Once-a-week or every-other-day urethra-sparing prostate cancer stereotactic body radiotherapy, a randomized phase II trial: 18 months follow-up results

Background To present the 18 months results from a prospective multicenter phase II randomized trial of short vs protracted urethra-sparing stereotactic body radiotherapy (SBRT) for localized prostate cancer (PCa).Methods Between 2012 and 2015, a total of 170 PCa patients were randomized to 36.25 Gy in 5 fractions (6.5 Gy x 5 to the urethra) delivered either every other day (EOD, arm A, n = 84) or once a week (QW, arm B, n = 86). Genitourinary (GU) and gastrointestinal (GI) toxicity (CTCAE v4.0 scale), IPSS, and QoL scores were assessed at baseline, at the 5th fraction (5fx), 12th weeks (12W), and every 6 months after SBRT. The primary endpoint was biochemical control at 18 months and grade >= 3 toxicity (including grade >= 2 for urinary obstruction/retention) during the first 3 months.Results Among the 165 patients analyzed, the toxicity stopping rule was never activated during the acute phase. Maximum acute grade 2 GU toxicity rates at 5fx were 17% and 19% for arms A and B, respectively, with only 2 cases of grade 2 GI toxicity at 5fx in arm A. At month 18, grade >= 2 GU and GI toxicity decreased below 5% and 2% for both arms. No changes in EORTC QLQ-PR25 scores for GU, GI, and sexual domains were observed in both arms between baseline and month 18. Four biochemical failures were observed, 2 in each arm, rejecting the null hypothesis of an unfavorable response rate = 95% rate.Conclusions At 18 months, urethra-sparing SBRT showed a low toxicity profile, with minimal impact on QoL and favorable biochemical control rates, regardless of overall treatment time (EOD vs QW)

Fast growth associated with aberrant vasculature and hypoxia in fibroblast growth factor 8b (FGF8b) over-expressing PC-3 prostate tumour xenografts

Background: Prostate tumours are commonly poorly oxygenated which is associated with tumour progression and development of resistance to chemotherapeutic drugs and radiotherapy. Fibroblast growth factor 8b (FGF8b) is a mitogenic and angiogenic factor, which is expressed at an increased level in human prostate tumours and is associated with a poor prognosis. We studied the effect of FGF8b on tumour oxygenation and growth parameters in xenografts in comparison with vascular endothelial growth factor (VEGF)-expressing xenografts, representing another fast growing and angiogenic tumour model. Methods: Subcutaneous tumours of PC-3 cells transfected with FGF8b, VEGF or empty (mock) vectors were produced and studied for vascularity, cell proliferation, glucose metabolism and oxygenation. Tumours were evaluated by immunohistochemistry (IHC), flow cytometry, use of radiolabelled markers of energy metabolism ([F-18] FDG) and hypoxia ([F-18] EF5), and intratumoral polarographic measurements of pO(2). Results: Both FGF8b and VEGF tumours grew rapidly in nude mice and showed highly vascularised morphology. Perfusion studies, pO(2) measurements, [F-18] EF5 and [F-18] FDG uptake as well as IHC staining for glucose transport protein (GLUT1) and hypoxia inducible factor (HIF) 1 showed that VEGF xenografts were well-perfused and oxygenised, as expected, whereas FGF8b tumours were as hypoxic as mock tumours. These results suggest that FGF8b-induced tumour capillaries are defective. Nevertheless, the growth rate of hypoxic FGF8b tumours was highly increased, as that of well-oxygenised VEGF tumours, when compared with hypoxic mock tumour controls. Conclusion: FGF8b is able to induce fast growth in strongly hypoxic tumour microenvironment whereas VEGF-stimulated growth advantage is associated with improved perfusion and oxygenation of prostate tumour xenografts

GOBO: Gene Expression-Based Outcome for Breast Cancer Online

Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo), allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1) rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2) identification of co-expressed genes for creation of potential metagenes, 3) association with outcome for gene expression levels of single genes, sets of genes, or gene signatures in multiple subgroups of the 1881-sample breast cancer data set. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform

Prognostic value of Dicer expression in human breast cancers and association with the mesenchymal phenotype

Background: Dicer, a ribonuclease, is the key enzyme required for the biogenesis of microRNAs and small interfering RNAs and is essential for both mammalian development and cell differentiation. Recent evidence indicates that Dicer may also be involved in tumourigenesis. However, no studies have examined the clinical significance of Dicer at both the RNA and the protein levels in breast cancer.Methods: In this study, the biological and prognostic value of Dicer expression was assessed in breast cancer cell lines, breast cancer progression cellular models, and in two well-characterised sets of breast carcinoma samples obtained from patients with long-term follow-up using tissue microarrays and quantitative reverse transcription-PCR.Results: We have found that Dicer protein expression is significantly associated with hormone receptor status and cancer subtype in breast tumours (ER P=0.008; PR P=0.019; cancer subtype P=0.023, luminal A P=0.0174). Dicer mRNA expression appeared to have an independent prognostic impact in metastatic disease (hazard ratio=3.36, P=0.0032). In the breast cancer cell lines, lower Dicer expression was found in cells harbouring a mesenchymal phenotype and in metastatic bone derivatives of a breast cancer cell line. These findings suggest that the downregulation of Dicer expression may be related to the metastatic spread of tumours.Conclusion: Assessment of Dicer expression may facilitate prediction of distant metastases for patients suffering from breast cancer

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

BACKGROUND: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses. RESULTS: A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics. CONCLUSION: Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power

Biological Convergence of Cancer Signatures

Gene expression profiling has identified cancer prognostic and predictive signatures with superior performance to conventional histopathological or clinical parameters. Consequently, signatures are being incorporated into clinical practice and will soon influence everyday decisions in oncology. However, the slight overlap in the gene identity between signatures for the same cancer type or condition raises questions about their biological and clinical implications. To clarify these issues, better understanding of the molecular properties and possible interactions underlying apparently dissimilar signatures is needed. Here, we evaluated whether the signatures of 24 independent studies are related at the genome, transcriptome or proteome levels. Significant associations were consistently observed across these molecular layers, which suggest the existence of a common cancer cell phenotype. Convergence on cell proliferation and death supports the pivotal involvement of these processes in prognosis, metastasis and treatment response. In addition, functional and molecular associations were identified with the immune response in different cancer types and conditions that complement the contribution of cell proliferation and death. Examination of additional, independent, cancer datasets corroborated our observations. This study proposes a comprehensive strategy for interpreting cancer signatures that reveals common design principles and systems-level properties

Depth Profiling Photoelectron-Spectroscopic Study of an Organic Spin Valve with a Plasma-Modified Pentacene Spacer

[[abstract]]We report an enhanced magnetoresistance (MR) in an organic spin valve with an oxygen plasma-treated pentacene (PC) spacer. The spin valve containing PC without the treatment shows no MR effect, whereas those with moderately plasma-treated PC exhibit MR ratios up to 1.64% at room temperature. X-ray photoelectron spectroscopy with depth profiling is utilized to characterize the interfacial electronic properties of the plasma-treated PC spacer which shows the formation of a derivative oxide layer. The results suggest an alternative approach to improve the interface quality and in turn to enhance the MR performance in organic spin valves.[[incitationindex]]SCI[[booktype]]電子

Microenvironmental IL1 1 β promotes metastatic colonisation of breast cancer cells in the bone via activation of Wnt-dependent cancer stem cell activity

Dissemination of tumour cells to the bone marrow is an early event in breast cancer, however cells may lie dormant for many years before bone metastases develop. Treatment for bone metastases is not curative, therefore new adjuvant therapies which prevent the colonisation of disseminated cells into metastatic lesions are required. There is evidence that cancer stem cells (CSCs) within breast tumours are capable of metastasis, but the mechanism by which these colonise bone is unknown. Here, we establish that bone marrow-derived IL1β stimulates breast cancer cell colonisation in the bone by inducing intracellular NFkB and CREB signalling in breast cancer cells, leading to autocrine Wnt signalling and CSC colony formation. Importantly, we show that inhibition of this pathway prevents both CSC colony formation in the bone environment, and bone metastasis. These findings establish that targeting IL1β-NFKB/CREB-Wnt signalling should be considered for adjuvant therapy to prevent breast cancer bone metastasis

Crosstalk between Chemokine Receptor CXCR4 and Cannabinoid Receptor CB2 in Modulating Breast Cancer Growth and Invasion

Cannabinoids bind to cannabinoid receptors CB(1) and CB(2) and have been reported to possess anti-tumorigenic activity in various cancers. However, the mechanisms through which cannabinoids modulate tumor growth are not well known. In this study, we report that a synthetic non-psychoactive cannabinoid that specifically binds to cannabinoid receptor CB(2) may modulate breast tumor growth and metastasis by inhibiting signaling of the chemokine receptor CXCR4 and its ligand CXCL12. This signaling pathway has been shown to play an important role in regulating breast cancer progression and metastasis.We observed high expression of both CB(2) and CXCR4 receptors in breast cancer patient tissues by immunohistochemical analysis. We further found that CB(2)-specific agonist JWH-015 inhibits the CXCL12-induced chemotaxis and wound healing of MCF7 overexpressing CXCR4 (MCF7/CXCR4), highly metastatic clone of MDA-MB-231 (SCP2) and NT 2.5 cells (derived from MMTV-neu) by using chemotactic and wound healing assays. Elucidation of the molecular mechanisms using various biochemical techniques and confocal microscopy revealed that JWH-015 treatment inhibited CXCL12-induced P44/P42 ERK activation, cytoskeletal focal adhesion and stress fiber formation, which play a critical role in breast cancer invasion and metastasis. In addition, we have shown that JWH-015 significantly inhibits orthotopic tumor growth in syngenic mice in vivo using NT 2.5 cells. Furthermore, our studies have revealed that JWH-015 significantly inhibits phosphorylation of CXCR4 and its downstream signaling in vivo in orthotopic and spontaneous breast cancer MMTV-PyMT mouse model systems.This study provides novel insights into the crosstalk between CB(2) and CXCR4/CXCL12-signaling pathways in the modulation of breast tumor growth and metastasis. Furthermore, these studies indicate that CB(2) receptors could be used for developing innovative therapeutic strategies against breast cancer

Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

<p>Abstract</p> <p>Background</p> <p>Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes.</p> <p>Methods</p> <p>Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach.</p> <p>Results</p> <p>We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population.</p> <p>Conclusions</p> <p>Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.</p