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Record W4405042905 · doi:10.1182/blood-2024-200300

Pharmacodynamic Signatures and Correlatives of Response in Patients with Relapsed/Refractory Multiple Myeloma (RRMM) Treated with Talquetamab or Teclistamab Plus Daratumumab and Pomalidomide

2024· article· en· W4405042905 on OpenAlexaff
Deeksha Vishwamitra, Sheri Skerget, Diana S. Cortes, Kalpana Bakshi, Lien Vandenberk, Weili Sun, Jaszianne Tolbert, Colleen Kane, Hein Ludlage, Bas D. Koster, Julie S. Larsen, Tobias Kampfenkel, Ching Li, Farheen Zishan, Thomas J. Prior, Luciano J. Costa, Jesús G. Berdeja, Cyrille Touzeau, Aurore Perrot, Emma Searle, Jeffrey Matous, Ajai Chari, Donna Reece, Manisha Bhutani, Bhagirathbhai Dholaria, Anita D’Souza, Thomas G. Martin, John T. McKay, Alfred L. Garfall, Amrita Krishnan, Niels W.C.J. van de Donk, Nizar J. Bahlis, Ricardo M. Attar

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of CalgaryPrincess Margaret Cancer Centre
FundersKite PharmaAdaptive BiotechnologiesRegeneron PharmaceuticalsJanssen PharmaceuticalsGenentechCelgeneBristol-Myers SquibbGlaxoSmithKlineServierAmgen
KeywordsDaratumumabPomalidomideMedicineMultiple myelomaLenalidomideInternal medicineRefractory (planetary science)OncologyPharmacodynamicsBiologyPharmacokinetics

Abstract

fetched live from OpenAlex

Introduction: First-in-class bispecific antibodies (BsAbs) like talquetamab (tal; targeting G protein-coupled receptor class C group 5 member D) have shown deep, durable responses in RRMM. Targeting multiple epitopes with combination therapies may enhance antimyeloma activity. The anti-CD38 monoclonal antibody daratumumab (D) and immunomodulatory drugs (IMiDs) such as pomalidomide (P) are known to augment T-cell activity. D exhibits direct antitumor cytotoxicity, increases T-cell recruitment, and depletes CD38 immunoregulatory cells; P upregulates CD38 expression and increases natural killer (NK)-cell activity. We assessed immunologic pharmacodynamic profiles, correlatives of response, and associations with outcomes in patients (pts) treated with tal-DP from TRIMM-2 (NCT04108195) to better understand the potential of this regimen. Methods: Eligible pts had ≥3 prior lines of therapy, including a proteasome inhibitor (PI) and IMiD, or were double refractory to a PI and IMiD. Pts received tal 0.4 mg/kg weekly (QW) or 0.8 mg/kg biweekly (Q2W) with step-up dosing and approved schedules of D 1800 mg and P 2 mg. Peripheral blood samples collected at baseline (BL) and on treatment were analyzed by flow cytometry. Max-fold change was calculated per pt using the highest fold change relative to BL or cycle (C) 2 day 1 (when P was added to tal-D). Correlations with progression-free survival (PFS), duration of response (DOR), and best response groups (complete response [CR]/stringent CR [sCR], partial response [PR]/very good PR [VGPR], and stable disease [SD]/progressive disease [PD]) were performed. Results: Samples from 77 pts in TRIMM-2 were analyzed (tal QW, n=18; tal Q2W, n=59; CR/sCR, n=37; VGPR/PR, n=24; and SD/PD, n=6). Tal-D showed complementary pharmacodynamic effects, including T-cell margination, increased absolute T-cell recovery, expanded effector memory, and decreased naive-CD8 T cells during the first C, which were enhanced after addition of P in C2. While D treatment led to an initial reduction in CD38+ CD8 T cells, addition of tal transiently induced activation of this subset despite concurrent D dosing. After P administration, reinduction of CD38 on CD8 T cells was observed, indicating T-cell restimulation. D reduced immunosuppressive CD38+ regulatory T cells (Tregs), and addition of P rescued NK cells reduced by D. A subgroup analysis showed a pronounced impact of tal-DP on pts with prior BsAb exposure. Although a more dysfunctional, exhausted T-cell phenotype at BL was identified in these pts, tal-DP led to greater CD8 T-cell expansion, NK-cell recovery, CD38+ T-cell activation, and reduction of CD38+ Tregs vs pts without prior BsAb exposure. BL response signatures showed higher CD8 T-cell counts and lower expression of T-cell activation/coinhibitory receptor expression on CD8 T cells (CD38, PD-1/LAG-3, and PD-1/TIM-3) in pts with deeper responses that also demonstrated trends associated with improved PFS and DOR. Longitudinal correlative analyses showed greater, earlier recovery of absolute CD8 T cells in deeper responders to tal-DP that correlated with longer PFS and DOR, particularly following addition of P. NK-cell reduction was comparable across all response groups after tal-D; however, pts with deep responses exhibited NK-cell recovery after P was added, which was not evident in pts with less deep or no responses. Although D addition initially reduced CD38+ CD8 T cells, pts with deeper responses showed greater induction of CD38+ CD8 T-cell activation after addition of tal, which was sustained following P administration; in contrast, nonresponding pts displayed a shorter reactivation of CD38 T cells after P. Higher max-fold change in absolute NK-cell counts and CD38+ CD8 T cells showed trends toward improved PFS and DOR, notably in C2 after the addition of P. Finally, less persistent expression of coinhibitory receptors on CD8 T cells was observed in pts with deep responses, while a reduction in CD38+ Tregs was seen in all pts with tal-DP. Preliminary results with teclistamab (tec)-DP show comparable results to tal-DP, and further analyses are ongoing. Conclusions: Tal-DP exhibits a deep, long-term impact on efficacy through complementary mechanisms of action and may be especially beneficial in pts with prior BsAb exposure, who typically have unfavorable BL immune profiles. Ongoing analyses of tec-DP from MajesTEC-2 (NCT04722146) will be presented.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.267
Teacher spread0.257 · 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 designObservational
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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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