A Research Center's Experience of T-cell Redirecting Therapies in Triple-Class Refractory Multiple Myeloma
BLOOD ADVANCES(2024)
Hematology Department | Hosp Univ Salamanca
Abstract
The efficacies of chimeric antigen receptor T cells (CAR-Ts) and bispecific monoclonal antibodies (BiAbs) for triple-class refractory (TCR) myeloma have not previously been compared, and clinical data on how to rescue patients after relapse from these immunotherapies are limited. A retrospective study of 73 TCR patients included in trials was conducted: 36 received CAR-Ts and 37 received BiAbs. CAR-Ts produced a higher overall response rate (ORR) than BiAbs (97.1% vs 56.8%, P = .002). After a median of followup of 18.7 months, no significant difference in progression-free survival (PFS) was observed between the CAR-T and BiAbs groups (16.6 vs 10.8 months; P = .090), whereas overall survival (OS) was significantly longer in the CAR-T than in the BiAbs group (49.2 vs 22.6 months; P = .021). BiAbs after CAR-Ts yielded a higher ORR and longer PFS2 than did nonredirecting T-cell therapies after CAR-Ts (ORR: 87.5% vs 50.0%; PFS2: 22.9 vs 12.4 months). By contrast, BiAbs after BiAbs resulted in an ORR of 33% and PFS2 of 8.4 months, which was similar to that produced by the nonredirecting T-cell therapies (ORR: 28.6%; PFS2: 8.1 months). Although this is a pooled analysis of different trials with different products and the patient profile is different for CAR-Ts and BiAbs, both were effective therapies for TCR myeloma. However, in our experience, although the PFS was similar with the 2 approaches, CAR-T therapy resulted in better OS, mainly because of the efficacy of BiAbs as rescue therapy. Our results highlight the importance of treatment sequence in real- word experience.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2013
被引用1339 | 浏览
2017
被引用477 | 浏览
2017
被引用263 | 浏览
2016
被引用1886 | 浏览
2019
被引用377 | 浏览
2019
被引用1636 | 浏览
2022
被引用17 | 浏览
2022
被引用59 | 浏览
2022
被引用35 | 浏览
2023
被引用12 | 浏览
2023
被引用3 | 浏览
2023
被引用19 | 浏览
2023
被引用21 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper