TP53 Expression Correlates with TP53 Mutations and Is an Independent Predictor of Clinical Outcome in Patients with DLBCL Treated with R-CHOP
Notice bibliographique
Résumé
Background: Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous neoplasm with 40% of patients experiencing treatment failure following immuno-chemotherapy (R-CHOP). Both cell-of-origin (COO) and presence of concurrent MYC/BCL2 rearrangements (DHIT) are significantly associated with distinct inferior outcome. Recently, next-generation sequencing (NGS) studies have uncovered distinct genetic subtypes, including a sizable ABC/GCB-independent group characterized by more frequent TP53 abnormalities. The patterns of TP53 mutations and the prognostic significance in DLBCL have been previously reported. However, such information is rarely available at the time of diagnosis as diagnosis of DLBCL for most patients is based on morphology and phenotype, assessed by immunohistochemistry (IHC). To bridge the gap between genotype and phenotype, we examined the TP53 mutational status and TP53 protein over-expression (IHC) in a large population-based DLBCL cohort uniformly treated with R-CHOP (Ennishi et al. Blood 2017 129:2760-2770). Methods: We analyzed 347 newly diagnosed de novo DLBCL cases uniformly treated with R-CHOP in British Columbia. Comprehensive clinical annotation was available through the BC Cancer Lymphoid Cancer Database. Deep targeted re-sequencing of the coding exons of TP53 was performed using a Truseq Custom Amplicon assay (Illumina) on the Miseq platform. IHC staining for TP53 (DO7), TP21, COO (Hans) and break-apart FISH assays for MYC and BCL2 were performed on tissue microarrays (n=332). COO classification was also performed using the Lymph2Cx assay (NanoString) (n=324). Strong TP53 expression (TP53+) was defined as high intensity (3/3) expression in >50% of the malignant cells. Results: TP53 mutations (p53mut) were present in 72 cases (22.2%) with 84% being missense and 64% localized to the DNA binding-motifs. There were 54 TP53+ tumors by IHC (17%), of which 51 (94%) had p53mut, with a sensitivity of 70% for detection of p53mut. All but one TP53+ with p53mut (50/51 cases) had missense mutations. All TP53+ with missense p53mut (85% of all missense p53mut) showed strong nuclear expression, one recurrent nonsense p53mut showed combined cytoplasm/nuclear TP53+, while all splice site and frameshift variants were TP53+-negative. All TP53+ cases were negative for TP21; 30 (56%) cases were GCB (Hans) while 24 (44%) cases were non-GCB (Hans) and only 4 (16%) cases were DHIT. Both p53mut and TP53+ were associated with poor overall survival (OS) (p=0.004 and p=0.007, respectively) and disease-specific survival (DSS) (p=0.003 and p=0.001, respectively). In multivariate analysis with IPI, COO (Hans) and DHIT status, both p53mut and TP53+ were independent predictors of OS (HR=0.6, 95%CI=0.4-0.9, p=0.008 and HR=0.6, 95%CI=0.4-0.9, p=0.011, respectively) and DSS (HR=0.6, 95%CI=0.4-0.9, p=0.01 and HR=0.5, 95%CI=0.3-0.8, p=0.004, respectively). These results were consistent when the Lymph2Cx was used to assign COO (n = 324). Importantly, patients with TP53+ tumors showed significantly poorer outcome (OS and DSS) when compared with patients with tumors negative for TP53 stratified by both Hans COO subtypes (p=0.001 and p<0.001, respectively) and DHIT tumors (p=0.02 and p=0.003, respectively). Conclusion: TP53+ shows good correlation with the presence and type of TP53 mutations and can be readily performed in routine clinical practice. TP53+ is a strong predictor of clinical outcome in DLBCL patients treated with R-CHOP and independent of IPI, COO and DHIT. TP53+ can be easily performed in current diagnostic laboratories and complements known biomarkers to better stratify DLBCL patients and potentially improve their clinical management. Figure Disclosures Villa: Roche, Abbvie, Celgene, Seattle Genetics, Lundbeck, AstraZeneca, Nanostring, Janssen, Gilead: Consultancy, Honoraria. Savage:BMS, Merck, Novartis, Verastem, Abbvie, Servier, and Seattle Genetics: Consultancy, Honoraria; Seattle Genetics, Inc.: Consultancy, Honoraria, Research Funding. Sehn:Abbvie: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; F. Hoffmann-La Roche/Genentech: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; TEVA Pharmaceuticals Industries: Consultancy, Honoraria; Acerta: Consultancy, Honoraria; TG Therapeutics: Consultancy, Honoraria; Merck: Consultancy, Honoraria; TG Therapeutics: Consultancy, Honoraria; Lundbeck: Consultancy, Honoraria; Lundbeck: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; F. Hoffmann-La Roche/Genentech: Consultancy, Honoraria, Research Funding; Verastem: Consultancy, Honoraria; Apobiologix: Consultancy, Honoraria; Janssen-Ortho: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Acerta: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria; TEVA Pharmaceuticals Industries: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Janssen-Ortho: Honoraria; Merck: Consultancy, Honoraria; Astra Zeneca: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Kite Pharma: Consultancy, Honoraria; Kite Pharma: Consultancy, Honoraria; Astra Zeneca: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria. Steidl:Nanostring: Patents & Royalties: Filed patent on behalf of BC Cancer; Bristol-Myers Squibb: Research Funding; Roche: Consultancy; Bayer: Consultancy; Seattle Genetics: Consultancy; Juno Therapeutics: Consultancy; Tioma: Research Funding. Scott:Roche/Genentech: Research Funding; Celgene: Consultancy; Janssen: Consultancy, Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], 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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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 ».