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Circulating tumor DNA (ctDNA) has emerged as a potential new biomarker with diagnostic, predictive, and prognostic applications for various solid tumor types. Before beginning large prospective clinical trials to prove the added value of utilizing ctDNA in clinical practice, it is essential to investigate the effects of various preanalytical conditions on the quality of cell‐free DNA (cfDNA) in general and of ctDNA in particular in order to optimize and standardize these conditions. Whole blood samples were collected from patients with metastatic cancer bearing a known somatic variant. The following preanalytical conditions were investigated: (a) different time intervals to plasma isolation (1, 24, and 96 h) and (b) different preservatives in blood collection tubes (EDTA, CellSave, and BCT). The quality of cfDNA/ctDNA was assessed by DNA quantification, digital polymerase chain reaction (dPCR) for somatic variant detection and a β‐actin fragmentation assay for DNA contamination from lysed leukocytes. In 11 (69%) of our 16 patients, we were able to detect the known somatic variant in ctDNA. We observed a time‐dependent increase in cfDNA concentrations in EDTA tubes, which was positively correlated with an increase in wild‐type copy numbers and large DNA fragments (> 420 bp). Using different preservatives did not affect somatic variant detection ability, but did stabilize cfDNA concentrations over time. Variant allele frequency was affected by fluctuations in cfDNA concentration only in EDTA tubes at 96 h. Both CellSave and BCT tubes ensured optimal ctDNA quality in plasma processed within 96 h after blood collection for downstream somatic variant detection by dPCR. The effects of preanalytical conditions on the quality of circulating tumor DNA (ctDNA) were investigated using blood samples from patients with metastatic cancer. There was a time‐dependent increase in cell‐free DNA (cfDNA) in EDTA tubes, which decreased detection of somatic variant allele frequencies (VAF). Both CellSave and BCT tubes ensure optimal ctDNA quality for up to 96 h.

Genome-wide measures of gene expression can identify patterns of gene activity that subclassify tumours and might provide a better means than is currently available for individual risk assessment in patients with lymph-node-negative breast cancer. We analysed, with Affymetrix Human U133a GeneChips, the expression of 22 000 transcripts from total RNA of frozen tumour samples from 286 lymph-node-negative patients who had not received adjuvant systemic treatment. In a training set of 115 tumours, we identified a 76-gene signature consisting of 60 genes for patients positive for oestrogen receptors (ER) and 16 genes for ER-negative patients. This signature showed 93% sensitivity and 48% specificity in a subsequent independent testing set of 171 lymph-node-negative patients. The gene profile was highly informative in identifying patients who developed distant metastases within 5 years (hazard ratio 5·67 [95% CI 2·59–12·4]), even when corrected for traditional prognostic factors in multivariate analysis (5·55 [2·46–12·5]). The 76-gene profile also represented a strong prognostic factor for the development of metastasis in the subgroups of 84 premenopausal patients (9·60 [2·28–40·5]), 87 postmenopausal patients (4·04 [1·57–10·4]), and 79 patients with tumours of 10–20 mm (14·1 [3·34–59·2]), a group of patients for whom prediction of prognosis is especially difficult. The identified signature provides a powerful tool for identification of patients at high risk of distant recurrence. The ability to identify patients who have a favourable prognosis could, after independent confirmation, allow clinicians to avoid adjuvant systemic therapy or to choose less aggressive therapeutic options.

In this study, we quantified 249 mature micro-RNA (miRNA) transcripts in estrogen receptor-positive (ER⁺) primary breast tumors of patients with lymph node-negative (LNN) disease to identify miRNAs associated with metastatic capability. In addition, the prognostic value of the candidate miRNAs was determined in ER⁻/LNN breast cancer. Unsupervised analysis in a prescreening set of 38 patients identified three subgroups predominantly driven by three miRNA signatures: an ER-driven luminal B-associated miRNA signature, a stromal miRNA signature, and an overexpressed miRNA cluster located on chromosome 19q23, but these intrinsic miRNA signatures were not associated with tumor aggressiveness. Supervised analysis in the initial subset and subsequent analysis in additional tumors significantly linked four miRNAs (miR-7, miR-128a, miR-210, and miR-516-3p) to ER⁺/LNN breast cancer aggressiveness (n = 147) and one miRNA (miR-210) to metastatic capability in ER⁻/LNN breast cancer (n = 114) and in the clinically important triple-negative subgroup (n = 69) (all P < 0.05). Bioinformatic analysis coupled miR-210 to hypoxia/VEGF signaling, miR-7 and miR-516-3p to cell cycle progression and chromosomal instability, and miR-128a to cytokine signaling. In conclusion, our work connects four miRNAs to breast cancer progression and to several distinct biological processes involved therein.

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of “good” performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of “good” performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of “good” performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies.

Meta-analysis of individual patient data (IPD) is the “gold-standard” for synthesizing evidence across several studies. Some studies, however, may only provide aggregate data (AD). In this situation researchers might need to combine IPD with AD to utilize all the evidence available. Here, we review applied IPD meta-analysis articles to assess if and how AD is combined with IPD in practice. A systematic review of articles identified from bibliographic databases and searches. We identified 33 applied IPD articles that combined IPD and AD and 166 that did not. For each article, we recorded the proportion of total studies providing IPD, and found that articles combining IPD and AD had, on average, IPD available in only 64% of studies (compared to 90% in articles not combining IPD and AD). Two different methods were used to combine IPD and AD, the two-stage method and analysis of partially reconstructed IPD, but a review of methodological articles identified two further methods, multilevel modeling and Bayesian hierarchical related regression. We summarize each method to aid practitioners. Combining IPD and AD is a relevant issue for evidence synthesis, and the further development and validation of suitable meta-analysis methods is needed.

MicroRNAs (miRNAs) are small RNA molecules that modulate gene expression and which have been implicated in cancer. We evaluated whether five candidate predictive miRNAs, derived from a pilot study in which 249 miRNAs were assayed, were associated with clinical benefit of tamoxifen therapy in advanced breast cancer. These five miRNAs were measured in an independent series of 246 estrogen receptor (ER)-positive primary breast tumors of patients who received tamoxifen for advanced disease by quantitative Real Time PCR. Univariate analysis showed that higher expression levels of hsa-miR-30a-3p, hsa-miR-30c, and hsa-miR-182 were significantly associated with benefit of tamoxifen treatment and with longer PFS (all P -values <0.01). In multivariate analysis, corrected for the traditional predictive factors, only hsa-miRNA-30c was an independent predictor ( P -value <0.01). Finally, in an attempt to understand the biology connected to this miRNA, Global testing pathway analysis showed an association of hsa-miRNA-30c expression with HER and RAC1 signaling pathways. We identified hsa-miRNA-30c as an independent predictor for clinical benefit of tamoxifen therapy in patients with advanced breast cancer. Assessment of tumor levels and connected pathways could be helpful to improve treatment strategies.

In a previous study, we detected a significant association between phosphoserine aminotransferase 1 (PSAT1) hyper-methylation and mRNA levels to outcome to tamoxifen treatment in recurrent disease. We here aimed to study the association of PSAT1 protein levels to outcome upon tamoxifen treatment and to obtain more insight in its role in tamoxifen resistance. A cohort of ER positive, hormonal therapy naïve primary breast carcinomas was immunohistochemically (IHC) stained for PSAT1. Staining was analyzed for association with patient’s time to progression (TTP) and overall response on first-line tamoxifen for recurrent disease. PSAT1 mRNA levels were also assessed by reverse transcriptase quantitative polymerase chain reaction (RT-qPCR; n = 161) and Affymetrix GeneChip (n = 155). Association of PSAT1 to biological pathways on tamoxifen outcome were assessed by global test. PSAT1 protein and mRNA levels were significantly associated to poor outcome to tamoxifen treatment. When comparing PSAT1 protein and mRNA levels, IHC and RT-qPCR data showed a significant association. Global test results showed that cytokine and JAK-STAT signaling were associated to PSAT1 expression. We hereby report that PSAT1 protein and mRNA levels measured in ER positive primary tumors are associated with poor clinical outcome to tamoxifen.

Increased APOBEC3B mRNA levels are associated with a hypermutator phenotype and poor prognosis in ER-positive breast cancer patients. In addition, a 29.5 kb deletion polymorphism of APOBEC3B, resulting in an APOBEC3A-B hybrid transcript, has been associated with an increased breast cancer risk and the hypermutator phenotype. Here we evaluated whether the APOBEC3B deletion polymorphism also associates with clinical outcome of breast cancer. Copy number analysis was performed by quantitative PCR (qPCR) in primary tumors of 1,756 Dutch breast cancer patients. The APOBEC3B deletion was found in 187 patients of whom 16 carried a two-copy deletion and 171 carried a one-copy deletion. The prognostic value of the APOBEC3B deletion for the natural course of the disease was evaluated among 1,076 lymph-node negative (LNN) patients who did not receive adjuvant systemic treatment. No association was found between APOBEC3B copy number values and the length of metastasis-free survival (MFS; hazard ratio (HR) = 1.00, 95% confidence interval (CI) = 0.90-1.11, P = 0.96). Subgroup analysis by ER status also did not reveal an association between APOBEC3B copy number values and the length of MFS. The predictive value of the APOBEC3B deletion was assessed among 329 ER-positive breast cancer patients who received tamoxifen as the first-line therapy for recurrent disease and 226 breast cancer patients who received first-line chemotherapy for recurrent disease. No association between APOBEC3B copy number values and the overall response rate (ORR) to either tamoxifen (odds ratio (OR) = 0.88, 95% CI = 0.69-1.13, P = 0.31) or chemotherapy (OR = 0.97, 95% CI = 0.71-1.33, P = 0.87) was found. Thus, in contrast to APOBEC3B mRNA levels, the APOBEC3B deletion polymorphism has neither a prognostic nor a predictive value for breast cancer patients. Although a correlation exists between APOBEC3B copy number and mRNA expression, it is relatively weak. This suggests that other mechanisms exist that may affect and therefore determine the prognostic value of APOBEC3B mRNA levels.

PIK3CA is the most frequent somatic mutated oncogene in estrogen receptor (ER) positive breast cancer. We previously observed an association between PIK3CA genotype and aromatase inhibitors (AI) treatment outcome. This study now evaluates whether expression of mRNAs and miRs are linked to PIK3CA genotype and are independently related to AI therapy response in order to define potential expressed biomarkers for treatment outcome. The miR and mRNA expression levels were evaluated for their relationship with the PIK3CA genotype in two breast tumor datasets, i.e. 286 luminal cancers from the TCGA consortium and our set of 84 ER positive primary tumors of metastatic breast cancer patients who received first line AI. BRB Array tools class comparison was performed to define miRs and mRNAs whose expression associate with PIK3CA exon 9 and 20 status. Spearman correlations established miR–mRNA pairs and mRNAs with related expression. Next, a third dataset of 25 breast cancer patients receiving neo-adjuvant letrozole was evaluated, to compare expression levels of identified miRs and mRNAs in biopsies before and after treatment. Finally, to identify potential biomarkers miR and mRNA levels were related with overall survival (OS) and progression free survival (PFS) after first-line AI therapy. Expression of 3 miRs (miR-449a, miR-205-5p, miR-301a-3p) and 9 mRNAs (CCNO, FAM81B, LRG1, NEK10, PLCL1, PGR, SERPINA3, SORBS2, VTCN1) was related to the PIK3CA status in both datasets. All except miR-301a-3p had an increased expression in tumors with PIK3CA mutations. Validation in a publicly available dataset showed that LRG1, PGR, and SERPINA3 levels were decreased after neo-adjuvant AI-treatment. Six miR–mRNA pairs correlated significantly and stepdown analysis of all 12 factors revealed 3 mRNAs (PLCL1, LRG1, FAM81B) related to PFS. Further analyses showed LRG1 and PLCL1 expression to be unrelated with luminal subtype and to associate with OS and with PFS, the latter independent from traditional predictive factors. We showed in two datasets of ER positive and luminal breast tumors that the expression of 3 miRs and 9 mRNAs associate with the PIK3CA status. Expression of LRG1 is independent of luminal (A or B) subtype, decreased after neo-adjuvant AI-treatment, and is proposed as potential biomarker for AI therapy outcome. •Expression of 9 mRNAs and 3 miRs relates to PIK3CA genotype in 2 breast cancer cohorts.•All 9 mRNAs and 2 miRs were upregulated in tumors with PIK3CA mutations.•LRG1 and PLCL1 mRNA levels relate to PIK3CA status irrespective luminal subtype.•LRG1 and PLCL1 mRNA levels associate with aromatase inhibitor therapy outcome.•LRG1 expression is decreased after neo-adjuvant letrozole treatment.

The gastrin releasing peptide receptor (GRPR) and the somatostatin receptor 2 (SSTR2) are overexpressed on primary breast cancer (BC), making them ideal candidates for receptor-mediated nuclear imaging and therapy. The aim of this study was to determine whether these receptors are also suitable targets for metastatic BC. mRNA expression of human BC samples were studied by in vitro autoradiography and associated with radioligand binding. Next, GRPR and SSTR2 mRNA levels of 60 paired primary BCs and metastases from different sites were measured by quantitative reverse transcriptase polymerase chain reaction. Receptor mRNA expression levels were associated with clinico-pathological factors and expression levels of primary tumors and corresponding metastases were compared. Binding of GRPR and SSTR radioligands to tumor tissue correlated significantly with receptor mRNA expression. High GRPR and SSTR2 mRNA levels were associated with estrogen receptor (ESR1)-positive tumors (p<0.001 for both receptors). There was no significant difference in GRPR mRNA expression of primary tumors versus paired metastases. Regarding SSTR2 mRNA expression, there was also no significant difference in the majority of cases, apart from liver and ovarian metastases which showed a significantly lower expression compared to the corresponding primary tumors (p = 0.02 and p = 0.03, respectively). Targeting the GRPR and SSTR2 for nuclear imaging and/or treatment has the potential to improve BC care in primary as well as metastatic disease.