Early Relapse Following ASCT for Patients with MM: Identification of Predictor Factors in the Era of Novel Agents
Notice bibliographique
Résumé
Abstract Introduction Auto-SCT, still remains as the standard therapy for patients with MM deemed to be eligible for this approach. Unfortunately, even when most patients will respond to auto-SCT, 10-20% of cases will progress within a year. Over the last few years, a dramatic improvement on clinical outcomes has been made by using novel agents in the treatment of MM. Based on the above mentioned, we aimed to assess the incidence of Early Relapse (ER) for patients undergoing auto-SCT treated with novel-agents induction combinations at our center and to explore possible predictor factors. Methods All consecutive patients who underwent single auto-SCT at Tom Baker Cancer Center (TBCC) from 01/2006 to March/2016 were evaluated. ER was defined as per recent publications (<12 months from auto-SCT). Two-sided Fisher exact test was used to test for differences between categorical variables. A p value of <0.05 was considered significant. Survival curves were constructed according to the Kaplan-Meier method and compared using the log rank test. All statistical analyses were performed by using the SPSS 24.0 software. Results 232 consecutive patients with MM underwent single auto-SCT at our Institution over the defined period. Clinical characteristics are shown in Table 1. At the time of analysis, 172 patients are still alive and 112 have already progressed. Among these cases, 35 patients have relapsed in <12 months (ER) from auto-SCT (15.1%). 16 out of 35 patients with ER had HRC (high-risk cytogenetics) (45.7%). ER was seen in 25% of cases with HRC and 11% of patients with Standard Risk (SRC) (p=0.01). Patient with 0.5) was associated to a higher rate of ER. Median OS was shorter for the ER group (17.8 months) compared to an estimated 93 months for those patients relapsing >12 months. (p=0.0001) In conclusion, patients with ER after auto-SCT remain to be a challenge. Even with the advent of novel agents, patients with ER had poor outcomes. ER seems to be associated to HRC and low degree of response. Patients with these features should be considered for novel alternatives, aiming to achieve and sustain the deepest possible response. More biological insights on ER cases are needed to further improve survival outcomes. PFS according to level of response at day-100 post ASCT PFS according to level of response at day-100 post ASCT Figure 1 Overall survival according to the pattern of relapse Figure 1. Overall survival according to the pattern of relapse Disclosures Jimenez-Zepeda: Amgen: Honoraria; Takeda: Honoraria; Janssen: Honoraria; Celgene, Janssen, Amgen, Onyx: Honoraria. Neri:Celgene and Jannsen: Consultancy, Honoraria. Bahlis:Onyx: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Other: Travel Expenses, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Other: Travel Expenses, Research Funding, Speakers Bureau; BMS: Honoraria.
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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 | 0,001 | 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 | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».