PO.CL01.03 · 临床研究
多发性骨髓瘤中针对BCMA、GPRC5D和CD38靶向治疗的比较性评分框架
A comparative scoring framework for BCMA-, GPRC5D- and CD38-targeted therapies in multiple myeloma
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
在多发性骨髓瘤（MM）中，选择接受T细胞衔接器（TCE）和CAR-T治疗的患者主要依赖既往治疗线数，缺乏预测性生物标志物。目前尚无工具指导靶点（如BCMA vs GPRC5D）或治疗模式（如CAR-T vs TCE）的选择。我们开发了一个基于NGS的框架，计算“靶点就绪度”评分（ReadyScore，RS），利用恶性细胞和肿瘤微环境（TME）特征来预测治疗反应。
用于开发该框架的数据来自公开的MM队列，包括bulk测序（n=715、n=290）和scRNA-seq（n=6、n=5）。RS针对抗BCMA CAR-T和TCE、抗GPRC5D TCE以及抗CD38单克隆抗体（mAb）分别构建。RS是一个归一化的加权评分，整合了相关基因的表达活性（如靶点TNFRSF17、GPRC5D、CD38；耐药相关STAT3、gamma-secretase；免疫因子CD274（PD-L1）、CD55、CD59、ICAM1）以及相关的TME特征（如T细胞耗竭、T-reg比例）。
我们在MM中观察到异质性谱（CoMMpass）：36%的患者（pts）具有高RS BCMA-CAR-T/TCE，33%具有高RS CD38-mAb，31%具有高RS GPRC5D-TCE，反映了潜在的治疗获益。值得注意的是，21%的患者对所有受测免疫疗法均为低RS（< 0.5）。在这一“低RS”组中，该工具识别出可能与耐药相关的其他可干预通路（如2%高gamma-secretase，3%高STAT3），提示了干预机会。为说明该工具的预测效用，我们将其应用于接受抗BCMA CAR-T治疗的患者（表1）。全部3/3例具有高RS BCMA-CAR-T的患者均获得较长的PFS。对于CAR-T后PFS较短的患者，该工具提示了潜在的替代治疗：患者8的RS BCMA-TCE值高，患者32的RS GPRC5D-TCE值高，患者16的RS CD38-mAb值高。
本研究提出了一个分子分层框架，有望支持基于生物标志物为MM患者选择TCE和CAR-T治疗。尽管仍需在扩大的队列中进行验证，但该方法有助于优先识别可增强试验设计和转化决策的预测性谱型。
表1. 靶点ReadyScore应用于GSE210079数据集 患者 PFS RS BCMA-CAR-T RS BCMA-TCE RS GPRC5D-TCE RS CD38-mAb Pt 1 长 0.69 0.23 0.01 1 Pt 19 长 1 0.66 0.48 0 Pt 33 长 0.71 0 0 0.3 Pt 8 短 0 1 0.51 0.16 Pt 16 短 0.6 0.38 0.56 0.61 Pt 32 短 0.09 0.29 1 0.24
查看英文原文 English abstract
Selecting patients for T-cell engager (TCE) and CAR-T therapy in multiple myeloma (MM) relies on prior therapy lines, lacking predictive biomarkers. No tools guide target (e.g., BCMA vs GPRC5D) or modality (e.g., CAR-T vs TCE) selection. We developed an NGS-based framework computing a “Target Readiness” score (ReadyScore, RS) to predict response using malignant and tumor microenvironment (TME) features.
Public MM cohorts, including bulk (n=715, n=290) and scRNA-seq (n=6, n=5), were used to develop the framework. RS was developed for anti-BCMA CAR-T and TCE, anti-GPRC5D TCE, and anti-CD38 monoclonal antibodies (mAb). RS is a normalized, weighted score integrating expression activity of related genes (e.g., targets TNFRSF17, GPRC5D, CD38 ; resistance STAT3 , gamma-secretase; immune factors CD274 (PD-L1), CD55, CD59, ICAM1 ) and relevant TME signatures (e.g., T-cell exhaustion, T-reg fraction).
We observed heterogeneous profiles in MM (CoMMpass): 36% of patients (pts) had high RS BCMA-CAR-T/TCE , 33% high RS CD38-mAb , and 31% high RS GPRC5D-TCE , reflecting potential treatment benefit. Notably, 21% of pts had a low RS (< 0.5) for all tested immunotherapies. Within this 'low-RS' group, the tool identified other actionable pathways potentially linked to resistance (e.g., 2% high gamma-secretase, 3% high STAT3 ), highlighting intervention opportunities. To illustrate the tool's predictive utility, we applied it to anti-BCMA CAR-T treated pts (Table 1). All 3/3 pts with a high RS BCMA-CAR-T had long PFS. For pts with short PFS on CAR-T, the tool highlighted potential alternative therapies: pt 8 had a high value for RS BCMA-TCE , pt 32 for RS GPRC5D-TCE , and pt 16 for RS CD38-mAb .
This work introduces a molecular stratification framework with potential to support biomarker-driven selection of MM pts for TCE and CAR-T therapies. While validation in expanded cohorts is needed, this approach could help prioritize predictive profiles that can enhance trial design and translational decision-making.
Table 1. Target ReadyScore applied to the GSE210079 dataset Patient PFS RS BCMA-CAR-T RS BCMA-TCE RS GPRC5D-TCE RS CD38-mAb Pt 1 long 0.69 0.23 0.01 1 Pt 19 long 1 0.66 0.48 0 Pt 33 long 0.71 0 0 0.3 Pt 8 short 0 1 0.51 0.16 Pt 16 short 0.6 0.38 0.56 0.61 Pt 32 short 0.09 0.29 1 0.24
利益披露 Disclosure
K. Chernyshov,
BostonGene Corporation Employment, Stock Option, Patent.
S. Kurpe,
BostonGene Corporation Employment.
M. Kuzmicheva,
BostonGene Corporation Employment.
K. Fede,
BostonGene Corporation Employment, Stock Option.
S. Margaryan,
BostonGene Corporation Employment.
D. Goncharova,
BostonGene Corporation Employment.
A. Evdokimova,
BostonGene Corporation Employment.
D. Grachev,
BostonGene Corporation Employment.
O. Baranov,
BostonGene Corporation Employment, Stock Option, Patent.
P. Turova,
BostonGene Corporation Employment, Stock Option, Patent.
A. Nesmelov,
BostonGene Corporation Employment, Stock Option.
A. Kravets,
BostonGene Corporation Employment, Stock Option.
E. Shugaev-Mendosa,
BostonGene Corporation Employment.
N. Kotlov,
BostonGene Corporation Employment, Stock Option, Patent.