Characterization of the "Immune Evasion" Phenotype of Richter Syndrome and the Implications for Immune-Checkpoint Inhibitor Therapy
Notice bibliographique
Résumé
Richter syndrome (RS) is the transformation of chronic lymphocytic leukemia (CLL) to a high-grade B-cell lymphoma and is associated with an aggressive clinical course and poor prognosis. Conventional treatment options for RS are generally associated with low response rates and limited durability making this entity an area of significant unmet therapeutic need. Immune checkpoint inhibitor therapy has shown promise in the treatment of some aggressive lymphoma subtypes. In RS, modest benefits have been reported in small phase two trials of anti-PD-1 monotherapy and in combination with ibrutinib, however larger scale studies are lacking (Ding et al Blood, 2017; Jain et al Blood, 2016). We sought to characterise the immune-evasion phenotype of RS focussing on potential genetic biomarkers which may inform the selection of patients who are most likely to benefit from immune-directed therapies. We first assessed the gene expression of immune-checkpoint molecules given their potential clinical relevance and ability to be targeted by available therapeutic agents. Given immunohistochemical (IHC) assessment of immune-checkpoint molecules is recognized to be associated with high inter-observer variability and there is a high correlation between gene expression of immune-checkpoint molecules and IHC, we performed gene expression quantification using the Nanostring nCounter Human Immunology V2 panel (Nanostring Technologies, USA). Nanostring analysis was performed on samples from 17 patients with histologically confirmed RS (DLBCL subtype) and compared to 73 cases of de novo (non-transformed) DLBCL. Significant differences in the gene expression of checkpoint molecules was observed between RS and DLBCL biopsies, including higher expression of LAG3, PD1 and TIGIT in RS (p=0.0001, logFC 1.9; p=0.0017, logFC 1.1 and p=0.0437, logFC 0.7 vs DLBCL, respectively). PD-L2 and TIM3 gene expression were both significantly lower in RS compared to DLBCL (p = 0.0059, logFC 0.8; p = 0.012, logFC 0.8). PDL1 and CTLA4 gene expression did not significantly differ between RS and DLBCL. We next assessed the gene expression of T- and NK- cell markers (including CD3, CD4, CD8, FOXP3 and CD56) and the ratios of these markers to malignant B-cells (CD19). We observed no significant difference between RS and DLBCL, consistent with a similar relative quantity of immune cell infiltration between the two entities. Significantly higher gene expression of CD39, a marker of CD8+ T-cell exhaustion, was observed in RS than DLBCL (p = 0.031; logFC 0.5). Additional immune-related genes were next assessed, including those involved in antigen presentation (e.g. B2M, HLA molecules, TAP), immunosuppressive cytokine generation (e.g. ARG1, IDO1) and apoptosis resistance (e.g. FAS) which showed no significant differences in expression between RS and DLBCL. To assess whether these findings were consistent across other transformed lymphoma subtypes, we compared RS to a cohort of transformed follicular lymphoma (tFL, n=16) and transformed marginal zone lymphoma (tMZL, n=25). LAG3 expression was significantly higher in RS compared to both tFL and tMZL (p=0.0002, logFC 2.7; p=0.019, logFC 1.7). PD1 expression was also significantly higher in RS than tFL but not tMZL (p=0.0045, logFC 1.7; p=0.39, logFC 0.4). Given the established association of copy number amplifications involving immune checkpoint molecules (e.g. PD-L1/PD-L2 on 9p24.1) representing a potential predictive biomarker of response in other lymphomas, we performed hybridization-based NGS with whole genome copy number assessment to evaluate immune checkpoint gene loci in the three cohorts. No significant focal amplifications were detected in RS samples with overexpressed immune-checkpoint molecules. In contrast, three patients in the DLBCL/transformed cohort had focal copy number amplifications involving PD-L1. No copy number amplification of LAG3 was observed in either RS or DLBCL. In summary, we have observed significantly increased gene expression of LAG3, PD1 and TIGIT in RS compared to de novo DLBCL. Combined with increased gene expression of the exhausted cytotoxic T-cell marker CD39, these data provide a strong biological rationale for pursuing LAG3 inhibition either alone or in combination with other immune checkpoint blockade to enhance anti-tumour T cell responses in this difficult-to-treat entity. CG/JL/YK co-first authors Disclosures Gould: NovoNordisk: Other: Travel funding - domestic flights to attend education, May 2018. Villa:Roche, Abbvie, Celgene, Seattle Genetics, Lundbeck, AstraZeneca, Nanostring, Janssen, Gilead: Consultancy, Honoraria. Tam:Abbvie, Janssen: Research Funding; Abbvie, Janssen, Beigene, Roche, Novartis: Honoraria. Neeson:Roche Genetech: Research Funding; Allergan: Research Funding; Juno/Celgene: Research Funding; Compugen: Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Seymour:Roche: Consultancy, Research Funding, Speakers Bureau; Takeda: Consultancy; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau; Acerta: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Research Funding. Dickinson:Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Merck Sharpe and Dohme: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; GlaxoSmithKline: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Blombery:Invivoscribe: Honoraria; Novartis: Consultancy; Janssen: Honoraria.
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 | 0,000 |
| 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 ».