Novel method of minimal residual disease testing in myeloma: Liquid biopsies to enumerate and 3D telomere-profiling of circulating tumor cells
Notice bibliographique
Résumé
Abstract Introduction: Novel therapeutic approaches, especially cellular therapies, have significantly prolonged the survival of patients with multiple myeloma (MM). However, a cure is yet to be discovered, and the natural course of myeloma remains a series of disease relapses that become treatment refractory. Minimal residual disease (MRD), characterized by the presence of detectable clonal plasma cells in the bone marrow (BM) during or following therapy, is a critical prognostic and treatment-monitoring biomarker. Current approved MRD assessment technologies require invasive BM aspiration, which limits the ability to monitor the disease progression over time. Furthermore, the testing requires a baseline sample to identify tumor-specific sequences that do not predict the biological behavior of tumor cells and cannot account for the inherent variability of MM nor the development of treatment-resistant clones. In contrast to BM aspiration, liquid biopsy and evaluation of circulating tumor cells (CTCs) from peripheral blood (PB) offer a non-invasive, reproducible alternative that not only provides a comprehensive picture of the whole disease burden but also enables continuous monitoring of patients. However, due to the heterogeneity of MM, CTC enumeration alone cannot give a precise indication of the level of genomic instability related to MRD stability/progression. We recently demonstrated that the 3-dimensional (3D) profiles of telomeres, a marker of genomic instability, can predict disease progression in patients with smoldering multiple myeloma. Here, we describe a new method for MRD evaluation that combines the enumeration and immunophenotyping of individual MM CTCs in liquid biopsy with 3D telomere profiling to characterize the residual MM cells or clones, and determine MRD negativity or positivity, enabling continuous non-invasive follow-up. Methods: Intact CTCs from the PB of 14 MM patients were isolated at the point of diagnosis and at the time of disease relapse, with subsequent enumeration and immunophenotyping using CD56 and CD138 markers combined with 3D telomere profiling using the TeloView® software platform. Results: We consistently identified and enumerated CTCs in all patient samples with high sensitivity (1 in 107). Using the 6 parameters of quantitative 3D telomere measurements provided by the TeloView®, we compared the 3D telomere profiles of CD56+/Cd138+ MM CTCs and normal lymphocytes of the same patient. We demonstrate that MM cells have higher nuclear volume and a/c ratio (a measure of cell cycle progression/division), abnormal spatial telomere distribution within the nuclear space, and lower average telomere length. Conclusions: The novel workflow we present here successfully identifies and enumerates detectable CTCs not only at the point of diagnosis, but also at various times in the disease course: pre and post-ASCT, pre and post CAR-T and at disease progression. 3D telomere analysis of the isolated CTCs demonstrates 3D telomere profiles characteristic of MM and distinct from those of lymphocytes of the same patient. The proposed unique workflow allows for longitudinal and minimally invasive monitoring of MRD in multiple myeloma patients from the time of treatment. Unlike conventional approaches, this platform does not require a baseline sample and yields functionally and biologically actionable data on CTCs. It provides insights into disease stability or progression beyond simple enumeration, while avoiding the need for repeated bone marrow biopsies.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».