Multiple myeloma immunoglobulin λ translocations portend poor prognosis
Bibliographic record
Abstract
Abstract Multiple myeloma is a malignancy of antibody-secreting plasma cells. Most patients benefit from current therapies, however, 20% of patients relapse or die within two years and are deemed ‘high-risk’. To better understand and identify high-risk myeloma, we analyzed the translocation landscape of 826 newly-diagnosed patients by whole genome sequencing as part of the CoMMpass study. Translocations at the IgL locus were present in 10% of myeloma patients, and corresponded with poor prognosis. Importantly, 70% of IgL translocations co-occurred with hyperdiploid disease, a marker of standard risk, which is routinely diagnosed clinically whereas IgL-translocations are not. Thus, it is likely that the majority of IgL-translocated myeloma is being misclassified. The IgL enhancer is among the strongest in myeloma cells, indicating it can robustly drive oncogene expression when translocated. Consistent with this, IgL-translocated patients failed to benefit from immunomodulatory imide drugs (IMiDs), which target the lymphocyte-specific transcription factor Ikaros. These data implicate the IgL enhancer as resistant to IMiD-inhibition, and when translocated, as a driver of poor prognosis.
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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 | 0.001 |
| 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) | 0.004 | 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".