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Ohnmacht AJ, Stahler A, Stintzing S, Modest DP, Holch JW, Westphalen CB, Hölzel L, Schübel MK, Galhoz A, Farnoud A, Ud-Dean M, Vehling-Kaiser U, Decker T, Moehler M, Heinig M, Heinemann V, Menden MP. The Oncology Biomarker Discovery framework reveals cetuximab and bevacizumab response patterns in metastatic colorectal cancer. Nat Commun 2023; 14:5391. [PMID: 37666855 PMCID: PMC10477267 DOI: 10.1038/s41467-023-41011-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/17/2023] [Indexed: 09/06/2023] Open
Abstract
Precision medicine has revolutionised cancer treatments; however, actionable biomarkers remain scarce. To address this, we develop the Oncology Biomarker Discovery (OncoBird) framework for analysing the molecular and biomarker landscape of randomised controlled clinical trials. OncoBird identifies biomarkers based on single genes or mutually exclusive genetic alterations in isolation or in the context of tumour subtypes, and finally, assesses predictive components by their treatment interactions. Here, we utilise the open-label, randomised phase III trial (FIRE-3, AIO KRK-0306) in metastatic colorectal carcinoma patients, who received either cetuximab or bevacizumab in combination with 5-fluorouracil, folinic acid and irinotecan (FOLFIRI). We systematically identify five biomarkers with predictive components, e.g., patients with tumours that carry chr20q amplifications or lack mutually exclusive ERK signalling mutations benefited from cetuximab compared to bevacizumab. In summary, OncoBird characterises the molecular landscape and outlines actionable biomarkers, which generalises to any molecularly characterised randomised controlled trial.
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Affiliation(s)
- Alexander J Ohnmacht
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
- Department of Biology, Ludwig-Maximilians University Munich, 82152, Martinsried, Germany
| | - Arndt Stahler
- Charité Universitätsmedizin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Hematology, Oncology, and Cancer Immunology, Charitéplatz 1, 10117, Berlin, Germany
| | - Sebastian Stintzing
- Charité Universitätsmedizin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Hematology, Oncology, and Cancer Immunology, Charitéplatz 1, 10117, Berlin, Germany
- German Cancer Consortium (DKTK), partner sites Berlin and Munich, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
| | - Dominik P Modest
- Charité Universitätsmedizin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Hematology, Oncology, and Cancer Immunology, Charitéplatz 1, 10117, Berlin, Germany
| | - Julian W Holch
- German Cancer Consortium (DKTK), partner sites Berlin and Munich, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Department of Medicine III and Comprehensive Cancer Center Munich, University Hospital, Ludwig-Maximilians University Munich, 81377, Munich, Germany
| | - C Benedikt Westphalen
- Department of Medicine III and Comprehensive Cancer Center Munich, University Hospital, Ludwig-Maximilians University Munich, 81377, Munich, Germany
| | - Linus Hölzel
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
| | - Marisa K Schübel
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
- Department of Biology, Ludwig-Maximilians University Munich, 82152, Martinsried, Germany
| | - Ana Galhoz
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
- Department of Biology, Ludwig-Maximilians University Munich, 82152, Martinsried, Germany
| | - Ali Farnoud
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
| | - Minhaz Ud-Dean
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
| | | | | | - Markus Moehler
- Department of Medicine I and Research Center for Immunotherapy (FZI), Johannes Gutenberg-University Clinic, 55131, Mainz, Germany
| | - Matthias Heinig
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany
| | - Volker Heinemann
- Department of Medicine III and Comprehensive Cancer Center Munich, University Hospital, Ludwig-Maximilians University Munich, 81377, Munich, Germany.
| | - Michael P Menden
- Computational Health Center, Helmholtz Munich, 85764, Neuherberg, Germany.
- Department of Biology, Ludwig-Maximilians University Munich, 82152, Martinsried, Germany.
- Department of Biochemistry and Pharmacology, University of Melbourne, Victoria, 3010, Australia.
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Erber J, Kappler V, Haller B, Mijočević H, Galhoz A, Prazeres da Costa C, Gebhardt F, Graf N, Hoffmann D, Thaler M, Lorenz E, Roggendorf H, Kohlmayer F, Henkel A, Menden MP, Ruland J, Spinner CD, Protzer U, Knolle P, Lingor P. Infection Control Measures and Prevalence of SARS-CoV-2 IgG among 4,554 University Hospital Employees, Munich, Germany. Emerg Infect Dis 2022; 28:572-581. [PMID: 35195515 PMCID: PMC8888242 DOI: 10.3201/eid2803.204436] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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Caldi Gomes L, Galhoz A, Jain G, Roser A, Maass F, Carboni E, Barski E, Lenz C, Lohmann K, Klein C, Bähr M, Fischer A, Menden MP, Lingor P. Multi-omic landscaping of human midbrains identifies disease-relevant molecular targets and pathways in advanced-stage Parkinson's disease. Clin Transl Med 2022; 12:e692. [PMID: 35090094 PMCID: PMC8797064 DOI: 10.1002/ctm2.692] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 12/07/2021] [Accepted: 12/16/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence is rapidly increasing worldwide. The molecular mechanisms underpinning the pathophysiology of sporadic PD remain incompletely understood. Therefore, causative therapies are still elusive. To obtain a more integrative view of disease-mediated alterations, we investigated the molecular landscape of PD in human post-mortem midbrains, a region that is highly affected during the disease process. METHODS Tissue from 19 PD patients and 12 controls were obtained from the Parkinson's UK Brain Bank and subjected to multi-omic analyses: small and total RNA sequencing was performed on an Illumina's HiSeq4000, while proteomics experiments were performed in a hybrid triple quadrupole-time of flight mass spectrometer (TripleTOF5600+) following quantitative sequential window acquisition of all theoretical mass spectra. Differential expression analyses were performed with customized frameworks based on DESeq2 (for RNA sequencing) and with Perseus v.1.5.6.0 (for proteomics). Custom pipelines in R were used for integrative studies. RESULTS Our analyses revealed multiple deregulated molecular targets linked to known disease mechanisms in PD as well as to novel processes. We have identified and experimentally validated (quantitative real-time polymerase chain reaction/western blotting) several PD-deregulated molecular candidates, including miR-539-3p, miR-376a-5p, miR-218-5p and miR-369-3p, the valid miRNA-mRNA interacting pairs miR-218-5p/RAB6C and miR-369-3p/GTF2H3, as well as multiple proteins, such as CHI3L1, HSPA1B, FNIP2 and TH. Vertical integration of multi-omic analyses allowed validating disease-mediated alterations across different molecular layers. Next to the identification of individual molecular targets in all explored omics layers, functional annotation of differentially expressed molecules showed an enrichment of pathways related to neuroinflammation, mitochondrial dysfunction and defects in synaptic function. CONCLUSIONS This comprehensive assessment of PD-affected and control human midbrains revealed multiple molecular targets and networks that are relevant to the disease mechanism of advanced PD. The integrative analyses of multiple omics layers underscore the importance of neuroinflammation, immune response activation, mitochondrial and synaptic dysfunction as putative therapeutic targets for advanced PD.
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Affiliation(s)
- Lucas Caldi Gomes
- Department of NeurologyRechts der Isar HospitalTechnical University of MunichMünchenGermany
- Department of NeurologyUniversity Medical Center GöttingenGöttingenGermany
| | - Ana Galhoz
- Helmholtz Zentrum München GmbH ‐ German Research Center for Environmental HealthInstitute of Computational BiologyNeuherbergGermany
- Department of BiologyLudwig‐Maximilians University MunichMartinsriedGermany
| | - Gaurav Jain
- Department for Epigenetics and Systems Medicine in Neurodegenerative DiseasesGerman Center for Neurodegenerative Diseases (DZNE)GöttingenGermany
| | - Anna‐Elisa Roser
- Department of NeurologyUniversity Medical Center GöttingenGöttingenGermany
| | - Fabian Maass
- Department of NeurologyUniversity Medical Center GöttingenGöttingenGermany
| | - Eleonora Carboni
- Department of NeurologyUniversity Medical Center GöttingenGöttingenGermany
| | - Elisabeth Barski
- Department of NeurologyUniversity Medical Center GöttingenGöttingenGermany
| | - Christof Lenz
- Institute of Clinical ChemistryUniversity Medical Center GöttingenGöttingenGermany
- Bioanalytical Mass Spectrometry GroupMax Planck Institute for Biophysical ChemistryGöttingenGermany
| | - Katja Lohmann
- Institute of NeurogeneticsUniversity of LübeckLübeckGermany
| | | | - Mathias Bähr
- Department of NeurologyUniversity Medical Center GöttingenGöttingenGermany
- Department for Epigenetics and Systems Medicine in Neurodegenerative DiseasesGerman Center for Neurodegenerative Diseases (DZNE)GöttingenGermany
| | - André Fischer
- Department for Epigenetics and Systems Medicine in Neurodegenerative DiseasesGerman Center for Neurodegenerative Diseases (DZNE)GöttingenGermany
- Department of Psychiatry and PsychotherapyUniversity Medical Center GöttingenGöttingenGermany
| | - Michael P. Menden
- Helmholtz Zentrum München GmbH ‐ German Research Center for Environmental HealthInstitute of Computational BiologyNeuherbergGermany
- Department of BiologyLudwig‐Maximilians University MunichMartinsriedGermany
- German Centre for Diabetes Research (DZD e.V.)NeuherbergGermany
| | - Paul Lingor
- Department of NeurologyRechts der Isar HospitalTechnical University of MunichMünchenGermany
- German Center for Neurodegenerative Diseases (DZNE)MünchenGermany
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Wang D, Hensman J, Kutkaite G, Toh TS, Galhoz A, Dry JR, Saez-Rodriguez J, Garnett MJ, Menden MP, Dondelinger F. A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates. eLife 2020; 9:e60352. [PMID: 33274713 PMCID: PMC7746236 DOI: 10.7554/elife.60352] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 12/04/2020] [Indexed: 12/16/2022] Open
Abstract
High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.
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Affiliation(s)
- Dennis Wang
- Sheffield Institute for Translational Neuroscience, University of SheffieldSheffieldUnited Kingdom
- Department of Computer Science, University of SheffieldSheffieldUnited Kingdom
| | | | - Ginte Kutkaite
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental HealthNeuherbergGermany
- Department of Biology, Ludwig-Maximilians University MunichMartinsriedGermany
| | - Tzen S Toh
- The Medical School, University of SheffieldSheffieldUnited Kingdom
| | - Ana Galhoz
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental HealthNeuherbergGermany
- Department of Biology, Ludwig-Maximilians University MunichMartinsriedGermany
| | - Jonathan R Dry
- Research and Early Development, Oncology R&D, AstraZenecaBostonUnited States
| | - Julio Saez-Rodriguez
- Institute of Computational Biomedicine,Faculty of Medicine,Heidelberg Universityand Heidelberg University Hospital, BioquantHeidelbergGermany
| | | | - Michael P Menden
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental HealthNeuherbergGermany
- Department of Biology, Ludwig-Maximilians University MunichMartinsriedGermany
- German Center for Diabetes Research (DZD e.V.)NeuherbergGermany
| | - Frank Dondelinger
- Centre for Health Informatics, Computation and Statistics, Lancaster Medical School, Lancaster UniversityLancasterUnited Kingdom
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Ayestaran I, Galhoz A, Spiegel E, Sidders B, Dry JR, Dondelinger F, Bender A, McDermott U, Iorio F, Menden MP. Identification of Intrinsic Drug Resistance and Its Biomarkers in High-Throughput Pharmacogenomic and CRISPR Screens. Patterns (N Y) 2020; 1:100065. [PMID: 33205120 PMCID: PMC7660407 DOI: 10.1016/j.patter.2020.100065] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/14/2020] [Accepted: 06/08/2020] [Indexed: 12/15/2022]
Abstract
High-throughput drug screens in cancer cell lines test compounds at low concentrations, thereby enabling the identification of drug-sensitivity biomarkers, while resistance biomarkers remain underexplored. Dissecting meaningful drug responses at high concentrations is challenging due to cytotoxicity, i.e., off-target effects, thus limiting resistance biomarker discovery to frequently mutated cancer genes. To address this, we interrogate subpopulations carrying sensitivity biomarkers and consecutively investigate unexpectedly resistant (UNRES) cell lines for unique genetic alterations that may drive resistance. By analyzing the GDSC and CTRP datasets, we find 53 and 35 UNRES cases, respectively. For 24 and 28 of them, we highlight putative resistance biomarkers. We find clinically relevant cases such as EGFRT790M mutation in NCI-H1975 or PTEN loss in NCI-H1650 cells, in lung adenocarcinoma treated with EGFR inhibitors. Interrogating the underpinnings of drug resistance with publicly available CRISPR phenotypic assays assists in prioritizing resistance drivers, offering hypotheses for drug combinations.
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Affiliation(s)
- Iñigo Ayestaran
- Institute of Computational Biology, Helmholtz Zentrum München GmbH—German Research Center for Environmental Health, Neuherberg 85764, Germany
- CRUK Cambridge Centre Early Detection Programme, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Ana Galhoz
- Institute of Computational Biology, Helmholtz Zentrum München GmbH—German Research Center for Environmental Health, Neuherberg 85764, Germany
- Department of Biology, Ludwig-Maximilians University Munich, Martinsried 82152, Germany
| | - Elmar Spiegel
- Institute of Computational Biology, Helmholtz Zentrum München GmbH—German Research Center for Environmental Health, Neuherberg 85764, Germany
| | - Ben Sidders
- Research and Early Development, Oncology, AstraZeneca, Cambridge CB4 0WG, UK
| | - Jonathan R. Dry
- Research and Early Development, Oncology, AstraZeneca, Boston, MA 02451, USA
- Tempus Labs, Boston, MA, USA
| | - Frank Dondelinger
- Centre for Health Informatics, Computation and Statistics, Lancaster Medical School, Lancaster University, Lancaster LA1 4YW, UK
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
| | - Ultan McDermott
- Research and Early Development, Oncology, AstraZeneca, Cambridge CB4 0WG, UK
| | - Francesco Iorio
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1RQ, UK
- Human Technopole, Palazzo Italia Via Cristina Belgioioso 171, Milano 20157, Italy
| | - Michael P. Menden
- Institute of Computational Biology, Helmholtz Zentrum München GmbH—German Research Center for Environmental Health, Neuherberg 85764, Germany
- Department of Biology, Ludwig-Maximilians University Munich, Martinsried 82152, Germany
- German Centre for Diabetes Research (DZD e.V.), Neuherberg 85764, Germany
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