Daratumumab in Combination with Lenalidomide Plus Dexamethasone Results in Persistent Natural Killer (NK) Cells with a Distinct Phenotype and Expansion of Effector Memory T-Cells in Pollux, a Phase 3 Randomized Study
Bibliographic record
Abstract
Abstract Introduction: Daratumumab (DARA) is a human monoclonal IgG1κ CD38-targeted antibody that has several mechanisms of action, including complement-dependent cytotoxicity, antibody-dependent cellular cytotoxicity, antibody-dependent cellular phagocytosis, modulation of CD38 enzymatic activity, and induction of apoptosis. DARA (16 mg/kg) single-agent, phase 1/2 translational studies (MMY2002 and GEN501) revealed an additional, novel immunomodulatory mechanism of action that increased the adaptive immune response (Krejcik J et al, Blood 2016;128[3]:384-94). NK cells were reduced in these studies, with no effect on DARA efficacy or safety (Casneuf et al, Presented at EHA. June 9-12, 2016, Copenhagen, Denmark, Abstract P286; Adams et al, Presented at ASH. Dec 3-6, 2016, San Diego, CA. Abstract 4521). To explore the ability of DARA to promote adaptive T-cell responses and immune changes, and to investigate the immunophenotype of NK cells that persist, we have incorporated cytometry by time-of-flight (CyTOF®) technology and profiled patient blood samples at baseline and upon treatment with lenalidomide and dexamethasone (Rd) or DARA plus Rd (DRd). Methods: Relapsed/refractory multiple myeloma patient whole blood samples from both treatment arms of POLLUX were analyzed at baseline (DRd, n=40; Rd, n=45) and after two months of therapy (DRd, n=31; Rd, n=33). Samples were stained with a metal-conjugated antibody panel and evaluated on the CyTOF platform. Similar cellular events were clustered into nodes using the spanning tree progression of density normalized events (SPADE; Qui P, et al. Nature Biotechnology . 2011;29(10):886-891) algorithm and annotated into immune population bubbles via Cytobank® software. P-values derived from t-tests and single cell level bootstrap adjusted p-values corrected for multiple dependent hypothesis testing defined differences in marker intensity and cell populations within subgroups of this study. Results were visualized by SPADE blend trees, coloring each cluster using a combination of p-values related to marker intensity and cell population size changes, and Radviz projections. Results: Consistent with previous DARA monotherapy and combination therapy studies, a reduction in circulating NK cells was observed with DRd in POLLUX. Interestingly, the NK cells that persisted had a distinct phenotype: decreased expression of PD-1 and increased expression of HLA-DR, CD69, CD127 and CD27. These effects were not observed with Rd and may affect the adaptive immune response. The proportion of T-cells increased preferentially in deep responders (≥complete response) receiving DRd and correlated with a higher proportion of CD8+ vs. CD4+ T-cells. Regardless of the treatment received, the phenotype of all cells shifted toward CD45RO+. However, the DRd arm induced greater increases in HLA-DR expression, particularly for effector memory CD8+ T-cells. Furthermore, DRd led to a higher proportion of effector memory T-cells vs Rd. Consistent with observations from DARA monotherapy studies, CD38+ regulatory T-cells (Tregs), a cell population which we have demonstrated potently suppresses T-cell proliferation (Krejcik J et al), were exclusively decreased by DRd. Conclusion: DARA in combination with Rd specifically induced unique phenotypic changes in residual NK cells, suggesting that these cells are able to contribute to immune homeostasis. DARA also induced T-cell profile changes, including expansion of effector memory T-cells and increased expression of activation markers. This study supports the immunomodulatory mechanism of action of DARA and provides additional insight into changes in NK cells, T-cell subtypes and activation status following DARA-based therapy. Disclosures Van De Donk: Janssen, Celgene, Bristol-Myers Squibb, Amgen: Research Funding. Adams: Janssen: Employment. Vanhoof: Janssen: Employment. Van der Borght: Janssen: Employment. Casneuf: Janssen: Employment. Smets: Janssen: Employment. Axel: Janssen: Employment. Abraham: Janssen: Employment. Ceulmans: Janssen: Employment. Stevenaert: Janssen: Employment. Usmani: Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Millennium: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Research Funding; Onyx: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Array BioPharma: Honoraria, Research Funding; Pharmacyclics: Honoraria, Research Funding; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novartis: Speakers Bureau. Plesner: Janssen, Genmab: Membership on an entity's Board of Directors or advisory committees; Janssen, Takeda: Consultancy; Janssen: Research Funding. Lokhorst: OncoImmune: Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genmab: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding. Mutis: Celgene: Research Funding; Novartis: Research Funding; OncoImmune: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Genmab: Research Funding; Gilead: Research Funding. Bahlis: Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Schecter: Janssen: Employment. Chiu: Janssen: Employment. Avet-Loiseau: Celgene, Janssen, Amgen, Bristol-Myers Squibb, Sanofi: Honoraria, Speakers Bureau; Celgene, Janssen: Research Funding; Janssen, Sanofi, Celgene, Amgen: Consultancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".