Fas Mutations in Non-Hodgkin's Lymphoma (NHL): Implications for Disease Progression and Therapeutic Resistance
Notice bibliographique
Résumé
Introduction: Diffuse large B cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma and standard frontline treatment is carried out with R-CHOP chemotherapy. However, DLBCL remains an extremely heterogenous disease and refractory/relapse events are common. Recent sequencing experiments have found 18% of relapsed DLBCL contains Fas mutations, an increase from mutations seen at initial diagnosis. Fas receptor (FasR) is a transmembrane protein encoded by the Fas gene that is critical for the induction of extrinsic apoptosis. Once FasR is bound by Fas Ligand expressed on cytotoxic T cells, it initiates the formation of the death-inducing signaling complex and subsequent programmed cell death. Downregulation of FasR expression on B cells has also been a proposed method by which B cell lymphomas are able to evade immune surveillance. Therefore, we hypothesized that dysfunction in Fas signaling contributes to chemotherapy resistance and disease relapse in DLBCL. We used an immune-competent mouse model that can generate aggressive B cell lymphomas (Eμ-Myc) to investigate the role of Fas mutations in lymphomagenesis and response to chemotherapy. Methods: We designed a breeding program, in which we crossed heterozygous Lpr mice, which harbor a germline mutation in Fas, with Eμ-Myc mice. This led to the development of mice that grow spontaneous Eμ-Myc lymphomas with either FasWT or Fasmut gene alterations, hereafter designated WT and MUT. These mice were analyzed for disease progression and their lymphoma cells were intravenously injected into C57BL/6 mice to create 2nd generation lymphomas. A 3rd generation cohort was developed similarly from injecting 2nd generation lymphoma cells into another group of C57BL/6 mice. 3rd generation mice were treated with components of R-CHOP chemotherapy, namely doxorubicin, vincristine, and cyclophosphamide. Overall and disease-related survival were monitored for all cohorts, and lymph node and spleen tissues were preserved from all 3 generations as formalin-fixed paraffin-embedded (FFPE) blocks. A tissue microarray was created to analyze the tumor microenvironment and elements of extrinsic apoptosis/immune response. Immunohistochemistry staining of the microarray using T cell and lymphoma-related markers is currently ongoing (CD3, CD4, CD8, CD25, FoxP3, TP53, Nfkb, Bcl2). Results: In the 1st generation, 21/37 WT and 11/18 MUT mice developed lymphoma, with the time to lymphoma death being similar in both groups (170 days versus 140 days, respectively, p =0.32). Of the 32 primary NHLs generated, 3 didn't have sufficient viable cells to perform subsequent experiments. The remaining 29 NHLs were injected into at least two different C57BL/6 mice, with the exception of one who only had enough cells to inject into one mouse. Thus, the second generation included 57 mice transplanted with 29 primary NHLs. Lymphoma development was higher in the MUT cohort (12/37 WT and 14/20 MUT, p=0.011). The all-cause overall survival was not different between both genotypes (p=0.152), but lymphoma specific survival was significantly shorter in the MUT mice (67 days for MUT and 136 days for WT, p=0.026). In the 3rd generation, 93 mice developed lymphoma (54 WT and 39 MUT), of which 15 were used as untreated controls and 78 were treated with components of R-CHOP. Overall, WT mice appeared to have durable responses to therapy, as shown by increased survival when compared to controls across doxorubicin, vincristine, and cyclophosphamide groups (p=0.0147, 0.0406, 0.0321 respectively). However, no difference in survival was seen in the MUT cohort between treated and untreated controls. Conclusion: Fas mutations may provide survival advantages to lymphoma cells implanted into immune-competent mice. They may also promote resistance to R-CHOP, particularly vincristine, doxorubicin, and cyclophosphamide, but the exact mechanism by which this occurs is unclear. The immune-tumor cell interactions are being investigated by IHC and will be presented. Disclosures Johnson: Roche: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding; Abbvie: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Consultancy, Honoraria; BMS: Consultancy, Honoraria; BD Biosciences: Other: Provided a significant proportion of the antibodies used in this project free of cost.; Seattle Genetics: Honoraria; Lundbeck: Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding.
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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 | 0,000 | 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 ».