Abstract 2044: Three-dimensional nuclear telomeric architecture defines cytopenias of myelodysplastic syndrome and its transformation to acute myeloid leukemia
Bibliographic record
Abstract
Abstract Background Myelodysplastic Syndromes (MDS) are a group of disorders characterized by cytopenias, with a propensity for evolution into Acute Myeloid Leukemias (AML). This transformation is governed by acquisition of additional mutations, which in turn is driven by genomic instability and epigenetic events. However, how genomic instability occurs in this disease remains unknown. Telomere dysfunction might be the generator of genomic instability leading to cytopenias and disease progression from MDS to AML. Methods We undertook a prospective pilot study of 95 patients to investigate the nuclear telomere organization in MDS (57 patients) and AML (38 patients). Three-dimensional (3D) quantitative fluorescent in situ hybridization was performed to label telomeres of bone marrow samples. Nuclear telomere organization was analyzed using TeloViewTM and TeloScanTM. Findings Our cohort was composed of 57 cases of MDS and 38 AML. MDS cohort was constituted of Refractory Anaemia with Multilineage Dysplasia (RCMD = 32 cases), Refractory Anaemia (RA = 12 cases), Refractory Anaemia with Excess of Blasts 1 and 2 (RAEB-1 = 8 cases; RAEB-2 =1 case), Refractory Anaemia with Ring Sideroblasts (RARS = 2 cases), MDS associated with isolated del(5q) (1 case), and MDS unclassified (MDS-U = 1 case). AML were 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). We analyzed the 3D nuclear telomeric architecture, and determined the telomere numbers, presence of telomere aggregates, telomere signal intensities, nuclear volume, and telomere distribution. From these parameters we constructed 3D telomeric profiles, and we were able to subdivide the MDS patients into 9 subgroups and the AML patients into 6 subgroups. Each of the quantitative telomere parameters showed significant difference between MDS and AML. Furthermore, statistical analyses combining all the telomere parameters displayed significant difference between all subgroups. These profiles were linked to the evolution of telomere dysfunction in these subgroups allowing us to define a model of progression of MDS to AML. Our 3D telomeres profiling reflect better genomic abnormalities in MDS and AML than the cytogenetic categorisations. Telomere profiles seem to be a potential biomarker, which will improve the clinical management of these patients combined to other indexes such as IPSS and WPSS in MDS, and the cytogenetic risk factor classifications of AML. Interpretation Our results define for the first time the chronological and evolutionary process of telomere dysfunction in MDS and AML. These results are consistent with the hypothesis that telomere dysfunction generates genomic instability, leading to worsening cytopenias and to the transformation of MDS to AML. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2044. doi:1538-7445.AM2012-2044
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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 | 0.001 | 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) | 0.003 | 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".