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  1. Article: Connecting Chromatin Structures to Gene Regulation Using Dynamic Polymer Simulations.

    Fu, Yi / Clark, Finnegan / Nomikou, Sofia / Tsirigos, Aristotelis / Lionnet, Timothee

    bioRxiv : the preprint server for biology

    2023  

    Abstract: The transfer of regulatory information between distal loci on chromatin is thought to involve physical proximity, but key biophysical features of these contacts remain unclear. For instance, it is unknown how close and for how long two loci need to be in ...

    Abstract The transfer of regulatory information between distal loci on chromatin is thought to involve physical proximity, but key biophysical features of these contacts remain unclear. For instance, it is unknown how close and for how long two loci need to be in order to productively interact. The main challenge is that it is currently impossible to measure chromatin dynamics with high spatiotemporal resolution at scale. Polymer simulations provide an accessible and rigorous way to test biophysical models of chromatin regulation, yet there is a lack of simple and general methods for extracting the values of model parameters. Here we adapt the Nelder-Mead simplex optimization algorithm to select the best polymer model matching a given Hi-C dataset, using the
    Language English
    Publishing date 2023-11-10
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.11.07.566032
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Deep learning links histology, molecular signatures and prognosis in cancer.

    Coudray, Nicolas / Tsirigos, Aristotelis

    Nature cancer

    2020  Volume 1, Issue 8, Page(s) 755–757

    MeSH term(s) Deep Learning ; Histological Techniques ; Humans ; Neoplasms/diagnosis ; Prognosis
    Language English
    Publishing date 2020-07-06
    Publishing country England
    Document type Journal Article ; Comment
    ISSN 2662-1347
    ISSN (online) 2662-1347
    DOI 10.1038/s43018-020-0099-2
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  3. Article ; Online: Machine learning approaches to predict drug efficacy and toxicity in oncology.

    Badwan, Bara A / Liaropoulos, Gerry / Kyrodimos, Efthymios / Skaltsas, Dimitrios / Tsirigos, Aristotelis / Gorgoulis, Vassilis G

    Cell reports methods

    2023  Volume 3, Issue 2, Page(s) 100413

    Abstract: In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug repurposing, diagnostics, clinical trial design, and pharmaceutical ... ...

    Abstract In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug repurposing, diagnostics, clinical trial design, and pharmaceutical production. MLAs have the potential to provide valuable insights and predictions in these areas by representing both the disease state and the therapeutic agents used to treat it. To fully utilize the capabilities of MLAs in oncology, it is important to understand the fundamental concepts underlying these algorithms and how they can be applied to assess the efficacy and toxicity of therapeutics. In this perspective, we lay out approaches to represent both the disease state and the therapeutic agents used by MLAs to derive novel insights and make relevant predictions.
    MeSH term(s) Artificial Intelligence ; Machine Learning ; Algorithms ; Drug Discovery ; Medical Oncology
    Language English
    Publishing date 2023-02-21
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ISSN 2667-2375
    ISSN (online) 2667-2375
    DOI 10.1016/j.crmeth.2023.100413
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Novel Chromatin Insulating Activities Uncovered upon Eliminating Known Insulators

    Ortabozkoyun, Havva / Huang, Pin-Yao / Cho, Hyunwoo / Tsirigos, Aristotelis / Mazzoni, Esteban / Reinberg, Danny

    bioRxiv : the preprint server for biology

    2023  

    Abstract: CCCTC-binding factor (CTCF) and MAZ are recognized insulators required for shielding repressed posterior genes from active anterior genes within ... ...

    Abstract CCCTC-binding factor (CTCF) and MAZ are recognized insulators required for shielding repressed posterior genes from active anterior genes within the
    Language English
    Publishing date 2023-04-25
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.04.25.538167
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: On Epigenetic Plasticity and Genome Topology.

    Lazaris, Charalampos / Aifantis, Iannis / Tsirigos, Aristotelis

    Trends in cancer

    2020  Volume 6, Issue 3, Page(s) 177–180

    Abstract: Mounting evidence links genetic lesions with genome topology alterations and aberrant gene activation. However, the role of epigenetic plasticity remains elusive. Emerging studies implicate DNA methylation, transcriptional elongation, long noncoding RNAs ...

    Abstract Mounting evidence links genetic lesions with genome topology alterations and aberrant gene activation. However, the role of epigenetic plasticity remains elusive. Emerging studies implicate DNA methylation, transcriptional elongation, long noncoding RNAs (lncRNAs), and CCCTC-binding factor (CTCF)-RNA interactions, but systematic approaches are needed to fully decipher the role of epigenetic plasticity in genome integrity and function.
    MeSH term(s) Cell Transformation, Neoplastic/genetics ; Chromatin/genetics ; Chromatin/ultrastructure ; Epigenomics ; Gene Expression Regulation, Neoplastic/genetics ; Genome, Human ; Humans ; Models, Genetic ; Neoplasms/genetics ; Oncogenes ; Transcription, Genetic
    Chemical Substances Chromatin
    Language English
    Publishing date 2020-02-07
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; 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.2020.01.006
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  6. Article: Integrative CRISPR Activation and Small Molecule Inhibitor Screening for lncRNA Mediating BRAF Inhibitor Resistance in Melanoma.

    Shamloo, Sama / Kloetgen, Andreas / Petroulia, Stavroula / Hockemeyer, Kathryn / Sievers, Sonja / Tsirigos, Aristotelis / Aifantis, Ioannis / Imig, Jochen

    Biomedicines

    2023  Volume 11, Issue 7

    Abstract: The incidence of melanoma, being one of the most commonly occurring cancers, has been rising since the past decade. Patients at advanced stages of the disease have very poor prognoses, as opposed to at the earlier stages. The conventional targeted ... ...

    Abstract The incidence of melanoma, being one of the most commonly occurring cancers, has been rising since the past decade. Patients at advanced stages of the disease have very poor prognoses, as opposed to at the earlier stages. The conventional targeted therapy is well defined and effective for advanced-stage melanomas for patients not responding to the standard-of-care immunotherapy. However, targeted therapies do not prove to be as effective as patients inevitably develop V-Raf Murine Sarcoma Viral Oncogene Homolog B (BRAF)-inhibitor resistance to the respective drugs. Factors which are driving melanoma drug resistance mainly involve mutations in the mitogen-activated protein kinase (
    Language English
    Publishing date 2023-07-21
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2720867-9
    ISSN 2227-9059
    ISSN 2227-9059
    DOI 10.3390/biomedicines11072054
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: 3D Chromosomal Landscapes in Hematopoiesis and Immunity.

    Kloetgen, Andreas / Thandapani, Palaniraja / Tsirigos, Aristotelis / Aifantis, Iannis

    Trends in immunology

    2019  Volume 40, Issue 9, Page(s) 809–824

    Abstract: Epigenetic dysregulation plays a profound role in the pathogenesis of hematological malignancies, which is often the result of somatic mutations of chromatin regulators. Previously, these mutations were largely considered to alter gene expression in two ... ...

    Abstract Epigenetic dysregulation plays a profound role in the pathogenesis of hematological malignancies, which is often the result of somatic mutations of chromatin regulators. Previously, these mutations were largely considered to alter gene expression in two dimensions, by activating or repressing chromatin states; however, research in the last decade has highlighted the increasing impact of the 3D organization of the genome in gene regulation and disease pathogenesis. Here, we summarize the current principles of 3D chromatin organization, how the integrity of the 3D genome governs immune cell development and malignant transformation, as well as how underlying (epi-)genetic drivers of 3D chromatin alterations might act as potential novel therapeutic targets for hematological malignancies.
    MeSH term(s) Animals ; Chromatin/chemistry ; Chromatin/genetics ; Chromatin/immunology ; Hematopoiesis/genetics ; Hematopoiesis/immunology ; Humans ; Imaging, Three-Dimensional
    Chemical Substances Chromatin
    Language English
    Publishing date 2019-08-15
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2036831-8
    ISSN 1471-4981 ; 1471-4906
    ISSN (online) 1471-4981
    ISSN 1471-4906
    DOI 10.1016/j.it.2019.07.003
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1601: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 2: Show issues

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  8. Article ; Online: SETD2

    Contreras Yametti, Gloria P / Robbins, Gabriel / Chowdhury, Ashfiyah / Narang, Sonali / Ostrow, Talia H / Kilberg, Harrison / Greenberg, Joshua / Kramer, Lindsay / Raetz, Elizabeth / Tsirigos, Aristotelis / Evensen, Nikki A / Carroll, William L

    Leukemia & lymphoma

    2024  Volume 65, Issue 1, Page(s) 78–90

    Abstract: Mutations in genes encoding epigenetic regulators are commonly observed at relapse in B cell acute lymphoblastic leukemia (B-ALL). Loss-of-function mutations in SETD2, an H3K36 methyltransferase, have been observed in B-ALL and other cancers. Previous ... ...

    Abstract Mutations in genes encoding epigenetic regulators are commonly observed at relapse in B cell acute lymphoblastic leukemia (B-ALL). Loss-of-function mutations in SETD2, an H3K36 methyltransferase, have been observed in B-ALL and other cancers. Previous studies on mutated SETD2 in solid tumors and acute myelogenous leukemia support a role in promoting resistance to DNA damaging agents. We did not observe chemoresistance, an impaired DNA damage response, nor increased mutation frequency in response to thiopurines using CRISPR-mediated knockout in wild-type B-ALL cell lines. Likewise, restoration of SETD2 in cell lines with hemizygous mutations did not increase sensitivity.
    MeSH term(s) Humans ; Mutation ; Leukemia, Myeloid, Acute ; Recurrence ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/drug therapy ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/genetics
    Language English
    Publishing date 2024-01-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1042374-6
    ISSN 1029-2403 ; 1042-8194
    ISSN (online) 1029-2403
    ISSN 1042-8194
    DOI 10.1080/10428194.2023.2273752
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    Zs.A 2997: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  9. Article: Self-Supervised Learning Reveals Clinically Relevant Histomorphological Patterns for Therapeutic Strategies in Colon Cancer.

    Liu, Bojing / Polack, Meaghan / Coudray, Nicolas / Quiros, Adalberto Claudio / Sakellaropoulos, Theodore / Crobach, Augustinus S L P / van Krieken, J Han J M / Yuan, Ke / Tollenaar, Rob A E M / Mesker, Wilma E / Tsirigos, Aristotelis

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-and-eosin-stained whole-slide images (WSIs). We trained an SSL Barlow Twins-encoder on 435 TCGA colon adenocarcinoma WSIs to ... ...

    Abstract Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-and-eosin-stained whole-slide images (WSIs). We trained an SSL Barlow Twins-encoder on 435 TCGA colon adenocarcinoma WSIs to extract features from small image patches. Leiden community detection then grouped tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival was confirmed in an independent clinical trial cohort (N=1213 WSIs). This unbiased atlas resulted in 47 HPCs displaying unique and sharing clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analysis of these HPCs, including immune landscape and gene set enrichment analysis, and association to clinical outcomes, we shed light on the factors influencing survival and responses to treatments like standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil new insights and aid decision-making and personalized treatments for colon cancer patients.
    Language English
    Publishing date 2024-03-21
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.02.26.582106
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Assessing Drug Development Risk Using Big Data and Machine Learning.

    Vergetis, Vangelis / Skaltsas, Dimitrios / Gorgoulis, Vassilis G / Tsirigos, Aristotelis

    Cancer research

    2020  Volume 81, Issue 4, Page(s) 816–819

    Abstract: Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard ... ...

    Abstract Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard given the complexities of drug biology and clinical trials. This inherent risk is often misunderstood and mischaracterized, leading to inefficient allocation of resources and, as a result, an overall reduction in R&D productivity. Here we argue that the recent resurgence of Machine Learning in combination with the availability of data can provide a more accurate and unbiased estimate of drug development risk.
    MeSH term(s) Antineoplastic Agents/adverse effects ; Big Data ; Drug Delivery Systems/adverse effects ; Drug Delivery Systems/statistics & numerical data ; Drug Development/methods ; Drug Development/standards ; Drug Development/trends ; Drug-Related Side Effects and Adverse Reactions/epidemiology ; Drug-Related Side Effects and Adverse Reactions/etiology ; Female ; Humans ; Machine Learning/statistics & numerical data ; Male ; Neoplasms/drug therapy ; Neoplasms/epidemiology ; Patient Safety/standards ; Risk Assessment
    Chemical Substances Antineoplastic Agents
    Language English
    Publishing date 2020-12-22
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1432-1
    ISSN 1538-7445 ; 0008-5472
    ISSN (online) 1538-7445
    ISSN 0008-5472
    DOI 10.1158/0008-5472.CAN-20-0866
    Database MEDical Literature Analysis and Retrieval System OnLINE

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