Frontiers in Immunology (Mar 2022)

Identification of the Predictive Models for the Treatment Response of Refractory/Relapsed B-Cell ALL Patients Receiving CAR-T Therapy

  • Jingxian Gu,
  • Jingxian Gu,
  • Jingxian Gu,
  • Sining Liu,
  • Sining Liu,
  • Sining Liu,
  • Wei Cui,
  • Wei Cui,
  • Wei Cui,
  • Haiping Dai,
  • Haiping Dai,
  • Haiping Dai,
  • Qingya Cui,
  • Qingya Cui,
  • Qingya Cui,
  • Jia Yin,
  • Jia Yin,
  • Jia Yin,
  • Zheng Li,
  • Zheng Li,
  • Zheng Li,
  • Liqing Kang,
  • Huiying Qiu,
  • Huiying Qiu,
  • Huiying Qiu,
  • Yue Han,
  • Yue Han,
  • Yue Han,
  • Miao Miao,
  • Miao Miao,
  • Miao Miao,
  • Suning Chen,
  • Suning Chen,
  • Suning Chen,
  • Shengli Xue,
  • Shengli Xue,
  • Shengli Xue,
  • Ying Wang,
  • Ying Wang,
  • Ying Wang,
  • Zhengming Jin,
  • Zhengming Jin,
  • Zhengming Jin,
  • Xiaming Zhu,
  • Xiaming Zhu,
  • Xiaming Zhu,
  • Lei Yu,
  • Depei Wu,
  • Depei Wu,
  • Depei Wu,
  • Xiaowen Tang,
  • Xiaowen Tang,
  • Xiaowen Tang

DOI
https://doi.org/10.3389/fimmu.2022.858590
Journal volume & issue
Vol. 13

Abstract

Read online

Background/AimsChimeric antigen receptor (CAR) T cells for refractory or relapsed (r/r) B-cell acute lymphoblastic leukemia (ALL) patients have shown promising clinical effectiveness. However, the factors impacting the clinical response of CAR-T therapy have not been fully elucidated. We here aimed to identify the independent factors of CAR-T treatment response and construct the models for predicting the complete remission (CR) and minimal residual disease (MRD)-negative CR in r/r B-ALL patients after CAR-T cell infusion.MethodsUnivariate and multivariate logistic regression analyses were conducted to identify the independent factors of CR and MRD-negative CR. The predictive models for the probability of remission were constructed based on the identified independent factors. Discrimination and calibration of the established models were assessed by receiver operating characteristic (ROC) curves and calibration plots, respectively. The predictive models were further integrated and validated in the internal series. Moreover, the prognostic value of the integration risk model was also confirmed.ResultsThe predictive model for CR was formulated by the number of white blood cells (WBC), central neural system (CNS) leukemia, TP53 mutation, bone marrow blasts, and CAR-T cell generation while the model for MRD-negative CR was formulated by disease status, bone marrow blasts, and infusion strategy. The ROC curves and calibration plots of the two models displayed great discrimination and calibration ability. Patients and infusions were divided into different risk groups according to the integration model. High-risk groups showed significant lower CR and MRD-negative CR rates in both the training and validation sets (p < 0.01). Furthermore, low-risk patients exhibited improved overall survival (OS) (log-rank p < 0.01), higher 6-month event-free survival (EFS) rate (p < 0.01), and lower relapse rate after the allogeneic hematopoietic stem cell transplantation (allo-HSCT) following CAR-T cell infusion (p = 0.06).ConclusionsWe have established predictive models for treatment response estimation of CAR-T therapy. Our models also provided new clinical insights for the accurate diagnosis and targeted treatment of r/r B-ALL.

Keywords