NF-κB activation in inflammatory breast cancer is associated with oestrogen receptor downregulation, secondary to EGFR and/or ErbB2 overexpression and MAPK hyperactivation

Activation of NF-κB in inflammatory breast cancer (IBC) is associated with loss of estrogen receptor (ER) expression, indicating a potential crosstalk between NF-κB and ER. In this study, we examined the activation of NF-κB in IBC and non-IBC with respect to ER and EGFR and/or ErbB2 expression and MAPK hyperactivation. A qRT–PCR based ER signature was evaluated in tumours with and without transcriptionally active NF-κB, as well as correlated with the expression of eight NF-κB target genes. Using a combined ER/NF-κB signature, hierarchical clustering was executed. Hyperactivation of MAPK was investigated using a recently described MAPK signature (Creighton et al, 2006), and was linked to tumour phenotype, ER and EGFR and/or ErbB2 overexpression. The expression of most ER-modulated genes was significantly elevated in breast tumours without transcriptionally active NF-κB. In addition, the expression of most ER-modulated genes was significantly anticorrelated with the expression of most NF-κB target genes, indicating an inverse correlation between ER and NF-κB activation. Clustering using the combined ER and NF-κB signature revealed one cluster mainly characterised by low NF-κB target gene expression and a second one with elevated NF-κB target gene expression. The first cluster was mainly characterised by non-IBC specimens and IHC ER+ breast tumours (13 out of 18 and 15 out of 18 respectively), whereas the second cluster was mainly characterised by IBC specimens and IHC ER− breast tumours (12 out of 19 and 15 out of 19 respectively) (Pearson χ2, P<0.0001 and P<0.0001 respectively). Hyperactivation of MAPK was associated with both ER status and tumour phenotype by unsupervised hierarchical clustering using the MAPK signature and was significantly reflected by overexpression of EGFR and/or ErbB2. NF-κB activation is linked to loss of ER expression and activation in IBC and in breast cancer in general. The inverse correlation between NF-κB activation and ER activation is due to EGFR and/or ErbB2 overexpression, resulting in NF-κB activation and ER downregulation

NF-kappa B genes have a major role in Inflammatory Breast Cancer

<p>Abstract</p> <p>Background</p> <p>IBC (Inflammatory Breast cancer) is a rare form of breast cancer with a particular phenotype. New molecular targets are needed to improve the treatment of this rapidly fatal disease. Given the role of NF-κB-related genes in cell proliferation, invasiveness, angiogenesis and inflammation, we postulated that they might be deregulated in IBC.</p> <p>Methods</p> <p>We measured the mRNA expression levels of 60 NF-κB-related genes by using real-time quantitative RT-PCR in a well-defined series of 35 IBCs, by comparison with 22 stage IIB and III non inflammatory breast cancers. Twenty-four distant metastases of breast cancer served as "poor prognosis" breast tumor controls.</p> <p>Results</p> <p>Thirty-five (58%) of the 60 NF-κB-related genes were significantly upregulated in IBC compared with non IBC. The upregulated genes were NF-κB genes (<it>NFKB1</it>, <it>RELA</it>, <it>IKBKG</it>, <it>NFKBIB</it>, <it>NFKB2</it>, <it>REL</it>, <it>CHUK</it>), apoptosis genes (<it>MCL1L</it>, <it>TNFAIP3/A20</it>, <it>GADD45B</it>, <it>FASLG</it>, <it>MCL1S</it>, <it>IER3L</it>, <it>TNFRSF10B/TRAILR2</it>), immune response genes (<it>CD40</it>, <it>CD48</it>, <it>TNFSF11/RANKL</it>, <it>TNFRSF11A/RANK</it>, <it>CCL2/MCP-1</it>, <it>CD40LG</it>, <it>IL15</it>, <it>GBP1</it>), proliferation genes (<it>CCND2</it>, <it>CCND3</it>, <it>CSF1R</it>, <it>CSF1</it>, <it>SOD2</it>), tumor-promoting genes (<it>CXCL12</it>, <it>SELE</it>, <it>TNC</it>, <it>VCAM1</it>, <it>ICAM1</it>, <it>PLAU/UPA</it>) or angiogenesis genes (<it>PTGS2/COX2</it>, <it>CXCL1/GRO1</it>). Only two of these 35 genes (<it>PTGS2/COX2 </it>and <it>CXCL1/GRO1</it>)were also upregulated in breast cancer metastases. We identified a five-gene molecular signature that matched patient outcomes, consisting of <it>IL8 </it>and <it>VEGF </it>plus three NF-κB-unrelated genes that we had previously identified as prognostic markers in the same series of IBC.</p> <p>Conclusion</p> <p>The NF-κB pathway appears to play a major role in IBC, possibly contributing to the unusual phenotype and aggressiveness of this form of breast cancer. Some upregulated NF-κB-related genes might serve as novel therapeutic targets in IBC.</p

Phase-matched multi-layer based polarisation-independent spot-size converter for silicon nanowire

The efficient coupling of optical power from a silicon nanowire (NW) to an optical fibre is challenging for both the quasi-TE and quasi-TM polarisations. Here, we propose a polarisation-independent spot-size converter (PI-SSC) based on phase-matched multi-layer waveguides for efficient coupling between a silicon NW and an optical fibre for both the polarisations. The fabrication process of the proposed PI-SSC is compatible with the complementary metal-oxide-semiconductor (CMOS) process. The optimisation for the proposed PI-SSC is studied by using a numerically efficient algorithm, combining a rigorous H-field based full-vectorial finite element method (VFEM) and the least squares boundary residual (LSBR) method. The simulation results show that using an eleven-layer based PI-SSC, the coupling losses between a silicon NW and a lensed fibre of radius 2 μm can be reduced to only 0.34 dB and 0.25 dB for the quasi-TE and quasi-TM polarisations, respectively. Furthermore, the output multi-layer is horizontally tapered, which further reduces the coupling loss for both the polarisations and the end face is easy to be polished

Core module biomarker identification with network exploration for breast cancer metastasis

<p>Abstract</p> <p>Background</p> <p>In a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i.e. "driver" genes), while others represent closely-related first-degree neighbours in gene interaction space. The remaining genes consist of further removed "passenger" genes, which are often not directly related to the original cause of the disease. For prognostic and diagnostic purposes, it is crucial to be able to separate the group of "driver" genes and their first-degree neighbours, (i.e. "core module") from the general "disease module".</p> <p>Results</p> <p>We have developed COMBINER: COre Module Biomarker Identification with Network ExploRation. COMBINER is a novel pathway-based approach for selecting highly reproducible discriminative biomarkers. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. COMBINER-derived biomarkers exhibited 10-fold higher reproducibility than other methods, with up to 30-fold greater enrichment for known cancer-related genes, and 4-fold enrichment for known breast cancer susceptible genes. More than 50% and 40% of the resulting biomarkers were cancer and breast cancer specific, respectively. The identified modules were overlaid onto a map of intracellular pathways that comprehensively highlighted the hallmarks of cancer. Furthermore, we constructed a global regulatory network intertwining several functional clusters and uncovered 13 confident "driver" genes of breast cancer metastasis.</p> <p>Conclusions</p> <p>COMBINER can efficiently and robustly identify disease core module genes and construct their associated regulatory network. In the same way, it is potentially applicable in the characterization of any disease that can be probed with microarrays.</p

TP53 outperforms other androgen receptor biomarkers to predict abiraterone or enzalutamide outcome in metastatic castration-resistant prostate cancer.

PURPOSE: To infer the prognostic value of simultaneous androgen receptor (AR) and TP53 profiling in liquid biopsies from metastatic castration-resistant prostate cancer (mCRPC) patients starting a new line of AR signalling inhibitors (ARSi). EXPERIMENTAL DESIGN: Between March 2014 and April 2017, we recruited mCRPC patients (n=168) prior to ARSi in a cohort study encompassing 10 European centres. Blood samples were collected for comprehensive profiling of CellSearch-enriched circulating tumour cells (CTCs) and circulating tumour DNA (ctDNA). Targeted CTC RNA-seq allowed the detection of eight AR splice variants (ARVs). Low-pass whole-genome and targeted gene-body sequencing of AR and TP53 was applied to identify amplifications, loss-of-heterozygosity, mutations and structural rearrangements in ctDNA. Clinical or radiological progression-free survival (PFS) was estimated by Kaplan-Meier analysis, and independent associations were determined using multivariable Cox-regression models. RESULTS: Overall, no single AR perturbation remained associated with adverse prognosis after multivariable analysis. Instead, tumour burden estimates (CTC counts, ctDNA fraction, and visceral metastases) were significantly associated with PFS. TP53 inactivation harbored independent prognostic value (HR 1.88, 95%CI 1.18-3.00, p = 0.008), and outperformed ARV expression and detection of genomic AR alterations. Using Cox coefficient analysis of clinical parameters and TP53 status, we identified three prognostic groups with differing PFS estimates (median, 14.7 vs 7.51 vs 2.62 months, p < 0.0001), which was validated in an independent mCRPC cohort (n=202) starting first-line ARSi (median, 14.3 vs 6.39 vs 2.23 months, p < 0.0001). CONCLUSIONS: In an all-comer cohort, tumour burden estimates and TP53 outperform any AR perturbation to infer prognosis

FISim: A new similarity measure between transcription factor binding sites based on the fuzzy integral

Background Regulatory motifs describe sets of related transcription factor binding sites (TFBSs) and can be represented as position frequency matrices (PFMs). De novo identification of TFBSs is a crucial problem in computational biology which includes the issue of comparing putative motifs with one another and with motifs that are already known. The relative importance of each nucleotide within a given position in the PFMs should be considered in order to compute PFM similarities. Furthermore, biological data are inherently noisy and imprecise. Fuzzy set theory is particularly suitable for modeling imprecise data, whereas fuzzy integrals are highly appropriate for representing the interaction among different information sources.Results We propose FISim, a new similarity measure between PFMs, based on the fuzzy integral of the distance of the nucleotides with respect to the information content of the positions. Unlike existing methods, FISim is designed to consider the higher contribution of better conserved positions to the binding affinity. FISim provides excellent results when dealing with sets of randomly generated motifs, and outperforms the remaining methods when handling real datasets of related motifs. Furthermore, we propose a new cluster methodology based on kernel theory together with FISim to obtain groups of related motifs potentially bound by the same TFs, providing more robust results than existing approaches.Conclusion FISim corrects a design flaw of the most popular methods, whose measures favour similarity of low information content positions. We use our measure to successfully identify motifs that describe binding sites for the same TF and to solve real-life problems. In this study the reliability of fuzzy technology for motif comparison tasks is proven.This work has been carried out as part of projects P08-TIC-4299 of J. A., Sevilla and TIN2006-13177 of DGICT, Madrid

Markers of subtypes in inflammatory breast cancer studied by immunohistochemistry: Prominent expression of P-cadherin

<p>Abstract</p> <p>Background</p> <p>Inflammatory breast cancer (IBC) is a distinct and aggressive form of locally-advanced breast cancer with high metastatic potential. In Tunisia, IBC is associated with a high death rate. Among the major molecular subtypes, basal breast carcinomas are poorly differentiated, have metastatic potential and poor prognosis, but respond relatively well to chemotherapy. The aim of this study was to determine the distribution of molecular subtypes in IBC and identify factors that may explain the poor prognosis of IBC.</p> <p>Methods</p> <p>To determine breast cancer subtypes we studied by immunohistochemistry the expression of 12 proteins in a series of 91 Tunisian IBC and 541 non-IBC deposited in tissue microarrays.</p> <p>Results</p> <p>We considered infiltrating ductal cases only. We found 33.8% of basal cases in IBC vs 15.9% in non-IBC (p < 0.001), 33.3% of ERBB2-overexpressing cases in IBC vs 14.5% in non-IBC (p < 0.001), and 29.3% of luminal cases in IBC vs 59.9% in non-IBC (p < 0.001). The most differentially-expressed protein between IBCs and non-IBCs was P-cadherin. P-cadherin expression was found in 75.9% of all IBC vs 48.2% of all non-IBC (p < 0.001), 95% of IBC vs 69% of non-IBC (p = 0.02) in basal cases, and 82% of IBC vs 43% of non-IBC (p < 0.001) in luminal cases. Logistic regression determined that the most discriminating markers between IBCs and non-IBCs were P-cadherin (OR = 4.9, p = 0.0019) MIB1 (OR = 3.6, p = 0.001), CK14 (OR = 2.7, p = 0.02), and ERBB2 (OR = 2.3, p = 0.06).</p> <p>Conclusion</p> <p>Tunisian IBCs are characterized by frequent basal and ERBB2 phenotypes. Surprisingly, luminal IBC also express the basal marker P-cadherin. This profile suggests a specificity that needs further investigation.</p