Three-dimensional telomere profiling to predict risk of progression in smoldering multiple myeloma.
Notice bibliographique
Résumé
8056 Background: Multiple myeloma (MM) is preceded by monoclonal gammopathy of undetermined significance (MGUS). A transitional stage of smoldering multiple myeloma (SMM) can be identified between MGUS and MM. While MGUS carries a steady risk of progression of 1% per year, SMM is more heterogenous with nearly 40% of patients progressing in the first 5 years, 15% in the next 5 years, reaching the same low risk as MGUS after 10 years. SMM with its high risk of progression in the initial years after diagnosis presents a viable opportunity for early intervention. For implementing early intervention, the ability to identify SMM patients at the highest risk of progression is critical. This has led to the development of several risk stratification systems. Using these systems high risk SMM patients studied in phase 3 trials demonstrated delayed progression to MM and improved overall survival with early initiation of therapy. However, these approaches showed limited specificity exposing patients at lower risk of progression to therapy. To date, identifying high risk SMM patients and confirming disease stability in low risk SMM patients remain an important clinical need. Genomic instability has been shown to be a sensitive indicator of disease progression in cancer. Telomere dysfunction is an early event in genomic instability. The 3-dimensional spatial profiling of telomeres using TeloView technology allows for quantification of telomere dysfunction, and was shown to be instrumental in risk stratification of cancer patients generally, but particularly in selected hematological malignancies. Importantly, in a previous SMM proof-of-concept study telomeric parameters measured by TeloView technology was found to be significantly different between SMM patients who progressed to active MM within 2 years and those who remained stable for over 5 years. Methods: We analyzed a total of 162 SMM patients using TeloView technology. 88 patients were employed as training dataset in Receiver Operating Curve (ROC) modeling to develop a scoring model that stratifies individual SMM patients based on risk of progression to full stage MM. An additional cohort of 74 SMM patients was used for blind validation of the developed scoring model. Results: We report area-under-the-curve (AUC) in the ROC analysis of 0.8 (accuracy 80%) achieved by the scoring model developed using the training dataset. Furthermore, the independent blind validation achieved positive predictive value of 83% and negative predictive value of 71%, with sensitivity and specificity of 80% and 76% respectively. Conclusions: The result of this study supports presenting TeloView as an accurate prognostic biomarker which appears able to stratify SMM patients into their respective risk groups with high sensitivity and specificity. This will potentially allow for evidence-based treatment decisions for high risk SMM patients and confident monitoring of stable patients.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».