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1,746 results for “Oncogenes”
Supplementary data and scripts for Willemsen et al., 2019 "Genome plasticity in Papillomaviruses and de novo emergence of E5 oncogenes"
<p>Supplementary data for Willemsen et al., 2019 "Genome plasticity in Papillomaviruses and <em>de novo</em> emergence of E5 oncogenes". The data set consists of three folders: “Alignments”, “Bali-Phy” and “RandomPermutationTests”. The “Alignments” folder contains the different alignments used for phylogenetic tree construction and comparison. The “Bali-Phy” folder contains the final results and convergence diagnostics of the Common Ancestry tests obtained by using the Bali-Phy software. The “RandomPermutationTests” folder contains all the data and scripts to repeat the random permutation tests described in the manuscript. Please see the corresponding README files for more information.</p>
Mechanism-Based Redesign of GAP to Activate Oncogenic Ras
<p>Additional material for Berta et al. <em>J. Am. Chem. Soc.</em> 2023, 145, 37, 20302–20310</p> <ul> <li>Paths: minimised structures corresponding to the the GTP hydrolysis mechanism.</li> <li>MD setup: equilibriated inputs for classical MD simulations for WT and mutant systems in CHARMM/NAMD format, set up for the charmm36m FF.</li> <li>NBO: outputs for Natrual Bonding Orbitals analysis carried out for TS and RS structures.</li> <li>GAP mutants: optimised TS and RS structures of the most promising designed GAP mutants.</li> </ul>
Oncogenic signalling is coupled to colorectal cancer cell differentiation state
<p>Mass cytometry and single-cell RNA-sequencing data as well as R Markdown reports to reproduce the figures of our publication.</p> <p>Raw MC data were saved post de-convolution, spillover-compensation, and removal of calibration bead events. Gates for singlets and non-dead cells (low_Pt) are included as logical columns and should be applied prior to usage.</p> <p>As we performed random sampling to equalise cell numbers across conditions, batch normalisation, and used non-linear dimensionality reduction techniques (UMAP and Diffusion Maps), resulting plots may differ slightly from the published figures, yet still support the drawn conclusions. Already normalised and/or sampled data as well as pre-computed UMAP and Diffusion Map coordinates are included in this data set to reproduce the manuscript figures exactly, as shown in the included report “figures_only”. For all details on the batch normalisation and data analysis steps performed, please consult the report “data_analysis” instead.</p>
Illuminating oncogenic KRAS signaling by multi-dimensional chemical proteomics
<p><span>Mutated KRAS is among the most frequent activating genetic alterations in cancer. Drug discovery efforts have led to inhibitors that block mutant KRAS activity. To better understand the molecular basis of their cytostatic rather than cytotoxic effects, w</span>e performed comprehensive dose-dependent proteome-wide target deconvolution, pathway engagement, and protein expression characterization in response to KRAS, MEK, ERK, SHP2, and SOS1 inhibitors in pancreatic (KRAS G12C, G12D) and lung cancer (KRAS G12C) cell lines. Analysis of the dose-response curves available online revealed common and cell line-specific signaling networks dominated by KRAS activity. Time-dose experiments separated early ERK-driven effects from those that result from cell cycle arrest. The transition occurred without substantial proteome re-modelling but extensive changes in phosphorylation and ubiquitinylation. Our resource highlights the complexity of KRAS signaling in cancer and places a large number of new proteins and their modifications into this functional context for further exploration.</p> <p>We provide all dose-response curve data processed using internal pipelines or CurveCurator v0.5.0 (<a href="https://github.com/kusterlab/curve_curator" target="_blank" rel="noopener">https://github.com/kusterlab/curve_curator</a>). A README file is included with details about each file and a Meta table describing the experimental conditions. Each CurveCurator folder contains both the input data (including the TOML parameter file used for curve generation) and the output, which includes interactive dashboards (<strong>dashboard.html</strong>) and processed curve data (<strong>curves.txt</strong>).</p> <p>Phospho-proteome, whole proteome, ubiquitinome, Kinobead pulldown, and cysteine profiling data are provided in separate ZIP folders. Additionally, we include all aggregated supplementary tables and analysis output tables used for figure generation in the manuscript.</p>
Data S1. Human PGBD5 DNA transposase promotes site-specific oncogenic mutations in rhabdoid tumors
<p>Supplementary data S1 for Henssen et al. "</p> <p>Human PGBD5 DNA transposase promotes site-specific oncogenic mutations in rhabdoid tumors"</p>
Oncogenic role of a developmentally regulated NTRK2 splice variant
<p>Abstract</p> <p>Temporally-regulated alternative splicing choices are vital for proper development yet the wrong splice choice may be detrimental. Here we highlight a novel role for the neurotrophin receptor splice variant TrkB.T1 in neurodevelopment, embryogenesis, transformation, and oncogenesis across multiple tumor types in both humans and mice. TrkB.T1 is the predominant NTRK2 isoform across embryonic organogenesis and forced over-expression of this embryonic pattern causes multiple solid and nonsolid tumors in mice in the context of tumor suppressor loss. TrkB.T1 also emerges the predominant NTRK isoform expressed in a wide range of adult and pediatric tumors, including those harboring TRK fusions. Affinity purification-mass spectrometry (AP-MS) proteomic analysis reveals TrkB.T1 has distinct interactors with known developmental and oncogenic signaling pathways such as Wnt, TGF-ß, Hedgehog, and Ras. From alterations in splicing factors to changes in gene expression, the discovery of isoform specific oncogenes with embryonic ancestry has the potential to shape the way we think about developmental systems and oncology.</p>
Oncogenic calreticulin induces TGF-β expression and Treg expansion in the bone marrow microenvironment as a mechanism of immune escape
<p>This repository contains all necessary scRNA-seq inputs to reproduce the results described in "Oncogenic calreticulin induces TGF-β expression and Treg expansion in the bone marrow microenvironment as a mechanism of immune escape" by Schmidt et al. (Cancer Research 2024). </p> <p>Content:</p> <ol> <li>"MPN_calreticulin_bm.R" --> R script containing all code</li> <li>"cells_table.RDS" --> cells table containing, cell_id, UMAP coordinates, complexity, cell type annotation and metadata</li> <li>"normalized_matrix.RDS" --> quality control filtered, log2-normalized and centered expression matrix</li> <li>"reference_signatures.RDS" --> all external signatures used for this study</li> <li>"EV2_*", "EV5_*", "MPN2_*", "MPN5_*", --> cellranger outputs</li> </ol>
IGM Variant Oncogenicity Classifications
<p>IGM Variant oncogenicity classification data curated using the Variation Categorizer. </p>
Data for manuscript: The structural influence of the oncogenic driver mutation N642H in the STAT5B SH2 domain
<p>The data is provided as a part of the manuscript "<strong>The structural influence of the oncogenic driver mutation N642H in the STAT5B SH2 domain</strong>". </p> <p>This repository includes an archive with folders:</p> <div> </div> <div>MD_Data</div> <div>-- Contains shortened versions of the MD trajectories, and initial structure files used to run simulations</div> <div> </div> <div>FigureData </div> <div>-- Contains comma separated value files for each figure</div> <p> </p>
Excited state observation of active K-Ras reveals differential structural dynamics of wild-type versus oncogenic G12D and G12C mutants
<p>Despite the prominent role of the K-Ras protein in many different types of human cancer, major gaps in atomic-level information severely limit our understanding of K-Ras function in health and disease. Here, we report the quantitative backbone structural dynamics of K-Ras by solution NMR spectroscopy of the active state of wild-type K-Ras·GTP and two of its oncogenic P-loop mutants, G12D and G12C, using a novel nanoparticle-assisted spin relaxation method, relaxation dispersion and chemical exchange saturation transfer experiments covering the entire range of timescales from picosecond to milliseconds. Our combined experiments allow the detection and analysis of the functionally critical Switch I and Switch II regions that have previously remained largely unobservable by X-ray crystallography and NMR spectroscopy. Our data reveal cooperative transitions of K-Ras·GTP to a highly dynamic excited state that closely resembles the partially disordered K-Ras·GDP state. These results advance our understanding of differential GTPase activities and signaling properties of the WT versus mutants and may thus guide new strategies for the development of therapeutics.</p>
Functional anaysis of miR-143-3p/KSR2 interaction and oncogenic function in JURKAT and ALL-SIL T-cell acute lymphoblastic leukemia cell lines
<p>1. FCS files from GFP competition assay performed in ALL-SIL and JURKAT cell lines upon transduction with hsa-mir-143 expression vector (pCDH miR-143-3p) or empty vector (pCDH EV) as control.</p><p>2. Uncropped chemiluminescent immunoblot in JURKAT and ALL-SIL cell lines transduced with hsa-mir-143 expression vector (pCDH miR-143-3p) or empty vector (pCDH EV) as control. Upper band is KSR2 protein (~100 kDa) and lower band is loading control GAPDH protein (~37 kDa). Order of samples on the membrane: JURKAT pCDH miR-143-3p replicate 1, pCDH EV replicate 1, pCDH EV replicate 2, pCDH miR-143-3p replicate 2, pCDH EV replicate 3, pCDH miR-143-3p replicate 3, ALL-SIL pCDH miR-143-3p replicate 1, pCDH miR-143-3p replicate 2, pCDH EV replicate 1, pCDH miR-143-3p replicate 3, pCDH EV replicate 2, pCDH EV replicate 3.</p><p>3. RT-qPCR amplification data for relative quantification of <i>KSR2 </i>expression in reference to <i>ACTB </i>and <i>GAPDH </i>in JURKAT and ALL-SIL cell lines expressing deadCas9-KRAB system for transcriptional repression, upon transduction with sgRNA targeting <i>KSR2 </i>transcription start site vector (<i>KSR2 </i>sgRNA1 and <i>KSR2 </i>sgRNA2) or non-targeting sgRNA vector (NT) as control.</p><p>4. FCS files from GFP competition assay performed in ALL-SIL and JURKAT cell lines expressing deadCas9-KRAB system for transcriptional repression, upon transduction with sgRNA targeting <i>KSR2 </i>transcription start site vector (<i>KSR2 </i>sgRNA1 and <i>KSR2 </i>sgRNA2) or non-targeting sgRNA vector (NT) as control.</p>
Assess Efficacy & Safety of Selumetinib in Combination With Docetaxel in Patients Receiving 2nd Line Treatment for v-Ki-ras2 Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) Positive NSCLC
ClinicalTrials.gov study NCT01933932. IPD Sharing: YES. Countries: 26. Publications: 2.
Ph I Ipilimumab Vemurafenib Combo in Patients With v-Raf Murine Sarcoma Viral Oncogene Homolog B1 (BRAF)
ClinicalTrials.gov study NCT01400451. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Excited state observation of active K-Ras reveals differential structural dynamics of wild-type versus oncogenic G12D and G12C mutants
Open the record for dataset details and reuse information.
Data from: Oncogenic transformation by inhibitor-sensitive and resistant EGFR mutants
Open the record for dataset details and reuse information.
Supplementary Data for "Convergent organization of aberrant MYB complexes controls oncogenic gene expression in acute myeloid leukemia"
<p>These files contain the computational analysis of sequencing data and mass spectrometry data for "Convergent organization of aberrant MYB complexes controls oncogenic gene expression in acute myeloid leukemia" by Takao, Forbes, Uni, and Kentsis et al.</p>
Data from: Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues
Cancer cells are known to exhibit unusual metabolic activity and yet, few metabolic cancer driver genes are known. Genetic alterations and epigenetic modi cations of cancer cells result in the abnormal regulation of cellular metabolic pathways that are different when compared to normal cells. Such a metabolic reprogramming can be simulated using constraint-based modelling approaches towards predicting oncogenes. We introduced the tri-level optimization problem to use the metabolic reprogramming towards inferring oncogenes. The algorithm incorporated Recon 2.2 network with the Human Protein Atlas to reconstruct genome-scale metabolic network models of the tissue-speci fic cells at normal and cancer states, respectively. Such reconstructed models were applied to build the templates of the metabolic reprogramming between normal and cancer cell metabolism. The inference optimization problem was formulated to use the templates as a measure towards predicting oncogenes. The nested hybrid differential evolution algorithm was applied to solve the problem to overcome solving difficulty for transferring the inner optimization problem into the single one. Head and neck squamous cells were applied as a case study to evaluate the algorithm. We detected 13 of the top ranked one-hit dysregulations and 17 of the top ranked two-hit oncogenes with high similarity ratios to the templates. According literature survey, most inferred oncogenes are consistent with the observation in various tissues. Furthermore, the inferred oncogenes were highly connected with the TP53/AKT/IGF/MTOR signalling pathway through PTEN, which is one of the most frequently detected tumour suppressor genes in human cancer.
Oncogenic BRAF V600E induces glial proliferation through ERK and neuronal death through JNK
<p>Raw Data Files for the manuscript entitled "Oncogenic <em>BRAF</em> V600E induces glial proliferation through ERK and neuronal death through JNK". </p>
Raw single-molecule imaging data for "Tuning levels of low-complexity domain interactions to modulate endogenous oncogenic transcription"
<p><strong>Raw single-molecule imaging data for "Tuning levels of low-complexity domain interactions to modulate endogenous oncogenic transcription"</strong></p> <p>Shasha Chong<sup>1</sup>, Thomas G.W. Graham<sup>2</sup>, Claire Dugast-Darzacq<sup>2,5</sup>, Gina M. Dailey<sup>2</sup>, Xavier Darzacq<sup>2,5</sup>, Robert Tjian<sup>2,3,4,5</sup>*</p> <p><sup>1 </sup>Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA</p> <p><sup>2 </sup>Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.</p> <p><sup>3 </sup>Howard Hughes Medical Institute, University of California, Berkeley, CA, USA.</p> <p><sup>4</sup><sup> </sup>Li Ka Shing Center for Biomedical & Health Sciences, University of California, Berkeley, CA, USA.</p> <p><sup>5</sup><sup> </sup>CIRM Center of Excellence, University of California, Berkeley, CA. </p> <p>* Lead contact</p> <p><strong>Overview</strong></p> <p>This repository contains 1) movies of endogenously expressed EWS::FLI1-Halo in genome-edited A673 cells acquired using stroboscopic photo-activatable single particle tracking (spaSPT) and 2) images of exogenously expressed mNeonGreen-EWS-NPM1 fusion protein in the above cells before and after spaSPT movies were acquired. The uploaded files include data acquired from 80 live cells on 4 different days. The imaging data, after being processed, were used to generate Figure 4C-E of the manuscript in the title. </p> <p><strong>Method details</strong></p> <p>The genome-edited A673 cells (described in https://www.science.org/doi/10.1126/science.aar2555) with inducible expression of mNeonGreen-EWS-NPM1 were grown on 25 mm circular No. 1.5 cover glasses (Azer Scientific, 200251) that were plasma-cleaned prior to use. We induced the cells with 200 ng/ml of doxycycline for 96 hours, stained the cells with 20 nM PA-JF646 and 200 nM JFX549 HaloTag ligands, and performed single-molecule imaging of EWS::FLI1-Halo on a custom-built Nikon (Nikon Instruments Inc.) TI microscope described in (https://elifesciences.org/articles/25776). We took images with a 100x/NA 1.49 oil-immersion TIRF objective (Nikon apochromat CFI Apo TIRF 100x Oil) under highly inclined and laminated optical sheet (HILO) illumination (https://www.nature.com/articles/nmeth1171) using following laser lines: 488 nm for mNG; 561 nm for JFX549; 405 nm and 633 nm for photo-activation and excitation of PA-JF646, respectively. The incubation chamber maintained a humidified 37°C atmosphere with 5% CO<sub>2</sub> and the objective was similarly heated to 37°C for live-cell experiments. </p> <p>High-concentration JFX549 staining allows visualization of the intracellular distribution of EWS::FLI1-Halo. We chose cells with EWS::FLI1-Halo enriched in the nucleolus to perform spaSPT. The procedure of spaSPT largely follows what is described in (https://elifesciences.org/articles/25776). Both the excitation laser (633 nm) and the photo-activation laser (405 nm) for PA-JF646 were pulsed. Each frame consisted of a 7-ms camera exposure time followed by a ~500 μs camera ‘dead’ time. The excitation laser (633 nm) was pulsed for 1 ms starting at the beginning for the 7 ms camera exposure time. The photo-activation laser (405 nm) was pulsed during the ~500 μs camera ‘dead’ time, minimizing fluorescence background. Each cell was imaged for 20,000 frames corresponding to ~1.5 min. Images of mNeonGreen-EWS-NPM1 were collected with a camera exposure time of 500 ms before and after the acquisition of each spaSPT movie.</p>
Raw confocal imaging and FRAP data for "Tuning levels of low-complexity domain interactions to modulate endogenous oncogenic transcription"
<p><strong>Raw confocal imaging and FRAP data of "Tuning levels of low-complexity domain interactions to modulate endogenous oncogenic transcription"</strong></p> <p>Shasha Chong<sup>1</sup>, Thomas G.W. Graham<sup>2</sup>, Claire Dugast-Darzacq<sup>2,5</sup>, Gina M. Dailey<sup>2</sup>, Xavier Darzacq<sup>2,5</sup>, Robert Tjian<sup>2,3,4,5</sup>*</p> <p><sup>1 </sup>Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA</p> <p><sup>2 </sup>Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.</p> <p><sup>3 </sup>Howard Hughes Medical Institute, University of California, Berkeley, CA, USA.</p> <p><sup>4</sup><sup> </sup>Li Ka Shing Center for Biomedical & Health Sciences, University of California, Berkeley, CA, USA.</p> <p><sup>5</sup><sup> </sup>CIRM Center of Excellence, University of California, Berkeley, CA. </p> <p>* Lead contact</p> <p><strong>Overview</strong></p> <p>This repository contains 1) raw three-color confocal fluorescence images of a transiently expressed protein (mNeonGreen-EWS, mNeonGreen, EGFP-TAF15, EGFP, mNeonGreen-EWS-NPM1, or mNeonGreen-NPM1), endogenously expressed EWS::FLI1-Halo labeled with JFX549 Halo ligand, and intron RNA fluorescence in situ hybridization (FISH) targeting <em>ABHD6</em>, <em>CAV1</em>, or<em> GAPDH </em>in genome-edited A673 cells, 2) raw fluorescence recovery after photobleaching (FRAP) movies of endogenously expressed EWS::FLI1-Halo labeled with TMR Halo ligand in genome-edited A673 cells in the presence and absence of transient expression of mNeonGreen-EWS-NPM1. The imaging data, after being processed, were used to generate Figure 1D-G (also S1A, S3, and S4), 2E-G (also S5A and S7), 3C-E (also S9), 4A, S2, S6, and S8 of the manuscript in the title. </p> <p><strong>Method details</strong></p> <p>1. RNA fluorescence in situ hybridization (FISH)</p> <p>The genome-edited A673 cells (described in https://www.science.org/doi/10.1126/science.aar2555) were plated on 18 mm circular No. 1 cover glasses (VWR VistaVision, 16004-300) and transfected with a protein expression plasmid using Lipofectamine 3000. 24 hours after transfection, we stained the cells with 200 nM JFX549 HaloTag ligand following the protocol described above, fixed the cells, and then proceeded with RNA FISH. To measure nascent transcription levels of <em>ABHD6</em>, <em>CAV1</em>, and <em>GAPDH </em>genes, we performed intron RNA FISH following the published Stellaris RNA FISH protocol for adherent cells (https://biosearchassets.blob.core.windows.net/assets/bti_stellaris_protocol_adherent_cell.pdf) using Quasar 670-labeled FISH probes designed with the online software Stellaris Probe Designer (https://www.biosearchtech.com/support/tools/design-software/stellaris-probe-designer) and purchased from LGC Biosearch Technologies. </p> <p>2. Confocal fluorescence imaging of protein and nucleic acid distribution</p> <p>Two confocal microscopes were used to image intron RNA FISH samples. One is an inverted laser scanning confocal microscope (Zeiss, LSM 710 AxioObserver) equipped with 34-channel spectral detection, a motorized stage, a full incubation chamber maintaining 37°C and 5% CO<sub>2</sub>, a heated stage, an X-Cite 120 illumination source as well as several laser lines (405, 458, 488, 514, 561, 591, 633 nm). Images were acquired with a 40x Plan NeoFluar NA1.3 oil-immersion objective under control of the Zeiss Zen software. The other is an inverted laser scanning confocal microscope with Airyscan super-resolution capability (Zeiss, LSM 900 with Airyscan 2) and equipped with four laser lines (405, 488, 561, 640 nm). Images were acquired with a 40x oil objective (Zeiss Plan-Apochromat 40x/1.3 Oil DIC) in the confocal (CO) mode under control of the Zen software. We acquired z stacks of RNA FISH samples with a slice interval of 0.3 mm. 405 nm, 488 nm, 561 nm, and 633 or 640 nm lasers were used to excite the fluorescence of Hoechst-labeled nuclei, EGFP or mNeonGreen-labeled proteins, JFX549-labeled EWS::FLI1-Halo, and Quasar 670-labeled intron RNA FISH, respectively. Before acquiring any fluorescence image, we carefully set the laser intensity and microscope detectors to make sure that no pixel in the image was saturated. We used proper emission filters for sequential four-color imaging and ensured no bleed-through between the four channels by imaging cell samples that contain only one of the four fluorophores (Hoechst, EGFP or mNeonGreen, JFX549, and Quasar 670) under the four-color imaging settings.</p> <p>3. Fluorescence recovery after photobleaching (FRAP)</p> <p>FRAP was performed on the inverted laser scanning confocal microscope (Zeiss, LSM 710 AxioObserver) described above. The 561 nm laser and the epi-illumination mode were used for FRAP measurements. Images were acquired with a 40x Plan NeoFluar NA1.3 oil-immersion objective. The knock-in A673 cells were grown on glass-bottom (No. 1.5, 14 mm diameter) 35 mm dishes (MatTek, P35G-1.5-14-C). To measure the FRAP dynamics of EWS::FLI1-Halo in the nucleolus, we transfected the knock-in cells with a plasmid encoding mNG-EWS-NPM1 and stained the cells with 500 nM HaloTag TMR ligand (Promega, G8251) following the protocol described above. We acquired 1000 frames at one frame per 0.3 seconds with the first 5 frames acquired before the bleach pulse for the measurement of baseline fluorescence of the bleach spot and the whole nucleus. We chose to photobleach a circular spot with a radius of 1 μm within a nucleolus using the 561 nm laser at maximum intensity. To measure the FRAP dynamics of EWS::FLI1-Halo in the nucleoplasm, we followed the same procedure as above, except that the knock-in cells were not transfected and a circular bleach spot with a radius of 1 μm was chosen within the nucleoplasm of a cell and at least 1 μm from nuclear and nucleolar boundaries. </p>
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