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Patient, Disease and Treatment Characteristics of Long-Term Survivors of Multiple Myeloma

2014· article· en· W2475561659 on OpenAlexaff
Michael Kennah, Nastaran Noroozi, Esther Masih‐Khan, Tony Panzarella, Donna Reece, Suzanne Trudel, Vishal Kukreti, Rodger E. Tiedemann, Christine I. Chen

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineMultiple myelomaUnivariate analysisInternal medicineAutologous stem-cell transplantationRetrospective cohort studySurgeryStage (stratigraphy)Survival analysisTransplantationDiseaseMultivariate analysis

Abstract

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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.

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)0.000
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.000
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 head0.018
GPT teacher head0.268
Teacher spread0.250 · 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
Published2014
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