Progeny

progeny (n.)
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Example: Many of the first Puritans and their progenies were afraid of the dangers that would find them in the New World. The origin of progeny can be traced back to the Latin word meaning to "beget. The word progeny is first recorded in English in the 14th century. Darwin explains that the strongest and best adapted males of the animal kingdom will produce the most descendants, i. He assumes, like many scientists, that the abilities and characteristics of these males give them the advantage to have more offspring, in which case their positive qualities will be passed down.

Here, Milton is arguing against the censorship of literature. He asserts that, just as people have the freedom of speech, their written words, or progeny , should be given the same rights. Milton warns that censorship will suppress the ideas and beliefs of authors whose books embody their hearts and souls. Bring out the linguist in you! What is your own interpretation of progeny. Did you use progeny in a game? In contrast, pathway mapping only recovers known associations, where this effect is mediated by expression changes in pathway members, such as TP53 oncogene activation or copy number aberrations.

We applied PROGENy to a drug sensitivity data set, where the significant associations we obtained corresponded better to known drug—pathway interactions than those of competing methods. Overall, our results suggest that PROGENy provides a better measure of pathway activity than other pathway methods, irrespective of whether the latter was derived from gene sets or directed paths. The latter can be used for many more pathways, as information on the pathway components is more often available than perturbation experiments.

However, our results indicate that one should be cautious when interpreting the expression level of a pathway as its activity. We have shown that PROGENy is able to refine our understanding of the impact of mutations, as well as their utility for cell line drug response and patient survival.

It provides a strong evidence that in order to infer pathway activity, e. While PROGENy provides a good estimate of pathway activity in large and heterogeneous data sets, signatures derived from, for instance, a specific tissue may still more closely reflect activation status given the same context. We see a hint of this when applying the Gatza et al. We believe that our curated set of experiments and computational pipeline will be useful to further investigate this aspect of specialized vs.

We extracted the raw counts from the text files for each gene, discarded those that did not have a valid HGNC symbol, and averaged expression levels where more than one row corresponded to a given gene. The data used corresponds to 34 cancer types and a total of tumor and matched normal samples. From the clinical data, we extracted the vital status and used known survival time or known time of last follow-up as the survival time for the downstream analyses.

We converted the time in days to months by dividing by We obtained both mRNA expression levels as well as survival times for 10, patients, distributed across 33 cancer types. For comparing different pathway methods, we only used cancer types with tissue-matched controls, leaving samples in 13 cancer types. For computing pan-cancer associations, we used the subset with TCGA-like cancer type label, leaving cell lines. We used drug profiling data version —02—20 and drug metadata version —02— Before treatments, cells were starved in serum-free medium overnight.

Read quality was assessed using FastQC and sequencing adapters were trimmed using cutadapt Reads were mapped with STAR aligner v2. The preprocessing pipeline was written in Snakemake Raw read counts were then normalized with DESeq2 and variance stabilization transformed The beads and detection antibodies were diluted All primary antibodies were diluted and obtained from Cell Signaling Technology.

Electrophoresis was performed and lysates were transferred onto nitrocellulose membranes. Thereafter, specific proteins were detected by incubation with primary antibodies diluted in the same blocking buffer containing 0. The bands were quantified by determining the background corrected total intensities using ImageStudio software Li-COR. Our method is dependent on a sufficiently large number of available perturbation experiments that activate or inhibit one of the pathways we were looking at.

We then used those as query terms for public perturbation experiments in the ArrayExpress database 43 and included a total of submissions and experiments in our data set, where each experiment is a distinct comparison between basal and perturbed arrays. If there were multiple time points, different cells, different concentrations, or different perturbing agents within a single database submission, they were considered as different experiments.

Started from the curated list of perturbation-induced gene expression experiments, we included all single-channel microarrays with at least two replicates in the basal condition with raw data available that could be processed by either the limma 44 , oligo 45 , or affy 46 BioConductor packages and for which there was a respective annotation package available.

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Mnemonic Children who are prodigies often make the best progenies! Weir : Gentlemen! Oberoth is curious, however, when he learns that Weir's team has set up a base of operations in the Pegasus Galaxy , though she is careful not to reveal that it is Atlantis. We also have particular experience in dealing with Court of Protection matters and trust and probate administration. Over time, your circumstances will change and your priorities will shift. Niam believes that the only way it is possible, is for McKay to rewrite their Base code. Release Dates.

We first calculated a probe-level expression levels for full series of arrays, where we performed quality control of the raw data using RLE and NUSE cutoffs under 0. If after filtering less than two basal condition arrays remained, the whole experiment was discarded. For the remaining experiments we normalized expression data using the RMA algorithm and mapped the probe identifiers to HGNC symbols. We set aside 10 experiments for model validation. For the remainder and each HGNC symbol, we calculated a model based on mean and standard deviation of the gene expression level, and computed the z -score as average number of standard deviations that the expression level in the perturbed array was shifted from the basal arrays.

We then performed LOESS smoothing for all z -scores in a given experiment using our null model as described previously From the z -scores of all experiments and all pathways, we performed a linear regression with the pathway as input and the z -scores as response variable for each gene separately:. Where Z g is the z -score for a given gene g across all input experiments. From the result of the linear model, we selected the top genes per pathway according to their significance p value and took their estimate the fitted z -scores as coefficient.

We set all other gene coefficients to 0. Each column in the matrix of perturbation-response genes corresponds to a plane in gene expression space, in which each cell line or tumor sample is located. If you follow its normal vector from the origin, the distance it spans corresponds to the pathway score P , each sample is assigned matrix of samples in rows, pathways in columns. In practice, this is achieved by a simple matrix multiplication between the gene expression matrix samples in rows, genes in columns, values are expression levels and the model matrix genes in rows, pathways in columns, values are our top coefficients :.

We then scaled each pathway or gene set score to have a mean of zero and standard deviation of one, in order to factor out the difference in strength of gene expression signatures and thus be able to compare the relative scores across pathways and samples at the same time. We calculate pathway scores for all perturbation experiments in the direction of activation activated—control and control—inhibition. For both, we normalize the pathway scores per experiment because of the different strength of perturbations.

We then quantify how well each pathway signature ranks experiments, where a pathway was perturbed before experiments where a pathway was not perturbed by the Receiver Operator AUC. We quantify if a given method has a consistently higher AUC than another across pathways using a binomial test Supplementary Fig. We previously set aside 10 public perturbations experiments that measure both pathway activation mainly western Blots and gene expression upon perturbation, which were not included in any of the model building. For each of those experiments, we quantified the Blot bands in the original publication DOI and experimental details in Supplementary Fig.

We calculated PROGENy pathway scores for both the control and perturbed condition, and plotted the spread of the control scores vs. We set the median of the control to 0, and the total standard deviation of the control-perturbed pair to 1 for easier presentation without changing test statistics. We performed a one-tailed t test between each control and perturbed pair and report the p values Supplementary Fig. We then computed the pathway scores for all conditions, and scaled each pathway score to have a mean of 0 and standard deviation of 1.

We then computed the difference between the control condition BSA treatment and each perturbation. For this comparison, we plot the difference in means Fig. We matched our defined set of pathways to the publicly available pathway database Reactome 9 and Gene Ontology GO 8 categories, as well as Gatza et al. Signaling Pathway Impact Analysis SPIA 11 is a method that utilizes the directionality and signs in a KEGG 47 pathway graph to determine if in a given pathway structure the available species are more or less available to transduce a signal. As the species considered for a pathway are usually mRNAs of genes, this method infers signaling activity by the proxy of gene expression.

In order to do this, SPIA scores require the comparison with a normal condition in order to compute both their scores and their significance. We calculated our scores either for each cell line compared to the rest of a given tissue where no normals are available i. We normalized our gene expression data from both GDSC and TCGA using ranks to assign equally spaced values between 0 and 1 for each sample within a given tissue.

We calculate pathway scores for each of our curated experiments using all pathway methods. For comparing the impact of mutations across different pathway methods, we used TCGA cohorts, where tissue-matched controls were available, leaving samples across 13 cancer types. For mutated genes, we considered all genes that had a change of coding sequence SNP, small indels in MAF files as mutated and all others as not mutated.

We focussed our analysis of the mutations and copy number alterations on the subset of driver genes that were also used in the GDSC. We used the sets of mutations and CNAs to compute the linear associations between samples for all different methods we looked at. For pan-cancer associations, we used the cancer type as a covariate in order to discard the effect that different tissues have on the observed drug response.

While this will also remove genuine differences in pathway activation between different cancer types, we would not be able to distinguish between those and other confounders that impact the sensitivity of a certain cell line from a given tissue to a drug.

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Progeny is the progeny of the Latin verb progignere, meaning "to beget." That Latin word is itself an offspring of the prefix pro-, meaning "forth," and gignere, which can mean "to beget" or "to bring forth." Gignere has produced a large family of English descendants, including. Progeny offers family history and genetic pedigree software with integrated risk assessment tools for clinical genetic services, clinicians and researchers.

Our pan-cancer association are thus the same of intra-tissue differences in drug response explained by inferred our method, GO, or Reactome pathway scores. We selected four of our strongest associations to investigate whether they provide additional information of what is known by mutation data.

For the pan-cancer cohort, we fitted the model for each pathway and method separately, regressing out the study of origin and age of the patient. For the tissue-specific cohorts, we regressed out the age of the patients. We adjusted the p values using the FDR method for each method and study separately. In order to get distinct classes needed for interpretable Kaplan—Meier survival curves Fig. Phosphoprotein measurements are available as Supplementary Data 2. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Hanahan, D. The Hallmarks of Cancer. Cell , 57—70 International Cancer Genome Consortium. International network of cancer genome projects. Nature , — Garnett, M. Systematic identification of genomic markers of drug sensitivity in cancer cells. Iorio, F. A Landscape of Pharmacogenomic Interactions in Cancer. Cell , — Barretina, J. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity.

Pathway and network analysis of cancer genomes. Methods 12 , — Gene Ontology Consortium. The Gene Ontology GO database and informatics resource. Nucleic Acids Res. Croft, D. Reactome: a database of reactions, pathways and biological processes. Subramanian, A. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Natl Acad. USA , — Tarca, A.

A novel signaling pathway impact analysis.

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Bioinformatics 25 , 75—82 Vaske, C. Bioinformatics 26 , i—i Drier, Y. Pathway-based personalized analysis of cancer. Enabling personalized cancer medicine through analysis of gene-expression patterns. Lamb, J. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science , —