Diffuse Large B-Cell Lymphoma Patient-Derived Xenograft Models Capture Molecular and Biologic Heterogeneity and Inform Therapy
Notice bibliographique
Résumé
Abstract Diffuse large B-cell lymphoma (DLBCL) is a clinically and biologically heterogeneous disease defined by different transcriptional classifications, associated signaling and survival pathways and additional recurrent genetic alterations. In the cell-of-origin (COO) scheme, DLBCLs subtypes share certain features with normal germinal center B-cells (GCB) and activated B-cells (ABC). In comparison to GCB DLBCLs, ABC tumors have increased baseline NFκB activity and more frequent genetic alterations of NFκB pathway components. DLBCLs with shared functional features are also defined by the consensus clustering classification (CCC) which delineates B-cell receptor (BCR), Oxidative Phosphorylation (OxPhos) and Host Response (HR) tumors. BCR DLBCLs have increased reliance on BCR-signaling and survival pathways and aerobic glycolysis. BCR-dependent DLBCLs with high or low baseline NFκB activity (which largely correspond to ABC or GCB tumors, respectively) have distinct SYK-PI3K-dependent survival pathways and shared sensitivity to proximal BCR pathway inhibitors. Although DLBCLs have infrequent inactivating somatic mutations of TP53, these tumors commonly have copy number alterations (CNAs) of TP53 and genes encoding cell cycle pathway components. Given the clinical and molecular heterogeneity of DLBCL, we sought to develop faithful subtype-specific model systems to assess targeted therapies. Fresh tumor biopsies from 27 primary LBCLs were implanted under the renal capsule of immune compromised NSG mice. Nine of 27 tumors were successfully expanded in vivo, serially propagated for > 5 generations and considered stable LBCL PDX models. All models were EBV- and had clonal IgH rearrangements. Morphological and immunohistochemical signatures defined 8 PDX models as DLBCL and 1 as EBV- plasmablastic lymphoma (PBL). All LBCL PDX models were subjected to RNA-Seq and classified with respect to COO and CCC subtypes. Models were also evaluated by whole exome sequencing with a modified bait set which captured coding mutations and selected chromosomal rearrangements. Six of 9 DLBCL PDX models were ABC type. These models exhibited mutations of MYD88 alone or in association with PIM1 or CD79B with other alterations, as reported in primary ABC DLBCLs. The remaining 2 DLBCL PDX models were GCB type, with characteristic alterations of GNA13 and EZH2, and chromosomal translocations involving IgH and either BCL2 or MYC. Of note, BCL2 and MYC translocations are known adverse prognostic features of primary GCB DLBCL. Certain PDX models had additional mutations including B2M, MLL2, TNFAIP3, MEF2B and TP53. Only 25% (2/8) of the DLBCL PDX models harbored inactivating TP53 mutations whereas 75% (6/8) of tumors exhibited CNAs of TP53 or its upstream modifier, CDKN2A. These data are consistent with the reported incidence and type of TP53 pathway alterations in primary DLBCLs and contrast sharply with the near-uniform presence of TP53 mutations in DLBCL cell lines. Using the CCC classification, 6/8 DLBCL PDX models (both GCBs and 4 of 6 ABCs) were defined as BCR-subtype and 2 models as non-BCR type. To assess the utility of the DLBCL PDX models for functional analysis of BCR signaling, we first assessed cell surface immunoglobulin (sIg) expression by flow cytometry. All 6 BCR-type DLBCLs expressed sIgM whereas the 2 non-BCR DLBCL models and the PBL model lacked sIg. Next, we treated viable tumor cell suspensions with a selective SYK inhibitor, entospletinib (GS-9973). SYK inhibition significantly decreased the proliferation of all 6 BCR-type DLBCLs, but had no effect on the non-BCR-type DLBCLs or the PBL PDX. Given the distinctive SYK/PI3K-dependent signaling and survival pathways in DLBCLs with low or high baseline NFκB, we also assessed selective apoptotic pathway readouts in entospletinib-treated PDX cell suspensions. SYK inhibition selectively upregulated the pro-apoptotic BH3 family member, HRK, in BCR-dependent GCB-type DLBCL PDX samples and significantly downregulated the anti-apoptotic BCL2 family member, BCL2A1, in BCR- dependent ABC-type DLBCL PDX tumors, effects consistent with those previously observed in primary DLBCL samples. In summary, we have established and molecularly characterized faithful PDX models of DLBCL and PBL and demonstrated their usefulness in evaluating novel BCR pathway inhibitors. Disclosures Rodig: Perkin Elmer: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding. Shipp:Gilead: Consultancy; Sanofi: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees, 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 | 0,000 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».