{"entity": "researcher", "timestamp": "2026-11-06T19:58:07.199Z", "family": "Schwab", "given": "Matthias", "initials": "M", "orcid": "0000-0002-9984-075X", "affiliations": ["Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany.", "Image-Guided and Functionally Instructed Tumor Therapies Cluster of Excellence (iFIT), University of T\u00fcbingen, T\u00fcbingen, Germany.", "Department of Clinical Pharmacology, University of T\u00fcbingen, T\u00fcbingen, Germany.", "Department of Biochemistry and Pharmacy, University of T\u00fcbingen, T\u00fcbingen, Germany.", "German Cancer Consortium (DKTK), German Cancer Research Center, Partner Site T\u00fcbingen, T\u00fcbingen, Germany."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/a671991e1b85463e8a7bbad2e29fb1bd.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/a671991e1b85463e8a7bbad2e29fb1bd"}}, "publications": [{"entity": "publication", "iuid": "ac580aabc9cd4ad0a35c5d1bde9d60ca", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/ac580aabc9cd4ad0a35c5d1bde9d60ca.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/ac580aabc9cd4ad0a35c5d1bde9d60ca"}}, "title": "Integration of germline pharmacogenomic burden to predict fluoropyrimidine-related toxicity - A secondary analysis of the PREPARE trial.", "authors": [{"family": "De Mattia", "given": "Elena", "initials": "E", "orcid": "0000-0003-4948-8767", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e8a9a17bfbf04ae4bc963b8e9dd96991.json"}}, {"family": "Park", "given": "Yoomi", "initials": "Y"}, {"family": "Peruzzi", "given": "Elena", "initials": "E"}, {"family": "Zhou", "given": "Yitian", "initials": "Y", "orcid": "0000-0003-3066-5444", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c4e92db79d554ad283927ad7e6e54e8d.json"}}, {"family": "Roncato", "given": "Rossana", "initials": "R"}, {"family": "Polesel", "given": "Jerry", "initials": "J", "orcid": "0000-0001-9381-1520", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d222504eecb241b3824cad86688aff8b.json"}}, {"family": "Scarabel", "given": "Lucia", "initials": "L"}, {"family": "Schwab", "given": "Matthias", "initials": "M", "orcid": "0000-0002-9984-075X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a671991e1b85463e8a7bbad2e29fb1bd.json"}}, {"family": "Guchelaar", "given": "Henk-Jan", "initials": "HJ"}, {"family": "Swen", "given": "Jesse Joachim", "initials": "JJ"}, {"family": "Spina", "given": "Michele", "initials": "M"}, {"family": "Puglisi", "given": "Fabio", "initials": "F", "orcid": "0000-0003-0573-4938", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/17e87f4beb62427fb1155ca71a3c1388.json"}}, {"family": "Toffoli", "given": "Giuseppe", "initials": "G"}, {"family": "Lauschke", "given": "Volker M", "initials": "VM", "orcid": "0000-0002-1140-6204", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/82048d4da61547d08aa9d2bb3bae12dd.json"}}, {"family": "Cecchin", "given": "Erika", "initials": "E", "orcid": "0000-0001-7517-7490", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3018f538ff124402b050b2e8e6b8f5e0.json"}}], "type": "journal article", "published": "2025-11-00", "journal": {"title": "Oncogene", "issn": "1476-5594", "volume": "44", "issue": "45", "pages": "4352-4362", "issn-l": "0950-9232"}, "abstract": "Testing for four dihydropyrimidine dehydrogenase (DPYD) variants (DPYD*2 A, DPYD*13, c.2846 A > T, DPYD-HapB3) is currently implemented in clinical practice to prevent fluoropyrimidines (FLs) related toxicity but with limited sensitivity. This study aimed to identify novel genetic factors in FL-related genes to enhance risk prediction using data from the PREPARE trial (NCT03093818). Two hundred seventy-four patients receiving FL-based chemotherapy with severe toxicity were sequenced for 60 candidate genes. Gene and pathway-level association analyses focusing mainly on rare variants were performed using dedicated statistical tests, including gene-wise variant burden (GVB) analysis. DPYD germline variant burden beyond the four routinely tested markers emerged to contribute to toxicity, indicating that rarer genetic variants could help in refining the optimal FL dosage (p < 0.1). Functional rare variant burden in ABCB5, PARP1, ENOSF1, CYP3A4 and nuclear receptors pathway impacted on toxicity risk (p < 0.05 in at least one statistical test). GVB analysis confirmed ABCB5 as a significant risk gene and highlighted ABCC4, HNF4A, and XRCC3 as additional candidates. A predictive model combining genetic burden scores with clinical variables improved the identification of high-risk patients (sensitivity=0.71, specificity=0.74, accuracy=0.73). This study indicated a paradigm shift from population to individual-level arguing for an extension of testing beyond the four DPYD currently considered variants to predict FL-related toxicity.", "doi": "10.1038/s41388-025-03587-7", "pmid": "40999005", "labels": {"DDLS Fellow": null, "Yitian Zhou": null}, "xrefs": [{"db": "pii", "key": "10.1038/s41388-025-03587-7"}], "notes": [], "created": "2026-11-06T09:37:33.874Z", "modified": "2026-11-06T09:37:34.147Z"}, {"entity": "publication", "iuid": "f15ea5379f604dd5afc828e8ac6553fb", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f15ea5379f604dd5afc828e8ac6553fb.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f15ea5379f604dd5afc828e8ac6553fb"}}, "title": "Structural variation of the coding and non-coding human pharmacogenome.", "authors": [{"family": "Tremmel", "given": "Roman", "initials": "R", "orcid": "0000-0003-1564-0433", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5dcfb9d17c9340d69ea2c5fcbd06aeec.json"}}, {"family": "Zhou", "given": "Yitian", "initials": "Y"}, {"family": "Schwab", "given": "Matthias", "initials": "M", "orcid": "0000-0002-9984-075X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a671991e1b85463e8a7bbad2e29fb1bd.json"}}, {"family": "Lauschke", "given": "Volker M", "initials": "VM", "orcid": "0000-0002-1140-6204", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/82048d4da61547d08aa9d2bb3bae12dd.json"}}], "type": "journal article", "published": "2023-09-08", "journal": {"title": "NPJ Genom Med", "issn": "2056-7944", "volume": "8", "issue": "1", "pages": "24", "issn-l": null}, "abstract": "Genetic variants in drug targets and genes encoding factors involved in drug absorption, distribution, metabolism and excretion (ADME) can have pronounced impacts on drug pharmacokinetics, response, and toxicity. While the landscape of genetic variability at the level of single nucleotide variants (SNVs) has been extensively studied in these pharmacogenetic loci, their structural variation is only poorly understood. Thus, we systematically analyzed the genetic structural variability across 908 pharmacogenes (344 ADME genes and 564 drug targets) based on publicly available whole genome sequencing data from 10,847 unrelated individuals. Overall, we extracted 14,984 distinct structural variants (SVs) ranging in size from 50 bp to 106 Mb. Each individual harbored on average 10.3 and 1.5 SVs with putative functional effects that affected the coding regions of ADME genes and drug targets, respectively. In addition, by cross-referencing pharmacogenomic SVs with experimentally determined binding data of 224 transcription factors across 130 cell types, we identified 1276 non-coding SVs that overlapped with gene regulatory elements. Based on these data, we estimate that non-coding structural variants account for 22% of the genetically encoded pharmacogenomic variability. Combined, these analyses provide the first comprehensive map of structural variability across pharmacogenes, derive estimates for the functional impact of non-coding SVs and incentivize the incorporation of structural genomic data into personalized drug response predictions.", "doi": "10.1038/s41525-023-00371-y", "pmid": "37684227", "labels": {"DDLS Fellow": null, "Yitian Zhou": null}, "xrefs": [{"db": "pmc", "key": "PMC10491600"}, {"db": "pii", "key": "10.1038/s41525-023-00371-y"}], "notes": [], "created": "2026-11-06T09:33:14.437Z", "modified": "2026-11-06T09:33:14.477Z"}, {"entity": "publication", "iuid": "28a34a109dc24e86a66f6d9edaed4d04", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/28a34a109dc24e86a66f6d9edaed4d04.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/28a34a109dc24e86a66f6d9edaed4d04"}}, "title": "Cross-Ancestry Genome-Wide Association Study Defines the Extended CYP2D6 Locus as the Principal Genetic Determinant of Endoxifen Plasma Concentrations.", "authors": [{"family": "Khor", "given": "Chiea Chuen", "initials": "CC"}, {"family": "Winter", "given": "Stefan", "initials": "S"}, {"family": "Sutiman", "given": "Natalia", "initials": "N"}, {"family": "M\u00fcrdter", "given": "Thomas E", "initials": "TE"}, {"family": "Chen", "given": "Sylvia", "initials": "S", "orcid": "0000-0002-0445-6502", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5310f9983f7c447b8ccd17d80cb2d4a3.json"}}, {"family": "Lim", "given": "Joanne Siok Liu", "initials": "JSL"}, {"family": "Li", "given": "Zheng", "initials": "Z", "orcid": "0000-0002-7060-2213", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a014baeffa7c4317af45a704a24d17c9.json"}}, {"family": "Li", "given": "Jingmei", "initials": "J", "orcid": "0000-0001-8587-7511", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6027dea1185b4a00a77a6bca138eab20.json"}}, {"family": "Sim", "given": "Kar Seng", "initials": "KS", "orcid": "0000-0002-3822-7759", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/7f1c9943279444bfad3e051f04296a71.json"}}, {"family": "Ganchev", "given": "Boian", "initials": "B"}, {"family": "Eccles", "given": "Diana", "initials": "D"}, {"family": "Eccles", "given": "Bryony", "initials": "B"}, {"family": "Tapper", "given": "William", "initials": "W"}, {"family": "Zgheib", "given": "Nathalie K", "initials": "NK", "orcid": "0000-0002-0571-768X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/191f804995e74a6d91b2c3a150be69ee.json"}}, {"family": "Tfayli", "given": "Arafat", "initials": "A"}, {"family": "Ng", "given": "Raymond Chee Hui", "initials": "RCH"}, {"family": "Yap", "given": "Yoon Sim", "initials": "YS"}, {"family": "Lim", "given": "Elaine", "initials": "E"}, {"family": "Wong", "given": "Mabel", "initials": "M"}, {"family": "Wong", "given": "Nan Soon", "initials": "NS"}, {"family": "Ang", "given": "Peter Cher Siang", "initials": "PCS"}, {"family": "Dent", "given": "Rebecca", "initials": "R"}, {"family": "Tremmel", "given": "Roman", "initials": "R", "orcid": "0000-0003-1564-0433", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5dcfb9d17c9340d69ea2c5fcbd06aeec.json"}}, {"family": "Klein", "given": "Kathrin", "initials": "K"}, {"family": "Schaeffeler", "given": "Elke", "initials": "E"}, {"family": "Zhou", "given": "Yitian", "initials": "Y", "orcid": "0000-0003-3066-5444", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c4e92db79d554ad283927ad7e6e54e8d.json"}}, {"family": "Lauschke", "given": "Volker M", "initials": "VM", "orcid": "0000-0002-1140-6204", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/82048d4da61547d08aa9d2bb3bae12dd.json"}}, {"family": "Eichelbaum", "given": "Michel", "initials": "M"}, {"family": "Schwab", "given": "Matthias", "initials": "M", "orcid": "0000-0002-9984-075X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a671991e1b85463e8a7bbad2e29fb1bd.json"}}, {"family": "Brauch", "given": "Hiltrud B", "initials": "HB", "orcid": "0000-0001-7531-2736", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d50df7befe8b45989a8b81d3051004b5.json"}}, {"family": "Chowbay", "given": "Balram", "initials": "B", "orcid": "0000-0003-4775-9877", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a4b56e6cd97f40cd88668603d7c510f6.json"}}, {"family": "Schroth", "given": "Werner", "initials": "W", "orcid": "0000-0003-1412-4592", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/30be50cbd1474b8ebe8a0b31c5b47c91.json"}}], "type": "journal article", "published": "2023-03-00", "journal": {"title": "Clin. Pharmacol. Ther.", "issn": "1532-6535", "volume": "113", "issue": "3", "pages": "712-723", "issn-l": "0009-9236"}, "abstract": "The therapeutic efficacy of tamoxifen is predominantly mediated by its active metabolites 4-hydroxy-tamoxifen and endoxifen, whose formation is catalyzed by the polymorphic cytochrome P450 2D6 (CYP2D6). Yet, known CYP2D6 polymorphisms only partially determine metabolite concentrations in vivo. We performed the first cross-ancestry genome-wide association study with well-characterized patients of European, Middle-Eastern, and Asian descent (n = 497) to identify genetic factors impacting active and parent metabolite formation. Genome-wide significant variants were functionally evaluated in an independent liver cohort (n = 149) and in silico. Metabolite prediction models were validated in two independent European breast cancer cohorts (n = 287, n = 189). Within a single 1-megabase (Mb) region of chromosome 22q13 encompassing the CYP2D6 gene, 589 variants were significantly associated with tamoxifen metabolite concentrations, particularly endoxifen and metabolic ratio (MR) endoxifen/N-desmethyltamoxifen (minimal P = 5.4E-35 and 2.5E-65, respectively). Previously suggested other loci were not confirmed. Functional analyses revealed 66% of associated, mostly intergenic variants to be significantly correlated with hepatic CYP2D6 activity or expression (\u03c1 = 0.35 to -0.52), and six hotspot regions in the extended 22q13 locus impacting gene regulatory function. Machine learning models based on hotspot variants (n = 12) plus CYP2D6 activity score (AS) increased the explained variability (~ 9%) compared with AS alone, explaining up to 49% (median R2 ) and 72% of the variability in endoxifen and MR endoxifen/N-desmethyltamoxifen, respectively. Our findings suggest that the extended CYP2D6 locus at 22q13 is the principal genetic determinant of endoxifen plasma concentration. Long-distance haplotypes connecting CYP2D6 with adjacent regulatory sites and nongenetic factors may account for the unexplained portion of variability.", "doi": "10.1002/cpt.2846", "pmid": "36629403", "labels": {"DDLS Fellow": null, "Yitian Zhou": null}, "xrefs": [], "notes": [], "created": "2026-11-06T09:32:58.404Z", "modified": "2026-11-06T09:32:59.085Z"}]}