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A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications
- Source :
- Cancer Cell
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- The development of precision medicine approaches for diffuse large B cell lymphoma (DLBCL) is confounded by its pronounced genetic, phenotypic, and clinical heterogeneity. Recent multiplatform genomic studies revealed the existence of genetic subtypes of DLBCL using clustering methodologies. Here, we describe an algorithm that determines the probability that a patient's lymphoma belongs to one of seven genetic subtypes based on its genetic features. This classification reveals genetic similarities between these DLBCL subtypes and various indolent and extranodal lymphoma types, suggesting a shared pathogenesis. These genetic subtypes also have distinct gene expression profiles, immune microenvironments, and outcomes following immunochemotherapy. Functional analysis of genetic subtype models highlights distinct vulnerabilities to targeted therapy, supporting the use of this classification in precision medicine trials.
- Subjects :
- 0301 basic medicine
Cancer Research
medicine.medical_treatment
Apoptosis
Mice, SCID
Computational biology
Biology
Article
Targeted therapy
Genetic Heterogeneity
Mice
03 medical and health sciences
0302 clinical medicine
Mice, Inbred NOD
immune system diseases
hemic and lymphatic diseases
Clinical heterogeneity
Biomarkers, Tumor
Tumor Cells, Cultured
Tumor Microenvironment
medicine
Animals
Humans
Molecular Targeted Therapy
Precision Medicine
Cell Proliferation
Probabilistic classification
Gene Expression Profiling
Precision medicine
medicine.disease
Xenograft Model Antitumor Assays
Phenotype
Lymphoma
Gene Expression Regulation, Neoplastic
030104 developmental biology
Oncology
030220 oncology & carcinogenesis
Extranodal lymphoma
Female
Lymphoma, Large B-Cell, Diffuse
Diffuse large B-cell lymphoma
Subjects
Details
- ISSN :
- 15356108
- Volume :
- 37
- Database :
- OpenAIRE
- Journal :
- Cancer Cell
- Accession number :
- edsair.doi.dedup.....7a85fe84baf16958d489493aff9aa950