Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse.

Liu, Lunan; Ma, Chao; Zhang, Zhuoyu; Witkowski, Matthew T; Aifantis, Iannis; Ghassemi, Saba; Chen, Weiqiang

Liu, Lunan; Ma, Chao; Zhang, Zhuoyu; Witkowski, Matthew T; Aifantis, Iannis; Ghassemi, Saba; Chen, Weiqiang.

Afiliação

Liu L; Department of Mechanical and Aerospace Engineering, New York University, Brooklyn, New York, USA.
Ma C; Department of Mechanical and Aerospace Engineering, New York University, Brooklyn, New York, USA.
Zhang Z; Department of Biomedical Engineering, New York University, Brooklyn, New York, USA.
Witkowski MT; Department of Mechanical and Aerospace Engineering, New York University, Brooklyn, New York, USA.
Aifantis I; Perlmutter Cancer Center, NYU Langone Health, New York City, New York, USA.
Ghassemi S; Department of Pathology, NYU Langone Health, New York City, New York, USA.
Chen W; Perlmutter Cancer Center, NYU Langone Health, New York City, New York, USA.

J Immunother Cancer ; 10(12)2022 12.

Article em En | MEDLINE | ID: mdl-36600553

Assuntos

Leucemia; Receptores de Antígenos Quiméricos; Humanos; Imunoterapia; Linfócitos T; Recidiva; Antígenos CD19; Leucemia/terapia

Palavras-chave

computational biology; immunotherapy

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Leucemia / Receptores de Antígenos Quiméricos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Immunother Cancer Ano de publicação: 2022 Tipo de documento: Article

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