Clinical lymphoma, myeloma & leukemia · 2019
Using Informatics Tools to Identify Opportunities for Precision Medicine in Diffuse Large B-cell Lymphoma.
СтатьяПоддержка NIH: внешняя программаСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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Patel Sharvil P, Harkins R Andrew, Lee Michelle J, Flowers Christopher R, Koff Jean L
Аннотация
INTRODUCTION: Diffuse large B-cell lymphoma (DLBCL) is genetically and clinically heterogeneous. Despite advances in genomic subtyping, standard frontline chemoimmunotherapy has remained unchanged for years. As high-throughput analysis becomes more accessible, characterizing drug-gene interactions in DLBCL could support patient-specific treatment strategies.
MATERIALS AND METHODS: From our systematic literature review, we compiled a comprehensive list of somatic mutations implicated in DLBCL. We extracted reported and primary sequencing data for these mutations and assessed their association with signaling pathways, cell-of-origin subtypes, and clinical outcomes.
RESULTS: Twenty-two targetable mutations present in ≥ 5% of patients with DLBCL were associated with unfavorable outcomes, yielding a predicted population of 31.7% of DLBCL cases with poor-risk disease and candidacy for targeted therapy. A second review identified 256 studies that had characterized the drug-gene interactions for these mutations via in vitro studies, mouse models, and/or clinical trials.
CONCLUSIONS: Our novel approach linking the data from our systematic reviews with informatics tools identified high-risk DLBCL subgroups, DLBCL-specific drug-gene interactions, and potential populations for precision medicine trials.
MATERIALS AND METHODS: From our systematic literature review, we compiled a comprehensive list of somatic mutations implicated in DLBCL. We extracted reported and primary sequencing data for these mutations and assessed their association with signaling pathways, cell-of-origin subtypes, and clinical outcomes.
RESULTS: Twenty-two targetable mutations present in ≥ 5% of patients with DLBCL were associated with unfavorable outcomes, yielding a predicted population of 31.7% of DLBCL cases with poor-risk disease and candidacy for targeted therapy. A second review identified 256 studies that had characterized the drug-gene interactions for these mutations via in vitro studies, mouse models, and/or clinical trials.
CONCLUSIONS: Our novel approach linking the data from our systematic reviews with informatics tools identified high-risk DLBCL subgroups, DLBCL-specific drug-gene interactions, and potential populations for precision medicine trials.