Reanalysis of genotype distributions published in Neurology between 1999 and 2002

The authors tested 123 genotypes described in 54 papers published in the journal Neurology between 1999 and 2002 to ascertain whether these genotype distributions deviated from Hardy - Weinberg equilibrium (HWE). Unreported deviations from HWE in 19 genotype distributions described in 11 of the papers were discovered. The authors also report additional information that could have been extracted after calculating HWE and conclude that HWE values should be mandatory in population genetic studies published in Neurology

Comparative promoter analysis of doxorubicin resistance-associated genes suggests E47 as a key regulatory element

Working under the assumption that up- or down-regulation of genes implicated in chemoresistance may be the result of altered function of regulatory transcription factors (TF), over-represented TF-binding sites of gene lists previously associated with doxorubicin resistance were the target of our search. First, a data warehouse was set up containing 52 genes which were present in at least two gene lists; of those, proximal Promoter sequences (1 kb upstream and 0.05 kb downstream of the transcriptional start sites) could be retrieved from genomic databases for 45 genes using the EZ-Retrieve. The TOUCAN tool MotifScanner, which searches the TRANSTAC database, was used to detect TF-binding sites (TFBSs) in our set of sequences. The statistics tool of the Java program TOUCAN was applied to the data with the appropriate expected frequencies file to compare the measured prevalence to a background model. The most significantly over-represented TFBS was that of E47 (p=0.00024, prevalence: 0.2 vs. background: 8.19E-6). In summary, based on the results of our analysis it is hypothesized that the E47 transcription factor may contribute to doxorubicin resistance

Parallel Evolution under Chemotherapy Pressure in 29 Breast Cancer Cell Lines Results in Dissimilar Mechanisms of Resistance

Background: Developing chemotherapy resistant cell lines can help to identify markers of resistance. Instead of using a panel of highly heterogeneous cell lines, we assumed that truly robust and convergent pattern of resistance can be identified in multiple parallel engineered derivatives of only a few parental cell lines. Methods: Parallel cell populations were initiated for two breast cancer cell lines (MDA-MB-231 and MCF-7) and these were treated independently for 18 months with doxorubicin or paclitaxel. IC50 values against 4 chemotherapy agents were determined to measure cross-resistance. Chromosomal instability and karyotypic changes were determined by cytogenetics. TaqMan RT-PCR measurements were performed for resistance-candidate genes. Pgp activity was measured by FACS. Results: All together 16 doxorubicin- and 13 paclitaxel-treated cell lines were developed showing 2-46 fold and 3-28 fold increase in resistance, respectively. The RT-PCR and FACS analyses confirmed changes in tubulin isofom composition, TOP2A and MVP expression and activity of transport pumps (ABCB1, ABCG2). Cytogenetics showed less chromosomes but more structural aberrations in the resistant cells. Conclusion: We surpassed previous studies by parallel developing a massive number of cell lines to investigate chemoresistance. While the heterogeneity caused evolution of multiple resistant clones with different resistance characteristics, the activation of only a few mechanisms were sufficient in one cell line to achieve resistance. © 2012 Tegze et al

Identifying Resistance Mechanisms against Five Tyrosine Kinase Inhibitors Targeting the ERBB/RAS Pathway in 45 Cancer Cell Lines

Because of the low overall response rates of 10-47% to targeted cancer therapeutics, there is an increasing need for predictive biomarkers. We aimed to identify genes predicting response to five already approved tyrosine kinase inhibitors. We tested 45 cancer cell lines for sensitivity to sunitinib, erlotinib, lapatinib, sorafenib and gefitinib at the clinically administered doses. A resistance matrix was determined, and gene expression profiles of the subsets of resistant vs. sensitive cell lines were compared. Triplicate gene expression signatures were obtained from the caArray project. Significance analysis of microarrays and rank products were applied for feature selection. Ninety-five genes were also measured by RT-PCR. In case of four sunitinib resistance associated genes, the results were validated in clinical samples by immunohistochemistry. A list of 63 top genes associated with resistance against the five tyrosine kinase inhibitors was identified. Quantitative RT-PCR analysis confirmed 45 of 63 genes identified by microarray analysis. Only two genes (ANXA3 and RAB25) were related to sensitivity against more than three inhibitors. The immunohistochemical analysis of sunitinib-treated metastatic renal cell carcinomas confirmed the correlation between RAB17, LGALS8, and EPCAM and overall survival. In summary, we determined predictive biomarkers for five tyrosine kinase inhibitors, and validated sunitinib resistance biomarkers by immunohistochemistry in an independent patient cohort. © 2013 Pénzváltó et al

Challenging the heterogeneity of disease presentation in malignant melanoma-impact on patient treatment

There is an increasing global interest to support research areas that can assist in understanding disease and improving patient care. The National Cancer Institute (NIH) has identified precision medicine-based approaches as key research strategies to expedite advances in cancer research. The Cancer Moonshot program ( https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative ) is the largest cancer program of all time, and has been launched to accelerate cancer research that aims to increase the availability of therapies to more patients and, ultimately, to eradicate cancer. Mass spectrometry-based proteomics has been extensively used to study the molecular mechanisms of cancer, to define molecular subtypes of tumors, to map cancer-associated protein interaction networks and post-translational modifications, and to aid in the development of new therapeutics and new diagnostic and prognostic tests. To establish the basis for our melanoma studies, we have established the Southern Sweden Malignant Melanoma Biobank. Tissues collected over many years have been accurately characterized with respect to the tumor and patient information. The extreme variability displayed in the protein profiles and the detection of missense mutations has confirmed the complexity and heterogeneity of the disease. It is envisaged that the combined analysis of clinical, histological, and proteomic data will provide patients with a more personalized medical treatment. With respect to disease presentation, targeted treatment and medical mass spectrometry analysis and imaging, this overview report will outline and summarize the current achievements and status within malignant melanoma. We present data generated by our cancer research center in Lund, Sweden, where we have built extensive capabilities in biobanking, proteogenomics, and patient treatments over an extensive time period

Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets

The European Union multi-disciplinary Personalised RNA interference to Enhance the Delivery of Individualised Cytotoxic and Targeted therapeutics (PREDICT) consortium has recently initiated a framework to accelerate the development of predictive biomarkers of individual patient response to anti-cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway inhibitor. Through the analysis of tumor tissue derived from pre-operative renal cell carcinoma (RCC) clinical trials, the PREDICT consortium will use established and novel methods to integrate comprehensive tumor-derived genomic data with personalized tumor-derived small hairpin RNA and high-throughput small interfering RNA screens to identify and validate functionally important genomic or transcriptomic predictive biomarkers of individual drug response in patients. PREDICT's approach to predictive biomarker discovery differs from conventional associative learning approaches, which can be susceptible to the detection of chance associations that lead to overestimation of true clinical accuracy. These methods will identify molecular pathways important for survival and growth of RCC cells and particular targets suitable for therapeutic development. Importantly, our results may enable individualized treatment of RCC, reducing ineffective therapy in drug-resistant disease, leading to improved quality of life and higher cost efficiency, which in turn should broaden patient access to beneficial therapeutics, thereby enhancing clinical outcome and cancer survival. The consortium will also establish and consolidate a European network providing the technological and clinical platform for large-scale functional genomic biomarker discovery. Here we review our current understanding of molecular mechanisms driving resistance to anti-angiogenesis agents, the current limitations of laboratory and clinical trial strategies and how the PREDICT consortium will endeavor to identify a new generation of predictive biomarkers

miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients

PURPOSE: The proper validation of prognostic biomarkers is an important clinical issue in breast cancer research. MicroRNAs (miRNAs) have emerged as a new class of promising breast cancer biomarkers. In the present work, we developed an integrated online bioinformatic tool to validate the prognostic relevance of miRNAs in breast cancer. METHODS: A database was set up by searching the GEO, EGA, TCGA, and PubMed repositories to identify datasets with published miRNA expression and clinical data. Kaplan-Meier survival analysis was performed to validate the prognostic value of a set of 41 previously published survival-associated miRNAs. RESULTS: All together 2178 samples from four independent datasets were integrated into the system including the expression of 1052 distinct human miRNAs. In addition, the web-tool allows for the selection of patients, which can be filtered by receptors status, lymph node involvement, histological grade, and treatments. The complete analysis tool can be accessed online at: www.kmplot.com/mirpower . We used this tool to analyze a large number of deregulated miRNAs associated with breast cancer features and outcome, and confirmed the prognostic value of 26 miRNAs. A significant correlation in three out of four datasets was validated only for miR-29c and miR-101. CONCLUSIONS: In summary, we established an integrated platform capable to mine all available miRNA data to perform a survival analysis for the identification and validation of prognostic miRNA markers in breast cancer

Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer

In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer

Multiple scattering theory for superconducting heterostructures

We generalize the screened Korringa-Kohn-Rostoker (SKKR) method for solving the corresponding Kohn-Sham-Bogoliubov-de Gennes (KSBdG) equations for surfaces and interfaces. As an application of the newly developed theory we study the quasiparticle spectrum of Au overlayers on a Nb(100) host. We find that, within the superconducting gap region, the quasiparticle spectrum consists of Andreev bound states (ABS) with a dispersion which is closely connected to the underlying electronic structure of the overlayer. We also find that the spectrum has a strongly k-dependent induced gap. The properties of the gap is discussed in relation to the thickness of the overlayer, and it is shown that certain states do not participate in the Andreev scattering process.Comment: 11 pages, 7 figure

Cancer heterogeneity determined by functional proteomics

Current manuscript gives a synopsis of tumor heterogeneity related to patient samples analyzed by proteomics, protein expression analysis and imaging mass spectrometry.First, we discuss the pathophysiologocal background of cancer biology as a multifactorial and challenging diseases. Disease pathology forms the basis for protein target selection. Therefore, histopathological diagnostics and grading of tumors is highlighted. Pathology is the cornerstone of state-of-the-art diagnostics of tumors today both by establishing dignity and - when needed - describing molecular properties of the cancers. Drug development by the pharmaceutical industry utilizes proteomics studies to pinpoint the most relevant targets. Molecular studies profiling affinity-interactions of the protein(s) with targeted small drug molecules to reach efficacy and optimal patient safety are today requested by the FDA and other agencies for new drug development.An understading of basic mechanisms, controlling drug action and drug binding is central, as a new era of personalized medicine becomes an important milestone solution for the healthcare sector as well as the Pharma and Biotech industry. Development of further diagnostic, prognostic and predictive tests will aid current and future treatment of cancer patients.In the paper we present current status of Proteomics that we believe requires attention in order to collectively advance forward in the fight against cancer, addressing the burning opportunities and challenges