Digital image analysis outperforms manual biomarker assessment in breast cancer.

In the spectrum of breast cancers, categorization according to the four gene expression-based subtypes 'Luminal A,' 'Luminal B,' 'HER2-enriched,' and 'Basal-like' is the method of choice for prognostic and predictive value. As gene expression assays are not yet universally available, routine immunohistochemical stains act as surrogate markers for these subtypes. Thus, congruence of surrogate markers and gene expression tests is of utmost importance. In this study, 3 cohorts of primary breast cancer specimens (total n=436) with up to 28 years of survival data were scored for Ki67, ER, PR, and HER2 status manually and by digital image analysis (DIA). The results were then compared for sensitivity and specificity for the Luminal B subtype, concordance to PAM50 assays in subtype classification and prognostic power. The DIA system used was the Visiopharm Integrator System. DIA outperformed manual scoring in terms of sensitivity and specificity for the Luminal B subtype, widely considered the most challenging distinction in surrogate subclassification, and produced slightly better concordance and Cohen's κ agreement with PAM50 gene expression assays. Manual biomarker scores and DIA essentially matched each other for Cox regression hazard ratios for all-cause mortality. When the Nottingham combined histologic grade (Elston-Ellis) was used as a prognostic surrogate, stronger Spearman's rank-order correlations were produced by DIA. Prognostic value of Ki67 scores in terms of likelihood ratio χ(2) (LR χ(2)) was higher for DIA that also added significantly more prognostic information to the manual scores (LR-Δχ(2)). In conclusion, the system for DIA evaluated here was in most aspects a superior alternative to manual biomarker scoring. It also has the potential to reduce time consumption for pathologists, as many of the steps in the workflow are either automatic or feasible to manage without pathological expertise.

Sequencing of breast cancer stem cell populations indicates a dynamic conversion between differentiation states in vivo.

INTRODUCTION: The cancer stem cell model implies a hierarchical organization within breast tumors maintained by cancer stem-like cells (CSCs). Accordingly, CSCs are a subpopulation of cancer cells with capacity for self-renewal, differentiation and tumor initiation. These cells can be isolated through the phenotypic markers CD44+/CD24-, expression of ALDH1 and an ability to form nonadherent, multicellular spheres in vitro. However, controversies to describe the stem cell model exist; it is unclear whether the tumorigenicity of CSCs in vivo is solely a proxy for a certain genotype. Moreover, in vivo evidence is lacking to fully define the reversibility of CSC differentiation. METHODS: In order to answer these questions, we undertook exome sequencing of CSCs from 12 breast cancer patients, along with paired primary tumor samples. As suggested by stem classical cell biology, we assumed that the number of mutations in the CSC subpopulation should be lower and distinct compared to the differentiated tumor cells with higher proliferation. RESULTS: Our analysis revealed that the majority of somatic mutations are shared between CSCs and bulk primary tumor, with similar frequencies in the two. CONCLUSIONS: The data presented here exclude the possibility that CSCs are only a phenotypic consequence of certain somatic mutations, that is a distinct and non-reversible population of cells. In addition, our results imply that CSCs must be a population of cells that can dynamically switch from differentiated tumor cells, and vice versa. This finding increases our understanding of CSC function in tumor heterogeneity and the importance of identifying drugs to counter de-differentiation rather than targeting CSCs.

Low concordance of biomarkers in histopathological and cytological material from breast cancer.

AIMS: The aim of this study was to investigate in primary breast cancer the congruency of routine clinical predictive biomarker evaluations, including ER, PR and Ki67, obtained using immunocytochemistry (ICC) and immunohistochemistry (IHC). METHODS AND RESULTS: Clinicopathological data were collected on all women diagnosed with primary breast cancer at Karolinska University Hospital in 2011. A total of 346 patients were included in a retrospective paired comparison of predictive biomarker evaluations on direct smear ICC and IHC. This showed a low congruency between findings with the two methods, especially evident for Ki67 (κ = 0.35-0.42). By suggested adjustments to ICC cut-offs, we managed to improve the inter-rater agreement of Ki67 classification slightly to κ = 0.46. CONCLUSIONS: Our findings suggest that routine clinical ICC and IHC evaluations of predictive biomarkers produce discordant results. Consequently, basing therapeutic decisions on cytology with cut-offs defined for IHC induces a risk that patients will receive suboptimal therapy. However, our analysis shows that local adjustments to biomarker cut-off levels may improve congruency and increase the probability of correct classifications.

The dyslexia candidate gene DYX1C1 is a potential marker of poor survival in breast cancer.

BACKGROUND: The dyslexia candidate gene, DYX1C1, shown to regulate and interact with estrogen receptors and involved in the regulation of neuronal migration, has recently been proposed as a putative cancer biomarker. This study was undertaken to assess the prognostic value and therapy-predictive potential of DYX1C1 mRNA and protein expression in breast cancer. METHODS: DYX1C1 mRNA expression was assessed at the mRNA level in three independent population-derived patient cohorts. An association to estrogen/progesterone receptor status, Elston grade, gene expression subtype and lymph node status was analyzed within these cohorts. DYX1C1 protein expression was examined using immunohistochemistry in cancer and normal breast tissue. The statistical analyses were performed using the non-parametric Wilcoxon rank-sum test, ANOVA, Fisher's exact test and a multivariate proportional hazard (Cox) model. RESULTS: DYX1C1 mRNA is significantly more highly expressed in tumors that have been classified as estrogen receptor α and progesterone receptor-positive. The expression of DYX1C1 among the molecular subtypes shows the lowest median expression within the basal type tumors, which are considered to have the worst prognosis. The expression of DYX1C1 is significantly lower in tumors graded as Elston grade 3 compared with grades 1 and 2. DYX1C1 protein is expressed in 88% of tumors and in all 10 normal breast tissues examined. Positive protein expression was significantly correlated to overall survival (Hazard ratio 3.44 [CI 1.84-6.42]) of the patients but not to any of the variables linked with mRNA expression. CONCLUSION: We show that the expression of DYX1C1 in breast cancer is associated with several clinicopathological parameters and that loss of DYX1C1 correlates with a more aggressive disease, in turn indicating that DYX1C1 is a potential prognostic biomarker in breast cancer.

A genome-wide association scan on estrogen receptor-negative breast cancer.

INTRODUCTION: Breast cancer is a heterogeneous disease and may be characterized on the basis of whether estrogen receptors (ER) are expressed in the tumour cells. ER status of breast cancer is important clinically, and is used both as a prognostic indicator and treatment predictor. In this study, we focused on identifying genetic markers associated with ER-negative breast cancer risk. METHODS: We conducted a genome-wide association analysis of 285,984 single nucleotide polymorphisms (SNPs) genotyped in 617 ER-negative breast cancer cases and 4,583 controls. We also conducted a genome-wide pathway analysis on the discovery dataset using permutation-based tests on pre-defined pathways. The extent of shared polygenic variation between ER-negative and ER-positive breast cancers was assessed by relating risk scores, derived using ER-positive breast cancer samples, to disease state in independent, ER-negative breast cancer cases. RESULTS: Association with ER-negative breast cancer was not validated for any of the five most strongly associated SNPs followed up in independent studies (1,011 ER-negative breast cancer cases, 7,604 controls). However, an excess of small P-values for SNPs with known regulatory functions in cancer-related pathways was found (global P = 0.052). We found no evidence to suggest that ER-negative breast cancer shares a polygenic basis to disease with ER-positive breast cancer. CONCLUSIONS: ER-negative breast cancer is a distinct breast cancer subtype that merits independent analyses. Given the clinical importance of this phenotype and the likelihood that genetic effect sizes are small, greater sample sizes and further studies are required to understand the etiology of ER-negative breast cancers.

Estrogen Receptor ß as a Therapeutic Target in Breast Cancer Stem Cells.

Background: Breast cancer cells with tumor-initiating capabilities (BSCs) are considered to maintain tumor growth and govern metastasis. Hence, targeting BSCs will be crucial to achieve successful treatment of breast cancer. Methods: We characterized mammospheres derived from more than 40 cancer patients and two breast cancer cell lines for the expression of estrogen receptors (ERs) and stem cell markers. Mammosphere formation and proliferation assays were performed on cells from 19 cancer patients and five healthy individuals after incubation with ER-subtype selective ligands. Transcriptional analysis was performed to identify pathways activated in ERß-stimulated mammospheres and verified using in vitro experiments. Xenograft models (n = 4 or 5 per group) were used to study the role of ERs during tumorigenesis. Results: We identified an absence of ERα but upregulation of ERß in BSCs associated with phenotypic stem cell markers and responsible for the proliferative role of estrogens. Knockdown of ERß caused a reduction of mammosphere formation in cell lines and in patient-derived cancer cells (40.7%, 26.8%, and 39.1%, respectively). Gene set enrichment analysis identified glycolysis-related pathways (false discovery rate < 0.001) upregulated in ERß-activated mammospheres. We observed that tamoxifen or fulvestrant alone was insufficient to block proliferation of patient-derived BSCs while this could be accomplished by a selective inhibitor of ERß (PHTPP; 53.7% in luminal and 45.5% in triple-negative breast cancers). Furthermore, PHTPP reduced tumor initiation in two patient-derived xenografts (75.9% and 59.1% reduction in tumor volume, respectively) and potentiated tamoxifen-mediated inhibition of tumor growth in MCF7 xenografts. Conclusion: We identify ERß as a mediator of estrogen action in BSCs and a novel target for endocrine therapy.

Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers.

Sequencing-based breast cancer diagnostics have the potential to replace routine biomarkers and provide molecular characterization that enable personalized precision medicine. Here we investigate the concordance between sequencing-based and routine diagnostic biomarkers and to what extent tumor sequencing contributes clinically actionable information. We applied DNA- and RNA-sequencing to characterize tumors from 307 breast cancer patients with replication in up to 739 patients. We developed models to predict status of routine biomarkers (ER, HER2,Ki-67, histological grade) from sequencing data. Non-routine biomarkers, including mutations in BRCA1, BRCA2 and ERBB2(HER2), and additional clinically actionable somatic alterations were also investigated. Concordance with routine diagnostic biomarkers was high for ER status (AUC = 0.95;AUC(replication) = 0.97) and HER2 status (AUC = 0.97;AUC(replication) = 0.92). The transcriptomic grade model enabled classification of histological grade 1 and histological grade 3 tumors with high accuracy (AUC = 0.98;AUC(replication) = 0.94). Clinically actionable mutations in BRCA1, BRCA2 and ERBB2(HER2) were detected in 5.5% of patients, while 53% had genomic alterations matching ongoing or concluded breast cancer studies. Sequencing-based molecular profiling can be applied as an alternative to histopathology to determine ER and HER2 status, in addition to providing improved tumor grading and clinically actionable mutations and molecular subtypes. Our results suggest that sequencing-based breast cancer diagnostics in a near future can replace routine biomarkers.

Tumour nuclear oestrogen receptor beta 1 correlates inversely with parathyroid tumour weight.

Primary hyperparathyroidism (PHPT) is a common endocrinopathy, frequently caused by a parathyroid adenoma, rarely by a parathyroid carcinoma that lacks effective oncological treatment. As the majority of cases are present in postmenopausal women, oestrogen signalling has been implicated in the tumourigenesis. Oestrogen receptor beta 1 (ERB1) and ERB2 have been recently identified in parathyroid adenomas, the former inducing genes coupled to tumour apoptosis. We applied immunohistochemistry and slide digitalisation to quantify nuclear ERB1 and ERB2 in 172 parathyroid adenomas, atypical adenomas and carcinomas, and ten normal parathyroid glands. All the normal parathyroid glands expressed ERB1 and ERB2. The majority of tumours expressed ERB1 (70.6%) at varying intensities, and ERB2 (96.5%) at strong intensities. Parathyroid carcinomas expressed ERB1 in three out of six cases and ERB2 in five out of six cases. The intensity of tumour nuclear ERB1 staining significantly correlated inversely with tumour weight (P=0.011), and patients whose tumours were classified as ERB1-negative had significantly greater tumour weight as well as higher serum calcium (P=0.002) and parathyroid hormone levels (P=0.003). Additionally, tumour nuclear ERB1 was not expressed differentially with respect to sex or age of the patient. Levels of tumour nuclear ERB2 did not correlate with clinical characteristics. In conclusion, decreased ERB1 immunoreactivity is associated with increased tumour weight in parathyroid adenomas. Given the previously reported correlation with tumour-suppressive signalling, selective oestrogen receptor modulation (SERMs) may play a role in the treatment of parathyroid carcinomas. Future studies of SERMs and oestrogen treatment in PHPT should consider tumour weight as a potential factor in pharmacological responsiveness.

The rs3743205 SNP is important for the regulation of the dyslexia candidate gene DYX1C1 by estrogen receptor ß and DNA methylation.

Estrogen is involved in numerous physiological processes such as growth, differentiation, and function of the male and female reproductive tissues. In the developing brain, estrogen signaling has been linked to cognitive functions, such as learning and memory; however, the molecular mechanisms underlying this phenomenon are poorly understood. We have previously shown a link between developmental dyslexia and estrogen signaling, when we studied the functional interactions between the dyslexia candidate protein DYX1C1 and the estrogen receptors α (ERα) and ß (ERß). Here, we investigate the 17ß-estradiol (E2)-dependent regulation of dyslexia susceptibility 1 candidate 1 (DYX1C1) expression. We demonstrate that ERß, not ERα, binds to a transcriptionally active cis-regulatory region upstream of DYX1C1 transcriptional start site and that DYX1C1 expression is enhanced by E2 in a neuroblastoma cell line. This regulation is dependent on transcription factor II-I and liganded ERß recruitment to this region. In addition, we describe that a single nucleotide polymorphism previously shown to be associated with dyslexia and located in the cis-regulatory region of DYX1C1 may alter the epigenetic and endocrine regulation of this gene. Our data provide important molecular insights into the relationship between developmental dyslexia susceptibility and estrogen signaling.