Individual participant data meta-analysis for external validation, recalibration, and updating of a flexible parametric prognostic model.

Individual participant data (IPD) from multiple sources allows external validation of a prognostic model across multiple populations. Often this reveals poor calibration, potentially causing poor predictive performance in some populations. However, rather than discarding the model outright, it may be possible to modify the model to improve performance using recalibration techniques. We use IPD meta-analysis to identify the simplest method to achieve good model performance. We examine four options for recalibrating an existing time-to-event model across multiple populations: (i) shifting the baseline hazard by a constant, (ii) re-estimating the shape of the baseline hazard, (iii) adjusting the prognostic index as a whole, and (iv) adjusting individual predictor effects. For each strategy, IPD meta-analysis examines (heterogeneity in) model performance across populations. Additionally, the probability of achieving good performance in a new population can be calculated allowing ranking of recalibration methods. In an applied example, IPD meta-analysis reveals that the existing model had poor calibration in some populations, and large heterogeneity across populations. However, re-estimation of the intercept substantially improved the expected calibration in new populations, and reduced between-population heterogeneity. Comparing recalibration strategies showed that re-estimating both the magnitude and shape of the baseline hazard gave the highest predicted probability of good performance in a new population. In conclusion, IPD meta-analysis allows a prognostic model to be externally validated in multiple settings, and enables recalibration strategies to be compared and ranked to decide on the least aggressive recalibration strategy to achieve acceptable external model performance without discarding existing model information.

IGF1R signaling drives antiestrogen resistance through PAK2/PIX activation in luminal breast cancer.

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/ß as downstream targets of IGF1R signaling, which are independent from PI3K/ATK and MAPK/ERK pathways. PAK2 and PIXα/ß modulate IGF1R signaling-driven cell scattering. Targeting PIXα/ß 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.

Phosphoserine aminotransferase 1 is associated to poor outcome on tamoxifen therapy in recurrent breast cancer.

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.

Application of circulating tumor DNA in prospective clinical oncology trials - standardization of preanalytical conditions.

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 ß-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.

Prospects of Targeting the Gastrin Releasing Peptide Receptor and Somatostatin Receptor 2 for Nuclear Imaging and Therapy in Metastatic Breast Cancer.

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.

The 29.5 kb APOBEC3B Deletion Polymorphism Is Not Associated with Clinical Outcome of Breast Cancer.

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.

Prognostic significance of nuclear expression of UMP-CMP kinase in triple negative breast cancer patients.

We have previously identified UMP-CMP kinase (CMPK1) as a prognostic marker for triple negative breast cancer (TNBC) by mass spectrometry (MS). In this study we evaluated CMPK1 association to prognosis in an independent set of samples by immunohistochemistry (IHC) and assessed biological pathways associated to its expression through gene set enrichment analysis (GSEA). A total of 461 TNBC paraffin-embedded tissues were collected from different academic hospitals in Europe, incorporated into tissue micro-arrays (TMA), and stained for CMPK1 expression. We also collected gene expression data of 60 samples, which were also present in the TMA, for GSEA correlation analysis. CMPK1 IHC staining showed both cytoplasmic and nuclear components. While cytoplasmic CMPK1 did not show any association to metastasis free survival (MFS), nuclear CMPK1 was associated to poor prognosis independently from other prognostic factors in stratified Cox regression analyses. GSEA correlation analysis of the nuclear CMPK1-stratified gene expression dataset showed a significant enrichment of extracellular matrix (ECM; positive correlation) and cell cycle (negative correlation) associated genes. We have shown here that nuclear CMPK1 is indicative of poor prognosis in TNBCs and that its expression may be related to dysregulation of ECM and cell cycle molecules.

LRG1 mRNA expression in breast cancer associates with PIK3CA genotype and with aromatase inhibitor therapy outcome.

BACKGROUND: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Opioid treatment failure in cancer patients: the role of clinical and genetic factors.

AIM: To identify clinical and genetic factors associated with outcome of opioid treatment. PATIENTS & METHODS: We performed an exploratory analysis in a cohort of 353 patients treated with fentanyl, morphine, oxycodone and/or hydromorphone for cancer-related pain, exploring selected clinical and pharmacogenetic factors for a correlation with treatment failure for all and per type of opioid. RESULTS: Use of adjuvant pain medication, intensity of pain at rest and age were associated with treatment failure in the various cohorts. Only the genetic variants rs12948783 (RHBDF2) and rs7016778 (OPRK1) correlated statistically significant in univariate, but not in multivariable analysis. CONCLUSION: Several clinical and genetic factors were identified that warrant further study to clarify their role and use in opioid treatment.

SIAH2 protein expression in breast cancer is inversely related with ER status and outcome to tamoxifen therapy.

Our previous study demonstrated that high mRNA levels for Seven in Absentia Homolog 2 (SIAH2) correlated with high Estrogen Receptor (ER) mRNA levels and with longer progression-free survival (PFS) after first-line tamoxifen. Others showed high SIAH2 protein levels in ER-negative breast cancer associated with an unfavorable relapse-free survival. In the current study, we investigated SIAH2 protein expression to clarify the discrepancy between protein and mRNA findings and to determine its diagnostic value in breast cancer patients. Tissue microarrays (TMAs) containing core specimens of primary breast tumors were immunohistochemically stained for SIAH2 protein. The TMAs analyzed a cohort of 746 patients with primary breast cancer (PBC) and a cohort of 245 patients with ER-positive metastatic breast cancer (MBC) treated with first-line tamoxifen. SIAH2 staining was scored for intensity and proportion of positive tumor cells and evaluated for its relationship with metastasis-free survival (MFS) and PFS. Multivariate survival analyses included traditional prognostic or predictive factors, respectively. The PBC-cohort had 263 patients with high SIAH2 protein expression and decreased expression of ER protein and mRNA levels (P = 0.005 and P = 0.003, respectively). High SIAH2 levels correlated with significant unfavorable MFS in lymph node negative, ER-positive breast cancer patients. The MBC-cohort had 86 patients with increased SIAH2 protein expression. High SIAH2 expression was associated with an unfavorable PFS after first-line tamoxifen in multivariate analyses (HR = 1.45; 95% CI, 1.07-1.96; P = 0.015). In conclusion, SIAH2 protein expression is especially observed in ER-negative tumors. Its prognostic value in breast cancer does not add to current prognostic markers. The proportion of SIAH2-positive cells can be used as biomarker to predict tamoxifen treatment failure in MBC patients.

Mitochondrial DNA content in breast cancer: Impact on in vitro and in vivo phenotype and patient prognosis.

Reduced mitochondrial DNA (mtDNA) content in breast cancer cell lines has been associated with transition towards a mesenchymal phenotype, but its clinical consequences concerning breast cancer dissemination remain unidentified. Here, we aimed to clarify the link between mtDNA content and a mesenchymal phenotype and its relation to prognosis of breast cancer patients. We analyzed mtDNA content in 42 breast cancer cell lines and 207 primary breast tumor specimens using a combination of quantitative PCR and array-based copy number analysis. By associating mtDNA content with expression levels of genes involved in epithelial-to-mesenchymal transition (EMT) and with the intrinsic breast cancer subtypes, we could not identify a relation between low mtDNA content and mesenchymal properties in the breast cancer cell lines or in the primary breast tumors. In addition, we explored the relation between mtDNA content and prognosis in our cohort of primary breast tumor specimens that originated from patients with lymph node-negative disease who did not receive any (neo)adjuvant systemic therapy. When patients were divided based on the tumor quartile levels of mtDNA content, those in the lowest quarter (≤ 350 mtDNA molecules per cell) showed a poorer 10-year distant metastasis-free survival than patients with > 350 mtDNA molecules per cell (HR 0.50 [95% CI 0.29-0.87], P = 0.015). The poor prognosis was independent of established clinicopathological markers (HR 0.54 [95% CI 0.30-0.97], P = 0.038). We conclude that, despite a lack of evidence between mtDNA content and EMT, low mtDNA content might provide meaningful prognostic value for distant metastasis in breast cancer.

GATA3 mRNA expression, but not mutation, associates with longer progression-free survival in ER-positive breast cancer patients treated with first-line tamoxifen for recurrent disease.

In breast cancer, GATA3 mutations have been associated with a favorable prognosis and the response to neoadjuvant aromatase inhibitor treatment. Therefore, we investigated whether GATA3 mutations predict the outcome of tamoxifen treatment in the advanced setting. In a retrospective study consisting of 235 hormone-naive patients with ER-positive breast cancer who received tamoxifen as first-line treatment for recurrent disease, GATA3 mutations (in 14.0% of patients) did not significantly associate with either the overall response rate (ORR) or with the length of progression-free survival (PFS) after the start of tamoxifen therapy. Interestingly, among 148 patients for whom both mutation and mRNA expression data were available, GATA3 mutations associated with an increased expression of GATA3. However, only 23.7% of GATA3 high tumors had a mutation. Evaluation of the clinical significance of GATA3 mRNA revealed that it was associated with prolonged PFS, but not with the ORR, also in multivariate analysis. Thus, GATA3 mRNA expression, but not GATA3 mutation, is an independent predictor of prolonged PFS in ER-positive breast cancer patients who received first-line tamoxifen for recurrent disease. Besides GATA3 mutation, other mechanisms must exist that underlie increased GATA3 levels.

Annexin-A1 and caldesmon are associated with resistance to tamoxifen in estrogen receptor positive recurrent breast cancer.

Tamoxifen therapy resistance constitutes a major cause of death in patients with recurrent estrogen receptor (ER) positive breast cancer. Through high resolution mass spectrometry (MS), we previously generated a 4-protein predictive signature for tamoxifen therapy outcome in recurrent breast cancer. ANXA1 and CALD1, which were not included in the classifier, were however the most differentially expressed proteins. We first evaluated the clinical relevance of these markers in our MS cohort, followed by immunohistochemical (IHC) staining on an independent set of tumors incorporated in a tissue microarray (TMA) and regression analysis in relation to time to progression (TTP), clinical benefit and objective response. In order to assess which mechanisms ANXA1 and CALD1 might been involved in, we performed Ingenuity pathway analysis (IPA) on ANXA1 and CALD1 correlated proteins in our MS cohort. ANXA1 (Hazard ratio [HR] = 1.83; 95% confidence interval [CI]: 1.22-2.75; P = 0.003) and CALD1 (HR = 1.57; 95% CI: 1.04-2.36; P = 0.039) based patient stratification showed significant association to TTP, while IHC staining on TMA showed that both ANXA1 (HR = 1.82; 95% CI: 1.12-3.00; P = 0.016) and CALD1 (HR = 2.29; 95% CI: 1.40-3.75; P = 0.001) expression was associated with shorter TTP independently of traditional predictive factors. Pearson correlation analysis showed that the majority of proteins correlated to ANXA1 also correlated with CALD1. IPA indicated that ANXA1 and CALD1 were associated with ER-downregulation and NFκB signaling. We hereby report that ANXA1 and CALD1 proteins are independent markers for tamoxifen therapy outcome and are associated to fast tumor progression.

4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer.

Estrogen receptor (ER) positive tumors represent the majority of breast malignancies, and are effectively treated with hormonal therapies, such as tamoxifen. However, in the recurrent disease resistance to tamoxifen therapy is common and a major cause of death. In recent years, in-depth proteome analyses have enabled identification of clinically useful biomarkers, particularly, when heterogeneity in complex tumor tissue was reduced using laser capture microdissection (LCM). In the current study, we performed high resolution proteomic analysis on two cohorts of ER positive breast tumors derived from patients who either manifested good or poor outcome to tamoxifen treatment upon recurrence. A total of 112 fresh frozen tumors were collected from multiple medical centers and divided into two sets: an in-house training and a multi-center test set. Epithelial tumor cells were enriched with LCM and analyzed by nano-LC Orbitrap mass spectrometry (MS), which yielded >3000 and >4000 quantified proteins in the training and test sets, respectively. Raw data are available via ProteomeXchange with identifiers PXD000484 and PXD000485. Statistical analysis showed differential abundance of 99 proteins, of which a subset of 4 proteins was selected through a multivariate step-down to develop a predictor for tamoxifen treatment outcome. The 4-protein signature significantly predicted poor outcome patients in the test set, independent of predictive histopathological characteristics (hazard ratio [HR] = 2.17; 95% confidence interval [CI] = 1.15 to 4.17; multivariate Cox regression p value = 0.017). Immunohistochemical (IHC) staining of PDCD4, one of the signature proteins, on an independent set of formalin-fixed paraffin-embedded tumor tissues provided and independent technical validation (HR = 0.72; 95% CI = 0.57 to 0.92; multivariate Cox regression p value = 0.009). We hereby report the first validated protein predictor for tamoxifen treatment outcome in recurrent ER-positive breast cancer. IHC further showed that PDCD4 is an independent marker.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

OBJECTIVES: 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. STUDY DESIGN AND SETTING: 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. RESULTS: 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. CONCLUSION: 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.

Clinical Relevance of Targeting the Gastrin-Releasing Peptide Receptor, Somatostatin Receptor 2, or Chemokine C-X-C Motif Receptor 4 in Breast Cancer for Imaging and Therapy.

UNLABELLED: Imaging and therapy using radioligands targeting receptors overexpressed on tumor cells is successfully applied in neuroendocrine tumor patients. Because expression of the gastrin-releasing peptide receptor (GRPR), somatostatin receptor 2 (SSTR2), and chemokine C-X-C motif receptor 4 (CXCR4) has been demonstrated in breast cancer, targeting these receptors using radioligands might offer new imaging and therapeutic opportunities for breast cancer patients. The aim of this study was to correlate messenger RNA (mRNA) expression of GRPR, SSTR2, and CXCR4 with clinicopathologic and biologic factors, and with prognosis and prediction to therapy response, in order to identify specific breast cancer patient groups suited for the application of radioligands targeting these receptors. METHODS: First, we studied GRPR and SSTR2 expression in 13 clinical breast cancer specimens by in vitro autoradiography and correlated this with corresponding mRNA levels to investigate whether mRNA levels reliably represent cell surface expression. Next, GRPR, SSTR2, and CXCR4 mRNA levels were measured by quantitative reverse transcriptase polymerase chain reaction in 915 primary breast cancer tissues and correlated with known clinicopathologic and biologic factors, disease-free survival, distant metastasis-free survival, and overall survival (DFS, MFS, and OS, respectively). In 224 adjuvant hormonal treatment-naïve estrogen receptor (ER, ESR1)-positive patients who received tamoxifen as first-line therapy for recurrent or metastatic disease, the expression levels of the receptors were correlated with progression-free survival. RESULTS: Our results showed a significant positive correlation between GRPR and SSTR2 expression analyzed by in vitro autoradiography and by quantitative reverse transcriptase polymerase chain reaction (Spearman's rank correlation coefficient [Rs]=0.94, P<0.001, and Rs=0.73, P=0.0042, respectively). Furthermore, high GRPR and SSTR2 mRNA levels were observed more frequently in ESR1-positive specimens, whereas high CXCR4 expression was associated with ESR1-negative specimens. Also, high mRNA expression of CXCR4 was associated with a prolonged DFS, MFS, and OS (multivariate hazard ratio MFS=0.76 [95% confidence interval, 0.64-0.90], P=0.001), whereas high mRNA levels of GRPR were associated with a prolonged progression-free survival after the start of first-line tamoxifen treatment (multivariate hazard ratio=0.68 [95% confidence interval, 0.48-0.97], P=0.031). CONCLUSION: Our data indicate that imaging and therapy using GRPR or SSTR2 radioligands might especially be beneficial for ESR1-positive breast cancer and CXCR4 radioligands for ESR1-negative breast cancer.

Tumor cell migration screen identifies SRPK1 as breast cancer metastasis determinant.

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 ß3-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.

Growth and metastatic behavior of molecularly well-characterized human breast cancer cell lines in mice.

Breast cancer (BC) is a disease with intra- and inter-tumor heterogeneity, and models representing the complete variety of clinical BC phenotypes are not available. We explored the tumor growth potential and metastatic behavior of human BC cell lines and determined whether these cell lines can recapitulate subtype-related biological characteristics of tumors. Eighteen human BC cell lines were implanted under the mammary fat pad of nude mice. Subtype-specific differences in tumor growth, metastatic ability to distant sites, and tumor-related survival of mice were recorded. Eighty-nine percent of the cell lines gave rise to xenografts of which 56 % showed metastasis to distant sites. A clear difference was observed in growth of xenografts from cell lines of different molecular subtypes (P = 0.001; Kruskal-Wallis test). Mice bearing the basal-like and the normal-like xenografts showed poor tumor-related survival (HR: 10.50; P = 0.002 and HR: 9.89; P = 0.003, respectively) compared with those bearing the ERBB2-positive xenografts, which had the longest survival. Subtype-specific metastasis to distant sites between xenografts was not however observed. Comparable to clinical behavior in humans, we observed that the basal-like and the normal-like cell lines grew more aggressively in mice than the cell lines of other molecular subtypes. However, in contrast to clinical findings, we observed no relationships between intrinsic subtype and preferences for site of relapse. Importantly, we have established xenograft models from 16 phenotypically and molecularly diverse human BC cell lines, which can be exploited as useful tools to perform functional studies and screening of interfering drugs.

Elevated APOBEC3B correlates with poor outcomes for estrogen-receptor-positive breast cancers.

Recent observations connected DNA cytosine deaminase APOBEC3B to the genetic evolution of breast cancer. We addressed whether APOBEC3B is associated with breast cancer clinical outcomes. APOBEC3B messenger RNA (mRNA) levels were related in 1,491 primary breast cancers to disease-free (DFS), metastasis-free (MFS), and overall survival (OS). For independent validation, APOBEC3B mRNA expression was associated with patient outcome data in five additional cohorts (over 3,500 breast cancer cases). In univariate Cox regression analysis, increasing APOBEC3B expression as a continuous variable was associated with worse DFS, MFS, and OS (hazard ratio [HR] = 1.20, 1.21, and 1.24, respectively; all P < .001). Also, in untreated ER-positive (ER+), but not in ER-, lymph-node-negative patients, high APOBEC3B levels were associated with a poor DFS (continuous variable: HR = 1.29, P = .001; dichotomized at the median level, HR = 1.66, P = .0002). This implies that APOBEC3B is a marker of pure prognosis in ER + disease. These findings were confirmed in the analyses of five independent patient sets. In these analyses, APOBEC3B expression dichotomized at the median level was associated with adverse outcomes (METABRIC discovery and validation, 788 and 706 ER + cases, disease-specific survival (DSS), HR = 1.77 and HR = 1.77, respectively, both P < .001; Affymetrix dataset, 754 ER + cases, DFS, HR = 1.57, P = 2.46E-04; NKI295, 181 ER + cases, DFS, HR = 1.72, P = .054; and BIG 1-98, 1,219 ER + cases, breast-cancer-free interval (BCFI), HR = 1.42, P = 0.0079). APOBEC3B is a marker of pure prognosis and poor outcomes for ER + breast cancer, which strongly suggests that genetic aberrations induced by APOBEC3B contribute to breast cancer progression.

Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis.

Multilevel mixed effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, and individual participant data (IPD) meta-analyses, to investigate heterogeneity in baseline risk and covariate effects. In this paper, we extend parametric frailty models including the exponential, Weibull and Gompertz proportional hazards (PH) models and the log logistic, log normal, and generalized gamma accelerated failure time models to allow any number of normally distributed random effects. Furthermore, we extend the flexible parametric survival model of Royston and Parmar, modeled on the log-cumulative hazard scale using restricted cubic splines, to include random effects while also allowing for non-PH (time-dependent effects). Maximum likelihood is used to estimate the models utilizing adaptive or nonadaptive Gauss-Hermite quadrature. The methods are evaluated through simulation studies representing clinically plausible scenarios of a multicenter trial and IPD meta-analysis, showing good performance of the estimation method. The flexible parametric mixed effects model is illustrated using a dataset of patients with kidney disease and repeated times to infection and an IPD meta-analysis of prognostic factor studies in patients with breast cancer. User-friendly Stata software is provided to implement the methods.