Profiling Three-Dimensional Nuclear Telomeric Architecture of Myelodysplastic Syndromes and Acute Myeloid Leukemia Defines Patient Subgroups
Bibliographic record
Abstract
PURPOSE: Myelodysplastic syndromes (MDS) are a group of disorders characterized by cytopenias, with a propensity for evolution into acute myeloid leukemias (AML). This transformation is driven by genomic instability, but mechanisms remain unknown. Telomere dysfunction might generate genomic instability leading to cytopenias and disease progression. EXPERIMENTAL DESIGN: We undertook a pilot study of 94 patients with MDS (56 patients) and AML (38 patients). The MDS cohort consisted of refractory cytopenia with multilineage dysplasia (32 cases), refractory anemia (12 cases), refractory anemia with excess of blasts (RAEB)1 (8 cases), RAEB2 (1 case), refractory anemia with ring sideroblasts (2 cases), and MDS with isolated del(5q) (1 case). The AML cohort was composed of AML-M4 (12 cases), AML-M2 (10 cases), AML-M5 (5 cases), AML-M0 (5 cases), AML-M1 (2 cases), AML-M4eo (1 case), and AML with multidysplasia-related changes (1 case). Three-dimensional quantitative FISH of telomeres was carried out on nuclei from bone marrow samples and analyzed using TeloView. RESULTS: We defined three-dimensional nuclear telomeric profiles on the basis of telomere numbers, telomeric aggregates, telomere signal intensities, nuclear volumes, and nuclear telomere distribution. Using these parameters, we blindly subdivided the MDS patients into nine subgroups and the AML patients into six subgroups. Each of the parameters showed significant differences between MDS and AML. Combining all parameters revealed significant differences between all subgroups. Three-dimensional telomeric profiles are linked to the evolution of telomere dysfunction, defining a model of progression from MDS to AML. CONCLUSIONS: Our results show distinct three-dimensional telomeric profiles specific to patients with MDS and AML that help subgroup patients based on the severity of telomere dysfunction highlighted in the profiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".