LIVIVO - Das Suchportal für Lebenswissenschaften

switch to English language
Erweiterte Suche

Suchergebnis

Treffer 1 - 2 von insgesamt 2

Suchoptionen

  1. Artikel ; Online: Deep Learning Model for Tumor Type Prediction using Targeted Clinical Genomic Sequencing Data.

    Darmofal, Madison / Suman, Shalabh / Atwal, Gurnit / Toomey, Michael / Chen, Jie-Fu / Chang, Jason C / Vakiani, Efsevia / Varghese, Anna M / Balakrishnan Rema, Anoop / Syed, Aijazuddin / Schultz, Nikolaus / Berger, Michael F / Morris, Quaid

    Cancer discovery

    2024  

    Abstract: Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor type classifiers trained on genomic features have been explored, but the most ... ...

    Abstract Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a dataset of 39,787 solid tumors sequenced using a clinical targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivalling performance of WGS-based methods. GDD-ENS can also guide diagnoses on rare type and cancers of unknown primary, and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically-relevant tumor type predictions to guide treatment decisions in real time.
    Sprache Englisch
    Erscheinungsdatum 2024-02-27
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2625242-9
    ISSN 2159-8290 ; 2159-8274
    ISSN (online) 2159-8290
    ISSN 2159-8274
    DOI 10.1158/2159-8290.CD-23-0996
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Zusatzmaterialien

    Kategorien

    Verfügbar in ZB MED Köln/Königswinter

    Zs.A 6916: Hefte anzeigen Standort:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  2. Artikel ; Online: Enhanced clinical assessment of hematologic malignancies through routine paired tumor and normal sequencing.

    Ptashkin, Ryan N / Ewalt, Mark D / Jayakumaran, Gowtham / Kiecka, Iwona / Bowman, Anita S / Yao, JinJuan / Casanova, Jacklyn / Lin, Yun-Te David / Petrova-Drus, Kseniya / Mohanty, Abhinita S / Bacares, Ruben / Benhamida, Jamal / Rana, Satshil / Razumova, Anna / Vanderbilt, Chad / Balakrishnan Rema, Anoop / Rijo, Ivelise / Son-Garcia, Julie / de Bruijn, Ino /
    Zhu, Menglei / Lachhander, Sean / Wang, Wei / Haque, Mohammad S / Seshan, Venkatraman E / Wang, Jiajing / Liu, Ying / Nafa, Khedoudja / Borsu, Laetitia / Zhang, Yanming / Aypar, Umut / Suehnholz, Sarah P / Chakravarty, Debyani / Park, Jae H / Abdel-Wahab, Omar / Mato, Anthony R / Xiao, Wenbin / Roshal, Mikhail / Yabe, Mariko / Batlevi, Connie Lee / Giralt, Sergio / Salles, Gilles / Rampal, Raajit / Tallman, Martin / Stein, Eytan M / Younes, Anas / Levine, Ross L / Perales, Miguel-Angel / van den Brink, Marcel R M / Dogan, Ahmet / Ladanyi, Marc / Berger, Michael F / Brannon, A Rose / Benayed, Ryma / Zehir, Ahmet / Arcila, Maria E

    Nature communications

    2023  Band 14, Heft 1, Seite(n) 6895

    Abstract: Genomic profiling of hematologic malignancies has augmented our understanding of variants that contribute to disease pathogenesis and supported development of prognostic models that inform disease management in the clinic. Tumor only sequencing assays ... ...

    Abstract Genomic profiling of hematologic malignancies has augmented our understanding of variants that contribute to disease pathogenesis and supported development of prognostic models that inform disease management in the clinic. Tumor only sequencing assays are limited in their ability to identify definitive somatic variants, which can lead to ambiguity in clinical reporting and patient management. Here, we describe the MSK-IMPACT Heme cohort, a comprehensive data set of somatic alterations from paired tumor and normal DNA using a hybridization capture-based next generation sequencing platform. We highlight patterns of mutations, copy number alterations, and mutation signatures in a broad set of myeloid and lymphoid neoplasms. We also demonstrate the power of appropriate matching to make definitive somatic calls, including in patients who have undergone allogeneic stem cell transplant. We expect that this resource will further spur research into the pathobiology and clinical utility of clinical sequencing for patients with hematologic neoplasms.
    Mesh-Begriff(e) Humans ; Neoplasms/genetics ; Hematologic Neoplasms/diagnosis ; Hematologic Neoplasms/genetics ; Hematologic Neoplasms/therapy ; Mutation ; High-Throughput Nucleotide Sequencing ; DNA
    Chemische Substanzen DNA (9007-49-2)
    Sprache Englisch
    Erscheinungsdatum 2023-10-28
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-42585-9
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

Zum Seitenanfang