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Low-Risk Multiple Myeloma By SKY92+ISS Validated in the Multiple Myeloma Genomics Initiative Study

2015· article· en· W2479223216 on OpenAlexaff
Erik van Beers, M. van Vliet, Leonie de Best, Kenneth C. Anderson, Ajai Chari, Sundar Jagganath, Andrzej Jakubowiak, Shaji Kumar, Daniel Lebovic, Joan Levy, Daniel Auclair, Sagar Lonial, Donna Reece, Paul G. Richardson, David S. Siegel, A. Keith Stewart, Suzanne Trudel, Ravi Vij, Todd M. Zimmerman, Rafaël Fonseca

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMultiple myelomaMedicineInternal medicineOncology

Abstract

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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

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.307
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2015
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