Three‐dimensional telomere profiling predicts risk of progression in smoldering multiple myeloma
Bibliographic record
Abstract
Smoldering multiple myeloma (SMM) is a precursor stage that precedes multiple myeloma (MM). SMM is heterogenous with nearly 40% of patients progressing to MM in the first 5 years. The high rate of progression of SMM patients highlights the need for early intervention, which underscores the importance of identifying SMM patients with the highest risk of progression. Several risk stratification models showed utility in identifying high-risk SMM patients; however, these systems showed limited sensitivity. To date, identifying high-risk SMM patients remains an important clinical need. In this study, we present the 3-dimensional telomere profiling as a structural biomarker capable of stratifying SMM patients as a function of genomic instability. Quantifying telomere dysfunction using the TeloView technology showed utility in risk stratification of cancer patients, particularly hematological malignancies. In this study, we analyzed 168 SMM patients. We report an AUC in ROC analysis of 0.8 using a subset of the patients as a training dataset. We then conducted a blind validation on a different cohort and demonstrated a positive predictive value of 85% and negative predictive value of 73%, with sensitivity and specificity of 83% and 76%, respectively. We examined the correlation between the TeloView prediction and the 20-2-20 scoring system, and cytogenetic abnormalities. We report a correlation of 53% with the 20-2-20 scores and over 60% correlation with cytogenetic abnormalities. The result of this study presents the telomere profiling as an effective biomarker able to stratify SMM patients to their respective risk groups with high sensitivity and specificity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".