CA 15-3 is predictive of response and disease recurrence following treatment in locally advanced breast cancer

BACKGROUND: Primary chemotherapy (PC) is used for down-staging locally advanced breast cancer (LABC). CA 15-3 measures the protein product of the MUC1 gene and is the most widely used serum marker in breast cancer. METHODS: We retrospectively investigated the role of CA 15-3 in conjunction with other clinico-pathological variables as a predictor of response and time to disease recurrence following treatment in LABC. Pre and post primary chemotherapy serum concentrations of CA 15-3 together with other variables were reviewed and related to four outcomes following primary chemotherapy (clinical response, pathological response, time to recurrence and time to progression). Persistently elevated CA 15-3 after PC was considered as consecutively high levels above the cut off point during and after PC. RESULTS: 73 patients were included in this study. Patients received PC (AC or AC-T regimen) for locally advanced breast cancer. 54 patients underwent surgery. The median follow up was 790 days. Patients with high concentrations of CA 15-3 before PC treatment had a poor clinical (p = 0.013) and pathological (p = 0.044) response. Together with Her-2/neu expression (p = 0.009) and tumour lympho-vascular space invasion (LVI) (p = 0.001), a persistently elevated CA 15-3 post PC (p = 0.007) was an independent predictive factor of recurrence following treatment in LABC. CONCLUSION: Elevated CA 15-3 level is predictive of a poor response to chemotherapy. In addition, persistently elevated CA 15-3 levels post chemotherapy in conjunction with lympho-vascular invasion and HER2 status predict a reduced disease free survival following treatment in locally advanced breast cancer

Quantification of lentiviral vector copy numbers in individual hematopoietic colony-forming cells shows vector dose-dependent effects on the frequency and level of transduction

Lentiviral vectors are effective tools for gene transfer and integrate variable numbers of proviral DNA copies in variable proportions of cells. The levels of transduction of a cellular population may therefore depend upon experimental parameters affecting the frequency and/or the distribution of vector integration events in this population. Such analysis would require measuring vector copy numbers (VCN) in individual cells. To evaluate the transduction of hematopoietic progenitor cells at the single-cell level, we measured VCN in individual colony-forming cell (CFC) units, using an adapted quantitative PCR (Q-PCR) method. The feasibility, reproducibility and sensitivity of this approach were tested with characterized cell lines carrying known numbers of vector integration. The method was validated by correlating data in CFC with gene expression or with calculated values, and was found to slightly underestimate VCN. In spite of this, such Q-PCR on CFC was useful to compare transduction levels with different infection protocols and different vectors. Increasing the vector concentration and re-iterating the infection were two different strategies that improved transduction by increasing the frequency of transduced progenitor cells. Repeated infection also augmented the number of integrated copies and the magnitude of this effect seemed to depend on the vector preparation. Thus, the distribution of VCN in hematopoietic colonies may depend upon experimental conditions including features of vectors. This should be carefully evaluated in the context of ex vivo hematopoietic gene therapy studies

Recurrent and multiple bladder tumors show conserved expression profiles

<p>Abstract</p> <p>Background</p> <p>Urothelial carcinomas originate from the epithelial cells of the inner lining of the bladder and may appear as single or as multiple synchronous tumors. Patients with urothelial carcinomas frequently show recurrences after treatment making follow-up necessary. The leading hypothesis explaining the origin of meta- and synchronous tumors assumes a monoclonal origin. However, the genetic relationship among consecutive tumors has been shown to be complex in as much as the genetic evolution does not adhere to the chronological appearance of the metachronous tumors. Consequently, genetically less evolved tumors may appear chronologically later than genetically related but more evolved tumors.</p> <p>Methods</p> <p>Forty-nine meta- or synchronous urothelial tumors from 22 patients were analyzed using expression profiling, conventional CGH, LOH, and mutation analyses.</p> <p>Results</p> <p>We show by CGH that partial chromosomal losses in the initial tumors may not be present in the recurring tumors, by LOH that different haplotypes may be lost and that detected regions of LOH may be smaller in recurring tumors, and that mutations present in the initial tumor may not be present in the recurring ones. In contrast we show that despite apparent genomic differences, the recurrent and multiple bladder tumors from the same patients display remarkably similar expression profiles.</p> <p>Conclusion</p> <p>Our findings show that even though the vast majority of the analyzed meta- and synchronous tumors from the same patients are not likely to have originated directly from the preceding tumor they still show remarkably similar expressions profiles. The presented data suggests that an expression profile is established early in tumor development and that this profile is stable and maintained in recurring tumors.</p

The projection score - an evaluation criterion for variable subset selection in PCA visualization

<p>Abstract</p> <p>Background</p> <p>In many scientific domains, it is becoming increasingly common to collect high-dimensional data sets, often with an exploratory aim, to generate new and relevant hypotheses. The exploratory perspective often makes statistically guided visualization methods, such as Principal Component Analysis (PCA), the methods of choice. However, the clarity of the obtained visualizations, and thereby the potential to use them to formulate relevant hypotheses, may be confounded by the presence of the many non-informative variables. For microarray data, more easily interpretable visualizations are often obtained by filtering the variable set, for example by removing the variables with the smallest variances or by only including the variables most highly related to a specific response. The resulting visualization may depend heavily on the inclusion criterion, that is, effectively the number of retained variables. To our knowledge, there exists no objective method for determining the optimal inclusion criterion in the context of visualization.</p> <p>Results</p> <p>We present the projection score, which is a straightforward, intuitively appealing measure of the informativeness of a variable subset with respect to PCA visualization. This measure can be universally applied to find suitable inclusion criteria for any type of variable filtering. We apply the presented measure to find optimal variable subsets for different filtering methods in both microarray data sets and synthetic data sets. We note also that the projection score can be applied in general contexts, to compare the informativeness of any variable subsets with respect to visualization by PCA.</p> <p>Conclusions</p> <p>We conclude that the projection score provides an easily interpretable and universally applicable measure of the informativeness of a variable subset with respect to visualization by PCA, that can be used to systematically find the most interpretable PCA visualization in practical exploratory analysis.</p

Analyzing the Number of Common Integration Sites of Viral Vectors – New Methods and Computer Programs

Vectors based on γ-retroviruses or lentiviruses have been shown to stably express therapeutical transgenes and effectively cure different hematological diseases. Molecular follow up of the insertional repertoire of gene corrected cells in patients and preclinical animal models revealed different integration preferences in the host genome including clusters of integrations in small genomic areas (CIS; common integrations sites). In the majority, these CIS were found in or near genes, with the potential to influence the clonal fate of the affected cell. To determine whether the observed degree of clustering is statistically compatible with an assumed standard model of spatial distribution of integrants, we have developed various methods and computer programs for γ-retroviral and lentiviral integration site distribution. In particular, we have devised and implemented mathematical and statistical approaches for comparing two experimental samples with different numbers of integration sites with respect to the propensity to form CIS as well as for the analysis of coincidences of integration sites obtained from different blood compartments. The programs and statistical tools described here are available as workspaces in R code and allow the fast detection of excessive clustering of integration sites from any retrovirally transduced sample and thus contribute to the assessment of potential treatment-related risks in preclinical and clinical retroviral gene therapy studies

Gene expression of circulating tumour cells in breast cancer patients

<p>Abstract</p> <p>Background</p> <p>The diagnostic tools to predict the prognosis in patients suffering from breast cancer (BC) need further improvements. New technological achievements like the gene profiling of circulating tumour cells (CTC) could help identify new prognostic markers in the clinical setting. Furthermore, gene expression patterns of CTC might provide important informations on the mechanisms of tumour cell metastasation.</p> <p>Materials and methods</p> <p>We performed realtime-PCR and multiplex-PCR analyses following immunomagnetic separation of CTC. Peripheral blood (PB) samples of 63 patients with breast cancer of various stages were analyzed and compared to a control group of 14 healthy individuals. After reverse-transcription, we performed multiplex PCR using primers for the genes <it>ga733.3, muc-1 </it>and <it>c-erbB2. Mammaglobin1, spdef </it>and <it>c-erbB2 </it>were analyzed applying realtime-PCR.</p> <p>Results</p> <p><it>ga733.2 </it>overexpression was found in 12.7% of breast cancer cases, <it>muc-1 </it>in 15.9%, <it>mgb1 </it>in 9.1% and <it>spdef </it>in 12.1%. In this study, <it>c-erbB2 </it>did not show any significant correlation to BC, possibly due to a highly ambient expression. Besides single gene analyses, gene profiles were additionally evaluated. Highly significant correlations to BC were found in single gene analyses of <it>ga733.2 </it>and <it>muc-1 </it>and in gene profile analyses of <it>ga733.3</it>*<it>muc-1 </it>and GA7 <it>ga733.3</it>*muc-1*<it>mgb1</it>*<it>spdef</it>.</p> <p>Conclusion</p> <p>Our study reveals that the single genes <it>ga733.3, muc-1 </it>and the gene profiles <it>ga733.3</it>*<it>muc-1 </it>and <it>ga733.3</it>*3<it>muc-1</it>*<it>mgb1</it>*<it>spdef </it>can serve as markers for the detection of CTC in BC. The multigene analyses found highly positive levels in BC patients. Our study indicates that not single gene analyses but subtle patterns of multiple genes lead to rising accuracy and low loss of specificity in detection of breast cancer cases.</p

Towards a Clinically Relevant Lentiviral Transduction Protocol for Primary Human CD34+ Hematopoietic Stem/Progenitor Cells

Background: Hematopoietic stem cells (HSC), in particular mobilized peripheral blood stem cells, represent an attractive target for cell and gene therapy. Efficient gene delivery into these target cells without compromising self-renewal and multipotency is crucial for the success of gene therapy. We investigated factors involved in the ex vivo transduction of CD34 + HSCs in order to develop a clinically relevant transduction protocol for gene delivery. Specifically sought was a protocol that allows for efficient transduction with minimal ex vivo manipulation without serum or other reagents of animal origin. Methodology/Principal Findings: Using commercially available G-CSF mobilized peripheral blood (PB) CD34 + cells as the most clinically relevant target, we systematically examined factors including the use of serum, cytokine combinations, prestimulation time, multiplicity of infection (MOI), transduction duration and the use of spinoculation and/or retronectin. A self-inactivating lentiviral vector (SIN-LV) carrying enhanced green fluorescent protein (GFP) was used as the gene delivery vehicle. HSCs were monitored for transduction efficiency, surface marker expression and cellular function. We were able to demonstrate that efficient gene transduction can be achieved with minimal ex vivo manipulation while maintaining the cellular function of transduced HSCs without serum or other reagents of animal origin. Conclusions/Significance: This study helps to better define factors relevant towards developing a standard clinical protocol for the delivery of SIN-LV into CD34 + cells

The effect of the stromal component of breast tumours on prediction of clinical outcome using gene expression microarray analysis

INTRODUCTION: The aim of this study was to examine the effect of the cellular composition of biopsies on the error rates of multigene predictors of response of breast tumours to neoadjuvant adriamycin and cyclophosphamide (AC) chemotherapy. MATERIALS AND METHODS: Core biopsies were taken from primary breast tumours of 43 patients prior to AC, and subsequent clinical response was recorded. Post-chemotherapy (day 21) samples were available for 16 of these samples. Frozen sections of each core were used to estimate the proportion of invasive cancer and other tissue components at three levels. Transcriptional profiling was performed using a cDNA array containing 4,600 elements. RESULTS: Twenty-three (53%) patients demonstrated a 'good' and 20 (47%) a 'poor' clinical response. The percentage invasive tumour in core biopsies collected from these patients varied markedly. Despite this, agglomerative clustering of sample expression profiles showed that almost all biopsies from the same tumour aggregated as nearest neighbours. SAM (significance analysis of microarrays) regression analysis identified 144 genes which distinguished high- and low-percentage invasive tumour biopsies at a false discovery rate of not more than 5%. The misclassification error of prediction of clinical response using microarray data from pre-treatment biopsies (on leave-one-out cross-validation) was 28%. When prediction was performed on subsets of samples which were more homogeneous in their proportions of malignant and stromal cells, the misclassification error was considerably lower (8%–13%, p < 0.05 on permutation). CONCLUSION: The non-tumour content of breast cancer samples has a significant effect on gene expression profiles. Consideration of this factor improves accuracy of response prediction by expression array profiling. Future gene expression array prediction studies should be planned taking this into account

Real-Time Definition of Non-Randomness in the Distribution of Genomic Events

Features such as mutations or structural characteristics can be non-randomly or non-uniformly distributed within a genome. So far, computer simulations were required for statistical inferences on the distribution of sequence motifs. Here, we show that these analyses are possible using an analytical, mathematical approach. For the assessment of non-randomness, our calculations only require information including genome size, number of (sampled) sequence motifs and distance parameters. We have developed computer programs evaluating our analytical formulas for the real-time determination of expected values and p-values. This approach permits a flexible cluster definition that can be applied to most effectively identify non-random or non-uniform sequence motif distribution. As an example, we show the effectivity and reliability of our mathematical approach in clinical retroviral vector integration site distribution