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  • 1
    Online Resource
    Online Resource
    Wiley ; 2014
    In:  Proteins: Structure, Function, and Bioinformatics Vol. 82, No. 9 ( 2014-09), p. 1937-1946
    In: Proteins: Structure, Function, and Bioinformatics, Wiley, Vol. 82, No. 9 ( 2014-09), p. 1937-1946
    Type of Medium: Online Resource
    ISSN: 0887-3585
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2014
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  • 2
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 29, No. 7 ( 2023-04-03), p. 1317-1331
    Abstract: ALK-activating mutations are identified in approximately 10% of newly diagnosed neuroblastomas and ALK amplifications in a further 1%–2% of cases. Lorlatinib, a third-generation anaplastic lymphoma kinase (ALK) inhibitor, will soon be given alongside induction chemotherapy for children with ALK-aberrant neuroblastoma. However, resistance to single-agent treatment has been reported and therapies that improve the response duration are urgently required. We studied the preclinical combination of lorlatinib with chemotherapy, or with the MDM2 inhibitor, idasanutlin, as recent data have suggested that ALK inhibitor resistance can be overcome through activation of the p53-MDM2 pathway. Experimental Design: We compared different ALK inhibitors in preclinical models prior to evaluating lorlatinib in combination with chemotherapy or idasanutlin. We developed a triple chemotherapy (CAV: cyclophosphamide, doxorubicin, and vincristine) in vivo dosing schedule and applied this to both neuroblastoma genetically engineered mouse models (GEMM) and patient-derived xenografts (PDX). Results: Lorlatinib in combination with chemotherapy was synergistic in immunocompetent neuroblastoma GEMM. Significant growth inhibition in response to lorlatinib was only observed in the ALK-amplified PDX model with high ALK expression. In this PDX, lorlatinib combined with idasanutlin resulted in complete tumor regression and significantly delayed tumor regrowth. Conclusions: In our preclinical neuroblastoma models, high ALK expression was associated with lorlatinib response alone or in combination with either chemotherapy or idasanutlin. The synergy between MDM2 and ALK inhibition warrants further evaluation of this combination as a potential clinical approach for children with neuroblastoma.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 83, No. 7_Supplement ( 2023-04-04), p. 234-234
    Abstract: Advancements in state-of-the-art molecular profiling techniques have resulted in better understanding of pediatric cancers and driver events. It has become apparent that pediatric cancers are significantly more heterogeneous than previously thought as evidenced by the number of novel entities and subtypes that have been identified with distinct molecular and clinical characteristics. For most of these newly recognized entities there are extremely limited treatment options available. The ITCC-P4 consortium is an international collaboration between several European academic centers and pharmaceutical companies, with the overall aim to establish a sustainable platform of & gt;400 molecularly well-characterized PDX models of high-risk pediatric cancers, their tumors and matching controls and to use the PDX models for in vivo testing of novel mechanism-of-action based treatments. Currently, 251 models are fully characterized, including 182 brain and 69 non-brain PDX models, representing 112 primary models, 92 relapse, 42 metastasis and 4 progressions under treatment models. Using low coverage whole-genome and whole exome sequencing, somatic mutation calling, DNA copy number and methylation analysis we aim to define genetic features in our PDX models and estimate the molecular fidelity of PDX models compared to their patient tumor. Based on DNA methylation profiling we identified 43 different tumor subgroups within 18 cancer entities. Mutational landscape analysis identified key somatic and germline oncogenic drivers. Ependymoma PDX models displayed the C11orf95-RELA fusion event, YAP1, C11orf95 and RELA structural variants. Medulloblastoma models were driven by MYCN, TP53, GLI2, SUFU and PTEN. High-grade glioma samples showed TP53, ATRX, MYCN and PIK3CA somatic SNVs, along with focal deletions in CDKN2A in chromosome 9. Neuroblastoma models were enriched for ALK SNVs and/or MYCN focal amplification, ATRX SNVs and CDKN2A/B deletions. Tumor mutational burden across entities and copy number analysis was performed to identify allele-specific copy number detection in tumor-normal pairs. Large chromosomal aberrations (deletions, duplications) detected in the PDX models were concurrent with molecular alterations frequently observed in each tumor type -isochromosome 17 was detected in 5 medulloblastoma models, while deletion of chromosome arm 1p or gain of parts of 17q in neuroblastomas which correlate with tumor progression. We observe clonal evolution of somatic variants not only in certain PDX-tumor pairs but also between disease states. The multi-omics approach in this study provides insight into the mutational landscape and patterns of the PDX models thus providing an overview of molecular mechanisms facilitating the identification and prioritization of oncogenic drivers and potential biomarkers for optimal treatment therapies. Citation Format: Apurva Gopisetty, Aniello Federico, Didier Surdez, Yasmine Iddir, Sakina Zaidi, Alexandra Saint-Charles, Joshua Waterfall, Elnaz Saberi-Ansari, Justyna Wierzbinska, Andreas Schlicker, Norman Mack, Benjamin Schwalm, Christopher Previti, Lena Weiser, Ivo Buchhalter, Anna-Lisa Böttcher, Martin Sill, Robert Autry, Frank Estermann, David Jones, Richard Volckmann, Danny Zwijnenburg, Angelika Eggert, Olaf Heidenreich, Fatima Iradier, Irmela Jeremias, Heinrich Kovar, Jan-Henning Klusmann, Klaus-Michael Debatin, Simon Bomken, Petra Hamerlik, Maureen Hattersley, Olaf Witt, Louis Chesler, Alan Mackay, Johannes Gojo, Stefano Cairo, Julia Schueler, Johannes Schulte, Birgit Geoerger, Jan J. Molenaar, David J. Shields, Hubert N. Caron, Gilles Vassal, Louis F. Stancato, Stefan M. Pfister, Natalie Jaeger, Jan Koster, Marcel Kool, Gudrun Schleiermacher. ITCC-P4: Genomic profiling and analyses of pediatric patient tumor and patient-derived xenograft (PDX) models for high throughput in vivo testing [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 234.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 4
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_7 ( 2022-11-14), p. vii126-vii127
    Abstract: Advancements in state-of-the-art molecular profiling techniques has resulted in better understanding of pediatric cancers and their drivers. Conversely, it also became apparent that pediatric cancers are much more heterogeneous than previously thought. Many new types and subtypes of pediatric cancers have been identified with distinct molecular and clinical characteristics. However, for most newly recognized entities there is no specific treatment available yet. The ITCC-P4 consortium is a collaboration between many academic centers across Europe and several pharmaceutical companies involved in preclinical testing, with the overall aim to establish a sustainable platform of & gt;400 molecularly well-characterized PDX models of high-risk pediatric cancers and to use them for in vivo testing of novel mechanism-of-action based treatments. Currently, 340 models are fully established, including 87 brain and 253 non-brain tumor models, together representing different tumor types both from primary (113) and relapsed (92)/metastatic disease (42). 252 of these models have been fully molecularly characterized, representing 18 pediatric cancer entities and 43 different subtypes. Using low coverage whole-genome and whole exome sequencing, somatic mutation calling, DNA copy number, transcriptome analysis and methylation profiling we have observed that the molecular profile of most PDX models closely mimics their original tumors. Clonal evolution of somatic variants was only observed in some PDX-tumor pairs or so between disease states. Somatic copy number variant analysis highlights specific alterations for instance MYB, MYC, MYCN, NTRK3, PTEN loss differently distributed between PDX-patient tumor pairs in high-grade gliomas. Overall, our results show that we have established & gt;250 PDX models of solid pediatric cancers, that well represents the disease spectrum and that is currently being used for in vivo testing of standard of care drugs and targeted small molecules. Treatment responses will be directly linked to molecular data to identify potential biomarkers for prioritization or deprioritization of individual, patient-specific specific drugs.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
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  • 5
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 24, No. Supplement_1 ( 2022-06-03), p. i189-i189
    Abstract: Thanks to state-of-the-art molecular profiling techniques we by now have a much better understanding of pediatric cancers and what is driving them. On the other hand, we have also realized that pediatric cancers are much more heterogeneous than previously thought. Many new types and subtypes of pediatric cancers have been identified with distinct molecular and clinical characteristics. However, for many if not most of these new types and subtypes there is no specific treatment available, yet. In order to develop specific treatment protocols and to increase survival rates for pediatric cancer patients further, both at diagnosis and relapse/metastasis, we need a large collection of well-characterized preclinical models representing all the different types and subtypes. These models can be used for preclinical drug testing to prioritize the pediatric development of anticancer drugs that would be best targeting pediatric tumor biology. The ITCC-P4 consortium, which is a collaboration between many academic centers across Europe, several companies involved in in vivo preclinical testing, and ten pharmaceutical companies, started in 2017 with the overall aim to establish a sustainable platform of & gt;400 molecularly well-characterized PDX models of high-risk pediatric cancers and to use them for in vivo testing of novel mechanism-of-action based treatments. Currently, 340 models have been fully established, including 87 brain tumor models and 253 non-brain tumor models, together representing many different tumor types both from primary and relapsed/metastatic disease. Out of these 340 models, 252 have been fully molecularly characterized, most of them together with their matching original tumors, and almost of all these models are currently being subjected to in vivo testing using three standard of care drugs and six novel mechanism-of-action based drugs. In this presentation, an update on the current status of the ITCC-P4 platform and the data we collectively have generated thus far will be presented.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
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  • 6
    Online Resource
    Online Resource
    Elsevier BV ; 2017
    In:  Genomics, Proteomics & Bioinformatics Vol. 15, No. 6 ( 2017-12), p. 396-404
    In: Genomics, Proteomics & Bioinformatics, Elsevier BV, Vol. 15, No. 6 ( 2017-12), p. 396-404
    Type of Medium: Online Resource
    ISSN: 1672-0229
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2017
    detail.hit.zdb_id: 2233708-8
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  • 7
    In: Cell Reports, Elsevier BV, Vol. 42, No. 9 ( 2023-09), p. 113132-
    Type of Medium: Online Resource
    ISSN: 2211-1247
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2649101-1
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  • 8
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2009-12)
    Abstract: Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs. Results We present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of E. coli and S. cereviciae , and also on non-biological networks: a social and an electronic network. Conclusion The efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: http://Lbb.ut.ac.ir/Download/LBBsoft/Kavosh/ .
    Type of Medium: Online Resource
    ISSN: 1471-2105
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2009
    detail.hit.zdb_id: 2041484-5
    SSG: 12
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