Analysis of Relapse Biopsies in Classical Hodgkin Lymphoma Reveals Correlations with Outcome after Autologous Stem Cell Transplantation
Notice bibliographique
Résumé
Abstract Introduction : Classical Hodgkin lymphoma (cHL) is the most common lymphoma affecting individuals under the age of 30. Despite improvements in standard treatment, 25-30% of patients still relapse after first-line treatment with ABVD. Relapsed patients are then typically treated by high dose chemotherapy and autologous stem cell transplantation (HDC/aSCT). However, this salvage therapy only cures approximately 50% of relapsed patients, and the mechanisms of second-line treatment failure are unclear. Moreover, currently no reliable biological markers are available to predict the outcome of HDC/aSCT at the time of relapse. The specific aim of this study is to compare diagnostic and relapse biopsies along with integrating clinical outcome data to uncover biology associated with resistance to first-line therapy, and identify prognostic markers for HDC/aSCT. Materials & Methods: NanoString Technologies digital gene expression profiling was used to ascertain the gene expression of 785 genes in formalin-fixed paraffin embedded tissue of 245 biopsies sampled from 174 cHL patients. This cohort included 90 patients with single biopsies performed at first diagnosis, 13 patients with single biopsies taken at relapse, and 71 patients with paired biopsies taken at both first diagnosis and at relapse. All 174 patients relapsed following uniform first line treatment with ABVD. Of these, 151 patients went on to receive salvage treatment that included HDC/aSCT. The 785 genes reflect biological signatures representative of cell types typically found in the microenvironment of cHL, and those previously reported to be associated with outcome in cHL. A tissue microarray was constructed containing all biopsies for immunohistochemistry (IHC) using CD68 antibodies (clone KP1). Spearman correlation tests were used for comparative gene expression analyses and IHC correlations. Differential expression analyses were performed using a non-parametric Wilcoxon test. Gaussian mixture models were used to cluster patients into poorly and well-correlated biopsy pairs. Outcome correlations were performed using the log-rank test and Cox regression analysis. Results : 24% of patients had a histological subtype transition between diagnostic and relapse biopsies with the most common transition (46%) from mixed cellularity (at diagnosis) to nodular sclerosis (at relapse). Comparative gene expression analysis revealed that 17 of the 71 patients (24%) had poorly correlated biopsy pairs (R2 < 0.75). Specifically, genes associated with macrophage differentiation (e.g. CD68, MARCO; FDR < 0.1) were found to be more highly expressed in the relapse biopsies compared to the matching samples at first diagnosis. CD68 IHC staining was well correlated with mRNA expression (p < 0.001) and confirmed that CD68+ cells were significantly more frequent in the relapse biopsies (p = 0.023). Patients with poorly correlated biopsy pairs had an inferior post-HDC/aSCT failure-free survival (5-y FFS: poorly correlated 39% vs. well correlated 68%; log-rank p = 0.005). Additionally, we applied our previously published 23-gene predictor (Scott, JCO, 2013), that was originally developed for pre-treatment biopsies, to the relapse biopsies. Using the published thresholds, 19% of patients were designated high-risk and had significantly inferior outcomes post-HDC/aSCT (5 year post-HDC/aSCT overall survival 40% vs. 82%, log-rank p = 0.001; 5 year post-HDC/aSCT failure-free survival 39% vs. 72%, log-rank p = 0.004). Conclusions : Our comparative analysis of cHL pre-treatment and relapse biopsies reveals differences at both the histopathological and molecular levels. We have identified novel factors, such as a poor correlation between paired biopsies and the 23-gene predictor, that are predictive of an inferior post-HDC/aSCT outcome. These findings suggest that re-biopsying patients at relapse will yield important biological insight and superior predictive power for second-line treatment failure. Disclosures Gerrie: F Hoffmann-La Roche: Other. Savage:F Hoffmann-La Roche: Other.
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 | 0,000 |
| 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 ».