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Harraka P, Bruinsma F, Nguyen-Dumont T, Jordan S, Giles GG, Winship IM, Tucker K, Southey MC. Is renal cell carcinoma associated with MITF c.952G>A (p.E318K)? J Med Genet 2024:jmg-2024-109984. [PMID: 39089886 DOI: 10.1136/jmg-2024-109984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 07/20/2024] [Indexed: 08/04/2024]
Affiliation(s)
- Philip Harraka
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Fiona Bruinsma
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Burnet Institute, Melbourne, Victoria, Australia
| | - Tu Nguyen-Dumont
- Clinical Genomics, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Susan Jordan
- School of Public Health, The University of Queensland, Brisbane, Queensland, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Ingrid M Winship
- Genomic Medicine, The Royal Melbourne Hospital, Parkville, Victoria, Australia
- Department of Medicine, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kathy Tucker
- Hereditary Cancer Service, Prince of Wales Hospital NCCC, Randwick, New South Wales, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
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Szadai L, Bartha A, Parada IP, Lakatos AI, Pál DM, Lengyel AS, de Almeida NP, Jánosi ÁJ, Nogueira F, Szeitz B, Doma V, Woldmar N, Guedes J, Ujfaludi Z, Pahi ZG, Pankotai T, Kim Y, Győrffy B, Baldetorp B, Welinder C, Szasz AM, Betancourt L, Gil J, Appelqvist R, Kwon HJ, Kárpáti S, Kuras M, Murillo JR, Németh IB, Malm J, Fenyö D, Pawłowski K, Horvatovich P, Wieslander E, Kemény LV, Domont G, Marko-Varga G, Sanchez A. Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts. Front Oncol 2024; 14:1428182. [PMID: 39015503 PMCID: PMC11249723 DOI: 10.3389/fonc.2024.1428182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Accepted: 06/10/2024] [Indexed: 07/18/2024] Open
Abstract
Introduction While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Methods Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Results Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Discussion Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.
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Affiliation(s)
- Leticia Szadai
- Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary
| | - Aron Bartha
- Department of Bioinformatics, Semmelweis University, Budapest, Hungary
- Department of Pediatrics, Semmelweis University, Budapest, Hungary
| | - Indira Pla Parada
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | - Alexandra I.T. Lakatos
- HCEMM-SU Translational Dermatology Research Group, Semmelweis University, Budapest, Hungary
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Department of Dermatology, Venereology, and Dermatooncology, Semmelweis University, Budapest, Hungary
| | - Dorottya M.P. Pál
- HCEMM-SU Translational Dermatology Research Group, Semmelweis University, Budapest, Hungary
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Department of Dermatology, Venereology, and Dermatooncology, Semmelweis University, Budapest, Hungary
| | - Anna Sára Lengyel
- HCEMM-SU Translational Dermatology Research Group, Semmelweis University, Budapest, Hungary
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Department of Dermatology, Venereology, and Dermatooncology, Semmelweis University, Budapest, Hungary
| | - Natália Pinto de Almeida
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, Lund, Sweden
- Chemistry Institute Federal, University of Rio de Janeiro, Rio de Janiero, Brazil
| | - Ágnes Judit Jánosi
- Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary
| | - Fábio Nogueira
- Chemistry Institute Federal, University of Rio de Janeiro, Rio de Janiero, Brazil
| | - Beata Szeitz
- Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
| | - Viktória Doma
- Department of Dermatology, Venereology, and Dermatooncology, Semmelweis University, Budapest, Hungary
| | - Nicole Woldmar
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, Lund, Sweden
- Chemistry Institute Federal, University of Rio de Janeiro, Rio de Janiero, Brazil
| | - Jéssica Guedes
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Zsuzsanna Ujfaludi
- Department of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
- Competence Centre of the Life Sciences Cluster of the Centre of Excellence for Interdisciplinary Research, Development and Innovation, University of Szeged, Szeged, Hungary
| | - Zoltán Gábor Pahi
- Department of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
- Competence Centre of the Life Sciences Cluster of the Centre of Excellence for Interdisciplinary Research, Development and Innovation, University of Szeged, Szeged, Hungary
- Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Genome Integrity and DNA Repair Core Group, University of Szeged, Szeged, Hungary
| | - Tibor Pankotai
- Department of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
- Competence Centre of the Life Sciences Cluster of the Centre of Excellence for Interdisciplinary Research, Development and Innovation, University of Szeged, Szeged, Hungary
- Hungarian Centre of Excellence for Molecular Medicine (HCEMM), Genome Integrity and DNA Repair Core Group, University of Szeged, Szeged, Hungary
| | - Yonghyo Kim
- Therapeutics & Biotechnology Division, Korea Research Institute of Chemical Technology (KRICT), Daejeon, Republic of Korea
| | - Balázs Győrffy
- Department of Bioinformatics, Semmelweis University, Budapest, Hungary
- Research Centre for Natural Sciences, Institute of Molecular Life Sciences, Budapest, Hungary
| | - Bo Baldetorp
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Charlotte Welinder
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - A. Marcell Szasz
- Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
| | - Lazaro Betancourt
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Jeovanis Gil
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Roger Appelqvist
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Ho Jeong Kwon
- Chemical Genomics Global Research Lab, Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Republic of Korea
| | - Sarolta Kárpáti
- Department of Dermatology, Venereology, and Dermatooncology, Semmelweis University, Budapest, Hungary
| | - Magdalena Kuras
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, Lund, Sweden
| | | | - István Balázs Németh
- Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary
| | - Johan Malm
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | - David Fenyö
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, United States
| | - Krzysztof Pawłowski
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Peter Horvatovich
- Analytical Biochemistry, Department of Pharmacy, University of Groningen, Groningen, Netherlands
| | - Elisabet Wieslander
- Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Lajos V. Kemény
- HCEMM-SU Translational Dermatology Research Group, Semmelweis University, Budapest, Hungary
- Department of Physiology, Semmelweis University, Budapest, Hungary
- Department of Dermatology, Venereology, and Dermatooncology, Semmelweis University, Budapest, Hungary
| | - Gilberto Domont
- Chemistry Institute Federal, University of Rio de Janeiro, Rio de Janiero, Brazil
| | - György Marko-Varga
- Clinical Protein Science & Imaging, Biomedical Centre, Department of Biomedical Engineering, Lund University, Lund, Sweden
- First Department of Surgery, Tokyo Medical University, Nishishinjiku, Shinjiku-ku, Tokyo, Japan
| | - Aniel Sanchez
- Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
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Szadai L, Bartha A, Parada IP, Lakatos A, Pál D, Lengyel AS, de Almeida NP, Jánosi ÁJ, Nogueira F, Szeitz B, Doma V, Woldmar N, Guedes J, Ujfaludi Z, Pahi ZG, Pankotai T, Kim Y, Győrffy B, Baldetorp B, Welinder C, Szasz AM, Betancourt L, Gil J, Appelqvist R, Kwon HJ, Kárpáti S, Kuras M, Murillo JR, Németh IB, Malm J, Fenyö D, Pawłowski K, Horvatovich P, Wieslander E, Kemény LV, Domont G, MarkoVarga G, Sanchez A. Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.592032. [PMID: 38746333 PMCID: PMC11092593 DOI: 10.1101/2024.05.01.592032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making. Abstract Figure
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Wojcik GL, Murphy J, Edelson JL, Gignoux CR, Ioannidis AG, Manning A, Rivas MA, Buyske S, Hendricks AE. Opportunities and challenges for the use of common controls in sequencing studies. Nat Rev Genet 2022; 23:665-679. [PMID: 35581355 PMCID: PMC9765323 DOI: 10.1038/s41576-022-00487-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2022] [Indexed: 01/02/2023]
Abstract
Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.
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Affiliation(s)
- Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jessica Murphy
- Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
- Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA
| | - Jacob L Edelson
- Department of Biomedical Data Science, Stanford Medical School, Stanford, CA, USA
| | - Christopher R Gignoux
- Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA
- Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Alexander G Ioannidis
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Alisa Manning
- Metabolism Program, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Manuel A Rivas
- Department of Biomedical Data Science, Stanford Medical School, Stanford, CA, USA
| | - Steven Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
| | - Audrey E Hendricks
- Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA.
- Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA.
- Human Medical Genetics and Genomics Program, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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