116 research outputs found
Importance of pre-analytical steps for transcriptome and RT-qPCR analyses in the context of the phase II randomised multicentre trial REMAGUS02 of neoadjuvant chemotherapy in breast cancer patients
<p>Abstract</p> <p>Background</p> <p>Identification of predictive markers of response to treatment is a major objective in breast cancer. A major problem in clinical sampling is the variability of RNA templates, requiring accurate management of tumour material and subsequent analyses for future translation in clinical practice. Our aim was to establish the feasibility and reliability of high throughput RNA analysis in a prospective trial.</p> <p>Methods</p> <p>This study was conducted on RNA from initial biopsies, in a prospective trial of neoadjuvant chemotherapy in 327 patients with inoperable breast cancer. Four independent centres included patients and samples. Human U133 GeneChips plus 2.0 arrays for transcriptome analysis and quantitative RT-qPCR of 45 target genes and 6 reference genes were analysed on total RNA.</p> <p>Results</p> <p>Thirty seven samples were excluded because <it>i) </it>they contained less than 30% malignant cells, or <it>ii) </it>they provided RNA Integrity Number (RIN) of poor quality. Among the 290 remaining cases, taking into account strict quality control criteria initially defined to ensure good quality of sampling, 78% and 82% samples were eligible for transcriptome and RT-qPCR analyses, respectively. For RT-qPCR, efficiency was corrected by using standard curves for each gene and each plate. It was greater than 90% for all genes. Clustering analysis highlighted relevant breast cancer phenotypes for both techniques (ER+, PR+, HER2+, triple negative). Interestingly, clustering on trancriptome data also demonstrated a "centre effect", probably due to the sampling or extraction methods used in on of the centres. Conversely, the calibration of RT-qPCR analysis led to the centre effect withdrawing, allowing multicentre analysis of gene transcripts with high accuracy.</p> <p>Conclusions</p> <p>Our data showed that strict quality criteria for RNA integrity assessment and well calibrated and standardized RT-qPCR allows multicentre analysis of genes transcripts with high accuracy in the clinical context. More stringent criteria are needed for transcriptome analysis for clinical applications.</p
Establishment and characterisation of a new breast cancer xenograft obtained from a woman carrying a germline BRCA2 mutation
Effects of anharmonic strain on phase stability of epitaxial films and superlattices: applications to noble metals
Epitaxial strain energies of epitaxial films and bulk superlattices are
studied via first-principles total energy calculations using the local-density
approximation. Anharmonic effects due to large lattice mismatch, beyond the
reach of the harmonic elasticity theory, are found to be very important in
Cu/Au (lattice mismatch 12%), Cu/Ag (12%) and Ni/Au (15%). We find that
is the elastically soft direction for biaxial expansion of Cu and Ni, but it is
for large biaxial compression of Cu, Ag, and Au. The stability of
superlattices is discussed in terms of the coherency strain and interfacial
energies. We find that in phase-separating systems such as Cu-Ag the
superlattice formation energies decrease with superlattice period, and the
interfacial energy is positive. Superlattices are formed easiest on (001) and
hardest on (111) substrates. For ordering systems, such as Cu-Au and Ag-Au, the
formation energy of superlattices increases with period, and interfacial
energies are negative. These superlattices are formed easiest on (001) or (110)
and hardest on (111) substrates. For Ni-Au we find a hybrid behavior:
superlattices along and like in phase-separating systems, while for
they behave like in ordering systems. Finally, recent experimental
results on epitaxial stabilization of disordered Ni-Au and Cu-Ag alloys,
immiscible in the bulk form, are explained in terms of destabilization of the
phase separated state due to lattice mismatch between the substrate and
constituents.Comment: RevTeX galley format, 16 pages, includes 9 EPS figures, to appear in
Physical Review
Tumor aromatase expression as a prognostic factor for local control in young breast cancer patients after breast-conserving treatment
Optimisation of the RT-PCR detection of immunomagnetically enriched carcinoma cells
BACKGROUND: Immunomagnetic enrichment followed by RT-PCR (immunobead RT-PCR) is an efficient methodology to identify disseminated carcinoma cells in the blood and bone marrow. The RT-PCR assays must be both specific for the tumor cells and sufficiently sensitive to enable detection of single tumor cells. We have developed a method to test RT-PCR assays for any cancer. This has been investigated using a panel of RT-PCR markers suitable for the detection of breast cancer cells. METHODS: In the assay, a single cell line-derived tumor cell is added to 100 peripheral blood mononuclear cells (PBMNCs) after which mRNA is isolated and reverse transcribed for RT-PCR analysis. PBMNCs without added tumor cells are used as specificity controls. The previously studied markers epidermal growth factor receptor (EGFR), mammaglobin 1 (MGB1), epithelial cell adhesion molecule (EpCAM/TACSTD1), mucin 1 (MUC1), carcinoembryonic antigen (CEA) were tested. Two new epithelial-specific markers ELF3 and EphB4 were also tested. RESULTS: MUC1 was unsuitable as strong amplification was detected in 100 cell PBMNC controls. Expression of ELF3, EphB4, EpCAM, EGFR, CEA and MGB1 was found to be both specific for the tumor cell, as demonstrated by the absence of a signal in most 100 cell PBMNC controls, and sensitive enough to detect a single tumor cell in 100 PBMNCs using a single round of RT-PCR. CONCLUSIONS: ELF3, EphB4, EpCAM, EGFR, CEA and MGB1 are appropriate RT-PCR markers for use in a marker panel to detect disseminated breast cancer cells after immunomagnetic enrichment
TFRC and ACTB as the best reference genes to quantify Urokinase Plasminogen Activator in breast cancer
Association of high risk human papillomavirus and breast cancer : a UK based study
Infection by human papillomaviruses (HPVs) has been implicated in the aetiology of a variety of cancers. Studies evaluating the presence of HPVs in breast cancer (BC) have generated considerable controversy. To date, most studies have focused on the presence of viral DNA in BC; however there are important gaps in evidencing the role of HPV persistence in the invasiveness of BC. While these studies have been conducted in several countries, none, on the presence and biological activity of high risk (HR) HPV in BC has been done in the UK. Hence, we aimed to investigate these gaps by screening a total of 110 fresh breast tissue specimens from UK patients for the presence of twelve HR-HPV types DNA using PCR and Sanger sequencing. Samples positive for HPV-DNA were screened for viral oncoprotein expression using western blot and dot blot. Data obtained showed the presence of HR-HPVs in 42% of breast tissues of which the viral activity was only confirmed in a number of invasive carcinomas (5/26). This finding, the first to report in the UK, suggests that the selective expression of viral oncoprotein in invasive cases may propose a role for HR-HPVs in the development of some types of BC
Impact of the spotted microarray preprocessing method on fold-change compression and variance stability
<p>Abstract</p> <p>Background</p> <p>The standard approach for preprocessing spotted microarray data is to subtract the local background intensity from the spot foreground intensity, to perform a log2 transformation and to normalize the data with a global median or a lowess normalization. Although well motivated, standard approaches for background correction and for transformation have been widely criticized because they produce high variance at low intensities. Whereas various alternatives to the standard background correction methods and to log2 transformation were proposed, impacts of both successive preprocessing steps were not compared in an objective way.</p> <p>Results</p> <p>In this study, we assessed the impact of eight preprocessing methods combining four background correction methods and two transformations (the log2 and the glog), by using data from the MAQC study. The current results indicate that most preprocessing methods produce fold-change compression at low intensities. Fold-change compression was minimized using the Standard and the Edwards background correction methods coupled with a log2 transformation. The drawback of both methods is a high variance at low intensities which consequently produced poor estimations of the p-values. On the other hand, effective stabilization of the variance as well as better estimations of the p-values were observed after the glog transformation.</p> <p>Conclusion</p> <p>As both fold-change magnitudes and p-values are important in the context of microarray class comparison studies, we therefore recommend to combine the Edwards correction with a hybrid transformation method that uses the log2 transformation to estimate fold-change magnitudes and the glog transformation to estimate p-values.</p
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