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Enregistrement W2579680972 · doi:10.1182/blood.v126.23.2737.2737

Retrospective Analysis of Lymphomas in the Setting of Autoimmune Disease and the Impact of Immunosuppression

2015· article· en· W2579680972 sur OpenAlexaff
Adam M. Petrich, Stefan K. Barta, Frederick Lansigan, Trent Wang, Ananta Bhatt, Garrett T. Wasp, Addie Hill, Frank Passero, Amrit Kahalon, Roopesh Kansara, Mitul Gandhi, Graham W. Slack, Tatyana Feldman, Andrew M. Evens, Kerry J. Savage

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueViral-associated cancers and disorders
Établissements canadiensBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésMedicineRituximabImmunosuppressionInternal medicineLymphomaOncologyUnivariate analysisLymphoproliferative disordersProportional hazards modelRetrospective cohort studyInternational Prognostic IndexImmunologyMultivariate analysis

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Post-transplant lymphoproliferative disease (PTLD) encompasses a heterogeneous array of cases of lymphoma/lymphoma-like conditions arising in the setting of immunosuppression (IS) for prior organ or marrow transplant. Such pts face heightened risk of toxicity from exposure to cytotoxic chemotherapy, and may be best treated in the frontline with reduction of IS (RI) and anti-CD20 monoclonal antibody treatment (Trappe, 2012). Autoimmune (AI) disease has been associated with an increased risk of developing lymphoma; however, the relative impact of baseline clinical features, including prior IS, is unknown. Methods: We conducted a multicenter, retrospective analysis of adult pts with pre-existing AI conditions who were diagnosed with lymphoma since 1997. Baseline clinical features at diagnosis of lymphoid malignancy, including International Prognostic Index (IPI) risk factors; underlying AI disease; duration and type of IS; EBV status (by EBER in-situ hybridization); and primary therapy received (RI, rituximab [R] monotherapy, chemotherapy [+/- R]); were collected. Survival analyses were performed using Kaplan-Meier method. We then focused on those who had A) received IS other than corticosteroids (CS) alone; and B) those diagnosed with DLBCL. Those variables found to have significant correlation with OS by univariate analyses (UVA) were used to construct Cox proportional hazards model (multivariate analysis [MVA]) in order to determine which might have the strongest association with OS. Lastly, we sought to evaluate a potential role for RI and/or R as frontline therapy for those with DLBCL. Results: A total of 130 pts were included (Table 1). The most frequent AI disease was rheumatoid arthritis and for all cases, 76% had documented exposure to IS, for a median duration of 4.5 years (range 0.17-57 years) prior to diagnosis of lymphoma. The most common histologic subtype was DLBCL (52%). EBV status was reported for only 34% of pts, but was positive in 68% (25/37), all of whom had received prior exposure to IS beyond CS, and 80% of whom (20/25) were diagnosed with DLBCL. EBV status was infrequently tested in pts not previously exposed to IS (3/31). At a median follow-up of 61 months for the entire cohort, 2-year PFS and OS were 79% and 91%, respectively (Figure 1, Panel A). By UVA, age>60; PS>1; LDH> upper limit of normal (ULN); DLBCL (vs all other histologies); underlying rheumatoid arthritis (RA; vs all other AI diseases); and prior exposure to IS, each correlated with inferior OS (Table 1). By MVA, PS>1 and prior IS maintained significance (p<0.05). If those receiving only CS are grouped with those not previously exposed to IS, the correlation of this factor with OS was strengthened (p 0.008), and by MVA, PS>1 (p 0.002) and prior IS (p 0.010) maintain significance (data not shown). Among 67 pts with DLBCL, median age was 61 (range 26-90), 60% had advanced stage disease, and 32% had IPI of 4 or 5. At a median follow-up of 32 months, the 2-year PFS and OS were 82% and 84%, respectively. There were no differences in frequency of any IPI factors between patients exposed to prior IS (n=53) and those who were naïve to prior IS (n=14). For those not exposed to prior IS, the 2-year OS was 100%, compared to 80% in those who received prior IS (p 0.24); corresponding 2-year PFS were 92% and 79%, respectively (p 0.41). Age>60 and PS>1 were associated with an inferior OS but use of IS was not associated with outcome (Table 2). The 2 year OS for those treated with R plus CHOP(like) chemotherapy, CHOP(like) chemotherapy (without R), R alone (+/- RI), and with RI alone were 92%, 75%, 90%, and 67%, respectively (Figure 1, Panel B; log-rank p value 0.55). Patients who received CHOP-like therapy +/- R, as compared to R and/or RI were more likely to be naïve to IS therapy (15/46 vs 0/22, p = 0.003) and have 2 or more EN sites of disease (15/46 vs 2/22, =0.041). These differences notwithstanding, the 2-year PFS for the two groups were 86% and 74% (p 0.16), and 2-year OS for the two groups were 88% and 82%, respectively (Figure 1, Panel C; p 0.91). Conclusions: Pts with immunosuppression-related lymphoma have high rates of 2-year OS and in DLBCL, IS does not appear to be associated with an inferior outcome. Similar to evolving treatment paradigms in PTLD, rituximab monotherapy and other cytotoxic chemotherapy-free regimens as well as risk-adapted approaches may warrant further evaluation in IS-related DLBCL. Disclosures Petrich: Seattle Genetics: Consultancy, Honoraria, Research Funding. Barta:Seattle Genetics: Research Funding. Feldman:Celgene: Honoraria, Speakers Bureau; Pharmacyclics/JNJ: Honoraria, Speakers Bureau; Seattle Genetics: Honoraria, Speakers Bureau. Savage:Seattle Genetics: Honoraria, Speakers Bureau; BMS: Honoraria; Infinity: Honoraria; Roche: Other: Institutional 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 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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
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 Opus0,009
Tête enseignante GPT0,276
Écart entre enseignants0,267 · 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'étudeObservationnel
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

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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