An Actionability Score for BCMA-, GPRC5D-, and CD38-targeted therapy selection in multiple myeloma
Authors
Konstantin Chernyshov, Stanislav Kurpe, Maria Kuzmicheva, Kseniia Fede, Sargis Margaryan, Daria Goncharova MSc, Anastasiya Evdokimova, Dmitrii Grachev, Oleg Baranov, Polina Turova, Alexander Nesmelov, Andrey Kravetz, Eduardo Shugaev-Mendosa, Nikita Kotlov
- BostonGene Corporation, Waltham, MA, USA
Abstract
Background:
Selecting patients for T-cell engager (TCE) and CAR-T therapy in multiple myeloma (MM) currently relies on prior therapy lines, without predictive biomarkers to guide target (e.g., BCMA vs GPRC5D) or modality (e.g., CAR-T vs TCE) selection. We developed an NGS-based framework for computing an Actionability Score (AS) to predict response using malignant and tumor microenvironment features.
Methods: Thorough literature review and analysis of publicly accessible MM cohorts, including bulk RNA-seq (n=715, n=290) and scRNA-seq (n=6, n=5) cases, were used to develop the AS framework for anti-BCMA CAR-T and TCE, anti-GPRC5D TCE, and anti-CD38 monoclonal antibodies (mAb). The AS represents a normalized (0 to 1), weighted score integrating the expression of related target genes (TNFRSF17, GPRC5D, CD38), resistance factors (STAT3, γ-secretase), immune modulators (CD274 / PD-L1, CD55, CD59, ICAM1) and relevant TME signatures (e.g., T-cell exhaustion, T-reg fraction).
Results:
We observed heterogeneous profiles in MM (CoMMpass): 36% of patients had high ASBCMA-CAR-T/TCE, 33% high ASCD38-mAb, and 31% high ASGPRC5D-TCE, reflecting potential treatment benefit. Notably, 21% of patients had a low AS (<0.5) for all tested immunotherapies. Within this low-AS group, the AS framework identified other actionable pathways potentially linked to resistance (e.g., 2% high γ-secretase, 3% high STAT3), highlighting intervention opportunities. To illustrate the predictive utility of AS, we applied it to pre-treatment samples of anti-BCMA CAR-T-treated patients (pts) (GSE210079). All three pts with high (>0.6) ASBCMA-CAR-T had long (>6 months) PFS. For pts with short (<6 months) PFS on CAR-T treatment, AS highlighted the possibility for alternative therapies: pt 8 (ASBCMA-TCE=1.0), pt 32 (ASGPRC5D-TCE=1.0), and pt 16 (ASCD38-mAb=0.61). These data suggest that higher baseline ASBCMA-CAR-T may be associated with prolonged PFS in pts receiving anti-BCMA CAR-T.
Conclusions:
The AS represents a molecular stratification framework that can guide biomarker-driven selection of MM patients for TCE and CAR-T therapies. Pending validation in larger cohorts, AS yields biologically grounded, actionable insights for treatment selection in MM patients that can optimize clinical trial design, response prediction, and treatment sequencing for improved patient outcomes.
Adapted from a presentation at the 2026 AACR Annual Meeting with significant updates.
Selecting patients for T-cell engager (TCE) and CAR-T therapy in multiple myeloma (MM) currently relies on prior therapy lines, without predictive biomarkers to guide target (e.g., BCMA vs GPRC5D) or modality (e.g., CAR-T vs TCE) selection. We developed an NGS-based framework for computing an Actionability Score (AS) to predict response using malignant and tumor microenvironment features.
Methods: Thorough literature review and analysis of publicly accessible MM cohorts, including bulk RNA-seq (n=715, n=290) and scRNA-seq (n=6, n=5) cases, were used to develop the AS framework for anti-BCMA CAR-T and TCE, anti-GPRC5D TCE, and anti-CD38 monoclonal antibodies (mAb). The AS represents a normalized (0 to 1), weighted score integrating the expression of related target genes (TNFRSF17, GPRC5D, CD38), resistance factors (STAT3, γ-secretase), immune modulators (CD274 / PD-L1, CD55, CD59, ICAM1) and relevant TME signatures (e.g., T-cell exhaustion, T-reg fraction).
Results:
We observed heterogeneous profiles in MM (CoMMpass): 36% of patients had high ASBCMA-CAR-T/TCE, 33% high ASCD38-mAb, and 31% high ASGPRC5D-TCE, reflecting potential treatment benefit. Notably, 21% of patients had a low AS (<0.5) for all tested immunotherapies. Within this low-AS group, the AS framework identified other actionable pathways potentially linked to resistance (e.g., 2% high γ-secretase, 3% high STAT3), highlighting intervention opportunities. To illustrate the predictive utility of AS, we applied it to pre-treatment samples of anti-BCMA CAR-T-treated patients (pts) (GSE210079). All three pts with high (>0.6) ASBCMA-CAR-T had long (>6 months) PFS. For pts with short (<6 months) PFS on CAR-T treatment, AS highlighted the possibility for alternative therapies: pt 8 (ASBCMA-TCE=1.0), pt 32 (ASGPRC5D-TCE=1.0), and pt 16 (ASCD38-mAb=0.61). These data suggest that higher baseline ASBCMA-CAR-T may be associated with prolonged PFS in pts receiving anti-BCMA CAR-T.
Conclusions:
The AS represents a molecular stratification framework that can guide biomarker-driven selection of MM patients for TCE and CAR-T therapies. Pending validation in larger cohorts, AS yields biologically grounded, actionable insights for treatment selection in MM patients that can optimize clinical trial design, response prediction, and treatment sequencing for improved patient outcomes.
Adapted from a presentation at the 2026 AACR Annual Meeting with significant updates.
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