Immune and Tumor Mediators of Resistance to Daratumumab-IMiDs Based Therapies in Relapsed Multiple Myeloma
Bibliographic record
Abstract
Background: The use of the anti-CD38 monoclonal antibody daratumumab (Dara) together with IMiDs agents such as pomalidomide and lenalidomide has demonstrated high efficacy in newly diagnosed and relapsed multiple myeloma (MM) patients. However, MM eventually relapses and the mechanisms underlying acquired resistance to Dara-IMiDs based therapies are yet to fully defined. In this project, we aimed to identify at the single cell level the immune and tumor mediators of resistance to these therapies in MM. Methods: Serial bone marrow (BM) aspirates were collected from 31 RRMM patients treated with daratumumab-IMiDs combinations before initiation of therapy, at C3D1, and at time of relapse. Patients were considered as responders if achieving ≥ partial response (PR) for at least 4 months. BM mononuclear fractions were isolated via Ficoll density gradients coupled with magnetic sorting of CD138+ and CD138- cells. Unbiased single cell mRNA profiling of CD138+ and CD138- cells was performed using the GemCode system (10x Genomics) and paired-end sequencing done on the NexSeq Illumina platform. CellRanger and Seurat were used for sample de-multiplexing, barcode processing, single-cell 3′ gene counting and data analysis. A total of 270,835 cells were annotated based on their gene expression signature using scGate and ProjecTILs packages. Results: We first compared the baseline immunome profiling of 74,800 CD138- cells obtained from 29 patients (24 responders and 5 non-responders). An activated innate and adaptive immune microenvironment was observed in responders with significant enrichment of highly cytotoxic NK cells (CD56dim, CD16+, NCR3+, PRF1+, CD38-), CD4 CTLs (GNLY+, CX3CR1+, PRF1+, GZM+), central memory CD8 T cells (GZMB+, GZMH+, TOX-), and CD16 M1 monocytes (CD16+, TNF+, IL1B+, CLL3+). In contrast, the non-responders had a highly inflammatory microenvironment enriched of INF-secreting cDC2 cells (CLEC10A+, ISG15+, IFI6+, IFI44L+) and classical CD14+ monocytes (CCR1+, SELL+, CX3CR1-, CD16-) together with low cytotoxic NK cells (CD56+, CD16-, KLRB1+, CD38+) and more exhausted CD8 T cells (GZMB-, TCF7-, TOX+, LAG3+). Of interest, the non-responders were also characterized by a significantly high proportion of tolerogenic cDC1 CLEC9A+ expressing IDO1, known to interfere with T-cell activation and induce immune suppression. Among the 24 responders 4 patients relapsed after achieving PR for a minimum of 7 months. Comparison of the immunome profiling of CD138- cells at time of progression vs baseline revealed a loss of activation of the innate and adaptive immunity with reduction of cytotoxic NK cells (CD56+, CD16+, NCR3+, CD38+), loss of naive CD4 and CD8 T cells (TCF7+, CCR7+) together with an enrichment of exhausted CD4 and CD8 T cells (GZMB+, TOX+, LAG3+). In addition, an enrichment of CD16 monocytes lacking TNF and IL1B expression and increase of immunosuppressive cDC1 (CLEC9A+, IDO1+) were also observed at relapse. Lastly, the analysis of the MM cells transcriptome performed by comparing samples prior starting therapy and at the time of relapse revealed loss or downregulation of CD38 expression in 75% of the patients. In one patient, we observed a decrease of CD38 as well as increased expression of the complement inhibitor molecule CD59 and CD47 (“don't eat me” signal). In addition, significant MYC transcript amplification was noted in a primary refractory patient. Analysis of cell-cell interactions between tumor and immune cells using CellChat is currently ongoing and will be updated at the meeting. Conclusions: Through single cell transcriptomic studies, we have characterized the tumor microenvironment of RRMM patients treated with Dara-IMiDs based therapies and identified an activated immune microenvironment in responding patients. In contrast a highly inflammatory microenvironment with enrichment of exhausted T cells and increased numbers of immunosuppressive cDC1 and CD14 monocytes were observed in resistant patients. Amongst tumor-intrinsic factors, target CD38 downregulation was commonly noted. Taken together our findings underline the influence of the BM immune composition in promoting Dara-IMiDs resistance and support the integration of novel therapeutic strategies targeting the underlying inflammatory BM environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".