Caveolin-1 gene expression provides additional prognostic information combined with PAM50 risk of recurrence (ROR) score in breast cancer.

Combining information from the tumor microenvironment (TME) with PAM50 Risk of Recurrence (ROR) score could improve breast cancer prognostication. Caveolin-1 (CAV1) is a marker of an active TME. CAV1 is a membrane protein involved in cell signaling, extracellular matrix organization, and tumor-stroma interactions. We sought to investigate CAV1 gene expression in relation to PAM50 subtypes, ROR score, and their joint prognostic impact. CAV1 expression was compared between PAM50 subtypes and ROR categories in two cohorts (SCAN-B, n = 5326 and METABRIC, n = 1980). CAV1 expression was assessed in relation to clinical outcomes using Cox regression and adjusted for clinicopathological predictors. Effect modifications between CAV1 expression and ROR categories on clinical outcome were investigated using multiplicative and additive two-way interaction analyses. Differential gene expression and gene set enrichment analyses were applied to compare high and low expressing CAV1 tumors. All samples expressed CAV1 with the highest expression in the Normal-like subtype. Gene modules consistent with epithelial-mesenchymal transition (EMT), hypoxia, and stromal activation were associated with high CAV1 expression. CAV1 expression was inversely associated with ROR category. Interactions between CAV1 expression and ROR categories were observed in both cohorts. High expressing CAV1 tumors conferred worse prognosis only within the group classified as ROR high. ROR gave markedly different prognostic information depending on the underlying CAV1 expression. CAV1, a potential mediator between the malignant cells and TME, could be a useful biomarker that enhances and further refines PAM50 ROR risk stratification in patients with ROR high tumors and a potential therapeutic target.

Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer.

BACKGROUND: Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance. METHODS: This observational study is based on two patient cohorts (SöS-BC-4, N = 2421 (training and internal test); SCAN-B-Lund, N = 1262 (test)) that include routine histological whole-slide images (WSIs) together with patient outcomes. A deep convolutional neural network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort. RESULTS: We observed effect sizes (hazard ratio) for grade 3 versus 1, for the conventional NHG method (HR = 2.60 (1.18-5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI 1.07-4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for clinical NHG 2 versus 1 was estimated to be 2.59 (p-value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for predGrade 2 versus 1 HR = 2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for preGrade 3 versus 1 was observed in the independent external test set. In multivariable analysis, HR estimates for neither clinical NHG nor predGrade were found to be significant (p-value > 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade. CONCLUSION: Routine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.

Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification.

BACKGROUND: Genomic rearrangements in cancer cells can create fusion genes that encode chimeric proteins or alter the expression of coding and non-coding RNAs. In some cancer types, fusions involving specific kinases are used as targets for therapy. Fusion genes can be detected by whole genome sequencing (WGS) and targeted fusion panels, but RNA sequencing (RNA-Seq) has the advantageous capability of broadly detecting expressed fusion transcripts. RESULTS: We developed a pipeline for validation of fusion transcripts identified in RNA-Seq data using matched WGS data from The Cancer Genome Atlas (TCGA) and applied it to 910 tumors from 11 different cancer types. This resulted in 4237 validated gene fusions, 3049 of them with at least one identified genomic breakpoint. Utilizing validated fusions as true positive events, we trained a machine learning classifier to predict true and false positive fusion transcripts from RNA-Seq data. The final precision and recall metrics of the classifier were 0.74 and 0.71, respectively, in an independent dataset of 249 breast tumors. Application of this classifier to all samples with RNA-Seq data from these cancer types vastly extended the number of likely true positive fusion transcripts and identified many potentially targetable kinase fusions. Further analysis of the validated gene fusions suggested that many are created by intrachromosomal amplification events with microhomology-mediated non-homologous end-joining. CONCLUSIONS: A classifier trained on validated fusion events increased the accuracy of fusion transcript identification in samples without WGS data. This allowed the analysis to be extended to all samples with RNA-Seq data, facilitating studies of tumor biology and increasing the number of detected kinase fusions. Machine learning could thus be used in identification of clinically relevant fusion events for targeted therapy. The large dataset of validated gene fusions generated here presents a useful resource for development and evaluation of fusion transcript detection algorithms.

Perturbation and stability of PAM50 subtyping in population-based primary invasive breast cancer.

PAM50 gene expression subtypes represent a cornerstone in the molecular classification of breast cancer and are included in risk prediction models to guide therapy. We aimed to illustrate the impact of included genes and biological processes on subtyping while considering a tumor's underlying clinical subgroup defined by ER, PR, and HER2 status. To do this we used a population-representative and clinically annotated early-stage breast tumor cohort of 6233 samples profiled by RNA sequencing and applied a perturbation strategy of excluding co-expressed genes (gene sets). We demonstrate how PAM50 nearest-centroid classification depends on biological processes present across, but also within, ER/PR/HER2 subgroups and PAM50 subtypes themselves. Our analysis highlights several key aspects of PAM50 classification. Firstly, we demonstrate the tight connection between a tumor's nearest and second-nearest PAM50 centroid. Additionally, we show that the second-best subtype is associated with overall survival in ER-positive, HER2-negative, and node-negative disease. We also note that ERBB2 expression has little impact on PAM50 classification in HER2-positive disease regardless of ER status and that the Basal subtype is highly stable in contrast to the Normal subtype. Improved consciousness of the commonly used PAM50 subtyping scheme will aid in our understanding and interpretation of breast tumors that have seemingly conflicting PAM50 classification when compared to clinical biomarkers. Finally, our study adds further support in challenging the common misconception that PAM50 subtypes are distinct classes by illustrating that PAM50 subtypes in tumors represent a continuum with prognostic implications.

Matched analysis of circulating selenium with the breast cancer selenotranscriptome: a multicentre prospective study.

INTRODUCTION: Low serum selenium and altered tumour RNA expression of certain selenoproteins are associated with a poor breast cancer prognosis. Selenoprotein expression stringently depends on selenium availability, hence circulating selenium may interact with tumour selenoprotein expression. However, there is no matched analysis to date. METHODS: This study included 1453 patients with newly diagnosed breast cancer from the multicentric prospective Sweden Cancerome Analysis Network - Breast study. Total serum selenium, selenoprotein P and glutathione peroxidase 3 were analysed at time of diagnosis. Bulk RNA-sequencing was conducted in matched tumour tissues. Fully adjusted Cox regression models with an interaction term were employed to detect dose-dependent interactions of circulating selenium with the associations of tumour selenoprotein mRNA expression and mortality. RESULTS: 237 deaths were recorded within ~ 9 years follow-up. All three serum selenium biomarkers correlated positively (p < 0.001). All selenoproteins except for GPX6 were expressed in tumour tissues. Single cell RNA-sequencing revealed a heterogeneous expression pattern in the tumour microenvironment. Circulating selenium correlated positively with tumour SELENOW and SELENON expression (p < 0.001). In fully adjusted models, the associations of DIO1, DIO3 and SELENOM with mortality were dose-dependently modified by serum selenium (p < 0.001, p = 0.020, p = 0.038, respectively). With increasing selenium, DIO1 and SELENOM associated with lower, whereas DIO3 expression associated with higher mortality. Association of DIO1 with lower mortality was only apparent in patients with high selenium [above median (70.36 µg/L)], and the HR (95%CI) for one-unit increase in log(FPKM + 1) was 0.70 (0.50-0.98). CONCLUSIONS: This first unbiased analysis of serum selenium with the breast cancer selenotranscriptome identified an effect-modification of selenium on the associations of DIO1, SELENOM, and DIO3 with prognosis. Selenium substitution in patients with DIO1-expressing tumours merits consideration to improve survival.

Alternative promoters and splicing create multiple functionally distinct isoforms of oestrogen receptor alpha in breast cancer and healthy tissues.

BACKGROUND: Oestrogen receptor alpha (ER) is involved in cell growth and proliferation and functions as a transcription factor, a transcriptional coregulator, and in cytoplasmic signalling. It affects, for example, bone, endometrium, ovaries and mammary epithelium. It is a key biomarker in clinical management of breast cancer, where it is used as a prognostic and treatment-predictive factor, and a therapeutical target. Several ER isoforms have been described, but transcript annotation in public databases is incomplete and inconsistent, and functional differences are not well understood. METHODS: We have analysed short- and long-read RNA sequencing data from breast tumours, breast cancer cell lines, and normal tissues to create a comprehensive annotation of ER transcripts and combined it with experimental studies of full-length protein and six alternative isoforms. RESULTS: The isoforms have varying transcription factor activity, subcellular localisation, and response to the ER-targeting drugs tamoxifen and fulvestrant. Antibodies differ in ability to detect alternative isoforms, which raises concerns for the interpretation of ER-status in routine pathology. CONCLUSIONS: Future work should investigate the effects of alternative isoforms on patient survival and therapy response. An accurate annotation of ER isoforms will aid in interpretation of clinical data and inform functional studies to improve our understanding of the ER in health and disease.

FGF/FGFR1 system in paired breast tumor-adjacent and tumor tissues, associations with mammographic breast density and tumor characteristics.

Introduction: Mammographic breast density (MBD) is an established breast cancer risk factor, yet the underlying molecular mechanisms remain to be deciphered. Fibroblast growth factor receptor 1 (FGFR1) amplification is associated with breast cancer development and aberrant FGF signaling found in the biological processes related to both high mammographic density and breast cancer microenvironment. The aim of this study was to investigate the FGF/FGFR1 expression in-between paired tumor-adjacent and tumor tissues from the same patient, and its associations with MBD and tumor characteristics. Methods: FGFR1 expression in paired tissues from 426 breast cancer patients participating in the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort study was analyzed by immunohistochemistry. FGF ligand expression was obtained from RNA-sequencing data for 327 of the included patients. Results: FGFR1 levels were differently expressed in tumor-adjacent and tumor tissues, with increased FGFR1 levels detected in 58% of the tumors. High FGFR1 expression in tumor tissues was associated with less favorable tumor characteristics; high histological grade (OR=1.86, 95% CI 1.00-3.44), high Ki67 proliferative index (OR=2.18, 95% CI 1.18-4.02) as well as tumors of Luminal B-like subtype (OR=2.56, 95%CI 1.29-5.06). While no clear association between FGFR1 expression and MBD was found, FGF ligand (FGF1, FGF11, FGF18) expression was positively correlated with MBD. Discussion: Taken together, these findings support a role of the FGF/FGFR1 system in early breast cancer which warrants further investigation in the MBD-breast cancer context.

Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information.

BACKGROUND: Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer. METHODS: Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262). RESULTS: We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10-2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm). CONCLUSION: We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer. SIGNIFICANCE: Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.

Serum copper, zinc and copper/zinc ratio in relation to survival after breast cancer diagnosis: A prospective multicenter cohort study.

BACKGROUND: The essential trace elements copper and zinc, and their ratio (copper/zinc), are important for maintaining redox homeostasis. Previous studies suggest that these elements may impact breast cancer survival. However, no epidemiological study has so far been conducted on the potential association between copper and copper/zinc levels and survival after breast cancer diagnosis. In this study, we aimed to examine the relationship between serum copper, zinc and copper/zinc levels and survival following breast cancer diagnosis. PATIENTS AND METHODS: The Sweden Cancerome Analysis Network - Breast Initiative (SCAN-B) is a population-based cohort study including multiple participating hospitals in Sweden. A total of 1998 patients diagnosed with primary invasive breast cancer were followed for approximately nine years. Serum levels of copper and zinc and their ratio at the time of diagnosis was analyzed in relation to breast cancer survival using multivariate Cox regression, yielding hazard ratios (HR) with 95% confidence intervals. RESULTS: A higher copper/zinc ratio was associated with lower overall survival after breast cancer diagnosis. Comparing patients with a copper/zinc ratio in quartile 4 vs 1, the crude HR was 2.29 (1.65-3.19) (Ptrend <0.01) and the fully adjusted HR was 1.58 (1.11-2.25) (Ptrend = 0.01). No overall associations were seen between serum copper or zinc levels on their own and survival after breast cancer diagnosis, although a tendency toward lower breast cancer survival was seen for higher copper levels and lower zinc levels. CONCLUSION: There is evidence that the serum copper/zinc ratio provides an independent predictive value for overall survival following breast cancer diagnosis.

Molecular characteristics of breast tumors in patients screened for germline predisposition from a population-based observational study.

BACKGROUND: Pathogenic germline variants (PGVs) in certain genes are linked to higher lifetime risk of developing breast cancer and can influence preventive surgery decisions and therapy choices. Public health programs offer genetic screening based on criteria designed to assess personal risk and identify individuals more likely to carry PGVs, dividing patients into screened and non-screened groups. How tumor biology and clinicopathological characteristics differ between these groups is understudied and could guide refinement of screening criteria. METHODS: Six thousand six hundred sixty breast cancer patients diagnosed in South Sweden during 2010-2018 were included with available clinicopathological and RNA sequencing data, 900 (13.5%) of which had genes screened for PGVs through routine clinical screening programs. We compared characteristics of screened patients and tumors to non-screened patients, as well as between screened patients with (n = 124) and without (n = 776) PGVs. RESULTS: Broadly, breast tumors in screened patients showed features of a more aggressive disease. However, few differences related to tumor biology or patient outcome remained significant after stratification by clinical subgroups or PAM50 subtypes. Triple-negative breast cancer (TNBC), the subgroup most enriched for PGVs, showed the most differences between screening subpopulations (e.g., higher tumor proliferation in screened cases). Significant differences in PGV prevalence were found between clinical subgroups/molecular subtypes, e.g., TNBC cases were enriched for BRCA1 PGVs. In general, clinicopathological differences between screened and non-screened patients mimicked those between patients with and without PGVs, e.g., younger age at diagnosis for positive cases. However, differences in tumor biology/microenvironment such as immune cell composition were additionally seen within PGV carriers/non-carriers in ER + /HER2 - cases, but not between screening subpopulations in this subgroup. CONCLUSIONS: Characterization of molecular tumor features in patients clinically screened and not screened for PGVs represents a relevant read-out of guideline criteria. The general lack of molecular differences between screened/non-screened patients after stratification by relevant breast cancer subsets questions the ability to improve the identification of screening candidates based on currently used patient and tumor characteristics, pointing us towards universal screening. Nevertheless, while that is not attained, molecular differences identified between PGV carriers/non-carriers suggest the possibility of further refining patient selection within certain patient subsets using RNA-seq through, e.g., gene signatures. TRIAL REGISTRATION: The Sweden Cancerome Analysis Network - Breast (SCAN-B) was prospectively registered at ClinicalTrials.gov under the identifier NCT02306096.

Choice of High-Throughput Proteomics Method Affects Data Integration with Transcriptomics and the Potential Use in Biomarker Discovery.

In recent years, several advances have been achieved in breast cancer (BC) classification and treatment. However, overdiagnosis, overtreatment, and recurrent disease are still significant causes of complication and death. Here, we present the development of a protocol aimed at parallel transcriptome and proteome analysis of BC tissue samples using mass spectrometry, via Data Dependent and Independent Acquisitions (DDA and DIA). Protein digestion was semi-automated and performed on flowthroughs after RNA extraction. Data for 116 samples were acquired in DDA and DIA modes and processed using MaxQuant, EncyclopeDIA, or DIA-NN. DIA-NN showed an increased number of identified proteins, reproducibility, and correlation with matching RNA-seq data, therefore representing the best alternative for this setup. Gene Set Enrichment Analysis pointed towards complementary information being found between transcriptomic and proteomic data. A decision tree model, designed to predict the intrinsic subtypes based on differentially abundant proteins across different conditions, selected protein groups that recapitulate important clinical features, such as estrogen receptor status, HER2 status, proliferation, and aggressiveness. Taken together, our results indicate that the proposed protocol performed well for the application. Additionally, the relevance of the selected proteins points to the possibility of using such data as a biomarker discovery tool for personalized medicine.

How Reliable Are Gene Expression-Based and Immunohistochemical Biomarkers Assessed on a Core-Needle Biopsy? A Study of Paired Core-Needle Biopsies and Surgical Specimens in Early Breast Cancer.

In early breast cancer, a preoperative core-needle biopsy (CNB) is vital to confirm the malignancy of suspected lesions and for assessing the expression of treatment predictive and prognostic biomarkers in the tumor to choose the optimal treatments, emphasizing the importance of obtaining reliable results when biomarker status is assessed on a CNB specimen. This study aims to determine the concordance between biomarker status assessed as part of clinical workup on a CNB compared to a medically untreated surgical specimen. Paired CNB and surgical specimens from 259 patients that were part of the SCAN-B cohort were studied. The concordance between immunohistochemical (IHC) and gene expression (GEX) based biomarker status was investigated. Biomarkers of interest included estrogen receptor (ER; specifically, the alpha variant), progesterone receptor (PgR), Ki67, HER2, and tumor molecular subtype. In general, moderate to very good correlation in biomarker status between the paired CNB and surgical specimens was observed for both IHC assessment (83-99% agreement, kappa range 0.474-0.917) and GEX assessment (70-97% agreement, kappa range 0.552-0.800), respectively. However, using IHC, 52% of cases with low Ki67 status in the CNB shifted to high Ki67 status in the surgical specimen (McNemar's p = 0.011). Similarly, when using GEX, a significant shift from negative to positive ER (47%) and from low to high Ki67 (16%) was observed between the CNB and surgical specimen (McNemar's p = 0.027 and p = 0.002 respectively). When comparing biomarker status between different techniques (IHC vs. GEX) performed on either CNBs or surgical specimens, the agreement in ER, PgR, and HER2 status was generally over 80% in both CNBs and surgical specimens (kappa range 0.395-0.708), but Ki67 and tumor molecular subtype showed lower concordance levels between IHC and GEX (48-62% agreement, kappa range 0.152-0.398). These results suggest that both the techniques used for collecting tissue samples and analyzing biomarker status have the potential to affect the results of biomarker assessment, potentially also impacting treatment decisions and patient survival outcomes.

RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer.

Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 (five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high (90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.

Subtype and cell type specific expression of lncRNAs provide insight into breast cancer.

Long non-coding RNAs (lncRNAs) are involved in breast cancer pathogenesis through chromatin remodeling, transcriptional and post-transcriptional gene regulation. We report robust associations between lncRNA expression and breast cancer clinicopathological features in two population-based cohorts: SCAN-B and TCGA. Using co-expression analysis of lncRNAs with protein coding genes, we discovered three distinct clusters of lncRNAs. In silico cell type deconvolution coupled with single-cell RNA-seq analyses revealed that these three clusters were driven by cell type specific expression of lncRNAs. In one cluster lncRNAs were expressed by cancer cells and were mostly associated with the estrogen signaling pathways. In the two other clusters, lncRNAs were expressed either by immune cells or fibroblasts of the tumor microenvironment. To further investigate the cis-regulatory regions driving lncRNA expression in breast cancer, we identified subtype-specific transcription factor (TF) occupancy at lncRNA promoters. We also integrated lncRNA expression with DNA methylation data to identify long-range regulatory regions for lncRNA which were validated using ChiA-Pet-Pol2 loops. lncRNAs play an important role in shaping the gene regulatory landscape in breast cancer. We provide a detailed subtype and cell type-specific expression of lncRNA, which improves the understanding of underlying transcriptional regulation in breast cancer.

Autoimmunity to selenoprotein P predicts breast cancer recurrence.

BACKGROUND: Low concentrations of serum selenium (Se) and its main transporter selenoprotein P (SELENOP) are associated with a poor prognosis following breast cancer diagnosis. Recently, natural autoantibodies (aAb) with antagonistic properties to SELENOP uptake have been identified in healthy subjects, and in patients with thyroid disease. Given the potential transport disrupting properties, we hypothesized that breast cancer patients with SELENOP-aAb may have a poor prognosis. METHODS: SELENOP-aAb along with serum Se, SELENOP and GPX3 activity were determined in serum samples of 1988 patients with a new diagnosis of breast cancer enrolled in the multicentre SCAN-B study. Patients were followed for ∼9 years and multivariate Cox regression models were applied to assess hazard ratios. RESULTS: Applying a cut-off based on outlier detection, we identified 7.65% of patients with SELENOP-aAb. Autoantibody titres correlated positively to total Se and SELENOP concentrations, but not to GPX3 activity, supporting a negative role of SELENOP-aAb on Se transport. SELENOP-aAb were associated with age, but independent of tumor characteristics. After fully adjusting for potential confounders, SELENOP-aAb were associated with higher recurrence, HR(95%CI) = 1.87(1.17-2.99), particularly in patients with low Se concentrations, HR(95%CI) = 2.16(1.20-3.88). Associations of SELENOP-aAb with recurrence and mortality were linear and dose-dependent, with fully adjusted HR(95%CI) per log increase of 1.25(1.01-1.55) and 1.31(1.13-1.51), respectively. CONCLUSION: Our results indicate a prognostic and pathophysiological relevance of SELENOP-aAb in breast cancer, with potential relevance for other malignancies. Assessment of SELENOP-aAb at time of diagnosis identifies patients with a distinctly elevated risk for a poor prognosis, independent of established prognostic factors, who may respond favourably to Se supplementation.

Regulatory networks and 5' partner usage of miRNA host gene fusions in breast cancer.

Genomic rearrangements in cancer cells can create gene fusions where the juxtaposition of two different genes leads to the production of chimeric proteins or altered gene expression through promoter-swapping. We have previously shown that fusion transcripts involving microRNA (miRNA) host genes contribute to deregulation of miRNA expression regardless of the protein-coding potential of these transcripts. Many different genes can also be used as 5' partners by a miRNA host gene in what we named recurrent miRNA-convergent fusions. Here, we have explored the properties of 5' partners in fusion transcripts that involve miRNA hosts in breast tumours from The Cancer Genome Atlas (TCGA). We hypothesised that firstly, 5' partner genes should belong to pathways and transcriptional programmes that reflect the tumour phenotype and secondly, there should be a selection for fusion events that shape miRNA expression to benefit the tumour cell through the known hallmarks of cancer. We found that the set of 5' partners in miRNA host fusions is non-random, with overrepresentation of highly expressed genes in pathways active in cancer including epithelial-to-mesenchymal transition, translational regulation and oestrogen signalling. Furthermore, many miRNAs were upregulated in samples with host gene fusions, including established oncogenic miRNAs such as mir-21 and the mir-106b~mir-93~mir-25 cluster. To the list of mechanisms for deregulation of miRNA expression, we have added fusion transcripts that change the promoter region. We propose that this adds material for genetic selection and tumour evolution in cancer cells and that miRNA host fusions can act as tumour 'drivers'.

Interval breast cancer is associated with interferon immune response.

BACKGROUND: The aggressive nature of breast cancers detected between planned mammographic screens, so-called interval cancers, remains elusive. Here, we aim to characterise underlying molecular features of interval cancer. METHODS: From 672 patients with invasive breast cancer, we analysed gene expression differences between 90 'true' interval cancer cases (i.e. women with low-dense breasts defined as per cent mammographic density <25%) and 310 screen-detected tumours while accounting for PAM50 subtypes and thus overall tumour aggressiveness. We computed an interval cancer gene expression profile (IC-Gx) in a total of 2270 breast tumours (regardless of interval cancer status) and tested for association with expression-based immune subtypes in breast cancer. In addition, we investigated the contribution of inherited and somatic genetic variants in distinct features of interval cancer. RESULTS: We identified 8331 genes nominally associated with interval cancer (P-value < 0.05, fold-change > 1.5). Gene set enrichment analysis showed immune-related pathways as key processes altered in interval cancer. Our IC-Gx, based on 47 genes with the strongest associations (false discovery rate < 0.05), was found to be associated mainly with immune subtypes involving interferon response. We isolated an interaction network of interval cancer and interferon genes for which a significant load of somatic and germline variants in class I interferon genes was observed. CONCLUSION: We identified novel molecular features of interval breast cancer highlighting interferon pathways as a potential target for prevention or treatment.

Sample Preparation Approach Influences PAM50 Risk of Recurrence Score in Early Breast Cancer.

The PAM50 gene expression subtypes and the associated risk of recurrence (ROR) score are used to predict the risk of recurrence and the benefits of adjuvant therapy in early-stage breast cancer. The Prosigna assay includes the PAM50 subtypes along with their clinicopathological features, and is approved for treatment recommendations for adjuvant hormonal therapy and chemotherapy in hormone-receptor-positive early breast cancer. The Prosigna test utilizes RNA extracted from macrodissected tumor cells obtained from formalin-fixed, paraffin-embedded (FFPE) tissue sections. However, RNA extracted from fresh-frozen (FF) bulk tissue without macrodissection is widely used for research purposes, and yields high-quality RNA for downstream analyses. To investigate the impact of the sample preparation approach on ROR scores, we analyzed 94 breast carcinomas included in an observational study that had available gene expression data from macrodissected FFPE tissue and FF bulk tumor tissue, along with the clinically approved Prosigna scores for the node-negative, hormone-receptor-positive, HER2-negative cases (n = 54). ROR scores were calculated in R; the resulting two sets of scores from FFPE and FF samples were compared, and treatment recommendations were evaluated. Overall, ROR scores calculated based on the macrodissected FFPE tissue were consistent with the Prosigna scores. However, analyses from bulk tissue yielded a higher proportion of cases classified as normal-like; these were samples with relatively low tumor cellularity, leading to lower ROR scores. When comparing ROR scores (low, intermediate, and high), discordant cases between the two preparation approaches were revealed among the luminal tumors; the recommended treatment would have changed in a minority of cases.

Serum selenium, selenoprotein P and glutathione peroxidase 3 as predictors of mortality and recurrence following breast cancer diagnosis: A multicentre cohort study.

The trace element selenium is of essential importance for the synthesis of a set of redox active proteins. We investigated three complementary serum selenium status biomarkers in relation to overall survival and recurrence following diagnosis of primary invasive breast cancer in a large prospective cohort study. The Sweden Cancerome Analysis Network - Breast Initiative (SCAN-B) is a prospective population-based study including multiple participating hospitals. Main analyses included 1996 patients with a new diagnosis of primary invasive breast cancer, with blood sampling at the time of diagnosis. In sera of the patients, total serum selenium, selenoprotein P (SELENOP), and glutathione peroxidase 3 (GPx3) activity was analysed. All three biomarkers showed a positive correlation (p < 0.001), supporting the high quality of samples and analytical techniques. During a total of 13,306 person years of follow-up, 310 deaths and 167 recurrent breast cancer events occurred. In fully adjusted Cox models, all three biomarkers correlated inversely with mortality (p trend <0.001) and compared with the lowest quintile, hazard ratios (95% confidence interval) for overall survival in the highest quintile of selenium, SELENOP and GPx3 were 0.42 (0.28-0.63), 0.51 (0.36-0.73) and 0.52 (0.36-0.75), respectively. Low GPx3 activity was associated with more recurrences (Q5 vs Q1: fully adjusted HR (95%CI); 0.57 (0.35-0.92), (p trend = 0.005). Patients with low selenium status according to all three biomarkers (triple deficient) had the highest mortality risk with an overall survival probability of ∼50% after 8 years, in particular as compared to those having at least one marker in the highest quintile; fully adjusted HR (95%CI); 0.30 (0.21-0.43). Prediction of mortality based on all three biomarkers outperformed established tumour characteristics like histologic grade, number of involved lymph nodes or tumour size. An assessment of Se status at breast cancer diagnosis identifies patients at exceptionally high risk for a poor prognosis.

Preexisting Somatic Mutations of Estrogen Receptor Alpha (ESR1) in Early-Stage Primary Breast Cancer.

Background: More than three-quarters of primary breast cancers are positive for estrogen receptor alpha (ER; encoded by the gene ESR1), the most important factor for directing anti-estrogenic endocrine therapy (ET). Recently, mutations in ESR1 were identified as acquired mechanisms of resistance to ET, found in 12% to 55% of metastatic breast cancers treated previously with ET. Methods: We analyzed 3217 population-based invasive primary (nonmetastatic) breast cancers (within the SCAN-B study, ClinicalTrials.gov NCT02306096), sampled from initial diagnosis prior to any treatment, for the presence of ESR1 mutations using RNA sequencing. Mutations were verified by droplet digital polymerase chain reaction on tumor and normal DNA. Patient outcomes were analyzed using Kaplan-Meier estimation and a series of 2-factor Cox regression multivariable analyses. Results: We identified ESR1 resistance mutations in 30 tumors (0.9%), of which 29 were ER positive (1.1%). In ET-treated disease, presence of ESR1 mutation was associated with poor relapse-free survival and overall survival (2-sided log-rank test P < .001 and P = .008, respectively), with hazard ratios of 3.00 (95% confidence interval = 1.56 to 5.88) and 2.51 (95% confidence interval = 1.24 to 5.07), respectively, which remained statistically significant when adjusted for other prognostic factors. Conclusions: These population-based results indicate that ESR1 mutations at diagnosis of primary breast cancer occur in about 1% of women and identify for the first time in the adjuvant setting that such preexisting mutations are associated to eventual resistance to standard hormone therapy. If replicated, tumor ESR1 screening should be considered in ER-positive primary breast cancer, and for patients with mutated disease, ER degraders such as fulvestrant or other therapeutic options may be considered as more appropriate.