Gene expression signatures predict response to therapy with growth hormone.
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ABSTRACT:
SUBMITTER: Stevens A
PROVIDER: S-EPMC8455334 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesSalivary glands are useful targets for gene therapeutics. After gene transfer into salivary glands, regulated secretory pathway proteins, such as human growth hormone, are secreted into saliva, whereas constitutive secretory pathway proteins, such as erythropoietin, are secreted into the bloodstream. Secretion of human growth hormone (hGH) into the saliva is not therapeutically useful. In this study, we attempted to redirect the secretion of transgenic hGH from the saliva to the serum by site-directed mutagenesis.Materials and methodsWe tested hGH mutants first in vitro with AtT20 cells, a model endocrine cell line that exhibits polarized secretion of regulated secretory pathway proteins. Selected mutants were further studied in vivo using adenoviral-mediated gene transfer to rat submandibular glands.ResultsWe identified two mutants with differences in secretion behavior compared to wild-type hGH. One mutant, ΔN1-6 , was detected in the serum of transduced rats, demonstrating that expression of this mutant in the salivary gland resulted in its secretion through the constitutive secretory pathway.ConclusionThis study demonstrates that mutagenesis of therapeutic proteins normally destined for the regulated secretory pathway may result in their secretion via the constitutive secretory pathway into the circulation for potential therapeutic benefit.
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Project description:Conventional development of multivariate gene expression models (GEM) predicting therapeutic response of cancer patients is based on analysis of patients treated with specific regimens, which limits generalization to different or novel drug combinations. We overcome this limitation by developing GEMs based on in vitro drug sensitivities and microarray analyses of the NCI-60 cancer cell line panel. These GEMs were evaluated in blind fashion as predictors of tumor response and/or patient survival in seven independent cohorts of patients with breast (n = 275), bladder (n = 59), and ovarian (n= 143) cancer treated with multiagent chemotherapy, of which 233 patients were from prospectively enrolled clinical trials. In all studies, GEMs effectively stratified tumor response and patient survival independent of established clinical and pathologic tumor variables. In bladder cancer patients treated with neoadjuvant methotrexate, vinblastine, Adriamycin (doxorubicin), and cisplatin, the 3-year overall survival for those with favorable GEM scores was 81% versus 33% for those with less favorable scores (P = 0.002). GEMs for breast cancer patients treated with 5-fluorouracil, Adriamycin (doxorubicin), and cyclophosphamide and ovarian cancer patients treated with platinum-containing regimens also stratified patient survival [5-year overall survival 100% versus 74% (P = 0.05) and 3-year overall survival 68% versus 43% (P = 0.008), respectively]. Importantly, clinical prediction using our in vitro GEM was superior to that of conventionally derived GEMs. We show a facile yet effective approach to GEM derivation that identifies patients most likely to benefit from selected multiagent therapy. Use of such in vitro-based GEMs may provide a robust and generalizable approach to personalized cancer therapy.
| S-EPMC2784269 | biostudies-literature
Project description:To respond to potential adverse exposures properly, health care providers need accurate indicators of exposure levels. The indicators are particularly important in the case of acetaminophen (APAP) intoxication, the leading cause of liver failure in the U.S. We hypothesized that gene expression patterns derived from blood cells would provide useful indicators of acute exposure levels. To test this hypothesis, we used a blood gene expression data set from rats exposed to APAP to train classifiers in two prediction algorithms and to extract patterns for prediction using a profiling algorithm. Prediction accuracy was tested on a blinded, independent rat blood test data set and ranged from 88.9% to 95.8%. Genomic markers outperformed predictions based on traditional clinical parameters. The expression profiles of the predictor genes from the patterns extracted from the blood exhibited remarkable (97% accuracy) transtissue APAP exposure prediction when liver gene expression data were used as a test set. Analysis of human samples revealed separation of APAP-intoxicated patients from control individuals based on blood expression levels of human orthologs of the rat discriminatory genes. The major biological signal in the discriminating genes was activation of an inflammatory response after exposure to toxic doses of APAP. These results support the hypothesis that gene expression data from peripheral blood cells can provide valuable information about exposure levels, well before liver damage is detected by classical parameters. It also supports the potential use of genomic markers in the blood as surrogates for clinical markers of potential acute liver damage.
| S-EPMC2084322 | biostudies-literature
Project description:Maintaining balanced growth in a changing environment is a fundamental systems-level challenge for cellular physiology, particularly in microorganisms. While the complete set of regulatory and functional pathways supporting growth and cellular proliferation are not yet known, portions of them are well understood. In particular, cellular proliferation is governed by mechanisms that are highly conserved from unicellular to multicellular organisms, and the disruption of these processes in metazoans is a major factor in the development of cancer. In this paper, we develop statistical methodology to identify quantitative aspects of the regulatory mechanisms underlying cellular proliferation in Saccharomyces cerevisiae. We find that the expression levels of a small set of genes can be exploited to predict the instantaneous growth rate of any cellular culture with high accuracy. The predictions obtained in this fashion are robust to changing biological conditions, experimental methods, and technological platforms. The proposed model is also effective in predicting growth rates for the related yeast Saccharomyces bayanus and the highly diverged yeast Schizosaccharomyces pombe, suggesting that the underlying regulatory signature is conserved across a wide range of unicellular evolution. We investigate the biological significance of the gene expression signature that the predictions are based upon from multiple perspectives: by perturbing the regulatory network through the Ras/PKA pathway, observing strong upregulation of growth rate even in the absence of appropriate nutrients, and discovering putative transcription factor binding sites, observing enrichment in growth-correlated genes. More broadly, the proposed methodology enables biological insights about growth at an instantaneous time scale, inaccessible by direct experimental methods. Data and tools enabling others to apply our methods are available at http://function.princeton.edu/growthrate.
| S-EPMC2599889 | biostudies-literature
Project description:Growth hormone (GH) plays a pivotal role in many physiological processes in humans, and in other mammalian and non-mammalian vertebrate species, through actions on somatic growth, tissue development and repair, and intermediary metabolism. This review will focus on mechanisms of GH actions on gene expression, primarily from the perspective of the genes that encode proteins stimulated by GH to regulate somatic growth, especially insulin-like growth factor 1 (IGF-I), but also others that are induced or repressed by GH. Topics to be discussed will include a brief overview of GH-mediated signal transduction pathways and how these cascades alter the functions of responsive transcription factors, with a specific focus on STAT5B, a key member of the signal transducers and activators of transcription family, characterization of essential GH-regulated genes, and elucidation of mechanisms of their regulation from biochemical, genetic, and genomic perspectives.
| S-EPMC7138728 | biostudies-literature
Project description:Functional gene expression signatures (FGES) from pretreatment biopsy samples have been used to predict the responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies. However, there are no predictive FGE signatures from patients receiving treatment. Here, using the Elastic Net Regression (ENLR) algorithm, we analyzed transcriptomic and matching clinical data from a dataset of patients with metastatic melanoma treated with ICB therapies and produced an FGE signature for pretreatment (FGES-PRE) and on-treatment (FGES-ON). Both the FGES-PRE and FGES-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.44-0.81 and 0.82-0.83, respectively. Then, we combined all test samples and obtained AUCs of 0.71 and 0.82 for the FGES-PRE and FGES-ON signatures, respectively. The FGES-ON signatures had a higher predictive value for prognosis than the FGES-PRE signatures. The FGES-PRE and FGES-ON signatures were divided into high- and low-risk scores using the signature score mean value. Patients with a high FGE signature score had better survival outcomes than those with low scores. Overall, we determined that the FGES-ON signature is an effective biomarker for metastatic melanoma patients receiving ICB therapy. This work would provide an important theoretical basis for applying FGE signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.
| S-EPMC9855743 | biostudies-literature
Project description:Gene expression-based signatures help identify pathways relevant to diseases and treatments, but are challenging to construct when there is a diversity of disease mechanisms and treatments in patients with complex diseases. To overcome this challenge, we present a new application of an in silico gene expression deconvolution method, ISOpure-S1, and apply it to identify a common gene expression signature corresponding to response to treatment in 33 juvenile idiopathic arthritis (JIA) patients. Using pre- and post-treatment gene expression profiles only, we found a gene expression signature that significantly correlated with a reduction in the number of joints with active arthritis, a measure of clinical outcome (Spearman rho = 0.44, p = 0.040, Bonferroni correction). This signature may be associated with a decrease in T-cells, monocytes, neutrophils and platelets. The products of most differentially expressed genes include known biomarkers for JIA such as major histocompatibility complexes and interleukins, as well as novel biomarkers including α-defensins. This method is readily applicable to expression datasets of other complex diseases to uncover shared mechanistic patterns in heterogeneous samples.
| S-EPMC4887077 | biostudies-literature
Project description:BackgroundThe capacity to assess environmental inputs to biological phenotypes is limited by methods that can accurately and quantitatively measure these contributions. One such example can be seen in the context of exposure to ionizing radiation.Methods and findingsWe have made use of gene expression analysis of peripheral blood (PB) mononuclear cells to develop expression profiles that accurately reflect prior radiation exposure. We demonstrate that expression profiles can be developed that not only predict radiation exposure in mice but also distinguish the level of radiation exposure, ranging from 50 cGy to 1,000 cGy. Likewise, a molecular signature of radiation response developed solely from irradiated human patient samples can predict and distinguish irradiated human PB samples from nonirradiated samples with an accuracy of 90%, sensitivity of 85%, and specificity of 94%. We further demonstrate that a radiation profile developed in the mouse can correctly distinguish PB samples from irradiated and nonirradiated human patients with an accuracy of 77%, sensitivity of 82%, and specificity of 75%. Taken together, these data demonstrate that molecular profiles can be generated that are highly predictive of different levels of radiation exposure in mice and humans.ConclusionsWe suggest that this approach, with additional refinement, could provide a method to assess the effects of various environmental inputs into biological phenotypes as well as providing a more practical application of a rapid molecular screening test for the diagnosis of radiation exposure.
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Project description: Not available
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Project description:Glucocorticoids act in part via glucocorticoid receptor binding to hormone response elements (HREs), but their direct target genes in vivo are still largely unknown. We developed the criterion that genomic occurrence of paired HREs at an inter-HRE distance less than 200 bp predicts hormone responsiveness, based on synergy of multiple HREs, and HRE information from known target genes. This criterion predicts a substantial number of novel responsive genes, when applied to genomic regions 10 kb upstream of genes. Multiple-tissue in situ hybridization showed that mRNA expression of 6 out of 10 selected genes was induced in a tissue-specific manner in mice treated with a single dose of corticosterone, with the spleen being the most responsive organ. Caveolin-1 was strongly responsive in several organs, and the HRE pair in its upstream region showed increased occupancy by glucocorticoid receptor in response to corticosterone. Our approach allowed for discovery of novel tissue specific glucocorticoid target genes, which may exemplify responses underlying the permissive actions of glucocorticoids.
| S-EPMC2809115 | biostudies-other