinical parameters to enable deeper understanding of cancer mechanisms and allow better decision making in the clinic. Here, using a large cohort of ∼550 breast tumours from The Cancer Genome Atlas, we systematically evaluate a number of expression-based schemes including at least eight molecular pathways implicated in breast cancer and three prognostic signatures, across a variety of classification scenarios covering molecular characteristics, biomarker status, tumour stages and survival patterns. We observe that a careful combination of these schemes yields better classification results compared with using them individually, thus confirming that molecular mechanisms and clinical outcomes are related and that an effective scheme should therefore integrate both these parameters to enable a deeper understanding of the cancer.

Subtype-dependent prognostic relevance of an interferon-induced pathway metagene in node-negative breast cancer

The majority of gene expression signatures developed to predict the likelihood to relapse in breast cancer (BC) patients assigns a high risk score to patients with Estrogen Receptor (ER) negative or highly proliferating tumors. We aimed to identify a signature of differentially expressed (DE) metagenes, rather than single DE genes, associated with distant metastases beyond classical risk factors. We used 105 gene expression profiles from consecutive BCs to identify metagenes whose prognostic role was defined on an independent series of 92 ESR1+/ERBB2- node-negative BCs (42 cases developing metastases within 5 years from diagnosis and 50 cases metastasis-free for more than 5 years, comparable for age, tumor size, ER status and surgery). Findings were validated on publicly available datasets of 684 node-negative BCs including all the subtypes. Only a metagene containing interferon-induced genes (IFN metagene) proved to be predictive of distant metastasis in our series of patients with ESR1+/ERBB2- tumors (P = 0.029), and such a finding was validated on 457 ESR1+/ERBB2- BCs from public datasets (P = 0.0424). Conversely, the IFN metagene was associated with a low risk of metastasis in 104 ERBB2+ tumors (P = 0.0099) whereas it did not prove to significantly affect prognosis in 123 ESR1-/ERBB2- tumors (P = 0.2235). A complex prognostic interaction was revealed in ESR1+/ERBB2- and ERBB2+ tumors when the association between the IFN metagene and a T-cell metagene was considered. The study confirms the importance of analyzing prognostic variables separately within BC subtypes, highlights the advantages of using metagenes rather than genes, and finally identifies in node-negative ESR1+/ERBB2- BCs, the unfavorable role of high IFN metagene expression.

Intratumoral CD8⁺ cytotoxic lymphocyte is a favorable prognostic marker in node-negative breast cancer

Background

The prognostic effect of tumor infiltrating CD8⁺ cytotoxic lymphocytes (CTLs) in breast cancer is controversial. We analyzed the association between CD8⁺ CTLs and survival of untreated node-negative breast cancer patients.

Material and Methods

CD8⁺ CTLs infiltrate was evaluated by immunostaining in a cohort of 332 node-negative breast cancer patients with a median follow-up of 152 months. The prognostic significance of CD8⁺ CTLs for disease-free survival (DFS) and breast cancer-specific overall survival (OS) was evaluated with Kaplan-Meier survival analysis as well as univariate analysis and multivariate Cox analysis adjusted for age at diagnosis, pT stage, histological grade, estrogen receptor (ER) status, progesterone receptor (PR) status, Ki-67 expression and human epidermal growth factor receptor 2 (HER-2) status.

Results

285 (85.8%) patients showed strong CD8⁺ CTLs infiltrate positive status. Univariate analysis showed that CD8⁺ CTLs had statistically significant association with DFS (P = 0.004, hazard ratio [HR] = 0.454, 95% confidence interval [CI] = 0.265–0.777) and OS (P = 0.014, HR = 0.430, 95% CI = 0.220–0.840) in the entire cohort. The significance of CD8⁺ CTLs was especially strong in ER negative, HER-2 negative and ER, PR, HER-2 triple-negative breast cancers. In Kaplan-Meier analysis, CD8⁺ CTLs had significant effect on prognosis of patients (Log-rank test: P = 0.003 for DFS and P = 0.011 for OS), independent of established clinical factors for DFS (P = 0.002, HR = 0.418, 95% CI = 0.242–0.724) as well as for OS (P = 0.009, HR = 0.401, 95% CI = 0.202–0.797).

The immune system and response to HER2-targeted treatment in breast cancer

The monoclonal antibody trastuzumab targets the growth factor receptor HER2 and has profoundly improved the course of disease and survival of women with HER2-overexpressing breast cancer. Because trastuzumab targets aberrant expression of HER2 in tumours addicted to HER2 activation, its clinical activity is credited largely to inhibition of intracellular signalling. A growing body of preclinical and clinical evidence shows that the immune system contributes substantially to the therapeutic effects of trastuzumab and other monoclonal antibodies in vivo. Furthermore, findings indicate that immune-related markers can provide useful predictive information and that increased clinical activity might follow activation of the immune system. Development of immunomodulatory drugs with remarkable activity in many solid tumours defines a scenario in which the combination of immune modulation with trastuzumab, or other HER2-directed drugs, will result in augmented response and clinical outcome.

Is carcinoma a mesenchymal disease? The role of the stromal microenvironment in carcinogenesis

Most research into the biology of carcinoma has focused on the epithelial cells therein; the inherent assumption has been that the tumour arises from epithelial cells 'gone bad', and that the surrounding stroma is simply an 'innocent bystander'. However, there is increasing evidence that there is a complex interplay between tumour cells and their surrounding microenvironment, and that the latter may be just as important in determining the development and clinical behaviour of a given tumour. Similarly, traditional oncological practice has been predominantly aimed at a perceived ideal goal of killing all the tumour epithelial cells, with only a few recently developed therapies seeking to affect other components (such as tumour vasculature); but identifying stromal factors involved in tumour growth and survival may well lead to the development of novel therapies. This review examines current understanding of the interplay between tumour epithelial cells and their microenvironment, and enumerates various stromal factors which appear to play a role in tumour progression and/or metastasis.

Markers of breast cancer stromal fibroblasts in the primary tumour site associated with lymph node metastasis: a systematic review including our case series

CAFs (cancer-associated fibroblasts), the most abundant cell type in breast cancer stroma, produce a plethora of chemokines, growth factors and ECM (extracellular matrix) proteins, that may contribute to dissemination and metastasis. Axillary nodes are the first metastatic site in breast cancer; however, to the present date, there is no consensus of which specific proteins, synthesized by CAFs, might be related with lymph node involvement. The purpose of this study was to perform a systematic review of CAF biomarkers associated with the presence of regional metastasis. PubMed was searched using the words: ‘breast cancer’ and ‘lymph node’ and fibroblast or stroma or microenvironment. After exclusions, eight studies evaluating biomarkers immunoexpression in CAFs and lymph node status were selected. Biomarkers evaluated in these studies may be divided in two groups, according to their ontology: extracellular matrix components [MMP13 (matrix metalloproteinase 13), TIMP2 (tissue inhibitor of metalloproteinases-2), THBS1 (thrombospondin 1), LGALS1 (lectin, galactoside-binding, soluble, 1)] and response to wounding [PDPN (podoplanin), PLAU (plasminogen activator, urokinase), PLAUR (plasminogen activator, urokinase receptor), CAV1 (caveolin 1), THBS1, LGALS1]. A positive expression of MMP13 and LGALS1 in CAFs was associated with enhanced OR (odds ratio) for regional metastasis. Contrariwise, CAV1 positive staining of fibroblasts was associated with decreased OR for nodal involvement. Expression of MMP13, PDPN and CAV1 was further tested in a new series of 65 samples of invasive ductal breast carcinomas by immunohistochemistry and no association between biomarkers expression in CAFs and nodal status was found. It was suggested that breast cancer subtypes may differentially affect CAFs behaviour. It would be interesting to evaluate the prognostic significance of these biomarkers in CAFs from different tumour types.

DNA repair gene patterns as prognostic and predictive factors in molecular breast cancer subtypes

DNA repair pathways can enable tumor cells to survive DNA damage induced by chemotherapy and thus provide prognostic and/or predictive value. We evaluated Affymetrix gene expression profiles for 145 DNA repair genes in untreated breast cancer (BC) patients (n = 684) and BC patients treated with regimens containing neoadjuvant taxane/anthracycline (n = 294) or anthracycline (n = 210). We independently assessed estrogen receptor (ER)-positive/HER2-negative, HER2-positive, and ER-negative/HER2-negative subgroups for differential expression, bimodal distribution, and the prognostic and predictive value of DNA repair gene expression. Twenty-two genes were consistently overexpressed in ER-negative tumors, and five genes were overexpressed in ER-positive tumors, but no differences in expression were associated with HER2 status. In ER-positive/HER2-negative tumors, the expression of nine genes (BUB1, FANCI, MNAT1, PARP2, PCNA, POLQ, RPA3, TOP2A, and UBE2V2) was associated with poor prognosis, and the expression of one gene (ATM) was associated with good prognosis. Furthermore, the prognostic value of specific genes did not correlate with proliferation. A few genes were associated with chemotherapy response in BC subtypes and treatment-specific manner. In ER-negative/HER2-negative tumors, the MSH2, MSH6, and FAN1 (previously MTMR15) genes were associated with pathological complete response and residual invasive cancer in taxane/anthracycline-treated patients. Conversely, PMS2 expression was associated with residual invasive cancer in treatments using anthracycline as a single agent. In HER2-positive tumors, TOP2A was associated with patient response to anthracyclines but not to taxane/anthracycline regimens. In genes expressed in a bimodal fashion, RECQL4 was significantly associated with clinical outcome. In vitro studies showed that defects in RECQL4 impair homologous recombination, sensitizing BC cells to DNA-damaging agents.

From High-Throughput Microarray-Based Screening to Clinical Application: The Development of a Second Generation Multigene Test for Breast Cancer Prognosis

Several multigene tests have been developed for breast cancer patients to predict the individual risk of recurrence. Most of the first generation tests rely on proliferation-associated genes and are commonly carried out in central reference laboratories. Here, we describe the development of a second generation multigene assay, the EndoPredict test, a prognostic multigene expression test for estrogen receptor (ER) positive, human epidermal growth factor receptor (HER2) negative (ER+/HER2−) breast cancer patients. The EndoPredict gene signature was initially established in a large high-throughput microarray-based screening study. The key steps for biomarker identification are discussed in detail, in comparison to the establishment of other multigene signatures. After biomarker selection, genes and algorithms were transferred to a diagnostic platform (reverse transcription quantitative PCR (RT-qPCR)) to allow for assaying formalin-fixed, paraffin-embedded (FFPE) samples. A comprehensive analytical validation was performed and a prospective proficiency testing study with seven pathological laboratories finally proved that EndoPredict can be reliably used in the decentralized setting. Three independent large clinical validation studies (n = 2,257) demonstrated that EndoPredict offers independent prognostic information beyond current clinicopathological parameters and clinical guidelines. The review article summarizes several important steps that should be considered for the development process of a second generation multigene test and offers a means for transferring a microarray signature from the research laboratory to clinical practice.

Profiling the immune stromal interface in breast cancer and its potential for clinical impact

Advances in DNA sequencing technologies, as well as refined bioinformatics methods for interpretation of complex datasets, have provided the opportunity to comprehensively assess gene expression in tumours and their surrounding microenvironment. More recently, these advances have highlighted the interplay between the immune effector mechanisms and breast cancer cell biology, emphasizing the long-recognized link between immunity and cancer. Studying immune-associated genes has not only resulted in further stratification within the broad pathological types of breast cancers, but also provided further biological insights into the complex heterogeneity within breast cancer subgroups. On the basis that anti-cancer therapies can modify the host-tumour interaction, investigators have focused their attention on the predictive value of immune parameters as markers of therapeutic anti-tumour response. We discuss the current status of immune signatures in breast cancer and some of the fundamental limitations that need to be overcome to move these discoveries into clinic.

Th-MYCN mice with caspase-8 deficiency develop advanced neuroblastoma with bone marrow metastasis

Neuroblastoma, the most common extracranial pediatric solid tumor, is responsible for 15% of all childhood cancer deaths. Patients frequently present at diagnosis with metastatic disease, particularly to the bone marrow. Advances in therapy and understanding of the metastatic process have been limited due, in part, to the lack of animal models harboring bone marrow disease. The widely used transgenic model, the Th-MYCN mouse, exhibits limited metastasis to this site. Here, we establish the first genetic immunocompetent mouse model for metastatic neuroblastoma with enhanced secondary tumors in the bone marrow. This model recapitulates 2 frequent alterations in metastatic neuroblastoma, overexpression of MYCN and loss of caspase-8 expression. Mouse caspase-8 gene was deleted in neural crest lineage cells by crossing a Th-Cre transgenic mouse with a caspase-8 conditional knockout mouse. This mouse was then crossed with the neuroblastoma prone Th-MYCN mouse. Although overexpression of MYCN by itself rarely caused bone marrow metastasis, combining MYCN overexpression and caspase-8 deletion significantly enhanced bone marrow metastasis (37% incidence). Microarray expression studies of the primary tumors mRNAs and microRNAs revealed extracellular matrix structural changes, increased expression of genes involved in epithelial to mesenchymal transition, inflammation, and downregulation of miR-7a and miR-29b. These molecular changes have been shown to be associated with tumor progression and activation of the cytokine TGF-β pathway in various tumor models. Cytokine TGF-β can preferentially promote single cell motility and blood-borne metastasis and therefore activation of this pathway may explain the enhanced bone marrow metastasis observed in this animal model.

CD4⁺ follicular helper T cell infiltration predicts breast cancer survival

CD4⁺ T cells are critical regulators of immune responses, but their functional role in human breast cancer is relatively unknown. The goal of this study was to produce an image of CD4⁺ T cells infiltrating breast tumors using limited ex vivo manipulation to better understand the in vivo differences associated with patient prognosis. We performed comprehensive molecular profiling of infiltrating CD4⁺ T cells isolated from untreated invasive primary tumors and found that the infiltrating T cell subpopulations included follicular helper T (Tfh) cells, which have not previously been found in solid tumors, as well as Th1, Th2, and Th17 effector memory cells and Tregs. T cell signaling pathway alterations included a mixture of activation and suppression characterized by restricted cytokine/chemokine production, which inversely paralleled lymphoid infiltration levels and could be reproduced in activated donor CD4⁺ T cells treated with primary tumor supernatant. A comparison of extensively versus minimally infiltrated tumors showed that CXCL13-producing CD4⁺ Tfh cells distinguish extensive immune infiltrates, principally located in tertiary lymphoid structure germinal centers. An 8-gene Tfh signature, signifying organized antitumor immunity, robustly predicted survival or preoperative response to chemotherapy. Our identification of CD4⁺ Tfh cells in breast cancer suggests that they are an important immune element whose presence in the tumor is a prognostic factor.

Immune aspects of the breast tumor microenvironment

The immune response in the tumor microenvironment is complex, consisting of cells from both the adaptive and innate immune systems. The phenotype and function of these cells are dictated by cytokines present in the microenvironment, as well as by the interactions of these cells with the tumor cells and each other. Technological advances have allowed investigators to better identify the specific immune cells present and immune-related gene signatures overexpressed in the tumor microenvironment. Increased knowledge of tumor immunology has allowed us to better understand how these cells and the developing tumor interact. Together, these advances have prompted the conduct of numerous studies investigating the prognostic and predictive significance of immune infiltrates.

Pretreatment levels of circulating Th1 and Th2 cytokines, and their ratios, are associated with ER-negative and triple negative breast cancers

Immune signatures in breast tumors differ by estrogen receptor (ER) status. The purpose of this study was to assess associations between ER phenotypes and circulating levels of cytokines that co-ordinate cell-mediated [T-helper type 1 (Th1)] and humoral [T-helper type 2 (Th2)] immunity. We conducted a case-case comparison of 523 women with newly diagnosed breast cancer to evaluate associations between 27 circulating cytokines, measured using Luminex XMap technology, and breast cancer phenotypes [ER(-) vs. ER(+); triple negative breast cancer (TNBC) vs. luminal A (LumA)]. Ratios of Th1 to Th2 cytokines were also evaluated. Levels of interleukin (IL)-5, a Th-2 cytokine, were higher in ER(-) than in ER(+) tumors. The highest tertile of IL-5 was more strongly associated with ER(-) (OR = 2.33, 95 % CI 1.40-3.90) and TNBCs (OR = 2.78, 95 % CI 1.53-5.06) compared to ER(+) and LumA cancers, respectively, particularly among premenopausal women (OR = 4.17, 95 % CI 1.86-9.34, ER(-) vs. ER(+); OR = 5.60, 95 % CI 2.09-15.01, TNBC vs. LumA). Elevated Th1 cytokines were also detected in women with ER(-) and TNBCs, with women in the highest tertile of interferon α2 (OR = 2.39, 95 % CI 1.31-4.35) or tumor necrosis factor-α (OR = 2.27, 95 % CI 1.21-4.26) being twice as likely to have TNBC versus LumA cancer. When cytokine ratios were examined, women with the highest ratios of Th1 cytokines to IL-5 levels were least likely to have ER(-) or TNBCs compared to ER(+) or LumA cancers, respectively. The strongest associations were in premenopausal women, who were up to 80 % less likely to have TNBC than LumA cancers (IL-12p40/IL-5, OR = 0.19, 95 % CI 0.07-0.56). These findings indicate that immune function is associated with ER(-) and TNBC and may be most relevant among younger women, who are likely to be diagnosed with these aggressive phenotypes.

Interactions between immunity, proliferation and molecular subtype in breast cancer prognosis

BACKGROUND

Gene expression signatures indicative of tumor proliferative capacity and tumor-immune cell interactions have emerged as principal biology-driven predictors of breast cancer outcomes. How these signatures relate to one another in biological and prognostic contexts remains to be clarified.

RESULTS

To investigate the relationship between proliferation and immune gene signatures, we analyzed an integrated dataset of 1,954 clinically annotated breast tumor expression profiles randomized into training and test sets to allow two-way discovery and validation of gene-survival associations. Hierarchical clustering revealed a large cluster of distant metastasis-free survival-associated genes with known immunological functions that further partitioned into three distinct immune metagenes likely reflecting B cells and/or plasma cells; T cells and natural killer cells; and monocytes and/or dendritic cells. A proliferation metagene allowed stratification of cases into proliferation tertiles. The prognostic strength of these metagenes was largely restricted to tumors within the highest proliferation tertile, though intrinsic subtype-specific differences were observed in the intermediate and low proliferation tertiles. In highly proliferative tumors, high tertile immune metagene expression equated with markedly reduced risk of metastasis whereas tumors with low tertile expression of any one of the three immune metagenes were associated with poor outcome despite higher expression of the other two metagenes.

CONCLUSIONS

These findings suggest that a productive interplay among multiple immune cell types at the tumor site promotes long-term anti-metastatic immunity in a proliferation-dependent manner. The emergence of a subset of effective immune responders among highly proliferative tumors has novel prognostic ramifications.

Characteristics of cross-hybridization and cross-alignment of expression in pseudo-xenograft samples by RNA-Seq and microarrays

Background

Exploring stromal changes associated with tumor growth and development is a growing area of oncologic research. In order to study molecular changes in the stroma it is recommended to separate tumor tissue from stromal tissue. This is relevant to xenograft models where tumors can be small and difficult to separate from host tissue. We introduce a novel definition of cross-alignment/cross-hybridization to compare qualitatively the ability of high-throughput mRNA sequencing, RNA-Seq, and microarrays to detect tumor and stromal expression from mixed ‘pseudo-xenograft’ samples vis-à-vis genes and pathways in cross-alignment (RNA-Seq) and cross-hybridization (microarrays). Samples consisted of normal mouse lung and human breast cancer cells; these were combined in fixed proportions to create a titration series of 25% steps. Our definition identifies genes in a given species (human or mouse) with undetectable expression in same-species RNA but detectable expression in cross-species RNA. We demonstrate the comparative value of this method and discuss its potential contribution in cancer research.

Results

Our method can identify genes from either species that demonstrate cross-hybridization and/or cross-alignment properties. Surprisingly, the set of genes identified using a simpler and more common approach (using a ‘pure’ cross-species sample and calling all detected genes as ‘crossers’) is not a superset of the genes identified using our technique. The observed levels of cross-hybridization are relatively low: 5.3% of human genes detected in mouse, and 3.5% of mouse genes detected in human. Observed levels of cross-alignment are practically comparable to the levels of cross-hybridization: 6.5% of human genes detected in mouse, and 2.3% of mouse genes detected in human. We also observed a relatively high percentage of orthologs: 40.3% of cross-hybridizing genes, and 32.2% of cross-aligning genes.

Normalizing the gene catalog to use Consensus Coding Sequence (CCDS) IDs (Genome Res 19:1316–1323, 2009), our results show that the observed levels of cross-hybridization are low: 2.7% of human CCDS IDs are detected in mouse, and 2.4% of mouse CCDS IDs are detected in human. Levels of cross-alignment using the RNA-Seq data are comparable for the mouse, 2.2% of mouse CCDS IDs detected in human, and 9.9% of human CCDS IDs detected in mouse. However, the lists of cross-aligning/cross-hybridizing genes contain many that are of specific interest to oncologic researchers.

Conclusions

The conservative definition that we propose identifies genes in mouse whose expression can be attributed to human RNA, and vice versa, as well as revealing genes with cross-alignment/cross-hybridization properties which could not be identified using a simpler but more established approach. The overall percentage of genes affected by cross-hybridization/cross-alignment is small, but includes genes that are of interest to oncologic researchers. Which platform to use with mixed xenograft samples, microarrays or RNA-Seq, appears to be primarily a question of cost and whether the detection and measurement of expression of specific genes of interest are likely to be affected by cross-hybridization or cross-alignment.

Prognostic value of single nucleotide polymorphisms of candidate genes associated with inflammation in early stage breast cancer

To examine the role of germline genetic variations in inflammatory pathways as modifiers of time to recurrence (TTR) in patients with early stage breast cancer (BC), DNA from 997 early stage BC patients was genotyped for 53 tagging single nucleotide polymorphisms (SNPs) in 12 genes involved in inflammation. SNPs were analyzed separately for Caucasians versus African-Americans and Hispanics. Cox proportional hazards models were used to evaluate the association between SNPs in the inflammatory genes and TTR, adjusted for clinical and pathologic covariates. In univariable analyses of Caucasian women, the homozygous genotype of 12 SNPs, including 6 NFKB1 SNPs, 4 IL4 SNPs, and 2 IL13 SNPs, were significantly associated with a decrease in TTR compared with the heterozygous and/or corresponding homozygous genotype (P < 0.05). The significant NFKB1 and IL4 SNPs were in an area of high linkage disequilibrium (D' > 0.8). After adjusting for stage, age, and treatment, carriage of the homozygous genotypes for NFKB1 rs230532 and IL13rs1800925 were independently associated with a shorter TTR (P = 0.001 and P = 0.034, respectively). In African-American and Hispanic patients, expression of NFKB1 rs3774932, TNFrs1799964, and IL4rs3024543 SNPs were associated with a shorter TTR in univariable model. Only NFKB1 rs3774932 (P = 0.02) and IL4Rrs3024543 (P = 0.03) had independent prognostic value in the multivariable model These data support the existence of host genetic susceptibility as a component in recurrence risk mediated by pro-inflammatory and immune factors, and suggest the potential for drugs which modify immune responses and inflammatory genes to improve prognosis in early stage BC.

Molecular profiling of aromatase inhibitor-treated postmenopausal breast tumors identifies immune-related correlates of resistance

PURPOSE

Estrogen withdrawal by treatment with aromatase inhibitors is the most effective form of endocrine therapy for postmenopausal estrogen receptor-positive (ER+) breast cancer. However, response to therapy varies markedly and understanding of the precise molecular effects of aromatase inhibitors and causes of resistance is limited. We aimed to identify in clinical breast cancer those genes and pathways most associated with resistance to aromatase inhibitors by examining the global transcriptional effects of AI treatment.

EXPERIMENTAL DESIGN

Baseline and 2-week posttreatment biopsies were obtained from 112 postmenopausal women with ER+ breast cancer receiving neoadjuvant anastrozole. Gene expression data were obtained from 81 baseline and 2-week paired samples. Pathway analysis identified (i) the most prevalent changes in expression and (ii) the pretreatment genes/pathways most related to poor antiproliferative response.

RESULTS

A total of 1,327 genes were differentially expressed after 2-week treatment (false discovery rate < 0.01). Proliferation-associated genes and classical estrogen-dependent genes were strongly downregulated whereas collagens and chemokines were upregulated. Pretreatment expression of an inflammatory signature correlated with antiproliferative response to anastrozole and this observation was validated in an independent study. Higher expression of immune-related genes such as SLAMF8 and TNF as well as lymphocytic infiltration were associated with poorer response (P < 0.001) and validated in an independent cohort.

CONCLUSIONS

The molecular response to aromatase inhibitor treatment varies greatly between patients consistent with the variable clinical benefit from aromatase inhibitor treatment. Higher baseline expression of an inflammatory signature is associated with poor antiproliferative response and should be assessed further as a novel biomarker and potential target for aromatase inhibitor-treated patients.

Proliferation and estrogen signaling can distinguish patients at risk for early versus late relapse among estrogen receptor positive breast cancers

INTRODUCTION

We examined if a combination of proliferation markers and estrogen receptor (ER) activity could predict early versus late relapses in ER-positive breast cancer and inform the choice and length of adjuvant endocrine therapy.

METHODS

Baseline affymetrix gene-expression profiles from ER-positive patients who received no systemic therapy (n = 559), adjuvant tamoxifen for 5 years (cohort-1: n = 683, cohort-2: n = 282) and from 58 patients treated with neoadjuvant letrozole for 3 months (gene-expression available at baseline, 14 and 90 days) were analyzed. A proliferation score based on the expression of mitotic kinases (MKS) and an ER-related score (ERS) adopted from Oncotype DX® were calculated. The same analysis was performed using the Genomic Grade Index as proliferation marker and the luminal gene score from the PAM50 classifier as measure of estrogen-related genes. Median values were used to define low and high marker groups and four combinations were created. Relapses were grouped into time cohorts of 0-2.5, 0-5, 5-10 years.

RESULTS

In the overall 10 years period, the proportional hazards assumption was violated for several biomarker groups indicating time-dependent effects. In tamoxifen-treated patients Low-MKS/Low-ERS cancers had continuously increasing risk of relapse that was higher after 5 years than Low-MKS/High-ERS cancers [0 to 10 year, HR 3.36; p = 0.013]. High-MKS/High-ERS cancers had low risk of early relapse [0-2.5 years HR 0.13; p = 0.0006], but high risk of late relapse which was higher than in the High-MKS/Low-ERS group [after 5 years HR 3.86; p = 0.007]. The High-MKS/Low-ERS subset had most of the early relapses [0 to 2.5 years, HR 6.53; p < 0.0001] especially in node negative tumors and showed minimal response to neoadjuvant letrozole. These findings were qualitatively confirmed in a smaller independent cohort of tamoxifen-treated patients. Using different biomarkers provided similar results.

CONCLUSIONS

Early relapses are highest in highly proliferative/low-ERS cancers, in particular in node negative tumors. Relapses occurring after 5 years of adjuvant tamoxifen are highest among the highly-proliferative/high-ERS tumors although their risk of recurrence is modest in the first 5 years on tamoxifen. These tumors could be the best candidates for extended endocrine therapy.

Signatures of tumor-immune interactions as biomarkers for breast cancer prognosis

Breast cancer mortality is usually due to distant recurrence of cancer at an advanced stage of the disease rather than from primary cancer. Therefore, prediction of breast cancer recurrence at the time of diagnosis could lead to advances in personalized treatment of cancer patients in order to prevent risk of recurrence. Two prognostic biomarkers that are currently being used in clinical practice are a 70-gene MammaPrint signature and a 21-gene Oncotype DX panel. These assays generate relative risks of recurrence, but they do not provide a 'yes' or 'no' answer about recurrence in a given patient. These tests include genes that are involved in the cell cycle, invasion, metastasis and angiogenesis related to breast cancer. Emerging evidence suggests that a signature of genes involved in tumor-immune interactions may provide a more accurate prognostic tool. This paper reviews recent advances in the discovery of prognostic biomarkers for breast cancer patients.

Immunoglobulin kappa C predicts overall survival in node-negative breast cancer

Background

Biomarkers of the immune system are currently not used as prognostic factors in breast cancer. We analyzed the association of the B cell/plasma cell marker immunoglobulin kappa C (IGKC) and survival of untreated node-negative breast cancer patients.

Material and Methods

IGKC expression was evaluated by immunostaining in a cohort of 335 node-negative breast cancer patients with a median follow-up of 152 months. The prognostic significance of IGKC for disease-free survival (DFS) and breast cancer-specific overall survival (OS) was evaluated with Kaplan-Meier survival analysis as well as univariate and multivariate Cox analysis adjusted for age at diagnosis, pT stage, histological grade, estrogen receptor (ER) status, progesterone receptor (PR) status, Ki-67 and human epidermal growth factor receptor 2 (HER-2) status.

Results

160 patients (47.7%) showed strong expression of IGKC. Univariate analysis showed that IGKC was significantly associated with DFS (P = 0.017, hazard ratio [HR] = 0.570, 95% confidence interval [CI] = 0.360–0.903) and OS (P = 0.011, HR = 0.438, 95% CI = 0.233–0.822) in the entire cohort. The significance of IGKC was especially strong in ER negative and in luminal B carcinomas. In multivariate analysis IGKC retained its significance independent of established clinical factors for DFS (P = 0.004, HR = 0.504, 95% CI = 0.315–0.804) as well as for OS (P = 0.002, HR = 0.371, 95% CI = 0.196–0.705).

Conclusion

Expression of IGKC has an independent protective impact on DFS and OS in node-negative breast cancer.

Stromal genes add prognostic information to proliferation and histoclinical markers: a basis for the next generation of breast cancer gene signatures

BACKGROUND

First-generation gene signatures that identify breast cancer patients at risk of recurrence are confined to estrogen-positive cases and are driven by genes involved in the cell cycle and proliferation. Previously we induced sets of stromal genes that are prognostic for both estrogen-positive and estrogen-negative samples. Creating risk-management tools that incorporate these stromal signatures, along with existing proliferation-based signatures and established clinicopathological measures such as lymph node status and tumor size, should better identify women at greatest risk for metastasis and death.

METHODOLOGY/PRINCIPAL FINDINGS

To investigate the strength and independence of the stromal and proliferation factors in estrogen-positive and estrogen-negative patients we constructed multivariate Cox proportional hazards models along with tree-based partitions of cancer cases for four breast cancer cohorts. Two sets of stromal genes, one consisting of DCN and FBLN1, and the other containing LAMA2, add substantial prognostic value to the proliferation signal and to clinical measures. For estrogen receptor-positive patients, the stromal-decorin set adds prognostic value independent of proliferation for three of the four datasets. For estrogen receptor-negative patients, the stromal-laminin set significantly adds prognostic value in two datasets, and marginally in a third. The stromal sets are most prognostic for the unselected population studies and may depend on the age distribution of the cohorts.

CONCLUSION

The addition of stromal genes would measurably improve the performance of proliferation-based first-generation gene signatures, especially for older women. Incorporating indicators of the state of stromal cell types would mark a conceptual shift from epithelial-centric risk assessment to assessment based on the multiple cell types in the cancer-altered tissue.

Systematic analysis of breast cancer morphology uncovers stromal features associated with survival

The morphological interpretation of histologic sections forms the basis of diagnosis and prognostication for cancer. In the diagnosis of carcinomas, pathologists perform a semiquantitative analysis of a small set of morphological features to determine the cancer's histologic grade. Physicians use histologic grade to inform their assessment of a carcinoma's aggressiveness and a patient's prognosis. Nevertheless, the determination of grade in breast cancer examines only a small set of morphological features of breast cancer epithelial cells, which has been largely unchanged since the 1920s. A comprehensive analysis of automatically quantitated morphological features could identify characteristics of prognostic relevance and provide an accurate and reproducible means for assessing prognosis from microscopic image data. We developed the C-Path (Computational Pathologist) system to measure a rich quantitative feature set from the breast cancer epithelium and stroma (6642 features), including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. These measurements were used to construct a prognostic model. We applied the C-Path system to microscopic images from two independent cohorts of breast cancer patients [from the Netherlands Cancer Institute (NKI) cohort, n = 248, and the Vancouver General Hospital (VGH) cohort, n = 328]. The prognostic model score generated by our system was strongly associated with overall survival in both the NKI and the VGH cohorts (both log-rank P ≤ 0.001). This association was independent of clinical, pathological, and molecular factors. Three stromal features were significantly associated with survival, and this association was stronger than the association of survival with epithelial characteristics in the model. These findings implicate stromal morphologic structure as a previously unrecognized prognostic determinant for breast cancer.

Why the stroma matters in breast cancer: insights into breast cancer patient outcomes through the examination of stromal biomarkers

Survival and recurrence rates in breast cancer are variable for common diagnoses, and therefore the biological underpinnings of the disease that determine those outcomes are yet to be fully understood. As a result, translational medicine is one of the fastest growing arenas of study in tumor biology. With advancements in genetic and imaging techniques, archived biopsies can be examined for purposes other than diagnosis. There is a great deal of evidence that points to the stroma as the major regulator of tumor progression following the initial stages of tumor formation, and the stroma may also contribute to risk factors determining tumor formation. Therefore, aspects of stromal biology are well-suited to be a focus for studies of patient outcome, where statistical differences in survival among patients provide evidence as to whether that stromal component is a signpost for tumor progression. In this review we summarize the latest research done where breast cancer patient survival was correlated with aspects of stromal biology, which have been put into four categories: reorganization of the extracellular matrix (ECM) to promote invasion, changes in the expression of stromal cell types, changes in stromal gene expression, and changes in cell biology signaling cascades to and from the stroma.

An "elite hacker": breast tumors exploit the normal microenvironment program to instruct their progression and biological diversity

The year 2011 marked the 40 year anniversary of Richard Nixon signing the National Cancer Act, thus declaring the beginning of the "War on Cancer" in the United States. Whereas we have made tremendous progress toward understanding the genetics of tumors in the past four decades, and in developing enabling technology to dissect the molecular underpinnings of cancer at unprecedented resolution, it is only recently that the important role of the stromal microenvironment has been studied in detail. Cancer is a tissue-specific disease, and it is becoming clear that much of what we know about breast cancer progression parallels the biology of the normal breast differentiation, of which there is still much to learn. In particular, the normal breast and breast tumors share molecular, cellular, systemic and microenvironmental influences necessary for their progression. It is therefore enticing to consider a tumor to be a "rogue hacker"--one who exploits the weaknesses of a normal program for personal benefit. Understanding normal mammary gland biology and its "security vulnerabilities" may thus leave us better equipped to target breast cancer. In this review, we will provide a brief overview of the heterotypic cellular and molecular interactions within the microenvironment of the developing mammary gland that are necessary for functional differentiation, provide evidence suggesting that similar biology--albeit imbalanced and exaggerated--is observed in breast cancer progression particularly during the transition from carcinoma in situ to invasive disease. Lastly we will present evidence suggesting that the multigene signatures currently used to model cancer heterogeneity and clinical outcome largely reflect signaling from a heterogeneous microenvironment-a recurring theme that could potentially be exploited therapeutically.