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  1. Article ; Online: The eternal quest for self-improvement of somatic cells.

    Campbell, Peter J

    Cell genomics

    2022  Volume 2, Issue 2, Page(s) 100094

    Abstract: Multiple somatic mutations drive cancer cell transformation, but the sequence of events is poorly understood. In this issue ... ...

    Abstract Multiple somatic mutations drive cancer cell transformation, but the sequence of events is poorly understood. In this issue of
    Language English
    Publishing date 2022-02-09
    Publishing country United States
    Document type News
    ISSN 2666-979X
    ISSN (online) 2666-979X
    DOI 10.1016/j.xgen.2022.100094
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  2. Article ; Online: Demystifying the black box: from ignorance to observation to mechanism in cancer research.

    Campbell, Peter J

    European journal of epidemiology

    2022  Volume 38, Issue 12, Page(s) 1265–1267

    MeSH term(s) Humans ; Neoplasms ; Research
    Language English
    Publishing date 2022-11-03
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 632614-6
    ISSN 1573-7284 ; 0393-2990
    ISSN (online) 1573-7284
    ISSN 0393-2990
    DOI 10.1007/s10654-022-00935-9
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  3. Article ; Online: Improving services requires resources not targets.

    Campbell, Peter J

    BMJ (Clinical research ed.)

    2019  Volume 364, Page(s) l830

    MeSH term(s) Efficiency, Organizational/standards ; Emergency Service, Hospital/standards ; Humans ; Quality Improvement/organization & administration ; State Medicine/standards ; United Kingdom
    Language English
    Publishing date 2019-02-22
    Publishing country England
    Document type Letter
    ZDB-ID 1362901-3
    ISSN 1756-1833 ; 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    ISSN (online) 1756-1833
    ISSN 0959-8154 ; 0959-8146 ; 0959-8138 ; 0959-535X ; 1759-2151
    DOI 10.1136/bmj.l830
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  4. Article ; Online: The scope of artificial intelligence in retinopathy of prematurity (ROP) management.

    Maitra, Puja / Shah, Parag K / Campbell, Peter J / Rishi, Pukhraj

    Indian journal of ophthalmology

    2024  

    Abstract: Artificial Intelligence (AI) is a revolutionary technology that has the potential to develop into a widely implemented system that could reduce the dependence on qualified professionals/experts for screening the large at-risk population, especially in ... ...

    Abstract Artificial Intelligence (AI) is a revolutionary technology that has the potential to develop into a widely implemented system that could reduce the dependence on qualified professionals/experts for screening the large at-risk population, especially in the Indian scenario. Deep learning involves learning without being explicitly told what to focus on and utilizes several layers of artificial neural networks (ANNs) to create a robust algorithm that is capable of high-complexity tasks. Convolutional neural networks (CNNs) are a subset of ANNs that are particularly useful for image processing as well as cognitive tasks. Training of these algorithms involves inputting raw human-labeled data, which are then processed through the algorithm's multiple layers and allow CNN to develop their own learning of image features. AI systems must be validated using different population datasets since the performance of the AI system would vary according to the population. Indian datasets have been used in AI-based risk model that could predict whether an infant would develop treatment-requiring retinopathy of prematurity (ROP). AI also served as an epidemiological tool by objectively showing that a higher ROP severity was in Neonatal intensive care units (NICUs) that did not have the resources to monitor and titrate oxygen. There are rising concerns about the medicolegal aspect of AI implementation as well as discussion on the possibilities of catastrophic life-threatening diseases like retinoblastoma and lipemia retinalis being missed by AI. Computer-based systems have the advantage over humans in not being susceptible to biases or fatigue. This is especially relevant in a country like India with an increased rate of ROP and a preexisting strained doctor-to-preterm child ratio. Many AI algorithms can perform in a way comparable to or exceeding human experts, and this opens possibilities for future large-scale prospective studies.
    Language English
    Publishing date 2024-03-08
    Publishing country India
    Document type Journal Article
    ZDB-ID 187392-1
    ISSN 1998-3689 ; 0301-4738
    ISSN (online) 1998-3689
    ISSN 0301-4738
    DOI 10.4103/IJO.IJO_2544_23
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  5. Article ; Online: Cliques and Schisms of Cancer Genes.

    Campbell, Peter J

    Cancer cell

    2017  Volume 32, Issue 2, Page(s) 129–130

    Abstract: With a few exceptions, cancers typically carry more than one driver mutation, sometimes five, ten, or more, and these driver mutations do not necessarily assort randomly. In this issue of Cancer Cell, Mina et al. systematically characterize patterns of ... ...

    Abstract With a few exceptions, cancers typically carry more than one driver mutation, sometimes five, ten, or more, and these driver mutations do not necessarily assort randomly. In this issue of Cancer Cell, Mina et al. systematically characterize patterns of co-mutation and mutual exclusivity in 6,456 cancers across 23 tumor types.
    MeSH term(s) Algorithms ; Computational Biology ; Humans ; Mutation ; Neoplasms/genetics ; Oncogenes
    Language English
    Publishing date 2017--14
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 2078448-X
    ISSN 1878-3686 ; 1535-6108
    ISSN (online) 1878-3686
    ISSN 1535-6108
    DOI 10.1016/j.ccell.2017.07.009
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  6. Article ; Online: Genome Sequencing during a Patient's Journey through Cancer.

    Nangalia, Jyoti / Campbell, Peter J

    The New England journal of medicine

    2019  Volume 381, Issue 22, Page(s) 2145–2156

    MeSH term(s) Genome ; Germ-Line Mutation ; Humans ; Interviews as Topic ; Mutation ; Neoplasms/genetics ; Precision Medicine ; Risk Assessment/methods ; Whole Genome Sequencing/methods
    Language English
    Publishing date 2019-11-24
    Publishing country United States
    Document type Journal Article ; Review ; Video-Audio Media
    ZDB-ID 207154-x
    ISSN 1533-4406 ; 0028-4793
    ISSN (online) 1533-4406
    ISSN 0028-4793
    DOI 10.1056/NEJMra1910138
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  7. Article ; Online: Reconstructing phylogenetic trees from genome-wide somatic mutations in clonal samples.

    Coorens, Tim H H / Spencer Chapman, Michael / Williams, Nicholas / Martincorena, Inigo / Stratton, Michael R / Nangalia, Jyoti / Campbell, Peter J

    Nature protocols

    2024  

    Abstract: Phylogenetic trees are a powerful means to display the evolutionary history of species, pathogens and, more recently, individual cells of the human body. Whole-genome sequencing of laser capture microdissections or expanded stem cells has allowed the ... ...

    Abstract Phylogenetic trees are a powerful means to display the evolutionary history of species, pathogens and, more recently, individual cells of the human body. Whole-genome sequencing of laser capture microdissections or expanded stem cells has allowed the discovery of somatic mutations in clones, which can be used as natural barcodes to reconstruct the developmental history of individual cells. Here we describe Sequoia, our pipeline to reconstruct lineage trees from clones of normal cells. Candidate somatic mutations are called against the human reference genome and filtered to exclude germline mutations and artifactual variants. These filtered somatic mutations form the basis for phylogeny reconstruction using a maximum parsimony framework. Lastly, we use a maximum likelihood framework to explicitly map mutations to branches in the phylogenetic tree. The resulting phylogenies can then serve as a basis for many subsequent analyses, including investigating embryonic development, tissue dynamics in health and disease, and mutational signatures. Sequoia can be readily applied to any clonal somatic mutation dataset, including single-cell DNA sequencing datasets, using the commands and scripts provided. Moreover, Sequoia is highly flexible and can be easily customized. Typically, the runtime of the core script ranges from minutes to an hour for datasets with a moderate number (50,000-150,000) of variants. Competent bioinformatic skills, including in-depth knowledge of the R programming language, are required. A high-performance computing cluster (one that is capable of running mutation-calling algorithms and other aspects of the analysis at scale) is also required, especially if handling large datasets.
    Language English
    Publishing date 2024-02-23
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2244966-8
    ISSN 1750-2799 ; 1754-2189
    ISSN (online) 1750-2799
    ISSN 1754-2189
    DOI 10.1038/s41596-024-00962-8
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  8. Article ; Online: Bayesian networks elucidate complex genomic landscapes in cancer.

    Angelopoulos, Nicos / Chatzipli, Aikaterini / Nangalia, Jyoti / Maura, Francesco / Campbell, Peter J

    Communications biology

    2022  Volume 5, Issue 1, Page(s) 306

    Abstract: Bayesian networks (BNs) are disciplined, explainable Artificial Intelligence models that can describe structured joint probability spaces. In the context of understanding complex relations between a number of variables in biological settings, they can be ...

    Abstract Bayesian networks (BNs) are disciplined, explainable Artificial Intelligence models that can describe structured joint probability spaces. In the context of understanding complex relations between a number of variables in biological settings, they can be constructed from observed data and can provide a guiding, graphical tool in exploring such relations. Here we propose BNs for elucidating the relations between driver events in large cancer genomic datasets. We present a methodology that is specifically tailored to biologists and clinicians as they are the main producers of such datasets. We achieve this by using an optimal BN learning algorithm based on well established likelihood functions and by utilising just two tuning parameters, both of which are easy to set and have intuitive readings. To enhance value to clinicians, we introduce (a) the use of heatmaps for families in each network, and (b) visualising pairwise co-occurrence statistics on the network. For binary data, an optional step of fitting logic gates can be employed. We show how our methodology enhances pairwise testing and how biologists and clinicians can use BNs for discussing the main relations among driver events in large genomic cohorts. We demonstrate the utility of our methodology by applying it to 5 cancer datasets revealing complex genomic landscapes. Our networks identify central patterns in all datasets including a central 4-way mutual exclusivity between HDR, t(4,14), t(11,14) and t(14,16) in myeloma, and a 3-way mutual exclusivity of three major players: CALR, JAK2 and MPL, in myeloproliferative neoplasms. These analyses demonstrate that our methodology can play a central role in the study of large genomic cancer datasets.
    MeSH term(s) Algorithms ; Artificial Intelligence ; Bayes Theorem ; Genomics ; Humans ; Neoplasms/genetics
    Language English
    Publishing date 2022-04-04
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ISSN 2399-3642
    ISSN (online) 2399-3642
    DOI 10.1038/s42003-022-03243-w
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  9. Article ; Online: Telomeres and cancer: from crisis to stability to crisis to stability.

    Campbell, Peter J

    Cell

    2012  Volume 148, Issue 4, Page(s) 633–635

    Abstract: Telomere attrition unleashes genomic instability, promoting cancer development. Once established, however, the malignant clone often re-establishes genomic stability through overexpression of telomerase. In two papers, one in this issue of Cell and one ... ...

    Abstract Telomere attrition unleashes genomic instability, promoting cancer development. Once established, however, the malignant clone often re-establishes genomic stability through overexpression of telomerase. In two papers, one in this issue of Cell and one in the subsequent issue, DePinho and colleagues explore the consequences of telomerase re-expression and its validity as a therapeutic target in mouse models of cancer.
    Language English
    Publishing date 2012-02-16
    Publishing country United States
    Document type Comment ; Journal Article
    ZDB-ID 187009-9
    ISSN 1097-4172 ; 0092-8674
    ISSN (online) 1097-4172
    ISSN 0092-8674
    DOI 10.1016/j.cell.2012.01.043
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  10. Article ; Online: Somatic mutation in cancer and normal cells.

    Martincorena, Iñigo / Campbell, Peter J

    Science (New York, N.Y.)

    2015  Volume 349, Issue 6255, Page(s) 1483–1489

    Abstract: Spontaneously occurring mutations accumulate in somatic cells throughout a person's lifetime. The majority of these mutations do not have a noticeable effect, but some can alter key cellular functions. Early somatic mutations can cause developmental ... ...

    Abstract Spontaneously occurring mutations accumulate in somatic cells throughout a person's lifetime. The majority of these mutations do not have a noticeable effect, but some can alter key cellular functions. Early somatic mutations can cause developmental disorders, whereas the progressive accumulation of mutations throughout life can lead to cancer and contribute to aging. Genome sequencing has revolutionized our understanding of somatic mutation in cancer, providing a detailed view of the mutational processes and genes that drive cancer. Yet, fundamental gaps remain in our knowledge of how normal cells evolve into cancer cells. We briefly summarize a number of the lessons learned over 5 years of cancer genome sequencing and discuss their implications for our understanding of cancer progression and aging.
    MeSH term(s) Aging/genetics ; DNA Mutational Analysis ; Evolution, Molecular ; Genome, Human ; Humans ; Mutagenesis ; Mutation ; Neoplasms/epidemiology ; Neoplasms/genetics
    Language English
    Publishing date 2015-09-25
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.aab4082
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