Three-Dimensional Nuclear Telomeric Organization (3D) of Chronic Myeloid Leukemia Patients Predicts Accelerated Phase and Blast Crisis.
Notice bibliographique
Résumé
Abstract Abstract 2771 One major cause of genomic instability and cellular apoptosis is telomere dysfunction. Telomere loss or dysfunction results in breakage–bridge –fusion cycles, aneuploidy, and ongoing chromosomal abnormalities. The three-dimensional (3D) nuclear organization of telomeres allows for a distinction between normal and tumor cells: nuclei of the latter tend to be disorganized and commonly contain telomeric aggregates. However, few studies have addressed the impact of telomeres dynamics in CML progression. The frequency of additional chromosomal abnormalities in CML is around 5% in chronic phase and increases to 50–80% in the advanced phases. Clonal evolution often precedes progression and is predictive for inferior therapeutic outcome. In order to better understand cellular and molecular mechanisms in CML progression, the objectives of this investigation were examine telomere dysfunction and alterations in the 3D nuclear telomere architecture. Eighteen CML patients, in total, 54 bone marrow samples (chronic phase, accelerated phase and blast crisis) were eligible for 3D nuclear telomeric investigation. The quantitative FISH (QFISH), cytologic diagnosis and the cytogenetic determination for additional chromosomal abnormalities were assessed according to standard protocols. 3D image analysis on 30 interphase nuclei per slide was obtained by using an Axio Imager A1 microscope (Carl Zeiss, USA). Sixty z-stacks were acquired at a sampling distance of x,y: 102 nm and z: 200 nm for each slice of the stack. AxioVision 4.8 software (Carl Zeiss, Canada) was used for 3D image acquisition, and deconvolution analysis. Three CML subgroups were defined on the basis of their 3D telomeric profiles. The telomeric parameters (number, length, telomere aggregates and nuclear volumes) were compared between these three subgroups. Distribution of telomere intensities in CML phases was compared between the patient's subgroups. All patients of a same subgroup displayed similar 3D telomeric profiles. Comparison with clinical diagnosis after the classification according to telomere profile showed that all CML patients were classified in the three distinct subgroups. Statistical analyses showed significant differences between the CML subgroups (P<0.001). Each of the quantitative telomere parameters exhibited significant differences. Furthermore, statistical analyses combining all 3D telomere parameters revealed significant differences between all subgroups (P<0.05). According to our data these profiles are correlated to the disease evolution and increased telomere dysfunction in these subgroups. It seems that the evolution of CML progresses from low to high level of telomere dysfunction, that is, from early stage to more aggressive stage, followed by disease transformation. We concluded that telomere 3D organization is a highly accurate tool to distinguish CML stages. We propose that monitoring 3D telomere dysfunction might be a very powerful marker to measure this transformation. Furthermore, it may be a better indicator of therapeutic response because an optimal response will lead to a normal cellular biology, including elimination of abnormal telomeric aggregates and the increase of normal 3D telomeric profiles. Financial support: FAPESP (2011/01647-2). Disclosures: No relevant conflicts of interest to declare.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».