Integrated Genomic and Transcriptomic Analysis Improves Disease Classification and Risk Stratification of MDS with Ring Sideroblasts
Bibliographic record
Abstract
PURPOSE: Ring sideroblasts (RS) define the low-risk myelodysplastic neoplasm (MDS) subgroup with RS but may also reflect erythroid dysplasia in higher risk myeloid neoplasm. The benign behavior of MDS with RS (MDSRS+) is limited to SF3B1-mutated cases without additional high-risk genetic events, but one third of MDSRS+ carry no SF3B1 mutation, suggesting that different molecular mechanisms may underlie RS formation. We integrated genomic and transcriptomic analyses to evaluate whether transcriptome profiles may improve current risk stratification. EXPERIMENTAL DESIGN: We studied a prospective cohort of MDSRS+ patients irrespective of World Health Organization (WHO) class with regard to somatic mutations, copy-number alterations, and bone marrow CD34+ cell transcriptomes to assess whether transcriptome profiles add to prognostication and provide input on disease classification. RESULTS: SF3B1, SRSF2, or TP53 multihit mutations were found in 89% of MDSRS+ cases, and each mutation category was associated with distinct clinical outcome, gene expression, and alternative splicing profiles. Unsupervised clustering analysis identified three clusters with distinct hemopoietic stem and progenitor (HSPC) composition, which only partially overlapped with mutation groups. IPSS-M and the transcriptome-defined proportion of megakaryocyte/erythroid progenitors (MEP) independently predicted survival in multivariable analysis. CONCLUSIONS: These results provide essential input on the molecular basis of SF3B1-unmutated MDSRS+ and propose HSPC quantification as a prognostic marker in myeloid neoplasms with RS.
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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 | 0.001 | 0.001 |
| 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".