Reduced Antigen Presentation May Contribute to Immunomodulatory Drug Resistance in Multiple Myeloma
Bibliographic record
Abstract
Introduction: Maintenance therapy with the immunomodulatory drug (IMiD) lenalidomide improves progression-free survival (PFS) and overall survival among patients with multiple myeloma (MM). It has been suggested that IMiDs enhance immune-mediated anti-tumor responses through increased activation and proliferation of T cells and natural killer (NK) cells as well as inhibition of regulatory T cells. Despite the effectiveness of this therapy, nearly all patients relapse for reasons that remain uncharacterized. We sought to elucidate mechanisms of resistance to lenalidomide maintenance through investigation of mutations in cell-free DNA (cfDNA) and identification of changes in the innate and adaptive immune system by single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs). While MM is a cancer of plasma cells that primarily reside in the bone marrow, we focused on the peripheral blood which offers a comprehensive view of multiple sites of disease including extramedullary locations commonly found at the time of relapse. Methods: Twenty-four patients with MM and no minimal residual disease by 10-color flow cytometry and IGHVnext-generation sequencing (Invivoscribe) after induction therapy with or without an autologous stem cell transplant were selected for analysis. All patients were enrolled in NCT02538198 at Memorial Sloan Kettering Cancer Center. Peripheral blood was collected serially including timepoints prior to lenalidomide maintenance and after disease progression. Plasma and PBMCs were isolated by Ficoll-Paque density gradient centrifugation. cfDNA was extracted from plasma and targeted next-generation sequencing using a 70-gene panel of recurrently mutated genes in myeloma was performed using the QIAseq Targeted DNA kit (QIAGEN). DNA sequencing was also performed using the same gene panel on granulocytes to exclude germline variants. On PBMC samples, 5' gene expression and T and B cell receptor single-cell sequencing were performed using the Chromium Single Cell V(D)J kit (10X Genomics). Analysis of single-cell sequencing was performed on the top 100 principal components and visualized by Uniform Manifold Approximation and Projection (UMAP). A regression-based classifier was trained to automatically assign clustered cells into 26 immune cell phenotypes using lists of genes reported to be uniquely expressed by each cell type. Results: cfDNA sequencing was performed on 12 patients characterized as durable responders (PFS > 57 months) and 12 early progressors (median PFS 25 months). Prior to maintenance therapy, no significant difference was observed in the number of cfDNA mutations between these groups. The number of non-synonymous somatic mutations in cfDNA tended to increase over time and was significantly higher among patients taking lenalidomide for more than 2 years compared to patients at the time of relapsed disease (mean number of mutations was 7 in responders versus 3 in progressors).ZNF292was among the genes most significantly mutated in responders compared to progressors. ZNF292encodes a zinc finger protein involved in RNA binding and has previously been identified as a potential source of tumor-associated antigens in patients with monoclonal gammopathy of undetermined significance. To identify changes in immune cell phenotype and function that associate with disease progression, we performed single-cell RNA sequencing on PBMCs from the 12 progressors before maintenance and at the time of relapse. The median number of cells sequenced per PBMC sample was 3,132. Over 90% of cells could automatically be classified into a specific immune cell type by their gene expression. We observed the frequency of NK cells and plasmablasts were significantly increased at the time progression on lenalidomide while monocytes, dendritic cells, and basophils were decreased. Conclusion: Mechanisms of resistance among patients receiving treatment with IMiDs is not well known. Using targeted sequencing of cfDNA, we identify patients responding to maintenance tended to have a higher mutational load in cfDNA including genes previously reported to encode tumor-associated antigens. Additionally, we show that a decline in antigen-presenting dendritic cells is more common at the time of disease progression. Taken together, these results suggest reduced neoantigen production and a decline in antigen presentation may contribute to IMiD resistance. Disclosures Landgren: Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Other: IDMC; Theradex: Other: IDMC; Adaptive: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees. Green:Celgene: Consultancy; Cellectar Biosciences: Research Funding; Seattle Genetics: Research Funding; Juno Therapeutics: Consultancy, Patents & Royalties, Research Funding; GSK: 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".