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  • 1
    Online-Ressource
    Online-Ressource
    Elsevier BV ; 2023
    In:  Advanced Drug Delivery Reviews Vol. 199 ( 2023-08), p. 114974-
    In: Advanced Drug Delivery Reviews, Elsevier BV, Vol. 199 ( 2023-08), p. 114974-
    Materialart: Online-Ressource
    ISSN: 0169-409X
    Sprache: Englisch
    Verlag: Elsevier BV
    Publikationsdatum: 2023
    ZDB Id: 2020327-5
    SSG: 15,3
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 2504-2504
    Kurzfassung: Malignancy in cancer is a consequence of the progressive accumulation of mutations in a tumor, with profound implications for drug selection and treatment. However, in human studies, inter-patient variability obscures molecular signatures of tumor progression because patients usually present with a single mammary tumor. In contrast, dogs frequently exhibit multiple naturally occurring mammary tumors in the same individual. Moreover, canine mammary tumors (CMTs) and human breast cancer have similar histopathological profiles and clinical presentation. We leverage the CMT model to elucidate genome-wide molecular changes clinically relevant in human breast cancer, focusing on signals underlying tumor development. We develop a robust, generally applicable, computational analysis framework (FREYA) for analysis of CMTs for comparative oncology. Using FREYA, we RNA profile 89 samples from 16 dogs, and demonstrate that CMTs recapitulate human breast cancer subtypes. We then extract molecular profiles of breast cancer progression at three distinct stages (normal, pre-malignant and malignant) and identify signatures of gene expression reflective of tumor progression. Focusing on the transitions to malignancy, we identify transcriptional patterns and biological pathways specific to malignant tumors and distinct from those characterizing pre-malignant tumors or normal tissue. We find that human breast cancer patients whose tumors exhibit strong CMT malignancy signatures have significantly decreased survival, indicative of the importance of the tumor progression processes identified in CMTs to human breast cancer prognosis. Altogether, our comprehensive genomic characterization demonstrates that CMTs are a powerful translational model of breast cancer, providing insights that inform our understanding of tumor development in humans. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we publicly share all of our data and provide FREYA, a robust data processing pipeline and statistical analyses framework, at freya.flatironinstitute.org. Citation Format: Kiley Graim, Dmitriy Gorenshteyn, David G. Robinson, Nicholas J. Carriero, James Cahill, Rumela Chakrabarti, Michael H. Goldschmidt, Amy C. Durham, Julien Funk, John D. Storey, Vessela N. Kristensen, Chandra L. Theesfeld, Karin U. Sorenmo, Olga G. Troyanskaya. Modeling molecular development of breast cancer in canine mammary tumors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2504.
    Materialart: Online-Ressource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Sprache: Englisch
    Verlag: American Association for Cancer Research (AACR)
    Publikationsdatum: 2020
    ZDB Id: 2036785-5
    ZDB Id: 1432-1
    ZDB Id: 410466-3
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 3
    In: Clinical and Translational Science, Wiley, Vol. 17, No. 3 ( 2024-03)
    Kurzfassung: The purpose of this study was to investigate changes in the lipidome of patients with sepsis to identify signaling lipids associated with poor outcomes that could be linked to future therapies. Adult patients with sepsis were enrolled within 24h of sepsis recognition. Patients meeting Sepsis‐3 criteria were enrolled from the emergency department or intensive care unit and blood samples were obtained. Clinical data were collected and outcomes of rapid recovery, chronic critical illness (CCI), or early death were adjudicated by clinicians. Lipidomic analysis was performed on two platforms, the Sciex™ 5500 device to perform a lipidomic screen of 1450 lipid species and a targeted signaling lipid panel using liquid‐chromatography tandem mass spectrometry. For the lipidomic screen, there were 274 patients with sepsis: 192 with rapid recovery, 47 with CCI, and 35 with early deaths. CCI and early death patients were grouped together for analysis. Fatty acid (FA) 12:0 was decreased in CCI/early death, whereas FA 17:0 and 20:1 were elevated in CCI/early death, compared to rapid recovery patients. For the signaling lipid panel analysis, there were 262 patients with sepsis: 189 with rapid recovery, 45 with CCI, and 28 with early death. Pro‐inflammatory signaling lipids from ω‐6 poly‐unsaturated fatty acids (PUFAs), including 15‐hydroxyeicosatetraenoic (HETE), 12‐HETE, and 11‐HETE (oxidation products of arachidonic acid [AA]) were elevated in CCI/early death patients compared to rapid recovery. The pro‐resolving lipid mediator from ω‐3 PUFAs, 14(S)‐hydroxy docosahexaenoic acid (14S‐HDHA), was also elevated in CCI/early death compared to rapid recovery. Signaling lipids of the AA pathway were elevated in poor‐outcome patients with sepsis and may serve as targets for future therapies.
    Materialart: Online-Ressource
    ISSN: 1752-8054 , 1752-8062
    URL: Issue
    Sprache: Englisch
    Verlag: Wiley
    Publikationsdatum: 2024
    ZDB Id: 2433157-0
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 4
    In: Genome Research, Cold Spring Harbor Laboratory, Vol. 31, No. 2 ( 2021-02), p. 337-347
    Kurzfassung: Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue and benign and malignant tumors from each patient. We showed human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We showed that multiple histological samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework.
    Materialart: Online-Ressource
    ISSN: 1088-9051 , 1549-5469
    RVK:
    Sprache: Englisch
    Verlag: Cold Spring Harbor Laboratory
    Publikationsdatum: 2021
    ZDB Id: 1483456-X
    SSG: 12
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 5
    In: Critical Care Explorations, Ovid Technologies (Wolters Kluwer Health), Vol. 5, No. 6 ( 2023-06), p. e0929-
    Materialart: Online-Ressource
    ISSN: 2639-8028
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2023
    ZDB Id: 3015728-6
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 6
    Online-Ressource
    Online-Ressource
    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 10_Supplement ( 2022-05-15), p. A020-A020
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 10_Supplement ( 2022-05-15), p. A020-A020
    Kurzfassung: Osteosarcoma is a rare pediatric cancer characterized by poor prognosis and a high likelihood of metastasis, for which no systemic treatments have been successfully developed in the past 30 years [1]. The lack of effective biologically relevant osteosarcoma tumor models is a significant obstacle to the development of effective therapeutics [2] . Therefore, developing a framework that addresses this issue is essential in further advancing therapeutics studies. We postulate that a comparative genomics approach, evaluating whole-transcriptome osteosarcoma response in species closely related to humans, would highlight immune and other differences in each species that will better guide the design of human therapeutics. To investigate this, we compile RNA-seq data from studies of mice, human, and canine osteosarcoma [3], and perform an integrated analysis of disease in these three species. We build a tri-species multi-layer network of regulatory interactions in osteosarcomas to identify evolutionary links between osteosarcomas. Each layer of our network represents the interactions in one species, and the integrated network captures interactions preserved across species. We perform enrichment analysis on these networks and statistically evaluate differences between the topologies of the regulatory networks of these three species, investigating the functional impact of species-specific interactions and how each affects drug response. Additionally, we survey the literature to identify mutations in osteosarcomas from each species, and calculate diffusion scores for mutated genes in each layer of the network compared to genes mutated in osteosarcoma. As future work, we aim to utilize our comparative genomics approach and leverage the differences between interaction networks of related species to compute the likelihood of identifying early-stage therapeutic trials that will fail in later stages, thus avoiding costly experiments and guiding drug development. Citation Format: Tina Salehi Torabi, Soumya Gottipati, Tamer Kahveci, Kiley Graim. Multi-species regulatory analysis of osteosarcoma to reduce clinical trial failure rates [abstract] . In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A020.
    Materialart: Online-Ressource
    ISSN: 1538-7445
    Sprache: Englisch
    Verlag: American Association for Cancer Research (AACR)
    Publikationsdatum: 2022
    ZDB Id: 2036785-5
    ZDB Id: 1432-1
    ZDB Id: 410466-3
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 7
    In: Shock, Ovid Technologies (Wolters Kluwer Health), Vol. 58, No. 1 ( 2022-7), p. 20-27
    Kurzfassung: Objective: The aim of this study was to characterize early urinary gene expression differences between patients with sepsis and patients with sterile inflammation and summarize in terms of a reproducible sepsis probability score. Design: This was a prospective observational cohort study. Setting: The study was conducted in a quaternary care academic hospital. Patients: One hundred eighty-six sepsis patients and 78 systemic inflammatory response syndrome (SIRS) patients enrolled between January 2015 and February 2018. Interventions: Whole-genome transcriptomic analysis of RNA was extracted from urine obtained from sepsis patients within 12 hours of sepsis onset and from patients with surgery-acquired SIRS within 4 hours after major inpatient surgery. Measurements and Main Results: We identified 422 of 23,956 genes (1.7%) that were differentially expressed between sepsis and SIRS patients. Differentially expressed probes were provided to a collection of machine learning feature selection models to identify focused probe sets that differentiate between sepsis and SIRS. These probe sets were combined to find an optimal probe set (UrSepsisModel) and calculate a urinary sepsis score (UrSepsisScore), which is the geometric mean of downregulated genes subtracted from the geometric mean of upregulated genes. This approach summarizes the expression values of all decisive genes as a single sepsis score. The UrSepsisModel and UrSepsisScore achieved area under the receiver operating characteristic curves 0.91 (95% confidence interval, 0.86–0.96) and 0.80 (95% confidence interval, 0.70–0.88) on the validation cohort, respectively. Functional analyses of probes associated with sepsis demonstrated metabolic dysregulation manifest as reduced oxidative phosphorylation, decreased amino acid metabolism, and decreased oxidation of lipids and fatty acids. Conclusions: Whole-genome transcriptomic profiling of urinary cells revealed focused probe panels that can function as an early diagnostic tool for differentiating sepsis from sterile SIRS. Functional analysis of differentially expressed genes demonstrated a distinct metabolic dysregulation signature in sepsis.
    Materialart: Online-Ressource
    ISSN: 1073-2322
    Sprache: Englisch
    Verlag: Ovid Technologies (Wolters Kluwer Health)
    Publikationsdatum: 2022
    ZDB Id: 2011863-6
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 8
    Online-Ressource
    Online-Ressource
    American Association for Cancer Research (AACR) ; 2023
    In:  Cancer Research Vol. 83, No. 7_Supplement ( 2023-04-04), p. 1922-1922
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 1922-1922
    Kurzfassung: Rapid advancements in genomic sequencing technologies have catalyzed the molecular identification and treatment of previously elusive diseases. Despite these genomic advancements, disparities in precision medicine access and data have resulted in disparate impacts on different socioeconomic groups. Many of the gold standard datasets largely exclude minority populations, compounding their exclusion from medical research. This has resulted in a restricted understanding of cancer and other complex diseases. We demonstrate the effects of ancestral bias in gold standard genomics datasets through ancestral analysis of cancer-related genes in the Cancer Gene Census, spanning 17 ancestral populations. Additionally, we present a machine learning framework, PhyloFrame, that incorporates population genomics data to correct for ancestral bias by creating disease signatures representative of all ancestries. Our ancestral analysis results show that while a majority of the current cancer-related genes in the Cancer Gene Census have below average mutation frequency in non-European populations, there are peaks of ancestrally enriched mutations in ancestry-specific genes related to cancer, which can be targeted using PhyloFrame. PhyloFrame prioritizes gene expression from the cancer genes with high frequency mutations in a given human population in order to capture genes driving disease in each ancestry. It builds on existing disease gene signatures and big-data functional interaction networks to identify ancestry-relevant genes related to a disease, outputing an ancestry-agnostic disease signature. We test PhyloFrame on TCGA cancers with diverse patient populations, such as breast cancer, and compare PhyloFrame's disease signature output to the disease signature output of elastic net runs on cancer samples from a single ancestry. Our results demonstrate that the incorporation of ancestral information allows PhyloFrame to recapitulate disease signatures trained on only one ancestry in a dataset with individuals from many unquantified ancestries. With the incorporation of ancestral information, PhyloFrame is able to create disease signatures with genes pertinent to each ancestral population, even when individuals from those populations are not included in the training data. This work offers a quick and cheap alternative to the mass sequencing that would be required to capture disease-driving genes in minority populations in hopes to contribute to equitable representation in medical research. Citation Format: Leslie A. Smith, James A. Cahill, Kiley Graim. PhyloFrame: A machine learning framework for ancestry agnostic disease signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1922.
    Materialart: Online-Ressource
    ISSN: 1538-7445
    Sprache: Englisch
    Verlag: American Association for Cancer Research (AACR)
    Publikationsdatum: 2023
    ZDB Id: 2036785-5
    ZDB Id: 1432-1
    ZDB Id: 410466-3
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 9
    Online-Ressource
    Online-Ressource
    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 5030-5030
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 5030-5030
    Kurzfassung: Despite tremendous improvements in breast cancer detection, it remains unclear which early-stage tumors will later become aggressive. Dogs provide a unique opportunity to unravel early-stage tumor evolution, as dogs frequently develop multiple naturally occurring mammary tumors. To identify which early-stage human breast tumors will later become malignant, we analyzed mutational profiles of mammary tumors from two canine cohorts to identify patterns of tumor evolution. We compared our dog tumor signatures with human breast cancers. We found that dogs and humans share many known cancer driver mutations. Additionally, three of the five dogs with at least five mammary tumors had a single driver mutation present in most of their tumors. Despite sharing patient-level exposures and environment, passenger mutations significantly differ between these tumors, suggesting independent primaries initiated by a catalyzing factor causing the same driver mutation in each tumor. However, tumors from the same dog with the same driver gene mutations rarely exhibit the same expression-based breast cancer subtypes. Our analysis highlights the mutational similarities of dog and human breast cancers and provides insight into their development. Citation Format: Kiley Graim. Intra-patient tumor evolution analysis in dogs with many mammary tumors identifies signatures of tumor aggression in early-stage breast cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5030.
    Materialart: Online-Ressource
    ISSN: 1538-7445
    Sprache: Englisch
    Verlag: American Association for Cancer Research (AACR)
    Publikationsdatum: 2022
    ZDB Id: 2036785-5
    ZDB Id: 1432-1
    ZDB Id: 410466-3
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 10
    In: npj Precision Oncology, Springer Science and Business Media LLC, Vol. 6, No. 1 ( 2022-04-25)
    Kurzfassung: Leiomyosarcoma (LMS) is a rare, aggressive, mesenchymal tumor. Subsets of LMS have been identified to harbor genomic alterations associated with homologous recombination deficiency (HRD); particularly alterations in BRCA2 . Whereas genomic loss of heterozygosity (gLOH) has been used as a surrogate marker of HRD in other solid tumors, the prognostic or clinical value of gLOH in LMS (gLOH-LMS) remains poorly defined. We explore the genomic drivers associated with gLOH-LMS and their clinical import. Although the distribution of gLOH-LMS scores are similar to that of carcinomas, outside of BRCA2 , there was no overlap with previously published gLOH-associated genes from studies in carcinomas. We note that early stage tumors with elevated gLOH demonstrated a longer disease-free interval following resection in LMS patients. Taken together, and despite similarities to carcinomas in gLOH distribution and clinical import, gLOH-LMS are driven by different genomic signals. Additional studies will be required to isolate and confirm the unique differences in biological factors driving these differences.
    Materialart: Online-Ressource
    ISSN: 2397-768X
    Sprache: Englisch
    Verlag: Springer Science and Business Media LLC
    Publikationsdatum: 2022
    ZDB Id: 2891458-2
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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