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Canales-Herrerias P, Uzzan M, Seki A, Czepielewski RS, Verstockt B, Livanos AE, Raso F, Dunn A, Dai D, Wang A, Al-Taie Z, Martin J, Laurent T, Ko HM, Tokuyama M, Tankelevich M, Meringer H, Cossarini F, Jha D, Krek A, Paulsen JD, Taylor MD, Nakadar MZ, Wong J, Erlich EC, Mintz RL, Onufer EJ, Helmink BA, Sharma K, Rosenstein A, Ganjian D, Chung G, Dawson T, Juarez J, Yajnik V, Cerutti A, Faith JJ, Suarez-Farinas M, Argmann C, Petralia F, Randolph GJ, Polydorides AD, Reboldi A, Colombel JF, Mehandru S. Gut-associated lymphoid tissue attrition associates with response to anti-α4β7 therapy in ulcerative colitis. Sci Immunol 2024; 9:eadg7549. [PMID: 38640252 DOI: 10.1126/sciimmunol.adg7549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 03/20/2024] [Indexed: 04/21/2024]
Abstract
Vedolizumab (VDZ) is a first-line treatment in ulcerative colitis (UC) that targets the α4β7- mucosal vascular addressin cell adhesion molecule 1 (MAdCAM-1) axis. To determine the mechanisms of action of VDZ, we examined five distinct cohorts of patients with UC. A decrease in naïve B and T cells in the intestines and gut-homing (β7+) plasmablasts in circulation of VDZ-treated patients suggested that VDZ targets gut-associated lymphoid tissue (GALT). Anti-α4β7 blockade in wild-type and photoconvertible (KikGR) mice confirmed a loss of GALT size and cellularity because of impaired cellular entry. In VDZ-treated patients with UC, treatment responders demonstrated reduced intestinal lymphoid aggregate size and follicle organization and a reduction of β7+IgG+ plasmablasts in circulation, as well as IgG+ plasma cells and FcγR-dependent signaling in the intestine. GALT targeting represents a previously unappreciated mechanism of action of α4β7-targeted therapies, with major implications for this therapeutic paradigm in UC.
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Affiliation(s)
- Pablo Canales-Herrerias
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mathieu Uzzan
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Paris Est Créteil University UPEC, Assistance Publique-Hôpitaux de Paris (AP-HP), Henri Mondor Hospital, Gastroenterology Department, Fédération Hospitalo-Universitaire TRUE (InnovaTive theRapy for immUne disordErs), Créteil F-94010, France
| | - Akihiro Seki
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rafael S Czepielewski
- Department of Pathology, Washington University School of Medicine, St. Louis, MO, USA
| | - Bram Verstockt
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
- Translational Research in Gastrointestinal Disorders, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Alexandra E Livanos
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fiona Raso
- Department of Pathology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Alexandra Dunn
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel Dai
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew Wang
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zainab Al-Taie
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jerome Martin
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nantes Université, CHU Nantes, Inserm, Centre de Recherche Translationelle en Transplantation et Immunologie, UMR 1064, Nantes, France
- CHU Nantes, Nantes Université, Laboratoire d'Immunologie, CIMNA, Nantes, France
| | - Thomas Laurent
- Nantes Université, CHU Nantes, Inserm, Centre de Recherche Translationelle en Transplantation et Immunologie, UMR 1064, Nantes, France
- CHU Nantes, Nantes Université, Laboratoire d'Immunologie, CIMNA, Nantes, France
| | - Huaibin M Ko
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Minami Tokuyama
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Tankelevich
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hadar Meringer
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Francesca Cossarini
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Divya Jha
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John D Paulsen
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew D Taylor
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mohammad Zuber Nakadar
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua Wong
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Emma C Erlich
- Department of Pathology, Washington University School of Medicine, St. Louis, MO, USA
| | - Rachel L Mintz
- Department of Pathology, Washington University School of Medicine, St. Louis, MO, USA
| | - Emily J Onufer
- Division of Pediatric Surgery, Department of Surgery, St. Louis Children's Hospital, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Beth A Helmink
- Department of Surgery, Section of Surgical Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Keshav Sharma
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adam Rosenstein
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Danielle Ganjian
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Grace Chung
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Travis Dawson
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | - Andrea Cerutti
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Translational Clinical Research Program, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Catalan Institute for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Jeremiah J Faith
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mayte Suarez-Farinas
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Carmen Argmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Francesca Petralia
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gwendalyn J Randolph
- Department of Pathology, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexandros D Polydorides
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrea Reboldi
- Department of Pathology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Jean-Frederic Colombel
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saurabh Mehandru
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Cohen O, Kundel V, Robson P, Al-Taie Z, Suárez-Fariñas M, Shah NA. Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review. J Clin Med 2024; 13:1415. [PMID: 38592223 PMCID: PMC10932326 DOI: 10.3390/jcm13051415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/13/2024] [Accepted: 02/17/2024] [Indexed: 04/10/2024] Open
Abstract
Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea-hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.
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Affiliation(s)
- Oren Cohen
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (O.C.); (V.K.)
| | - Vaishnavi Kundel
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (O.C.); (V.K.)
| | - Philip Robson
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Zainab Al-Taie
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.A.-T.); (M.S.-F.)
| | - Mayte Suárez-Fariñas
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.A.-T.); (M.S.-F.)
| | - Neomi A. Shah
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (O.C.); (V.K.)
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Cohen O, Sánchez-de-la-Torre M, Al-Taie Z, Khan S, Kundel V, Kovacic JC, Gracia-Lavedan E, de Batlle J, Nadkarni G, Barbé F, Suárez-Fariñas M, Shah NA. Heterogeneous Effects of CPAP in Non-Sleepy OSA on CVD Outcomes: Post-hoc Machine Learning Analysis of the ISAACC Trial (ECSACT Study). Ann Am Thorac Soc 2024. [PMID: 38358332 DOI: 10.1513/annalsats.202309-799oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 02/13/2024] [Indexed: 02/16/2024] Open
Abstract
RATIONALE Randomized controlled trials of continuous positive airway pressure (CPAP) therapy for cardiovascular disease (CVD) prevention among patients with obstructive sleep apnea (OSA) have been largely neutral. However, given OSA is a heterogeneous disease, there may be unidentified subgroups demonstrating differential treatment effects. OBJECTIVES Apply a novel data-drive approach to identify non-sleepy OSA subgroups with heterogeneous effects of CPAP on CVD outcomes within the ISAACC study. METHODS Participants were randomly partitioned into two datasets. One for training (70%) our machine learning model and a second (30%) for validation of significant findings. Model-based recursive partitioning was applied to identify subgroups with heterogeneous treatment effects. Survival analysis was conducted to compare treatment (CPAP versus usual care [UC]) outcomes within subgroups. RESULTS A total of 1,224 non-sleepy OSA participants were included. Of fifty-five features entered into our model only two appeared in the final model (i.e., average OSA event duration and hypercholesterolemia). Among participants at or below the model-derived average event duration threshold (19.5 seconds), CPAP was protective for a composite of CVD events (training Hazard Ratio [HR] 0.46, p=0.002). For those with longer event duration (>19.5 seconds), an additional split occurred by hypercholesterolemia status. Among participants with longer event duration and hypercholesterolemia, CPAP resulted in more CVD events compared to UC (training HR 2.24, p=0.011). The point estimate for this harmful signal was also replicated in the testing dataset (HR 1.83, p=0.118). CONCLUSIONS We discovered subgroups of non-sleepy OSA participants within the ISAACC study with heterogeneous effects of CPAP. Among the training dataset, those with longer OSA event duration and hypercholesterolemia had nearly 2.5-times more CVD events with CPAP compared to UC, while those with shorter OSA event duration had roughly half the rate of CVD events if randomized to CPAP.
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Affiliation(s)
- Oren Cohen
- Icahn School of Medicine at Mount Sinai, 5925, Division of Pulmonary, Critical Care and Sleep Medicine, New York, New York, United States;
| | - Manuel Sánchez-de-la-Torre
- University of Lleida, 16739, Precision Medicine in Chronic Diseases, Hospital Universitari Arnau de Vilanova-Santa Maria, IRB Lleida, Department of Nursing and Physiotherapy, Faculty of Nursing and Physiotherapy, Lleida, Catalunya, Spain
- Centro de Investigacion Biomedica en Red, 540869, de Enfermedades Respiratorias, Madrid, Comunidad de Madrid, Spain
| | - Zainab Al-Taie
- Icahn School of Medicine at Mount Sinai, 5925, Center for Biostatistics, Department of Population Health Science and Policy, New York, New York, United States
| | - Samira Khan
- Icahn School of Medicine at Mount Sinai, 5925, Division of Pulmonary, Critical Care and Sleep Medicine, New York, New York, United States
| | - Vaishnavi Kundel
- Icahn School of Medicine at Mount Sinai, 5925, Division of Pulmonary, Critical Care and Sleep Medicine, New York, New York, United States
| | - Jason C Kovacic
- Icahn School of Medicine at Mount Sinai, 5925, Cardiovascular Research Institute, New York, New York, United States
- Victor Chang Cardiac Research Institute, 104338, Darlinghurst, New South Wales, Australia
- University of New South Wales, 7800, St. Vincent's Clinical School, Sydney, New South Wales, Australia
| | - Esther Gracia-Lavedan
- University of Lleida, 16739, Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova-Santa Maria, IRB Lleida, Lleida, Catalunya, Spain
- Centro de Investigacion Biomedica en Red, 540869, de Enfermedades Respiratorias, Madrid, Comunidad de Madrid, Spain
| | - Jordi de Batlle
- University of Lleida, 16739, Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova-Santa Maria, IRB Lleida, Lleida, Catalunya, Spain
- Centro de Investigacion Biomedica en Red, 540869, de Enfermedades Respiratorias, Madrid, Comunidad de Madrid, Spain
| | - Girish Nadkarni
- Icahn School of Medicine at Mount Sinai, 5925, Division of Data Driven and Digital Medicine (D3M); Charles Bronfman Institute for Personalized Medicine, New York, New York, United States
| | - Ferran Barbé
- University of Lleida, 16739, Translational Research in Respiratory Medicine, Hospital Universitari Arnau de Vilanova-Santa Maria, IRB Lleida, Lleida, Catalunya, Spain
- Centro de Investigacion Biomedica en Red, 540869, de Enfermedades Respiratorias, Madrid, Comunidad de Madrid, Spain
| | - Mayte Suárez-Fariñas
- Icahn School of Medicine at Mount Sinai, 5925, Center for Biostatistics, Department of Population Health Science and Policy, New York, New York, United States
| | - Neomi A Shah
- Icahn School of Medicine at Mount Sinai, 5925, Division of Pulmonary, Critical Care and Sleep Medicine, New York, New York, United States
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Jha D, Al-Taie Z, Krek A, Eshghi ST, Fantou A, Laurent T, Tankelevich M, Cao X, Meringer H, Livanos AE, Tokuyama M, Cossarini F, Bourreille A, Josien R, Hou R, Canales-Herrerias P, Ungaro RC, Kayal M, Marion J, Polydorides AD, Ko HM, D’souza D, Merand R, Kim-Schulze S, Hackney JA, Nguyen A, McBride JM, Yuan GC, Colombel JF, Martin JC, Argmann C, Suárez-Fariñas M, Petralia F, Mehandru S. Myeloid cell influx into the colonic epithelium is associated with disease severity and non-response to anti-Tumor Necrosis Factor Therapy in patients with Ulcerative Colitis. bioRxiv 2023:2023.06.02.542863. [PMID: 37333091 PMCID: PMC10274630 DOI: 10.1101/2023.06.02.542863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Ulcerative colitis (UC) is an idiopathic chronic inflammatory disease of the colon with sharply rising global prevalence. Dysfunctional epithelial compartment (EC) dynamics are implicated in UC pathogenesis although EC-specific studies are sparse. Applying orthogonal high-dimensional EC profiling to a Primary Cohort (PC; n=222), we detail major epithelial and immune cell perturbations in active UC. Prominently, reduced frequencies of mature BEST4+OTOP2+ absorptive and BEST2+WFDC2+ secretory epithelial enterocytes were associated with the replacement of homeostatic, resident TRDC+KLRD1+HOPX+ γδ+ T cells with RORA+CCL20+S100A4+ TH17 cells and the influx of inflammatory myeloid cells. The EC transcriptome (exemplified by S100A8, HIF1A, TREM1, CXCR1) correlated with clinical, endoscopic, and histological severity of UC in an independent validation cohort (n=649). Furthermore, therapeutic relevance of the observed cellular and transcriptomic changes was investigated in 3 additional published UC cohorts (n=23, 48 and 204 respectively) to reveal that non-response to anti-Tumor Necrosis Factor (anti-TNF) therapy was associated with EC related myeloid cell perturbations. Altogether, these data provide high resolution mapping of the EC to facilitate therapeutic decision-making and personalization of therapy in patients with UC.
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Affiliation(s)
- Divya Jha
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zainab Al-Taie
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, New York City, NY, USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Shadi Toghi Eshghi
- Biomarker Discovery, OMNI, Genentech Inc. South SanFrancisco, CA, USA
- OMNI Biomarker Development, Genentech Inc. South SanFrancisco, CA, USA
| | - Aurelie Fantou
- Université de Nantes, Inserm, CHU Nantes, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ITUN, F-44000 Nantes, France
| | - Thomas Laurent
- Université de Nantes, Inserm, CHU Nantes, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ITUN, F-44000 Nantes, France
| | - Michael Tankelevich
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xuan Cao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Hadar Meringer
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexandra E Livanos
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Minami Tokuyama
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Francesca Cossarini
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arnaud Bourreille
- Université de Nantes, Inserm, CHU Nantes, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ITUN, F-44000 Nantes, France
| | - Regis Josien
- Université de Nantes, Inserm, CHU Nantes, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ITUN, F-44000 Nantes, France
| | - Ruixue Hou
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, New York City, NY, USA
| | - Pablo Canales-Herrerias
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ryan C. Ungaro
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maia Kayal
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - James Marion
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Huaibin M. Ko
- Department of Pathology and Cell Biology, Columbia University Medical Center-New York Presbyterian Hospital, New York, New York
| | - Darwin D’souza
- Human Immune Monitoring Core, Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Raphael Merand
- Human Immune Monitoring Core, Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Seunghee Kim-Schulze
- Human Immune Monitoring Core, Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jason A. Hackney
- Biomarker Discovery, OMNI, Genentech Inc. South SanFrancisco, CA, USA
- OMNI Biomarker Development, Genentech Inc. South SanFrancisco, CA, USA
| | - Allen Nguyen
- Biomarker Discovery, OMNI, Genentech Inc. South SanFrancisco, CA, USA
- OMNI Biomarker Development, Genentech Inc. South SanFrancisco, CA, USA
| | - Jacqueline M. McBride
- Biomarker Discovery, OMNI, Genentech Inc. South SanFrancisco, CA, USA
- OMNI Biomarker Development, Genentech Inc. South SanFrancisco, CA, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Jean Frederic Colombel
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jerome C. Martin
- Université de Nantes, Inserm, CHU Nantes, Centre de Recherche en Transplantation et Immunologie, UMR 1064, ITUN, F-44000 Nantes, France
| | - Carmen Argmann
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, New York City, NY, USA
| | - Mayte Suárez-Fariñas
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, New York City, NY, USA
| | - Francesca Petralia
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
| | - Saurabh Mehandru
- Henry D. Janowitz Division of Gastroenterology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Institute of Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Argmann C, Hou R, Ungaro RC, Irizar H, Al-Taie Z, Huang R, Kosoy R, Venkat S, Song WM, Di'Narzo AF, Losic B, Hao K, Peters L, Comella PH, Wei G, Atreja A, Mahajan M, Iuga A, Desai PT, Branigan P, Stojmirovic A, Perrigoue J, Brodmerkel C, Curran M, Friedman JR, Hart A, Lamousé-Smith E, Wehkamp J, Mehandru S, Schadt EE, Sands BE, Dubinsky MC, Colombel JF, Kasarskis A, Suárez-Fariñas M. Biopsy and blood-based molecular biomarker of inflammation in IBD. Gut 2022:gutjnl-2021-326451. [PMID: 36109152 PMCID: PMC10014487 DOI: 10.1136/gutjnl-2021-326451] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 08/22/2022] [Indexed: 12/13/2022]
Abstract
OBJECTIVE IBD therapies and treatments are evolving to deeper levels of remission. Molecular measures of disease may augment current endpoints including the potential for less invasive assessments. DESIGN Transcriptome analysis on 712 endoscopically defined inflamed (Inf) and 1778 non-inflamed (Non-Inf) intestinal biopsies (n=498 Crohn's disease, n=421 UC and 243 controls) in the Mount Sinai Crohn's and Colitis Registry were used to identify genes differentially expressed between Inf and Non-Inf biopsies and to generate a molecular inflammation score (bMIS) via gene set variance analysis. A circulating MIS (cirMIS) score, reflecting intestinal molecular inflammation, was generated using blood transcriptome data. bMIS/cirMIS was validated as indicators of intestinal inflammation in four independent IBD cohorts. RESULTS bMIS/cirMIS was strongly associated with clinical, endoscopic and histological disease activity indices. Patients with the same histologic score of inflammation had variable bMIS scores, indicating that bMIS describes a deeper range of inflammation. In available clinical trial data sets, both scores were responsive to IBD treatment. Despite similar baseline endoscopic and histologic activity, UC patients with lower baseline bMIS levels were more likely treatment responders compared with those with higher levels. Finally, among patients with UC in endoscopic and histologic remission, those with lower bMIS levels were less likely to have a disease flare over time. CONCLUSION Transcriptionally based scores provide an alternative objective and deeper quantification of intestinal inflammation, which could augment current clinical assessments used for disease monitoring and have potential for predicting therapeutic response and patients at higher risk of disease flares.
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Affiliation(s)
- Carmen Argmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ruixue Hou
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ryan C Ungaro
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Haritz Irizar
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Zainab Al-Taie
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ruiqi Huang
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Roman Kosoy
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Won-Min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Antonio F Di'Narzo
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Sema4, Stamford, Connecticut, USA
| | - Bojan Losic
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Sema4, Stamford, Connecticut, USA
| | - Lauren Peters
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Phillip H Comella
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Gabrielle Wei
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ashish Atreja
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Milind Mahajan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Sema4, Stamford, Connecticut, USA
| | - Alina Iuga
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | | | | | | | | | - Mark Curran
- Janssen R&D, Spring House, Pennsylvania, USA
| | | | - Amy Hart
- Janssen R&D, Spring House, Pennsylvania, USA
| | | | - Jan Wehkamp
- Janssen R&D, Spring House, Pennsylvania, USA
| | - Saurabh Mehandru
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Sema4, Stamford, Connecticut, USA
| | - Bruce E Sands
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Marla C Dubinsky
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jean-Frederic Colombel
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Andrew Kasarskis
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Sema4, Stamford, Connecticut, USA
| | - Mayte Suárez-Fariñas
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA .,Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Al-Taie Z, Liu D, Mitchem JB, Papageorgiou C, Kaifi JT, Warren WC, Shyu CR. Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential. J Biomed Inform 2021; 118:103792. [PMID: 33915273 DOI: 10.1016/j.jbi.2021.103792] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 03/26/2021] [Accepted: 04/21/2021] [Indexed: 01/02/2023]
Abstract
Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.
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Affiliation(s)
- Zainab Al-Taie
- Institute for Data Science & Informatics, University of Missouri, Columbia, MO 65211, USA; Department of Computer Science, College of Science for Women, University of Baghdad, Baghdad, Iraq
| | - Danlu Liu
- Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211, USA
| | - Jonathan B Mitchem
- Institute for Data Science & Informatics, University of Missouri, Columbia, MO 65211, USA; Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA; Harry S. Truman Memorial Veterans' Hospital, Columbia, MO 65201, USA.
| | - Christos Papageorgiou
- Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA
| | - Jussuf T Kaifi
- Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA; Harry S. Truman Memorial Veterans' Hospital, Columbia, MO 65201, USA
| | - Wesley C Warren
- Institute for Data Science & Informatics, University of Missouri, Columbia, MO 65211, USA; Department of Surgery, School of Medicine, University of Missouri, Columbia, MO 65212, USA; Department of Animal Sciences, Bond Life Sciences Center, University of Missouri, 1201 Rollins Street, Columbia, MO 65211, USA
| | - Chi-Ren Shyu
- Institute for Data Science & Informatics, University of Missouri, Columbia, MO 65211, USA; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211, USA; Department of Medicine, School of Medicine, University of Missouri, Columbia, MO 65212, USA.
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7
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Giannaris PS, Al-Taie Z, Kovalenko M, Thanintorn N, Kholod O, Innokenteva Y, Coberly E, Frazier S, Laziuk K, Popescu M, Shyu CR, Xu D, Hammer RD, Shin D. Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports. J Pathol Inform 2020; 11:4. [PMID: 32166042 PMCID: PMC7045509 DOI: 10.4103/jpi.jpi_30_19] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 12/18/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. METHODS In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). RESULTS Evaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts' assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. CONCLUSION The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine.
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Affiliation(s)
- Pericles S. Giannaris
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Zainab Al-Taie
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Computer Science, College of Science for Women, University of Baghdad, Baghdad, Iraq
| | - Mikhail Kovalenko
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Nattapon Thanintorn
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Olha Kholod
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Yulia Innokenteva
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
| | - Emily Coberly
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Shellaine Frazier
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Katsiarina Laziuk
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Mihail Popescu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
- Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Chi-Ren Shyu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
| | - Dong Xu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
| | - Richard D. Hammer
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
| | - Dmitriy Shin
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, United States
- Department of Pathology and Anatomical Sciences, School of Medicine, University of Missouri, Columbia, MO 65212, United States
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Missouri, Columbia, MO 65211, United States
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Thanintorn N, Wang J, Ersoy I, Al-Taie Z, Jiang Y, Wang D, Verma M, Joshi T, Hammer R, Xu D, Shin D. RDF SKETCH MAPS - KNOWLEDGE COMPLEXITY REDUCTION FOR PRECISION MEDICINE ANALYTICS. Pac Symp Biocomput 2016; 21:417-428. [PMID: 26776205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Realization of precision medicine ideas requires significant research effort to be able to spot subtle differences in complex diseases at the molecular level to develop personalized therapies. It is especially important in many cases of highly heterogeneous cancers. Precision diagnostics and therapeutics of such diseases demands interrogation of vast amounts of biological knowledge coupled with novel analytic methodologies. For instance, pathway-based approaches can shed light on the way tumorigenesis takes place in individual patient cases and pinpoint to novel drug targets. However, comprehensive analysis of hundreds of pathways and thousands of genes creates a combinatorial explosion, that is challenging for medical practitioners to handle at the point of care. Here we extend our previous work on mapping clinical omics data to curated Resource Description Framework (RDF) knowledge bases to derive influence diagrams of interrelationships of biomarker proteins, diseases and signal transduction pathways for personalized theranostics. We present RDF Sketch Maps - a computational method to reduce knowledge complexity for precision medicine analytics. The method of RDF Sketch Maps is inspired by the way a sketch artist conveys only important visual information and discards other unnecessary details. In our case, we compute and retain only so-called RDF Edges - places with highly important diagnostic and therapeutic information. To do this we utilize 35 maps of human signal transduction pathways by transforming 300 KEGG maps into highly processable RDF knowledge base. We have demonstrated potential clinical utility of RDF Sketch Maps in hematopoietic cancers, including analysis of pathways associated with Hairy Cell Leukemia (HCL) and Chronic Myeloid Leukemia (CML) where we achieved up to 20-fold reduction in the number of biological entities to be analyzed, while retaining most likely important entities. In experiments with pathways associated with HCL a generated RDF Sketch Map of the top 30% paths retained important information about signaling cascades leading to activation of proto-oncogene BRAF, which is usually associated with a different cancer, melanoma. Recent reports of successful treatments of HCL patients by the BRAF-targeted drug vemurafenib support the validity of the RDF Sketch Maps findings. We therefore believe that RDF Sketch Maps will be invaluable for hypothesis generation for precision diagnostics and therapeutics as well as drug repurposing studies.
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