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Enregistrement W2575183386 · doi:10.1182/blood.v118.21.1617.1617

Chemosensitization of Diffuse Large B Cell Lymphoma by Demethylating Nucleoside Analogues

2011· article· en· W2575183386 sur OpenAlexaff
Thomas Clozel, ShaoNing Yang, Matthías Kormáksson, Rebecca Elstrom, Aparna Vasanthakumar, Sarah Brennan, Biljana Čuljković, Micheal Leser, Peter Martin, Katherine L. B. Borden, Y. Lynn Wang, Shuhua Cheng, Wayne Tam, Rita Shaknovich, Amy Chadburn, Lucy A. Godley, John P. Leonard, Ari Melnick, Leandro Cerchietti

Notice bibliographique

RevueBlood · 2011
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer-related gene regulation
Établissements canadiensUniversité de MontréalInstitute for Research in Immunology and Cancer
Organismes subventionnairesnon disponible
Mots-clésDecitabineDemethylating agentDiffuse large B-cell lymphomaCancer researchAzacitidineDNA methylationBiologyLymphomaCytarabineGeneImmunologyGene expressionGenetics

Résumé

récupéré en direct d'OpenAlex

Abstract Abstract 1617 Diffuse Large B cell Lymphoma (DLBCL) is a heterogeneous disease in which 40 to 50% of the patients will either no respond or relapse within the first five years after the administration of combinatorial chemotherapy. Taking together the observed occurrence of aberrant DNA methylation patterning in DLBCL and the possibility that hypermethylation might suppress genes that could play a role in chemo-responsiveness, we hypothesized that DNMT inhibitors might induce re-expression of genes in a least a subset of resistant DLBCLs in order to induce chemosensitization. To characterize the responsiveness of a genetically diverse set of DLBCL cells to DNMT inhibitors, a panel of 30 DLBCL cell lines was exposed to increasing concentrations of 5-aza-2'-deoxycytidine (decitabine), 5-azacytidine and 6 DLBCL-active chemotherapy agents. We found that resistance to demethylating nucleoside analogues is mostly non-overlapping with resistance to other chemotherapy drugs used in DLBCL. Features such as cellular uptake and retention on H3-decitabine at 24 h and 48 h, TP53 mutation, expression of membrane transporters and nucleoside metabolic enzymes that have been suggested to influence sensitivity of other tumor types were not associated with the response to decitabine suggesting that the difference in sensitivity among the panel of DLBCL may be biological in nature. To guide combinatorial therapy of drugs we performed a supervised analysis of gene expression profiles of 6 decitabine sensitive and 8 resistant cell lines. We next used C-mapping to screen for drugs that induce the differential expression of this signature, based on the hypothesis that such drugs would synergize with decitabine. Six drugs signatures from C-map were positively associated with our decitabine signature, including doxorubicin and mitoxantrone, which are highly active drugs in DLBCL. Accordingly, in chemo-sensitive cells, short-course low-dose administration of decitabine synergizes the anti-lymphoma effect of doxorubicin in vitro and in mice DLBCL xenograft experiments. The synergistic effect on cell viability was independent of whether the drugs were administered sequentially or concurrently. Therefore, for DLBCL cells individually responsive to 5-aza-2'-deoxycytidine and doxorubicin, combination therapy resulted in enhanced therapeutic efficacy. We wondered whether more decitabine and doxorubicin-resistant DLBCL cells may benefit from longer exposure to decitabine. Accordingly, low-dose (no significant DNA damage induction) longer administration of decitabine induced DNA demethylation, growth retardation and, most remarkably, doxorubicin sensitization in chemotherapy-resistant DLBCL cells in vitro and in mice xenografts. Tumor chemosensitization was achieved without toxicity to normal tissues. To identify candidate epigenetically silenced genes that might contribute to doxorubicin resistance, we compared the gene expression and DNA methylation profiles of the panel of five doxorubicin-resistant vs. six doxorubicin-sensitive cell lines. We found 15 unique genes specifically hypermethylated and repressed in resistant cell lines. Among these, we found 2 that were significally reactivated in doxorubicin-resistant cells after prolonged low-dose administration of decitabine, arguing that cell re-programming occurs upon treatment with a demethylating agent. One of these two genes was SMAD1. We found that SMAD1 is epigenetically silenced in resistant DLBCL cells. Accordingly, DNA demethylating agents reactivated SMAD1 and rescue experiments showed that it contributed to the chemosensitization effect in DLBCL cells and in 5 primary patients samples treated ex vivo. In two different cohorts of patients, both SMAD1 expression and methylation were correlated with outcome and overall survival in DLBCL patients, after correction for IPI and COO subtypes. Pre and post 5'azacytidine treatment biopsies in a high risk DLBCL patient confirmed SMAD1 demethylation and chemo-sensitization in the clinical setting, delineating a personalized strategy for the clinical use of DNMT inhibitors. In summary, our work helped to found potential meaningful combinations for treatment of DLBCL, exposed a tumor-suppressive role for SMAD1 in DLBCL biology and defined an epigenetically mechanism used by lymphoma cells to escape TGFB growth inhibitory effects. Disclosures: Off Label Use: Use of DNA demethylating agents prior to classical chemotherapy regimen in DLBCL. Martin:Millennium Pharmaceuticals Inc.: Employment, Research Funding. Leonard:Millenium: Consultancy; Hospira: Consultancy; Biogen IDEC: Consultancy; Cephalon: Consultancy; Calistoga: Consultancy; Johnson and Johnson: Consultancy; Celgene: Consultancy; Cell Therapeutics: Consultancy; Immunomedics: Honoraria; Pfizer: Consultancy; Sanofi Aventis: Consultancy; EMD Serono: Consultancy; glaxosmithkline: Consultancy; Novartis: Consultancy; Abbott: Consultancy; Seattle Genetics: Consultancy.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,007
Tête enseignante GPT0,196
Écart entre enseignants0,189 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2011
Routes d'admission1
Résumé présentoui

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