Genetic Analysis of the PI3K/AKT/mTOR Signaling Pathway

Cancer is a leading cause of human death, and it is fundamentally attributable to dysfunctional cell signaling. The PI3K/AKT/mTOR pathway is an important pro-growth intracellular signaling cascade that is often inappropriately activated in a wide array of cancers. Efforts to develop anticancer drugs have therefore focused, in part, on identifying PI3K/AKT/mTOR pathway inhibitors. However, patient response to some such inhibitors is mixed, with some patients experiencing a paradoxical activation of the pathway following treatment. It is therefore necessary to better understand the nature of the PI3K/AKT/mTOR pathway and how it varies in different individuals. The work presented here used cell lines from families to measure the activity of three PI3K/AKT/mTOR pathway members: AKT1, p70S6K and 4E-BP1) in a variety of contexts, including under baseline cell growth conditions and in response to treatment with different PI3K/AKT/mTOR pathway inhibitors. Traditional genetic analyses were used to identify pathway activation phenotypes that were influenced by genetic variation, and genomic regions harboring variation were identified. A new tool for ranking candidate genes was developed and used to select promising genes within these regions for follow-up. Genotyping and association tests of SNPs in these genes identified four variants that were associated with two baseline PI3K/AKT/mTOR pathway activation phenotypes. These represent the first studies to find genetic variants that influence post-translational protein modifications. In addition, the identified SNPs may shed light on normal pathway function as well as new mechanisms for pathway inhibition

Determination of minimal transcriptional signatures of compounds for target prediction

The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation

CANDID: a flexible method for prioritizing candidate genes for complex human traits

Genomewide studies and localized candidate gene approaches have become everyday study designs for identifying polymorphisms in genes that influence complex human traits. Yet, in general, the number of significant findings and the need to focus in smaller regions require a prioritization of genes for further study. Some candidate gene identification algorithms have been proposed in recent years to attempt to streamline this prioritization, but many suffer from limitations imposed by the source data or are difficult to use and understand. CANDID is a prioritization algorithm designed to produce impartial, accurate rankings of candidate genes that influence complex human traits. CANDID can use information from publications, protein domain descriptions, cross-species conservation measures, gene expression profiles, and protein-protein interactions in its analysis. Additionally, users may supplement these data sources with results from linkage, association and other studies. CANDID was tested on well-known complex trait genes using data from the Online Mendelian Inheritance in Man (OMIM) database. Additionally, CANDID was evaluated in a modeled gene discovery environment, where it ranked genes whose trait associations were published after CANDID’s databases were compiled. In all settings, CANDID exhibited high sensitivity and specificity, indicating an improvement upon previously published algorithms. Its accuracy and ease of use make CANDID a highly useful tool in study design and analysis for complex human traits

Novel polymorphisms and lack of mutations in the ACD gene in patients with ACTH resistance syndromes

Objective  ACTH resistance is a feature of several human syndromes with known genetic causes, including familial glucocorticoid deficiency (types 1 and 2) and triple A syndrome. However, many patients with ACTH resistance lack an identifiable genetic aetiology. The human homolog of the Acd gene, mutated in a mouse model of adrenal insufficiency, was sequenced in 25 patients with a clinical diagnosis of familial glucocorticoid deficiency or triple A syndrome. Design  A 3·4 kilobase genomic fragment containing the entire ACD gene was analysed for mutations in all 25 patients. Setting  Samples were obtained by three investigators from different institutions. Patients  The primary cohort consisted of 25 unrelated patients, primarily of European or Middle Eastern descent, with a clinical diagnosis of either familial glucocorticoid deficiency (FGD) or triple A syndrome. Patients lacked mutations in other genes known to cause ACTH resistance, including AAAS for patients diagnosed with triple A syndrome and MC2R and MRAP for patients diagnosed with familial glucocorticoid deficiency. Thirty-five additional patients with adrenal disease phenotypes were added to form an expanded cohort of 60 patients. Measurements  Identification of DNA sequence changes in the ACD gene in the primary cohort and analysis of putative ACD haplotypes in the expanded cohort. Results  No disease-causing mutations were found, but several novel single nucleotide polymorphisms (SNPs) and two putative haplotypes were identified. The overall frequency of SNPs in ACD is low compared to other gene families. Conclusions  No mutations were identified in ACD in this collection of patients with ACTH resistance phenotypes. However, the newly identified SNPs in ACD should be more closely examined for possible links to disease.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73948/1/j.1365-2265.2007.02855.x.pd

Genomewide Analysis of Inherited Variation Associated with Phosphorylation of PI3K/AKT/mTOR Signaling Proteins

While there exists a wealth of information about genetic influences on gene expression, less is known about how inherited variation influences the expression and post-translational modifications of proteins, especially those involved in intracellular signaling. The PI3K/AKT/mTOR signaling pathway contains several such proteins that have been implicated in a number of diseases, including a variety of cancers and some psychiatric disorders. To assess whether the activation of this pathway is influenced by genetic factors, we measured phosphorylated and total levels of three key proteins in the pathway (AKT1, p70S6K, 4E-BP1) by ELISA in 122 lymphoblastoid cell lines from 14 families. Interestingly, the phenotypes with the highest proportion of genetic influence were the ratios of phosphorylated to total protein for two of the pathway members: AKT1 and p70S6K. Genomewide linkage analysis suggested several loci of interest for these phenotypes, including a linkage peak for the AKT1 phenotype that contained the AKT1 gene on chromosome 14. Linkage peaks for the phosphorylated:total protein ratios of AKT1 and p70S6K also overlapped on chromosome 3. We selected and genotyped candidate genes from under the linkage peaks, and several statistically significant associations were found. One polymorphism in HSP90AA1 was associated with the ratio of phosphorylated to total AKT1, and polymorphisms in RAF1 and GRM7 were associated with the ratio of phosphorylated to total p70S6K. These findings, representing the first genomewide search for variants influencing human protein phosphorylation, provide useful information about the PI3K/AKT/mTOR pathway and serve as a valuable proof of concept for studies integrating human genomics and proteomics

Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer

Gene expression data is often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. However, pathway enrichment is typically computed with DEGs rather than ‘upstream’ nodes that are potentially causal of ‘downstream’ changes. Here we present graph-based models to predict causal targets using compound-microarray data. We test several approaches to traversing network topology for interactions of varying confidence levels. We found that larger, less-canonical networks outperformed linear canonical interactions. In addition, combining network topology scoring methods with a consensus minimum-rank score beat individual methods and could highly rank compound targets among all network nodes. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To extend our validation, we used integrated datasets from the The Cancer Genome Atlas to define driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including EGFR/PI3K/AKT/MAPK growth pathway and ATR/p53/BRCA DNA damage pathway, as well as unexpected pathways, such as TGF/WNT cytoskeleton remodeling, TNFR/IAP apoptosis, and IL12-induced IFN-gamma production. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer

The multidimensional perturbation value: A single metric to measure similarity and activity of treatments in high-throughput multidimensional screens

Screens using high-throughput, information-rich technologies such as microarrays, high-content screening (HCS), and next-generation sequencing (NGS) have become increasingly widespread. Compared with single-readout assays, these methods produce a more comprehensive picture of the effects of screened treatments. However, interpreting such multidimensional readouts is challenging. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional profiles, leaving no obvious way to answer common screening questions such as "Is treatment X active in this assay?" and "Is treatment X different from (or equivalent to) treatment Y?" We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. Here, we demonstrate application of the mp-value to three data sets: a multiplexed gene expression screen of compounds and genomic reagents, a microarray-based gene expression screen of compounds, and an HCS compound screen. In all data sets, active treatments were successfully identified using the mp-value, and simulations and follow-up analyses supported the mp-value's statistical and biological validity. We believe the mp-value represents a promising way to simplify the analysis of multidimensional data while taking full advantage of its richness. © 2012 Society for Laboratory Automation and Screening