A Tissue Microarray Classification in Multiple Myeloma (MM) Predicts Survival Post Autologous Stem Cell Transplant (ASCT).
Notice bibliographique
Résumé
Abstract Background: The prognosis of MM patients undergoing ASCT is predicted BY the ISS in conjunction with cytogenetic studies. Along these parameters, GEP based molecular classification was proven to be a dominant and independent predictor of survival. However the wide implementation of the molecular classification is lacking due to the complexity of the methodology involved in the analysis of such approach. Methods: After analysis of the GEP molecular classification we have constructed a four “biomarker” based decision tree for an immunohistochemical classification of MM. Immunoperoxidase results for cyclin B2, FGFR3, cyclin D2 and Integrin beta 7 were used to subclassify MM cases into a High risk (HR) and Low risk (LR) subgroups as shown below. Additional staining for PAX5 and PBX1 and cyclins D1, D3 and B1 were also performed. A tissue microarray containing diagnostic bone marrow biopsies of 52 newly diagnosed MM patients uniformly treated with a dexamethasone based regimen followed by ASCT was used as a training set to validate the proposed prognostic model. The clinical parameters, response criteria and survival outcomes (PFS and OS) of this testing cohort were defined according to the international uniform response criteria. FISH studies for del13 and t(4;14) were also performed. For immunohistochemical analysis (IHC) a pathologist who was blinded with regards to the clinical outcome of these patients scored these cases as positive or negative. The Kaplan-Meier method was used to estimate OS and TTP. Multivariate analysis was performed using the Cox regression method. Results: 52 patients were included in this testing cohort: the median age was 59 yrs (39–72), 25.7% had ISS stage III, median beta2-microglobulin was 3.43 mg/L (1.16–21.81). Del13q, t(4;14) and del17p13 were detected in 38.9%, 26.1% and 11 25% of patients, respectively. Post ASCT, 31.1% achieved a CR or VGPR with a 4 yrs PFS and OS of 24.1% and 67.8% respectively. Expression of FGFR3 was seen 9.8% of the patients, cyclin B2 in 58.1%, cyclin D2 in 72.1% and integrin-beta7 in 33%. In univariate analysis expression of FGFR3 was associated with a significantly shorter PFS (P=0.003) but not OS (P=0.228). Similarly integrin-beta7 predicted for longer PFS (P=0.014) but not OS (P=0.745). Cyclin B2 predicted for worse PFS (P=0.002) and OS (P=0.032), whereas the expression of cyclin D2 did not predict for OS or PFS. Out of 43 evaluable cases, 24 (55.8%) were considered as High risk by TMA analysis and had a significantly shorter PFS (P=0.001) and OS (P=0.044) compared with the Low risk group. The 3 yrs PFS for the Low risk group was 62.3% compared to only 7.6% for the High risk group. The 5 yrs OS for the Low risk group was 83.9% compared to 60.7% for the High risk group. Multivariate analysis was performed using ISS, FGFR3, cylin B2 and the risk group classification as variables. The TMA classification and FGFR3 were the only independent predictors of PFS with the high risk group having 5.9 fold greater risk of relapse. Conclusion: we found that expression of FGFR3, cyclinB2, cylinD2 and Integrin-beta7 in a tissue based array is powerful predictor of survival post ASCT in MM. A validation of the results in a larger cohort is underway. Figure Figure
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 | 0,001 |
| 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 | 0,001 |
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 ».