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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.

Tumor cell migration is a key process for cancer cell dissemination and metastasis that is controlled by signal-mediated cytoskeletal and cell matrix adhesion remodeling. Using a phagokinetic track assay with migratory H1299 cells, we performed an siRNA screen of almost 1,500 genes encoding kinases/phosphatases and adhesome- and migration-related proteins to identify genes that affect tumor cell migration speed and persistence. Thirty candidate genes that altered cell migration were validated in live tumor cell migration assays. Eight were associated with metastasis-free survival in breast cancer patients, with integrin β3-binding protein (ITGB3BP), MAP3K8, NIMA-related kinase (NEK2), and SHC-transforming protein 1 (SHC1) being the most predictive. Examination of genes that modulate migration indicated that SRPK1, encoding the splicing factor kinase SRSF protein kinase 1, is relevant to breast cancer outcomes, as it was highly expressed in basal breast cancer. Furthermore, high SRPK1 expression correlated with poor breast cancer disease outcome and preferential metastasis to the lungs and brain. In 2 independent murine models of breast tumor metastasis, stable shRNA-based SRPK1 knockdown suppressed metastasis to distant organs, including lung, liver, and spleen, and inhibited focal adhesion reorganization. Our study provides comprehensive information on the molecular determinants of tumor cell migration and suggests that SRPK1 has potential as a drug target for limiting breast cancer metastasis.

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.

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.

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.

Antiestrogen resistance in estrogen receptor positive (ER + ) breast cancer is associated with increased expression and activity of insulin-like growth factor 1 receptor (IGF1R). Here, a kinome siRNA screen has identified 10 regulators of IGF1R-mediated antiestrogen with clinical significance. These include the tamoxifen resistance suppressors BMPR1B , CDK10 , CDK5 , EIF2AK1 , and MAP2K5 , and the tamoxifen resistance inducers CHEK1 , PAK2 , RPS6KC1 , TTK , and TXK . The p21-activated kinase 2, PAK2 , is the strongest resistance inducer. Silencing of the tamoxifen resistance inducing genes, particularly PAK2 , attenuates IGF1R-mediated resistance to tamoxifen and fulvestrant. High expression of PAK2 in ER + metastatic breast cancer patients is correlated with unfavorable outcome after first-line tamoxifen monotherapy. Phospho-proteomics has defined PAK2 and the PAK-interacting exchange factors PIXα/β as downstream targets of IGF1R signaling, which are independent from PI3K/ATK and MAPK/ERK pathways. PAK2 and PIXα/β modulate IGF1R signaling-driven cell scattering. Targeting PIXα/β entirely mimics the effect of PAK2 silencing on antiestrogen re-sensitization. These data indicate PAK2/PIX as an effector pathway in IGF1R-mediated antiestrogen resistance.