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  1. Article ; Online: Next batter up! Targeting cancers with KRAS-G12D mutations.

    Zeissig, Mara N / Ashwood, Lauren M / Kondrashova, Olga / Sutherland, Kate D

    Trends in cancer

    2023  Volume 9, Issue 11, Page(s) 955–967

    Abstract: KRAS is the most frequently mutated oncogene in cancer. Activating mutations in codon 12, especially G12D, have the highest prevalence across a range of carcinomas and adenocarcinomas. With inhibitors to KRAS-G12D now entering clinical trials, ... ...

    Abstract KRAS is the most frequently mutated oncogene in cancer. Activating mutations in codon 12, especially G12D, have the highest prevalence across a range of carcinomas and adenocarcinomas. With inhibitors to KRAS-G12D now entering clinical trials, understanding the biology of KRAS-G12D cancers, and identifying biomarkers that predict therapeutic response is crucial. In this Review, we discuss the genomics and biology of KRAS-G12D adenocarcinomas, including histological features, transcriptional landscape, the immune microenvironment, and how these factors influence response to therapy. Moreover, we explore potential therapeutic strategies using novel G12D inhibitors, leveraging knowledge gained from clinical trials using G12C inhibitors.
    MeSH term(s) Humans ; Proto-Oncogene Proteins p21(ras)/genetics ; Mutation ; Adenocarcinoma ; Tumor Microenvironment/genetics
    Chemical Substances Proto-Oncogene Proteins p21(ras) (EC 3.6.5.2) ; KRAS protein, human
    Language English
    Publishing date 2023-08-15
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ZDB-ID 2852626-0
    ISSN 2405-8025 ; 2405-8033 ; 2405-8033
    ISSN (online) 2405-8025 ; 2405-8033
    ISSN 2405-8033
    DOI 10.1016/j.trecan.2023.07.010
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: The role of aberrant DNA methylation in cancer initiation and clinical impacts.

    Geissler, Franziska / Nesic, Ksenija / Kondrashova, Olga / Dobrovic, Alexander / Swisher, Elizabeth M / Scott, Clare L / J Wakefield, Matthew

    Therapeutic advances in medical oncology

    2024  Volume 16, Page(s) 17588359231220511

    Abstract: Epigenetic alterations, including aberrant DNA methylation, are now recognized ... ...

    Abstract Epigenetic alterations, including aberrant DNA methylation, are now recognized as
    Language English
    Publishing date 2024-01-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 2503443-1
    ISSN 1758-8359 ; 1758-8340
    ISSN (online) 1758-8359
    ISSN 1758-8340
    DOI 10.1177/17588359231220511
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Clarifying the role of EMSY in DNA repair in ovarian cancer.

    Kondrashova, Olga / Scott, Clare L

    Cancer

    2019  Volume 125, Issue 16, Page(s) 2720–2724

    MeSH term(s) DNA Repair/physiology ; Female ; Gene Expression Regulation, Neoplastic ; Humans ; Neoplasm Proteins/genetics ; Nuclear Proteins/genetics ; Ovarian Neoplasms/genetics ; Rad51 Recombinase/genetics ; Repressor Proteins/genetics
    Chemical Substances EMSY protein, human ; Neoplasm Proteins ; Nuclear Proteins ; Repressor Proteins ; RAD51 protein, human (EC 2.7.7.-) ; Rad51 Recombinase (EC 2.7.7.-)
    Language English
    Publishing date 2019-06-02
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 1429-1
    ISSN 1097-0142 ; 0008-543X ; 1934-662X
    ISSN (online) 1097-0142
    ISSN 0008-543X ; 1934-662X
    DOI 10.1002/cncr.32135
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    In stock of ZB MED Cologne/Königswinter

    Uh III Zs.146: Show issues Location:
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  4. Article ; Online: SpliceAI-10k calculator for the prediction of pseudoexonization, intron retention, and exon deletion.

    Canson, Daffodil M / Davidson, Aimee L / de la Hoya, Miguel / Parsons, Michael T / Glubb, Dylan M / Kondrashova, Olga / Spurdle, Amanda B

    Bioinformatics (Oxford, England)

    2023  Volume 39, Issue 4

    Abstract: Summary: SpliceAI is a widely used splicing prediction tool and its most common application relies on the maximum delta score to assign variant impact on splicing. We developed the SpliceAI-10k calculator (SAI-10k-calc) to extend use of this tool to ... ...

    Abstract Summary: SpliceAI is a widely used splicing prediction tool and its most common application relies on the maximum delta score to assign variant impact on splicing. We developed the SpliceAI-10k calculator (SAI-10k-calc) to extend use of this tool to predict: the splicing aberration type including pseudoexonization, intron retention, partial exon deletion, and (multi)exon skipping using a 10 kb analysis window; the size of inserted or deleted sequence; the effect on reading frame; and the altered amino acid sequence. SAI-10k-calc has 95% sensitivity and 96% specificity for predicting variants that impact splicing, computed from a control dataset of 1212 single-nucleotide variants (SNVs) with curated splicing assay results. Notably, it has high performance (≥84% accuracy) for predicting pseudoexon and partial intron retention. The automated amino acid sequence prediction allows for efficient identification of variants that are expected to result in mRNA nonsense-mediated decay or translation of truncated proteins.
    Availability and implementation: SAI-10k-calc is implemented in R (https://github.com/adavi4/SAI-10k-calc) and also available as a Microsoft Excel spreadsheet. Users can adjust the default thresholds to suit their target performance values.
    MeSH term(s) Introns ; Exons ; RNA Splicing ; RNA, Messenger/metabolism ; Amino Acid Sequence
    Chemical Substances RNA, Messenger
    Language English
    Publishing date 2023-04-20
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btad179
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2374: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  5. Article: Histone Modifying Enzymes in Gynaecological Cancers.

    Ramarao-Milne, Priya / Kondrashova, Olga / Barry, Sinead / Hooper, John D / Lee, Jason S / Waddell, Nicola

    Cancers

    2021  Volume 13, Issue 4

    Abstract: Genetic and epigenetic factors contribute to the development of cancer. Epigenetic dysregulation is common in gynaecological cancers and includes altered methylation at CpG islands in gene promoter regions, global demethylation that leads to genome ... ...

    Abstract Genetic and epigenetic factors contribute to the development of cancer. Epigenetic dysregulation is common in gynaecological cancers and includes altered methylation at CpG islands in gene promoter regions, global demethylation that leads to genome instability and histone modifications. Histones are a major determinant of chromosomal conformation and stability, and unlike DNA methylation, which is generally associated with gene silencing, are amenable to post-translational modifications that induce facultative chromatin regions, or condensed transcriptionally silent regions that decondense resulting in global alteration of gene expression. In comparison, other components, crucial to the manipulation of chromatin dynamics, such as histone modifying enzymes, are not as well-studied. Inhibitors targeting DNA modifying enzymes, particularly histone modifying enzymes represent a potential cancer treatment. Due to the ability of epigenetic therapies to target multiple pathways simultaneously, tumours with complex mutational landscapes affected by multiple driver mutations may be most amenable to this type of inhibitor. Interrogation of the actionable landscape of different gynaecological cancer types has revealed that some patients have biomarkers which indicate potential sensitivity to epigenetic inhibitors. In this review we describe the role of epigenetics in gynaecological cancers and highlight how it may exploited for treatment.
    Language English
    Publishing date 2021-02-16
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers13040816
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Deep learning in cancer diagnosis, prognosis and treatment selection.

    Tran, Khoa A / Kondrashova, Olga / Bradley, Andrew / Williams, Elizabeth D / Pearson, John V / Waddell, Nicola

    Genome medicine

    2021  Volume 13, Issue 1, Page(s) 152

    Abstract: Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare ...

    Abstract Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.
    MeSH term(s) Artificial Intelligence ; Deep Learning ; Genomics ; Humans ; Machine Learning ; Medical Oncology ; Neoplasms/diagnosis ; Neoplasms/genetics ; Neural Networks, Computer ; Pharmacogenetics ; Precision Medicine/methods ; Prognosis ; Tumor Microenvironment
    Language English
    Publishing date 2021-09-27
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2484394-5
    ISSN 1756-994X ; 1756-994X
    ISSN (online) 1756-994X
    ISSN 1756-994X
    DOI 10.1186/s13073-021-00968-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Diverse mechanisms of PARP inhibitor resistance in ovarian cancer.

    Wakefield, Matthew John / Nesic, Ksenija / Kondrashova, Olga / Scott, Clare L

    Biochimica et biophysica acta. Reviews on cancer

    2019  Volume 1872, Issue 2, Page(s) 188307

    MeSH term(s) Antineoplastic Agents/therapeutic use ; Drug Resistance, Neoplasm ; Female ; Humans ; Mutation ; Ovarian Neoplasms/drug therapy ; Ovarian Neoplasms/enzymology ; Ovarian Neoplasms/genetics ; Poly (ADP-Ribose) Polymerase-1/genetics ; Poly(ADP-ribose) Polymerase Inhibitors/therapeutic use
    Chemical Substances Antineoplastic Agents ; Poly(ADP-ribose) Polymerase Inhibitors ; PARP1 protein, human (EC 2.4.2.30) ; Poly (ADP-Ribose) Polymerase-1 (EC 2.4.2.30)
    Language English
    Publishing date 2019-08-02
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2918802-7
    ISSN 1879-2561 ; 0304-419X
    ISSN (online) 1879-2561
    ISSN 0304-419X
    DOI 10.1016/j.bbcan.2019.08.002
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 7162: Show issues Location:
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  8. Article ; Online: Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology.

    MacDonald, Samual / Foley, Helena / Yap, Melvyn / Johnston, Rebecca L / Steven, Kaiah / Koufariotis, Lambros T / Sharma, Sowmya / Wood, Scott / Addala, Venkateswar / Pearson, John V / Roosta, Fred / Waddell, Nicola / Kondrashova, Olga / Trzaskowski, Maciej

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 7395

    Abstract: Uncertainty estimation is crucial for understanding the reliability of deep learning (DL) predictions, and critical for deploying DL in the clinic. Differences between training and production datasets can lead to incorrect predictions with underestimated ...

    Abstract Uncertainty estimation is crucial for understanding the reliability of deep learning (DL) predictions, and critical for deploying DL in the clinic. Differences between training and production datasets can lead to incorrect predictions with underestimated uncertainty. To investigate this pitfall, we benchmarked one pointwise and three approximate Bayesian DL models for predicting cancer of unknown primary, using three RNA-seq datasets with 10,968 samples across 57 cancer types. Our results highlight that simple and scalable Bayesian DL significantly improves the generalisation of uncertainty estimation. Moreover, we designed a prototypical metric-the area between development and production curve (ADP), which evaluates the accuracy loss when deploying models from development to production. Using ADP, we demonstrate that Bayesian DL improves accuracy under data distributional shifts when utilising 'uncertainty thresholding'. In summary, Bayesian DL is a promising approach for generalising uncertainty, improving performance, transparency, and safety of DL models for deployment in the real world.
    MeSH term(s) Bayes Theorem ; Deep Learning ; Reproducibility of Results ; Uncertainty ; Medical Oncology
    Language English
    Publishing date 2023-05-06
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-31126-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Lentiviral Transduction-based CRISPR/Cas9 Editing of

    Du, Xiaofeng / McManus, Donald P / French, Juliet D / Sivakumaran, Haran / Johnston, Rebecca L / Kondrashova, Olga / Fogarty, Conor E / Jones, Malcolm K / You, Hong

    Current genomics

    2023  Volume 24, Issue 3, Page(s) 155–170

    Abstract: Background: Recent studies on CRISPR/Cas9-mediated gene editing in : Methods: To improve the efficiency of CRISPR/Cas9 genome editing in schistosomes, we used lentivirus, which has been effectively used for gene editing in mammalian cells, to deliver ...

    Abstract Background: Recent studies on CRISPR/Cas9-mediated gene editing in
    Methods: To improve the efficiency of CRISPR/Cas9 genome editing in schistosomes, we used lentivirus, which has been effectively used for gene editing in mammalian cells, to deliver plasmid DNA encoding Cas9 nuclease, a sgRNA targeting acetylcholinesterase (
    Results: MCherry fluorescence was observed in transduced eggs, schistosomula, and adult worms, indicating that the CRISPR components had been delivered into these parasite stages by lentivirus. In addition, clearly changed phenotypes were observed in
    Conclusion: Taken together, electroporation is more efficient than lentiviral transduction in the delivery of CRISPR/Cas9 into schistosomes for programmed genome editing. The exploration of tactics for enhancing CRISPR/Cas9 gene editing provides the basis for the future improvement of programmed genome editing in
    Language English
    Publishing date 2023-12-21
    Publishing country United Arab Emirates
    Document type Journal Article
    ZDB-ID 2033677-9
    ISSN 1875-5488 ; 1389-2029
    ISSN (online) 1875-5488
    ISSN 1389-2029
    DOI 10.2174/1389202924666230823094608
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5571: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  10. Article ; Online: Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures.

    Tran, Khoa A / Addala, Venkateswar / Johnston, Rebecca L / Lovell, David / Bradley, Andrew / Koufariotis, Lambros T / Wood, Scott / Wu, Sunny Z / Roden, Daniel / Al-Eryani, Ghamdan / Swarbrick, Alexander / Williams, Elizabeth D / Pearson, John V / Kondrashova, Olga / Waddell, Nicola

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 5758

    Abstract: Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate ...

    Abstract Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.
    MeSH term(s) Animals ; Tumor Microenvironment ; Mammary Neoplasms, Animal ; Cell Lineage ; Music ; RNA-Seq
    Language English
    Publishing date 2023-09-16
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-41385-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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