High-throughput phenotyping of lung cancer somatic mutations [main experiment]
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE83744 | GEO | 2016/06/28
SECONDARY ACCESSION(S): PRJNA326921
REPOSITORIES: GEO
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Project description:Recent genome sequencing efforts have identified millions of somatic mutations in cancer. However, the functional impact of most variants is poorly understood. Here we characterize 194 somatic mutations identified in primary lung adenocarcinomas using L1000 high-throughput gene-expression assays followed by expression-based variant impact phenotyping (eVIP), a method that uses gene expression changes to distinguish impactful from netural somatic mutations. This series represents the main experiment of the study where 8 replicates of wild-type and mutant ORFs are introduced into A549 cell lines. An ORF library containing wild-type and mutated versions of genes found to be mutated in lung cancer are introduced in A549 cell lines and measurements are made using the L1000 high-throughput gene-expression assay. These are done with 8 replicate experiemnts. The data are processed through a computational system, that converts raw fluorescence intensities into differential gene expression signatures. The data at each stage of the pre-processing are available: (LXB) - raw, unprocessed flow cytometry data from Luminex scanners. One LXB file is generated for each well of a 384-well plate, and each file contains a fluorescence intensity value for every observed analyte in the well. (Q2NORM) - gene expression profiles of directly measured landmark transcripts. Normalized using invariant set scaling followed by quantile normalization. (Z-SCORES) - signatures with differentially expressed genes computed by robust z-scores for each profile relative to control (relative to plate population as control)
2016-06-28 | E-GEOD-83744 | biostudies-arrayexpress
Project description:Genome sequencing studies have identified millions of somatic variants in cancer, but it remains challenging to predict the phenotypic impact of most. Experimental approaches to distinguish impactful variants often use phenotypic assays that report on predefined gene-specific functional effects in bulk cell populations. Here, we develop an approach to functionally assess variant impact in single cells by pooled Perturb-seq. We measured the impact of 200 TP53 and KRAS variants on RNA profiles in over 300,000 single lung cancer cells, and used the profiles to categorize variants into phenotypic subsets to distinguish gain-of-function, loss-of-function and dominant negative variants, which we validated by comparison with orthogonal assays. We discovered that KRAS variants did not merely fit into discrete functional categories, but spanned a continuum of gain-of-function phenotypes, and that their functional impact could not have been predicted solely by their frequency in patient cohorts. Our work provides a scalable, gene-agnostic method for coding variant impact phenotyping, with potential applications in multiple disease settings.
2021-10-01 | GSE161824 | GEO
Project description:While advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene variants. The eVIP method uses a decision tree-based algorithm to predict the functional impact of somatic variants by comparing gene expression signatures induced by introduction of wild-type versus mutant cDNAs in cell lines. The method distinguishes between variants that are gain-of-function, loss-of-function, change-of-function, or neutral. We present eVIP2, software that allows for pathway analysis (eVIP Pathways) and usage with RNA-seq data. To demonstrate the eVIP2 software and approach, we characterized two recurrent frameshift variants in RNF43, a negative regulator of Wnt signaling, frequently mutated in colorectal, gastric and endometrial cancer. RNF43 WT, RNF43 R117fs, RNF43 G659fs, or GFP control cDNA were overexpressed in HEK293T cells. Analysis with eVIP2 predicted that the frameshift at position 117 was a loss-of-function mutation, as expected. The second frameshift at position 659, was, surprisingly, predicted to be a gain-of-function mutation. Additional eVIP Pathways analysis of RNF43 G659fs predicted 10 pathways to be significantly altered, including TNF alpha via NFKB signaling, KRAS signaling, and hypoxia. To validate these predictions, we performed reporter assays and found that all eVIP2 impactful pathways tested in the assay were activated by expression of RNF43 G659fs, but not by expression of RNF43 WT, supporting that RNF43 G659fs is a gain-of-function mutation and its effect on the identified pathways. The eVIP2 method is an important step towards overcoming the current challenge of variant interpretation in the implementation of precision medicine. eVIP2 is available at https://github.com/BrooksLabUCSC/eVIP2
2019-12-13 | GSE141963 | GEO
Project description:These samples are part of the ENCODE consortium’s proposed time-limited Pilot Study for confirmation of the utility of RNA abundance measurements as a standard reference phenotyping tool. Keywords: cell type comparison For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Each batch of H1-ES cells cultured by Cellular Dynamics International for use by the ENCODE labs was processed on Affymetrix Exon 1.0 ST arrays to obtain phenotyping data.
2009-02-16 | E-GEOD-14863 | biostudies-arrayexpress
Project description:These samples are part of the ENCODE consortium’s proposed time-limited Pilot Study for confirmation of the utility of RNA abundance measurements as a standard reference phenotyping tool. Keywords: cell type comparison For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Each of the 7 ENCODE laboratories submitted biological triplicates for at least one of the four Tier2 cell lines. These were processed on Affymetrix Exon 1.0 ST arrays to obtain phenotyping data for each cell ine.
2009-08-24 | E-GEOD-17778 | biostudies-arrayexpress
Project description:Molecular phenotyping quantifies expression of pathway reproter genes by next-generation sequencing of pre-defined amplicons. We applied the technology to quantify pathway-level impact of ASOs to PTECs in the presence and absence of exogeneous EGF.
2017-03-13 | GSE83392 | GEO
Project description:The impact of different carcinogenic exposures on the specific patterns of somatic mutations in human tumors remains unclear. To clarify this issue, we profiled 209 cholangiocarcinomas (CCAs) from Asia and Europe, including 108 cases caused by liver fluke Opisthorchis viverrini (OV)-infection and 101 cases due to non-OV etiologies. Whole-exome (N = 15) and prevalence screening (N = 194) revealed recurrent somatic mutations in BAP1 and ARID1A, neither of which has been previously reported to be mutated in CCA. Comparisons between intrahepatic OV and non-OV CCAs demonstrated statistically significant different mutation patterns: BAP1 and IDH1/2 were more frequently mutated in non-OV CCAs, while TP53 displayed the reciprocal pattern. Functional studies demonstrated tumor suppressive roles of BAP1 and ARID1A, establishing the role of chromatin modulators in CCA pathogenesis. These findings indicate that different causative etiologies may induce distinct somatic alterations even within the same tumor type. Illumina 450k methylation array profiling performed on wild-type (n=23) and mutant (n=9) CCA (cholangiocarcinoma) samples, with adjacent normal tissue (n=4)
2013-10-20 | E-GEOD-49656 | biostudies-arrayexpress
Project description:The impact of different carcinogenic exposures on the specific patterns of somatic mutations in human tumors remains unclear. To clarify this issue, we profiled 209 cholangiocarcinomas (CCAs) from Asia and Europe, including 108 cases caused by liver fluke Opisthorchis viverrini (OV)-infection and 101 cases due to non-OV etiologies. Whole-exome (N = 15) and prevalence screening (N = 194) revealed recurrent somatic mutations in BAP1 and ARID1A, neither of which has been previously reported to be mutated in CCA. Comparisons between intrahepatic OV and non-OV CCAs demonstrated statistically significant different mutation patterns: BAP1 and IDH1/2 were more frequently mutated in non-OV CCAs, while TP53 displayed the reciprocal pattern. Functional studies demonstrated tumor suppressive roles of BAP1 and ARID1A, establishing the role of chromatin modulators in CCA pathogenesis. These findings indicate that different causative etiologies may induce distinct somatic alterations even within the same tumor type. Affymetrix SNP6 arrays were performed according to the manufacturer's directions on DNA extracted from the 15 tumors and the 15 matched normal discovery samples.
2013-11-03 | E-GEOD-49666 | biostudies-arrayexpress
Project description:High-throughput phenotyping of lung cancer somatic mutations [main experiment]
| PRJNA326921 | ENA
Project description:The impact of different carcinogenic exposures on the specific patterns of somatic mutations in human tumors remains unclear. To clarify this issue, we profiled 209 cholangiocarcinomas (CCAs) from Asia and Europe, including 108 cases caused by liver fluke Opisthorchis viverrini (OV)-infection and 101 cases due to non-OV etiologies. Whole-exome (N = 15) and prevalence screening (N = 194) revealed recurrent somatic mutations in BAP1 and ARID1A, neither of which has been previously reported to be mutated in CCA. Comparisons between intrahepatic OV and non-OV CCAs demonstrated statistically significant different mutation patterns: BAP1 and IDH1/2 were more frequently mutated in non-OV CCAs, while TP53 displayed the reciprocal pattern. Functional studies demonstrated tumor suppressive roles of BAP1 and ARID1A, establishing the role of chromatin modulators in CCA pathogenesis. These findings indicate that different causative etiologies may induce distinct somatic alterations even within the same tumor type.
2013-10-20 | GSE49656 | GEO