Lymphoma Microenvironment Deconvolution Links M1 Macrophage Infiltration to Clinical Outcome in Diffuse Large B-Cell Lymphoma
Notice bibliographique
Résumé
Introduction: The lymphoma microenvironment is increasingly recognized as crucial to sustaining lymphoma cell growth and an important contributor to treatment outcome, especially in the context of immunotherapies. CD20-targeted monoclonal antibodies (e.g. obinutuzumab [G] and rituximab [R]) function by several mechanisms, including antibody-dependent cellular cytotoxicity/phagocytosis (ADCC/ADCP). Immune effector cells, such as natural killer (NK) cells and phagocytes (i.e. macrophages and dendritic cells), and the Fc gamma receptor (FcγR) found on the surface of these cells, are critical to antibody treatment efficacy. Here we evaluated how the lymphoma microenvironment may affect clinical outcome in patients (pts) with previously untreated diffuse large B-cell lymphoma (DLBCL) receiving immunochemotherapy. Methods: We leveraged two large Phase III clinical trials of pts with previously untreated DLBCL (GOYA [NCT01287741] and MAIN [NCT00486759]) to produce comprehensive lymphoma immune microenvironment profiles from 604 tissue biopsies from pts treated with R plus cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) or G plus CHOP (G-CHOP) using the RNA-Seq deconvolution and marker gene methods: quanTIseq and xCell. The infiltration scores in each pt for various immune and stromal cell types were assessed, and their contribution to disease biology and treatment outcome was examined. Results: The extent of lymphoma microenvironment heterogeneity highlighted by the deconvolution analyses was consistent with previous studies (Figure A). Of the infiltrating cell types analyzed, the M1 macrophage signature quantified by either quanTIseq or xCell was most strongly associated with lower risk of progression (progression-free survival [PFS]; quanTIseq: HR, 0.596; 95% CI: 0.441-0.805; 24-month PFS: 82% [M1 high] vs 68% [M1 low] and xCell: HR, 0.627; 95% CI: 0.465-0.844; 24-month PFS: 80% [M1 high] vs 70% [M1 Low]; Figure B, C, D) and improved overall survival (OS; quanTIseq: HR, 0.465; 95% CI: 0.318-0.679; and xCell: HR, 0.527; 95% CI: 0.365-0.762). This finding was confirmed by both algorithms. This prognostic trend was stronger amongst G-treated pts than R-treated pts, consistent with the previous finding that G exhibits higher ADCC versus R (Mössner, et al. Blood 2010). Pts with PFS >24 months had significantly higher levels of M1 macrophage scores than pts with PFS <24 months. Despite the correlation with delayed disease progression, there was no differential enrichment of M1 macrophages in pts with complete response versus non-responders at end of treatment, or depending on International Prognostic Index. M1 scores did not significantly differ depending on cell of origin, although there was a trend for higher M1 macrophage scores in germinal center B-cell DLBCL. Aside from M1 macrophages, CD4+Th2 cells showed the strongest prognostic trend in DLBCL (PFS; HR, 0.745; 95% CI: 0.553-1.000; Figure C). In contrast to M1 macrophages, pts with M2 macrophage infiltration tended to have shorter PFS and OS although relatively low levels were observed for these signatures (Figure B, C). This suggests that lymphoma-infiltrating macrophages more commonly resemble the classically activated M1 polarization phenotype and are linked to prolonged PFS, while alternatively activated M2 macrophages, although their frequency is lower in DLBCL, are associated with shorter PFS. Consistent with previous work showing that programmed death-ligand 1 (PD-L1) levels correlate with a macrophage signature in DLBCL (McCord, et al. Blood Adv 2019), M1, but not M2, macrophage infiltration correlated with PD-L1 mRNA expression. M1 enrichment was highly correlated with CD8+ T cell signatures (including central and effector memory CD8+ T cells) in DLBCL. Conclusions: Data suggest macrophage polarization may be an important contributor to immunochemotherapy outcome in DLBCL. Previous studies aiming to link tumor-associated macrophages to R-CHOP outcome have yielded conflicting results, perhaps as most relied on CD68/CD163 staining alone as markers. Although R and G are thought to function via NK cell-mediated ADCC, FcγR-dependent stimulation of M1 macrophage-mediated ADCP may be key to sustaining their anti-lymphoma activity. Strategies facilitating the recruitment of M1 macrophages or macrophage repolarization may augment responses to immunochemotherapy in DLBCL. Disclosures Yan: F. Hoffmann-La Roche: Current Employment, Current equity holder in publicly-traded company. Chang:F. Hoffmann-La Roche: Current Employment, Current equity holder in private company. Raghavan:F. Hoffmann-La Roche: Current Employment. Dong:In graduate school: University of Toronto, MSc Biostatistics: Ended employment in the past 24 months; F. Hoffmann-La Roche, Mississauga, Biometrics: Current Employment. Klein:Roche: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties. Nielsen:F. Hoffmann-La Roche: Current Employment, Current equity holder in publicly-traded company. Paulson:Genentech, Inc: Current Employment; F. Hoffmann-La Roche: Current equity holder in private company, Current equity holder in publicly-traded company. Hatzi:F. Hoffmann-La Roche: Current equity holder in publicly-traded company; Genentech, Inc.: Current Employment.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 ».