Three-Dimensional Organization of Telomeres: An Emerging Prognostic Biomarker in Multiple Myeloma
Bibliographic record
Abstract
A crucial role of genome instability and telomeric dysfunction was demonstrated in multiple cancers, including multiple myeloma (MM). MM accounts for approximately 10% of all hematologic malignancies and includes asymptomatic pre-malignant monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). Due to the highly heterogeneous nature of the disease, there is an ongoing need for precise risk stratification and subsequent development of risk-adapted treatment strategies at every stage of disease and during disease progression. Telomere numbers, intensity, aggregates, and spatial arrangement within the nucleus were identified as prognostic biomarkers. Recent studies demonstrated that the three-dimensional (3D) analysis of key telomeric parameters is a reliable marker of the high risk of relapse in newly diagnosed MM (NDMM) patients and can predict the risk of progression of SMM patients. Telomeric parameters of malignant MM cells from the peripheral blood and bone marrow were similar, suggesting that 3D telomere profiling may assess MRD in liquid biopsies of MM patients. This review focuses on the prognostic value of 3D telomere profiling in MM. 3D spatial telomere analysis may potentially address a critical unmet clinical need in managing MM and, if incorporated into current guidelines, help to accurately predict disease status, progression risk, overall survival, and response to treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".