Single-cell transcriptional analysis of the immune tumour microenvironment during myeloma disease evolution
Bibliographic record
Abstract
Abstract Multiple myeloma is universally preceded by a premalignant disease state. However, efforts to develop preventative therapeutic strategies are hindered by an incomplete understanding of the immune mechanisms associated with progression. Using single-cell RNA-sequencing, we profiled 104,880 cells derived from the bone marrow of Vκ*MYC mice across the myeloma progression spectrum, of which 97,720 were identified as non-malignant cells of the tumour microenvironment. Analysis of the non-malignant cells comprising the immune microenvironment identified mechanisms associated with disease progression in innate and adaptive immune cell populations. This included activation of IL-17 signaling in myeloid cells from precursor mice, accompanied by upregulation of Il6 gene expression in basophils. In the T/Natural killer cell compartment, we identified Tox-expressing CD8+ T cells enriched in the tumour microenvironment of mice with overt disease, with co-expression of LAG3 and PD-1, as well as elevated T cell exhaustion signatures in mice with early disease. We subsequently showed that early intervention with combinatorial blockade of LAG3 and PD-1 using neutralizing monoclonal antibodies delayed tumor progression and improved survival of Vκ*MYC mice. Together, this work provides insight into the biology of myeloma evolution and nominates a treatment strategy for early disease.
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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) | 0.001 | 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".