Low-Risk Multiple Myeloma By SKY92+ISS Validated in the Multiple Myeloma Genomics Initiative Study
Notice bibliographique
Résumé
Abstract Introduction Gene Expression Profiling studies have resulted in signatures capable of providing robust prognosis for Multiple Myeloma (MM) patients, such as the EMC92 [SKY92, Kuiper et al. Leukemia 2012]. Recently, data from 4720 MM patients from the HOVON-65/GMMG-HD4, UAMS-TT2, UAMS-TT3, MRC-IX, APEX and IFM trials were employed to assessed the majority of currently identified prognostic markers and their combinations (GEP, FISH, and biochemistry data, [Kuiper et al ASH 2014]). The "SKY92 + ISS" was identified and validated as the statistically most optimal (i.e. most significant and robust) prognostic marker combination for MM patients. The combination of the EMC92 (=SKY92) and ISS prognostic features proves to be a very powerful, unprecedented and straightforward prognostic system. It identifies four prognostic risk classes: low risk (ISS I-SKY92 standard risk (SR)), intermediate-low (ISS II-SKY92 SR), intermediate-high (ISS III-SKY92 SR) and high risk (ISS I-III, SKY92 high risk). Previously, the SKY92 has been validated on the 91 MM cases in the MMGI cohort [Van Beers et al. ASH 2013]. Here we present an extension of that validation with the SKY92 + ISS combination on the 78 MM cases for whom both GEP and ISS is available, by both assessing as the "4 risk group" model defined above, but also a "3 risk group" model (the two intermediate groups combined) as this may be more relevant and useful for clinical application. Materials and Methods A public untreated MM dataset (Multiple Myeloma Genomics Initiative, MMGI) had n=78 cases for which OS, GEP, and ISS were available for analysis. The prognostic markers SKY92 and ISS were applied as proposed [Kuiper et al ASH 2014] to classify cases into the risk categories. Results The risks for the 4 group classification model are shown in Table 1 and Figure 1, and the 3 risk group model is shown in Table 2 and Figure 1. In the 4 group model, the intermediate-low group (SKY92 standard + ISS II) was a small and not significantly different (p=0.79) from the low risk. The intermediate-high group (SKY92 standard + ISS III) was also a small group, with significantly worse outcome compared with the low risk group (p=0.012). Although stratification into four groups was statistically superior in the training and validation data [Kuiper et al ASH 2014] the interpretability benefits from aggregation of the middle two groups [Fig 1 right]. Table 1. Classification results for the four risk groups (Fig 1 left) Risk group n % Median OS HR vs low risk SKY92 High Risk regardless of ISS 19 24 28.4 m 10.8 SKY92 standard risk + ISS-III 10 13 63.0 m 4.1 SKY92 standard risk + ISS-II 16 21 NR (0.8) SKY92 standard risk + ISS-I (low risk) 33 42 NR NA NR= Not Reached at 96 months, HR= hazard ratio Table 2. Classification results for the three risk groups (Fig 1 right) Risk group n % Median OS HR vs low risk SKY92 High Risk regardless of ISS 19 24 28.4 m 10.1 SKY92 standard risk + ISS-II/III 26 34 78.5 m (1.8) SKY92 standard risk + ISS-I 33 42 NR NA NR= Not Reached at 96 months, HR= hazard ratio, () not significant Conclusions By applying the SKY92 + ISS risk stratification model in an independent validation cohort of newly diagnosed Multiple Myeloma patients, besides a high risk group of 19 patients (24%), a group of 33 patients (42%) with superior prognosis could be predicted (SKY92 standard risk and ISS I) that translated into 62% OS at 96 months. The 19 high risk (SKY92 high risk) cases had very poor prognosis (median survival of 28 months). The combination of SKY92 Standard Risk and ISS II and III seems useful for definition of "intermediate risk" although sample size currently is insufficient for significance compared to low risk. The intention is to also perform this validation on the relapsed samples from the MMGI cohort, once OS data has been collected. This validated risk model could play a role in the design of future treatment strategies for high and low risk MM patients. Figure 1. Kaplan Meier curves on the 78 MMGI cases, split into four (left) or three (right) risk groups based on the combination of SKY92 + ISS. Hazard Ratios (HR) are from a Cox proportional Hazards model comparing a particular group to the low risk group. Figure 1. Kaplan Meier curves on the 78 MMGI cases, split into four (left) or three (right) risk groups based on the combination of SKY92 + ISS. Hazard Ratios (HR) are from a Cox proportional Hazards model comparing a particular group to the low risk group. Disclosures van Beers: SkylineDx: Employment. van Vliet:SkylineDx: Employment. de Best:SkylineDx: Employment. Chari:Novartis: Consultancy, Research Funding; Millennium/Takeda: Consultancy, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Biotest: Other: Institutional Research Funding; Array Biopharma: Consultancy, Other: Institutional Research Funding, Research Funding; Onyx: Consultancy, Research Funding. Jagganath:Millennium: Honoraria; Celgene: Honoraria. Jakubowiak:Sanofi-Aventis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: institutional funding for support of clinical trial conduct, Speakers Bureau; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; SkylineDx: Membership on an entity's Board of Directors or advisory committees; Onyx: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi-Aventis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Millennium: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Kumar:Celgene, Millenium, Sanofi, Skyline, BMS, Onyx, Noxxon,: Other: Consultant, no compensation,; Janssen: Research Funding; AbbVie: Research Funding; Sanofi: Research Funding; Millenium/Takeda: Research Funding; Celgene: Research Funding; Onyx: Research Funding; Skyline, Noxxon: Honoraria. Lebovic:Onyx: Speakers Bureau; Celgene: Speakers Bureau. Lonial:Millennium: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Onyx: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Reece:Onyx: Honoraria; Novartis: Honoraria; Millennium: Research Funding; Merck: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Research Funding. Richardson:Millennium Takeda: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gentium S.p.A.: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees. Siegel:Celgene Corporation: Consultancy, Speakers Bureau; Amgen: Speakers Bureau; Takeda: Speakers Bureau; Novartis: Speakers Bureau; Merck: Speakers Bureau. Stewart:SkylineDx: Membership on an entity's Board of Directors or advisory committees. Vij:Onyx: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Millennium: Honoraria, Speakers Bureau; BMS: Consultancy; Takeda: Consultancy, Research Funding; Novartis: Consultancy; Sanofi: Consultancy; Janssen: Consultancy; Merck: Consultancy. Zimmerman:Celgene: Honoraria, Speakers Bureau; Millennium: Honoraria, Speakers Bureau; Onyx: Honoraria; Amgen: Honoraria, Speakers Bureau. Fonseca:Onyx/Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Bayer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; Binding S
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».