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Record W3212668607 · doi:10.1182/blood-2021-151249

Prognostic Factors for Early (<2 years) and Late (>5 years) Relapse in Multiple Myeloma- Pivotal Role of Cytogenetic Changes

2021· article· en· W3212668607 on OpenAlexaff
Sarah Goldman‐Mazur, Alissa Visram, S. Vincent Rajkumar, Prashant Kapoor, Angela Dispenzieri, Martha Q. Lacy, Morie A. Gertz, Francis K. Buadi, Suzanne R. Hayman, David Dingli, Taxiarchis Kourelis, Wilson I. Gonsalves, Rahma Warsame, Eli Muchtar, Nelson Leung, Robert A. Kyle, Shaji Kumar

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineInternal medicineCohortMultivariate analysisOdds ratioMultiple myelomaOncology

Abstract

fetched live from OpenAlex

Abstract Background Multiple myeloma (MM) is an incurable disease, and its prognosis is largely influenced by clinicopathological features, response to therapy, and relapse characteristics. Despite novel agents and the attempts to individualize treatment strategies based on baseline risk stratification, significant variations in progression-free survivals (PFS) and overall survivals (OS) are observed between patients. We examined the outcomes of MM patients stratified according to timing of first relapse into early (<2 years) and late (>5 years). Methods We retrospectively assessed 1441 MM patients seen at Mayo Clinic between 2003 and 2018. Patients were 18 years or older and had at least one disease relapse that required an additional line of treatment. The study cohort was divided into three groups based on time of first relapse: early relapse (<2 years from diagnosis), relapse between 2 to 5 years from diagnosis, and late relapse (>5 years from diagnosis). The independent predictors of early/late relapse were identified using a forward stepwise multivariate logistic regression. Odds ratios in multivariate models were adjusted for age and sex. Results Early relapse has been recognized in 758 patients (52.6%), relapse between 2-5 years in 561 patients (38.9%), late relapse in 122 patients (8.5%). Six patients had a PFS >10 years. In comparison to patients with late relapse, patients with early relapse were older (median 63 vs 61 years, p=0.04), more frequently ISS stage III (40% vs 20%, p<0.001) had higher bone marrow plasma cell infiltration (median 60% vs 40%, p<0.001), and were more likely to have high-risk (HR) FISH (defined as translocation t[4;14], t[14;16], t[14;20], deletion 17p or p53 mutation; 28% vs 11%, p<0.001). At diagnosis, early relapse group more often presented with anemia (35% vs 21%, p=0.004), hypercalcemia (15% vs 5%, p<0.001), renal insufficiency (19% vs 5%, p<0.001) and higher serum beta-2-microglobulin (median 4.4 vs 3.4 mg/l, p<0.001). In terms of first line treatment, novel agents use was higher in early relapse group in comparison to late relapse group (82% vs 72%, p=0.007). Early relapse patients more often received PI-based therapy (30% vs 10%, p<0.001) or PI+IMID-based therapy (22% vs 12%, p=0.01), whereas late relapse patients received more often IMID-based therapy (63% vs 37%, p<0.001). No differences in the maintenance therapy were observed. Early relapse patients were less frequently treated with upfront autologous stem cell transplantation (ASCT, 35% vs 60%, p<0.001). Progression on active treatment/maintenance was observed more often in the early relapse group (55% vs 18%, p<0.001). On multivariable logistic regression model early relapse (vs all remaining patients) was predicted by HR cytogenetic features (odds ratio [OR] 3.06, 95% confidence interval [CI] 1.71-5.46, p<0.001), non-IgG isotype disease (OR 2.17, 95% CI 1.33-3.54, p=0.02), a non-ASCT pathway (OR 2.78, 95% CI 1.71-4.52, p<0.01), and by achieving less than a very good partial remission (VGPR; OR 3.23, 95% CI 1.96-5.35, p<0.01). The only factor associated with decreased chances of late relapse (vs all remaining patients) on multivariate logistic regression model was the presence of HR FISH features (OR 0.18, 95% CI 0.04-0.82, p=0.03). Median PFS from first relapse for the whole population was 13.9 months (95% CI 12.9-15.1), median OS from first relapse - 44.6 months (95% CI 41.7-183.0). Early relapse group exhibited worse median PFS and OS from first relapse (median PFS 9.1 months; median OS 26.6 months) than patients who relapsed 2-5 years after diagnosis (median PFS 18.5 months; OS 71.9 months), or late relapse group (median PFS 31.6 months; median OS 87.8 months; p<0.001) (Figure 1). Conclusions Early relapse (<2 years) is an indicator for shorter duration of response to subsequent treatments, and worse OS. Treatment with upfront ASCT and achieving VGPR or better after first line therapy lower the risk of early relapse. The only parameter that is predictive for both early and late relapse is HR FISH features. Although factors that predict worse survival in MM are well defined, further studies are needed to identify predictors of a more indolent disease course so that future therapeutic approaches can be tailored to each individual. Figure 1 Figure 1. Disclosures Kapoor: AbbVie: Research Funding; Glaxo SmithKline: Research Funding; Takeda: Research Funding; Karyopharm: Research Funding; Sanofi: Research Funding; Karyopharm: Consultancy; Cellectar: Consultancy; BeiGene: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy; Amgen: Research Funding; Ichnos Sciences: Research Funding; Regeneron Pharmaceuticals: Research Funding. Dispenzieri: Sorrento Therapeutics: Consultancy; Oncopeptides: Consultancy; Pfizer: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding. Gertz: Akcea Therapeutics, Alnylam Pharmaceuticals Inc, Prothena: Consultancy; Ionis Pharmaceuticals: Other: Advisory Board; AbbVie Inc, Celgene Corporation: Other: Data Safetly & Monitoring; Aurora Biopharma: Other: Stock option; Akcea Therapeutics, Ambry Genetics, Amgen Inc, Celgene Corporation, Janssen Biotech Inc, Karyopharm Therapeutics, Pfizer Inc (to Institution), Sanofi Genzyme: Honoraria. Dingli: Alexion: Consultancy; Janssen: Consultancy; Novartis: Research Funding; GSK: Consultancy; Sanofi: Consultancy; Apellis: Consultancy. Kumar: Astra-Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding; Tenebio: Research Funding; Roche-Genentech: Consultancy, Research Funding; Beigene: Consultancy; Oncopeptides: Consultancy; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Antengene: Consultancy, Honoraria; Carsgen: Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bluebird Bio: Consultancy; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Research Funding.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.285
Teacher spread0.246 · 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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Citations0
Published2021
Admission routes1
Has abstractyes

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