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Oncogenes

Oncogenees are genes that have the potential to cause cancer when mutated or expressed at high levels.
They play a key role in the uncontrolled cell growth and division that characterizes malignant tumors.
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Most cited protocols related to «Oncogenes»

Comprehensive Cancer Cell Line Profiling

A total of 947 independent cancer cell lines were profiled at the genomic level (data available at www.broadinstitute.org/ccle and Gene Expression Omnibus (GEO) using accession numbers GSE36139) and compound sensitivity data was obtained for 479 lines (Supplementary Table 11). Mutation information was obtained both by using massively parallel sequencing of >1,600 genes (Supplementary Table 12) and by mass spectrometric genotyping (OncoMap), which interrogated 492 mutations in 33 known oncogenes and tumor suppressors. Genotyping/copy number analysis was performed using Affymetrix Genome-Wide Human SNP Array 6.0 and expression analysis using the GeneChip Human Genome U133 Plus 2.0 Array. 8-point dose response curves were generated for 24 anticancer drugs using an automated compound-screening platform. Compound sensitivity data were used for two types of predictive models that utilized the naive Bayes classifier or the elastic net regression algorithm. The effects of AHR expression silencing on cell viability were assessed by stable expression of shRNA lentiviral vectors targeting either this gene or luciferase as control. The effect of compound treatment on AHR target gene expression was assessed by quantitative RT-PCR. A full description of the Methods is included in the Supplementary Information.

Barretina J., Caponigro G., Stransky N., Venkatesan K., Margolin A.A., Kim S., Wilson C.J., Lehár J., Kryukov G.V., Sonkin D., Reddy A., Liu M., Murray L., Berger M.F., Monahan J.E., Morais P., Meltzer J., Korejwa A., Jané-Valbuena J., Mapa F.A., Thibault J., Bric-Furlong E., Raman P., Shipway A., Engels I.H., Cheng J., Yu G.K., Yu J., Aspesi P J.r., de Silva M., Jagtap K., Jones M.D., Wang L., Hatton C., Palescandolo E., Gupta S., Mahan S., Sougnez C., Onofrio R.C., Liefeld T., MacConaill L., Winckler W., Reich M., Li N., Mesirov J.P., Gabriel S.B., Getz G., Ardlie K., Chan V., Myer V.E., Weber B.L., Porter J., Warmuth M., Finan P., Harris J.L., Meyerson M., Golub T.R., Morrissey M.P., Sellers W.R., Schlegel R, & Garraway L.A. (2012). The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity. Nature, 483(7391), 603-607.

Publication 2012

Cell Lines Cell Survival Cloning Vectors Gene Chips Gene Expression Genes Genome, Human Hypersensitivity Luciferases Malignant Neoplasms Mass Spectrometry Mutation Oncogenes Pharmaceutical Preparations Reverse Transcriptase Polymerase Chain Reaction Short Hairpin RNA Tumor Suppressor Genes

Benchmark of Computational Mutation Prediction

A collection of five human mutation datasets from online databases and the literature were downloaded and used in this study (Table 1). First, inherited disease-causing AASs annotated as DMs (damaging mutations) in the Human Gene Mutation Database [Stenson et al., 2009 (link)] (HGMD—November 2011; http://www.hgmd.org) and inherited putative functionally neutral AASs in the UniProt database [Apweiler et al., 2004 (link)] (UniProt—November 2011; http://www.uniprot.org/docs/humsavar) were downloaded and used to calculate the pathogenicity weights implemented in our weighted/species-specific method. Next, we obtained two human mutation datasets to assess the performance of FATHMM against the performance of other computational prediction algorithms previously reported in the literature: the VariBench database (VariBench—November 2011; http://bioinf.uta.fi/VariBench) used in a comprehensive review [Thusberg et al., 2011 (link)] of nine other computational prediction methods [Adzhubei et al., 2010 (link); Bao et al., 2005 (link); Bromberg and Rost, 2007 (link); Calabrese et al., 2009 (link); Capriotti et al., 2006 (link); Li et al., 2009 (link); Mort et al., 2010 (link); Ng and Henikoff, 2001 (link); Ramensky et al., 2002 (link); Thomas et al., 2003 (link)] and 267 AASs in four cancer-associated genes (BRCA1, MSH2, MLH1, and TP53) used in a recent review [Hicks et al., 2011 (link)] of four alternative computational prediction algorithms [Adzhubei et al., 2010 (link); Ng and Henikoff, 2001 (link); Reva et al., 2011 (link); Tavtigian et al., 2006 (link)]. Finally, we downloaded a human mutation dataset consisting of disease-associated and putative functionally neutral AASs from the SwissVar portal [Mottaz et al., 2010 (link)] (SwissVar—February 2011; http://swissvar.expasy.org) and performed an independent benchmark of FATHMM against eight other computational prediction algorithms [Adzhubei et al., 2010 (link); Calabrese et al., 2009 (link); Capriotti et al., 2006 (link); Ferrer-Costa et al., 2004 (link); Li et al., 2009 (link); Mort et al., 2010 (link); Ng and Henikoff, 2001 (link); Ramensky et al., 2002 (link); Thomas et al., 2003 (link)].

Shihab H.A., Gough J., Cooper D.N., Stenson P.D., Barker G.L., Edwards K.J., Day I.N, & Gaunt T.R. (2012). Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models. Human Mutation, 34(1), 57-65.

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Publication 2012

BRCA1 protein, human Gene, Cancer Homo sapiens MLH1 protein, human Mutation Oncogenes Pathogenicity Ribs STK35 protein, human TP53 protein, human

Identifying Mut-Driver Genes Through Mutation Patterns

If mutation frequency, corrected for mutation context, gene length, and other parameters, cannot reliably identify modestly mutated driver genes, what can? In our experience, the best way to identify Mut-driver genes is through their pattern of mutation rather than through their mutation frequency. The patterns of mutations in well-studied oncogenes and tumor suppressor genes are highly characteristic and nonrandom. Oncogenes are recurrently mutated at the same amino acid positions, whereas tumor suppressor genes are mutated through protein-truncating alterations throughout their length (Fig. 4 and table S2A).
On the basis of these mutation patterns rather than frequencies, we can determine which of the 18,306 mutated genes containing a total of 404,863 subtle mutations that have been recorded in the Catalogue of Somatic Mutations in Cancer (COSMIC) database (30 (link)) are Mut-driver genes and whether they are likely to function as oncogenes or tumor suppressor genes. To be classified as an oncogene, we simply require that >20% of the recorded mutations in the gene are at recurrent positions and are missense (see legend to table S2A). To be classified as a tumor suppressor gene, we analogously require that >20% of the recorded mutations in the gene are inactivating. This “20/20 rule” is lenient in that all well-documented cancer genes far surpass these criteria (table S2A).
The following examples illustrate the value of the 20/20 rule. When IDH1 mutations were first identified in brain tumors, their role in tumorigenesis was unknown (2 (link), 31 (link)). Initial functional studies suggested that IDH1 was a tumor suppressor gene and that mutations inactivated this gene (32 (link)). However, nearly all of the mutations in IDH1 were at the identical amino acid, codon 132 (Fig. 4). As assessed by the 20/20 rule, this distribution unambiguously indicated that IDH1 was an oncogene rather than a tumor suppressor gene, and this conclusion was eventually supported by biochemical experiments (33 (link), 34 (link)). Another example is provided by mutations in NOTCH1. In this case, some functional studies suggested that NOTCH1 was an oncogene, whereas others suggested it was a tumor suppressor gene (35 (link), 36 (link)). The situation could be clarified through the application of the 20/20 rule to NOTCH1 mutations in cancers. In “liquid tumors” such as lymphomas and leukemias, the mutations were often recurrent and did not truncate the predicted protein (37 (link)). In squamous cell carcinomas, the mutations were not recurrent and were usually inactivating (38 (link)–40 (link)). Thus, the genetic data clearly indicated that NOTCH1 functions differently in different tumor types. The idea that the same gene can function in completely opposite ways in different cell types is important for understanding cell signaling pathways.

Vogelstein B., Papadopoulos N., Velculescu V.E., Zhou S., Diaz LA J.r, & Kinzler K.W. (2013). Cancer Genome Landscapes. Science (New York, N.Y.), 339(6127), 1546-1558.

Publication 2013

Amino Acids Ataxia Telangiectasia Mutated Proteins Brain Neoplasms Cells Codon Cosmic composite resin Diploid Cell Gene, Cancer Genes Leukemia Lymphoma Malignant Neoplasms Mutation Neoplasms Neoplastic Cell Transformation Oncogenes Proteins Reproduction Signal Transduction Pathways Squamous Cell Carcinoma Tumor Suppressor Genes

Robust Gene Set Clustering Protocol

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Publication 2015

Chromatin Copy Number Polymorphism Cytoplasmic Granules Gene Clusters Genes Genetic Heterogeneity Malignant Neoplasms Oncogenes

Comprehensive Genomic Profiling of Prostate Cancer

Flash-frozen needle biopsies and matched normal samples underwent nucleic acid extraction as previously described (5 (link)). Extracted DNA underwent whole-exome library construction and somatic mutation analysis as previously described. BAM files were aligned to the hg19 human genome build. Copy-number aberrations were quantified and reported for each gene as previously described (38 (link), 39 (link)). Amplifications and homozygous deletions for a set of 20 genes previously implicated in prostate cancer (SI Appendix, Table S3) underwent further confirmatory review of segmentation files. Annotation of known or likely oncogenic SNVs was performed using the OncoKB platform (16 (link)).
Transcriptome libraries were prepared as previously described (5 (link)), using polyA+ RNA isolation, or captured using Agilent SureSelect Human All Exon V4 reagents, or in some cases using both polyA and capture methods. Library quality assessment and sequencing were performed as previously described. Paired-end transcriptome-sequencing reads were aligned to the human reference genome (GRCh38) using STAR (40 (link)). Gene expression as fragments per kilobase of exon per million fragments mapped (FPKMs) was determined using featureCounts against protein-coding genes from the Gencode v26 reference. Fusions in ETS genes (ERG, ETV1, ETV4, ETV5, FLI1) and RAF1/BRAF were detected using CODAC (41 (link)) and assessed manually in all cases where RNA-sequencing data were available. In addition, the presence of AR splice variants was quantified as the number of reads across specific splice junctions in splice reads per million (SRPMs) and as the ratio of reads across a specific splice junction to the sum of AR promoter 1 and promoter 2 reads (a surrogate of total AR expression), separately for polyA and capture libraries.
NEPC and AR signaling scores were computed by the Pearson’s correlation coefficient between the log2-transformed FPKM values of each score’s gene list and a reference gene expression vector, as previously described (7 (link), 32 (link)). CCP and RB loss scores were computed by the average (i.e., mean) Z score-transformed expression levels across each score’s gene list, as previously described (42 (link), 43 (link)). A high correlation (R ≥ 0.95, P < 0.001, Pearson’s correlation test) was noted between scores derived from polyA versus capture RNA-sequencing libraries (SI Appendix, Fig. S8), allowing for joint analysis of samples sequenced with either library construction method.
All data from SNV, copy-number, and expression analysis as well as clinical characteristics and outcomes measures (Dataset S1) have been made available in cBioPortal (44 (link)) (www.cbioportal.org), and have been deposited in GitHub, https://github.com/cBioPortal/datahub/tree/master/public/prad_su2c_2019.

Abida W., Cyrta J., Heller G., Prandi D., Armenia J., Coleman I., Cieslik M., Benelli M., Robinson D., Van Allen E.M., Sboner A., Fedrizzi T., Mosquera J.M., Robinson B.D., De Sarkar N., Kunju L.P., Tomlins S., Wu Y.M., Nava Rodrigues D., Loda M., Gopalan A., Reuter V.E., Pritchard C.C., Mateo J., Bianchini D., Miranda S., Carreira S., Rescigno P., Filipenko J., Vinson J., Montgomery R.B., Beltran H., Heath E.I., Scher H.I., Kantoff P.W., Taplin M.E., Schultz N., deBono J.S., Demichelis F., Nelson P.S., Rubin M.A., Chinnaiyan A.M, & Sawyers C.L. (2019). Genomic correlates of clinical outcome in advanced prostate cancer. Proceedings of the National Academy of Sciences of the United States of America, 116(23), 11428-11436.

Publication 2019

BRAF protein, human Diploid Cell DNA Library Exome Exons Freezing Gene Deletion Gene Expression Gene Fusion Gene Products, Protein Genes Genetic Vectors Genome, Human Homo sapiens Homozygote isolation Joints Mutation Needle Biopsies Nucleic Acids Oncogenes Poly A Prostate Cancer Raf1 protein, human RNA, Polyadenylated Transcriptome Trees

Most recents protocols related to «Oncogenes»

Inhibition of STAT3 Signaling in Multiple Myeloma

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Example 3

Human multiple myeloma cancer cells are known to undergo increased cell division through IL-6-triggered STAT3 signaling. Numerous studies have shown that let7a-3p miRNA (SEQ ID NO:1), let7a-5p miRNA (SEQ ID NO:2), miR17-3p miRNA (SEQ ID NO:3), miR17-5p miRNA (SEQ ID NO:4), or miR218-5p miRNA (SEQ ID NO:5) inhibits the activity of transcription factor Signal Transducer and Activator of Transcription 3 (STAT3). Human multiple myeloma cells MM.1S were incubated for 48 hrs daily with 10 μg/ml modified miRNA as indicated and expression of the STAT3 target genes was analyzed by RT-PCR. As shown in FIGS. 2C, 4C, 6C, 8C and 10C, incubation with PS-modified let7a-3p miRNA (SEQ ID NO:1), let7a-5p miRNA (SEQ ID NO:2), miR17-3p miRNA (SEQ ID NO:3), miR17-5p miRNA (SEQ ID NO:4), or miR218-5p miRNA (SEQ ID NO:5) inhibited expression of STAT3 target genes, for example, oncogenic Bcl-xL and/or IL-6 genes.

US11857633B2. Phosphorothioate-conjugated miRNAs and methods of using the same (2024-01-02). City of Hope [US]. Inventors: Andreas Herrmann [US], Hua Yu [US].

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Patent 2024

Cells Division, Cell DNA, Single-Stranded Figs Gene Expression Genes Homo sapiens Malignant Neoplasms MicroRNAs Multiple Myeloma Oligonucleotides Oncogenes Reverse Transcriptase Polymerase Chain Reaction STAT3 Protein Transcription Factor

Let7a-5p miRNA Inhibits STAT3 Signaling in Multiple Myeloma

Not available on PMC !

Example 6

Human multiple myeloma cancer cells are known to undergo increased cell division through IL-6-triggered STAT3 signaling. Numerous studies have shown that let7a-5p miRNA (SEQ ID NO:2) inhibits the activity of Signal Transducer and Activator of Transcription 3 (STAT3). Human multiple myeloma cells MM.1S were incubated for 48 hrs daily with 10 μg/ml polymer-modified let7a-5p miRNA as indicated and expression of the STAT3 target gene, oncogenic Bcl-xL gene, was analyzed by RT-PCR. As shown in FIG. 12B, incubation with PS polymer-modified let7a-5p miRNA inhibited expression of Bcl-xL gene.

US11857633B2. Phosphorothioate-conjugated miRNAs and methods of using the same (2024-01-02). City of Hope [US]. Inventors: Andreas Herrmann [US], Hua Yu [US].

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Patent 2024

Cells Division, Cell Gene Expression Genes Homo sapiens Malignant Neoplasms MicroRNAs Multiple Myeloma Oncogenes Polymers Reverse Transcriptase Polymerase Chain Reaction STAT3 Protein Sugar Phosphates Vertebral Column

Targeting ADAR1 in High Interferon Tumors

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Example 17

Since interferon signaling is spontaneously activated in a subset of cancer cells and exposes potential therapeutic vulnerabilities, it was tested whether there is evidence for similar endogenous interferon activation in primary human tumors. An IFN-GES threshold was computed to predict ADAR dependency across the CCLE cell lines and was determined to be a z-score above 2.26 (FIG. 66, panel A). This threshold was applied to The Cancer Genome Atlas (TCGA) tumors, to identify primary cancers with similarly high interferon activation. Restricting the analysis to the 4,072 samples analyzed by TCGA with at least 70% tumor purity as estimated by the ABSOLUTE algorithm (Carter et al. (2012) Nat. Biotechnol. 30:413-421), 2.7% of TCGA tumors displayed IFN-GESs above this threshold (FIG. 66, panel B and. GSEA of amplified genes in these high purity, high interferon tumors revealed the top pathway as “Type I Interferon Receptor Binding”, comprising 17 genes that all encode type I interferons and are clustered on chromosome 9p21.3 (FIG. 67).

Furthermore, analysis of TCGA copy number data showed that the interferon gene cluster including IFN-β (IFNβI), IFN-ε (IFNE), IFN-ω (IFNWI), and all 13 subtypes of IFN-α on chromosome 9p21.3, proximal to the CDKN2A/CDKN2B tumor suppressor locus, is one of the most frequently homozygously deleted regions in the cancer genome. The interferon genes comprise 16 of the 26 most frequently deleted coding genes across 9,853 TCGA cancer specimens for which ABSOLUTE copy number data are available (FIG. 66, panels C and D). Interferon signaling and activation, both in tumors with high IFN-GESs or deletions in chromosome 9p, therefore represent a biomarker to stratify patients who benefit from interferon modulating therapies.

In summary, specific cancer cell lines have been identified with elevated IFN-β signaling triggered by an activated cytosolic DNA sensing pathway, conferring dependence on the RNA editing enzyme, ADAR1. In cells with low, basal interferon signaling, the cGAS-STING pathway is inactive and PKR levels are reduced (FIG. 68, panel A). Upon cGAS-STING activation, interferon signaling and PKR protein levels are elevated but ADAR1 is still able to suppress PKR activation (FIG. 68, panel B). However, once ADAR1 is deleted, the abundant PKR becomes activated and leads to downstream signaling and cell death (FIG. 68, panel C). This is also shown in normal cells lines (e.g. A549 and NCI-H1437) once exogenous interferon is introduced (FIG. 68, panel D). ADAR1 deficiency in cell lines with high interferon levels, whether from endogenous or exogenous sources, led to phosphorylation and activation of PKR, ATF4-mediated gene expression, and apoptosis. Recent studies have shown that cGAS activation and innate interferon signaling, induced by cytosolic DNA released from the nucleus by DNA damage and genome instability (Mackenzie et al. (2017) Nature 548:461-465; Harding et al. (2017) Nature 548:466-470), led to elevated interferon-related gene expression signatures, which have been linked to resistance to DNA damage, chemotherapy, and radiation in cancer cells (Weichselbaum et al. (2008) Proc. Natl. Acad. Sci. USA 105:18490-18495). In high-interferon tumors, blocking ADAR1 might be effective to induce PKR-mediated apoptotic pathways while upregulating type I interferon signaling, which could contribute to anti-tumor immune responses (Parker et al. (2016) Nature 16:131-144). Alternatively, in tumors without activated interferon signaling, ADAR1 inhibition can be combined with localized interferon inducers, such as STING agonists, chemotherapy, or radiation. Generation of specific small molecule inhibitors targeting ADAR1 exploits this novel vulnerability in lung and other cancers and serves to enhance innate immunity in combination with immune checkpoint inhibitors.

US11873486B2. Modulating dsRNA editing, sensing, and metabolism to increase tumor immunity and improve the efficacy of cancer immunotherapy and/or modulators of intratumoral interferon (2024-01-16). Dana-Farber Cancer Institute, Inc. [US]. Inventors: Matthew Meyerson [US], William N. Haining [US], Jeffrey Ishizuka [US], Robert Manguso [US], Hugh Gannon [US].

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Patent 2024

agonists Apoptosis ATF4 protein, human Biological Markers CDKN2A Gene Cell Death Cell Lines Cell Nucleus Cells Chromogranin A Chromosome Deletion Chromosomes, Human, Pair 3 Cytosol DNA Damage Electromagnetic Radiation Enzymes Gene, Cancer Gene Clusters Gene Expression Genes Genome Genomic Instability Homo sapiens IFNAR2 protein, human Immune Checkpoint Inhibitors Immunity, Innate inhibitors Interferon-alpha Interferon Inducers interferon omega 1 Interferons Interferon Type I Lung Malignant Neoplasms Neoplasms Oncogenes Patients Pharmacotherapy Phosphorylation Proteins Psychological Inhibition Response, Immune Tumor Suppressor Genes

Integrative Analysis of Sorafenib-Resistant Liver Cancer

The two datasets, sorafenib-resistant dataset for Huh7 cells (GSE94550, [9 (link)]) and the Roessler Liver microarray dataset (GSE14520, [10 (link)]) were retrieved, and utilized > 2-fold change as the criterion for selection of sorafenib-modulated molecules in sorafenib-resistant Huh7 cells compared to parental cells (GSE94550) and > 1.2-fold as the criterion for choosing oncogenes related to survival in the bottom 25% vs. the top 25% of HCC patients from the Roessler Liver microarray dataset (GSE14520) to intersect the potential candidates.

Tsai H.W., Chen Y.L., Wang C.I., Hsieh C., Lin Y.H., Chu P.M., Wu Y.H., Huang Y.C, & Chen C.Y. (2023). Anterior gradient 2 induces resistance to sorafenib via endoplasmic reticulum stress regulation in hepatocellular carcinoma. Cancer Cell International, 23, 42.

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Publication 2023

Cells Liver Microarray Analysis Oncogenes Parent Patients Sorafenib

Upscaling Cervical Cancer Screening and Treatment for WLWH in Tanzania

This is a before-and after study to evaluate the impact and feasibility of upscaling CC screening and treatment services for WLWH attending a rural referral CTC in Tanzania. The main objective is to evaluate the uptake by WLWH attending screening after implementation of HPV testing on a self-sampled cervico-vaginal smear, compared to a retrospective cohort screened by VIA. HPV testing has been implemented in a bundle with: a) a smartphone integrating a mobile colposcope (EVA system, Mobile ODT, Israel) for digital enhancement of VIA examination with cervix magnification and second look/quality control; b) thermal ablation in place of cryotherapy (thus avoiding the need for replenishing nitrogen gas cartridges); and c) LEEP.
We adopted an uncontrolled before-and-after design to compare proportion of WLWH attending screening before and after implementing mentioned interventions. A sub-study with cross-sectional design aims to explore diagnostic performance of two novel tests: the first, QuantiGene-Molecular-Profiling-Histology (QG-MPH), is based on transcriptomic biomarker analysis, while the second is a serological assay to detect antibodies against HPV16-L1 [29 (link)], either with a qualitative (Prevo-Check®) or quantitative (PT Monitor®) approach (Table 1. Supplementary material Annex A1 and A2). Further objectives are to determine the adherence to recommendations after screening, to assess the prevalence of HPV genotype-specific infection as well as other co-infections, and to assess feasibility, acceptability and costs of the new implemented screening and treatment plan.Table 1

Novel assays

Test	Name	Target	Description
Lateral Flow Assay (LFA)	PT Monitor® (Abviris GmbH, Germany)	HPV16-L1 Ab	Blood-based (serum) competitive immunoassay assessing the presence of epitope-specific antibodies against HPV16-L1. Elevated levels of these antibodies are associated with the presence of HPV16-induced cancer or pre-cancer. A quantitative readout is possible with an optical table-top reader (aLF reader by Qiagen, Germany). CE-marked IVD
Rapid Lateral Flow Assay (rLFA)	Prevo-Check® (Abviris GmbH, Germany)	HPV16-L1 Ab	Qualitative (yes/no) output of LFA (PT Monitor®) in form of rapid capillary point-of-care test with a cut-off of HPV16-L1 Ab > 1000 ng/ml. CE-IVD-marked for the detection of HPV16-induced anal and oropharyngeal cancers
Probe-based RNA Assay	HPV and dysplasia test – QuantiGene-Molecular-Profiling-Histology (QG-MPH)	mRNA of HPV oncogenes and cellular biomarkers	Cell-based. Quantitative detection of HPV16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82 and cellular biomarkers, correlating with severity of a dysplastic lesion. The emergence and strength of biomarkers define the lesion stage. The QuantiGene 2.0 platform (ThermoFisher) is used 2 Experimental molecular IVD, Charité-University Hospital Berlin, DE (WO2020/161285 A1)

Test

Name

Target

Description

Lateral Flow Assay (LFA)

PT Monitor® (Abviris GmbH, Germany)

HPV16-L1 Ab

Blood-based (serum) competitive immunoassay assessing the presence of epitope-specific antibodies against HPV16-L1. Elevated levels of these antibodies are associated with the presence of HPV16-induced cancer or pre-cancer. A quantitative readout is possible with an optical table-top reader (aLF reader by Qiagen, Germany). CE-marked IVD

Rapid Lateral Flow Assay (rLFA)

Prevo-Check® (Abviris GmbH, Germany)

HPV16-L1 Ab

Qualitative (yes/no) output of LFA (PT Monitor®) in form of rapid capillary point-of-care test with a cut-off of HPV16-L1 Ab > 1000 ng/ml. CE-IVD-marked for the detection of HPV16-induced anal and oropharyngeal cancers

Probe-based RNA Assay

HPV and dysplasia test – QuantiGene-Molecular-Profiling-Histology (QG-MPH)

mRNA of HPV oncogenes and cellular biomarkers

Cell-based. Quantitative detection of HPV16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82 and cellular biomarkers, correlating with severity of a dysplastic lesion. The emergence and strength of biomarkers define the lesion stage. The QuantiGene 2.0 platform (ThermoFisher) is used

2 Experimental molecular IVD, Charité-University Hospital Berlin, DE (WO2020/161285 A1)

Di Salvo I., Mnzava D., Nicoletti G.J., Senkoro E., Ndege R.C., Huang D.J., Makunja N.T., Kassiga G.I., Kaufmann A.M., Weisser M, & Kind A.B. (2023). Upscaling cervical cancer screening and treatment for women living with HIV at a rural referral hospital in Tanzania: protocol of a before-and-after study exploring HPV testing and novel diagnostics. BMC Health Services Research, 23, 234.

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Publication 2023

Antibodies Anus Biological Assay Biological Markers Capillaries Cells Cervix Uteri Coinfection Colposcopes Cryotherapy Epitopes Gene Expression Profiling Genotype Human papillomavirus 16 Immunoassay Malignant Neoplasms Nitrogen Oncogenes Oropharynxs Papillomavirus Infections, Human Point-of-Care Testing RNA, Messenger Second Look Surgery Serum Tests, Diagnostic Vaginal Smears

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What are the different types or variations of Oncogenes?

Oncogenes can be classified into several categories based on their cellular function and mechanism of action. Some common types include growth factor oncogenes (e.g., EGFR, PDGFR), signal transduction oncogenes (e.g., RAS, RAF, PI3K), and transcription factor oncogenes (e.g., MYC, JUN). Each type plays a distinct role in promoting uncontrolled cell growth and division, leading to the development of cancer.

How can PubCompare.ai help with Oncogene research?

PubCompare.ai's AI-driven platform can greatly assist Oncogene research in several ways. First, it allows you to screen protocol literature more efficiently, helping you identify the most effective protocols related to Oncogenes for your specific research goals. Additionally, the platform's advanced AI analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy in your Oncogene studies. This ensures you make informed decisions and enhance the quality of your research.

What are some common challenges in working with Oncogenes?

One of the key challenges in working with Oncogenes is the complex and dynamic nature of their regulation and interactions within cellular signaling pathways. Oncogenes can be activated through various mechanisms, such as mutations, amplifications, or overexpression, which can lead to different phenotypic outcomes. Researchers must also carefully consider the context-dependent effects of Oncogenes, as their impact can vary depending on the cell type, tissue, and stage of cancer development. Effective study design and the use of appropriate models are crucial to addressing these complexities.

How can PubCompare.ai help overcome challenges in Oncogene research?

PubCompare.ai's AI-driven platform can help overcome several challenges in Oncogene research. By streamlining access to the best protocols, products, and data-driven insights from literature, preprints, and patents, PubCompare.ai enhances reproducibility and accuracy. Its innovative AI comparisons can highlight key differences in protocol effectiveness, enabling researchers to choose the most appropriate approaches for their specific Oncogene studies. This helps address the complexities of Oncogene regulation and ensures informed decisions are made throughout the research process.

What are some specific applications of Oncogenes in cancer research and treatment?

Oncogenes have a wide range of applications in cancer research and treatment. They are commonly used as biomarkers for cancer diagnosis, prognosis, and monitoring disease progression. Oncogenes also serve as targets for cancer therapies, with the development of targeted inhibitors and other interventions aimed at disrupting their aberrant signaling. Additionally, understanding the role of Oncogenes in the initiation and maintenance of malignant phenotypes is crucial for developing more effective prevention and treatment strategies. Researchers leveraging PubCompare.ai can stay up-to-date with the latest advancements in this dynamic field.

How can PubCompare.ai help researchers stay informed about Oncogene research?

PubCompare.ai's AI-driven platform can helpe researchers stay informed about the latest developments in Oncogene research. By providing access to the best protocols, products, and data-driven insights from literature, preprints, and patents, the platform enables users to stay at the forefront of this rapidly evolving field. Its innovative AI comparisons can highlight emerging trends, key findings, and the most effective approaches, allowing researchers to make informed decisions and adapt their Oncogene studies accordingly. This ensures researchers can continually enhance the quality and impact of their work in this critical area of cancer research.

More about "Oncogenes"

Oncogenes are genes that have the potential to cause cancer when mutated or expressed at abnormally high levels.
These genes play a crucial role in the uncontrolled cell growth and division that characterize malignant tumors.
Understanding the complex mechanisms of oncogenes is essential for cancer research and drug development.
PubCompare.ai's innovative AI platform helps researchers unravel the intricacies of oncogenes by streamlining access to the best protocols, products, and data-driven insights from literature, preprints, and patents.
This enhances reproducibility and accuracy, ensuring informed decisions for your oncogene studies.
Synonyms and Related Terms: - Proto-oncogenes - Tumor-promoting genes - Oncogenic drivers - Cell growth regulators - Cell cycle control genes - Proliferation-associated genes Key Subtopics: - Oncogene activation mechanisms (e.g., mutations, gene amplification, chromosomal rearrangements) - Oncogene signaling pathways (e.g., RAS, PI3K, MAPK) - Oncogene-induced cellular transformation - Oncogene-targeted therapies (e.g., tyrosine kinase inhibitors, monoclonal antibodies) - Clinical implications of oncogene alterations in cancer diagnosis and prognosis Relevant Products and Techniques: - QIAamp DNA FFPE Tissue Kit: for high-quality DNA extraction from formalin-fixed, paraffin-embedded (FFPE) tissue samples - Ion AmpliSeq Cancer Hotspot Panel v2: for targeted sequencing of oncogenes and other cancer-related genes - FBS (Fetal Bovine Serum): a common cell culture supplement that supports cell growth and proliferation - NextSeq 500 and HiSeq 2500: high-throughput sequencing platforms for genomic and transcriptomic analyses - Ion AmpliSeq Library Kit 2.0: for preparing targeted amplicon libraries for sequencing - DMEM (Dulbecco's Modified Eagle Medium): a widely used cell culture medium - Penicillin/Streptomycin: antibiotics used to prevent microbial contamination in cell culture - RNeasy Mini Kit: for efficient RNA extraction and purification - KAPA Hyper Prep Kit: a library preparation kit for next-generation sequencing By leveraging PubCompare.ai's data-driven insights and streamlined access to the best protocols and products, researchers can enhance the reproducibility and accuracy of their oncogene studies, leading to more informed decisions and advancements in cancer research and treatment.