The Tumor Microenvironment Measured by Flow Cytometry Predicts Overall Survival (OS) and Transformation Risk (TR) in Follicular Lymphoma.
Notice bibliographique
Résumé
Abstract Background: FL is an indolent but heterogeneous lymphoid neoplasm with a variable clinical course. Transformation into an aggressive lymphoma is a dominant clinical event that is frequently followed by shorter survival. There is no consistent biological prognostic marker for TR. Recent studies have highlighted the role of the microenvironment in helping to determine the prognosis of FL. However, its impact on TR is largely unknown. In this study we used diagnostic flow cytometry (FC) analysis to assess the role of non-malignant cells in determining OS and TR in FL. Methods: We identified 567 patients with FL diagnosed at the BCCA over a 5-year period between 1997 and 2001. Of these, 270 cases had diagnostic FC and histological review, of which 137 were nodal and had complete clinical data in our electronic database. FC results were re-analyzed. The antibodies studied included anti-CD3, CD4, CD5, CD8, CD10, CD14, CD19, CD20, CD23, CD45, FMC-7 and IG kappa and lambda light chains. To ensure that biopsies were representative, the sum of the % gated events for CD3 and CD20 had to equal 100% +/− 20%. Light chain restriction was present in all cases and established clonality. The estimate of neoplastic B cells was determined by examining CD19 and CD20 frequency and the ratio of clonal light chain vs non-clonal light chain. Non-neoplastic B cells were estimated using CD19/20 and the amount of non-clonal light chain × 0.5 (λ clonal) or × 2 (κ clonal). Clinical characteristics, different subsets of T cells and the cell content of reactive, non-neoplastic B cells were evaluated using SPSS® software. Results: The median age of the 137 patients was 57 years, 51.8% were male and 38.6% had a high IPI (4/5). There were 97 grade 1, 27 grade 2 and 13 grade 3a FL. The median ratio of CD4/CD8 was 4.4. Patients were given a variety of treatments, including observation if asymptomatic, precluding an analysis of progression-free survival. The median follow-up of the living patients was 5.8 years and the estimated 5-year OS and TR were 70% and 20%, respectively. The IPI was predictive of OS (p<0.0001). Two biological variables showed a significant impact on survival. Firstly, cases in which CD8+ cells represented more than 25% of the total (CD3+) T cells had shorter OS (p = 0.028) and increased TR (p = 0.013). The CD8 ratio (p = 0.026) affected OS independently of IPI (p = 0.046). Secondly, cases with a low content of reactive, non-neoplastic B cells, defined by the ratio between IG light chains >1/15 had shorter OS (p = 0.003) and increased TR (p=0.002). The impact of a reduction in normal reactive B cells (p = 0.014) on transformation risk was independent of the IPI (p = 0.05). Conclusion: Two features of the FL microenvironment studied by diagnostic FC demonstrated an impact on prognosis. The proportion of CD8+ T cells relative to the total T cells and the number of residual, non-neoplastic B cells were both predictors of OS. Importantly, both predict, independently of the IPI, the risk of transformation. These biomarkers are easily measured and may be used to better stratify patients, choose initial treatment options and predict transformation risk in patients with FL. Microenvironment & Transformation Risk in Follicular Lymphoma Microenvironment & Transformation Risk in Follicular Lymphoma
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 | 0,000 | 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) | 0,001 | 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 ».