Highly multiplexed quantitative phosphosite assay for biology and preclinical studies
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens
SUBMITTER: 
Hasmik Keshishian
PROVIDER: PXD026939 | panorama | Fri Oct 08 00:00:00 BST 2021
REPOSITORIES: PanoramaPublic
Publications
A highly multiplexed quantitative phosphosite assay for biology and preclinical studies.
Keshishian Hasmik H McDonald E Robert ER Mundt Filip F Melanson Randy R Krug Karsten K Porter Dale A DA Wallace Luke L Forestier Dominique D Rabasha Bokang B Marlow Sara E SE Jane-Valbuena Judit J Todres Ellen E Specht Harrison H Robinson Margaret Lea ML Jean Beltran Pierre M PM Babur Ozgun O Olive Meagan E ME Golji Javad J Kuhn Eric E Burgess Michael M MacMullan Melanie A MA Rejtar Tomas T Wang Karen K Mani D R DR Satpathy Shankha S Gillette Michael A MA Sellers William R WR Carr Steven A SA
Molecular systems biology 20210901 9
Reliable methods to quantify dynamic signaling changes across diverse pathways are needed to better understand the effects of disease and drug treatment in cells and tissues but are presently lacking. Here, we present SigPath, a targeted mass spectrometry (MS) assay that measures 284 phosphosites in 200 phosphoproteins of biological interest. SigPath probes a broad swath of signaling biology with high throughput and quantitative precision. We applied the assay to investigate changes in phospho-s ...[more]
Similar Datasets
Project description:Notum is a carboxylesterase that suppresses Wnt signaling through deacylation of an essential palmitoleate group on Wnt proteins. There is a growing understanding of the role Notum plays in human disease such as colorectal cancer and Alzheimer’s disease supporting the need to discover improved inhibitors, especially for use in models of neurodegeneration. Here, we describe the discovery and profile of 8I (ARUK3001185) as a potent, selective and brain pentrant inhibitor of Notum activity suitable for oral dosing in rodent models of disease. Crystallographic fragment screening of the Diamond-SGC Poised Library for binding to Notum, supported by a biochemical enzyme assay to rank inhibition activity, identifed a pair of outstanding hits. Fragment development of these delivered 8I that restored Wnt signaling in the presence of Notum in a cell-based reporter assay. Assessment in proteomic and pharmacology screens showed 8I to be selective against serine hydrolases, kinases and drug targets.
2022-06-09 | PXD031338 | Pride
Project description:High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.<br>Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFa, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFa-induced perturbation for each network model when compared against NF-kB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.<br>The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.
2012-04-15 | E-MTAB-1027 | biostudies-arrayexpress
Project description:High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFa, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFa-induced perturbation for each network model when compared against NF-kB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined. The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.
2012-12-15 | E-MTAB-1311 | biostudies-arrayexpress
Project description:Transcriptomic changes in human liver cancer cell lines caused by the demethylating drug zebularine. Epigenomic changes such as aberrant hypermethylation and subsequent atypical gene silencing are characteristic features of human cancer. Here, we report a comprehensive characterization of epigenomic modulation caused by zebularine, an effective DNA methylation inhibitor, in human liver cancer. Using transcriptomic and epigenomic profiling, we identified a zebularine signature that classified liver cancer cell lines into two major subtypes with different drug-responses. In drug-sensitive cell lines, zebularine caused inhibition of proliferation coupled with increased apoptosis, whereas drug-resistant cell lines were associated with upregulation of oncogenic networks (e.g. E2F1, MYC, and TNF) driving liver cancer growth in vitro and in mice. Assessment of zebularine-based therapy in xenograft mouse models demonstrated potent therapeutic effects against tumors established from zebularine-sensitive but not zebularine-resistant liver cancer cells leading to increased survival and decreased pulmonary metastasis. Integration of zebularine gene expression and demethylation response signatures differentiated patients with HCC according to their survival and disease recurrence and identified a subclass of patients within the poor survivors likely to benefit from therapeutic agents that target the cancer epigenome. Each cell line was mock treated or treated with 100uM and 200uM zebularine for 7 days, respectively *** This Series represents the gene expression component of the study.
2011-07-30 | E-GEOD-23991 | biostudies-arrayexpress
Project description:Background: Human breast cancer is a heterogeneous disease consisting of multiple molecular subtypes. Genetically engineered mouse models (GEMMs) are useful resources for studying breast cancers in vivo under genetically controlled and immune competent conditions. Identifying murine models with conserved human tumor features will facilitate etiology determinations, highlight the effects of mutations on pathway activation, and improve preclinical drug validation. Results: Transcriptomic profiles of 27 murine models of mammary carcinoma and normal mammary tissue were determined using gene expression microarrays. Hierarchical clustering analysis identified 17 distinct murine subtypes (classes). Across species analyses using three independent human breast cancer datasets identified eight murine classes that represent specific human breast cancer subtypes. Multiple models were associated with human basal-like tumors including TgC3(1)-Tag, TgWap-Myc, and Trp53-/-. Interestingly, the TgWAPCre-Etv6 model mimicked the HER2-enriched subtype, a group of human tumors without a murine counterpart in previous comparative studies. Gene signature analysis identified hundreds of commonly expressed pathways between linked mouse and human subtypes, highlighting potentially common genetic drivers of tumorigenesis and candidate pathways for therapeutic intervention. Conclusion: This study consolidates murine models of breast carcinoma into the largest comprehensive transcriptomic dataset to date to identify human-mouse disease subtype counterparts. This approach illustrates the value of comparisons between species to identify murine models that faithfully mimic the human condition and indicates that multiple GEMMs are needed to represent the diversity of human breast cancers. These trans-species associations should guide model selection during preclinical study design to ensure appropriate representatives of the human disease subtypes are used. Keywords: breast cancer, comparative genomics, genetically engineered mouse models, and molecular pathway signatures reference x sample
2013-11-04 | E-GEOD-42640 | biostudies-arrayexpress
Project description:We previously generated genetically engineered mouse (GEM) models based on perturbation of Tp53, Rb with or without Brca1 or Brca2 that develop serous epithelial ovarian cancer (SEOC) closely resembling the human disease on histologic and molecular levels. We have adapted these GEM models to orthotopic allografts that uniformly develop tumors with short latency in immunocompetent recipients and are ideally suited for routine preclinical studies. To monitor passaged tumors at the molecular level, we analyzed transcriptional profiles of a set of primary SEOC and matching derived passaged tumors. We have merged this dataset with previously published ( doi: 10.1158/0008-5472.CAN-11-3834; PMID 22617326) dataset of murine primary ovarian tumors from our GEM models (GSE46169) and merged and compared them to expression profiles of human dataset published previously (doi: 10.1038/nature10166). The high mortality rate from ovarian cancers can be attributed to late-stage diagnosis and lack of effective treatment. Despite enormous effort to develop better targeted therapies, platinum-based chemotherapy still remains the standard of care for ovarian cancer patients, and resistance occurs at a high rate. One of the rate limiting factors for translation of new drug discoveries into clinical treatments has been the lack of suitable preclinical cancer models with high predictive value. We previously generated genetically engineered mouse (GEM) models based on perturbation of Tp53, Rb with or without Brca1 or Brca2 that develop serous epithelial ovarian cancer (SEOC) closely resembling the human disease on histologic and molecular levels. Here, we describe an adaptation of these GEM models to orthotopic allografts that uniformly develop tumors with short latency and are ideally suited for routine preclinical studies. RNA was isolated from flash frozen ovarian tumors using Trizol and Qiagen RNeasy columns
2013-10-30 | E-GEOD-51927 | biostudies-arrayexpress
Project description:Transcriptomic changes in human liver cancer cell lines caused by the demethylating drug zebularine. Epigenomic changes such as aberrant hypermethylation and subsequent atypical gene silencing are characteristic features of human cancer. Here, we report a comprehensive characterization of epigenomic modulation caused by zebularine, an effective DNA methylation inhibitor, in human liver cancer. Using transcriptomic and epigenomic profiling, we identified a zebularine signature that classified liver cancer cell lines into two major subtypes with different drug-responses. In drug-sensitive cell lines, zebularine caused inhibition of proliferation coupled with increased apoptosis, whereas drug-resistant cell lines were associated with upregulation of oncogenic networks (e.g. E2F1, MYC, and TNF) driving liver cancer growth in vitro and in mice. Assessment of zebularine-based therapy in xenograft mouse models demonstrated potent therapeutic effects against tumors established from zebularine-sensitive but not zebularine-resistant liver cancer cells leading to increased survival and decreased pulmonary metastasis. Integration of zebularine gene expression and demethylation response signatures differentiated patients with HCC according to their survival and disease recurrence and identified a subclass of patients within the poor survivors likely to benefit from therapeutic agents that target the cancer epigenome.
2011-07-31 | GSE23991 | GEO
Project description:Background Worldwide, gastric cancer is the fourth most common malignancy and the most common cancer in East Asia. Development of targeted therapies for this disease has focused on a few known oncogenes but has had limited effects. Objective To determine oncogenic mechanisms and novel therapeutic targets specific for gastric cancer by identifying commonly dys-regulated genes from the tumors of both Asian-Pacific and Caucasian patients. Design We generated transcriptomic profiles of 22 Caucasian gastric cancer tumors and their matched non-cancerous samples, and performed an integrative analysis across different gastric cancer gene expression datasets. We examined the inhibition of commonly overexpressed oncogenes and their constituent signaling pathways by RNAi and/or pharmacologic inhibition. Results We found that HNF4α upregulation was a key signaling event in gastric tumors from both Caucasian and Asian patients, and HNF4α antagonism was antineoplastic. Perturbation experiments in GC tumor cell lines and xenograft models further demonstrated that HNF4α is downregulated by AMPKα signaling and the AMPK agonist metformin; blockade of HNF4α activity resulted in cyclin downregulation, cell cycle arrest, and tumor growth inhibition. HNF4α also regulated WNT signaling through its target gene WNT5A, a potential prognostic marker of diffuse type gastric tumors. Conclusions Our results indicate that HNF4α is a targetable oncoprotein in gastric cancer, is regulated by AMPK signaling through AMPKα, and resides upstream of WNT signaling. HNF4α may regulate “metabolic switch” characteristic of a general malignant phenotype and its target WNT5A has potential prognostic values. The AMPKα-HNF4α-WNT5A signaling cascade represents a potentially targetable pathway for drug development. Integrative analysis of Caucasian and Asian-Pacific gastric tumor expression datasets (including newly generated transcriptomic profiling of 22 tumors in this study) revealed a relatively small common sets of highly overexpressed genes.
2014-11-15 | E-GEOD-63288 | biostudies-arrayexpress
Project description:Prostate cancer discovery and translational research are hampered by a lack of preclinical models which accurately reproduce the biological heterogeneity observed in patients. Accordingly, we have established a bank of transplantable patient-derived prostate tumor xenograft lines, using subrenal capsule grafting of human tumor tissue into immuno-deficient mice. This panel includes the first lines generated from primary prostate cancer tissue, and also new lines from metastatic tissue. Critically, the lines retained salient features of the original patient tumors, including histopathology, clinical marker expression, chromosomal aberration and gene expression profiles. Furthermore, they span major histopathological and molecular subtypes of prostate cancer, capturing diverse inter- and intra-tumoral heterogeneity. Host castration led to the development of castrate-resistant tumors, including the first model of complete neuroendocrine transdifferentiation. This publicly-available resource provides novel tools to advance mechanistic understanding of disease progression and response to therapy, and delivers clinically-relevant model systems for evaluation of preclinical drug efficacy. 3 primary tumors and 21 xenograft tumors
2013-01-12 | E-GEOD-41188 | biostudies-arrayexpress
Project description:Prostate cancer discovery and translational research are hampered by a lack of preclinical models which accurately reproduce the biological heterogeneity observed in patients. Accordingly, we have established a bank of transplantable patient-derived prostate tumor xenograft lines, using subrenal capsule grafting of human tumor tissue into immuno-deficient mice. This panel includes the first lines generated from primary prostate cancer tissue, and also new lines from metastatic tissue. Critically, the lines retained salient features of the original patient tumors, including histopathology, clinical marker expression, chromosomal aberration and gene expression profiles. Furthermore, they span major histopathological and molecular subtypes of prostate cancer, capturing diverse inter- and intra-tumoral heterogeneity. Host castration led to the development of castrate-resistant tumors, including the first model of complete neuroendocrine transdifferentiation. This publicly-available resource provides novel tools to advance mechanistic understanding of disease progression and response to therapy, and delivers clinically-relevant model systems for evaluation of preclinical drug efficacy. 3 primary tumors and 22 xenograft tumors
2013-01-12 | E-GEOD-41192 | biostudies-arrayexpress