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Record W4417006357 · doi:10.1182/blood-2025-5728

Pomalidomide promotes activation induced cell death of pre-activated, memory BCMA CAR-T cells in a model of high, but not low, tumor burden multiple myeloma

2025· article· en· W4417006357 on OpenAlexaff
Kirsten Pfeffer, Yuan Xiao Zhu, Chang‐Xin Shi, Lorenzo Lindo, Kevin A. Hay, Erin W. Meermeier, P. Leif Bergsagel, Marta Chesi

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsInstitute of Cancer ResearchSpinal Cord Injury BC
Fundersnot available
KeywordsPomalidomideMultiple myelomaCancerFlow cytometryCancer cellT cellTumor microenvironmentBortezomib

Abstract

fetched live from OpenAlex

Abstract Introduction High disease burden has been associated with reduced efficacy and resistance to BCMA CAR-T therapy in relapsed/refractory multiple myeloma (van de Donk, Blood Cancer Discov, 2021; Martin, J Clin Oncol, 2023). While CAR-T cells can still induce remissions in these patients, outcomes are often worse compared to those with a lower tumor load. In addition to tumor debulking, novel strategies are needed to improve responses and durability of CAR-T cells in these patients. We previously demonstrated that immunomodulatory drugs (IMiDs) improve in vivo responses to T cell engagers (TCE) in high tumor burden setting by boosting T cell activation (Meermeier, Blood Cancer Discov, 2021; Meermeier, Blood, in press). IMiDs have also been shown to promote CAR-T cell antitumor activity in moderate to low tumor burden conditions (E:T ratios ≥1:1) (Yan, J Transl Med, 2024; Wang, Clin Cancer Res, 2018). We hypothesized that combining CAR-T with an IMiD may improve CAR-T function in high tumor burden setting. Methods We generated murine anti-BCMA.CD28 CAR-T cells by transducing Con-A stimulated T lymphocytes extracted from hCRBN+ mice, expanded with IL-7 and IL-15 to express a memory phenotype. We then tested CAR-T, or untransduced T cells, ability to kill in vitro IMiD resistant, hCRBN-, Vk*MYC myeloma cells in low (E:T 1:1) and high (E:T 1:≥5) tumor burden, with and without pomalidomide (POM) and IL-2. Tumor killing and T cell phenotype were assessed by flow cytometry at 24-72 hours. This controlled system allowed us to focus on putative CAR-T cell intrinsic impact of IMiDs. Results In low tumor burden setting, CAR-T effectively killed tumor at all timepoints, with or without POM; addition of POM increased T cell number, confirming that IMiDs support CAR-T expansion. Unexpectedly, in high tumor burden (E:T 1:≥5), adding POM diminished CAR-T mediated killing over time and hindered CAR-T expansion. Moreover, combination with POM decreased granzyme B, while increased expression of exhaustion marker TOX, ultimately leading to CAR-T apoptosis. This phenotype was further intensified by increasing concentrations of IL-2 in the co-culture, resulting in increased expression of CAR-T expressing LAG-3, TIM-3, and TIGIT, suggesting that CAR-T cells may be overactivated by POM and IL-2. Our manufacturing protocol preferentially expands memory CAR-T cells, favored for their durability, proliferative capacity, and considered to rapidly expand upon encounter with antigen. We reasoned that CAR-T are already sufficiently activated by CAR/CD3z stimulation, especially with a CD28 co-stimulatory domain (versus 4-1BB), known promote rapid, effector memory expansion upon activation (Kawalekar, Immunity, 2016). Activation is intensified by high tumor burden where antigen is highly abundant. We hypothesize under intense, sustained antigen exposure in high tumor burden, the addition of POM paradoxically triggers activation induced cell death (AICD). Conclusion Although it is well accepted that POM can boost T cell function and proliferation, we demonstrate that in high tumor burden, POM drives memory CAR-T to increase exhaustion markers and apoptotic cell death by overactivation, further exacerbated by exogenous IL-2. Interestingly, we have shown opposite effects of preclinically combining POM with TCE therapy in high tumor burden, based on dosing schedule. Concomitant administration transiently boosted T cell activation improving response rates but rapidly led to T cell exhaustion (Meermeier, Blood Cancer Discov, 2021). In contrast, IMiD pretreatment in the context of step-up dosing TCE administration improved T cell fitness and longevity (Meermeier, Blood, in press).The effect of IMiDs on T cells transcriptional program is likely to differ depending on differentiation stage and mode of T cell activation. Memory cells have more sensitive TCR signaling (Kumar, Immunity, 2011) and can be hyperactivated, leading to AICD. Thus, timing of IMiD administration, when tumor burden is low, may be a critical consideration as different T cell subsets require different levels of activation. Conversely, TCE provide only initial activation via TCR signaling, therefore POM may help in providing necessary co-stimulation in this setting. Future studies will evaluate different co-stimulatory domains and test in vivo effects of POM on CAR-T expansion and persistence, particularly as CAR-T cells diminish over time as tumor burden increases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.277
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes1
Has abstractyes

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