KAOS: A new automated computational method for the identification of overexpressed genes

Background: Kinase over-expression and activation as a consequence of gene amplification or gene fusion events is a well-known mechanism of tumorigenesis. The search for novel rearrangements of kinases or other druggable genes may contribute to understanding the biology of cancerogenesis, as well as lead to the identification of new candidate targets for drug discovery. However this requires the ability to query large datasets to identify rare events occurring in very small fractions (1-3 %) of different tumor subtypes. This task is different from what is normally done by conventional tools that are able to find genes differentially expressed between two experimental conditions. Results: We propose a computational method aimed at the automatic identification of genes which are selectively over-expressed in a very small fraction of samples within a specific tissue. The method does not require a healthy counterpart or a reference sample for the analysis and can be therefore applied also to transcriptional data generated from cell lines. In our implementation the tool can use gene-expression data from microarray experiments, as well as data generated by RNASeq technologies. Conclusions: The method was implemented as a publicly available, user-friendly tool called KAOS (Kinase Automatic Outliers Search). The tool enables the automatic execution of iterative searches for the identification of extreme outliers and for the graphical visualization of the results. Filters can be applied to select the most significant outliers. The performance of the tool was evaluated using a synthetic dataset and compared to state-of-the-art tools. KAOS performs particularly well in detecting genes that are overexpressed in few samples or when an extreme outlier stands out on a high variable expression background. To validate the method on real case studies, we used publicly available tumor cell line microarray data, and we were able to identify genes which are known to be overexpressed in specific samples, as well as novel ones

A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response

Cancer cells within a tumour have heterogeneous phenotypes and exhibit dynamic plasticity. How to evaluate such heterogeneity and its impact on outcome and drug response is still unclear. Here, we transcriptionally profile 35,276 individual cells from 32 breast cancer cell lines to yield a single cell atlas. We find high degree of heterogeneity in the expression of biomarkers. We then train a deconvolution algorithm on the atlas to determine cell line composition from bulk gene expression profiles of tumour biopsies, thus enabling cell line-based patient stratification. Finally, we link results from large-scale in vitro drug screening in cell lines to the single cell data to computationally predict drug responses starting from single-cell profiles. We find that transcriptional heterogeneity enables cells with differential drug sensitivity to co-exist in the same population. Our work provides a framework to determine tumour heterogeneity in terms of cell line composition and drug response

Hematopoietic stem cell transplantation is effective curative treatment in pediatric refractory/aggressive Langerhans cell histiocytosis

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Iron up-modulates the expression of transferrin receptors during monocyte-macrophage maturation

Abstract We have investigated the effect of iron on the expression of transferrin receptors (TrfRs) and ferritin chains in cultures of human peripheral blood monocytes maturing to macrophages. Monocyte-macrophage maturation is associated with a gradual rise of Trf-binding capacity in the absence of cell proliferation. At all culture times, treatment with ferric ammonium citrate induces a dose-dependent rise of the Trf-binding level as compared with nontreated cells. Scatchard analysis revealed that this phenomenon is due to an increase in receptor number rather than an alteration in ligand-receptor affinity. Biosynthesis experiments indicated that the rise in number of TrfRs is due to an increase of receptor synthesis, which is associated with a sustained elevation of the TrfR RNA level. The up-regulation of TrfR synthesis is specific in that expression of other macrophage membrane proteins is not affected by iron addition. Conversely, addition of an iron chelator induced a slight decrease of TrfR synthesis. The expression of heavy and light ferritin chains at RNA and protein levels was markedly more elevated in cultured macrophages than in fresh monocytes, thus suggesting modulation of ferritin genes at transcriptional or post-transcriptional levels. Addition of iron salts to monocyte-macrophage cultures sharply stimulated ferritin synthesis but only slightly enhanced the level of ferritin RNA, thus indicating a modulation at the translational level. These results suggests that in cultured human monocytes-macrophages, iron up-regulates TrfR expression, thus in sharp contrast to the negative feedback reported in a variety of other cell types. These observations may shed light on the mechanism(s) of iron storage in tissue macrophages under normal conditions and possibly on the pathogenesis of diseases characterized by abnormal iron storage

Establishment and genomic characterization of the new chordoma cell line Chor-IN-1

Chordomas are rare, slowly growing tumors with high medical need, arising in the axial skeleton from notochord remnants. The transcription factor "brachyury" represents a distinctive molecular marker and a key oncogenic driver of chordomas. Tyrosine kinase receptors are also expressed, but so far kinase inhibitors have not shown clear clinical efficacy in chordoma patients. The need for effective therapies is extremely high, but the paucity of established chordoma cell lines has limited preclinical research. Here we describe the isolation of the new Chor-IN-1 cell line from a recurrent sacral chordoma and its characterization as compared to other chordoma cell lines. Chor-IN-1 displays genomic identity to the tumor of origin and has morphological features, growth characteristics and chromosomal abnormalities typical of chordoma, with expression of brachyury and other relevant biomarkers. Chor-IN-1 gene variants, copy number alterations and kinome gene expression were analyzed in comparison to other four chordoma cell lines, generating large scale DNA and mRNA genomic data that can be exploited for the identification of novel pharmacological targets and candidate predictive biomarkers of drug sensitivity in chordoma. The establishment of this new, well characterized chordoma cell line provides a useful tool for the identification of drugs active in chordoma

Molecular dynamics simulations of p97 including covalent, allosteric and ATP-competitive inhibitors

Binary (nucleotide-protein dimer and hexamer complexes) and ternary (nucleotide-protein-inhibitor complexes) p97 complexes were subjected to molecular dynamics simulations in an attempt to further our understanding of the p97 protein oligomer domain stability and, more importantly, of the recently reported diverse molecular mechanisms of inhibition including allosteric, ATP-competitive and covalent inhibitors. Analysis of stable states following equilibration phases indicated a higher intrinsic stability of the homohexamer as opposed to the dimer, and of N-D1 domains as opposed to the D2 domain. The molecular dynamics of the proposed allosteric binding model reproduced important molecular interactions identified experimentally with high frequency throughout the trajectory. Observed conformational changes occurring in the D2 nucleotide binding site provided a novel bind-rearrange-react hypothesis of stepwise molecular events involved in the specific covalent inhibitor mode of actio

Identification and characterization of a novel SCYL3-NTRK1 rearrangement in a colorectal cancer patient

In colorectal cancer patients, chromosomal rearrangements involving NTRK1 gene (encoding the TRKA protein) are shown in a small subset of patients and are associated with the constitutive activation of the kinase domain of TRKA. In turn, activated TRKA-fusion proteins are associated with proliferation and survival in colorectal cancer tumors. Here we report the identification and functional characterization of a new SCYL3-NTRK1 fusion gene in a 61-year-old colorectal cancer patient. To our knowledge, this fusion protein has never been previously documented in oncological patients. We show that this novel fusion is oncogenic and sensitive to TRKA inhibitors. As suggested by other pieces of evidence, entrectinib - an orally available pan- TRK, ROS1 and ALK inhibitor - may have particular efficacy in patients with NTRK rearrangements. Therefore, screening for rearrangements involving NTRK genes may help identifying a subset of patients able to derive benefit from treatment with entrectinib or other targeted inhibitors

Cross platform microarray analysis for robust identification of differentially expressed genes

<p>Abstract</p> <p>Background</p> <p>Microarrays have been widely used for the analysis of gene expression and several commercial platforms are available. The combined use of multiple platforms can overcome the inherent biases of each approach, and may represent an alternative that is complementary to RT-PCR for identification of the more robust changes in gene expression profiles.</p> <p>In this paper, we combined statistical and functional analysis for the cross platform validation of two oligonucleotide-based technologies, Affymetrix (AFFX) and Applied Biosystems (ABI), and for the identification of differentially expressed genes.</p> <p>Results</p> <p>In this study, we analysed differentially expressed genes after treatment of an ovarian carcinoma cell line with a cell cycle inhibitor. Treated versus control RNA was analysed for expression of 16425 genes represented on both platforms.</p> <p>We assessed reproducibility between replicates for each platform using CAT plots, and we found it high for both, with better scores for AFFX. We then applied integrative correlation analysis to assess reproducibility of gene expression patterns across studies, bypassing the need for normalizing expression measurements across platforms. We identified 930 genes as differentially expressed on AFFX and 908 on ABI, with ~80% common to both platforms. Despite the different absolute values, the range of intensities of the differentially expressed genes detected by each platform was similar. ABI showed a slightly higher dynamic range in FC values, which might be associated with its detection system. 62/66 genes identified as differentially expressed by Microarray were confirmed by RT-PCR.</p> <p>Conclusion</p> <p>In this study we present a cross-platform validation of two oligonucleotide-based technologies, AFFX and ABI. We found good reproducibility between replicates, and showed that both platforms can be used to select differentially expressed genes with substantial agreement. Pathway analysis of the affected functions identified themes well in agreement with those expected for a cell cycle inhibitor, suggesting that this procedure is appropriate to facilitate the identification of biologically relevant signatures associated with compound treatment. The high rate of confirmation found for both common and platform-specific genes suggests that the combination of platforms may overcome biases related to probe design and technical features, thereby accelerating the identification of trustworthy differentially expressed genes.</p