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  • 1
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 361, No. 6405 ( 2018-08-31)
    Abstract: Sarcomas are cancers of the bone and soft tissue often defined by gene fusions. Ewing sarcoma involves fusions between EWSR1 , a gene encoding an RNA binding protein, and E26 transformation-specific (ETS) transcription factors. We explored how and when EWSR1-ETS fusions arise by studying the whole genomes of Ewing sarcomas. In 52 of 124 (42%) of tumors, the fusion gene arises by a sudden burst of complex, loop-like rearrangements, a process called chromoplexy, rather than by simple reciprocal translocations. These loops always contained the disease-defining fusion at the center, but they disrupted multiple additional genes. The loops occurred preferentially in early replicating and transcriptionally active genomic regions. Similar loops forming canonical fusions were found in three other sarcoma types. Chromoplexy-generated fusions appear to be associated with an aggressive form of Ewing sarcoma. These loops arise early, giving rise to both primary and relapse Ewing sarcoma tumors, which can continue to evolve in parallel.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2018
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    detail.hit.zdb_id: 2066996-3
    detail.hit.zdb_id: 2060783-0
    SSG: 11
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  • 2
    In: Cell Reports, Elsevier BV, Vol. 16, No. 7 ( 2016-08), p. 2032-2046
    Type of Medium: Online Resource
    ISSN: 2211-1247
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2016
    detail.hit.zdb_id: 2649101-1
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  • 3
    In: Applied Sciences, MDPI AG, Vol. 13, No. 10 ( 2023-05-18), p. 6209-
    Abstract: Our goal was to develop and characterize a Natural Language Processing (NLP) algorithm to extract Eastern Cooperative Oncology Group Performance Status (ECOG PS) from unstructured electronic health record (EHR) sources to enhance observational datasets. By scanning unstructured EHR-derived documents from a real-world database, the NLP algorithm assigned ECOG PS scores to patients diagnosed with one of 21 cancer types who lacked structured ECOG PS numerical scores, anchored to the initiation of treatment lines. Manually abstracted ECOG PS scores were used as a source of truth to both develop the algorithm and evaluate accuracy, sensitivity, and positive predictive value (PPV). Algorithm performance was further characterized by investigating the prognostic value of composite ECOG PS scores in patients with advanced non-small cell lung cancer receiving first line treatment. Of N = 480,825 patient-lines, structured ECOG PS scores were available for 290,343 (60.4%). After applying NLP-extraction, the availability increased to 73.2%. The algorithm’s overall accuracy, sensitivity, and PPV were 93% (95% CI: 92–94%), 88% (95% CI: 87–89%), and 88% (95% CI: 87–89%), respectively across all cancer types. In a cohort of N = 51,948 aNSCLC patients receiving 1L therapy, the algorithm improved ECOG PS completeness from 61.5% to 75.6%. Stratification by ECOG PS showed worse real-world overall survival (rwOS) for patients with worse ECOG PS scores. We developed an NLP algorithm to extract ECOG PS scores from unstructured EHR documents with high accuracy, improving data completeness for EHR-derived oncology cohorts.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704225-X
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  • 4
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 2506-2506
    Abstract: Background, Rationale and Experimental Approach Oncogenic fusions are generated via chromosomal rearrangements resulting in an exchange of coding or regulatory DNA sequences. These mutations play an important role in disease onset and subsequent cancer progression, however the exact timing and mechanisms by which they arise are unknown. Through the SickKids clinical sequencing program, KiCS, we explored how and when canonical fusions arise by studying the whole-genomes of childhood cancers with diagnostic or driver fusions. Our investigation began with the pediatric bone cancer, Ewing sarcoma, and later expanded to include other solid, blood, and brain cancers such as papillary thyroid carcinoma, myeloid leukemia, and ependymoma among others. Results The starting point of our investigation was ES, where we sequenced the whole-genomes of 124 cases. Ewing sarcoma (ES) represents the prototypical fusion-driven sarcoma as it is characterized and driven by the EWSR1-ETS fusion. In ~42% of cases, we found that the ES fusion gene arises by chromoplexy, a sudden burst of complex, loop-like rearrangements, rather than by simple reciprocal translocations as previously thought. We show that these rearrangements rapidly and dramatically altered the chromosomal landscape of ES tumors, producing the driver EWSR1-ETS fusion and disrupting numerous other genes in a short time. Remarkably, these complex rearrangements are enriched for genes, including those with a clear role in oncogenesis, and are associated with the earliest replicating portions of the genome. We then sequenced the genomes of 30 other childhood cancers with oncogenic fusions to study their timing and formation mechanisms. In doing so, we have identified several novel fusions in many cancer types, which have been validated by RNA sequencing and cytogenetics. In some cases, the presence of these chromoplectic fusions indicates these patients may benefit from targeted therapy due to the generation of druggable fusions. . Conclusions Our findings provide fundamental insights into the pathogenesis of gene fusions in human cancer. They reveal complex DNA rearrangements to be a mutational process underpinning gene fusions in cancer that influences tumorigenesis. Citation Format: Nathaniel D. Anderson, Richard de Borja, Matthew D. Young, Fabio Fuligni, Andrej Rosic, Nicola D. Roberts, Nischalan Pillay, Jeffrey A. Toretsky, Yoshida Akihiko, Tatsuhiro Shibata, Markus Metzler, Gino Somers, Stephen W. Scherer, Adrienne M. Flanagan, Peter J. Campbell, Joshua D. Schiffman, Mary Shago, Ludmil B. Alexandrov, Jay S. Wunder, Irene L. Andrulis, David Malkin, Sam Behjati, Adam Shlien. Exploring the complex etiology of oncogenic fusions in childhood cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2506.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
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    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 5
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2015
    In:  Cancer Research Vol. 75, No. 22_Supplement_2 ( 2015-11-15), p. B1-12-B1-12
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 22_Supplement_2 ( 2015-11-15), p. B1-12-B1-12
    Abstract: The transcriptomes of cancer cells differ structurally and numerically from normal tissue and between different types of cancer. However, a comprehensive large-scale analysis of structural transcriptomic events has been intractable so far due to computational limitations and the lack of an integrative software tool to detect a complete list of events. We developed an optimized computation pipeline, named RNA Architect, which enables the detection of such events from RNA sequencing data for thousands of cancer samples. RNA Architect detects events by discordant read pair and split-read analysis down to the resolution of individual breakpoints. Beside different kinds of fusion events (as in-frame, out of frame, fusions to intergenic regions), it reports a comprehensive overview of other transcriptional events including specific and novel splice-forms, inversions, cryptic and alternative splice sites, exon skips, exon re-usages, and early polyadenylation sites. In this poster, we describe the individual steps of the algorithm as well as our latest benchmarking results on a compilation of literature curated gene fusions: the approach shows a high sensitivity and recovers 92% of known fusion events. We also describe the post-processing of the data, which enables us to delineate relevant events from non-cancer specific ones and noise. We especially discuss optimizations implemented to run the software efficiently on a state of the art compute cluster. These optimizations enable the computation of a large amount of samples in a reasonable time frame (~ 8h on a 100 CPU system for the core split-read algorithm and ~3 hours for the discordant pair analysis on a 50 CPU system per sample). The high performance of the pipeline allows generating a comprehensive catalogue of all transcriptional events from large sets of cancer samples. RNA Architect is also fast enough to be used in time critical clinical settings as a potential diagnostic tool. Citation Format: Roland Chrisitian Arnold, Andrej Rosic, Reid JP Hayes, Adam Shlien. RNA Architect: High performance tools to exhaustively detect cancer-specific structural events in RNA sequencing data. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-12.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
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    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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