An inflammatory state defines a high-risk T-lineage acute lymphoblastic leukemia subgroup

IF 15.8 1区 医学 Q1 CELL BIOLOGY
Mark Gower, Ximing Li, Alicia G. Aguilar-Navarro, Brian Lin, Minerva Fernandez, Gibran Edun, Mursal Nader, Vincent Rondeau, Andrea Arruda, Anne Tierens, Anna Eames Seffernick, Petri Pölönen, Juliette Durocher, Elvin Wagenblast, Lin Yang, Ho Seok Lee, Charles G. Mullighan, David Teachey, Marissa Rashkovan, Cedric S. Tremblay, Daniel Herranz, Tomer Itkin, Sanam Loghavi, John E. Dick, Gregory Schwartz, Maria Agustina Perusini, Hassan Sibai, Johann Hitzler, Tanja A. Gruber, Mark Minden, Courtney L. Jones, Igor Dolgalev, Soheil Jahangiri, Anastasia N. Tikhonova
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引用次数: 0

Abstract

T-lineage acute lymphoblastic leukemia (ALL) is an aggressive cancer comprising diverse subtypes that are challenging to stratify using conventional immunophenotyping. To gain insights into subset-specific therapeutic vulnerabilities, we performed an integrative multiomics analysis of bone marrow samples from newly diagnosed T cell ALL, early T cell precursor ALL, and T/myeloid mixed phenotype acute leukemia. Leveraging cellular indexing of transcriptomes and epitopes in conjunction with T cell receptor sequencing, we identified a subset of patient samples characterized by activation of inflammatory and stem gene programs. These inflammatory T-lineage samples exhibited distinct biological features compared with other T-lineage ALL samples, including the production of proinflammatory cytokines, prevalence of mutations affecting cytokine signaling and chromatin remodeling, an altered immune microenvironment, and poor treatment responses. Moreover, we found that, although inflammatory T-lineage ALL samples were less sensitive to dexamethasone, they exhibited unique sensitivity to a BCL-2 inhibitor, venetoclax. To facilitate classification of patients with T-lineage ALL, we developed a computational inflammatory gene signature scoring system, which stratified patients and was associated with disease prognosis in three additional patient cohorts. By identifying a high-risk T-lineage ALL subtype on the basis of an inflammatory score, our study provides a framework for targeted therapeutic approaches for these challenging-to-treat cancers.
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来源期刊
Science Translational Medicine
Science Translational Medicine CELL BIOLOGY-MEDICINE, RESEARCH & EXPERIMENTAL
CiteScore
26.70
自引率
1.20%
发文量
309
审稿时长
1.7 months
期刊介绍: Science Translational Medicine is an online journal that focuses on publishing research at the intersection of science, engineering, and medicine. The goal of the journal is to promote human health by providing a platform for researchers from various disciplines to communicate their latest advancements in biomedical, translational, and clinical research. The journal aims to address the slow translation of scientific knowledge into effective treatments and health measures. It publishes articles that fill the knowledge gaps between preclinical research and medical applications, with a focus on accelerating the translation of knowledge into new ways of preventing, diagnosing, and treating human diseases. The scope of Science Translational Medicine includes various areas such as cardiovascular disease, immunology/vaccines, metabolism/diabetes/obesity, neuroscience/neurology/psychiatry, cancer, infectious diseases, policy, behavior, bioengineering, chemical genomics/drug discovery, imaging, applied physical sciences, medical nanotechnology, drug delivery, biomarkers, gene therapy/regenerative medicine, toxicology and pharmacokinetics, data mining, cell culture, animal and human studies, medical informatics, and other interdisciplinary approaches to medicine. The target audience of the journal includes researchers and management in academia, government, and the biotechnology and pharmaceutical industries. It is also relevant to physician scientists, regulators, policy makers, investors, business developers, and funding agencies.
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