Immunome Single Cell Profiling Reveals T Cell Exhaustion with Upregulation of Checkpoint Inhibitors LAG3 and Tigit on Marrow Infiltrating T Lymphocytes in Daratumumab and IMiDs Resistant Patients
Bibliographic record
Abstract
Abstract Background: The addition of immunomodulatory (IMiDs) drugs to monoclonal antibodies targeting the transmembrane glycoprotein CD38 has demonstrated very encouraging and durable responses in myeloma patients. It is believed that the synergistic effects observed with anti-CD38 antibodies and IMiDs are derived from their co-modulation of the host adaptive and innate immunity and therefore it is plausible to speculate that acquired resistance to daratumumab and IMiDs may be largely immune-mediated. The aim of the present study was to 1) interrogate at the single cell level the bone marrow immune repertoire of daratumumab sensitive and resistant patients, 2) identify cellular mediators of resistance to anti-CD38 antibodies and 3) define potential means to reinstate sensitivity to daratumumab and IMiDs. Methods and Results: Serial BM aspirates (n=44) were collected from patients treated with single agent daratumumab (MMY3012 trial) or daratumumab + pomalidomide (MM014 trial) prior to initiation of therapy, C3D1 and at relapse. Bone marrow mononuclear fractions were isolated through ficoll density gradients coupled with magnetic sorting of CD138pos and CD138neg cells. Unbiased mRNA profiling of BM CD138neg cells was performed by single-cell RNA-seq (scRNA-seq) using the GemCode system (10x Genomics). Paired-end sequencing was performed on Illumina NEXTseq and NOVAseq platforms. Cell Ranger Single and Seurat were used for sample de-multiplexing, barcode processing, single-cell 3′ gene counting and data analysis. Sequencing data were analyzed by principal component analysis (PCA), clustering with multi-sample batch correction and then visualized by t-distributed stochastic neighbor embedding (t-SNE) projection. Comparison of the single cell transcriptomes of CD138neg cells from responding patients pre- and post- treatment revealed that Daratumumab and Pomalidomide dramatically modify the immune cells composition (immunome) of the bone marrow niches leading to: 1) expansion of effector T cells (KLRG1high, GZMAhigh, CCL5high), 2) significant depletion of CD38high / FCGR3Ahigh NK cells with retained population of cytotoxic NK cells (CD27high, KLRB1high, NCR3high, GZMApos, PRF1pos), 3) depletion of FCGR3Ahigh / CD14low monocytes, 4) expansion of M1 inflammatory macrophages and depletion of plasmacytoid dendritic cells. Similar changes were seen in patients treated with single agent daratumumab (without IMiDs) however with a lesser expansion of effector T cells and in particular reduced marrow infiltrating inflammatory macrophages. In contrast, the immunome of daratumumab and pomalidomide resistant patients was characterized by a reduced central memory T cells (TCM), and a largely exhausted effector T cells populations that are CD28neg and expressing checkpoint inhibitors (LAG3and TIGIT significantly more than PDCD1) as well as high expression of TIM3 (HAVCR2) on marrow macrophages. Upregulation of LAG3 and TIGIT expression on T cells was also confirmed at the antigenic level by flow cytometry. Consistent with the non-bystander and suppressive effect of LAG3 and TIGIT on the function of effector T cells, activation (CD107a expression) and proliferation of LAGpos and/or TIGITpos sorted bone marrow T cells from resistant patients were significantly reduced in response to autologous myeloma cells stimulation or CD3/CD28 crosslinking. Lastly, a higher proportion and number of clonal T cell (through single cell TCR sequencing) was also observed in responding (≥ PR) vs non-responding (< PR) patients. Interrogation of the myeloma cells transcriptome, showed little to no loss of CD38 transcript at the time of acquired resistance with rather upregulation of complement inhibitory molecule CD59 and NFκB signature genes. Conclusion: A systematic unsupervised interrogation of the bone marrow immunome of daratumumab and IMiDs treated MM patients demonstrated a significant activation of adaptive and innate immunity in responding patients and revealed an expansion of exhausted T cells with upregulation of the checkpoint inhibitors LAG3 and TIGIT in resistant patients. Our findings warrant the exploration of LAG3- and/or TIGIT-blocking strategies as potential means to reinstate sensitivity to daratumumab and IMiDs in myeloma patients. Disclosures Neri: Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. McCulloch:Celgene: Honoraria; Takeda: Other: Travel expenses. Thakurta:Celgene Corporation: Employment, Equity Ownership. Bahlis:Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".