Protein Profiling Identify Distinct Tumor-Tissue Protein Expression Pattern Specific for Relapsing Diffuse Large B-Cell Lymphoma
Notice bibliographique
Résumé
Abstract Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease. Several genetic drivers have been suggested based on gene-expression and RNA sequencing. However, clinical trials based on these molecular subclassifications have failed to improve treatment results and standard of care in DLBCL is still R-CHOP (rituximab, cyclophosphamide, doxorubicin, prednisone) based therapy, which results in approximately an 70% 5-year overall survival rate. Nordic Lymphoma Group (NLG) has conducted two large phase II trials (NLG-LBC-04; NCT 01502982 and NLG-LBC-05; NCT 01325194) during the last decade with the aim of improving the treatment outcome for young (<65 years) high-risk patients with primary DLBCL. Both protocols were based on biweekly administered R-CHOP-14 with etoposide (R-CHOEP) and systemic central nervous system (CNS) prophylaxis. Various factors acting at different biological levels may influence pathology of the disease, and discrepancies between gene expression and the final combination of the functional proteins may be present due to, e.g., translational inhibition/activation and posttranslational modifications. Mass spectrometry-based protein profiling is a high throughput methodology that enables identification of the proteins in a given cell or tissue and the global protein expression can be compared between patients. The aim of our study was to search for prognostic markers and possible therapeutic targets at the protein level in a uniformly treated patient cohort from the two Nordic trials (n=64) with available tumor-tissue. Thus, in this study, we investigated the protein expression pattern in the pre-therapeutic formalin-fixed paraffin embedded tumor samples by nano liquid-chromatography coupled to a mass spectrometer (Orbitrap Fusion) through an EASY-Spray nano-electrospray ion source (nLC-MS/MS). We identified 4,622 proteins with at least two unique peptides across all samples. In the combined cohort, we were able to, based on differential protein expression of 190 proteins between patients (p<0.05) with treatment sensitive (n=53) and relapsing (n=11) lymphoma, identify three clusters with one of the clusters containing only patients with experienced relapsing disease. Interestingly, among other identified differentially expressed proteins, we emphasize the identification of voltage dependent anion channel 3 (VDAC3) and the GTPase RAC1. This pattern of protein expression suggests disturbance in reactive oxidative species (ROS) signaling and regulation of tumor cell proliferation between relapsing and therapy-responding patients. Assessment of these specific protein expression patterns in the three clusters may provide a useful parameter for prediction of relapsing disease in DLBCL patients but still awaits further validation in a separate patient cohort. Disclosures Holte: Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Nordic: Membership on an entity's Board of Directors or advisory committees; Nanovector: Membership on an entity's Board of Directors or advisory committees, Other: lectures honorarias; Novartis: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Jørgensen: Gilead/Kite: Consultancy; Novartis: Consultancy; Celgene/BMS: Consultancy; Roche: Consultancy.
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 ».