Serum Biomarkers Predict Outcomes in Advanced Hodgkin Lymphoma Independent of International Prognostic Score (IPS) and Treatment: Correlative Analysis from a Large North American Cooperative Group Trial
Notice bibliographique
Résumé
Abstract Introduction: Progress in the treatment of patients with classical Hodgkin lymphoma (cHL) depends on identifying methods to better risk-stratify patients and assess prognosis and treatment response. While prognostic scores based on clinical characteristics have utility, inflammatory markers and signaling proteins may better reflect tumor biology, the microenvironment, or host response and might serve as prognostic factors. Increased expression of tumor-associated macrophage markers in tumor tissue, such as CD68 or CD163, has been shown to be associated with inferior survival outcomes in cHL patients. Blood-based markers are more practical in that specimens are readily and serially obtainable. Such markers, if shown to track with tumor response and/or prognosticate outcomes, can be used throughout treatment and follow-up. Some soluble markers, such as TARC (chemokine (C-C motif) ligand (CCL)-17) and soluble CD163 (sCD163), have been shown to reflect tumor burden and active disease, and markers such as soluble CD30 (sCD30), interleukin (IL)-6, chemokine (C-X-C motif) ligand (CXCL)-10/IP10, and IL-2 receptor have been associated with failure-free survival (FFS). Methods: Newly diagnosed cHL patients with locally extensive or advanced stage disease were prospectively enrolled in the Intergroup E2496 randomized controlled trial, which compared ABVD with Stanford V chemotherapy. A panel of serum cytokines, chemokines, and other soluble markers including TARC, CCL22, CCL24, sCD30, sCD163, CXCL10/IP10, CXCL13, soluble CD14 (sCD14), IL-6, IL-10, IL-12, and IL-13, IL1-receptor antagonist (IL1RA)) were measured in pretreatment serum specimens from 301 cHL patients (out of 854 on study) using multiplex (Luminex) bead array immunoassay (R&D Systems). Serum marker values were log-transformed for all analyses. Epstein-Barr virus (EBV) tumor status was determined by EBER in situ hybridization. A linear regression model was used to assess the association between pre-treatment serum marker levels and baseline clinical characteristics. A stratified Cox proportional hazards regression model was used to evaluate the association between serum marker levels as continuous variables and survival outcomes, including FFS (time from registration to disease progression/relapse or death) and overall survival (OS). Patients were divided into quartile groups based on serum marker levels and Kaplan-Meier curves were constructed with comparison using the stratified log-rank test. Three stratification factors were used in all modeling/testing include: stage I-II bulky vs. stage III-IV; IPS 0-2 vs 3-7; and treatment arms (Stanford V vs. ABVD). Two-sided p-values were reported. Results: Increased pre-treatment levels of CCL24, sCD30, sCD163, IP10, sCD14, IL-6, and IL-10 were associated with the presence of B symptoms, independent of age, tumor histology, and disease stage, in multivariate analysis. Higher sCD163 and IP10 levels were associated with EBV positive tumors and higher TARC, CCL22, and CXCL13 levels were associated with EBV negative tumors, after adjusting for age and histology. Several markers were associated with factors in the International Prognostic Score (Table 1). Adjusting for IPS, stage and treatment arms, high levels of IL1RA, sCD30, sCD163, IP10, and IL-10, were significantly (p<0.05) associated with inferior FFS. While high levels of TARC (p=0.02, hazard ratio (HR) 0.88, 95% confidence interval (CI) 0.79-0.98), and CXCL13 (p=0.04, HR 0.84, 95% CI 0.72-0.99) were associated with better OS, high levels of CD14 (p=0.02, HR 2.2, 95% CI 1.11-4.38), IP10 (p<0.0001, HR 2.22, 95% CI 1.64-3.00) and sCD163 (p=0.0002, HR 2.89, 95% CI 1.65-5.06) were associated with significantly inferior OS (Figure 1). Conclusion: These findings support prior work demonstrating the prognostic significance of markers such as CD163 in cHL tumor tissue with similar findings using a more readily available (blood-based) method of assessment. The study expands upon the number of blood-based markers shown to be prognostic in cHL, and associates high pre-treatment levels of several markers with IPS factors and other baseline clinical characteristics. Further, and most importantly, IP10 and sCD163 predict inferior FFS and OS independent of IPS and treatment. Disclosures Cheson: Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Research Funding; Acerta: Membership on an entity's Board of Directors or advisory committees, Research Funding. Winter:Pharmacyclics: Research Funding; Medivation: Other: Provision of investigational agent for clinical trial; GSK: Research Funding; Seattle Genetics: Research Funding. Friedberg:Bayer: Honoraria, Other: Data Safety Monitoring Board. Kahl:This study was coordinated by the ECOG-ACRIN Cancer Research Group (Robert L. Comis, MD and Mitchell D. Schnall, MD, PhD, Group Co-Chairs) and supported by the National Cancer Institute of the National Institutes of Health under the following award number: Research Funding.
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 | 0,008 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».