Patient, Disease and Treatment Characteristics of Long-Term Survivors of Multiple Myeloma
Notice bibliographique
Résumé
Abstract Introduction With routine use of autologous stem cell transplantation (ASCT) and novel agents, survival of patients with multiple myeloma (MM) has improved in recent years. Yet, MM remains incurable and long-term survivors (LTS) of ≥10 years from diagnosis remain uncommon. This study aims to identify patient, disease and treatment characteristics of MM LTS, with particular interest in the effect of novel therapies. Methods A retrospective analysis was conducted of MM patients diagnosed between 1998 and 2002 and treated at Princess Margaret Cancer Centre, a tertiary care institution. LTS were identified by survival of ≥10 years from diagnosis and were compared with patients diagnosed and followed contemporaneously at our institution with survival <10 years from diagnosis. Candidate predictor variables were identified using univariate and multivariate logistic regression analysis; a p value <0.05 was considered statistically significant. Results Seventy-five patients were identified as LTS, with a control group of 119 patients with survival <10 years. The median survival for all patients was 7.3 years (range 0.6-14.5 years). Comparison of patient, disease and treatment characteristics between groups are detailed in Table 1. Patient and disease characteristics: At diagnosis, LTS were younger (p = 0.0005) and at earlier ISS stage (p = 0.02) than non-LTS. At diagnosis, LTS had a higher baseline mean hemoglobin level (p = 0.02) and platelet count (p = 0.003), and less frequently had lytic bone lesions (p = 0.03), consistent with earlier stage at diagnosis. There were no significant differences in baseline mean leukocyte count, serum calcium and creatinine. Cytogenetics were not routinely performed during this time period. Treatment characteristics: Of the LTS, 95% received an ASCT, as compared to 86% of non-LTS (p = 0.77). Median age at transplant was younger in the LTS (p = 0.003). LTS experienced a longer time from transplant to disease progression (TTP) than non-LTS (p < 0.0001) despite achieving similar rates of complete response (CR) and very good partial response (VGPR). Exposure to novel agents was common in both the LTS and control groups (73% vs. 82%, p = 0.24). Length of exposure to thalidomide (p = 0.01) and lenalidomide (p = 0.002) was greater in LTS, leading to higher quality responses and longer TTP with both agents (p < 0.0001 and p = 0.002, respectively). Similarly, bortezomib exposure was longer in the LTS (p = 0.02) with a longer TTP over that achieved in non-LTS (p = 0.008), although the quality of response was not significantly different. In a multivariate analysis, a longer TTP after ASCT (OR = 1.004; 95% CI 1.002-1.006, p = 0.0008), thalidomide (OR = 34; 95% CI 1.7-690.6; p = 0.023) and bortezomib (OR = 28.2; 95% CI 3.5-228; p = 0.002) treatment, though not after lenalidomide, were independently predictive of LTS. Table 1. Comparison of characteristics between LTS and non-LTS Disease characteristics LTS (n=75) Non-LTS (n=119) p -value Age (y) 53.2 59.1 0.0005 ISS stage I 66 43 0.02 II 20 33 III 14 24 Hemoglobin (g/L) 109 102 0.03 Leukocytes (x 109/L) 5.97 6.29 0.45 Platelets (x 109/L) 255 218 0.003 Calcium (mmol/L) 2.41 2.46 0.39 Creatinine (umol/L) 107.5 148.6 0.16 Presence of lytic lesions (%) 55 70 0.03 Treatment characteristics Autologous stem cell transplant Age (median, years) 53.3 59.4 0.003 Response (CR or VGPR, %) 47 44 0.62 TTP (median, months) 59 19.9 0.001 Thalidomide Age (median, years) 11.4 8.2 0.01 Response (CR or VGPR, %) 40 23 0.02 TTP (median, months) 32.4 9.6 <0.0001 Lenalidomide Age (median, years) 23.2 8.1 0.002 Response (CR or VGPR, %) 56 35 0.04 TTP (median, months) 24 10.8 0.002 Bortezomib Age (median, years) 6.8 3.3 0.02 Response (CR or VGPR, %) 40 28 0.24 TTP (median, months) 18 6 0.008 Conclusion LTS with MM received prolonged therapy and achieved higher quality responses to both transplant and novel agents. Our analysis suggests that LTS have baseline characteristics (age, early-stage disease, greater marrow reserve) that may enable them to tolerate more intensive or prolonged therapy. However, it is possible that LTS have disease more indolent or sensitive to therapeutic interventions. The retrospective nature of the study limits our ability to further characterize this. Regardless, these data suggest that the practice of continued exposure to novel agents may contribute to long-term survival in MM. Disclosures Reece: Otsuka: Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Millennium: Honoraria, Research Funding; Merck: Research Funding; BMS: Research Funding; Novartis: Honoraria, Research Funding; Amgen: Honoraria. Trudel:Celgene: Honoraria; Novartis: Honoraria; Glaxo Smith Kline: Honoraria, Research Funding; Oncoethix: Research Funding. Kukreti:Celgene: Consultancy, Honoraria. Tiedemann:Janssen: Honoraria. Chen:Celgene: Honoraria; Janssen: Honoraria.
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 | 0,002 |
| 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 | 0,000 | 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) | 0,002 | 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 ».