An APOBEC/Inflammation-based classifier improves the stratification of multiple myeloma patients and identifies novel risk subgroups
Bibliographic record
Abstract
Abstract Background: Recent insights into the pathogenesis of multiple myeloma (MM) have highlighted inflammation and genome editing, e.g. by APOBEC enzymes, as major drivers of disease onset and progression. We hypothesized that inclusion of molecular features corresponding to these two mechanisms can be utilized to define novel MM risk groups at initial diagnosis. Methods: Using two independent patient cohorts (MMRF and IFM/DFCI 2009), we developed and validated an easy-to-calculate novel risk-score that is based on mRNA expression levels of APOBEC2 and APOBEC3B, as well as inflammatory cytokines (IL11, TGFB1 and TGFB3) and serum levels of ß2-microglobulin and LDH. Results: Performance of the Editor- and Inflammation-based score (EI-score) was superior to current cytogenetics-based risk classifiers. Moreover, the EI-score was able to identify previously unrecognized MM patients who experience favourable outcomes despite carrying adverse risk cytogenetics. Conclusions: Through accurate risk stratification we can identify patients who are currently over-or undertreated. The EI-score is a contemporary and superior prognostic score, calculated based on transcript levels at diagnosis, allowing the identification of unrecognized MM risk subgroups potentially leading to adjustment of clinical treatment and improvement of patient outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".